diff --git "a/exp/log/log-train-2024-03-11-23-34-29-4" "b/exp/log/log-train-2024-03-11-23-34-29-4" new file mode 100644--- /dev/null +++ "b/exp/log/log-train-2024-03-11-23-34-29-4" @@ -0,0 +1,5364 @@ +2024-03-11 23:34:29,689 INFO [train.py:805] (4/6) Training started +2024-03-11 23:34:29,690 INFO [train.py:815] (4/6) Device: cuda:4 +2024-03-11 23:34:29,690 INFO [tts_datamodule.py:322] (4/6) About to get train cuts +2024-03-11 23:34:29,693 INFO [tts_datamodule.py:337] (4/6) About to get speakers +2024-03-11 23:34:29,694 INFO [train.py:827] (4/6) {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': -1, 'log_interval': 50, 'valid_interval': 200, 'env_info': {'k2-version': '1.24.4', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '2989b0b1186fa6022932804f5b39fbb2781ebf42', 'k2-git-date': 'Fri Nov 24 11:34:10 2023', 'lhotse-version': '1.22.0.dev+git.d8ed1bbb.dirty', 'torch-version': '1.11.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.9', 'icefall-git-branch': 'dev/tts/vctk/tokenizer', 'icefall-git-sha1': 'e69b60e5-clean', 'icefall-git-date': 'Mon Mar 11 23:14:14 2024', 'icefall-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/icefall-1.0-py3.9.egg', 'k2-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/k2-1.24.4.dev20231207+cuda10.2.torch1.11.0-py3.9-linux-x86_64.egg/k2/__init__.py', 'lhotse-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/lhotse-1.22.0.dev0+git.d8ed1bbb.dirty-py3.9.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-9-0208143539-7dbf569d4f-r7nrb', 'IP address': '10.177.13.150'}, 'sampling_rate': 22050, 'frame_shift': 256, 'frame_length': 1024, 'feature_dim': 513, 'n_mels': 80, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'world_size': 6, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 1000, 'start_epoch': 1, 'exp_dir': PosixPath('vits/exp_fp32'), 'tokens': 'data/tokens.txt', 'lr': 0.0002, 'seed': 42, 'print_diagnostics': False, 'inf_check': False, 'save_every_n': 20, 'use_fp16': False, 'manifest_dir': PosixPath('data/spectrogram'), 'speakers': PosixPath('data/speakers.txt'), 'max_duration': 200, 'bucketing_sampler': True, 'num_buckets': 30, 'on_the_fly_feats': False, 'shuffle': True, 'drop_last': True, 'return_cuts': False, 'num_workers': 8, 'input_strategy': 'PrecomputedFeatures', 'blank_id': 0, 'vocab_size': 159, 'num_spks': 108} +2024-03-11 23:34:29,694 INFO [train.py:829] (4/6) About to create model +2024-03-11 23:34:32,707 INFO [train.py:835] (4/6) Number of parameters in generator: 39004210 +2024-03-11 23:34:32,708 INFO [train.py:837] (4/6) Number of parameters in discriminator: 50974956 +2024-03-11 23:34:32,709 INFO [train.py:838] (4/6) Total number of parameters: 89979166 +2024-03-11 23:34:39,931 INFO [train.py:845] (4/6) Using DDP +2024-03-11 23:34:41,292 INFO [tts_datamodule.py:175] (4/6) About to create train dataset +2024-03-11 23:34:41,293 INFO [tts_datamodule.py:201] (4/6) Using DynamicBucketingSampler. +2024-03-11 23:34:42,567 INFO [tts_datamodule.py:218] (4/6) About to create train dataloader +2024-03-11 23:34:42,568 INFO [tts_datamodule.py:327] (4/6) About to get validation cuts +2024-03-11 23:34:42,570 INFO [tts_datamodule.py:241] (4/6) About to create dev dataset +2024-03-11 23:34:42,583 INFO [tts_datamodule.py:270] (4/6) About to create valid dataloader +2024-03-11 23:34:42,584 INFO [train.py:725] (4/6) Sanity check -- see if any of the batches in epoch 1 would cause OOM. +2024-03-11 23:35:04,586 INFO [train.py:780] (4/6) Maximum memory allocated so far is 7898MB +2024-03-11 23:35:06,618 INFO [train.py:780] (4/6) Maximum memory allocated so far is 8278MB +2024-03-11 23:35:10,774 INFO [train.py:780] (4/6) Maximum memory allocated so far is 18951MB +2024-03-11 23:35:13,688 INFO [train.py:780] (4/6) Maximum memory allocated so far is 18951MB +2024-03-11 23:35:18,922 INFO [train.py:780] (4/6) Maximum memory allocated so far is 27413MB +2024-03-11 23:35:22,780 INFO [train.py:780] (4/6) Maximum memory allocated so far is 27413MB +2024-03-11 23:35:22,791 INFO [train.py:919] (4/6) Start epoch 1 +2024-03-11 23:35:44,658 INFO [train.py:527] (4/6) Epoch 1, batch 0, global_batch_idx: 0, batch size: 72, loss[discriminator_loss=6.107, discriminator_real_loss=6.106, discriminator_fake_loss=0.001034, generator_loss=1489, generator_mel_loss=109.3, generator_kl_loss=1373, generator_dur_loss=1.817, generator_adv_loss=4.809, generator_feat_match_loss=0.1784, over 72.00 samples.], tot_loss[discriminator_loss=6.107, discriminator_real_loss=6.106, discriminator_fake_loss=0.001034, generator_loss=1489, generator_mel_loss=109.3, generator_kl_loss=1373, generator_dur_loss=1.817, generator_adv_loss=4.809, generator_feat_match_loss=0.1784, over 72.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:35:44,660 INFO [train.py:581] (4/6) Computing validation loss +2024-03-11 23:35:53,001 INFO [train.py:591] (4/6) Epoch 1, validation: discriminator_loss=4.882, discriminator_real_loss=4.807, discriminator_fake_loss=0.07482, generator_loss=763.5, generator_mel_loss=112.9, generator_kl_loss=643.8, generator_dur_loss=1.851, generator_adv_loss=4.809, generator_feat_match_loss=0.1531, over 100.00 samples. +2024-03-11 23:35:53,003 INFO [train.py:592] (4/6) Maximum memory allocated so far is 27413MB +2024-03-11 23:38:13,711 INFO [train.py:527] (4/6) Epoch 1, batch 50, global_batch_idx: 50, batch size: 74, loss[discriminator_loss=2.746, discriminator_real_loss=1.445, discriminator_fake_loss=1.301, generator_loss=116.3, generator_mel_loss=48.53, generator_kl_loss=62.53, generator_dur_loss=1.718, generator_adv_loss=1.83, generator_feat_match_loss=1.678, over 74.00 samples.], tot_loss[discriminator_loss=3.113, discriminator_real_loss=1.754, discriminator_fake_loss=1.359, generator_loss=217.9, generator_mel_loss=61.38, generator_kl_loss=152.5, generator_dur_loss=1.657, generator_adv_loss=1.795, generator_feat_match_loss=0.5479, over 2863.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:40:30,837 INFO [train.py:527] (4/6) Epoch 1, batch 100, global_batch_idx: 100, batch size: 74, loss[discriminator_loss=2.663, discriminator_real_loss=1.523, discriminator_fake_loss=1.14, generator_loss=87.79, generator_mel_loss=49.18, generator_kl_loss=33.47, generator_dur_loss=1.73, generator_adv_loss=1.824, generator_feat_match_loss=1.596, over 74.00 samples.], tot_loss[discriminator_loss=2.912, discriminator_real_loss=1.616, discriminator_fake_loss=1.296, generator_loss=158.3, generator_mel_loss=54.52, generator_kl_loss=99.16, generator_dur_loss=1.682, generator_adv_loss=1.858, generator_feat_match_loss=1.09, over 5784.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:41:34,706 INFO [train.py:919] (4/6) Start epoch 2 +2024-03-11 23:43:11,635 INFO [train.py:527] (4/6) Epoch 2, batch 26, global_batch_idx: 150, batch size: 83, loss[discriminator_loss=2.834, discriminator_real_loss=1.36, discriminator_fake_loss=1.474, generator_loss=70.68, generator_mel_loss=40.67, generator_kl_loss=25.26, generator_dur_loss=1.886, generator_adv_loss=1.725, generator_feat_match_loss=1.142, over 83.00 samples.], tot_loss[discriminator_loss=2.787, discriminator_real_loss=1.479, discriminator_fake_loss=1.309, generator_loss=76.23, generator_mel_loss=44.57, generator_kl_loss=26.34, generator_dur_loss=1.817, generator_adv_loss=1.901, generator_feat_match_loss=1.6, over 1557.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:45:31,871 INFO [train.py:527] (4/6) Epoch 2, batch 76, global_batch_idx: 200, batch size: 42, loss[discriminator_loss=2.838, discriminator_real_loss=1.587, discriminator_fake_loss=1.251, generator_loss=64.7, generator_mel_loss=41.78, generator_kl_loss=18.02, generator_dur_loss=1.862, generator_adv_loss=1.793, generator_feat_match_loss=1.249, over 42.00 samples.], tot_loss[discriminator_loss=2.814, discriminator_real_loss=1.511, discriminator_fake_loss=1.303, generator_loss=70.25, generator_mel_loss=42.85, generator_kl_loss=22.13, generator_dur_loss=1.848, generator_adv_loss=1.882, generator_feat_match_loss=1.545, over 4550.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:45:31,873 INFO [train.py:581] (4/6) Computing validation loss +2024-03-11 23:45:39,716 INFO [train.py:591] (4/6) Epoch 2, validation: discriminator_loss=2.887, discriminator_real_loss=1.544, discriminator_fake_loss=1.343, generator_loss=55.76, generator_mel_loss=38.74, generator_kl_loss=12.24, generator_dur_loss=2.086, generator_adv_loss=1.775, generator_feat_match_loss=0.9245, over 100.00 samples. +2024-03-11 23:45:39,717 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28465MB +2024-03-11 23:47:45,078 INFO [train.py:919] (4/6) Start epoch 3 +2024-03-11 23:48:14,631 INFO [train.py:527] (4/6) Epoch 3, batch 2, global_batch_idx: 250, batch size: 66, loss[discriminator_loss=2.876, discriminator_real_loss=1.583, discriminator_fake_loss=1.293, generator_loss=54.58, generator_mel_loss=36.41, generator_kl_loss=13.7, generator_dur_loss=1.866, generator_adv_loss=1.756, generator_feat_match_loss=0.8454, over 66.00 samples.], tot_loss[discriminator_loss=2.891, discriminator_real_loss=1.485, discriminator_fake_loss=1.406, generator_loss=57.04, generator_mel_loss=38.6, generator_kl_loss=13.95, generator_dur_loss=1.892, generator_adv_loss=1.747, generator_feat_match_loss=0.8489, over 152.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:50:34,551 INFO [train.py:527] (4/6) Epoch 3, batch 52, global_batch_idx: 300, batch size: 12, loss[discriminator_loss=3.084, discriminator_real_loss=1.447, discriminator_fake_loss=1.638, generator_loss=52.37, generator_mel_loss=37.16, generator_kl_loss=10.72, generator_dur_loss=1.84, generator_adv_loss=1.708, generator_feat_match_loss=0.9434, over 12.00 samples.], tot_loss[discriminator_loss=2.884, discriminator_real_loss=1.478, discriminator_fake_loss=1.406, generator_loss=53.06, generator_mel_loss=36.37, generator_kl_loss=12.18, generator_dur_loss=1.928, generator_adv_loss=1.688, generator_feat_match_loss=0.8931, over 3137.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:52:54,857 INFO [train.py:527] (4/6) Epoch 3, batch 102, global_batch_idx: 350, batch size: 62, loss[discriminator_loss=2.916, discriminator_real_loss=1.698, discriminator_fake_loss=1.218, generator_loss=44.8, generator_mel_loss=31.07, generator_kl_loss=9.048, generator_dur_loss=1.923, generator_adv_loss=1.764, generator_feat_match_loss=0.9961, over 62.00 samples.], tot_loss[discriminator_loss=2.865, discriminator_real_loss=1.476, discriminator_fake_loss=1.39, generator_loss=51.54, generator_mel_loss=35.94, generator_kl_loss=10.93, generator_dur_loss=1.925, generator_adv_loss=1.727, generator_feat_match_loss=1.014, over 5997.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:53:55,678 INFO [train.py:919] (4/6) Start epoch 4 +2024-03-11 23:55:37,873 INFO [train.py:527] (4/6) Epoch 4, batch 28, global_batch_idx: 400, batch size: 62, loss[discriminator_loss=2.656, discriminator_real_loss=1.328, discriminator_fake_loss=1.327, generator_loss=46.05, generator_mel_loss=32.87, generator_kl_loss=7.77, generator_dur_loss=1.919, generator_adv_loss=1.762, generator_feat_match_loss=1.733, over 62.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.448, discriminator_fake_loss=1.313, generator_loss=46.63, generator_mel_loss=33.53, generator_kl_loss=7.831, generator_dur_loss=1.936, generator_adv_loss=1.877, generator_feat_match_loss=1.453, over 1594.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:55:37,875 INFO [train.py:581] (4/6) Computing validation loss +2024-03-11 23:55:46,451 INFO [train.py:591] (4/6) Epoch 4, validation: discriminator_loss=2.851, discriminator_real_loss=1.361, discriminator_fake_loss=1.49, generator_loss=43.7, generator_mel_loss=32.79, generator_kl_loss=5.991, generator_dur_loss=2.147, generator_adv_loss=1.682, generator_feat_match_loss=1.097, over 100.00 samples. +2024-03-11 23:55:46,451 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28465MB +2024-03-11 23:58:04,803 INFO [train.py:527] (4/6) Epoch 4, batch 78, global_batch_idx: 450, batch size: 52, loss[discriminator_loss=2.633, discriminator_real_loss=1.329, discriminator_fake_loss=1.304, generator_loss=46.09, generator_mel_loss=34.62, generator_kl_loss=5.732, generator_dur_loss=1.91, generator_adv_loss=1.989, generator_feat_match_loss=1.836, over 52.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.448, discriminator_fake_loss=1.285, generator_loss=45.59, generator_mel_loss=33.04, generator_kl_loss=7.145, generator_dur_loss=1.941, generator_adv_loss=1.883, generator_feat_match_loss=1.582, over 4455.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:00:09,338 INFO [train.py:919] (4/6) Start epoch 5 +2024-03-12 00:00:45,079 INFO [train.py:527] (4/6) Epoch 5, batch 4, global_batch_idx: 500, batch size: 77, loss[discriminator_loss=2.777, discriminator_real_loss=1.513, discriminator_fake_loss=1.264, generator_loss=41.28, generator_mel_loss=30.39, generator_kl_loss=5.611, generator_dur_loss=1.995, generator_adv_loss=1.891, generator_feat_match_loss=1.395, over 77.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.494, discriminator_fake_loss=1.264, generator_loss=43.06, generator_mel_loss=32.02, generator_kl_loss=5.669, generator_dur_loss=1.941, generator_adv_loss=1.844, generator_feat_match_loss=1.582, over 273.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:03:05,357 INFO [train.py:527] (4/6) Epoch 5, batch 54, global_batch_idx: 550, batch size: 61, loss[discriminator_loss=2.759, discriminator_real_loss=1.446, discriminator_fake_loss=1.313, generator_loss=39.83, generator_mel_loss=29.54, generator_kl_loss=4.598, generator_dur_loss=1.976, generator_adv_loss=2.012, generator_feat_match_loss=1.705, over 61.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.443, discriminator_fake_loss=1.32, generator_loss=41.11, generator_mel_loss=30.61, generator_kl_loss=5.171, generator_dur_loss=1.941, generator_adv_loss=1.83, generator_feat_match_loss=1.561, over 2892.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:05:22,130 INFO [train.py:527] (4/6) Epoch 5, batch 104, global_batch_idx: 600, batch size: 83, loss[discriminator_loss=2.698, discriminator_real_loss=1.512, discriminator_fake_loss=1.186, generator_loss=39.55, generator_mel_loss=29.71, generator_kl_loss=4.158, generator_dur_loss=1.992, generator_adv_loss=1.883, generator_feat_match_loss=1.807, over 83.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.441, discriminator_fake_loss=1.306, generator_loss=40.39, generator_mel_loss=30.16, generator_kl_loss=4.782, generator_dur_loss=1.95, generator_adv_loss=1.864, generator_feat_match_loss=1.639, over 5791.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:05:22,132 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 00:05:31,117 INFO [train.py:591] (4/6) Epoch 5, validation: discriminator_loss=2.816, discriminator_real_loss=1.506, discriminator_fake_loss=1.31, generator_loss=36.96, generator_mel_loss=28.4, generator_kl_loss=3.509, generator_dur_loss=2.138, generator_adv_loss=1.789, generator_feat_match_loss=1.124, over 100.00 samples. +2024-03-12 00:05:31,118 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28488MB +2024-03-12 00:06:23,845 INFO [train.py:919] (4/6) Start epoch 6 +2024-03-12 00:08:08,940 INFO [train.py:527] (4/6) Epoch 6, batch 30, global_batch_idx: 650, batch size: 48, loss[discriminator_loss=2.767, discriminator_real_loss=1.318, discriminator_fake_loss=1.449, generator_loss=39.29, generator_mel_loss=30.03, generator_kl_loss=3.83, generator_dur_loss=1.9, generator_adv_loss=1.693, generator_feat_match_loss=1.84, over 48.00 samples.], tot_loss[discriminator_loss=2.771, discriminator_real_loss=1.446, discriminator_fake_loss=1.325, generator_loss=38.95, generator_mel_loss=29.4, generator_kl_loss=3.871, generator_dur_loss=1.954, generator_adv_loss=1.918, generator_feat_match_loss=1.8, over 1622.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:10:28,030 INFO [train.py:527] (4/6) Epoch 6, batch 80, global_batch_idx: 700, batch size: 61, loss[discriminator_loss=2.808, discriminator_real_loss=1.323, discriminator_fake_loss=1.486, generator_loss=36.65, generator_mel_loss=27.59, generator_kl_loss=3.534, generator_dur_loss=1.981, generator_adv_loss=2.099, generator_feat_match_loss=1.443, over 61.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.443, discriminator_fake_loss=1.296, generator_loss=38.23, generator_mel_loss=28.9, generator_kl_loss=3.676, generator_dur_loss=1.96, generator_adv_loss=1.904, generator_feat_match_loss=1.783, over 4603.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:12:30,294 INFO [train.py:919] (4/6) Start epoch 7 +2024-03-12 00:13:11,204 INFO [train.py:527] (4/6) Epoch 7, batch 6, global_batch_idx: 750, batch size: 52, loss[discriminator_loss=2.782, discriminator_real_loss=1.401, discriminator_fake_loss=1.381, generator_loss=34.04, generator_mel_loss=25.7, generator_kl_loss=3.026, generator_dur_loss=1.964, generator_adv_loss=1.969, generator_feat_match_loss=1.378, over 52.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.406, discriminator_fake_loss=1.36, generator_loss=36.21, generator_mel_loss=27.56, generator_kl_loss=3.052, generator_dur_loss=1.948, generator_adv_loss=1.886, generator_feat_match_loss=1.768, over 370.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:15:30,346 INFO [train.py:527] (4/6) Epoch 7, batch 56, global_batch_idx: 800, batch size: 59, loss[discriminator_loss=2.822, discriminator_real_loss=1.529, discriminator_fake_loss=1.293, generator_loss=33.8, generator_mel_loss=25.71, generator_kl_loss=2.857, generator_dur_loss=1.934, generator_adv_loss=1.956, generator_feat_match_loss=1.336, over 59.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.437, discriminator_fake_loss=1.317, generator_loss=36.03, generator_mel_loss=27.45, generator_kl_loss=2.922, generator_dur_loss=1.968, generator_adv_loss=1.897, generator_feat_match_loss=1.79, over 3255.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:15:30,349 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 00:15:38,549 INFO [train.py:591] (4/6) Epoch 7, validation: discriminator_loss=2.831, discriminator_real_loss=1.662, discriminator_fake_loss=1.168, generator_loss=33.21, generator_mel_loss=25.58, generator_kl_loss=2.468, generator_dur_loss=2.112, generator_adv_loss=1.932, generator_feat_match_loss=1.119, over 100.00 samples. +2024-03-12 00:15:38,550 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28731MB +2024-03-12 00:17:56,925 INFO [train.py:527] (4/6) Epoch 7, batch 106, global_batch_idx: 850, batch size: 64, loss[discriminator_loss=2.617, discriminator_real_loss=1.36, discriminator_fake_loss=1.257, generator_loss=36.23, generator_mel_loss=27.86, generator_kl_loss=2.538, generator_dur_loss=2.006, generator_adv_loss=1.846, generator_feat_match_loss=1.982, over 64.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.44, discriminator_fake_loss=1.31, generator_loss=35.78, generator_mel_loss=27.34, generator_kl_loss=2.81, generator_dur_loss=1.965, generator_adv_loss=1.883, generator_feat_match_loss=1.781, over 6090.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:18:47,352 INFO [train.py:919] (4/6) Start epoch 8 +2024-03-12 00:20:42,585 INFO [train.py:527] (4/6) Epoch 8, batch 32, global_batch_idx: 900, batch size: 39, loss[discriminator_loss=2.719, discriminator_real_loss=1.416, discriminator_fake_loss=1.304, generator_loss=33.04, generator_mel_loss=24.89, generator_kl_loss=2.511, generator_dur_loss=1.915, generator_adv_loss=1.808, generator_feat_match_loss=1.916, over 39.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.428, discriminator_fake_loss=1.32, generator_loss=34.19, generator_mel_loss=26.11, generator_kl_loss=2.474, generator_dur_loss=1.975, generator_adv_loss=1.869, generator_feat_match_loss=1.761, over 1971.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:23:05,349 INFO [train.py:527] (4/6) Epoch 8, batch 82, global_batch_idx: 950, batch size: 56, loss[discriminator_loss=2.782, discriminator_real_loss=1.537, discriminator_fake_loss=1.245, generator_loss=34.67, generator_mel_loss=26.79, generator_kl_loss=2.312, generator_dur_loss=1.908, generator_adv_loss=1.885, generator_feat_match_loss=1.77, over 56.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.432, discriminator_fake_loss=1.315, generator_loss=34.33, generator_mel_loss=26.22, generator_kl_loss=2.384, generator_dur_loss=1.973, generator_adv_loss=1.901, generator_feat_match_loss=1.848, over 4929.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:24:59,393 INFO [train.py:919] (4/6) Start epoch 9 +2024-03-12 00:25:44,828 INFO [train.py:527] (4/6) Epoch 9, batch 8, global_batch_idx: 1000, batch size: 50, loss[discriminator_loss=2.625, discriminator_real_loss=1.454, discriminator_fake_loss=1.171, generator_loss=33.76, generator_mel_loss=25.89, generator_kl_loss=2.144, generator_dur_loss=1.866, generator_adv_loss=1.948, generator_feat_match_loss=1.91, over 50.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.433, discriminator_fake_loss=1.286, generator_loss=34.12, generator_mel_loss=26.2, generator_kl_loss=2.24, generator_dur_loss=1.93, generator_adv_loss=1.876, generator_feat_match_loss=1.873, over 434.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:25:44,831 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 00:25:52,726 INFO [train.py:591] (4/6) Epoch 9, validation: discriminator_loss=2.716, discriminator_real_loss=1.498, discriminator_fake_loss=1.218, generator_loss=34.19, generator_mel_loss=26.16, generator_kl_loss=2.238, generator_dur_loss=2.052, generator_adv_loss=1.915, generator_feat_match_loss=1.823, over 100.00 samples. +2024-03-12 00:25:52,729 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28731MB +2024-03-12 00:28:16,698 INFO [train.py:527] (4/6) Epoch 9, batch 58, global_batch_idx: 1050, batch size: 52, loss[discriminator_loss=2.677, discriminator_real_loss=1.304, discriminator_fake_loss=1.374, generator_loss=35.55, generator_mel_loss=27.15, generator_kl_loss=2.145, generator_dur_loss=1.859, generator_adv_loss=2.224, generator_feat_match_loss=2.179, over 52.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.415, discriminator_fake_loss=1.287, generator_loss=34.19, generator_mel_loss=26.2, generator_kl_loss=2.148, generator_dur_loss=1.944, generator_adv_loss=1.921, generator_feat_match_loss=1.985, over 3266.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:30:36,622 INFO [train.py:527] (4/6) Epoch 9, batch 108, global_batch_idx: 1100, batch size: 58, loss[discriminator_loss=2.773, discriminator_real_loss=1.5, discriminator_fake_loss=1.273, generator_loss=35.72, generator_mel_loss=27.24, generator_kl_loss=2.003, generator_dur_loss=1.963, generator_adv_loss=2.284, generator_feat_match_loss=2.233, over 58.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.421, discriminator_fake_loss=1.286, generator_loss=33.98, generator_mel_loss=26.04, generator_kl_loss=2.111, generator_dur_loss=1.945, generator_adv_loss=1.914, generator_feat_match_loss=1.978, over 6134.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:31:17,677 INFO [train.py:919] (4/6) Start epoch 10 +2024-03-12 00:33:17,688 INFO [train.py:527] (4/6) Epoch 10, batch 34, global_batch_idx: 1150, batch size: 39, loss[discriminator_loss=2.674, discriminator_real_loss=1.336, discriminator_fake_loss=1.338, generator_loss=34.73, generator_mel_loss=26.35, generator_kl_loss=1.974, generator_dur_loss=1.917, generator_adv_loss=2.19, generator_feat_match_loss=2.302, over 39.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.397, discriminator_fake_loss=1.292, generator_loss=33.57, generator_mel_loss=25.77, generator_kl_loss=1.89, generator_dur_loss=1.91, generator_adv_loss=1.941, generator_feat_match_loss=2.055, over 1918.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:35:38,820 INFO [train.py:527] (4/6) Epoch 10, batch 84, global_batch_idx: 1200, batch size: 80, loss[discriminator_loss=2.718, discriminator_real_loss=1.415, discriminator_fake_loss=1.303, generator_loss=31, generator_mel_loss=23.75, generator_kl_loss=1.937, generator_dur_loss=1.922, generator_adv_loss=1.579, generator_feat_match_loss=1.812, over 80.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.398, discriminator_fake_loss=1.286, generator_loss=33.24, generator_mel_loss=25.45, generator_kl_loss=1.827, generator_dur_loss=1.922, generator_adv_loss=1.94, generator_feat_match_loss=2.099, over 5018.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:35:38,822 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 00:35:47,143 INFO [train.py:591] (4/6) Epoch 10, validation: discriminator_loss=2.786, discriminator_real_loss=1.171, discriminator_fake_loss=1.615, generator_loss=31.34, generator_mel_loss=24.62, generator_kl_loss=1.472, generator_dur_loss=2.13, generator_adv_loss=1.501, generator_feat_match_loss=1.612, over 100.00 samples. +2024-03-12 00:35:47,144 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28731MB +2024-03-12 00:37:36,583 INFO [train.py:919] (4/6) Start epoch 11 +2024-03-12 00:38:30,533 INFO [train.py:527] (4/6) Epoch 11, batch 10, global_batch_idx: 1250, batch size: 70, loss[discriminator_loss=2.724, discriminator_real_loss=1.507, discriminator_fake_loss=1.217, generator_loss=32.06, generator_mel_loss=24.77, generator_kl_loss=1.661, generator_dur_loss=1.976, generator_adv_loss=1.906, generator_feat_match_loss=1.744, over 70.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.394, discriminator_fake_loss=1.305, generator_loss=32.89, generator_mel_loss=25.35, generator_kl_loss=1.677, generator_dur_loss=1.946, generator_adv_loss=1.907, generator_feat_match_loss=2.011, over 634.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:40:53,412 INFO [train.py:527] (4/6) Epoch 11, batch 60, global_batch_idx: 1300, batch size: 88, loss[discriminator_loss=2.65, discriminator_real_loss=1.411, discriminator_fake_loss=1.239, generator_loss=32.73, generator_mel_loss=24.93, generator_kl_loss=1.583, generator_dur_loss=2.002, generator_adv_loss=2.099, generator_feat_match_loss=2.121, over 88.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.403, discriminator_fake_loss=1.29, generator_loss=32.83, generator_mel_loss=25.14, generator_kl_loss=1.664, generator_dur_loss=1.955, generator_adv_loss=1.941, generator_feat_match_loss=2.127, over 3489.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:43:12,176 INFO [train.py:527] (4/6) Epoch 11, batch 110, global_batch_idx: 1350, batch size: 59, loss[discriminator_loss=2.562, discriminator_real_loss=1.289, discriminator_fake_loss=1.273, generator_loss=33.91, generator_mel_loss=25.68, generator_kl_loss=1.586, generator_dur_loss=1.952, generator_adv_loss=2.061, generator_feat_match_loss=2.628, over 59.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.399, discriminator_fake_loss=1.284, generator_loss=32.93, generator_mel_loss=25.21, generator_kl_loss=1.628, generator_dur_loss=1.957, generator_adv_loss=1.946, generator_feat_match_loss=2.186, over 6261.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:43:49,434 INFO [train.py:919] (4/6) Start epoch 12 +2024-03-12 00:45:56,813 INFO [train.py:527] (4/6) Epoch 12, batch 36, global_batch_idx: 1400, batch size: 80, loss[discriminator_loss=2.805, discriminator_real_loss=1.535, discriminator_fake_loss=1.27, generator_loss=32.15, generator_mel_loss=24.77, generator_kl_loss=1.464, generator_dur_loss=1.99, generator_adv_loss=1.712, generator_feat_match_loss=2.216, over 80.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.433, discriminator_fake_loss=1.305, generator_loss=32.85, generator_mel_loss=25.11, generator_kl_loss=1.555, generator_dur_loss=1.969, generator_adv_loss=1.977, generator_feat_match_loss=2.246, over 2064.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:45:56,814 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 00:46:04,727 INFO [train.py:591] (4/6) Epoch 12, validation: discriminator_loss=2.896, discriminator_real_loss=1.381, discriminator_fake_loss=1.515, generator_loss=31.16, generator_mel_loss=24.6, generator_kl_loss=1.302, generator_dur_loss=2.125, generator_adv_loss=1.658, generator_feat_match_loss=1.471, over 100.00 samples. +2024-03-12 00:46:04,728 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 00:48:26,226 INFO [train.py:527] (4/6) Epoch 12, batch 86, global_batch_idx: 1450, batch size: 50, loss[discriminator_loss=2.732, discriminator_real_loss=1.435, discriminator_fake_loss=1.297, generator_loss=32.01, generator_mel_loss=24.91, generator_kl_loss=1.426, generator_dur_loss=1.929, generator_adv_loss=1.754, generator_feat_match_loss=1.989, over 50.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.426, discriminator_fake_loss=1.309, generator_loss=32.47, generator_mel_loss=24.96, generator_kl_loss=1.532, generator_dur_loss=1.966, generator_adv_loss=1.929, generator_feat_match_loss=2.082, over 4859.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:50:11,536 INFO [train.py:919] (4/6) Start epoch 13 +2024-03-12 00:51:09,440 INFO [train.py:527] (4/6) Epoch 13, batch 12, global_batch_idx: 1500, batch size: 55, loss[discriminator_loss=2.742, discriminator_real_loss=1.422, discriminator_fake_loss=1.32, generator_loss=33.22, generator_mel_loss=25.88, generator_kl_loss=1.549, generator_dur_loss=1.874, generator_adv_loss=1.883, generator_feat_match_loss=2.038, over 55.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.419, discriminator_fake_loss=1.296, generator_loss=32.67, generator_mel_loss=25.06, generator_kl_loss=1.506, generator_dur_loss=1.922, generator_adv_loss=1.963, generator_feat_match_loss=2.215, over 618.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:53:26,373 INFO [train.py:527] (4/6) Epoch 13, batch 62, global_batch_idx: 1550, batch size: 44, loss[discriminator_loss=2.74, discriminator_real_loss=1.664, discriminator_fake_loss=1.076, generator_loss=31.9, generator_mel_loss=24.41, generator_kl_loss=1.506, generator_dur_loss=1.947, generator_adv_loss=1.927, generator_feat_match_loss=2.106, over 44.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.415, discriminator_fake_loss=1.32, generator_loss=32.19, generator_mel_loss=24.76, generator_kl_loss=1.459, generator_dur_loss=1.951, generator_adv_loss=1.933, generator_feat_match_loss=2.095, over 3435.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:55:42,375 INFO [train.py:527] (4/6) Epoch 13, batch 112, global_batch_idx: 1600, batch size: 48, loss[discriminator_loss=2.742, discriminator_real_loss=1.334, discriminator_fake_loss=1.408, generator_loss=32.48, generator_mel_loss=25.14, generator_kl_loss=1.515, generator_dur_loss=1.969, generator_adv_loss=1.967, generator_feat_match_loss=1.892, over 48.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.412, discriminator_fake_loss=1.317, generator_loss=32.2, generator_mel_loss=24.78, generator_kl_loss=1.445, generator_dur_loss=1.956, generator_adv_loss=1.925, generator_feat_match_loss=2.098, over 5942.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:55:42,376 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 00:55:51,189 INFO [train.py:591] (4/6) Epoch 13, validation: discriminator_loss=2.687, discriminator_real_loss=1.474, discriminator_fake_loss=1.213, generator_loss=31.94, generator_mel_loss=25.06, generator_kl_loss=1.229, generator_dur_loss=2.103, generator_adv_loss=1.918, generator_feat_match_loss=1.634, over 100.00 samples. +2024-03-12 00:55:51,190 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 00:56:23,200 INFO [train.py:919] (4/6) Start epoch 14 +2024-03-12 00:58:32,770 INFO [train.py:527] (4/6) Epoch 14, batch 38, global_batch_idx: 1650, batch size: 47, loss[discriminator_loss=2.733, discriminator_real_loss=1.404, discriminator_fake_loss=1.329, generator_loss=32.91, generator_mel_loss=25.36, generator_kl_loss=1.481, generator_dur_loss=1.878, generator_adv_loss=1.996, generator_feat_match_loss=2.195, over 47.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.419, discriminator_fake_loss=1.316, generator_loss=31.7, generator_mel_loss=24.37, generator_kl_loss=1.382, generator_dur_loss=1.974, generator_adv_loss=1.916, generator_feat_match_loss=2.064, over 2355.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:00:50,089 INFO [train.py:527] (4/6) Epoch 14, batch 88, global_batch_idx: 1700, batch size: 42, loss[discriminator_loss=2.679, discriminator_real_loss=1.337, discriminator_fake_loss=1.342, generator_loss=32.47, generator_mel_loss=25.09, generator_kl_loss=1.395, generator_dur_loss=1.925, generator_adv_loss=1.911, generator_feat_match_loss=2.148, over 42.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.416, discriminator_fake_loss=1.332, generator_loss=31.64, generator_mel_loss=24.33, generator_kl_loss=1.36, generator_dur_loss=1.983, generator_adv_loss=1.902, generator_feat_match_loss=2.069, over 5459.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:02:30,070 INFO [train.py:919] (4/6) Start epoch 15 +2024-03-12 01:03:32,289 INFO [train.py:527] (4/6) Epoch 15, batch 14, global_batch_idx: 1750, batch size: 55, loss[discriminator_loss=2.777, discriminator_real_loss=1.399, discriminator_fake_loss=1.378, generator_loss=32.45, generator_mel_loss=25.44, generator_kl_loss=1.21, generator_dur_loss=1.982, generator_adv_loss=1.937, generator_feat_match_loss=1.883, over 55.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.418, discriminator_fake_loss=1.332, generator_loss=31.66, generator_mel_loss=24.45, generator_kl_loss=1.337, generator_dur_loss=1.967, generator_adv_loss=1.873, generator_feat_match_loss=2.039, over 841.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:05:50,105 INFO [train.py:527] (4/6) Epoch 15, batch 64, global_batch_idx: 1800, batch size: 68, loss[discriminator_loss=2.8, discriminator_real_loss=1.453, discriminator_fake_loss=1.346, generator_loss=30.99, generator_mel_loss=23.92, generator_kl_loss=1.34, generator_dur_loss=1.994, generator_adv_loss=1.857, generator_feat_match_loss=1.875, over 68.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.419, discriminator_fake_loss=1.333, generator_loss=31.65, generator_mel_loss=24.4, generator_kl_loss=1.322, generator_dur_loss=1.981, generator_adv_loss=1.883, generator_feat_match_loss=2.057, over 3905.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:05:50,107 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 01:05:57,888 INFO [train.py:591] (4/6) Epoch 15, validation: discriminator_loss=2.818, discriminator_real_loss=1.518, discriminator_fake_loss=1.3, generator_loss=31.04, generator_mel_loss=24.12, generator_kl_loss=1.255, generator_dur_loss=2.117, generator_adv_loss=1.833, generator_feat_match_loss=1.711, over 100.00 samples. +2024-03-12 01:05:57,889 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 01:08:15,644 INFO [train.py:527] (4/6) Epoch 15, batch 114, global_batch_idx: 1850, batch size: 66, loss[discriminator_loss=2.711, discriminator_real_loss=1.557, discriminator_fake_loss=1.153, generator_loss=33.21, generator_mel_loss=25.95, generator_kl_loss=1.274, generator_dur_loss=2.068, generator_adv_loss=1.719, generator_feat_match_loss=2.2, over 66.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.423, discriminator_fake_loss=1.328, generator_loss=31.76, generator_mel_loss=24.49, generator_kl_loss=1.317, generator_dur_loss=1.98, generator_adv_loss=1.89, generator_feat_match_loss=2.082, over 6801.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:08:41,763 INFO [train.py:919] (4/6) Start epoch 16 +2024-03-12 01:10:58,020 INFO [train.py:527] (4/6) Epoch 16, batch 40, global_batch_idx: 1900, batch size: 42, loss[discriminator_loss=2.709, discriminator_real_loss=1.526, discriminator_fake_loss=1.183, generator_loss=32.38, generator_mel_loss=25.47, generator_kl_loss=1.096, generator_dur_loss=1.935, generator_adv_loss=1.676, generator_feat_match_loss=2.208, over 42.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.421, discriminator_fake_loss=1.315, generator_loss=31.75, generator_mel_loss=24.56, generator_kl_loss=1.308, generator_dur_loss=1.962, generator_adv_loss=1.876, generator_feat_match_loss=2.045, over 2132.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:13:14,114 INFO [train.py:527] (4/6) Epoch 16, batch 90, global_batch_idx: 1950, batch size: 83, loss[discriminator_loss=2.741, discriminator_real_loss=1.327, discriminator_fake_loss=1.414, generator_loss=31.05, generator_mel_loss=23.89, generator_kl_loss=1.227, generator_dur_loss=2.066, generator_adv_loss=1.814, generator_feat_match_loss=2.047, over 83.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.418, discriminator_fake_loss=1.333, generator_loss=31.49, generator_mel_loss=24.29, generator_kl_loss=1.289, generator_dur_loss=1.974, generator_adv_loss=1.883, generator_feat_match_loss=2.055, over 5026.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:14:46,886 INFO [train.py:919] (4/6) Start epoch 17 +2024-03-12 01:15:55,089 INFO [train.py:527] (4/6) Epoch 17, batch 16, global_batch_idx: 2000, batch size: 70, loss[discriminator_loss=2.788, discriminator_real_loss=1.353, discriminator_fake_loss=1.436, generator_loss=31.95, generator_mel_loss=25.31, generator_kl_loss=1.163, generator_dur_loss=1.971, generator_adv_loss=1.754, generator_feat_match_loss=1.749, over 70.00 samples.], tot_loss[discriminator_loss=2.844, discriminator_real_loss=1.449, discriminator_fake_loss=1.395, generator_loss=30.56, generator_mel_loss=23.65, generator_kl_loss=1.238, generator_dur_loss=1.991, generator_adv_loss=1.841, generator_feat_match_loss=1.844, over 1111.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:15:55,091 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 01:16:02,946 INFO [train.py:591] (4/6) Epoch 17, validation: discriminator_loss=2.88, discriminator_real_loss=1.485, discriminator_fake_loss=1.395, generator_loss=29.27, generator_mel_loss=23.01, generator_kl_loss=1.108, generator_dur_loss=2.102, generator_adv_loss=1.687, generator_feat_match_loss=1.358, over 100.00 samples. +2024-03-12 01:16:02,947 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 01:18:23,012 INFO [train.py:527] (4/6) Epoch 17, batch 66, global_batch_idx: 2050, batch size: 39, loss[discriminator_loss=2.867, discriminator_real_loss=1.736, discriminator_fake_loss=1.131, generator_loss=29.33, generator_mel_loss=22.28, generator_kl_loss=1.299, generator_dur_loss=1.985, generator_adv_loss=2.173, generator_feat_match_loss=1.591, over 39.00 samples.], tot_loss[discriminator_loss=2.791, discriminator_real_loss=1.433, discriminator_fake_loss=1.358, generator_loss=30.75, generator_mel_loss=23.77, generator_kl_loss=1.265, generator_dur_loss=1.975, generator_adv_loss=1.835, generator_feat_match_loss=1.901, over 3877.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:20:38,077 INFO [train.py:527] (4/6) Epoch 17, batch 116, global_batch_idx: 2100, batch size: 56, loss[discriminator_loss=2.771, discriminator_real_loss=1.465, discriminator_fake_loss=1.306, generator_loss=30.64, generator_mel_loss=23.68, generator_kl_loss=1.334, generator_dur_loss=1.965, generator_adv_loss=1.842, generator_feat_match_loss=1.818, over 56.00 samples.], tot_loss[discriminator_loss=2.78, discriminator_real_loss=1.43, discriminator_fake_loss=1.35, generator_loss=30.91, generator_mel_loss=23.85, generator_kl_loss=1.267, generator_dur_loss=1.972, generator_adv_loss=1.846, generator_feat_match_loss=1.968, over 6562.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:20:57,377 INFO [train.py:919] (4/6) Start epoch 18 +2024-03-12 01:23:20,728 INFO [train.py:527] (4/6) Epoch 18, batch 42, global_batch_idx: 2150, batch size: 58, loss[discriminator_loss=2.943, discriminator_real_loss=1.73, discriminator_fake_loss=1.213, generator_loss=30.52, generator_mel_loss=23.79, generator_kl_loss=1.189, generator_dur_loss=1.938, generator_adv_loss=1.859, generator_feat_match_loss=1.745, over 58.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.41, discriminator_fake_loss=1.359, generator_loss=31.09, generator_mel_loss=23.8, generator_kl_loss=1.285, generator_dur_loss=1.98, generator_adv_loss=1.9, generator_feat_match_loss=2.124, over 2433.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:25:39,853 INFO [train.py:527] (4/6) Epoch 18, batch 92, global_batch_idx: 2200, batch size: 42, loss[discriminator_loss=2.748, discriminator_real_loss=1.312, discriminator_fake_loss=1.436, generator_loss=31.9, generator_mel_loss=24.3, generator_kl_loss=1.387, generator_dur_loss=1.949, generator_adv_loss=2.126, generator_feat_match_loss=2.135, over 42.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.416, discriminator_fake_loss=1.348, generator_loss=30.93, generator_mel_loss=23.72, generator_kl_loss=1.268, generator_dur_loss=1.977, generator_adv_loss=1.885, generator_feat_match_loss=2.084, over 5215.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:25:39,854 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 01:25:48,601 INFO [train.py:591] (4/6) Epoch 18, validation: discriminator_loss=2.828, discriminator_real_loss=1.563, discriminator_fake_loss=1.265, generator_loss=31.05, generator_mel_loss=24.18, generator_kl_loss=1.084, generator_dur_loss=2.103, generator_adv_loss=1.899, generator_feat_match_loss=1.786, over 100.00 samples. +2024-03-12 01:25:48,602 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 01:27:10,323 INFO [train.py:919] (4/6) Start epoch 19 +2024-03-12 01:28:25,342 INFO [train.py:527] (4/6) Epoch 19, batch 18, global_batch_idx: 2250, batch size: 17, loss[discriminator_loss=2.623, discriminator_real_loss=1.377, discriminator_fake_loss=1.246, generator_loss=34.92, generator_mel_loss=26.95, generator_kl_loss=1.508, generator_dur_loss=1.892, generator_adv_loss=1.834, generator_feat_match_loss=2.738, over 17.00 samples.], tot_loss[discriminator_loss=2.812, discriminator_real_loss=1.483, discriminator_fake_loss=1.329, generator_loss=30.85, generator_mel_loss=23.68, generator_kl_loss=1.267, generator_dur_loss=1.974, generator_adv_loss=1.918, generator_feat_match_loss=2.007, over 1084.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:30:45,145 INFO [train.py:527] (4/6) Epoch 19, batch 68, global_batch_idx: 2300, batch size: 53, loss[discriminator_loss=2.722, discriminator_real_loss=1.595, discriminator_fake_loss=1.127, generator_loss=30.84, generator_mel_loss=22.92, generator_kl_loss=1.308, generator_dur_loss=1.977, generator_adv_loss=2.451, generator_feat_match_loss=2.194, over 53.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.436, discriminator_fake_loss=1.327, generator_loss=30.89, generator_mel_loss=23.56, generator_kl_loss=1.253, generator_dur_loss=1.981, generator_adv_loss=1.93, generator_feat_match_loss=2.168, over 4035.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:33:04,829 INFO [train.py:527] (4/6) Epoch 19, batch 118, global_batch_idx: 2350, batch size: 47, loss[discriminator_loss=2.753, discriminator_real_loss=1.408, discriminator_fake_loss=1.344, generator_loss=31.09, generator_mel_loss=23.98, generator_kl_loss=1.363, generator_dur_loss=1.928, generator_adv_loss=1.829, generator_feat_match_loss=1.992, over 47.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.433, discriminator_fake_loss=1.332, generator_loss=30.99, generator_mel_loss=23.62, generator_kl_loss=1.25, generator_dur_loss=1.977, generator_adv_loss=1.943, generator_feat_match_loss=2.205, over 6952.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:33:21,287 INFO [train.py:919] (4/6) Start epoch 20 +2024-03-12 01:35:47,061 INFO [train.py:527] (4/6) Epoch 20, batch 44, global_batch_idx: 2400, batch size: 44, loss[discriminator_loss=2.65, discriminator_real_loss=1.313, discriminator_fake_loss=1.338, generator_loss=33.32, generator_mel_loss=25.51, generator_kl_loss=1.37, generator_dur_loss=1.965, generator_adv_loss=1.919, generator_feat_match_loss=2.563, over 44.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.395, discriminator_fake_loss=1.346, generator_loss=30.61, generator_mel_loss=23.36, generator_kl_loss=1.259, generator_dur_loss=1.984, generator_adv_loss=1.868, generator_feat_match_loss=2.142, over 2666.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:35:47,063 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 01:35:54,746 INFO [train.py:591] (4/6) Epoch 20, validation: discriminator_loss=2.762, discriminator_real_loss=1.446, discriminator_fake_loss=1.316, generator_loss=29.25, generator_mel_loss=22.69, generator_kl_loss=1.153, generator_dur_loss=2.09, generator_adv_loss=1.794, generator_feat_match_loss=1.518, over 100.00 samples. +2024-03-12 01:35:54,748 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 01:38:14,719 INFO [train.py:527] (4/6) Epoch 20, batch 94, global_batch_idx: 2450, batch size: 13, loss[discriminator_loss=2.709, discriminator_real_loss=1.354, discriminator_fake_loss=1.355, generator_loss=34.61, generator_mel_loss=26.85, generator_kl_loss=1.497, generator_dur_loss=1.82, generator_adv_loss=2.069, generator_feat_match_loss=2.373, over 13.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.401, discriminator_fake_loss=1.335, generator_loss=30.88, generator_mel_loss=23.56, generator_kl_loss=1.27, generator_dur_loss=1.981, generator_adv_loss=1.884, generator_feat_match_loss=2.188, over 5493.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:39:34,391 INFO [train.py:919] (4/6) Start epoch 21 +2024-03-12 01:41:48,434 INFO [train.py:527] (4/6) Epoch 21, batch 20, global_batch_idx: 2500, batch size: 36, loss[discriminator_loss=2.779, discriminator_real_loss=1.585, discriminator_fake_loss=1.194, generator_loss=31.69, generator_mel_loss=24.59, generator_kl_loss=1.272, generator_dur_loss=1.933, generator_adv_loss=1.873, generator_feat_match_loss=2.018, over 36.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.404, discriminator_fake_loss=1.339, generator_loss=31.36, generator_mel_loss=23.97, generator_kl_loss=1.273, generator_dur_loss=1.976, generator_adv_loss=1.905, generator_feat_match_loss=2.237, over 1214.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:44:07,471 INFO [train.py:527] (4/6) Epoch 21, batch 70, global_batch_idx: 2550, batch size: 45, loss[discriminator_loss=2.807, discriminator_real_loss=1.536, discriminator_fake_loss=1.272, generator_loss=31.1, generator_mel_loss=23.79, generator_kl_loss=1.339, generator_dur_loss=1.906, generator_adv_loss=1.739, generator_feat_match_loss=2.318, over 45.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.397, discriminator_fake_loss=1.337, generator_loss=30.89, generator_mel_loss=23.49, generator_kl_loss=1.271, generator_dur_loss=1.979, generator_adv_loss=1.899, generator_feat_match_loss=2.243, over 4286.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:46:24,404 INFO [train.py:527] (4/6) Epoch 21, batch 120, global_batch_idx: 2600, batch size: 48, loss[discriminator_loss=2.803, discriminator_real_loss=1.449, discriminator_fake_loss=1.354, generator_loss=31.23, generator_mel_loss=23.99, generator_kl_loss=1.336, generator_dur_loss=1.895, generator_adv_loss=1.918, generator_feat_match_loss=2.099, over 48.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.403, discriminator_fake_loss=1.334, generator_loss=30.84, generator_mel_loss=23.4, generator_kl_loss=1.277, generator_dur_loss=1.979, generator_adv_loss=1.924, generator_feat_match_loss=2.262, over 7044.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:46:24,406 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 01:46:33,227 INFO [train.py:591] (4/6) Epoch 21, validation: discriminator_loss=2.716, discriminator_real_loss=1.447, discriminator_fake_loss=1.27, generator_loss=30.54, generator_mel_loss=23.51, generator_kl_loss=1.136, generator_dur_loss=2.084, generator_adv_loss=1.928, generator_feat_match_loss=1.873, over 100.00 samples. +2024-03-12 01:46:33,228 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 01:46:43,669 INFO [train.py:919] (4/6) Start epoch 22 +2024-03-12 01:49:19,672 INFO [train.py:527] (4/6) Epoch 22, batch 46, global_batch_idx: 2650, batch size: 64, loss[discriminator_loss=2.597, discriminator_real_loss=1.27, discriminator_fake_loss=1.327, generator_loss=32.4, generator_mel_loss=24.21, generator_kl_loss=1.356, generator_dur_loss=1.94, generator_adv_loss=2.017, generator_feat_match_loss=2.878, over 64.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.41, discriminator_fake_loss=1.325, generator_loss=30.53, generator_mel_loss=23.06, generator_kl_loss=1.297, generator_dur_loss=1.981, generator_adv_loss=1.906, generator_feat_match_loss=2.29, over 2800.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:51:39,034 INFO [train.py:527] (4/6) Epoch 22, batch 96, global_batch_idx: 2700, batch size: 45, loss[discriminator_loss=2.68, discriminator_real_loss=1.289, discriminator_fake_loss=1.391, generator_loss=31.51, generator_mel_loss=23.51, generator_kl_loss=1.501, generator_dur_loss=1.913, generator_adv_loss=2.115, generator_feat_match_loss=2.473, over 45.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.403, discriminator_fake_loss=1.328, generator_loss=30.63, generator_mel_loss=23.15, generator_kl_loss=1.281, generator_dur_loss=1.98, generator_adv_loss=1.917, generator_feat_match_loss=2.303, over 5693.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:52:49,840 INFO [train.py:919] (4/6) Start epoch 23 +2024-03-12 01:54:13,351 INFO [train.py:527] (4/6) Epoch 23, batch 22, global_batch_idx: 2750, batch size: 64, loss[discriminator_loss=2.719, discriminator_real_loss=1.452, discriminator_fake_loss=1.267, generator_loss=31.54, generator_mel_loss=24.21, generator_kl_loss=1.222, generator_dur_loss=1.921, generator_adv_loss=1.833, generator_feat_match_loss=2.357, over 64.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.394, discriminator_fake_loss=1.357, generator_loss=30.57, generator_mel_loss=23.18, generator_kl_loss=1.281, generator_dur_loss=1.992, generator_adv_loss=1.889, generator_feat_match_loss=2.227, over 1503.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:56:31,413 INFO [train.py:527] (4/6) Epoch 23, batch 72, global_batch_idx: 2800, batch size: 31, loss[discriminator_loss=2.784, discriminator_real_loss=1.416, discriminator_fake_loss=1.368, generator_loss=30.69, generator_mel_loss=23.31, generator_kl_loss=1.319, generator_dur_loss=1.829, generator_adv_loss=2.156, generator_feat_match_loss=2.084, over 31.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.412, discriminator_fake_loss=1.345, generator_loss=30.79, generator_mel_loss=23.24, generator_kl_loss=1.276, generator_dur_loss=1.983, generator_adv_loss=1.951, generator_feat_match_loss=2.345, over 4493.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:56:31,414 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 01:56:39,134 INFO [train.py:591] (4/6) Epoch 23, validation: discriminator_loss=2.76, discriminator_real_loss=1.551, discriminator_fake_loss=1.209, generator_loss=29.39, generator_mel_loss=22.55, generator_kl_loss=1.05, generator_dur_loss=2.078, generator_adv_loss=2.038, generator_feat_match_loss=1.676, over 100.00 samples. +2024-03-12 01:56:39,135 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 01:58:56,576 INFO [train.py:527] (4/6) Epoch 23, batch 122, global_batch_idx: 2850, batch size: 31, loss[discriminator_loss=2.757, discriminator_real_loss=1.423, discriminator_fake_loss=1.334, generator_loss=30.41, generator_mel_loss=23.32, generator_kl_loss=1.402, generator_dur_loss=1.814, generator_adv_loss=1.92, generator_feat_match_loss=1.954, over 31.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.412, discriminator_fake_loss=1.339, generator_loss=30.63, generator_mel_loss=23.15, generator_kl_loss=1.273, generator_dur_loss=1.97, generator_adv_loss=1.93, generator_feat_match_loss=2.302, over 7244.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:59:02,487 INFO [train.py:919] (4/6) Start epoch 24 +2024-03-12 02:01:38,327 INFO [train.py:527] (4/6) Epoch 24, batch 48, global_batch_idx: 2900, batch size: 88, loss[discriminator_loss=2.729, discriminator_real_loss=1.281, discriminator_fake_loss=1.448, generator_loss=29.69, generator_mel_loss=22.31, generator_kl_loss=1.204, generator_dur_loss=2.021, generator_adv_loss=1.925, generator_feat_match_loss=2.235, over 88.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.408, discriminator_fake_loss=1.348, generator_loss=30.32, generator_mel_loss=22.96, generator_kl_loss=1.269, generator_dur_loss=1.958, generator_adv_loss=1.897, generator_feat_match_loss=2.236, over 2669.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:03:56,254 INFO [train.py:527] (4/6) Epoch 24, batch 98, global_batch_idx: 2950, batch size: 59, loss[discriminator_loss=2.734, discriminator_real_loss=1.446, discriminator_fake_loss=1.288, generator_loss=29.84, generator_mel_loss=22.55, generator_kl_loss=1.186, generator_dur_loss=1.943, generator_adv_loss=1.967, generator_feat_match_loss=2.197, over 59.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.405, discriminator_fake_loss=1.34, generator_loss=30.41, generator_mel_loss=22.9, generator_kl_loss=1.261, generator_dur_loss=1.969, generator_adv_loss=1.938, generator_feat_match_loss=2.338, over 5600.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:05:07,825 INFO [train.py:919] (4/6) Start epoch 25 +2024-03-12 02:06:39,769 INFO [train.py:527] (4/6) Epoch 25, batch 24, global_batch_idx: 3000, batch size: 64, loss[discriminator_loss=2.802, discriminator_real_loss=1.7, discriminator_fake_loss=1.103, generator_loss=29.85, generator_mel_loss=22.32, generator_kl_loss=1.203, generator_dur_loss=1.954, generator_adv_loss=2.198, generator_feat_match_loss=2.177, over 64.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.431, discriminator_fake_loss=1.321, generator_loss=29.99, generator_mel_loss=22.48, generator_kl_loss=1.214, generator_dur_loss=2.001, generator_adv_loss=1.972, generator_feat_match_loss=2.321, over 1642.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:06:39,770 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 02:06:47,849 INFO [train.py:591] (4/6) Epoch 25, validation: discriminator_loss=2.911, discriminator_real_loss=1.657, discriminator_fake_loss=1.254, generator_loss=29.63, generator_mel_loss=22.44, generator_kl_loss=1.237, generator_dur_loss=2.038, generator_adv_loss=2.074, generator_feat_match_loss=1.843, over 100.00 samples. +2024-03-12 02:06:47,850 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 02:09:03,743 INFO [train.py:527] (4/6) Epoch 25, batch 74, global_batch_idx: 3050, batch size: 15, loss[discriminator_loss=2.774, discriminator_real_loss=1.304, discriminator_fake_loss=1.47, generator_loss=31.62, generator_mel_loss=23.83, generator_kl_loss=1.43, generator_dur_loss=1.801, generator_adv_loss=2.327, generator_feat_match_loss=2.229, over 15.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.42, discriminator_fake_loss=1.324, generator_loss=30.15, generator_mel_loss=22.68, generator_kl_loss=1.254, generator_dur_loss=1.977, generator_adv_loss=1.94, generator_feat_match_loss=2.296, over 4317.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:11:21,438 INFO [train.py:919] (4/6) Start epoch 26 +2024-03-12 02:11:45,848 INFO [train.py:527] (4/6) Epoch 26, batch 0, global_batch_idx: 3100, batch size: 39, loss[discriminator_loss=2.809, discriminator_real_loss=1.313, discriminator_fake_loss=1.495, generator_loss=30.56, generator_mel_loss=23.31, generator_kl_loss=1.39, generator_dur_loss=1.895, generator_adv_loss=1.729, generator_feat_match_loss=2.229, over 39.00 samples.], tot_loss[discriminator_loss=2.809, discriminator_real_loss=1.313, discriminator_fake_loss=1.495, generator_loss=30.56, generator_mel_loss=23.31, generator_kl_loss=1.39, generator_dur_loss=1.895, generator_adv_loss=1.729, generator_feat_match_loss=2.229, over 39.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:14:06,936 INFO [train.py:527] (4/6) Epoch 26, batch 50, global_batch_idx: 3150, batch size: 77, loss[discriminator_loss=2.675, discriminator_real_loss=1.251, discriminator_fake_loss=1.424, generator_loss=30.8, generator_mel_loss=23.17, generator_kl_loss=1.11, generator_dur_loss=2.039, generator_adv_loss=1.919, generator_feat_match_loss=2.558, over 77.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.409, discriminator_fake_loss=1.343, generator_loss=29.83, generator_mel_loss=22.43, generator_kl_loss=1.248, generator_dur_loss=1.979, generator_adv_loss=1.924, generator_feat_match_loss=2.25, over 3097.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:16:25,891 INFO [train.py:527] (4/6) Epoch 26, batch 100, global_batch_idx: 3200, batch size: 61, loss[discriminator_loss=2.664, discriminator_real_loss=1.397, discriminator_fake_loss=1.266, generator_loss=30.53, generator_mel_loss=22.81, generator_kl_loss=1.324, generator_dur_loss=1.926, generator_adv_loss=1.78, generator_feat_match_loss=2.684, over 61.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.404, discriminator_fake_loss=1.331, generator_loss=30.03, generator_mel_loss=22.57, generator_kl_loss=1.269, generator_dur_loss=1.973, generator_adv_loss=1.923, generator_feat_match_loss=2.298, over 6011.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:16:25,892 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 02:16:34,497 INFO [train.py:591] (4/6) Epoch 26, validation: discriminator_loss=2.733, discriminator_real_loss=1.372, discriminator_fake_loss=1.361, generator_loss=28.89, generator_mel_loss=22.07, generator_kl_loss=1.013, generator_dur_loss=2.064, generator_adv_loss=1.783, generator_feat_match_loss=1.959, over 100.00 samples. +2024-03-12 02:16:34,498 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 02:17:37,710 INFO [train.py:919] (4/6) Start epoch 27 +2024-03-12 02:19:15,732 INFO [train.py:527] (4/6) Epoch 27, batch 26, global_batch_idx: 3250, batch size: 14, loss[discriminator_loss=2.718, discriminator_real_loss=1.283, discriminator_fake_loss=1.435, generator_loss=32.99, generator_mel_loss=25.38, generator_kl_loss=1.385, generator_dur_loss=1.789, generator_adv_loss=1.738, generator_feat_match_loss=2.698, over 14.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.428, discriminator_fake_loss=1.325, generator_loss=30.34, generator_mel_loss=22.7, generator_kl_loss=1.246, generator_dur_loss=1.979, generator_adv_loss=1.977, generator_feat_match_loss=2.442, over 1637.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:21:36,371 INFO [train.py:527] (4/6) Epoch 27, batch 76, global_batch_idx: 3300, batch size: 53, loss[discriminator_loss=2.724, discriminator_real_loss=1.288, discriminator_fake_loss=1.436, generator_loss=30.26, generator_mel_loss=22.43, generator_kl_loss=1.336, generator_dur_loss=1.898, generator_adv_loss=2.135, generator_feat_match_loss=2.462, over 53.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.417, discriminator_fake_loss=1.336, generator_loss=30.07, generator_mel_loss=22.47, generator_kl_loss=1.247, generator_dur_loss=1.968, generator_adv_loss=1.978, generator_feat_match_loss=2.405, over 4566.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:23:48,061 INFO [train.py:919] (4/6) Start epoch 28 +2024-03-12 02:24:17,419 INFO [train.py:527] (4/6) Epoch 28, batch 2, global_batch_idx: 3350, batch size: 77, loss[discriminator_loss=2.931, discriminator_real_loss=1.448, discriminator_fake_loss=1.483, generator_loss=28.38, generator_mel_loss=21.27, generator_kl_loss=1.117, generator_dur_loss=2.074, generator_adv_loss=1.81, generator_feat_match_loss=2.104, over 77.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.392, discriminator_fake_loss=1.368, generator_loss=29.45, generator_mel_loss=21.75, generator_kl_loss=1.208, generator_dur_loss=2.004, generator_adv_loss=2.044, generator_feat_match_loss=2.447, over 199.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:26:35,486 INFO [train.py:527] (4/6) Epoch 28, batch 52, global_batch_idx: 3400, batch size: 58, loss[discriminator_loss=2.779, discriminator_real_loss=1.287, discriminator_fake_loss=1.492, generator_loss=29.25, generator_mel_loss=21.93, generator_kl_loss=1.413, generator_dur_loss=1.97, generator_adv_loss=1.801, generator_feat_match_loss=2.137, over 58.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.425, discriminator_fake_loss=1.345, generator_loss=29.91, generator_mel_loss=22.47, generator_kl_loss=1.248, generator_dur_loss=1.953, generator_adv_loss=1.925, generator_feat_match_loss=2.309, over 2815.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:26:35,487 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 02:26:43,331 INFO [train.py:591] (4/6) Epoch 28, validation: discriminator_loss=2.762, discriminator_real_loss=1.354, discriminator_fake_loss=1.408, generator_loss=30.33, generator_mel_loss=23.31, generator_kl_loss=1.208, generator_dur_loss=2.058, generator_adv_loss=1.726, generator_feat_match_loss=2.028, over 100.00 samples. +2024-03-12 02:26:43,332 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28733MB +2024-03-12 02:29:00,586 INFO [train.py:527] (4/6) Epoch 28, batch 102, global_batch_idx: 3450, batch size: 77, loss[discriminator_loss=2.707, discriminator_real_loss=1.446, discriminator_fake_loss=1.261, generator_loss=30.36, generator_mel_loss=22.41, generator_kl_loss=1.291, generator_dur_loss=1.979, generator_adv_loss=2.076, generator_feat_match_loss=2.6, over 77.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.409, discriminator_fake_loss=1.338, generator_loss=29.98, generator_mel_loss=22.53, generator_kl_loss=1.26, generator_dur_loss=1.95, generator_adv_loss=1.922, generator_feat_match_loss=2.322, over 5585.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:30:00,952 INFO [train.py:919] (4/6) Start epoch 29 +2024-03-12 02:31:42,453 INFO [train.py:527] (4/6) Epoch 29, batch 28, global_batch_idx: 3500, batch size: 47, loss[discriminator_loss=2.662, discriminator_real_loss=1.328, discriminator_fake_loss=1.334, generator_loss=30.04, generator_mel_loss=22.3, generator_kl_loss=1.361, generator_dur_loss=1.869, generator_adv_loss=1.908, generator_feat_match_loss=2.608, over 47.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.385, discriminator_fake_loss=1.334, generator_loss=29.71, generator_mel_loss=22.31, generator_kl_loss=1.261, generator_dur_loss=1.954, generator_adv_loss=1.887, generator_feat_match_loss=2.3, over 1649.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:34:01,813 INFO [train.py:527] (4/6) Epoch 29, batch 78, global_batch_idx: 3550, batch size: 55, loss[discriminator_loss=2.676, discriminator_real_loss=1.325, discriminator_fake_loss=1.351, generator_loss=31.48, generator_mel_loss=23.5, generator_kl_loss=1.24, generator_dur_loss=1.961, generator_adv_loss=2.151, generator_feat_match_loss=2.625, over 55.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.407, discriminator_fake_loss=1.331, generator_loss=29.84, generator_mel_loss=22.36, generator_kl_loss=1.258, generator_dur_loss=1.95, generator_adv_loss=1.932, generator_feat_match_loss=2.341, over 4441.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:36:07,231 INFO [train.py:919] (4/6) Start epoch 30 +2024-03-12 02:36:42,546 INFO [train.py:527] (4/6) Epoch 30, batch 4, global_batch_idx: 3600, batch size: 31, loss[discriminator_loss=2.739, discriminator_real_loss=1.573, discriminator_fake_loss=1.167, generator_loss=28.76, generator_mel_loss=21.86, generator_kl_loss=1.272, generator_dur_loss=1.76, generator_adv_loss=1.766, generator_feat_match_loss=2.103, over 31.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.442, discriminator_fake_loss=1.235, generator_loss=29.48, generator_mel_loss=21.86, generator_kl_loss=1.287, generator_dur_loss=1.946, generator_adv_loss=2.034, generator_feat_match_loss=2.353, over 247.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:36:42,548 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 02:36:50,361 INFO [train.py:591] (4/6) Epoch 30, validation: discriminator_loss=2.709, discriminator_real_loss=1.298, discriminator_fake_loss=1.41, generator_loss=28.39, generator_mel_loss=21.72, generator_kl_loss=1.022, generator_dur_loss=2.038, generator_adv_loss=1.708, generator_feat_match_loss=1.903, over 100.00 samples. +2024-03-12 02:36:50,364 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 02:39:06,974 INFO [train.py:527] (4/6) Epoch 30, batch 54, global_batch_idx: 3650, batch size: 56, loss[discriminator_loss=2.728, discriminator_real_loss=1.353, discriminator_fake_loss=1.374, generator_loss=29.3, generator_mel_loss=22.18, generator_kl_loss=1.25, generator_dur_loss=1.933, generator_adv_loss=1.851, generator_feat_match_loss=2.083, over 56.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.406, discriminator_fake_loss=1.322, generator_loss=29.96, generator_mel_loss=22.33, generator_kl_loss=1.254, generator_dur_loss=1.938, generator_adv_loss=1.968, generator_feat_match_loss=2.464, over 2842.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:41:24,076 INFO [train.py:527] (4/6) Epoch 30, batch 104, global_batch_idx: 3700, batch size: 48, loss[discriminator_loss=2.619, discriminator_real_loss=1.41, discriminator_fake_loss=1.208, generator_loss=31.51, generator_mel_loss=23.37, generator_kl_loss=1.291, generator_dur_loss=1.839, generator_adv_loss=1.905, generator_feat_match_loss=3.11, over 48.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.401, discriminator_fake_loss=1.323, generator_loss=29.95, generator_mel_loss=22.36, generator_kl_loss=1.247, generator_dur_loss=1.943, generator_adv_loss=1.949, generator_feat_match_loss=2.45, over 5670.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:42:19,597 INFO [train.py:919] (4/6) Start epoch 31 +2024-03-12 02:44:05,429 INFO [train.py:527] (4/6) Epoch 31, batch 30, global_batch_idx: 3750, batch size: 74, loss[discriminator_loss=2.882, discriminator_real_loss=1.575, discriminator_fake_loss=1.307, generator_loss=28.71, generator_mel_loss=20.84, generator_kl_loss=1.218, generator_dur_loss=2.038, generator_adv_loss=2.066, generator_feat_match_loss=2.548, over 74.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.407, discriminator_fake_loss=1.339, generator_loss=30.03, generator_mel_loss=22.28, generator_kl_loss=1.258, generator_dur_loss=1.946, generator_adv_loss=2.002, generator_feat_match_loss=2.539, over 1777.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:46:25,070 INFO [train.py:527] (4/6) Epoch 31, batch 80, global_batch_idx: 3800, batch size: 64, loss[discriminator_loss=2.754, discriminator_real_loss=1.303, discriminator_fake_loss=1.451, generator_loss=27.98, generator_mel_loss=21.05, generator_kl_loss=1.249, generator_dur_loss=1.913, generator_adv_loss=1.685, generator_feat_match_loss=2.077, over 64.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.399, discriminator_fake_loss=1.343, generator_loss=29.84, generator_mel_loss=22.23, generator_kl_loss=1.225, generator_dur_loss=1.953, generator_adv_loss=1.964, generator_feat_match_loss=2.469, over 4812.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:46:25,072 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 02:46:33,133 INFO [train.py:591] (4/6) Epoch 31, validation: discriminator_loss=2.739, discriminator_real_loss=1.32, discriminator_fake_loss=1.419, generator_loss=27.75, generator_mel_loss=21.26, generator_kl_loss=1.047, generator_dur_loss=2.012, generator_adv_loss=1.689, generator_feat_match_loss=1.744, over 100.00 samples. +2024-03-12 02:46:33,134 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 02:48:32,244 INFO [train.py:919] (4/6) Start epoch 32 +2024-03-12 02:49:14,968 INFO [train.py:527] (4/6) Epoch 32, batch 6, global_batch_idx: 3850, batch size: 44, loss[discriminator_loss=2.683, discriminator_real_loss=1.385, discriminator_fake_loss=1.298, generator_loss=31.05, generator_mel_loss=23.56, generator_kl_loss=1.225, generator_dur_loss=1.878, generator_adv_loss=1.895, generator_feat_match_loss=2.49, over 44.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.376, discriminator_fake_loss=1.351, generator_loss=29.61, generator_mel_loss=22.11, generator_kl_loss=1.273, generator_dur_loss=1.95, generator_adv_loss=1.913, generator_feat_match_loss=2.369, over 433.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:51:36,074 INFO [train.py:527] (4/6) Epoch 32, batch 56, global_batch_idx: 3900, batch size: 48, loss[discriminator_loss=2.69, discriminator_real_loss=1.394, discriminator_fake_loss=1.296, generator_loss=30.77, generator_mel_loss=23.21, generator_kl_loss=1.255, generator_dur_loss=1.855, generator_adv_loss=1.886, generator_feat_match_loss=2.571, over 48.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=29.51, generator_mel_loss=21.94, generator_kl_loss=1.256, generator_dur_loss=1.948, generator_adv_loss=1.947, generator_feat_match_loss=2.423, over 3445.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:53:54,857 INFO [train.py:527] (4/6) Epoch 32, batch 106, global_batch_idx: 3950, batch size: 83, loss[discriminator_loss=2.761, discriminator_real_loss=1.407, discriminator_fake_loss=1.354, generator_loss=30.16, generator_mel_loss=22.48, generator_kl_loss=1.165, generator_dur_loss=2.015, generator_adv_loss=1.949, generator_feat_match_loss=2.546, over 83.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.398, discriminator_fake_loss=1.338, generator_loss=29.59, generator_mel_loss=22.04, generator_kl_loss=1.25, generator_dur_loss=1.95, generator_adv_loss=1.937, generator_feat_match_loss=2.414, over 6460.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:54:41,313 INFO [train.py:919] (4/6) Start epoch 33 +2024-03-12 02:56:35,261 INFO [train.py:527] (4/6) Epoch 33, batch 32, global_batch_idx: 4000, batch size: 31, loss[discriminator_loss=2.73, discriminator_real_loss=1.516, discriminator_fake_loss=1.213, generator_loss=32.66, generator_mel_loss=24.46, generator_kl_loss=1.476, generator_dur_loss=1.761, generator_adv_loss=1.933, generator_feat_match_loss=3.031, over 31.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.4, discriminator_fake_loss=1.332, generator_loss=29.99, generator_mel_loss=22.27, generator_kl_loss=1.223, generator_dur_loss=1.934, generator_adv_loss=1.986, generator_feat_match_loss=2.582, over 1929.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:56:35,262 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 02:56:43,046 INFO [train.py:591] (4/6) Epoch 33, validation: discriminator_loss=2.715, discriminator_real_loss=1.291, discriminator_fake_loss=1.424, generator_loss=28.62, generator_mel_loss=21.9, generator_kl_loss=1.076, generator_dur_loss=2.005, generator_adv_loss=1.742, generator_feat_match_loss=1.901, over 100.00 samples. +2024-03-12 02:56:43,047 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 02:59:01,964 INFO [train.py:527] (4/6) Epoch 33, batch 82, global_batch_idx: 4050, batch size: 42, loss[discriminator_loss=2.807, discriminator_real_loss=1.361, discriminator_fake_loss=1.447, generator_loss=29.32, generator_mel_loss=21.84, generator_kl_loss=1.295, generator_dur_loss=1.843, generator_adv_loss=2.074, generator_feat_match_loss=2.267, over 42.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.413, discriminator_fake_loss=1.337, generator_loss=29.86, generator_mel_loss=22.17, generator_kl_loss=1.239, generator_dur_loss=1.919, generator_adv_loss=1.999, generator_feat_match_loss=2.532, over 4577.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:00:56,706 INFO [train.py:919] (4/6) Start epoch 34 +2024-03-12 03:01:42,812 INFO [train.py:527] (4/6) Epoch 34, batch 8, global_batch_idx: 4100, batch size: 68, loss[discriminator_loss=2.699, discriminator_real_loss=1.249, discriminator_fake_loss=1.45, generator_loss=28.57, generator_mel_loss=21.02, generator_kl_loss=1.2, generator_dur_loss=1.96, generator_adv_loss=1.949, generator_feat_match_loss=2.438, over 68.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.403, discriminator_fake_loss=1.324, generator_loss=29.35, generator_mel_loss=22.07, generator_kl_loss=1.196, generator_dur_loss=1.916, generator_adv_loss=1.896, generator_feat_match_loss=2.267, over 480.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:04:02,946 INFO [train.py:527] (4/6) Epoch 34, batch 58, global_batch_idx: 4150, batch size: 36, loss[discriminator_loss=2.869, discriminator_real_loss=1.372, discriminator_fake_loss=1.497, generator_loss=29.52, generator_mel_loss=22.43, generator_kl_loss=1.342, generator_dur_loss=1.89, generator_adv_loss=1.778, generator_feat_match_loss=2.085, over 36.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.396, discriminator_fake_loss=1.345, generator_loss=29.66, generator_mel_loss=22.19, generator_kl_loss=1.224, generator_dur_loss=1.931, generator_adv_loss=1.915, generator_feat_match_loss=2.392, over 3351.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:06:21,468 INFO [train.py:527] (4/6) Epoch 34, batch 108, global_batch_idx: 4200, batch size: 25, loss[discriminator_loss=2.586, discriminator_real_loss=1.186, discriminator_fake_loss=1.4, generator_loss=32.58, generator_mel_loss=24.13, generator_kl_loss=1.459, generator_dur_loss=1.689, generator_adv_loss=2.064, generator_feat_match_loss=3.23, over 25.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.399, discriminator_fake_loss=1.343, generator_loss=29.56, generator_mel_loss=22.03, generator_kl_loss=1.225, generator_dur_loss=1.932, generator_adv_loss=1.944, generator_feat_match_loss=2.426, over 6313.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:06:21,469 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 03:06:30,178 INFO [train.py:591] (4/6) Epoch 34, validation: discriminator_loss=2.732, discriminator_real_loss=1.566, discriminator_fake_loss=1.165, generator_loss=28.72, generator_mel_loss=21.75, generator_kl_loss=1.073, generator_dur_loss=1.998, generator_adv_loss=1.98, generator_feat_match_loss=1.921, over 100.00 samples. +2024-03-12 03:06:30,179 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 03:07:11,456 INFO [train.py:919] (4/6) Start epoch 35 +2024-03-12 03:09:10,467 INFO [train.py:527] (4/6) Epoch 35, batch 34, global_batch_idx: 4250, batch size: 49, loss[discriminator_loss=2.669, discriminator_real_loss=1.294, discriminator_fake_loss=1.375, generator_loss=29.71, generator_mel_loss=21.92, generator_kl_loss=1.378, generator_dur_loss=1.849, generator_adv_loss=2.103, generator_feat_match_loss=2.463, over 49.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.394, discriminator_fake_loss=1.333, generator_loss=29.09, generator_mel_loss=21.69, generator_kl_loss=1.229, generator_dur_loss=1.936, generator_adv_loss=1.901, generator_feat_match_loss=2.336, over 2156.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:11:30,602 INFO [train.py:527] (4/6) Epoch 35, batch 84, global_batch_idx: 4300, batch size: 61, loss[discriminator_loss=2.806, discriminator_real_loss=1.238, discriminator_fake_loss=1.568, generator_loss=30.38, generator_mel_loss=22.51, generator_kl_loss=1.415, generator_dur_loss=1.928, generator_adv_loss=2.102, generator_feat_match_loss=2.419, over 61.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.398, discriminator_fake_loss=1.343, generator_loss=29.2, generator_mel_loss=21.72, generator_kl_loss=1.226, generator_dur_loss=1.941, generator_adv_loss=1.927, generator_feat_match_loss=2.387, over 5280.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:13:17,688 INFO [train.py:919] (4/6) Start epoch 36 +2024-03-12 03:14:10,380 INFO [train.py:527] (4/6) Epoch 36, batch 10, global_batch_idx: 4350, batch size: 53, loss[discriminator_loss=2.687, discriminator_real_loss=1.42, discriminator_fake_loss=1.266, generator_loss=30.41, generator_mel_loss=22.94, generator_kl_loss=1.133, generator_dur_loss=1.853, generator_adv_loss=1.774, generator_feat_match_loss=2.713, over 53.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.4, discriminator_fake_loss=1.334, generator_loss=29.65, generator_mel_loss=22.15, generator_kl_loss=1.227, generator_dur_loss=1.917, generator_adv_loss=1.904, generator_feat_match_loss=2.445, over 632.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:16:31,648 INFO [train.py:527] (4/6) Epoch 36, batch 60, global_batch_idx: 4400, batch size: 50, loss[discriminator_loss=2.711, discriminator_real_loss=1.312, discriminator_fake_loss=1.399, generator_loss=29.55, generator_mel_loss=21.86, generator_kl_loss=1.404, generator_dur_loss=1.811, generator_adv_loss=1.954, generator_feat_match_loss=2.523, over 50.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.413, discriminator_fake_loss=1.313, generator_loss=29.34, generator_mel_loss=21.86, generator_kl_loss=1.236, generator_dur_loss=1.917, generator_adv_loss=1.925, generator_feat_match_loss=2.403, over 3559.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:16:31,649 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 03:16:39,667 INFO [train.py:591] (4/6) Epoch 36, validation: discriminator_loss=2.713, discriminator_real_loss=1.416, discriminator_fake_loss=1.297, generator_loss=28.13, generator_mel_loss=21.35, generator_kl_loss=1.097, generator_dur_loss=1.984, generator_adv_loss=1.838, generator_feat_match_loss=1.856, over 100.00 samples. +2024-03-12 03:16:39,668 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 03:18:56,396 INFO [train.py:527] (4/6) Epoch 36, batch 110, global_batch_idx: 4450, batch size: 61, loss[discriminator_loss=2.687, discriminator_real_loss=1.458, discriminator_fake_loss=1.229, generator_loss=31.16, generator_mel_loss=23.34, generator_kl_loss=1.325, generator_dur_loss=1.911, generator_adv_loss=1.888, generator_feat_match_loss=2.694, over 61.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.406, discriminator_fake_loss=1.325, generator_loss=29.45, generator_mel_loss=21.94, generator_kl_loss=1.251, generator_dur_loss=1.917, generator_adv_loss=1.929, generator_feat_match_loss=2.419, over 6444.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:19:33,001 INFO [train.py:919] (4/6) Start epoch 37 +2024-03-12 03:21:34,210 INFO [train.py:527] (4/6) Epoch 37, batch 36, global_batch_idx: 4500, batch size: 45, loss[discriminator_loss=2.761, discriminator_real_loss=1.286, discriminator_fake_loss=1.474, generator_loss=29.85, generator_mel_loss=22.73, generator_kl_loss=1.279, generator_dur_loss=1.836, generator_adv_loss=1.799, generator_feat_match_loss=2.206, over 45.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.412, discriminator_fake_loss=1.351, generator_loss=29.33, generator_mel_loss=21.76, generator_kl_loss=1.265, generator_dur_loss=1.91, generator_adv_loss=1.942, generator_feat_match_loss=2.453, over 2096.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:23:54,459 INFO [train.py:527] (4/6) Epoch 37, batch 86, global_batch_idx: 4550, batch size: 59, loss[discriminator_loss=2.651, discriminator_real_loss=1.261, discriminator_fake_loss=1.391, generator_loss=29.34, generator_mel_loss=21.85, generator_kl_loss=1.135, generator_dur_loss=1.891, generator_adv_loss=1.92, generator_feat_match_loss=2.546, over 59.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.406, discriminator_fake_loss=1.345, generator_loss=29.22, generator_mel_loss=21.68, generator_kl_loss=1.247, generator_dur_loss=1.913, generator_adv_loss=1.935, generator_feat_match_loss=2.439, over 5047.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:25:40,678 INFO [train.py:919] (4/6) Start epoch 38 +2024-03-12 03:26:39,471 INFO [train.py:527] (4/6) Epoch 38, batch 12, global_batch_idx: 4600, batch size: 70, loss[discriminator_loss=2.756, discriminator_real_loss=1.514, discriminator_fake_loss=1.242, generator_loss=29.33, generator_mel_loss=22.09, generator_kl_loss=1.231, generator_dur_loss=1.961, generator_adv_loss=1.832, generator_feat_match_loss=2.221, over 70.00 samples.], tot_loss[discriminator_loss=2.775, discriminator_real_loss=1.449, discriminator_fake_loss=1.326, generator_loss=28.94, generator_mel_loss=21.57, generator_kl_loss=1.295, generator_dur_loss=1.903, generator_adv_loss=1.889, generator_feat_match_loss=2.285, over 732.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:26:39,474 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 03:26:47,327 INFO [train.py:591] (4/6) Epoch 38, validation: discriminator_loss=2.736, discriminator_real_loss=1.45, discriminator_fake_loss=1.286, generator_loss=27.59, generator_mel_loss=21.01, generator_kl_loss=1.146, generator_dur_loss=1.966, generator_adv_loss=1.795, generator_feat_match_loss=1.672, over 100.00 samples. +2024-03-12 03:26:47,327 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 03:29:05,461 INFO [train.py:527] (4/6) Epoch 38, batch 62, global_batch_idx: 4650, batch size: 44, loss[discriminator_loss=2.754, discriminator_real_loss=1.415, discriminator_fake_loss=1.339, generator_loss=28.74, generator_mel_loss=21.24, generator_kl_loss=1.23, generator_dur_loss=1.881, generator_adv_loss=2.056, generator_feat_match_loss=2.33, over 44.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.404, discriminator_fake_loss=1.338, generator_loss=29.11, generator_mel_loss=21.65, generator_kl_loss=1.264, generator_dur_loss=1.907, generator_adv_loss=1.893, generator_feat_match_loss=2.395, over 3624.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:31:25,946 INFO [train.py:527] (4/6) Epoch 38, batch 112, global_batch_idx: 4700, batch size: 50, loss[discriminator_loss=2.69, discriminator_real_loss=1.475, discriminator_fake_loss=1.215, generator_loss=28.38, generator_mel_loss=20.97, generator_kl_loss=1.326, generator_dur_loss=1.869, generator_adv_loss=1.835, generator_feat_match_loss=2.384, over 50.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.405, discriminator_fake_loss=1.337, generator_loss=29.08, generator_mel_loss=21.6, generator_kl_loss=1.259, generator_dur_loss=1.9, generator_adv_loss=1.907, generator_feat_match_loss=2.408, over 6467.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:31:59,583 INFO [train.py:919] (4/6) Start epoch 39 +2024-03-12 03:34:09,072 INFO [train.py:527] (4/6) Epoch 39, batch 38, global_batch_idx: 4750, batch size: 39, loss[discriminator_loss=2.679, discriminator_real_loss=1.194, discriminator_fake_loss=1.485, generator_loss=30.25, generator_mel_loss=22.56, generator_kl_loss=1.244, generator_dur_loss=1.795, generator_adv_loss=1.951, generator_feat_match_loss=2.696, over 39.00 samples.], tot_loss[discriminator_loss=2.775, discriminator_real_loss=1.432, discriminator_fake_loss=1.343, generator_loss=29.33, generator_mel_loss=21.72, generator_kl_loss=1.264, generator_dur_loss=1.891, generator_adv_loss=1.992, generator_feat_match_loss=2.467, over 2189.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:36:27,841 INFO [train.py:527] (4/6) Epoch 39, batch 88, global_batch_idx: 4800, batch size: 77, loss[discriminator_loss=2.699, discriminator_real_loss=1.315, discriminator_fake_loss=1.384, generator_loss=28.98, generator_mel_loss=21.22, generator_kl_loss=1.366, generator_dur_loss=1.993, generator_adv_loss=1.927, generator_feat_match_loss=2.478, over 77.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.423, discriminator_fake_loss=1.337, generator_loss=29.2, generator_mel_loss=21.65, generator_kl_loss=1.265, generator_dur_loss=1.886, generator_adv_loss=1.965, generator_feat_match_loss=2.435, over 4881.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:36:27,842 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 03:36:36,858 INFO [train.py:591] (4/6) Epoch 39, validation: discriminator_loss=2.785, discriminator_real_loss=1.46, discriminator_fake_loss=1.325, generator_loss=28.19, generator_mel_loss=21.57, generator_kl_loss=1.116, generator_dur_loss=1.951, generator_adv_loss=1.746, generator_feat_match_loss=1.807, over 100.00 samples. +2024-03-12 03:36:36,859 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 03:38:15,645 INFO [train.py:919] (4/6) Start epoch 40 +2024-03-12 03:39:17,970 INFO [train.py:527] (4/6) Epoch 40, batch 14, global_batch_idx: 4850, batch size: 61, loss[discriminator_loss=2.72, discriminator_real_loss=1.521, discriminator_fake_loss=1.199, generator_loss=29, generator_mel_loss=21.58, generator_kl_loss=1.295, generator_dur_loss=1.856, generator_adv_loss=1.93, generator_feat_match_loss=2.333, over 61.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.392, discriminator_fake_loss=1.339, generator_loss=28.67, generator_mel_loss=21.18, generator_kl_loss=1.245, generator_dur_loss=1.882, generator_adv_loss=1.951, generator_feat_match_loss=2.414, over 817.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:41:34,730 INFO [train.py:527] (4/6) Epoch 40, batch 64, global_batch_idx: 4900, batch size: 64, loss[discriminator_loss=2.6, discriminator_real_loss=1.355, discriminator_fake_loss=1.245, generator_loss=29.91, generator_mel_loss=21.21, generator_kl_loss=1.231, generator_dur_loss=1.793, generator_adv_loss=2.323, generator_feat_match_loss=3.347, over 64.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.414, discriminator_fake_loss=1.336, generator_loss=28.91, generator_mel_loss=21.39, generator_kl_loss=1.261, generator_dur_loss=1.895, generator_adv_loss=1.941, generator_feat_match_loss=2.425, over 3840.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:43:52,653 INFO [train.py:527] (4/6) Epoch 40, batch 114, global_batch_idx: 4950, batch size: 47, loss[discriminator_loss=2.724, discriminator_real_loss=1.391, discriminator_fake_loss=1.333, generator_loss=28.86, generator_mel_loss=21.71, generator_kl_loss=1.166, generator_dur_loss=1.806, generator_adv_loss=1.821, generator_feat_match_loss=2.35, over 47.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.425, discriminator_fake_loss=1.334, generator_loss=28.97, generator_mel_loss=21.42, generator_kl_loss=1.26, generator_dur_loss=1.889, generator_adv_loss=1.962, generator_feat_match_loss=2.439, over 6662.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:44:20,360 INFO [train.py:919] (4/6) Start epoch 41 +2024-03-12 03:46:42,091 INFO [train.py:527] (4/6) Epoch 41, batch 40, global_batch_idx: 5000, batch size: 55, loss[discriminator_loss=2.712, discriminator_real_loss=1.376, discriminator_fake_loss=1.336, generator_loss=29.46, generator_mel_loss=21.97, generator_kl_loss=1.228, generator_dur_loss=1.861, generator_adv_loss=1.889, generator_feat_match_loss=2.512, over 55.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.384, discriminator_fake_loss=1.334, generator_loss=29.01, generator_mel_loss=21.52, generator_kl_loss=1.317, generator_dur_loss=1.868, generator_adv_loss=1.902, generator_feat_match_loss=2.394, over 2121.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:46:42,093 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 03:46:50,134 INFO [train.py:591] (4/6) Epoch 41, validation: discriminator_loss=2.762, discriminator_real_loss=1.392, discriminator_fake_loss=1.37, generator_loss=28.57, generator_mel_loss=21.81, generator_kl_loss=1.047, generator_dur_loss=1.956, generator_adv_loss=1.772, generator_feat_match_loss=1.978, over 100.00 samples. +2024-03-12 03:46:50,135 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 03:49:08,361 INFO [train.py:527] (4/6) Epoch 41, batch 90, global_batch_idx: 5050, batch size: 70, loss[discriminator_loss=2.747, discriminator_real_loss=1.412, discriminator_fake_loss=1.335, generator_loss=27.73, generator_mel_loss=20.71, generator_kl_loss=1.216, generator_dur_loss=1.899, generator_adv_loss=1.845, generator_feat_match_loss=2.058, over 70.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.389, discriminator_fake_loss=1.337, generator_loss=29.06, generator_mel_loss=21.54, generator_kl_loss=1.298, generator_dur_loss=1.872, generator_adv_loss=1.909, generator_feat_match_loss=2.439, over 4897.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:50:42,219 INFO [train.py:919] (4/6) Start epoch 42 +2024-03-12 03:51:51,568 INFO [train.py:527] (4/6) Epoch 42, batch 16, global_batch_idx: 5100, batch size: 88, loss[discriminator_loss=2.64, discriminator_real_loss=1.307, discriminator_fake_loss=1.334, generator_loss=27.95, generator_mel_loss=20.54, generator_kl_loss=1.006, generator_dur_loss=2.003, generator_adv_loss=1.81, generator_feat_match_loss=2.595, over 88.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.391, discriminator_fake_loss=1.348, generator_loss=28.8, generator_mel_loss=21.38, generator_kl_loss=1.203, generator_dur_loss=1.899, generator_adv_loss=1.903, generator_feat_match_loss=2.414, over 1069.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:54:10,636 INFO [train.py:527] (4/6) Epoch 42, batch 66, global_batch_idx: 5150, batch size: 44, loss[discriminator_loss=2.762, discriminator_real_loss=1.41, discriminator_fake_loss=1.352, generator_loss=29.03, generator_mel_loss=21.6, generator_kl_loss=1.289, generator_dur_loss=1.795, generator_adv_loss=1.835, generator_feat_match_loss=2.511, over 44.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.404, discriminator_fake_loss=1.346, generator_loss=28.81, generator_mel_loss=21.29, generator_kl_loss=1.249, generator_dur_loss=1.887, generator_adv_loss=1.934, generator_feat_match_loss=2.451, over 3917.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:56:26,588 INFO [train.py:527] (4/6) Epoch 42, batch 116, global_batch_idx: 5200, batch size: 48, loss[discriminator_loss=2.729, discriminator_real_loss=1.529, discriminator_fake_loss=1.2, generator_loss=29.85, generator_mel_loss=22.49, generator_kl_loss=1.197, generator_dur_loss=1.799, generator_adv_loss=1.878, generator_feat_match_loss=2.489, over 48.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.396, discriminator_fake_loss=1.345, generator_loss=28.85, generator_mel_loss=21.34, generator_kl_loss=1.267, generator_dur_loss=1.874, generator_adv_loss=1.917, generator_feat_match_loss=2.45, over 6524.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:56:26,590 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 03:56:35,582 INFO [train.py:591] (4/6) Epoch 42, validation: discriminator_loss=2.725, discriminator_real_loss=1.443, discriminator_fake_loss=1.282, generator_loss=27.96, generator_mel_loss=21.22, generator_kl_loss=0.9713, generator_dur_loss=1.932, generator_adv_loss=1.878, generator_feat_match_loss=1.963, over 100.00 samples. +2024-03-12 03:56:35,583 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 03:56:56,614 INFO [train.py:919] (4/6) Start epoch 43 +2024-03-12 03:59:14,949 INFO [train.py:527] (4/6) Epoch 43, batch 42, global_batch_idx: 5250, batch size: 66, loss[discriminator_loss=2.674, discriminator_real_loss=1.383, discriminator_fake_loss=1.291, generator_loss=30.91, generator_mel_loss=23.05, generator_kl_loss=1.189, generator_dur_loss=1.875, generator_adv_loss=1.876, generator_feat_match_loss=2.917, over 66.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.397, discriminator_fake_loss=1.338, generator_loss=28.8, generator_mel_loss=21.29, generator_kl_loss=1.266, generator_dur_loss=1.873, generator_adv_loss=1.902, generator_feat_match_loss=2.471, over 2527.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:01:32,830 INFO [train.py:527] (4/6) Epoch 43, batch 92, global_batch_idx: 5300, batch size: 58, loss[discriminator_loss=2.871, discriminator_real_loss=1.492, discriminator_fake_loss=1.379, generator_loss=27.48, generator_mel_loss=20.39, generator_kl_loss=1.264, generator_dur_loss=1.758, generator_adv_loss=1.847, generator_feat_match_loss=2.227, over 58.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.409, discriminator_fake_loss=1.347, generator_loss=28.85, generator_mel_loss=21.27, generator_kl_loss=1.27, generator_dur_loss=1.875, generator_adv_loss=1.951, generator_feat_match_loss=2.481, over 5255.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:03:01,640 INFO [train.py:919] (4/6) Start epoch 44 +2024-03-12 04:04:18,520 INFO [train.py:527] (4/6) Epoch 44, batch 18, global_batch_idx: 5350, batch size: 56, loss[discriminator_loss=2.729, discriminator_real_loss=1.503, discriminator_fake_loss=1.227, generator_loss=28.57, generator_mel_loss=21.08, generator_kl_loss=1.306, generator_dur_loss=1.841, generator_adv_loss=1.867, generator_feat_match_loss=2.476, over 56.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.386, discriminator_fake_loss=1.342, generator_loss=28.91, generator_mel_loss=21.43, generator_kl_loss=1.257, generator_dur_loss=1.908, generator_adv_loss=1.91, generator_feat_match_loss=2.408, over 1252.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:06:38,382 INFO [train.py:527] (4/6) Epoch 44, batch 68, global_batch_idx: 5400, batch size: 96, loss[discriminator_loss=2.719, discriminator_real_loss=1.403, discriminator_fake_loss=1.316, generator_loss=28.35, generator_mel_loss=20.58, generator_kl_loss=1.301, generator_dur_loss=2.051, generator_adv_loss=1.966, generator_feat_match_loss=2.459, over 96.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.399, discriminator_fake_loss=1.345, generator_loss=28.73, generator_mel_loss=21.32, generator_kl_loss=1.261, generator_dur_loss=1.892, generator_adv_loss=1.881, generator_feat_match_loss=2.378, over 4206.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:06:38,384 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 04:06:46,212 INFO [train.py:591] (4/6) Epoch 44, validation: discriminator_loss=2.79, discriminator_real_loss=1.573, discriminator_fake_loss=1.217, generator_loss=27.42, generator_mel_loss=20.68, generator_kl_loss=1.082, generator_dur_loss=1.954, generator_adv_loss=1.905, generator_feat_match_loss=1.804, over 100.00 samples. +2024-03-12 04:06:46,213 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 04:09:04,561 INFO [train.py:527] (4/6) Epoch 44, batch 118, global_batch_idx: 5450, batch size: 55, loss[discriminator_loss=2.659, discriminator_real_loss=1.4, discriminator_fake_loss=1.259, generator_loss=30.05, generator_mel_loss=21.99, generator_kl_loss=1.288, generator_dur_loss=1.821, generator_adv_loss=2.291, generator_feat_match_loss=2.658, over 55.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.407, discriminator_fake_loss=1.342, generator_loss=28.74, generator_mel_loss=21.25, generator_kl_loss=1.262, generator_dur_loss=1.891, generator_adv_loss=1.918, generator_feat_match_loss=2.421, over 7143.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:09:19,652 INFO [train.py:919] (4/6) Start epoch 45 +2024-03-12 04:11:43,230 INFO [train.py:527] (4/6) Epoch 45, batch 44, global_batch_idx: 5500, batch size: 36, loss[discriminator_loss=2.789, discriminator_real_loss=1.407, discriminator_fake_loss=1.382, generator_loss=28.93, generator_mel_loss=21.33, generator_kl_loss=1.295, generator_dur_loss=1.834, generator_adv_loss=1.853, generator_feat_match_loss=2.614, over 36.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.395, discriminator_fake_loss=1.342, generator_loss=28.58, generator_mel_loss=21.15, generator_kl_loss=1.286, generator_dur_loss=1.871, generator_adv_loss=1.888, generator_feat_match_loss=2.382, over 2558.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:14:03,138 INFO [train.py:527] (4/6) Epoch 45, batch 94, global_batch_idx: 5550, batch size: 25, loss[discriminator_loss=2.661, discriminator_real_loss=1.239, discriminator_fake_loss=1.422, generator_loss=29.42, generator_mel_loss=21.58, generator_kl_loss=1.475, generator_dur_loss=1.641, generator_adv_loss=2.086, generator_feat_match_loss=2.639, over 25.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.4, discriminator_fake_loss=1.341, generator_loss=28.63, generator_mel_loss=21.16, generator_kl_loss=1.291, generator_dur_loss=1.871, generator_adv_loss=1.895, generator_feat_match_loss=2.414, over 5320.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:15:27,028 INFO [train.py:919] (4/6) Start epoch 46 +2024-03-12 04:16:45,580 INFO [train.py:527] (4/6) Epoch 46, batch 20, global_batch_idx: 5600, batch size: 74, loss[discriminator_loss=2.676, discriminator_real_loss=1.374, discriminator_fake_loss=1.302, generator_loss=29.32, generator_mel_loss=21.58, generator_kl_loss=1.254, generator_dur_loss=1.905, generator_adv_loss=1.782, generator_feat_match_loss=2.8, over 74.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.396, discriminator_fake_loss=1.337, generator_loss=28.95, generator_mel_loss=21.35, generator_kl_loss=1.271, generator_dur_loss=1.858, generator_adv_loss=1.934, generator_feat_match_loss=2.543, over 1187.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:16:45,581 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 04:16:53,514 INFO [train.py:591] (4/6) Epoch 46, validation: discriminator_loss=2.71, discriminator_real_loss=1.41, discriminator_fake_loss=1.301, generator_loss=27.62, generator_mel_loss=20.81, generator_kl_loss=1.172, generator_dur_loss=1.926, generator_adv_loss=1.793, generator_feat_match_loss=1.917, over 100.00 samples. +2024-03-12 04:16:53,515 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 04:19:10,818 INFO [train.py:527] (4/6) Epoch 46, batch 70, global_batch_idx: 5650, batch size: 74, loss[discriminator_loss=2.825, discriminator_real_loss=1.422, discriminator_fake_loss=1.403, generator_loss=27.59, generator_mel_loss=20.5, generator_kl_loss=1.271, generator_dur_loss=1.976, generator_adv_loss=1.904, generator_feat_match_loss=1.934, over 74.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.402, discriminator_fake_loss=1.336, generator_loss=28.78, generator_mel_loss=21.23, generator_kl_loss=1.282, generator_dur_loss=1.859, generator_adv_loss=1.906, generator_feat_match_loss=2.504, over 4044.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:21:29,309 INFO [train.py:527] (4/6) Epoch 46, batch 120, global_batch_idx: 5700, batch size: 31, loss[discriminator_loss=2.701, discriminator_real_loss=1.441, discriminator_fake_loss=1.26, generator_loss=29.35, generator_mel_loss=22.07, generator_kl_loss=1.338, generator_dur_loss=1.68, generator_adv_loss=1.805, generator_feat_match_loss=2.45, over 31.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.399, discriminator_fake_loss=1.334, generator_loss=28.78, generator_mel_loss=21.22, generator_kl_loss=1.281, generator_dur_loss=1.861, generator_adv_loss=1.895, generator_feat_match_loss=2.524, over 6999.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:21:39,716 INFO [train.py:919] (4/6) Start epoch 47 +2024-03-12 04:24:10,994 INFO [train.py:527] (4/6) Epoch 47, batch 46, global_batch_idx: 5750, batch size: 59, loss[discriminator_loss=2.756, discriminator_real_loss=1.457, discriminator_fake_loss=1.299, generator_loss=29.33, generator_mel_loss=21.51, generator_kl_loss=1.452, generator_dur_loss=1.809, generator_adv_loss=2.037, generator_feat_match_loss=2.529, over 59.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.427, discriminator_fake_loss=1.326, generator_loss=28.69, generator_mel_loss=21.06, generator_kl_loss=1.295, generator_dur_loss=1.857, generator_adv_loss=1.972, generator_feat_match_loss=2.499, over 2790.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:26:28,677 INFO [train.py:527] (4/6) Epoch 47, batch 96, global_batch_idx: 5800, batch size: 83, loss[discriminator_loss=2.716, discriminator_real_loss=1.47, discriminator_fake_loss=1.246, generator_loss=27.87, generator_mel_loss=20.31, generator_kl_loss=1.205, generator_dur_loss=1.928, generator_adv_loss=1.852, generator_feat_match_loss=2.573, over 83.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.409, discriminator_fake_loss=1.332, generator_loss=28.64, generator_mel_loss=21.07, generator_kl_loss=1.303, generator_dur_loss=1.844, generator_adv_loss=1.938, generator_feat_match_loss=2.483, over 5407.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:26:28,678 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 04:26:37,447 INFO [train.py:591] (4/6) Epoch 47, validation: discriminator_loss=2.724, discriminator_real_loss=1.397, discriminator_fake_loss=1.327, generator_loss=27.83, generator_mel_loss=20.92, generator_kl_loss=1.124, generator_dur_loss=1.896, generator_adv_loss=1.839, generator_feat_match_loss=2.052, over 100.00 samples. +2024-03-12 04:26:37,448 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 04:27:54,802 INFO [train.py:919] (4/6) Start epoch 48 +2024-03-12 04:29:21,040 INFO [train.py:527] (4/6) Epoch 48, batch 22, global_batch_idx: 5850, batch size: 47, loss[discriminator_loss=2.742, discriminator_real_loss=1.397, discriminator_fake_loss=1.345, generator_loss=28.34, generator_mel_loss=20.76, generator_kl_loss=1.399, generator_dur_loss=1.71, generator_adv_loss=1.986, generator_feat_match_loss=2.477, over 47.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.451, discriminator_fake_loss=1.319, generator_loss=28.84, generator_mel_loss=20.88, generator_kl_loss=1.306, generator_dur_loss=1.846, generator_adv_loss=2.052, generator_feat_match_loss=2.764, over 1294.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:31:38,658 INFO [train.py:527] (4/6) Epoch 48, batch 72, global_batch_idx: 5900, batch size: 59, loss[discriminator_loss=2.783, discriminator_real_loss=1.474, discriminator_fake_loss=1.31, generator_loss=29.15, generator_mel_loss=21.63, generator_kl_loss=1.351, generator_dur_loss=1.773, generator_adv_loss=2.028, generator_feat_match_loss=2.365, over 59.00 samples.], tot_loss[discriminator_loss=2.773, discriminator_real_loss=1.426, discriminator_fake_loss=1.347, generator_loss=28.53, generator_mel_loss=20.86, generator_kl_loss=1.297, generator_dur_loss=1.867, generator_adv_loss=1.976, generator_feat_match_loss=2.53, over 4389.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:33:57,416 INFO [train.py:527] (4/6) Epoch 48, batch 122, global_batch_idx: 5950, batch size: 42, loss[discriminator_loss=2.778, discriminator_real_loss=1.382, discriminator_fake_loss=1.397, generator_loss=28.27, generator_mel_loss=20.99, generator_kl_loss=1.443, generator_dur_loss=1.771, generator_adv_loss=1.84, generator_feat_match_loss=2.225, over 42.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.415, discriminator_fake_loss=1.347, generator_loss=28.41, generator_mel_loss=20.85, generator_kl_loss=1.302, generator_dur_loss=1.863, generator_adv_loss=1.931, generator_feat_match_loss=2.466, over 7256.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:34:02,192 INFO [train.py:919] (4/6) Start epoch 49 +2024-03-12 04:36:36,720 INFO [train.py:527] (4/6) Epoch 49, batch 48, global_batch_idx: 6000, batch size: 70, loss[discriminator_loss=2.762, discriminator_real_loss=1.396, discriminator_fake_loss=1.367, generator_loss=28.46, generator_mel_loss=20.62, generator_kl_loss=1.298, generator_dur_loss=1.928, generator_adv_loss=2.091, generator_feat_match_loss=2.523, over 70.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.398, discriminator_fake_loss=1.348, generator_loss=28.4, generator_mel_loss=20.93, generator_kl_loss=1.309, generator_dur_loss=1.853, generator_adv_loss=1.875, generator_feat_match_loss=2.438, over 2736.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:36:36,721 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 04:36:44,771 INFO [train.py:591] (4/6) Epoch 49, validation: discriminator_loss=2.812, discriminator_real_loss=1.581, discriminator_fake_loss=1.231, generator_loss=27.72, generator_mel_loss=20.69, generator_kl_loss=1.054, generator_dur_loss=1.903, generator_adv_loss=2.012, generator_feat_match_loss=2.069, over 100.00 samples. +2024-03-12 04:36:44,772 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28735MB +2024-03-12 04:39:03,216 INFO [train.py:527] (4/6) Epoch 49, batch 98, global_batch_idx: 6050, batch size: 80, loss[discriminator_loss=2.78, discriminator_real_loss=1.319, discriminator_fake_loss=1.461, generator_loss=29.12, generator_mel_loss=21.33, generator_kl_loss=1.345, generator_dur_loss=1.889, generator_adv_loss=2.051, generator_feat_match_loss=2.504, over 80.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.402, discriminator_fake_loss=1.349, generator_loss=28.35, generator_mel_loss=20.89, generator_kl_loss=1.321, generator_dur_loss=1.843, generator_adv_loss=1.877, generator_feat_match_loss=2.422, over 5481.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:40:16,382 INFO [train.py:919] (4/6) Start epoch 50 +2024-03-12 04:41:46,335 INFO [train.py:527] (4/6) Epoch 50, batch 24, global_batch_idx: 6100, batch size: 14, loss[discriminator_loss=2.758, discriminator_real_loss=1.487, discriminator_fake_loss=1.272, generator_loss=29.61, generator_mel_loss=21.81, generator_kl_loss=1.616, generator_dur_loss=1.754, generator_adv_loss=1.937, generator_feat_match_loss=2.497, over 14.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.41, discriminator_fake_loss=1.349, generator_loss=28.2, generator_mel_loss=20.71, generator_kl_loss=1.269, generator_dur_loss=1.833, generator_adv_loss=1.902, generator_feat_match_loss=2.48, over 1281.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:44:05,963 INFO [train.py:527] (4/6) Epoch 50, batch 74, global_batch_idx: 6150, batch size: 55, loss[discriminator_loss=2.61, discriminator_real_loss=1.154, discriminator_fake_loss=1.455, generator_loss=28.9, generator_mel_loss=20.55, generator_kl_loss=1.336, generator_dur_loss=1.782, generator_adv_loss=2.24, generator_feat_match_loss=2.987, over 55.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.396, discriminator_fake_loss=1.354, generator_loss=28.43, generator_mel_loss=20.85, generator_kl_loss=1.3, generator_dur_loss=1.849, generator_adv_loss=1.914, generator_feat_match_loss=2.521, over 4201.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:46:24,413 INFO [train.py:919] (4/6) Start epoch 51 +2024-03-12 04:46:48,867 INFO [train.py:527] (4/6) Epoch 51, batch 0, global_batch_idx: 6200, batch size: 74, loss[discriminator_loss=2.737, discriminator_real_loss=1.284, discriminator_fake_loss=1.452, generator_loss=27.67, generator_mel_loss=20.19, generator_kl_loss=1.405, generator_dur_loss=1.885, generator_adv_loss=1.78, generator_feat_match_loss=2.411, over 74.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.284, discriminator_fake_loss=1.452, generator_loss=27.67, generator_mel_loss=20.19, generator_kl_loss=1.405, generator_dur_loss=1.885, generator_adv_loss=1.78, generator_feat_match_loss=2.411, over 74.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:46:48,890 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 04:46:56,727 INFO [train.py:591] (4/6) Epoch 51, validation: discriminator_loss=2.728, discriminator_real_loss=1.345, discriminator_fake_loss=1.383, generator_loss=27.59, generator_mel_loss=20.65, generator_kl_loss=1.177, generator_dur_loss=1.902, generator_adv_loss=1.782, generator_feat_match_loss=2.077, over 100.00 samples. +2024-03-12 04:46:56,730 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 04:49:14,942 INFO [train.py:527] (4/6) Epoch 51, batch 50, global_batch_idx: 6250, batch size: 45, loss[discriminator_loss=2.748, discriminator_real_loss=1.315, discriminator_fake_loss=1.433, generator_loss=26.91, generator_mel_loss=19.65, generator_kl_loss=1.46, generator_dur_loss=1.713, generator_adv_loss=1.798, generator_feat_match_loss=2.294, over 45.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.398, discriminator_fake_loss=1.337, generator_loss=28.28, generator_mel_loss=20.77, generator_kl_loss=1.314, generator_dur_loss=1.827, generator_adv_loss=1.893, generator_feat_match_loss=2.479, over 2729.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:51:35,334 INFO [train.py:527] (4/6) Epoch 51, batch 100, global_batch_idx: 6300, batch size: 55, loss[discriminator_loss=2.761, discriminator_real_loss=1.374, discriminator_fake_loss=1.388, generator_loss=28.54, generator_mel_loss=21.33, generator_kl_loss=1.287, generator_dur_loss=1.783, generator_adv_loss=1.872, generator_feat_match_loss=2.265, over 55.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.413, discriminator_fake_loss=1.339, generator_loss=28.41, generator_mel_loss=20.86, generator_kl_loss=1.313, generator_dur_loss=1.83, generator_adv_loss=1.908, generator_feat_match_loss=2.5, over 5541.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:52:40,474 INFO [train.py:919] (4/6) Start epoch 52 +2024-03-12 04:54:18,864 INFO [train.py:527] (4/6) Epoch 52, batch 26, global_batch_idx: 6350, batch size: 52, loss[discriminator_loss=2.628, discriminator_real_loss=1.256, discriminator_fake_loss=1.372, generator_loss=28.09, generator_mel_loss=20.47, generator_kl_loss=1.209, generator_dur_loss=1.806, generator_adv_loss=2.032, generator_feat_match_loss=2.571, over 52.00 samples.], tot_loss[discriminator_loss=2.778, discriminator_real_loss=1.442, discriminator_fake_loss=1.336, generator_loss=28.42, generator_mel_loss=20.51, generator_kl_loss=1.324, generator_dur_loss=1.824, generator_adv_loss=2.064, generator_feat_match_loss=2.697, over 1485.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:56:38,056 INFO [train.py:527] (4/6) Epoch 52, batch 76, global_batch_idx: 6400, batch size: 77, loss[discriminator_loss=2.714, discriminator_real_loss=1.365, discriminator_fake_loss=1.349, generator_loss=27.88, generator_mel_loss=20, generator_kl_loss=1.332, generator_dur_loss=1.914, generator_adv_loss=2.036, generator_feat_match_loss=2.597, over 77.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.424, discriminator_fake_loss=1.336, generator_loss=28.24, generator_mel_loss=20.59, generator_kl_loss=1.325, generator_dur_loss=1.834, generator_adv_loss=1.967, generator_feat_match_loss=2.523, over 4337.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:56:38,057 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 04:56:46,044 INFO [train.py:591] (4/6) Epoch 52, validation: discriminator_loss=2.773, discriminator_real_loss=1.574, discriminator_fake_loss=1.199, generator_loss=26.39, generator_mel_loss=19.74, generator_kl_loss=1.16, generator_dur_loss=1.911, generator_adv_loss=1.953, generator_feat_match_loss=1.628, over 100.00 samples. +2024-03-12 04:56:46,045 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 04:58:55,690 INFO [train.py:919] (4/6) Start epoch 53 +2024-03-12 04:59:25,789 INFO [train.py:527] (4/6) Epoch 53, batch 2, global_batch_idx: 6450, batch size: 59, loss[discriminator_loss=2.763, discriminator_real_loss=1.379, discriminator_fake_loss=1.384, generator_loss=28.33, generator_mel_loss=20.81, generator_kl_loss=1.317, generator_dur_loss=1.81, generator_adv_loss=2.035, generator_feat_match_loss=2.361, over 59.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.443, discriminator_fake_loss=1.3, generator_loss=28.67, generator_mel_loss=21, generator_kl_loss=1.368, generator_dur_loss=1.775, generator_adv_loss=1.989, generator_feat_match_loss=2.537, over 151.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:01:44,717 INFO [train.py:527] (4/6) Epoch 53, batch 52, global_batch_idx: 6500, batch size: 68, loss[discriminator_loss=2.778, discriminator_real_loss=1.446, discriminator_fake_loss=1.332, generator_loss=27.11, generator_mel_loss=19.73, generator_kl_loss=1.295, generator_dur_loss=1.795, generator_adv_loss=1.866, generator_feat_match_loss=2.427, over 68.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.41, discriminator_fake_loss=1.345, generator_loss=28.18, generator_mel_loss=20.64, generator_kl_loss=1.323, generator_dur_loss=1.826, generator_adv_loss=1.901, generator_feat_match_loss=2.488, over 3017.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:04:04,453 INFO [train.py:527] (4/6) Epoch 53, batch 102, global_batch_idx: 6550, batch size: 80, loss[discriminator_loss=2.736, discriminator_real_loss=1.306, discriminator_fake_loss=1.43, generator_loss=28.44, generator_mel_loss=20.72, generator_kl_loss=1.357, generator_dur_loss=1.857, generator_adv_loss=1.694, generator_feat_match_loss=2.811, over 80.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.411, discriminator_fake_loss=1.346, generator_loss=28.09, generator_mel_loss=20.58, generator_kl_loss=1.33, generator_dur_loss=1.834, generator_adv_loss=1.883, generator_feat_match_loss=2.469, over 5877.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:05:04,131 INFO [train.py:919] (4/6) Start epoch 54 +2024-03-12 05:06:44,478 INFO [train.py:527] (4/6) Epoch 54, batch 28, global_batch_idx: 6600, batch size: 70, loss[discriminator_loss=2.713, discriminator_real_loss=1.259, discriminator_fake_loss=1.454, generator_loss=28.55, generator_mel_loss=21.07, generator_kl_loss=1.272, generator_dur_loss=1.918, generator_adv_loss=1.832, generator_feat_match_loss=2.463, over 70.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.405, discriminator_fake_loss=1.347, generator_loss=28.11, generator_mel_loss=20.69, generator_kl_loss=1.286, generator_dur_loss=1.82, generator_adv_loss=1.872, generator_feat_match_loss=2.443, over 1599.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:06:44,479 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 05:06:52,266 INFO [train.py:591] (4/6) Epoch 54, validation: discriminator_loss=2.733, discriminator_real_loss=1.331, discriminator_fake_loss=1.402, generator_loss=27.36, generator_mel_loss=20.51, generator_kl_loss=1.199, generator_dur_loss=1.892, generator_adv_loss=1.785, generator_feat_match_loss=1.97, over 100.00 samples. +2024-03-12 05:06:52,266 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 05:09:08,093 INFO [train.py:527] (4/6) Epoch 54, batch 78, global_batch_idx: 6650, batch size: 80, loss[discriminator_loss=2.759, discriminator_real_loss=1.35, discriminator_fake_loss=1.409, generator_loss=28.86, generator_mel_loss=20.98, generator_kl_loss=1.319, generator_dur_loss=1.898, generator_adv_loss=1.904, generator_feat_match_loss=2.76, over 80.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.402, discriminator_fake_loss=1.344, generator_loss=28.13, generator_mel_loss=20.59, generator_kl_loss=1.298, generator_dur_loss=1.839, generator_adv_loss=1.906, generator_feat_match_loss=2.499, over 4637.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:11:16,508 INFO [train.py:919] (4/6) Start epoch 55 +2024-03-12 05:11:50,267 INFO [train.py:527] (4/6) Epoch 55, batch 4, global_batch_idx: 6700, batch size: 55, loss[discriminator_loss=2.699, discriminator_real_loss=1.446, discriminator_fake_loss=1.253, generator_loss=28.94, generator_mel_loss=21.54, generator_kl_loss=1.358, generator_dur_loss=1.779, generator_adv_loss=1.701, generator_feat_match_loss=2.555, over 55.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.396, discriminator_fake_loss=1.36, generator_loss=27.98, generator_mel_loss=20.53, generator_kl_loss=1.238, generator_dur_loss=1.866, generator_adv_loss=1.893, generator_feat_match_loss=2.449, over 332.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:14:11,496 INFO [train.py:527] (4/6) Epoch 55, batch 54, global_batch_idx: 6750, batch size: 50, loss[discriminator_loss=2.841, discriminator_real_loss=1.384, discriminator_fake_loss=1.457, generator_loss=27.43, generator_mel_loss=20.07, generator_kl_loss=1.187, generator_dur_loss=1.777, generator_adv_loss=1.866, generator_feat_match_loss=2.531, over 50.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.403, discriminator_fake_loss=1.347, generator_loss=28.15, generator_mel_loss=20.49, generator_kl_loss=1.3, generator_dur_loss=1.839, generator_adv_loss=1.944, generator_feat_match_loss=2.577, over 3199.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:16:32,141 INFO [train.py:527] (4/6) Epoch 55, batch 104, global_batch_idx: 6800, batch size: 58, loss[discriminator_loss=2.723, discriminator_real_loss=1.346, discriminator_fake_loss=1.377, generator_loss=27.83, generator_mel_loss=20.39, generator_kl_loss=1.323, generator_dur_loss=1.819, generator_adv_loss=1.889, generator_feat_match_loss=2.41, over 58.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.404, discriminator_fake_loss=1.341, generator_loss=28.09, generator_mel_loss=20.47, generator_kl_loss=1.306, generator_dur_loss=1.838, generator_adv_loss=1.923, generator_feat_match_loss=2.549, over 6163.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:16:32,142 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 05:16:41,182 INFO [train.py:591] (4/6) Epoch 55, validation: discriminator_loss=2.695, discriminator_real_loss=1.381, discriminator_fake_loss=1.314, generator_loss=28.01, generator_mel_loss=20.84, generator_kl_loss=1.443, generator_dur_loss=1.868, generator_adv_loss=1.822, generator_feat_match_loss=2.032, over 100.00 samples. +2024-03-12 05:16:41,183 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 05:17:33,183 INFO [train.py:919] (4/6) Start epoch 56 +2024-03-12 05:19:21,132 INFO [train.py:527] (4/6) Epoch 56, batch 30, global_batch_idx: 6850, batch size: 59, loss[discriminator_loss=2.806, discriminator_real_loss=1.44, discriminator_fake_loss=1.365, generator_loss=27.86, generator_mel_loss=20.45, generator_kl_loss=1.329, generator_dur_loss=1.815, generator_adv_loss=1.94, generator_feat_match_loss=2.323, over 59.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.41, discriminator_fake_loss=1.342, generator_loss=28.22, generator_mel_loss=20.63, generator_kl_loss=1.324, generator_dur_loss=1.829, generator_adv_loss=1.873, generator_feat_match_loss=2.569, over 1762.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:21:42,298 INFO [train.py:527] (4/6) Epoch 56, batch 80, global_batch_idx: 6900, batch size: 44, loss[discriminator_loss=2.831, discriminator_real_loss=1.598, discriminator_fake_loss=1.234, generator_loss=27.22, generator_mel_loss=19.91, generator_kl_loss=1.35, generator_dur_loss=1.697, generator_adv_loss=1.85, generator_feat_match_loss=2.42, over 44.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.406, discriminator_fake_loss=1.345, generator_loss=28.01, generator_mel_loss=20.46, generator_kl_loss=1.318, generator_dur_loss=1.83, generator_adv_loss=1.876, generator_feat_match_loss=2.526, over 4700.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:23:44,939 INFO [train.py:919] (4/6) Start epoch 57 +2024-03-12 05:24:26,583 INFO [train.py:527] (4/6) Epoch 57, batch 6, global_batch_idx: 6950, batch size: 61, loss[discriminator_loss=2.756, discriminator_real_loss=1.285, discriminator_fake_loss=1.47, generator_loss=29.17, generator_mel_loss=21.07, generator_kl_loss=1.507, generator_dur_loss=1.833, generator_adv_loss=1.916, generator_feat_match_loss=2.849, over 61.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.408, discriminator_fake_loss=1.336, generator_loss=28.51, generator_mel_loss=20.65, generator_kl_loss=1.405, generator_dur_loss=1.857, generator_adv_loss=1.909, generator_feat_match_loss=2.698, over 449.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:26:47,948 INFO [train.py:527] (4/6) Epoch 57, batch 56, global_batch_idx: 7000, batch size: 50, loss[discriminator_loss=2.821, discriminator_real_loss=1.385, discriminator_fake_loss=1.437, generator_loss=26.96, generator_mel_loss=19.92, generator_kl_loss=1.394, generator_dur_loss=1.782, generator_adv_loss=1.842, generator_feat_match_loss=2.023, over 50.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.418, discriminator_fake_loss=1.349, generator_loss=28.2, generator_mel_loss=20.56, generator_kl_loss=1.315, generator_dur_loss=1.836, generator_adv_loss=1.917, generator_feat_match_loss=2.562, over 3279.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:26:47,950 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 05:26:56,056 INFO [train.py:591] (4/6) Epoch 57, validation: discriminator_loss=2.915, discriminator_real_loss=1.59, discriminator_fake_loss=1.324, generator_loss=27.21, generator_mel_loss=20.49, generator_kl_loss=1.026, generator_dur_loss=1.886, generator_adv_loss=1.882, generator_feat_match_loss=1.929, over 100.00 samples. +2024-03-12 05:26:56,057 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 05:29:16,445 INFO [train.py:527] (4/6) Epoch 57, batch 106, global_batch_idx: 7050, batch size: 59, loss[discriminator_loss=2.782, discriminator_real_loss=1.372, discriminator_fake_loss=1.41, generator_loss=29.13, generator_mel_loss=21.21, generator_kl_loss=1.412, generator_dur_loss=1.819, generator_adv_loss=1.985, generator_feat_match_loss=2.706, over 59.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.409, discriminator_fake_loss=1.35, generator_loss=28.12, generator_mel_loss=20.51, generator_kl_loss=1.32, generator_dur_loss=1.843, generator_adv_loss=1.907, generator_feat_match_loss=2.545, over 6257.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:30:06,573 INFO [train.py:919] (4/6) Start epoch 58 +2024-03-12 05:32:02,589 INFO [train.py:527] (4/6) Epoch 58, batch 32, global_batch_idx: 7100, batch size: 36, loss[discriminator_loss=2.742, discriminator_real_loss=1.4, discriminator_fake_loss=1.342, generator_loss=28.83, generator_mel_loss=20.9, generator_kl_loss=1.476, generator_dur_loss=1.759, generator_adv_loss=1.886, generator_feat_match_loss=2.813, over 36.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.4, discriminator_fake_loss=1.34, generator_loss=28.1, generator_mel_loss=20.43, generator_kl_loss=1.355, generator_dur_loss=1.809, generator_adv_loss=1.906, generator_feat_match_loss=2.592, over 1712.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:34:24,752 INFO [train.py:527] (4/6) Epoch 58, batch 82, global_batch_idx: 7150, batch size: 55, loss[discriminator_loss=2.636, discriminator_real_loss=1.409, discriminator_fake_loss=1.227, generator_loss=29.17, generator_mel_loss=21.3, generator_kl_loss=1.425, generator_dur_loss=1.737, generator_adv_loss=1.886, generator_feat_match_loss=2.814, over 55.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.403, discriminator_fake_loss=1.347, generator_loss=27.97, generator_mel_loss=20.37, generator_kl_loss=1.334, generator_dur_loss=1.823, generator_adv_loss=1.898, generator_feat_match_loss=2.545, over 4499.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:36:22,331 INFO [train.py:919] (4/6) Start epoch 59 +2024-03-12 05:37:09,841 INFO [train.py:527] (4/6) Epoch 59, batch 8, global_batch_idx: 7200, batch size: 59, loss[discriminator_loss=2.679, discriminator_real_loss=1.439, discriminator_fake_loss=1.239, generator_loss=27.02, generator_mel_loss=19.14, generator_kl_loss=1.312, generator_dur_loss=1.832, generator_adv_loss=2.201, generator_feat_match_loss=2.527, over 59.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.388, discriminator_fake_loss=1.337, generator_loss=27.94, generator_mel_loss=20.24, generator_kl_loss=1.296, generator_dur_loss=1.825, generator_adv_loss=1.981, generator_feat_match_loss=2.606, over 499.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:37:09,844 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 05:37:17,482 INFO [train.py:591] (4/6) Epoch 59, validation: discriminator_loss=2.651, discriminator_real_loss=1.434, discriminator_fake_loss=1.216, generator_loss=28.18, generator_mel_loss=20.64, generator_kl_loss=1.036, generator_dur_loss=1.874, generator_adv_loss=2.041, generator_feat_match_loss=2.591, over 100.00 samples. +2024-03-12 05:37:17,484 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 05:39:37,543 INFO [train.py:527] (4/6) Epoch 59, batch 58, global_batch_idx: 7250, batch size: 39, loss[discriminator_loss=2.747, discriminator_real_loss=1.469, discriminator_fake_loss=1.278, generator_loss=27.1, generator_mel_loss=19.58, generator_kl_loss=1.362, generator_dur_loss=1.776, generator_adv_loss=1.967, generator_feat_match_loss=2.423, over 39.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.408, discriminator_fake_loss=1.344, generator_loss=27.99, generator_mel_loss=20.33, generator_kl_loss=1.31, generator_dur_loss=1.824, generator_adv_loss=1.938, generator_feat_match_loss=2.587, over 3333.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:42:00,667 INFO [train.py:527] (4/6) Epoch 59, batch 108, global_batch_idx: 7300, batch size: 72, loss[discriminator_loss=2.756, discriminator_real_loss=1.453, discriminator_fake_loss=1.303, generator_loss=27.36, generator_mel_loss=20, generator_kl_loss=1.375, generator_dur_loss=1.855, generator_adv_loss=1.776, generator_feat_match_loss=2.349, over 72.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.402, discriminator_fake_loss=1.35, generator_loss=28.01, generator_mel_loss=20.39, generator_kl_loss=1.307, generator_dur_loss=1.824, generator_adv_loss=1.913, generator_feat_match_loss=2.576, over 6323.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:42:44,544 INFO [train.py:919] (4/6) Start epoch 60 +2024-03-12 05:44:45,997 INFO [train.py:527] (4/6) Epoch 60, batch 34, global_batch_idx: 7350, batch size: 66, loss[discriminator_loss=2.783, discriminator_real_loss=1.495, discriminator_fake_loss=1.288, generator_loss=28.27, generator_mel_loss=21.01, generator_kl_loss=1.189, generator_dur_loss=1.863, generator_adv_loss=1.842, generator_feat_match_loss=2.364, over 66.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.398, discriminator_fake_loss=1.352, generator_loss=27.79, generator_mel_loss=20.25, generator_kl_loss=1.31, generator_dur_loss=1.851, generator_adv_loss=1.878, generator_feat_match_loss=2.493, over 2118.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:47:06,753 INFO [train.py:527] (4/6) Epoch 60, batch 84, global_batch_idx: 7400, batch size: 61, loss[discriminator_loss=2.821, discriminator_real_loss=1.523, discriminator_fake_loss=1.298, generator_loss=27.44, generator_mel_loss=20.43, generator_kl_loss=1.429, generator_dur_loss=1.767, generator_adv_loss=1.74, generator_feat_match_loss=2.074, over 61.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.408, discriminator_fake_loss=1.353, generator_loss=27.93, generator_mel_loss=20.32, generator_kl_loss=1.308, generator_dur_loss=1.84, generator_adv_loss=1.918, generator_feat_match_loss=2.549, over 5120.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:47:06,754 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 05:47:15,706 INFO [train.py:591] (4/6) Epoch 60, validation: discriminator_loss=2.809, discriminator_real_loss=1.414, discriminator_fake_loss=1.395, generator_loss=25.96, generator_mel_loss=19.55, generator_kl_loss=1.112, generator_dur_loss=1.884, generator_adv_loss=1.7, generator_feat_match_loss=1.717, over 100.00 samples. +2024-03-12 05:47:15,707 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 05:49:05,423 INFO [train.py:919] (4/6) Start epoch 61 +2024-03-12 05:50:53,555 INFO [train.py:527] (4/6) Epoch 61, batch 10, global_batch_idx: 7450, batch size: 25, loss[discriminator_loss=2.705, discriminator_real_loss=1.338, discriminator_fake_loss=1.367, generator_loss=30.46, generator_mel_loss=22.16, generator_kl_loss=1.661, generator_dur_loss=1.65, generator_adv_loss=1.877, generator_feat_match_loss=3.117, over 25.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.398, discriminator_fake_loss=1.316, generator_loss=28.29, generator_mel_loss=20.47, generator_kl_loss=1.362, generator_dur_loss=1.82, generator_adv_loss=1.887, generator_feat_match_loss=2.758, over 614.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:53:16,109 INFO [train.py:527] (4/6) Epoch 61, batch 60, global_batch_idx: 7500, batch size: 50, loss[discriminator_loss=2.771, discriminator_real_loss=1.418, discriminator_fake_loss=1.353, generator_loss=27.12, generator_mel_loss=19.94, generator_kl_loss=1.351, generator_dur_loss=1.751, generator_adv_loss=1.743, generator_feat_match_loss=2.33, over 50.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.408, discriminator_fake_loss=1.338, generator_loss=27.95, generator_mel_loss=20.28, generator_kl_loss=1.326, generator_dur_loss=1.818, generator_adv_loss=1.919, generator_feat_match_loss=2.605, over 3255.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:55:37,013 INFO [train.py:527] (4/6) Epoch 61, batch 110, global_batch_idx: 7550, batch size: 72, loss[discriminator_loss=2.739, discriminator_real_loss=1.372, discriminator_fake_loss=1.368, generator_loss=28.62, generator_mel_loss=20.87, generator_kl_loss=1.281, generator_dur_loss=1.897, generator_adv_loss=1.755, generator_feat_match_loss=2.823, over 72.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.401, discriminator_fake_loss=1.343, generator_loss=27.96, generator_mel_loss=20.29, generator_kl_loss=1.331, generator_dur_loss=1.831, generator_adv_loss=1.901, generator_feat_match_loss=2.605, over 6191.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:56:16,603 INFO [train.py:919] (4/6) Start epoch 62 +2024-03-12 05:58:26,282 INFO [train.py:527] (4/6) Epoch 62, batch 36, global_batch_idx: 7600, batch size: 47, loss[discriminator_loss=2.816, discriminator_real_loss=1.505, discriminator_fake_loss=1.311, generator_loss=27.32, generator_mel_loss=19.83, generator_kl_loss=1.422, generator_dur_loss=1.795, generator_adv_loss=1.772, generator_feat_match_loss=2.499, over 47.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.408, discriminator_fake_loss=1.349, generator_loss=28.08, generator_mel_loss=20.42, generator_kl_loss=1.345, generator_dur_loss=1.822, generator_adv_loss=1.887, generator_feat_match_loss=2.604, over 2181.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 05:58:26,284 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 05:58:34,412 INFO [train.py:591] (4/6) Epoch 62, validation: discriminator_loss=2.777, discriminator_real_loss=1.434, discriminator_fake_loss=1.344, generator_loss=26.63, generator_mel_loss=20.06, generator_kl_loss=1.08, generator_dur_loss=1.882, generator_adv_loss=1.807, generator_feat_match_loss=1.799, over 100.00 samples. +2024-03-12 05:58:34,413 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 06:00:56,848 INFO [train.py:527] (4/6) Epoch 62, batch 86, global_batch_idx: 7650, batch size: 48, loss[discriminator_loss=2.788, discriminator_real_loss=1.211, discriminator_fake_loss=1.577, generator_loss=26.62, generator_mel_loss=19.17, generator_kl_loss=1.527, generator_dur_loss=1.719, generator_adv_loss=1.737, generator_feat_match_loss=2.466, over 48.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.412, discriminator_fake_loss=1.354, generator_loss=27.97, generator_mel_loss=20.3, generator_kl_loss=1.336, generator_dur_loss=1.817, generator_adv_loss=1.91, generator_feat_match_loss=2.603, over 4928.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:02:41,440 INFO [train.py:919] (4/6) Start epoch 63 +2024-03-12 06:03:38,515 INFO [train.py:527] (4/6) Epoch 63, batch 12, global_batch_idx: 7700, batch size: 66, loss[discriminator_loss=2.858, discriminator_real_loss=1.337, discriminator_fake_loss=1.521, generator_loss=28.46, generator_mel_loss=20.29, generator_kl_loss=1.339, generator_dur_loss=1.82, generator_adv_loss=2.425, generator_feat_match_loss=2.586, over 66.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.403, discriminator_fake_loss=1.353, generator_loss=27.95, generator_mel_loss=20.25, generator_kl_loss=1.338, generator_dur_loss=1.8, generator_adv_loss=1.904, generator_feat_match_loss=2.656, over 756.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:05:54,397 INFO [train.py:527] (4/6) Epoch 63, batch 62, global_batch_idx: 7750, batch size: 31, loss[discriminator_loss=2.795, discriminator_real_loss=1.49, discriminator_fake_loss=1.306, generator_loss=28.5, generator_mel_loss=20.79, generator_kl_loss=1.572, generator_dur_loss=1.708, generator_adv_loss=1.942, generator_feat_match_loss=2.485, over 31.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.404, discriminator_fake_loss=1.364, generator_loss=27.74, generator_mel_loss=20.16, generator_kl_loss=1.337, generator_dur_loss=1.795, generator_adv_loss=1.882, generator_feat_match_loss=2.566, over 3389.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:08:16,507 INFO [train.py:527] (4/6) Epoch 63, batch 112, global_batch_idx: 7800, batch size: 48, loss[discriminator_loss=2.74, discriminator_real_loss=1.569, discriminator_fake_loss=1.171, generator_loss=27.36, generator_mel_loss=19.84, generator_kl_loss=1.36, generator_dur_loss=1.842, generator_adv_loss=1.87, generator_feat_match_loss=2.453, over 48.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.407, discriminator_fake_loss=1.358, generator_loss=27.72, generator_mel_loss=20.14, generator_kl_loss=1.322, generator_dur_loss=1.809, generator_adv_loss=1.882, generator_feat_match_loss=2.567, over 6414.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:08:16,508 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 06:08:25,394 INFO [train.py:591] (4/6) Epoch 63, validation: discriminator_loss=2.713, discriminator_real_loss=1.429, discriminator_fake_loss=1.284, generator_loss=26.21, generator_mel_loss=19.45, generator_kl_loss=1.161, generator_dur_loss=1.878, generator_adv_loss=1.771, generator_feat_match_loss=1.95, over 100.00 samples. +2024-03-12 06:08:25,395 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 06:08:57,934 INFO [train.py:919] (4/6) Start epoch 64 +2024-03-12 06:11:06,464 INFO [train.py:527] (4/6) Epoch 64, batch 38, global_batch_idx: 7850, batch size: 61, loss[discriminator_loss=2.76, discriminator_real_loss=1.319, discriminator_fake_loss=1.441, generator_loss=27.67, generator_mel_loss=20.04, generator_kl_loss=1.349, generator_dur_loss=1.776, generator_adv_loss=1.779, generator_feat_match_loss=2.726, over 61.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.387, discriminator_fake_loss=1.352, generator_loss=28.01, generator_mel_loss=20.29, generator_kl_loss=1.348, generator_dur_loss=1.805, generator_adv_loss=1.888, generator_feat_match_loss=2.676, over 2163.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:13:29,492 INFO [train.py:527] (4/6) Epoch 64, batch 88, global_batch_idx: 7900, batch size: 58, loss[discriminator_loss=2.783, discriminator_real_loss=1.383, discriminator_fake_loss=1.4, generator_loss=29.41, generator_mel_loss=21.26, generator_kl_loss=1.283, generator_dur_loss=1.825, generator_adv_loss=2.107, generator_feat_match_loss=2.941, over 58.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.393, discriminator_fake_loss=1.346, generator_loss=27.98, generator_mel_loss=20.24, generator_kl_loss=1.342, generator_dur_loss=1.82, generator_adv_loss=1.905, generator_feat_match_loss=2.674, over 4974.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:15:02,933 INFO [train.py:919] (4/6) Start epoch 65 +2024-03-12 06:16:05,240 INFO [train.py:527] (4/6) Epoch 65, batch 14, global_batch_idx: 7950, batch size: 80, loss[discriminator_loss=2.875, discriminator_real_loss=1.712, discriminator_fake_loss=1.163, generator_loss=27.1, generator_mel_loss=20.03, generator_kl_loss=1.154, generator_dur_loss=1.901, generator_adv_loss=1.515, generator_feat_match_loss=2.498, over 80.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.421, discriminator_fake_loss=1.305, generator_loss=27.99, generator_mel_loss=20.27, generator_kl_loss=1.356, generator_dur_loss=1.827, generator_adv_loss=1.886, generator_feat_match_loss=2.649, over 903.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:18:22,626 INFO [train.py:527] (4/6) Epoch 65, batch 64, global_batch_idx: 8000, batch size: 59, loss[discriminator_loss=2.666, discriminator_real_loss=1.352, discriminator_fake_loss=1.314, generator_loss=28.06, generator_mel_loss=19.71, generator_kl_loss=1.446, generator_dur_loss=1.801, generator_adv_loss=1.965, generator_feat_match_loss=3.133, over 59.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.404, discriminator_fake_loss=1.348, generator_loss=27.9, generator_mel_loss=20.15, generator_kl_loss=1.331, generator_dur_loss=1.838, generator_adv_loss=1.914, generator_feat_match_loss=2.671, over 4056.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:18:22,628 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 06:18:31,351 INFO [train.py:591] (4/6) Epoch 65, validation: discriminator_loss=2.641, discriminator_real_loss=1.246, discriminator_fake_loss=1.395, generator_loss=28.02, generator_mel_loss=20.6, generator_kl_loss=1.285, generator_dur_loss=1.878, generator_adv_loss=1.843, generator_feat_match_loss=2.41, over 100.00 samples. +2024-03-12 06:18:31,352 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 06:20:51,832 INFO [train.py:527] (4/6) Epoch 65, batch 114, global_batch_idx: 8050, batch size: 83, loss[discriminator_loss=2.845, discriminator_real_loss=1.584, discriminator_fake_loss=1.262, generator_loss=27.28, generator_mel_loss=19.7, generator_kl_loss=1.357, generator_dur_loss=1.901, generator_adv_loss=1.868, generator_feat_match_loss=2.456, over 83.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.401, discriminator_fake_loss=1.349, generator_loss=27.86, generator_mel_loss=20.1, generator_kl_loss=1.324, generator_dur_loss=1.833, generator_adv_loss=1.933, generator_feat_match_loss=2.677, over 7093.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:21:18,635 INFO [train.py:919] (4/6) Start epoch 66 +2024-03-12 06:23:34,272 INFO [train.py:527] (4/6) Epoch 66, batch 40, global_batch_idx: 8100, batch size: 61, loss[discriminator_loss=2.65, discriminator_real_loss=1.438, discriminator_fake_loss=1.212, generator_loss=29.81, generator_mel_loss=21.54, generator_kl_loss=1.42, generator_dur_loss=1.806, generator_adv_loss=1.916, generator_feat_match_loss=3.128, over 61.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.402, discriminator_fake_loss=1.346, generator_loss=27.85, generator_mel_loss=20.21, generator_kl_loss=1.334, generator_dur_loss=1.816, generator_adv_loss=1.879, generator_feat_match_loss=2.616, over 2259.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:25:54,591 INFO [train.py:527] (4/6) Epoch 66, batch 90, global_batch_idx: 8150, batch size: 58, loss[discriminator_loss=2.656, discriminator_real_loss=1.195, discriminator_fake_loss=1.462, generator_loss=28.53, generator_mel_loss=20.42, generator_kl_loss=1.419, generator_dur_loss=1.775, generator_adv_loss=2.071, generator_feat_match_loss=2.838, over 58.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.399, discriminator_fake_loss=1.352, generator_loss=27.79, generator_mel_loss=20.13, generator_kl_loss=1.333, generator_dur_loss=1.828, generator_adv_loss=1.885, generator_feat_match_loss=2.621, over 5101.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:27:27,239 INFO [train.py:919] (4/6) Start epoch 67 +2024-03-12 06:28:32,182 INFO [train.py:527] (4/6) Epoch 67, batch 16, global_batch_idx: 8200, batch size: 13, loss[discriminator_loss=2.732, discriminator_real_loss=1.422, discriminator_fake_loss=1.31, generator_loss=29.41, generator_mel_loss=20.68, generator_kl_loss=1.688, generator_dur_loss=1.651, generator_adv_loss=2.137, generator_feat_match_loss=3.26, over 13.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.417, discriminator_fake_loss=1.333, generator_loss=27.82, generator_mel_loss=20.14, generator_kl_loss=1.356, generator_dur_loss=1.801, generator_adv_loss=1.924, generator_feat_match_loss=2.598, over 901.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:28:32,184 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 06:28:40,207 INFO [train.py:591] (4/6) Epoch 67, validation: discriminator_loss=2.852, discriminator_real_loss=1.627, discriminator_fake_loss=1.225, generator_loss=27.61, generator_mel_loss=20.41, generator_kl_loss=1.213, generator_dur_loss=1.846, generator_adv_loss=2.016, generator_feat_match_loss=2.123, over 100.00 samples. +2024-03-12 06:28:40,208 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 06:30:59,897 INFO [train.py:527] (4/6) Epoch 67, batch 66, global_batch_idx: 8250, batch size: 44, loss[discriminator_loss=2.756, discriminator_real_loss=1.585, discriminator_fake_loss=1.171, generator_loss=29.16, generator_mel_loss=21.2, generator_kl_loss=1.317, generator_dur_loss=1.772, generator_adv_loss=1.853, generator_feat_match_loss=3.026, over 44.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.405, discriminator_fake_loss=1.347, generator_loss=27.74, generator_mel_loss=20.08, generator_kl_loss=1.34, generator_dur_loss=1.809, generator_adv_loss=1.895, generator_feat_match_loss=2.616, over 3948.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:33:19,325 INFO [train.py:527] (4/6) Epoch 67, batch 116, global_batch_idx: 8300, batch size: 59, loss[discriminator_loss=2.845, discriminator_real_loss=1.33, discriminator_fake_loss=1.515, generator_loss=29.02, generator_mel_loss=21.05, generator_kl_loss=1.229, generator_dur_loss=1.74, generator_adv_loss=2.169, generator_feat_match_loss=2.832, over 59.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.403, discriminator_fake_loss=1.356, generator_loss=27.74, generator_mel_loss=20.09, generator_kl_loss=1.327, generator_dur_loss=1.807, generator_adv_loss=1.901, generator_feat_match_loss=2.619, over 6799.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:33:38,456 INFO [train.py:919] (4/6) Start epoch 68 +2024-03-12 06:35:59,112 INFO [train.py:527] (4/6) Epoch 68, batch 42, global_batch_idx: 8350, batch size: 66, loss[discriminator_loss=2.739, discriminator_real_loss=1.307, discriminator_fake_loss=1.432, generator_loss=27.1, generator_mel_loss=19.71, generator_kl_loss=1.16, generator_dur_loss=1.866, generator_adv_loss=1.993, generator_feat_match_loss=2.369, over 66.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.395, discriminator_fake_loss=1.354, generator_loss=27.75, generator_mel_loss=19.98, generator_kl_loss=1.318, generator_dur_loss=1.825, generator_adv_loss=1.95, generator_feat_match_loss=2.676, over 2499.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:38:17,961 INFO [train.py:527] (4/6) Epoch 68, batch 92, global_batch_idx: 8400, batch size: 53, loss[discriminator_loss=2.818, discriminator_real_loss=1.304, discriminator_fake_loss=1.514, generator_loss=28.23, generator_mel_loss=20.41, generator_kl_loss=1.245, generator_dur_loss=1.768, generator_adv_loss=2.21, generator_feat_match_loss=2.597, over 53.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.399, discriminator_fake_loss=1.341, generator_loss=27.76, generator_mel_loss=20.03, generator_kl_loss=1.326, generator_dur_loss=1.819, generator_adv_loss=1.922, generator_feat_match_loss=2.659, over 5272.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:38:17,962 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 06:38:26,712 INFO [train.py:591] (4/6) Epoch 68, validation: discriminator_loss=2.963, discriminator_real_loss=1.622, discriminator_fake_loss=1.34, generator_loss=27.87, generator_mel_loss=20.22, generator_kl_loss=1.24, generator_dur_loss=1.867, generator_adv_loss=2.181, generator_feat_match_loss=2.365, over 100.00 samples. +2024-03-12 06:38:26,713 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 06:39:51,711 INFO [train.py:919] (4/6) Start epoch 69 +2024-03-12 06:41:07,163 INFO [train.py:527] (4/6) Epoch 69, batch 18, global_batch_idx: 8450, batch size: 36, loss[discriminator_loss=2.777, discriminator_real_loss=1.481, discriminator_fake_loss=1.296, generator_loss=27.26, generator_mel_loss=19.35, generator_kl_loss=1.484, generator_dur_loss=1.734, generator_adv_loss=1.889, generator_feat_match_loss=2.807, over 36.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.388, discriminator_fake_loss=1.368, generator_loss=27.82, generator_mel_loss=20.04, generator_kl_loss=1.352, generator_dur_loss=1.809, generator_adv_loss=1.949, generator_feat_match_loss=2.671, over 1124.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:43:24,061 INFO [train.py:527] (4/6) Epoch 69, batch 68, global_batch_idx: 8500, batch size: 68, loss[discriminator_loss=2.713, discriminator_real_loss=1.366, discriminator_fake_loss=1.346, generator_loss=27.99, generator_mel_loss=20.19, generator_kl_loss=1.217, generator_dur_loss=1.835, generator_adv_loss=1.929, generator_feat_match_loss=2.822, over 68.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.396, discriminator_fake_loss=1.353, generator_loss=27.66, generator_mel_loss=19.97, generator_kl_loss=1.353, generator_dur_loss=1.813, generator_adv_loss=1.913, generator_feat_match_loss=2.612, over 4130.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:45:42,043 INFO [train.py:527] (4/6) Epoch 69, batch 118, global_batch_idx: 8550, batch size: 68, loss[discriminator_loss=2.778, discriminator_real_loss=1.427, discriminator_fake_loss=1.351, generator_loss=26.9, generator_mel_loss=19.39, generator_kl_loss=1.325, generator_dur_loss=1.762, generator_adv_loss=1.951, generator_feat_match_loss=2.471, over 68.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.395, discriminator_fake_loss=1.355, generator_loss=27.73, generator_mel_loss=20.04, generator_kl_loss=1.355, generator_dur_loss=1.802, generator_adv_loss=1.896, generator_feat_match_loss=2.639, over 6941.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:45:59,344 INFO [train.py:919] (4/6) Start epoch 70 +2024-03-12 06:48:24,416 INFO [train.py:527] (4/6) Epoch 70, batch 44, global_batch_idx: 8600, batch size: 83, loss[discriminator_loss=2.856, discriminator_real_loss=1.558, discriminator_fake_loss=1.297, generator_loss=27.48, generator_mel_loss=20.01, generator_kl_loss=1.141, generator_dur_loss=1.907, generator_adv_loss=1.837, generator_feat_match_loss=2.59, over 83.00 samples.], tot_loss[discriminator_loss=2.779, discriminator_real_loss=1.441, discriminator_fake_loss=1.338, generator_loss=27.95, generator_mel_loss=19.96, generator_kl_loss=1.324, generator_dur_loss=1.798, generator_adv_loss=2.042, generator_feat_match_loss=2.825, over 2448.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:48:24,418 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 06:48:32,172 INFO [train.py:591] (4/6) Epoch 70, validation: discriminator_loss=2.833, discriminator_real_loss=1.384, discriminator_fake_loss=1.449, generator_loss=27.25, generator_mel_loss=20.62, generator_kl_loss=1.145, generator_dur_loss=1.861, generator_adv_loss=1.695, generator_feat_match_loss=1.926, over 100.00 samples. +2024-03-12 06:48:32,173 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 06:50:51,332 INFO [train.py:527] (4/6) Epoch 70, batch 94, global_batch_idx: 8650, batch size: 58, loss[discriminator_loss=2.756, discriminator_real_loss=1.477, discriminator_fake_loss=1.28, generator_loss=27.13, generator_mel_loss=19.54, generator_kl_loss=1.377, generator_dur_loss=1.769, generator_adv_loss=1.901, generator_feat_match_loss=2.541, over 58.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.414, discriminator_fake_loss=1.347, generator_loss=27.75, generator_mel_loss=19.96, generator_kl_loss=1.328, generator_dur_loss=1.816, generator_adv_loss=1.963, generator_feat_match_loss=2.686, over 5403.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:52:11,743 INFO [train.py:919] (4/6) Start epoch 71 +2024-03-12 06:53:31,206 INFO [train.py:527] (4/6) Epoch 71, batch 20, global_batch_idx: 8700, batch size: 61, loss[discriminator_loss=2.762, discriminator_real_loss=1.494, discriminator_fake_loss=1.268, generator_loss=27.9, generator_mel_loss=20.17, generator_kl_loss=1.441, generator_dur_loss=1.732, generator_adv_loss=1.79, generator_feat_match_loss=2.772, over 61.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.405, discriminator_fake_loss=1.332, generator_loss=27.83, generator_mel_loss=20.04, generator_kl_loss=1.386, generator_dur_loss=1.766, generator_adv_loss=1.913, generator_feat_match_loss=2.728, over 1197.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:55:49,080 INFO [train.py:527] (4/6) Epoch 71, batch 70, global_batch_idx: 8750, batch size: 83, loss[discriminator_loss=2.771, discriminator_real_loss=1.419, discriminator_fake_loss=1.352, generator_loss=27.55, generator_mel_loss=19.77, generator_kl_loss=1.309, generator_dur_loss=1.842, generator_adv_loss=1.88, generator_feat_match_loss=2.747, over 83.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.397, discriminator_fake_loss=1.35, generator_loss=27.84, generator_mel_loss=20.06, generator_kl_loss=1.383, generator_dur_loss=1.782, generator_adv_loss=1.904, generator_feat_match_loss=2.713, over 4087.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:58:06,885 INFO [train.py:527] (4/6) Epoch 71, batch 120, global_batch_idx: 8800, batch size: 68, loss[discriminator_loss=2.736, discriminator_real_loss=1.537, discriminator_fake_loss=1.199, generator_loss=28.4, generator_mel_loss=20.17, generator_kl_loss=1.227, generator_dur_loss=1.805, generator_adv_loss=2.142, generator_feat_match_loss=3.055, over 68.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.399, discriminator_fake_loss=1.352, generator_loss=27.85, generator_mel_loss=20.04, generator_kl_loss=1.366, generator_dur_loss=1.787, generator_adv_loss=1.922, generator_feat_match_loss=2.728, over 7024.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:58:06,886 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 06:58:15,683 INFO [train.py:591] (4/6) Epoch 71, validation: discriminator_loss=2.67, discriminator_real_loss=1.398, discriminator_fake_loss=1.273, generator_loss=28.15, generator_mel_loss=20.67, generator_kl_loss=1.155, generator_dur_loss=1.83, generator_adv_loss=1.908, generator_feat_match_loss=2.588, over 100.00 samples. +2024-03-12 06:58:15,684 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 06:58:25,722 INFO [train.py:919] (4/6) Start epoch 72 +2024-03-12 07:00:55,663 INFO [train.py:527] (4/6) Epoch 72, batch 46, global_batch_idx: 8850, batch size: 56, loss[discriminator_loss=2.777, discriminator_real_loss=1.382, discriminator_fake_loss=1.395, generator_loss=27.93, generator_mel_loss=20.4, generator_kl_loss=1.476, generator_dur_loss=1.759, generator_adv_loss=2.018, generator_feat_match_loss=2.275, over 56.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.4, discriminator_fake_loss=1.357, generator_loss=27.69, generator_mel_loss=19.95, generator_kl_loss=1.337, generator_dur_loss=1.793, generator_adv_loss=1.931, generator_feat_match_loss=2.674, over 2622.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:03:14,428 INFO [train.py:527] (4/6) Epoch 72, batch 96, global_batch_idx: 8900, batch size: 39, loss[discriminator_loss=2.721, discriminator_real_loss=1.448, discriminator_fake_loss=1.274, generator_loss=26.99, generator_mel_loss=19.58, generator_kl_loss=1.259, generator_dur_loss=1.812, generator_adv_loss=1.872, generator_feat_match_loss=2.476, over 39.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.394, discriminator_fake_loss=1.356, generator_loss=27.72, generator_mel_loss=19.99, generator_kl_loss=1.338, generator_dur_loss=1.799, generator_adv_loss=1.901, generator_feat_match_loss=2.694, over 5651.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:04:31,556 INFO [train.py:919] (4/6) Start epoch 73 +2024-03-12 07:05:57,023 INFO [train.py:527] (4/6) Epoch 73, batch 22, global_batch_idx: 8950, batch size: 52, loss[discriminator_loss=2.938, discriminator_real_loss=1.48, discriminator_fake_loss=1.458, generator_loss=28.3, generator_mel_loss=20.23, generator_kl_loss=1.424, generator_dur_loss=1.712, generator_adv_loss=1.909, generator_feat_match_loss=3.027, over 52.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.415, discriminator_fake_loss=1.353, generator_loss=27.84, generator_mel_loss=19.81, generator_kl_loss=1.309, generator_dur_loss=1.807, generator_adv_loss=2.03, generator_feat_match_loss=2.881, over 1468.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:08:14,529 INFO [train.py:527] (4/6) Epoch 73, batch 72, global_batch_idx: 9000, batch size: 70, loss[discriminator_loss=2.741, discriminator_real_loss=1.303, discriminator_fake_loss=1.438, generator_loss=27.84, generator_mel_loss=20.16, generator_kl_loss=1.398, generator_dur_loss=1.837, generator_adv_loss=1.77, generator_feat_match_loss=2.676, over 70.00 samples.], tot_loss[discriminator_loss=2.775, discriminator_real_loss=1.42, discriminator_fake_loss=1.355, generator_loss=27.73, generator_mel_loss=19.85, generator_kl_loss=1.332, generator_dur_loss=1.805, generator_adv_loss=1.999, generator_feat_match_loss=2.747, over 4370.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:08:14,530 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 07:08:23,139 INFO [train.py:591] (4/6) Epoch 73, validation: discriminator_loss=2.808, discriminator_real_loss=1.416, discriminator_fake_loss=1.392, generator_loss=27.31, generator_mel_loss=20.49, generator_kl_loss=1.148, generator_dur_loss=1.855, generator_adv_loss=1.733, generator_feat_match_loss=2.082, over 100.00 samples. +2024-03-12 07:08:23,140 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 07:10:40,057 INFO [train.py:527] (4/6) Epoch 73, batch 122, global_batch_idx: 9050, batch size: 56, loss[discriminator_loss=2.759, discriminator_real_loss=1.477, discriminator_fake_loss=1.282, generator_loss=28.03, generator_mel_loss=20.36, generator_kl_loss=1.272, generator_dur_loss=1.83, generator_adv_loss=1.852, generator_feat_match_loss=2.718, over 56.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.41, discriminator_fake_loss=1.345, generator_loss=27.75, generator_mel_loss=19.92, generator_kl_loss=1.341, generator_dur_loss=1.791, generator_adv_loss=1.958, generator_feat_match_loss=2.734, over 7226.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:10:45,748 INFO [train.py:919] (4/6) Start epoch 74 +2024-03-12 07:13:21,552 INFO [train.py:527] (4/6) Epoch 74, batch 48, global_batch_idx: 9100, batch size: 50, loss[discriminator_loss=2.763, discriminator_real_loss=1.511, discriminator_fake_loss=1.252, generator_loss=27.69, generator_mel_loss=19.84, generator_kl_loss=1.277, generator_dur_loss=1.718, generator_adv_loss=2.047, generator_feat_match_loss=2.804, over 50.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.402, discriminator_fake_loss=1.357, generator_loss=27.45, generator_mel_loss=19.81, generator_kl_loss=1.338, generator_dur_loss=1.788, generator_adv_loss=1.877, generator_feat_match_loss=2.634, over 2849.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:15:39,573 INFO [train.py:527] (4/6) Epoch 74, batch 98, global_batch_idx: 9150, batch size: 55, loss[discriminator_loss=2.812, discriminator_real_loss=1.342, discriminator_fake_loss=1.471, generator_loss=27.13, generator_mel_loss=19.61, generator_kl_loss=1.367, generator_dur_loss=1.772, generator_adv_loss=2.072, generator_feat_match_loss=2.309, over 55.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.407, discriminator_fake_loss=1.356, generator_loss=27.45, generator_mel_loss=19.79, generator_kl_loss=1.343, generator_dur_loss=1.799, generator_adv_loss=1.879, generator_feat_match_loss=2.638, over 5732.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:16:51,204 INFO [train.py:919] (4/6) Start epoch 75 +2024-03-12 07:18:20,482 INFO [train.py:527] (4/6) Epoch 75, batch 24, global_batch_idx: 9200, batch size: 83, loss[discriminator_loss=2.737, discriminator_real_loss=1.337, discriminator_fake_loss=1.4, generator_loss=27.96, generator_mel_loss=20.34, generator_kl_loss=1.234, generator_dur_loss=1.84, generator_adv_loss=1.864, generator_feat_match_loss=2.685, over 83.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.402, discriminator_fake_loss=1.362, generator_loss=27.56, generator_mel_loss=19.84, generator_kl_loss=1.337, generator_dur_loss=1.821, generator_adv_loss=1.887, generator_feat_match_loss=2.678, over 1517.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:18:20,484 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 07:18:28,222 INFO [train.py:591] (4/6) Epoch 75, validation: discriminator_loss=2.814, discriminator_real_loss=1.479, discriminator_fake_loss=1.335, generator_loss=27.15, generator_mel_loss=20.35, generator_kl_loss=1.058, generator_dur_loss=1.861, generator_adv_loss=1.815, generator_feat_match_loss=2.065, over 100.00 samples. +2024-03-12 07:18:28,223 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 07:20:44,834 INFO [train.py:527] (4/6) Epoch 75, batch 74, global_batch_idx: 9250, batch size: 16, loss[discriminator_loss=2.712, discriminator_real_loss=1.308, discriminator_fake_loss=1.404, generator_loss=29.96, generator_mel_loss=21.38, generator_kl_loss=1.599, generator_dur_loss=1.681, generator_adv_loss=1.925, generator_feat_match_loss=3.374, over 16.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.399, discriminator_fake_loss=1.364, generator_loss=27.58, generator_mel_loss=19.89, generator_kl_loss=1.354, generator_dur_loss=1.814, generator_adv_loss=1.879, generator_feat_match_loss=2.64, over 4350.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:23:01,837 INFO [train.py:919] (4/6) Start epoch 76 +2024-03-12 07:23:25,973 INFO [train.py:527] (4/6) Epoch 76, batch 0, global_batch_idx: 9300, batch size: 70, loss[discriminator_loss=2.641, discriminator_real_loss=1.25, discriminator_fake_loss=1.391, generator_loss=27.49, generator_mel_loss=19.64, generator_kl_loss=1.391, generator_dur_loss=1.847, generator_adv_loss=1.802, generator_feat_match_loss=2.815, over 70.00 samples.], tot_loss[discriminator_loss=2.641, discriminator_real_loss=1.25, discriminator_fake_loss=1.391, generator_loss=27.49, generator_mel_loss=19.64, generator_kl_loss=1.391, generator_dur_loss=1.847, generator_adv_loss=1.802, generator_feat_match_loss=2.815, over 70.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:25:43,622 INFO [train.py:527] (4/6) Epoch 76, batch 50, global_batch_idx: 9350, batch size: 50, loss[discriminator_loss=2.715, discriminator_real_loss=1.382, discriminator_fake_loss=1.332, generator_loss=27.27, generator_mel_loss=19.71, generator_kl_loss=1.416, generator_dur_loss=1.757, generator_adv_loss=2.073, generator_feat_match_loss=2.312, over 50.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.388, discriminator_fake_loss=1.334, generator_loss=27.56, generator_mel_loss=19.81, generator_kl_loss=1.34, generator_dur_loss=1.784, generator_adv_loss=1.95, generator_feat_match_loss=2.68, over 2791.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:28:05,369 INFO [train.py:527] (4/6) Epoch 76, batch 100, global_batch_idx: 9400, batch size: 96, loss[discriminator_loss=2.706, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=27.45, generator_mel_loss=19.34, generator_kl_loss=1.158, generator_dur_loss=1.921, generator_adv_loss=2.006, generator_feat_match_loss=3.022, over 96.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.384, discriminator_fake_loss=1.34, generator_loss=27.66, generator_mel_loss=19.84, generator_kl_loss=1.339, generator_dur_loss=1.805, generator_adv_loss=1.928, generator_feat_match_loss=2.747, over 5797.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:28:05,370 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 07:28:14,095 INFO [train.py:591] (4/6) Epoch 76, validation: discriminator_loss=2.765, discriminator_real_loss=1.503, discriminator_fake_loss=1.262, generator_loss=26.53, generator_mel_loss=19.46, generator_kl_loss=1.181, generator_dur_loss=1.839, generator_adv_loss=1.956, generator_feat_match_loss=2.087, over 100.00 samples. +2024-03-12 07:28:14,095 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 07:29:15,259 INFO [train.py:919] (4/6) Start epoch 77 +2024-03-12 07:30:53,597 INFO [train.py:527] (4/6) Epoch 77, batch 26, global_batch_idx: 9450, batch size: 44, loss[discriminator_loss=2.75, discriminator_real_loss=1.278, discriminator_fake_loss=1.472, generator_loss=27.8, generator_mel_loss=20.24, generator_kl_loss=1.409, generator_dur_loss=1.77, generator_adv_loss=1.848, generator_feat_match_loss=2.532, over 44.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=27.98, generator_mel_loss=20, generator_kl_loss=1.397, generator_dur_loss=1.786, generator_adv_loss=1.909, generator_feat_match_loss=2.886, over 1466.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:33:12,747 INFO [train.py:527] (4/6) Epoch 77, batch 76, global_batch_idx: 9500, batch size: 53, loss[discriminator_loss=2.753, discriminator_real_loss=1.5, discriminator_fake_loss=1.253, generator_loss=26.55, generator_mel_loss=19.3, generator_kl_loss=1.335, generator_dur_loss=1.678, generator_adv_loss=1.763, generator_feat_match_loss=2.479, over 53.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.404, discriminator_fake_loss=1.355, generator_loss=27.75, generator_mel_loss=19.86, generator_kl_loss=1.377, generator_dur_loss=1.79, generator_adv_loss=1.934, generator_feat_match_loss=2.788, over 4122.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:35:22,380 INFO [train.py:919] (4/6) Start epoch 78 +2024-03-12 07:35:52,850 INFO [train.py:527] (4/6) Epoch 78, batch 2, global_batch_idx: 9550, batch size: 50, loss[discriminator_loss=2.753, discriminator_real_loss=1.409, discriminator_fake_loss=1.344, generator_loss=27.04, generator_mel_loss=19.41, generator_kl_loss=1.481, generator_dur_loss=1.706, generator_adv_loss=1.904, generator_feat_match_loss=2.54, over 50.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.379, discriminator_fake_loss=1.364, generator_loss=27.4, generator_mel_loss=19.68, generator_kl_loss=1.411, generator_dur_loss=1.76, generator_adv_loss=1.845, generator_feat_match_loss=2.71, over 170.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:38:10,269 INFO [train.py:527] (4/6) Epoch 78, batch 52, global_batch_idx: 9600, batch size: 25, loss[discriminator_loss=2.584, discriminator_real_loss=1.272, discriminator_fake_loss=1.312, generator_loss=30.41, generator_mel_loss=21.2, generator_kl_loss=1.591, generator_dur_loss=1.584, generator_adv_loss=2.048, generator_feat_match_loss=3.99, over 25.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.396, discriminator_fake_loss=1.35, generator_loss=27.4, generator_mel_loss=19.67, generator_kl_loss=1.357, generator_dur_loss=1.792, generator_adv_loss=1.867, generator_feat_match_loss=2.716, over 3036.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:38:10,270 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 07:38:18,167 INFO [train.py:591] (4/6) Epoch 78, validation: discriminator_loss=2.832, discriminator_real_loss=1.553, discriminator_fake_loss=1.28, generator_loss=27.19, generator_mel_loss=19.96, generator_kl_loss=1.056, generator_dur_loss=1.858, generator_adv_loss=1.962, generator_feat_match_loss=2.352, over 100.00 samples. +2024-03-12 07:38:18,168 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 07:40:37,224 INFO [train.py:527] (4/6) Epoch 78, batch 102, global_batch_idx: 9650, batch size: 42, loss[discriminator_loss=2.756, discriminator_real_loss=1.448, discriminator_fake_loss=1.309, generator_loss=27.22, generator_mel_loss=19.88, generator_kl_loss=1.28, generator_dur_loss=1.718, generator_adv_loss=1.668, generator_feat_match_loss=2.673, over 42.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.392, discriminator_fake_loss=1.352, generator_loss=27.45, generator_mel_loss=19.67, generator_kl_loss=1.359, generator_dur_loss=1.799, generator_adv_loss=1.875, generator_feat_match_loss=2.743, over 5887.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:41:36,964 INFO [train.py:919] (4/6) Start epoch 79 +2024-03-12 07:43:17,146 INFO [train.py:527] (4/6) Epoch 79, batch 28, global_batch_idx: 9700, batch size: 80, loss[discriminator_loss=2.714, discriminator_real_loss=1.431, discriminator_fake_loss=1.283, generator_loss=27.16, generator_mel_loss=19.53, generator_kl_loss=1.271, generator_dur_loss=1.833, generator_adv_loss=1.859, generator_feat_match_loss=2.672, over 80.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.403, discriminator_fake_loss=1.354, generator_loss=27.7, generator_mel_loss=19.83, generator_kl_loss=1.372, generator_dur_loss=1.795, generator_adv_loss=1.912, generator_feat_match_loss=2.797, over 1618.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:45:39,643 INFO [train.py:527] (4/6) Epoch 79, batch 78, global_batch_idx: 9750, batch size: 45, loss[discriminator_loss=2.686, discriminator_real_loss=1.312, discriminator_fake_loss=1.374, generator_loss=27.66, generator_mel_loss=19.75, generator_kl_loss=1.488, generator_dur_loss=1.696, generator_adv_loss=1.811, generator_feat_match_loss=2.923, over 45.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.402, discriminator_fake_loss=1.36, generator_loss=27.53, generator_mel_loss=19.72, generator_kl_loss=1.364, generator_dur_loss=1.793, generator_adv_loss=1.894, generator_feat_match_loss=2.755, over 4376.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:47:44,962 INFO [train.py:919] (4/6) Start epoch 80 +2024-03-12 07:48:20,368 INFO [train.py:527] (4/6) Epoch 80, batch 4, global_batch_idx: 9800, batch size: 16, loss[discriminator_loss=2.663, discriminator_real_loss=1.374, discriminator_fake_loss=1.289, generator_loss=27.93, generator_mel_loss=19.99, generator_kl_loss=1.563, generator_dur_loss=1.595, generator_adv_loss=1.864, generator_feat_match_loss=2.918, over 16.00 samples.], tot_loss[discriminator_loss=2.775, discriminator_real_loss=1.397, discriminator_fake_loss=1.378, generator_loss=27.61, generator_mel_loss=19.76, generator_kl_loss=1.354, generator_dur_loss=1.81, generator_adv_loss=1.895, generator_feat_match_loss=2.79, over 287.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:48:20,373 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 07:48:28,385 INFO [train.py:591] (4/6) Epoch 80, validation: discriminator_loss=2.778, discriminator_real_loss=1.448, discriminator_fake_loss=1.329, generator_loss=27.23, generator_mel_loss=20.25, generator_kl_loss=1.085, generator_dur_loss=1.86, generator_adv_loss=1.811, generator_feat_match_loss=2.225, over 100.00 samples. +2024-03-12 07:48:28,388 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 07:50:45,587 INFO [train.py:527] (4/6) Epoch 80, batch 54, global_batch_idx: 9850, batch size: 83, loss[discriminator_loss=2.833, discriminator_real_loss=1.43, discriminator_fake_loss=1.403, generator_loss=27.89, generator_mel_loss=20.26, generator_kl_loss=1.416, generator_dur_loss=1.795, generator_adv_loss=1.669, generator_feat_match_loss=2.745, over 83.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.393, discriminator_fake_loss=1.355, generator_loss=27.55, generator_mel_loss=19.73, generator_kl_loss=1.379, generator_dur_loss=1.788, generator_adv_loss=1.888, generator_feat_match_loss=2.773, over 3241.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:53:04,970 INFO [train.py:527] (4/6) Epoch 80, batch 104, global_batch_idx: 9900, batch size: 83, loss[discriminator_loss=2.791, discriminator_real_loss=1.528, discriminator_fake_loss=1.263, generator_loss=26.8, generator_mel_loss=19.48, generator_kl_loss=1.276, generator_dur_loss=1.883, generator_adv_loss=1.636, generator_feat_match_loss=2.528, over 83.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.392, discriminator_fake_loss=1.353, generator_loss=27.55, generator_mel_loss=19.72, generator_kl_loss=1.378, generator_dur_loss=1.79, generator_adv_loss=1.884, generator_feat_match_loss=2.778, over 6084.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:53:59,007 INFO [train.py:919] (4/6) Start epoch 81 +2024-03-12 07:56:20,651 INFO [train.py:527] (4/6) Epoch 81, batch 30, global_batch_idx: 9950, batch size: 47, loss[discriminator_loss=2.842, discriminator_real_loss=1.553, discriminator_fake_loss=1.288, generator_loss=26.98, generator_mel_loss=19.4, generator_kl_loss=1.43, generator_dur_loss=1.811, generator_adv_loss=1.909, generator_feat_match_loss=2.431, over 47.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.423, discriminator_fake_loss=1.342, generator_loss=27.49, generator_mel_loss=19.59, generator_kl_loss=1.353, generator_dur_loss=1.8, generator_adv_loss=1.917, generator_feat_match_loss=2.827, over 1672.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:58:38,409 INFO [train.py:527] (4/6) Epoch 81, batch 80, global_batch_idx: 10000, batch size: 62, loss[discriminator_loss=2.739, discriminator_real_loss=1.37, discriminator_fake_loss=1.369, generator_loss=27.9, generator_mel_loss=20.07, generator_kl_loss=1.41, generator_dur_loss=1.78, generator_adv_loss=1.912, generator_feat_match_loss=2.732, over 62.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.405, discriminator_fake_loss=1.354, generator_loss=27.57, generator_mel_loss=19.72, generator_kl_loss=1.361, generator_dur_loss=1.804, generator_adv_loss=1.912, generator_feat_match_loss=2.77, over 4511.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:58:38,411 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 07:58:46,879 INFO [train.py:591] (4/6) Epoch 81, validation: discriminator_loss=2.771, discriminator_real_loss=1.479, discriminator_fake_loss=1.292, generator_loss=27.36, generator_mel_loss=20.11, generator_kl_loss=1.106, generator_dur_loss=1.827, generator_adv_loss=1.929, generator_feat_match_loss=2.384, over 100.00 samples. +2024-03-12 07:58:46,879 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 08:00:49,150 INFO [train.py:919] (4/6) Start epoch 82 +2024-03-12 08:01:27,365 INFO [train.py:527] (4/6) Epoch 82, batch 6, global_batch_idx: 10050, batch size: 55, loss[discriminator_loss=2.761, discriminator_real_loss=1.384, discriminator_fake_loss=1.377, generator_loss=26.62, generator_mel_loss=18.9, generator_kl_loss=1.282, generator_dur_loss=1.76, generator_adv_loss=1.924, generator_feat_match_loss=2.751, over 55.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.368, discriminator_fake_loss=1.338, generator_loss=27.07, generator_mel_loss=19.11, generator_kl_loss=1.29, generator_dur_loss=1.85, generator_adv_loss=1.944, generator_feat_match_loss=2.878, over 473.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:03:45,528 INFO [train.py:527] (4/6) Epoch 82, batch 56, global_batch_idx: 10100, batch size: 83, loss[discriminator_loss=2.698, discriminator_real_loss=1.35, discriminator_fake_loss=1.348, generator_loss=27.01, generator_mel_loss=19.2, generator_kl_loss=1.268, generator_dur_loss=1.819, generator_adv_loss=1.947, generator_feat_match_loss=2.777, over 83.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.373, discriminator_fake_loss=1.334, generator_loss=27.64, generator_mel_loss=19.59, generator_kl_loss=1.346, generator_dur_loss=1.797, generator_adv_loss=1.948, generator_feat_match_loss=2.957, over 3342.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:06:04,750 INFO [train.py:527] (4/6) Epoch 82, batch 106, global_batch_idx: 10150, batch size: 66, loss[discriminator_loss=2.745, discriminator_real_loss=1.469, discriminator_fake_loss=1.275, generator_loss=28.51, generator_mel_loss=20.49, generator_kl_loss=1.303, generator_dur_loss=1.84, generator_adv_loss=1.968, generator_feat_match_loss=2.905, over 66.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.39, discriminator_fake_loss=1.338, generator_loss=27.59, generator_mel_loss=19.64, generator_kl_loss=1.347, generator_dur_loss=1.804, generator_adv_loss=1.935, generator_feat_match_loss=2.86, over 6345.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:06:53,026 INFO [train.py:919] (4/6) Start epoch 83 +2024-03-12 08:08:44,644 INFO [train.py:527] (4/6) Epoch 83, batch 32, global_batch_idx: 10200, batch size: 68, loss[discriminator_loss=2.715, discriminator_real_loss=1.357, discriminator_fake_loss=1.358, generator_loss=27.9, generator_mel_loss=19.65, generator_kl_loss=1.441, generator_dur_loss=1.844, generator_adv_loss=1.902, generator_feat_match_loss=3.067, over 68.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.39, discriminator_fake_loss=1.378, generator_loss=27.34, generator_mel_loss=19.56, generator_kl_loss=1.375, generator_dur_loss=1.82, generator_adv_loss=1.879, generator_feat_match_loss=2.702, over 1992.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:08:44,646 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 08:08:52,604 INFO [train.py:591] (4/6) Epoch 83, validation: discriminator_loss=2.828, discriminator_real_loss=1.457, discriminator_fake_loss=1.371, generator_loss=26.67, generator_mel_loss=19.9, generator_kl_loss=1.167, generator_dur_loss=1.845, generator_adv_loss=1.756, generator_feat_match_loss=2.008, over 100.00 samples. +2024-03-12 08:08:52,605 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 08:11:12,301 INFO [train.py:527] (4/6) Epoch 83, batch 82, global_batch_idx: 10250, batch size: 70, loss[discriminator_loss=2.689, discriminator_real_loss=1.416, discriminator_fake_loss=1.272, generator_loss=28.17, generator_mel_loss=19.8, generator_kl_loss=1.257, generator_dur_loss=1.867, generator_adv_loss=2.109, generator_feat_match_loss=3.135, over 70.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.401, discriminator_fake_loss=1.362, generator_loss=27.33, generator_mel_loss=19.55, generator_kl_loss=1.368, generator_dur_loss=1.804, generator_adv_loss=1.887, generator_feat_match_loss=2.726, over 4854.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:13:05,268 INFO [train.py:919] (4/6) Start epoch 84 +2024-03-12 08:13:51,784 INFO [train.py:527] (4/6) Epoch 84, batch 8, global_batch_idx: 10300, batch size: 48, loss[discriminator_loss=2.716, discriminator_real_loss=1.433, discriminator_fake_loss=1.282, generator_loss=27.07, generator_mel_loss=19.22, generator_kl_loss=1.505, generator_dur_loss=1.664, generator_adv_loss=1.763, generator_feat_match_loss=2.921, over 48.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.386, discriminator_fake_loss=1.345, generator_loss=27.46, generator_mel_loss=19.59, generator_kl_loss=1.416, generator_dur_loss=1.759, generator_adv_loss=1.891, generator_feat_match_loss=2.806, over 518.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:16:09,566 INFO [train.py:527] (4/6) Epoch 84, batch 58, global_batch_idx: 10350, batch size: 58, loss[discriminator_loss=2.745, discriminator_real_loss=1.387, discriminator_fake_loss=1.359, generator_loss=27.29, generator_mel_loss=19.37, generator_kl_loss=1.293, generator_dur_loss=1.767, generator_adv_loss=1.985, generator_feat_match_loss=2.88, over 58.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.385, discriminator_fake_loss=1.343, generator_loss=27.55, generator_mel_loss=19.6, generator_kl_loss=1.392, generator_dur_loss=1.782, generator_adv_loss=1.911, generator_feat_match_loss=2.868, over 3403.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:18:30,348 INFO [train.py:527] (4/6) Epoch 84, batch 108, global_batch_idx: 10400, batch size: 53, loss[discriminator_loss=2.761, discriminator_real_loss=1.49, discriminator_fake_loss=1.27, generator_loss=27.54, generator_mel_loss=19.67, generator_kl_loss=1.549, generator_dur_loss=1.676, generator_adv_loss=1.6, generator_feat_match_loss=3.048, over 53.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.396, discriminator_fake_loss=1.343, generator_loss=27.47, generator_mel_loss=19.59, generator_kl_loss=1.391, generator_dur_loss=1.771, generator_adv_loss=1.899, generator_feat_match_loss=2.816, over 5950.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:18:30,349 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 08:18:39,356 INFO [train.py:591] (4/6) Epoch 84, validation: discriminator_loss=2.949, discriminator_real_loss=1.299, discriminator_fake_loss=1.649, generator_loss=26.47, generator_mel_loss=19.88, generator_kl_loss=1.111, generator_dur_loss=1.823, generator_adv_loss=1.488, generator_feat_match_loss=2.164, over 100.00 samples. +2024-03-12 08:18:39,357 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 08:19:18,506 INFO [train.py:919] (4/6) Start epoch 85 +2024-03-12 08:21:17,415 INFO [train.py:527] (4/6) Epoch 85, batch 34, global_batch_idx: 10450, batch size: 36, loss[discriminator_loss=2.747, discriminator_real_loss=1.393, discriminator_fake_loss=1.354, generator_loss=27.28, generator_mel_loss=19.62, generator_kl_loss=1.597, generator_dur_loss=1.745, generator_adv_loss=1.916, generator_feat_match_loss=2.406, over 36.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.411, discriminator_fake_loss=1.342, generator_loss=27.56, generator_mel_loss=19.61, generator_kl_loss=1.36, generator_dur_loss=1.775, generator_adv_loss=1.955, generator_feat_match_loss=2.857, over 1927.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:23:35,895 INFO [train.py:527] (4/6) Epoch 85, batch 84, global_batch_idx: 10500, batch size: 68, loss[discriminator_loss=2.78, discriminator_real_loss=1.511, discriminator_fake_loss=1.269, generator_loss=26.4, generator_mel_loss=18.88, generator_kl_loss=1.438, generator_dur_loss=1.806, generator_adv_loss=1.81, generator_feat_match_loss=2.469, over 68.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.406, discriminator_fake_loss=1.349, generator_loss=27.44, generator_mel_loss=19.6, generator_kl_loss=1.358, generator_dur_loss=1.769, generator_adv_loss=1.913, generator_feat_match_loss=2.796, over 4781.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:25:24,045 INFO [train.py:919] (4/6) Start epoch 86 +2024-03-12 08:26:14,494 INFO [train.py:527] (4/6) Epoch 86, batch 10, global_batch_idx: 10550, batch size: 44, loss[discriminator_loss=2.764, discriminator_real_loss=1.238, discriminator_fake_loss=1.526, generator_loss=28.66, generator_mel_loss=20.56, generator_kl_loss=1.567, generator_dur_loss=1.719, generator_adv_loss=1.699, generator_feat_match_loss=3.119, over 44.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.41, discriminator_fake_loss=1.354, generator_loss=27.74, generator_mel_loss=19.82, generator_kl_loss=1.429, generator_dur_loss=1.735, generator_adv_loss=1.915, generator_feat_match_loss=2.848, over 549.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:28:39,022 INFO [train.py:527] (4/6) Epoch 86, batch 60, global_batch_idx: 10600, batch size: 48, loss[discriminator_loss=2.737, discriminator_real_loss=1.237, discriminator_fake_loss=1.5, generator_loss=26.81, generator_mel_loss=18.69, generator_kl_loss=1.492, generator_dur_loss=1.646, generator_adv_loss=2.077, generator_feat_match_loss=2.911, over 48.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.394, discriminator_fake_loss=1.346, generator_loss=27.37, generator_mel_loss=19.47, generator_kl_loss=1.357, generator_dur_loss=1.785, generator_adv_loss=1.911, generator_feat_match_loss=2.849, over 3642.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:28:39,023 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 08:28:46,851 INFO [train.py:591] (4/6) Epoch 86, validation: discriminator_loss=2.49, discriminator_real_loss=1.263, discriminator_fake_loss=1.227, generator_loss=27, generator_mel_loss=19.12, generator_kl_loss=1.179, generator_dur_loss=1.824, generator_adv_loss=2.144, generator_feat_match_loss=2.727, over 100.00 samples. +2024-03-12 08:28:46,852 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 08:31:04,072 INFO [train.py:527] (4/6) Epoch 86, batch 110, global_batch_idx: 10650, batch size: 62, loss[discriminator_loss=2.692, discriminator_real_loss=1.393, discriminator_fake_loss=1.3, generator_loss=28.23, generator_mel_loss=20.26, generator_kl_loss=1.324, generator_dur_loss=1.791, generator_adv_loss=1.931, generator_feat_match_loss=2.924, over 62.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.395, discriminator_fake_loss=1.331, generator_loss=27.69, generator_mel_loss=19.49, generator_kl_loss=1.364, generator_dur_loss=1.792, generator_adv_loss=2.016, generator_feat_match_loss=3.028, over 6606.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:31:40,261 INFO [train.py:919] (4/6) Start epoch 87 +2024-03-12 08:33:42,647 INFO [train.py:527] (4/6) Epoch 87, batch 36, global_batch_idx: 10700, batch size: 50, loss[discriminator_loss=2.703, discriminator_real_loss=1.417, discriminator_fake_loss=1.286, generator_loss=26.95, generator_mel_loss=19.06, generator_kl_loss=1.551, generator_dur_loss=1.755, generator_adv_loss=1.833, generator_feat_match_loss=2.756, over 50.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.396, discriminator_fake_loss=1.346, generator_loss=27.33, generator_mel_loss=19.41, generator_kl_loss=1.405, generator_dur_loss=1.796, generator_adv_loss=1.909, generator_feat_match_loss=2.81, over 2137.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:36:02,589 INFO [train.py:527] (4/6) Epoch 87, batch 86, global_batch_idx: 10750, batch size: 70, loss[discriminator_loss=2.749, discriminator_real_loss=1.399, discriminator_fake_loss=1.35, generator_loss=27.17, generator_mel_loss=19.33, generator_kl_loss=1.437, generator_dur_loss=1.877, generator_adv_loss=1.769, generator_feat_match_loss=2.754, over 70.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.391, discriminator_fake_loss=1.345, generator_loss=27.36, generator_mel_loss=19.44, generator_kl_loss=1.383, generator_dur_loss=1.806, generator_adv_loss=1.899, generator_feat_match_loss=2.836, over 5145.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:37:45,647 INFO [train.py:919] (4/6) Start epoch 88 +2024-03-12 08:38:43,579 INFO [train.py:527] (4/6) Epoch 88, batch 12, global_batch_idx: 10800, batch size: 39, loss[discriminator_loss=2.712, discriminator_real_loss=1.316, discriminator_fake_loss=1.396, generator_loss=29.09, generator_mel_loss=21.02, generator_kl_loss=1.546, generator_dur_loss=1.735, generator_adv_loss=1.883, generator_feat_match_loss=2.903, over 39.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.395, discriminator_fake_loss=1.356, generator_loss=27.71, generator_mel_loss=19.81, generator_kl_loss=1.376, generator_dur_loss=1.762, generator_adv_loss=1.874, generator_feat_match_loss=2.894, over 703.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:38:43,581 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 08:38:51,181 INFO [train.py:591] (4/6) Epoch 88, validation: discriminator_loss=2.811, discriminator_real_loss=1.449, discriminator_fake_loss=1.362, generator_loss=27.26, generator_mel_loss=20.33, generator_kl_loss=1.1, generator_dur_loss=1.832, generator_adv_loss=1.811, generator_feat_match_loss=2.186, over 100.00 samples. +2024-03-12 08:38:51,182 INFO [train.py:592] (4/6) Maximum memory allocated so far is 28739MB +2024-03-12 08:41:09,937 INFO [train.py:527] (4/6) Epoch 88, batch 62, global_batch_idx: 10850, batch size: 72, loss[discriminator_loss=2.784, discriminator_real_loss=1.512, discriminator_fake_loss=1.272, generator_loss=26.52, generator_mel_loss=18.74, generator_kl_loss=1.203, generator_dur_loss=1.877, generator_adv_loss=1.734, generator_feat_match_loss=2.967, over 72.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.395, discriminator_fake_loss=1.355, generator_loss=27.37, generator_mel_loss=19.52, generator_kl_loss=1.384, generator_dur_loss=1.769, generator_adv_loss=1.872, generator_feat_match_loss=2.825, over 3363.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:43:28,419 INFO [train.py:527] (4/6) Epoch 88, batch 112, global_batch_idx: 10900, batch size: 25, loss[discriminator_loss=2.645, discriminator_real_loss=1.348, discriminator_fake_loss=1.297, generator_loss=28.91, generator_mel_loss=20.2, generator_kl_loss=1.759, generator_dur_loss=1.639, generator_adv_loss=1.909, generator_feat_match_loss=3.403, over 25.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.412, discriminator_fake_loss=1.358, generator_loss=27.36, generator_mel_loss=19.48, generator_kl_loss=1.382, generator_dur_loss=1.773, generator_adv_loss=1.921, generator_feat_match_loss=2.805, over 6170.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:44:02,239 INFO [train.py:919] (4/6) Start epoch 89 +2024-03-12 08:46:13,292 INFO [train.py:527] (4/6) Epoch 89, batch 38, global_batch_idx: 10950, batch size: 64, loss[discriminator_loss=2.731, discriminator_real_loss=1.331, discriminator_fake_loss=1.4, generator_loss=27.09, generator_mel_loss=19.18, generator_kl_loss=1.423, generator_dur_loss=1.811, generator_adv_loss=1.962, generator_feat_match_loss=2.717, over 64.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=27.62, generator_mel_loss=19.63, generator_kl_loss=1.357, generator_dur_loss=1.811, generator_adv_loss=1.908, generator_feat_match_loss=2.908, over 2253.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:48:28,943 INFO [train.py:527] (4/6) Epoch 89, batch 88, global_batch_idx: 11000, batch size: 59, loss[discriminator_loss=2.723, discriminator_real_loss=1.474, discriminator_fake_loss=1.249, generator_loss=27.01, generator_mel_loss=19.11, generator_kl_loss=1.292, generator_dur_loss=1.761, generator_adv_loss=2.013, generator_feat_match_loss=2.835, over 59.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.377, discriminator_fake_loss=1.359, generator_loss=27.53, generator_mel_loss=19.57, generator_kl_loss=1.37, generator_dur_loss=1.802, generator_adv_loss=1.898, generator_feat_match_loss=2.885, over 5047.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:48:28,945 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 08:48:37,872 INFO [train.py:591] (4/6) Epoch 89, validation: discriminator_loss=2.752, discriminator_real_loss=1.557, discriminator_fake_loss=1.195, generator_loss=26.74, generator_mel_loss=19.27, generator_kl_loss=1.154, generator_dur_loss=1.835, generator_adv_loss=2.016, generator_feat_match_loss=2.461, over 100.00 samples. +2024-03-12 08:48:37,873 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 08:50:16,787 INFO [train.py:919] (4/6) Start epoch 90 +2024-03-12 08:51:22,961 INFO [train.py:527] (4/6) Epoch 90, batch 14, global_batch_idx: 11050, batch size: 68, loss[discriminator_loss=2.717, discriminator_real_loss=1.422, discriminator_fake_loss=1.296, generator_loss=26.27, generator_mel_loss=18.35, generator_kl_loss=1.351, generator_dur_loss=1.8, generator_adv_loss=1.978, generator_feat_match_loss=2.792, over 68.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.386, discriminator_fake_loss=1.342, generator_loss=27.16, generator_mel_loss=19.2, generator_kl_loss=1.371, generator_dur_loss=1.77, generator_adv_loss=1.951, generator_feat_match_loss=2.869, over 852.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:53:43,239 INFO [train.py:527] (4/6) Epoch 90, batch 64, global_batch_idx: 11100, batch size: 52, loss[discriminator_loss=2.765, discriminator_real_loss=1.28, discriminator_fake_loss=1.484, generator_loss=27.79, generator_mel_loss=19.96, generator_kl_loss=1.303, generator_dur_loss=1.759, generator_adv_loss=1.941, generator_feat_match_loss=2.822, over 52.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.394, discriminator_fake_loss=1.351, generator_loss=27.42, generator_mel_loss=19.51, generator_kl_loss=1.379, generator_dur_loss=1.794, generator_adv_loss=1.894, generator_feat_match_loss=2.845, over 3797.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:56:00,747 INFO [train.py:527] (4/6) Epoch 90, batch 114, global_batch_idx: 11150, batch size: 25, loss[discriminator_loss=2.639, discriminator_real_loss=1.413, discriminator_fake_loss=1.227, generator_loss=27.4, generator_mel_loss=19.27, generator_kl_loss=1.577, generator_dur_loss=1.647, generator_adv_loss=1.949, generator_feat_match_loss=2.965, over 25.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.386, discriminator_fake_loss=1.35, generator_loss=27.45, generator_mel_loss=19.5, generator_kl_loss=1.373, generator_dur_loss=1.802, generator_adv_loss=1.897, generator_feat_match_loss=2.881, over 6700.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:56:23,498 INFO [train.py:919] (4/6) Start epoch 91 +2024-03-12 08:58:38,291 INFO [train.py:527] (4/6) Epoch 91, batch 40, global_batch_idx: 11200, batch size: 50, loss[discriminator_loss=2.698, discriminator_real_loss=1.347, discriminator_fake_loss=1.351, generator_loss=26.89, generator_mel_loss=19.32, generator_kl_loss=1.415, generator_dur_loss=1.663, generator_adv_loss=1.897, generator_feat_match_loss=2.597, over 50.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.383, discriminator_fake_loss=1.36, generator_loss=27.38, generator_mel_loss=19.48, generator_kl_loss=1.394, generator_dur_loss=1.775, generator_adv_loss=1.884, generator_feat_match_loss=2.854, over 2314.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:58:38,292 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 08:58:46,107 INFO [train.py:591] (4/6) Epoch 91, validation: discriminator_loss=2.709, discriminator_real_loss=1.359, discriminator_fake_loss=1.35, generator_loss=27.02, generator_mel_loss=19.51, generator_kl_loss=1.237, generator_dur_loss=1.81, generator_adv_loss=1.85, generator_feat_match_loss=2.611, over 100.00 samples. +2024-03-12 08:58:46,109 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 09:01:04,322 INFO [train.py:527] (4/6) Epoch 91, batch 90, global_batch_idx: 11250, batch size: 31, loss[discriminator_loss=2.765, discriminator_real_loss=1.386, discriminator_fake_loss=1.379, generator_loss=26.88, generator_mel_loss=19.33, generator_kl_loss=1.367, generator_dur_loss=1.705, generator_adv_loss=1.79, generator_feat_match_loss=2.681, over 31.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.387, discriminator_fake_loss=1.353, generator_loss=27.42, generator_mel_loss=19.51, generator_kl_loss=1.393, generator_dur_loss=1.764, generator_adv_loss=1.896, generator_feat_match_loss=2.853, over 5089.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:02:37,811 INFO [train.py:919] (4/6) Start epoch 92 +2024-03-12 09:03:48,778 INFO [train.py:527] (4/6) Epoch 92, batch 16, global_batch_idx: 11300, batch size: 72, loss[discriminator_loss=2.727, discriminator_real_loss=1.394, discriminator_fake_loss=1.334, generator_loss=26.94, generator_mel_loss=19.19, generator_kl_loss=1.153, generator_dur_loss=1.814, generator_adv_loss=2.061, generator_feat_match_loss=2.718, over 72.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.421, discriminator_fake_loss=1.339, generator_loss=27.3, generator_mel_loss=19.45, generator_kl_loss=1.342, generator_dur_loss=1.775, generator_adv_loss=1.95, generator_feat_match_loss=2.784, over 970.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:06:10,305 INFO [train.py:527] (4/6) Epoch 92, batch 66, global_batch_idx: 11350, batch size: 68, loss[discriminator_loss=2.751, discriminator_real_loss=1.322, discriminator_fake_loss=1.429, generator_loss=28.62, generator_mel_loss=20.23, generator_kl_loss=1.498, generator_dur_loss=1.743, generator_adv_loss=2.042, generator_feat_match_loss=3.111, over 68.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.396, discriminator_fake_loss=1.348, generator_loss=27.38, generator_mel_loss=19.48, generator_kl_loss=1.375, generator_dur_loss=1.783, generator_adv_loss=1.906, generator_feat_match_loss=2.831, over 3932.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:08:27,319 INFO [train.py:527] (4/6) Epoch 92, batch 116, global_batch_idx: 11400, batch size: 48, loss[discriminator_loss=2.826, discriminator_real_loss=1.499, discriminator_fake_loss=1.327, generator_loss=26.38, generator_mel_loss=18.89, generator_kl_loss=1.474, generator_dur_loss=1.701, generator_adv_loss=1.918, generator_feat_match_loss=2.402, over 48.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.395, discriminator_fake_loss=1.346, generator_loss=27.44, generator_mel_loss=19.49, generator_kl_loss=1.366, generator_dur_loss=1.774, generator_adv_loss=1.93, generator_feat_match_loss=2.879, over 6897.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:08:27,320 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 09:08:36,554 INFO [train.py:591] (4/6) Epoch 92, validation: discriminator_loss=2.79, discriminator_real_loss=1.531, discriminator_fake_loss=1.259, generator_loss=27.22, generator_mel_loss=19.99, generator_kl_loss=1.191, generator_dur_loss=1.808, generator_adv_loss=1.89, generator_feat_match_loss=2.342, over 100.00 samples. +2024-03-12 09:08:36,555 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 09:08:55,508 INFO [train.py:919] (4/6) Start epoch 93 +2024-03-12 09:11:18,246 INFO [train.py:527] (4/6) Epoch 93, batch 42, global_batch_idx: 11450, batch size: 64, loss[discriminator_loss=2.651, discriminator_real_loss=1.204, discriminator_fake_loss=1.448, generator_loss=27.57, generator_mel_loss=19.24, generator_kl_loss=1.408, generator_dur_loss=1.734, generator_adv_loss=1.925, generator_feat_match_loss=3.257, over 64.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.395, discriminator_fake_loss=1.349, generator_loss=27.44, generator_mel_loss=19.48, generator_kl_loss=1.369, generator_dur_loss=1.777, generator_adv_loss=1.915, generator_feat_match_loss=2.901, over 2480.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:13:34,464 INFO [train.py:527] (4/6) Epoch 93, batch 92, global_batch_idx: 11500, batch size: 47, loss[discriminator_loss=2.702, discriminator_real_loss=1.448, discriminator_fake_loss=1.254, generator_loss=27.28, generator_mel_loss=19.53, generator_kl_loss=1.484, generator_dur_loss=1.703, generator_adv_loss=1.827, generator_feat_match_loss=2.726, over 47.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.398, discriminator_fake_loss=1.349, generator_loss=27.44, generator_mel_loss=19.49, generator_kl_loss=1.382, generator_dur_loss=1.754, generator_adv_loss=1.914, generator_feat_match_loss=2.9, over 5284.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:15:01,356 INFO [train.py:919] (4/6) Start epoch 94 +2024-03-12 09:16:10,263 INFO [train.py:527] (4/6) Epoch 94, batch 18, global_batch_idx: 11550, batch size: 56, loss[discriminator_loss=2.729, discriminator_real_loss=1.459, discriminator_fake_loss=1.27, generator_loss=26.34, generator_mel_loss=18.45, generator_kl_loss=1.256, generator_dur_loss=1.723, generator_adv_loss=2.002, generator_feat_match_loss=2.906, over 56.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.359, discriminator_fake_loss=1.364, generator_loss=27.3, generator_mel_loss=19.34, generator_kl_loss=1.319, generator_dur_loss=1.755, generator_adv_loss=1.916, generator_feat_match_loss=2.964, over 1203.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:18:29,018 INFO [train.py:527] (4/6) Epoch 94, batch 68, global_batch_idx: 11600, batch size: 72, loss[discriminator_loss=2.756, discriminator_real_loss=1.343, discriminator_fake_loss=1.413, generator_loss=26.98, generator_mel_loss=19.04, generator_kl_loss=1.303, generator_dur_loss=1.769, generator_adv_loss=1.982, generator_feat_match_loss=2.887, over 72.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.38, discriminator_fake_loss=1.36, generator_loss=27.15, generator_mel_loss=19.3, generator_kl_loss=1.344, generator_dur_loss=1.762, generator_adv_loss=1.9, generator_feat_match_loss=2.846, over 4236.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:18:29,019 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 09:18:37,811 INFO [train.py:591] (4/6) Epoch 94, validation: discriminator_loss=2.789, discriminator_real_loss=1.553, discriminator_fake_loss=1.236, generator_loss=27.3, generator_mel_loss=19.89, generator_kl_loss=1.23, generator_dur_loss=1.789, generator_adv_loss=1.981, generator_feat_match_loss=2.414, over 100.00 samples. +2024-03-12 09:18:37,812 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 09:20:57,130 INFO [train.py:527] (4/6) Epoch 94, batch 118, global_batch_idx: 11650, batch size: 70, loss[discriminator_loss=2.625, discriminator_real_loss=1.319, discriminator_fake_loss=1.307, generator_loss=27.88, generator_mel_loss=19.35, generator_kl_loss=1.474, generator_dur_loss=1.716, generator_adv_loss=2.021, generator_feat_match_loss=3.322, over 70.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.383, discriminator_fake_loss=1.35, generator_loss=27.36, generator_mel_loss=19.4, generator_kl_loss=1.371, generator_dur_loss=1.736, generator_adv_loss=1.921, generator_feat_match_loss=2.93, over 6852.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:21:13,559 INFO [train.py:919] (4/6) Start epoch 95 +2024-03-12 09:23:40,680 INFO [train.py:527] (4/6) Epoch 95, batch 44, global_batch_idx: 11700, batch size: 61, loss[discriminator_loss=2.819, discriminator_real_loss=1.536, discriminator_fake_loss=1.283, generator_loss=26.05, generator_mel_loss=18.71, generator_kl_loss=1.401, generator_dur_loss=1.706, generator_adv_loss=1.79, generator_feat_match_loss=2.447, over 61.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.409, discriminator_fake_loss=1.345, generator_loss=27.32, generator_mel_loss=19.43, generator_kl_loss=1.361, generator_dur_loss=1.738, generator_adv_loss=1.904, generator_feat_match_loss=2.89, over 2540.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:26:04,430 INFO [train.py:527] (4/6) Epoch 95, batch 94, global_batch_idx: 11750, batch size: 62, loss[discriminator_loss=2.83, discriminator_real_loss=1.323, discriminator_fake_loss=1.507, generator_loss=27.23, generator_mel_loss=19.4, generator_kl_loss=1.404, generator_dur_loss=1.732, generator_adv_loss=1.927, generator_feat_match_loss=2.767, over 62.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.407, discriminator_fake_loss=1.343, generator_loss=27.26, generator_mel_loss=19.39, generator_kl_loss=1.362, generator_dur_loss=1.736, generator_adv_loss=1.908, generator_feat_match_loss=2.866, over 5429.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:27:30,538 INFO [train.py:919] (4/6) Start epoch 96 +2024-03-12 09:28:50,151 INFO [train.py:527] (4/6) Epoch 96, batch 20, global_batch_idx: 11800, batch size: 70, loss[discriminator_loss=2.715, discriminator_real_loss=1.347, discriminator_fake_loss=1.367, generator_loss=27.96, generator_mel_loss=19.95, generator_kl_loss=1.36, generator_dur_loss=1.786, generator_adv_loss=1.987, generator_feat_match_loss=2.887, over 70.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.385, discriminator_fake_loss=1.346, generator_loss=27.45, generator_mel_loss=19.44, generator_kl_loss=1.4, generator_dur_loss=1.767, generator_adv_loss=1.905, generator_feat_match_loss=2.94, over 1180.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:28:50,152 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 09:28:58,042 INFO [train.py:591] (4/6) Epoch 96, validation: discriminator_loss=2.716, discriminator_real_loss=1.489, discriminator_fake_loss=1.227, generator_loss=26.48, generator_mel_loss=19.32, generator_kl_loss=1.108, generator_dur_loss=1.819, generator_adv_loss=1.963, generator_feat_match_loss=2.267, over 100.00 samples. +2024-03-12 09:28:58,043 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 09:31:21,630 INFO [train.py:527] (4/6) Epoch 96, batch 70, global_batch_idx: 11850, batch size: 58, loss[discriminator_loss=2.695, discriminator_real_loss=1.264, discriminator_fake_loss=1.432, generator_loss=27.23, generator_mel_loss=19.33, generator_kl_loss=1.289, generator_dur_loss=1.759, generator_adv_loss=1.979, generator_feat_match_loss=2.88, over 58.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.393, discriminator_fake_loss=1.365, generator_loss=27.41, generator_mel_loss=19.33, generator_kl_loss=1.388, generator_dur_loss=1.761, generator_adv_loss=1.96, generator_feat_match_loss=2.977, over 3834.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:33:43,330 INFO [train.py:527] (4/6) Epoch 96, batch 120, global_batch_idx: 11900, batch size: 55, loss[discriminator_loss=2.983, discriminator_real_loss=1.222, discriminator_fake_loss=1.76, generator_loss=26.28, generator_mel_loss=18.81, generator_kl_loss=1.344, generator_dur_loss=1.664, generator_adv_loss=1.814, generator_feat_match_loss=2.655, over 55.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.397, discriminator_fake_loss=1.355, generator_loss=27.34, generator_mel_loss=19.33, generator_kl_loss=1.382, generator_dur_loss=1.771, generator_adv_loss=1.944, generator_feat_match_loss=2.919, over 6877.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:33:55,013 INFO [train.py:919] (4/6) Start epoch 97 +2024-03-12 09:36:27,959 INFO [train.py:527] (4/6) Epoch 97, batch 46, global_batch_idx: 11950, batch size: 39, loss[discriminator_loss=2.717, discriminator_real_loss=1.409, discriminator_fake_loss=1.308, generator_loss=27.74, generator_mel_loss=19.54, generator_kl_loss=1.588, generator_dur_loss=1.719, generator_adv_loss=1.992, generator_feat_match_loss=2.894, over 39.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.385, discriminator_fake_loss=1.347, generator_loss=27.47, generator_mel_loss=19.51, generator_kl_loss=1.372, generator_dur_loss=1.765, generator_adv_loss=1.902, generator_feat_match_loss=2.921, over 2557.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:38:50,876 INFO [train.py:527] (4/6) Epoch 97, batch 96, global_batch_idx: 12000, batch size: 62, loss[discriminator_loss=2.791, discriminator_real_loss=1.378, discriminator_fake_loss=1.413, generator_loss=26.99, generator_mel_loss=19.09, generator_kl_loss=1.425, generator_dur_loss=1.81, generator_adv_loss=1.972, generator_feat_match_loss=2.694, over 62.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.391, discriminator_fake_loss=1.346, generator_loss=27.36, generator_mel_loss=19.39, generator_kl_loss=1.381, generator_dur_loss=1.785, generator_adv_loss=1.905, generator_feat_match_loss=2.903, over 5457.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:38:50,877 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 09:38:59,575 INFO [train.py:591] (4/6) Epoch 97, validation: discriminator_loss=2.715, discriminator_real_loss=1.468, discriminator_fake_loss=1.247, generator_loss=26.83, generator_mel_loss=19.48, generator_kl_loss=1.2, generator_dur_loss=1.834, generator_adv_loss=1.95, generator_feat_match_loss=2.361, over 100.00 samples. +2024-03-12 09:38:59,575 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 09:40:12,071 INFO [train.py:919] (4/6) Start epoch 98 +2024-03-12 09:41:40,379 INFO [train.py:527] (4/6) Epoch 98, batch 22, global_batch_idx: 12050, batch size: 96, loss[discriminator_loss=2.777, discriminator_real_loss=1.556, discriminator_fake_loss=1.221, generator_loss=26.77, generator_mel_loss=19.18, generator_kl_loss=1.343, generator_dur_loss=1.9, generator_adv_loss=1.694, generator_feat_match_loss=2.662, over 96.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.383, discriminator_fake_loss=1.355, generator_loss=27.34, generator_mel_loss=19.38, generator_kl_loss=1.37, generator_dur_loss=1.816, generator_adv_loss=1.885, generator_feat_match_loss=2.891, over 1499.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:44:00,442 INFO [train.py:527] (4/6) Epoch 98, batch 72, global_batch_idx: 12100, batch size: 64, loss[discriminator_loss=2.746, discriminator_real_loss=1.449, discriminator_fake_loss=1.298, generator_loss=27.32, generator_mel_loss=19.37, generator_kl_loss=1.264, generator_dur_loss=1.785, generator_adv_loss=1.827, generator_feat_match_loss=3.071, over 64.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.406, discriminator_fake_loss=1.351, generator_loss=27.57, generator_mel_loss=19.42, generator_kl_loss=1.388, generator_dur_loss=1.79, generator_adv_loss=1.961, generator_feat_match_loss=3.019, over 4351.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:46:23,177 INFO [train.py:527] (4/6) Epoch 98, batch 122, global_batch_idx: 12150, batch size: 31, loss[discriminator_loss=2.715, discriminator_real_loss=1.326, discriminator_fake_loss=1.389, generator_loss=28.76, generator_mel_loss=20.42, generator_kl_loss=1.598, generator_dur_loss=1.738, generator_adv_loss=1.9, generator_feat_match_loss=3.102, over 31.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.399, discriminator_fake_loss=1.35, generator_loss=27.56, generator_mel_loss=19.41, generator_kl_loss=1.385, generator_dur_loss=1.789, generator_adv_loss=1.946, generator_feat_match_loss=3.032, over 7069.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:46:27,367 INFO [train.py:919] (4/6) Start epoch 99 +2024-03-12 09:49:09,444 INFO [train.py:527] (4/6) Epoch 99, batch 48, global_batch_idx: 12200, batch size: 80, loss[discriminator_loss=2.738, discriminator_real_loss=1.511, discriminator_fake_loss=1.228, generator_loss=27.23, generator_mel_loss=19.2, generator_kl_loss=1.314, generator_dur_loss=1.876, generator_adv_loss=1.899, generator_feat_match_loss=2.943, over 80.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.378, discriminator_fake_loss=1.347, generator_loss=27.45, generator_mel_loss=19.42, generator_kl_loss=1.351, generator_dur_loss=1.778, generator_adv_loss=1.918, generator_feat_match_loss=2.986, over 2830.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:49:09,445 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 09:49:17,400 INFO [train.py:591] (4/6) Epoch 99, validation: discriminator_loss=2.729, discriminator_real_loss=1.43, discriminator_fake_loss=1.299, generator_loss=27.87, generator_mel_loss=20.36, generator_kl_loss=1.145, generator_dur_loss=1.836, generator_adv_loss=1.867, generator_feat_match_loss=2.665, over 100.00 samples. +2024-03-12 09:49:17,401 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 09:51:38,140 INFO [train.py:527] (4/6) Epoch 99, batch 98, global_batch_idx: 12250, batch size: 47, loss[discriminator_loss=2.718, discriminator_real_loss=1.295, discriminator_fake_loss=1.423, generator_loss=28.45, generator_mel_loss=20.1, generator_kl_loss=1.471, generator_dur_loss=1.664, generator_adv_loss=1.978, generator_feat_match_loss=3.238, over 47.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.391, discriminator_fake_loss=1.347, generator_loss=27.39, generator_mel_loss=19.41, generator_kl_loss=1.368, generator_dur_loss=1.782, generator_adv_loss=1.897, generator_feat_match_loss=2.933, over 5630.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:52:49,174 INFO [train.py:919] (4/6) Start epoch 100 +2024-03-12 09:54:22,506 INFO [train.py:527] (4/6) Epoch 100, batch 24, global_batch_idx: 12300, batch size: 74, loss[discriminator_loss=2.766, discriminator_real_loss=1.343, discriminator_fake_loss=1.423, generator_loss=27.15, generator_mel_loss=19.2, generator_kl_loss=1.359, generator_dur_loss=1.81, generator_adv_loss=1.881, generator_feat_match_loss=2.899, over 74.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.407, discriminator_fake_loss=1.353, generator_loss=27.32, generator_mel_loss=19.32, generator_kl_loss=1.375, generator_dur_loss=1.77, generator_adv_loss=1.893, generator_feat_match_loss=2.961, over 1467.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:56:41,905 INFO [train.py:527] (4/6) Epoch 100, batch 74, global_batch_idx: 12350, batch size: 64, loss[discriminator_loss=2.621, discriminator_real_loss=1.351, discriminator_fake_loss=1.269, generator_loss=28.51, generator_mel_loss=19.47, generator_kl_loss=1.235, generator_dur_loss=1.843, generator_adv_loss=2.349, generator_feat_match_loss=3.61, over 64.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.4, discriminator_fake_loss=1.351, generator_loss=27.43, generator_mel_loss=19.36, generator_kl_loss=1.368, generator_dur_loss=1.79, generator_adv_loss=1.915, generator_feat_match_loss=3.003, over 4425.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:59:04,088 INFO [train.py:919] (4/6) Start epoch 101 +2024-03-12 09:59:33,451 INFO [train.py:527] (4/6) Epoch 101, batch 0, global_batch_idx: 12400, batch size: 48, loss[discriminator_loss=2.718, discriminator_real_loss=1.362, discriminator_fake_loss=1.357, generator_loss=26.91, generator_mel_loss=19.19, generator_kl_loss=1.42, generator_dur_loss=1.731, generator_adv_loss=1.844, generator_feat_match_loss=2.728, over 48.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.362, discriminator_fake_loss=1.357, generator_loss=26.91, generator_mel_loss=19.19, generator_kl_loss=1.42, generator_dur_loss=1.731, generator_adv_loss=1.844, generator_feat_match_loss=2.728, over 48.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:59:33,454 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 09:59:41,317 INFO [train.py:591] (4/6) Epoch 101, validation: discriminator_loss=2.747, discriminator_real_loss=1.379, discriminator_fake_loss=1.368, generator_loss=27.1, generator_mel_loss=19.92, generator_kl_loss=1.113, generator_dur_loss=1.817, generator_adv_loss=1.748, generator_feat_match_loss=2.501, over 100.00 samples. +2024-03-12 09:59:41,319 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 10:02:00,884 INFO [train.py:527] (4/6) Epoch 101, batch 50, global_batch_idx: 12450, batch size: 31, loss[discriminator_loss=2.733, discriminator_real_loss=1.383, discriminator_fake_loss=1.35, generator_loss=28.74, generator_mel_loss=20.54, generator_kl_loss=1.552, generator_dur_loss=1.714, generator_adv_loss=1.887, generator_feat_match_loss=3.039, over 31.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.382, discriminator_fake_loss=1.366, generator_loss=27.41, generator_mel_loss=19.38, generator_kl_loss=1.383, generator_dur_loss=1.797, generator_adv_loss=1.897, generator_feat_match_loss=2.953, over 2957.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 10:04:19,963 INFO [train.py:527] (4/6) Epoch 101, batch 100, global_batch_idx: 12500, batch size: 53, loss[discriminator_loss=2.834, discriminator_real_loss=1.44, discriminator_fake_loss=1.394, generator_loss=26.47, generator_mel_loss=19.15, generator_kl_loss=1.498, generator_dur_loss=1.724, generator_adv_loss=1.691, generator_feat_match_loss=2.406, over 53.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.387, discriminator_fake_loss=1.355, generator_loss=27.44, generator_mel_loss=19.36, generator_kl_loss=1.392, generator_dur_loss=1.784, generator_adv_loss=1.911, generator_feat_match_loss=2.99, over 5644.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 10:05:29,004 INFO [train.py:919] (4/6) Start epoch 102 +2024-03-12 10:07:05,811 INFO [train.py:527] (4/6) Epoch 102, batch 26, global_batch_idx: 12550, batch size: 53, loss[discriminator_loss=2.742, discriminator_real_loss=1.506, discriminator_fake_loss=1.236, generator_loss=27.53, generator_mel_loss=19.37, generator_kl_loss=1.389, generator_dur_loss=1.683, generator_adv_loss=2.091, generator_feat_match_loss=3.003, over 53.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.401, discriminator_fake_loss=1.351, generator_loss=27.58, generator_mel_loss=19.5, generator_kl_loss=1.401, generator_dur_loss=1.761, generator_adv_loss=1.909, generator_feat_match_loss=3.01, over 1443.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:09:26,926 INFO [train.py:527] (4/6) Epoch 102, batch 76, global_batch_idx: 12600, batch size: 48, loss[discriminator_loss=2.756, discriminator_real_loss=1.385, discriminator_fake_loss=1.372, generator_loss=26.39, generator_mel_loss=18.72, generator_kl_loss=1.371, generator_dur_loss=1.718, generator_adv_loss=1.774, generator_feat_match_loss=2.804, over 48.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.394, discriminator_fake_loss=1.352, generator_loss=27.37, generator_mel_loss=19.3, generator_kl_loss=1.369, generator_dur_loss=1.788, generator_adv_loss=1.931, generator_feat_match_loss=2.986, over 4394.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:09:26,927 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 10:09:35,877 INFO [train.py:591] (4/6) Epoch 102, validation: discriminator_loss=2.726, discriminator_real_loss=1.317, discriminator_fake_loss=1.409, generator_loss=26.52, generator_mel_loss=19.64, generator_kl_loss=1.136, generator_dur_loss=1.834, generator_adv_loss=1.754, generator_feat_match_loss=2.162, over 100.00 samples. +2024-03-12 10:09:35,878 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 10:11:48,031 INFO [train.py:919] (4/6) Start epoch 103 +2024-03-12 10:12:19,602 INFO [train.py:527] (4/6) Epoch 103, batch 2, global_batch_idx: 12650, batch size: 58, loss[discriminator_loss=2.736, discriminator_real_loss=1.351, discriminator_fake_loss=1.385, generator_loss=26.67, generator_mel_loss=18.92, generator_kl_loss=1.458, generator_dur_loss=1.761, generator_adv_loss=1.808, generator_feat_match_loss=2.725, over 58.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.401, discriminator_fake_loss=1.347, generator_loss=27.19, generator_mel_loss=19.27, generator_kl_loss=1.429, generator_dur_loss=1.749, generator_adv_loss=1.923, generator_feat_match_loss=2.82, over 163.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:14:41,464 INFO [train.py:527] (4/6) Epoch 103, batch 52, global_batch_idx: 12700, batch size: 74, loss[discriminator_loss=2.768, discriminator_real_loss=1.365, discriminator_fake_loss=1.402, generator_loss=27.28, generator_mel_loss=19.28, generator_kl_loss=1.441, generator_dur_loss=1.757, generator_adv_loss=1.999, generator_feat_match_loss=2.795, over 74.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.393, discriminator_fake_loss=1.347, generator_loss=27.24, generator_mel_loss=19.22, generator_kl_loss=1.379, generator_dur_loss=1.754, generator_adv_loss=1.914, generator_feat_match_loss=2.976, over 3216.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:17:01,125 INFO [train.py:527] (4/6) Epoch 103, batch 102, global_batch_idx: 12750, batch size: 96, loss[discriminator_loss=2.746, discriminator_real_loss=1.374, discriminator_fake_loss=1.373, generator_loss=26.43, generator_mel_loss=18.74, generator_kl_loss=1.192, generator_dur_loss=1.822, generator_adv_loss=1.875, generator_feat_match_loss=2.807, over 96.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.396, discriminator_fake_loss=1.346, generator_loss=27.27, generator_mel_loss=19.26, generator_kl_loss=1.371, generator_dur_loss=1.757, generator_adv_loss=1.916, generator_feat_match_loss=2.969, over 6063.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:18:03,692 INFO [train.py:919] (4/6) Start epoch 104 +2024-03-12 10:19:45,547 INFO [train.py:527] (4/6) Epoch 104, batch 28, global_batch_idx: 12800, batch size: 58, loss[discriminator_loss=2.711, discriminator_real_loss=1.466, discriminator_fake_loss=1.244, generator_loss=27.81, generator_mel_loss=19.58, generator_kl_loss=1.501, generator_dur_loss=1.718, generator_adv_loss=1.92, generator_feat_match_loss=3.092, over 58.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=27.31, generator_mel_loss=19.35, generator_kl_loss=1.409, generator_dur_loss=1.741, generator_adv_loss=1.862, generator_feat_match_loss=2.949, over 1628.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:19:45,548 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 10:19:53,816 INFO [train.py:591] (4/6) Epoch 104, validation: discriminator_loss=2.756, discriminator_real_loss=1.366, discriminator_fake_loss=1.39, generator_loss=26.69, generator_mel_loss=19.5, generator_kl_loss=1.266, generator_dur_loss=1.804, generator_adv_loss=1.795, generator_feat_match_loss=2.324, over 100.00 samples. +2024-03-12 10:19:53,817 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 10:22:18,398 INFO [train.py:527] (4/6) Epoch 104, batch 78, global_batch_idx: 12850, batch size: 66, loss[discriminator_loss=2.74, discriminator_real_loss=1.365, discriminator_fake_loss=1.375, generator_loss=26.85, generator_mel_loss=19.05, generator_kl_loss=1.485, generator_dur_loss=1.771, generator_adv_loss=1.862, generator_feat_match_loss=2.686, over 66.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.396, discriminator_fake_loss=1.359, generator_loss=27.29, generator_mel_loss=19.28, generator_kl_loss=1.389, generator_dur_loss=1.764, generator_adv_loss=1.869, generator_feat_match_loss=2.984, over 4656.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:24:24,859 INFO [train.py:919] (4/6) Start epoch 105 +2024-03-12 10:25:02,175 INFO [train.py:527] (4/6) Epoch 105, batch 4, global_batch_idx: 12900, batch size: 96, loss[discriminator_loss=2.799, discriminator_real_loss=1.379, discriminator_fake_loss=1.42, generator_loss=26.38, generator_mel_loss=18.41, generator_kl_loss=1.24, generator_dur_loss=1.931, generator_adv_loss=1.82, generator_feat_match_loss=2.979, over 96.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.366, discriminator_fake_loss=1.401, generator_loss=27.32, generator_mel_loss=19.29, generator_kl_loss=1.357, generator_dur_loss=1.825, generator_adv_loss=1.854, generator_feat_match_loss=2.985, over 281.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:27:25,787 INFO [train.py:527] (4/6) Epoch 105, batch 54, global_batch_idx: 12950, batch size: 39, loss[discriminator_loss=2.722, discriminator_real_loss=1.39, discriminator_fake_loss=1.333, generator_loss=26.33, generator_mel_loss=18.73, generator_kl_loss=1.463, generator_dur_loss=1.743, generator_adv_loss=1.831, generator_feat_match_loss=2.564, over 39.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.402, discriminator_fake_loss=1.354, generator_loss=27.23, generator_mel_loss=19.2, generator_kl_loss=1.403, generator_dur_loss=1.788, generator_adv_loss=1.897, generator_feat_match_loss=2.94, over 3159.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:29:48,021 INFO [train.py:527] (4/6) Epoch 105, batch 104, global_batch_idx: 13000, batch size: 25, loss[discriminator_loss=2.706, discriminator_real_loss=1.299, discriminator_fake_loss=1.407, generator_loss=27.24, generator_mel_loss=19.11, generator_kl_loss=1.583, generator_dur_loss=1.611, generator_adv_loss=1.85, generator_feat_match_loss=3.084, over 25.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.397, discriminator_fake_loss=1.355, generator_loss=27.26, generator_mel_loss=19.22, generator_kl_loss=1.4, generator_dur_loss=1.777, generator_adv_loss=1.896, generator_feat_match_loss=2.962, over 5869.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:29:48,022 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 10:29:57,124 INFO [train.py:591] (4/6) Epoch 105, validation: discriminator_loss=2.617, discriminator_real_loss=1.249, discriminator_fake_loss=1.368, generator_loss=26.98, generator_mel_loss=19.23, generator_kl_loss=1.241, generator_dur_loss=1.776, generator_adv_loss=1.931, generator_feat_match_loss=2.793, over 100.00 samples. +2024-03-12 10:29:57,125 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 10:30:50,383 INFO [train.py:919] (4/6) Start epoch 106 +2024-03-12 10:32:41,698 INFO [train.py:527] (4/6) Epoch 106, batch 30, global_batch_idx: 13050, batch size: 25, loss[discriminator_loss=2.609, discriminator_real_loss=1.309, discriminator_fake_loss=1.3, generator_loss=28.53, generator_mel_loss=20.08, generator_kl_loss=1.599, generator_dur_loss=1.615, generator_adv_loss=1.902, generator_feat_match_loss=3.333, over 25.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.394, discriminator_fake_loss=1.353, generator_loss=27.28, generator_mel_loss=19.27, generator_kl_loss=1.409, generator_dur_loss=1.755, generator_adv_loss=1.919, generator_feat_match_loss=2.927, over 1674.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:35:02,022 INFO [train.py:527] (4/6) Epoch 106, batch 80, global_batch_idx: 13100, batch size: 56, loss[discriminator_loss=2.778, discriminator_real_loss=1.297, discriminator_fake_loss=1.481, generator_loss=28.1, generator_mel_loss=20.2, generator_kl_loss=1.323, generator_dur_loss=1.754, generator_adv_loss=2.1, generator_feat_match_loss=2.718, over 56.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.392, discriminator_fake_loss=1.352, generator_loss=27.39, generator_mel_loss=19.34, generator_kl_loss=1.394, generator_dur_loss=1.755, generator_adv_loss=1.914, generator_feat_match_loss=2.989, over 4551.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:37:09,104 INFO [train.py:919] (4/6) Start epoch 107 +2024-03-12 10:37:52,302 INFO [train.py:527] (4/6) Epoch 107, batch 6, global_batch_idx: 13150, batch size: 72, loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=27.34, generator_mel_loss=19.17, generator_kl_loss=1.303, generator_dur_loss=1.784, generator_adv_loss=1.902, generator_feat_match_loss=3.181, over 72.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.38, discriminator_fake_loss=1.349, generator_loss=27.06, generator_mel_loss=19.13, generator_kl_loss=1.325, generator_dur_loss=1.772, generator_adv_loss=1.887, generator_feat_match_loss=2.949, over 475.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:40:17,640 INFO [train.py:527] (4/6) Epoch 107, batch 56, global_batch_idx: 13200, batch size: 16, loss[discriminator_loss=2.788, discriminator_real_loss=1.324, discriminator_fake_loss=1.463, generator_loss=29.49, generator_mel_loss=20.81, generator_kl_loss=1.821, generator_dur_loss=1.565, generator_adv_loss=1.997, generator_feat_match_loss=3.304, over 16.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.394, discriminator_fake_loss=1.355, generator_loss=27.17, generator_mel_loss=19.1, generator_kl_loss=1.364, generator_dur_loss=1.756, generator_adv_loss=1.932, generator_feat_match_loss=3.023, over 3259.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:40:17,642 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 10:40:25,395 INFO [train.py:591] (4/6) Epoch 107, validation: discriminator_loss=2.792, discriminator_real_loss=1.582, discriminator_fake_loss=1.21, generator_loss=26.08, generator_mel_loss=18.85, generator_kl_loss=1.137, generator_dur_loss=1.792, generator_adv_loss=1.94, generator_feat_match_loss=2.356, over 100.00 samples. +2024-03-12 10:40:25,396 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 10:42:43,001 INFO [train.py:527] (4/6) Epoch 107, batch 106, global_batch_idx: 13250, batch size: 64, loss[discriminator_loss=2.751, discriminator_real_loss=1.379, discriminator_fake_loss=1.372, generator_loss=27.48, generator_mel_loss=18.88, generator_kl_loss=1.488, generator_dur_loss=1.812, generator_adv_loss=1.996, generator_feat_match_loss=3.31, over 64.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.398, discriminator_fake_loss=1.348, generator_loss=27.18, generator_mel_loss=19.13, generator_kl_loss=1.376, generator_dur_loss=1.753, generator_adv_loss=1.918, generator_feat_match_loss=2.999, over 6091.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:43:31,746 INFO [train.py:919] (4/6) Start epoch 108 +2024-03-12 10:45:23,070 INFO [train.py:527] (4/6) Epoch 108, batch 32, global_batch_idx: 13300, batch size: 31, loss[discriminator_loss=2.613, discriminator_real_loss=1.212, discriminator_fake_loss=1.401, generator_loss=27.33, generator_mel_loss=19.12, generator_kl_loss=1.614, generator_dur_loss=1.688, generator_adv_loss=1.797, generator_feat_match_loss=3.103, over 31.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.386, discriminator_fake_loss=1.354, generator_loss=27.58, generator_mel_loss=19.39, generator_kl_loss=1.399, generator_dur_loss=1.757, generator_adv_loss=1.925, generator_feat_match_loss=3.101, over 1685.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:47:44,047 INFO [train.py:527] (4/6) Epoch 108, batch 82, global_batch_idx: 13350, batch size: 62, loss[discriminator_loss=2.687, discriminator_real_loss=1.398, discriminator_fake_loss=1.289, generator_loss=28.07, generator_mel_loss=19.7, generator_kl_loss=1.603, generator_dur_loss=1.767, generator_adv_loss=1.907, generator_feat_match_loss=3.087, over 62.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=27.38, generator_mel_loss=19.26, generator_kl_loss=1.395, generator_dur_loss=1.755, generator_adv_loss=1.924, generator_feat_match_loss=3.052, over 4339.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:49:42,673 INFO [train.py:919] (4/6) Start epoch 109 +2024-03-12 10:50:30,630 INFO [train.py:527] (4/6) Epoch 109, batch 8, global_batch_idx: 13400, batch size: 62, loss[discriminator_loss=2.752, discriminator_real_loss=1.593, discriminator_fake_loss=1.159, generator_loss=28.44, generator_mel_loss=19.44, generator_kl_loss=1.36, generator_dur_loss=1.752, generator_adv_loss=2.432, generator_feat_match_loss=3.455, over 62.00 samples.], tot_loss[discriminator_loss=2.798, discriminator_real_loss=1.425, discriminator_fake_loss=1.372, generator_loss=28.07, generator_mel_loss=19.45, generator_kl_loss=1.381, generator_dur_loss=1.772, generator_adv_loss=2.122, generator_feat_match_loss=3.344, over 566.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:50:30,633 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 10:50:38,651 INFO [train.py:591] (4/6) Epoch 109, validation: discriminator_loss=2.703, discriminator_real_loss=1.603, discriminator_fake_loss=1.1, generator_loss=27.09, generator_mel_loss=19.11, generator_kl_loss=1.1, generator_dur_loss=1.82, generator_adv_loss=2.323, generator_feat_match_loss=2.736, over 100.00 samples. +2024-03-12 10:50:38,653 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 10:53:02,543 INFO [train.py:527] (4/6) Epoch 109, batch 58, global_batch_idx: 13450, batch size: 83, loss[discriminator_loss=2.821, discriminator_real_loss=1.437, discriminator_fake_loss=1.384, generator_loss=27.41, generator_mel_loss=19.22, generator_kl_loss=1.395, generator_dur_loss=1.805, generator_adv_loss=2.067, generator_feat_match_loss=2.918, over 83.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.413, discriminator_fake_loss=1.348, generator_loss=27.46, generator_mel_loss=19.28, generator_kl_loss=1.387, generator_dur_loss=1.774, generator_adv_loss=1.991, generator_feat_match_loss=3.03, over 3471.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:55:20,980 INFO [train.py:527] (4/6) Epoch 109, batch 108, global_batch_idx: 13500, batch size: 52, loss[discriminator_loss=2.712, discriminator_real_loss=1.397, discriminator_fake_loss=1.315, generator_loss=27.6, generator_mel_loss=19.39, generator_kl_loss=1.466, generator_dur_loss=1.728, generator_adv_loss=1.876, generator_feat_match_loss=3.139, over 52.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.41, discriminator_fake_loss=1.353, generator_loss=27.36, generator_mel_loss=19.29, generator_kl_loss=1.385, generator_dur_loss=1.756, generator_adv_loss=1.936, generator_feat_match_loss=2.998, over 6119.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:56:04,671 INFO [train.py:919] (4/6) Start epoch 110 +2024-03-12 10:58:07,910 INFO [train.py:527] (4/6) Epoch 110, batch 34, global_batch_idx: 13550, batch size: 61, loss[discriminator_loss=2.751, discriminator_real_loss=1.342, discriminator_fake_loss=1.41, generator_loss=27.88, generator_mel_loss=19.52, generator_kl_loss=1.488, generator_dur_loss=1.692, generator_adv_loss=2.033, generator_feat_match_loss=3.147, over 61.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=27.01, generator_mel_loss=19.04, generator_kl_loss=1.375, generator_dur_loss=1.763, generator_adv_loss=1.878, generator_feat_match_loss=2.955, over 2109.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:00:32,648 INFO [train.py:527] (4/6) Epoch 110, batch 84, global_batch_idx: 13600, batch size: 64, loss[discriminator_loss=2.666, discriminator_real_loss=1.348, discriminator_fake_loss=1.318, generator_loss=27.24, generator_mel_loss=18.98, generator_kl_loss=1.359, generator_dur_loss=1.854, generator_adv_loss=1.964, generator_feat_match_loss=3.088, over 64.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.393, discriminator_fake_loss=1.358, generator_loss=27.17, generator_mel_loss=19.11, generator_kl_loss=1.4, generator_dur_loss=1.77, generator_adv_loss=1.888, generator_feat_match_loss=2.995, over 5023.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:00:32,649 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 11:00:41,510 INFO [train.py:591] (4/6) Epoch 110, validation: discriminator_loss=2.734, discriminator_real_loss=1.415, discriminator_fake_loss=1.319, generator_loss=26.16, generator_mel_loss=19.01, generator_kl_loss=1.163, generator_dur_loss=1.823, generator_adv_loss=1.86, generator_feat_match_loss=2.299, over 100.00 samples. +2024-03-12 11:00:41,511 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 11:02:28,817 INFO [train.py:919] (4/6) Start epoch 111 +2024-03-12 11:03:24,127 INFO [train.py:527] (4/6) Epoch 111, batch 10, global_batch_idx: 13650, batch size: 31, loss[discriminator_loss=2.754, discriminator_real_loss=1.318, discriminator_fake_loss=1.436, generator_loss=27.24, generator_mel_loss=18.88, generator_kl_loss=1.648, generator_dur_loss=1.693, generator_adv_loss=1.898, generator_feat_match_loss=3.121, over 31.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.384, discriminator_fake_loss=1.363, generator_loss=27.28, generator_mel_loss=19.13, generator_kl_loss=1.407, generator_dur_loss=1.772, generator_adv_loss=1.927, generator_feat_match_loss=3.038, over 656.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:05:46,908 INFO [train.py:527] (4/6) Epoch 111, batch 60, global_batch_idx: 13700, batch size: 53, loss[discriminator_loss=2.726, discriminator_real_loss=1.451, discriminator_fake_loss=1.276, generator_loss=27.34, generator_mel_loss=19.28, generator_kl_loss=1.395, generator_dur_loss=1.752, generator_adv_loss=1.989, generator_feat_match_loss=2.924, over 53.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.392, discriminator_fake_loss=1.342, generator_loss=27.34, generator_mel_loss=19.19, generator_kl_loss=1.4, generator_dur_loss=1.753, generator_adv_loss=1.921, generator_feat_match_loss=3.076, over 3466.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:08:05,502 INFO [train.py:527] (4/6) Epoch 111, batch 110, global_batch_idx: 13750, batch size: 44, loss[discriminator_loss=2.859, discriminator_real_loss=1.386, discriminator_fake_loss=1.473, generator_loss=27.51, generator_mel_loss=19.87, generator_kl_loss=1.52, generator_dur_loss=1.711, generator_adv_loss=1.716, generator_feat_match_loss=2.691, over 44.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.395, discriminator_fake_loss=1.355, generator_loss=27.42, generator_mel_loss=19.23, generator_kl_loss=1.402, generator_dur_loss=1.762, generator_adv_loss=1.936, generator_feat_match_loss=3.089, over 6178.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:08:44,639 INFO [train.py:919] (4/6) Start epoch 112 +2024-03-12 11:10:51,371 INFO [train.py:527] (4/6) Epoch 112, batch 36, global_batch_idx: 13800, batch size: 88, loss[discriminator_loss=2.804, discriminator_real_loss=1.433, discriminator_fake_loss=1.372, generator_loss=26.99, generator_mel_loss=19.16, generator_kl_loss=1.298, generator_dur_loss=1.864, generator_adv_loss=1.805, generator_feat_match_loss=2.866, over 88.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.385, discriminator_fake_loss=1.346, generator_loss=27.24, generator_mel_loss=19.07, generator_kl_loss=1.351, generator_dur_loss=1.817, generator_adv_loss=1.912, generator_feat_match_loss=3.091, over 2189.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:10:51,373 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 11:10:59,109 INFO [train.py:591] (4/6) Epoch 112, validation: discriminator_loss=2.8, discriminator_real_loss=1.409, discriminator_fake_loss=1.391, generator_loss=26.38, generator_mel_loss=19.18, generator_kl_loss=1.216, generator_dur_loss=1.816, generator_adv_loss=1.727, generator_feat_match_loss=2.443, over 100.00 samples. +2024-03-12 11:10:59,110 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 11:13:21,087 INFO [train.py:527] (4/6) Epoch 112, batch 86, global_batch_idx: 13850, batch size: 25, loss[discriminator_loss=2.656, discriminator_real_loss=1.353, discriminator_fake_loss=1.303, generator_loss=28.11, generator_mel_loss=19.5, generator_kl_loss=1.574, generator_dur_loss=1.519, generator_adv_loss=2.063, generator_feat_match_loss=3.454, over 25.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.384, discriminator_fake_loss=1.355, generator_loss=27.28, generator_mel_loss=19.15, generator_kl_loss=1.369, generator_dur_loss=1.793, generator_adv_loss=1.899, generator_feat_match_loss=3.068, over 5182.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:15:04,484 INFO [train.py:919] (4/6) Start epoch 113 +2024-03-12 11:16:04,046 INFO [train.py:527] (4/6) Epoch 113, batch 12, global_batch_idx: 13900, batch size: 44, loss[discriminator_loss=2.512, discriminator_real_loss=1.266, discriminator_fake_loss=1.247, generator_loss=28.59, generator_mel_loss=19.41, generator_kl_loss=1.622, generator_dur_loss=1.739, generator_adv_loss=2.109, generator_feat_match_loss=3.714, over 44.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.342, generator_loss=27.72, generator_mel_loss=19.29, generator_kl_loss=1.452, generator_dur_loss=1.754, generator_adv_loss=1.998, generator_feat_match_loss=3.225, over 659.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:18:23,843 INFO [train.py:527] (4/6) Epoch 113, batch 62, global_batch_idx: 13950, batch size: 66, loss[discriminator_loss=2.748, discriminator_real_loss=1.481, discriminator_fake_loss=1.266, generator_loss=27.5, generator_mel_loss=19.46, generator_kl_loss=1.295, generator_dur_loss=1.777, generator_adv_loss=1.942, generator_feat_match_loss=3.025, over 66.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.395, discriminator_fake_loss=1.359, generator_loss=27.31, generator_mel_loss=19.15, generator_kl_loss=1.386, generator_dur_loss=1.768, generator_adv_loss=1.958, generator_feat_match_loss=3.05, over 3358.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:20:48,891 INFO [train.py:527] (4/6) Epoch 113, batch 112, global_batch_idx: 14000, batch size: 31, loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=26.92, generator_mel_loss=18.91, generator_kl_loss=1.28, generator_dur_loss=1.724, generator_adv_loss=1.862, generator_feat_match_loss=3.148, over 31.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.396, discriminator_fake_loss=1.35, generator_loss=27.28, generator_mel_loss=19.14, generator_kl_loss=1.383, generator_dur_loss=1.771, generator_adv_loss=1.93, generator_feat_match_loss=3.052, over 6184.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:20:48,893 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 11:20:57,870 INFO [train.py:591] (4/6) Epoch 113, validation: discriminator_loss=2.751, discriminator_real_loss=1.385, discriminator_fake_loss=1.366, generator_loss=25.82, generator_mel_loss=18.77, generator_kl_loss=1.134, generator_dur_loss=1.803, generator_adv_loss=1.791, generator_feat_match_loss=2.326, over 100.00 samples. +2024-03-12 11:20:57,871 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 11:21:28,959 INFO [train.py:919] (4/6) Start epoch 114 +2024-03-12 11:23:41,678 INFO [train.py:527] (4/6) Epoch 114, batch 38, global_batch_idx: 14050, batch size: 66, loss[discriminator_loss=2.723, discriminator_real_loss=1.342, discriminator_fake_loss=1.381, generator_loss=27.71, generator_mel_loss=19.57, generator_kl_loss=1.363, generator_dur_loss=1.71, generator_adv_loss=1.849, generator_feat_match_loss=3.22, over 66.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.384, discriminator_fake_loss=1.356, generator_loss=27.3, generator_mel_loss=19.2, generator_kl_loss=1.406, generator_dur_loss=1.743, generator_adv_loss=1.885, generator_feat_match_loss=3.073, over 2154.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:26:06,586 INFO [train.py:527] (4/6) Epoch 114, batch 88, global_batch_idx: 14100, batch size: 56, loss[discriminator_loss=2.738, discriminator_real_loss=1.372, discriminator_fake_loss=1.366, generator_loss=26.78, generator_mel_loss=18.65, generator_kl_loss=1.442, generator_dur_loss=1.734, generator_adv_loss=1.996, generator_feat_match_loss=2.96, over 56.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.388, discriminator_fake_loss=1.354, generator_loss=27.25, generator_mel_loss=19.14, generator_kl_loss=1.39, generator_dur_loss=1.743, generator_adv_loss=1.904, generator_feat_match_loss=3.066, over 4974.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:27:47,057 INFO [train.py:919] (4/6) Start epoch 115 +2024-03-12 11:28:53,543 INFO [train.py:527] (4/6) Epoch 115, batch 14, global_batch_idx: 14150, batch size: 42, loss[discriminator_loss=2.763, discriminator_real_loss=1.406, discriminator_fake_loss=1.357, generator_loss=26.05, generator_mel_loss=18.08, generator_kl_loss=1.363, generator_dur_loss=1.76, generator_adv_loss=2.175, generator_feat_match_loss=2.668, over 42.00 samples.], tot_loss[discriminator_loss=2.771, discriminator_real_loss=1.428, discriminator_fake_loss=1.342, generator_loss=27.1, generator_mel_loss=19.01, generator_kl_loss=1.405, generator_dur_loss=1.766, generator_adv_loss=1.922, generator_feat_match_loss=2.999, over 825.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:31:17,606 INFO [train.py:527] (4/6) Epoch 115, batch 64, global_batch_idx: 14200, batch size: 45, loss[discriminator_loss=2.748, discriminator_real_loss=1.471, discriminator_fake_loss=1.277, generator_loss=27.04, generator_mel_loss=19.03, generator_kl_loss=1.543, generator_dur_loss=1.714, generator_adv_loss=1.878, generator_feat_match_loss=2.872, over 45.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.401, discriminator_fake_loss=1.346, generator_loss=27.27, generator_mel_loss=19.05, generator_kl_loss=1.381, generator_dur_loss=1.781, generator_adv_loss=1.951, generator_feat_match_loss=3.103, over 3849.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:31:17,608 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 11:31:25,537 INFO [train.py:591] (4/6) Epoch 115, validation: discriminator_loss=2.747, discriminator_real_loss=1.47, discriminator_fake_loss=1.277, generator_loss=26.49, generator_mel_loss=19.07, generator_kl_loss=1.219, generator_dur_loss=1.805, generator_adv_loss=1.859, generator_feat_match_loss=2.54, over 100.00 samples. +2024-03-12 11:31:25,538 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 11:33:42,864 INFO [train.py:527] (4/6) Epoch 115, batch 114, global_batch_idx: 14250, batch size: 31, loss[discriminator_loss=2.788, discriminator_real_loss=1.427, discriminator_fake_loss=1.362, generator_loss=28.23, generator_mel_loss=20.04, generator_kl_loss=1.533, generator_dur_loss=1.685, generator_adv_loss=1.926, generator_feat_match_loss=3.049, over 31.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.398, discriminator_fake_loss=1.352, generator_loss=27.37, generator_mel_loss=19.16, generator_kl_loss=1.387, generator_dur_loss=1.78, generator_adv_loss=1.933, generator_feat_match_loss=3.105, over 6621.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:34:08,705 INFO [train.py:919] (4/6) Start epoch 116 +2024-03-12 11:36:27,834 INFO [train.py:527] (4/6) Epoch 116, batch 40, global_batch_idx: 14300, batch size: 53, loss[discriminator_loss=2.797, discriminator_real_loss=1.364, discriminator_fake_loss=1.432, generator_loss=26.59, generator_mel_loss=18.92, generator_kl_loss=1.437, generator_dur_loss=1.708, generator_adv_loss=1.877, generator_feat_match_loss=2.644, over 53.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.4, discriminator_fake_loss=1.355, generator_loss=27.27, generator_mel_loss=19.18, generator_kl_loss=1.421, generator_dur_loss=1.761, generator_adv_loss=1.899, generator_feat_match_loss=3.006, over 2127.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:38:53,134 INFO [train.py:527] (4/6) Epoch 116, batch 90, global_batch_idx: 14350, batch size: 64, loss[discriminator_loss=2.72, discriminator_real_loss=1.352, discriminator_fake_loss=1.368, generator_loss=27.54, generator_mel_loss=19.26, generator_kl_loss=1.33, generator_dur_loss=1.764, generator_adv_loss=2.033, generator_feat_match_loss=3.157, over 64.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.396, discriminator_fake_loss=1.355, generator_loss=27.22, generator_mel_loss=19.09, generator_kl_loss=1.403, generator_dur_loss=1.777, generator_adv_loss=1.894, generator_feat_match_loss=3.053, over 5066.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:40:23,287 INFO [train.py:919] (4/6) Start epoch 117 +2024-03-12 11:41:34,035 INFO [train.py:527] (4/6) Epoch 117, batch 16, global_batch_idx: 14400, batch size: 72, loss[discriminator_loss=2.672, discriminator_real_loss=1.31, discriminator_fake_loss=1.362, generator_loss=27, generator_mel_loss=18.51, generator_kl_loss=1.413, generator_dur_loss=1.827, generator_adv_loss=1.92, generator_feat_match_loss=3.33, over 72.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.371, discriminator_fake_loss=1.362, generator_loss=27.3, generator_mel_loss=19.06, generator_kl_loss=1.391, generator_dur_loss=1.81, generator_adv_loss=1.905, generator_feat_match_loss=3.142, over 1088.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:41:34,037 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 11:41:42,045 INFO [train.py:591] (4/6) Epoch 117, validation: discriminator_loss=2.698, discriminator_real_loss=1.388, discriminator_fake_loss=1.31, generator_loss=26.84, generator_mel_loss=19.33, generator_kl_loss=1.169, generator_dur_loss=1.842, generator_adv_loss=1.867, generator_feat_match_loss=2.631, over 100.00 samples. +2024-03-12 11:41:42,046 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 11:44:05,220 INFO [train.py:527] (4/6) Epoch 117, batch 66, global_batch_idx: 14450, batch size: 53, loss[discriminator_loss=2.726, discriminator_real_loss=1.334, discriminator_fake_loss=1.392, generator_loss=26.68, generator_mel_loss=19.08, generator_kl_loss=1.34, generator_dur_loss=1.711, generator_adv_loss=1.987, generator_feat_match_loss=2.564, over 53.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.387, discriminator_fake_loss=1.358, generator_loss=27.28, generator_mel_loss=19.11, generator_kl_loss=1.38, generator_dur_loss=1.784, generator_adv_loss=1.904, generator_feat_match_loss=3.097, over 4055.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:46:21,783 INFO [train.py:527] (4/6) Epoch 117, batch 116, global_batch_idx: 14500, batch size: 25, loss[discriminator_loss=2.624, discriminator_real_loss=1.272, discriminator_fake_loss=1.352, generator_loss=27.22, generator_mel_loss=18.73, generator_kl_loss=1.599, generator_dur_loss=1.558, generator_adv_loss=1.984, generator_feat_match_loss=3.346, over 25.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.39, discriminator_fake_loss=1.361, generator_loss=27.28, generator_mel_loss=19.13, generator_kl_loss=1.389, generator_dur_loss=1.768, generator_adv_loss=1.907, generator_feat_match_loss=3.089, over 6748.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:46:41,565 INFO [train.py:919] (4/6) Start epoch 118 +2024-03-12 11:49:04,529 INFO [train.py:527] (4/6) Epoch 118, batch 42, global_batch_idx: 14550, batch size: 55, loss[discriminator_loss=2.664, discriminator_real_loss=1.346, discriminator_fake_loss=1.319, generator_loss=27.99, generator_mel_loss=19.73, generator_kl_loss=1.426, generator_dur_loss=1.806, generator_adv_loss=1.759, generator_feat_match_loss=3.271, over 55.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.404, discriminator_fake_loss=1.352, generator_loss=27.39, generator_mel_loss=19.28, generator_kl_loss=1.428, generator_dur_loss=1.744, generator_adv_loss=1.878, generator_feat_match_loss=3.063, over 2255.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:51:25,225 INFO [train.py:527] (4/6) Epoch 118, batch 92, global_batch_idx: 14600, batch size: 66, loss[discriminator_loss=2.793, discriminator_real_loss=1.413, discriminator_fake_loss=1.38, generator_loss=26.35, generator_mel_loss=18.71, generator_kl_loss=1.333, generator_dur_loss=1.786, generator_adv_loss=1.842, generator_feat_match_loss=2.679, over 66.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.397, discriminator_fake_loss=1.357, generator_loss=27.34, generator_mel_loss=19.2, generator_kl_loss=1.398, generator_dur_loss=1.775, generator_adv_loss=1.892, generator_feat_match_loss=3.076, over 5445.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:51:25,226 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 11:51:33,692 INFO [train.py:591] (4/6) Epoch 118, validation: discriminator_loss=2.765, discriminator_real_loss=1.428, discriminator_fake_loss=1.338, generator_loss=26.6, generator_mel_loss=19.28, generator_kl_loss=1.279, generator_dur_loss=1.827, generator_adv_loss=1.81, generator_feat_match_loss=2.411, over 100.00 samples. +2024-03-12 11:51:33,693 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 11:53:02,355 INFO [train.py:919] (4/6) Start epoch 119 +2024-03-12 11:54:18,461 INFO [train.py:527] (4/6) Epoch 119, batch 18, global_batch_idx: 14650, batch size: 47, loss[discriminator_loss=2.77, discriminator_real_loss=1.336, discriminator_fake_loss=1.435, generator_loss=26.89, generator_mel_loss=18.98, generator_kl_loss=1.445, generator_dur_loss=1.705, generator_adv_loss=1.79, generator_feat_match_loss=2.973, over 47.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.365, discriminator_fake_loss=1.356, generator_loss=27.19, generator_mel_loss=18.96, generator_kl_loss=1.424, generator_dur_loss=1.736, generator_adv_loss=1.936, generator_feat_match_loss=3.13, over 952.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:56:42,132 INFO [train.py:527] (4/6) Epoch 119, batch 68, global_batch_idx: 14700, batch size: 58, loss[discriminator_loss=2.743, discriminator_real_loss=1.368, discriminator_fake_loss=1.375, generator_loss=26.97, generator_mel_loss=18.82, generator_kl_loss=1.355, generator_dur_loss=1.828, generator_adv_loss=1.933, generator_feat_match_loss=3.03, over 58.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.397, discriminator_fake_loss=1.352, generator_loss=27.19, generator_mel_loss=19.06, generator_kl_loss=1.399, generator_dur_loss=1.746, generator_adv_loss=1.904, generator_feat_match_loss=3.084, over 3701.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:58:56,869 INFO [train.py:527] (4/6) Epoch 119, batch 118, global_batch_idx: 14750, batch size: 56, loss[discriminator_loss=2.766, discriminator_real_loss=1.271, discriminator_fake_loss=1.495, generator_loss=26.83, generator_mel_loss=18.94, generator_kl_loss=1.399, generator_dur_loss=1.762, generator_adv_loss=1.851, generator_feat_match_loss=2.877, over 56.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.393, discriminator_fake_loss=1.357, generator_loss=27.21, generator_mel_loss=19.08, generator_kl_loss=1.402, generator_dur_loss=1.754, generator_adv_loss=1.892, generator_feat_match_loss=3.083, over 6621.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:59:12,212 INFO [train.py:919] (4/6) Start epoch 120 +2024-03-12 12:01:37,947 INFO [train.py:527] (4/6) Epoch 120, batch 44, global_batch_idx: 14800, batch size: 45, loss[discriminator_loss=2.727, discriminator_real_loss=1.408, discriminator_fake_loss=1.32, generator_loss=25.88, generator_mel_loss=18.35, generator_kl_loss=1.465, generator_dur_loss=1.669, generator_adv_loss=1.901, generator_feat_match_loss=2.494, over 45.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.399, discriminator_fake_loss=1.349, generator_loss=27.16, generator_mel_loss=19.02, generator_kl_loss=1.382, generator_dur_loss=1.77, generator_adv_loss=1.92, generator_feat_match_loss=3.069, over 2725.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:01:37,948 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 12:01:45,880 INFO [train.py:591] (4/6) Epoch 120, validation: discriminator_loss=2.76, discriminator_real_loss=1.43, discriminator_fake_loss=1.33, generator_loss=26.73, generator_mel_loss=19.22, generator_kl_loss=1.226, generator_dur_loss=1.797, generator_adv_loss=1.813, generator_feat_match_loss=2.683, over 100.00 samples. +2024-03-12 12:01:45,881 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 12:04:04,615 INFO [train.py:527] (4/6) Epoch 120, batch 94, global_batch_idx: 14850, batch size: 50, loss[discriminator_loss=2.84, discriminator_real_loss=1.674, discriminator_fake_loss=1.166, generator_loss=28.2, generator_mel_loss=19.99, generator_kl_loss=1.485, generator_dur_loss=1.626, generator_adv_loss=1.638, generator_feat_match_loss=3.457, over 50.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.394, discriminator_fake_loss=1.349, generator_loss=27.19, generator_mel_loss=19.05, generator_kl_loss=1.378, generator_dur_loss=1.758, generator_adv_loss=1.917, generator_feat_match_loss=3.094, over 5686.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:05:27,007 INFO [train.py:919] (4/6) Start epoch 121 +2024-03-12 12:06:45,350 INFO [train.py:527] (4/6) Epoch 121, batch 20, global_batch_idx: 14900, batch size: 47, loss[discriminator_loss=2.786, discriminator_real_loss=1.383, discriminator_fake_loss=1.403, generator_loss=27.3, generator_mel_loss=19.44, generator_kl_loss=1.336, generator_dur_loss=1.694, generator_adv_loss=1.892, generator_feat_match_loss=2.931, over 47.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.395, discriminator_fake_loss=1.355, generator_loss=27.39, generator_mel_loss=19.14, generator_kl_loss=1.398, generator_dur_loss=1.747, generator_adv_loss=1.963, generator_feat_match_loss=3.14, over 1191.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:09:06,763 INFO [train.py:527] (4/6) Epoch 121, batch 70, global_batch_idx: 14950, batch size: 14, loss[discriminator_loss=2.64, discriminator_real_loss=1.236, discriminator_fake_loss=1.404, generator_loss=28.66, generator_mel_loss=19.85, generator_kl_loss=1.726, generator_dur_loss=1.616, generator_adv_loss=1.885, generator_feat_match_loss=3.581, over 14.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.402, discriminator_fake_loss=1.361, generator_loss=27.27, generator_mel_loss=19.15, generator_kl_loss=1.375, generator_dur_loss=1.768, generator_adv_loss=1.91, generator_feat_match_loss=3.071, over 4229.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:11:25,473 INFO [train.py:527] (4/6) Epoch 121, batch 120, global_batch_idx: 15000, batch size: 25, loss[discriminator_loss=2.712, discriminator_real_loss=1.339, discriminator_fake_loss=1.373, generator_loss=27.47, generator_mel_loss=18.69, generator_kl_loss=1.544, generator_dur_loss=1.618, generator_adv_loss=2.016, generator_feat_match_loss=3.605, over 25.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.392, discriminator_fake_loss=1.355, generator_loss=27.22, generator_mel_loss=19.05, generator_kl_loss=1.384, generator_dur_loss=1.767, generator_adv_loss=1.909, generator_feat_match_loss=3.112, over 7348.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:11:25,474 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 12:11:34,416 INFO [train.py:591] (4/6) Epoch 121, validation: discriminator_loss=2.75, discriminator_real_loss=1.455, discriminator_fake_loss=1.294, generator_loss=26.64, generator_mel_loss=19.12, generator_kl_loss=1.093, generator_dur_loss=1.827, generator_adv_loss=1.899, generator_feat_match_loss=2.698, over 100.00 samples. +2024-03-12 12:11:34,418 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 12:11:44,013 INFO [train.py:919] (4/6) Start epoch 122 +2024-03-12 12:14:17,642 INFO [train.py:527] (4/6) Epoch 122, batch 46, global_batch_idx: 15050, batch size: 44, loss[discriminator_loss=2.667, discriminator_real_loss=1.363, discriminator_fake_loss=1.305, generator_loss=27.75, generator_mel_loss=18.88, generator_kl_loss=1.438, generator_dur_loss=1.679, generator_adv_loss=2.167, generator_feat_match_loss=3.582, over 44.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.398, discriminator_fake_loss=1.356, generator_loss=27.28, generator_mel_loss=19.01, generator_kl_loss=1.387, generator_dur_loss=1.752, generator_adv_loss=1.949, generator_feat_match_loss=3.183, over 2710.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:16:36,057 INFO [train.py:527] (4/6) Epoch 122, batch 96, global_batch_idx: 15100, batch size: 68, loss[discriminator_loss=2.727, discriminator_real_loss=1.358, discriminator_fake_loss=1.369, generator_loss=27.3, generator_mel_loss=19.05, generator_kl_loss=1.308, generator_dur_loss=1.778, generator_adv_loss=1.832, generator_feat_match_loss=3.327, over 68.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.402, discriminator_fake_loss=1.351, generator_loss=27.19, generator_mel_loss=18.99, generator_kl_loss=1.382, generator_dur_loss=1.768, generator_adv_loss=1.932, generator_feat_match_loss=3.124, over 5775.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:17:48,094 INFO [train.py:919] (4/6) Start epoch 123 +2024-03-12 12:19:14,202 INFO [train.py:527] (4/6) Epoch 123, batch 22, global_batch_idx: 15150, batch size: 31, loss[discriminator_loss=2.715, discriminator_real_loss=1.316, discriminator_fake_loss=1.399, generator_loss=27.14, generator_mel_loss=19.06, generator_kl_loss=1.502, generator_dur_loss=1.675, generator_adv_loss=2.059, generator_feat_match_loss=2.841, over 31.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.373, discriminator_fake_loss=1.365, generator_loss=27.18, generator_mel_loss=19.05, generator_kl_loss=1.369, generator_dur_loss=1.763, generator_adv_loss=1.877, generator_feat_match_loss=3.119, over 1379.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:21:34,576 INFO [train.py:527] (4/6) Epoch 123, batch 72, global_batch_idx: 15200, batch size: 55, loss[discriminator_loss=2.774, discriminator_real_loss=1.448, discriminator_fake_loss=1.326, generator_loss=27.21, generator_mel_loss=19.21, generator_kl_loss=1.421, generator_dur_loss=1.71, generator_adv_loss=1.895, generator_feat_match_loss=2.972, over 55.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.395, discriminator_fake_loss=1.357, generator_loss=27.25, generator_mel_loss=19.08, generator_kl_loss=1.387, generator_dur_loss=1.759, generator_adv_loss=1.891, generator_feat_match_loss=3.128, over 4023.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:21:34,578 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 12:21:42,581 INFO [train.py:591] (4/6) Epoch 123, validation: discriminator_loss=2.71, discriminator_real_loss=1.427, discriminator_fake_loss=1.282, generator_loss=26.89, generator_mel_loss=19.56, generator_kl_loss=1.188, generator_dur_loss=1.806, generator_adv_loss=1.883, generator_feat_match_loss=2.453, over 100.00 samples. +2024-03-12 12:21:42,581 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 12:24:00,581 INFO [train.py:527] (4/6) Epoch 123, batch 122, global_batch_idx: 15250, batch size: 62, loss[discriminator_loss=2.762, discriminator_real_loss=1.354, discriminator_fake_loss=1.407, generator_loss=27.5, generator_mel_loss=19.45, generator_kl_loss=1.269, generator_dur_loss=1.764, generator_adv_loss=1.86, generator_feat_match_loss=3.154, over 62.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.391, discriminator_fake_loss=1.353, generator_loss=27.16, generator_mel_loss=19.02, generator_kl_loss=1.388, generator_dur_loss=1.76, generator_adv_loss=1.888, generator_feat_match_loss=3.104, over 6732.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:24:05,488 INFO [train.py:919] (4/6) Start epoch 124 +2024-03-12 12:26:41,473 INFO [train.py:527] (4/6) Epoch 124, batch 48, global_batch_idx: 15300, batch size: 74, loss[discriminator_loss=2.772, discriminator_real_loss=1.481, discriminator_fake_loss=1.291, generator_loss=27.22, generator_mel_loss=18.96, generator_kl_loss=1.356, generator_dur_loss=1.832, generator_adv_loss=2.115, generator_feat_match_loss=2.958, over 74.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.401, discriminator_fake_loss=1.353, generator_loss=27.56, generator_mel_loss=19.13, generator_kl_loss=1.392, generator_dur_loss=1.767, generator_adv_loss=1.979, generator_feat_match_loss=3.288, over 2658.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:28:58,239 INFO [train.py:527] (4/6) Epoch 124, batch 98, global_batch_idx: 15350, batch size: 96, loss[discriminator_loss=2.78, discriminator_real_loss=1.465, discriminator_fake_loss=1.314, generator_loss=26.58, generator_mel_loss=18.59, generator_kl_loss=1.274, generator_dur_loss=1.938, generator_adv_loss=1.918, generator_feat_match_loss=2.853, over 96.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.406, discriminator_fake_loss=1.347, generator_loss=27.32, generator_mel_loss=19.07, generator_kl_loss=1.395, generator_dur_loss=1.764, generator_adv_loss=1.947, generator_feat_match_loss=3.143, over 5487.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:30:10,236 INFO [train.py:919] (4/6) Start epoch 125 +2024-03-12 12:31:41,027 INFO [train.py:527] (4/6) Epoch 125, batch 24, global_batch_idx: 15400, batch size: 80, loss[discriminator_loss=2.721, discriminator_real_loss=1.429, discriminator_fake_loss=1.292, generator_loss=27.22, generator_mel_loss=19.34, generator_kl_loss=1.241, generator_dur_loss=1.86, generator_adv_loss=1.768, generator_feat_match_loss=3.018, over 80.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.393, discriminator_fake_loss=1.347, generator_loss=27.31, generator_mel_loss=19.09, generator_kl_loss=1.394, generator_dur_loss=1.789, generator_adv_loss=1.878, generator_feat_match_loss=3.163, over 1454.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:31:41,028 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 12:31:49,013 INFO [train.py:591] (4/6) Epoch 125, validation: discriminator_loss=2.708, discriminator_real_loss=1.334, discriminator_fake_loss=1.374, generator_loss=25.91, generator_mel_loss=18.78, generator_kl_loss=1.215, generator_dur_loss=1.829, generator_adv_loss=1.705, generator_feat_match_loss=2.374, over 100.00 samples. +2024-03-12 12:31:49,014 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 12:34:10,625 INFO [train.py:527] (4/6) Epoch 125, batch 74, global_batch_idx: 15450, batch size: 88, loss[discriminator_loss=2.657, discriminator_real_loss=1.329, discriminator_fake_loss=1.328, generator_loss=27.58, generator_mel_loss=18.89, generator_kl_loss=1.284, generator_dur_loss=1.799, generator_adv_loss=2.128, generator_feat_match_loss=3.485, over 88.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.393, discriminator_fake_loss=1.349, generator_loss=27.22, generator_mel_loss=19.01, generator_kl_loss=1.4, generator_dur_loss=1.768, generator_adv_loss=1.9, generator_feat_match_loss=3.141, over 4364.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:36:26,650 INFO [train.py:919] (4/6) Start epoch 126 +2024-03-12 12:36:50,421 INFO [train.py:527] (4/6) Epoch 126, batch 0, global_batch_idx: 15500, batch size: 66, loss[discriminator_loss=2.767, discriminator_real_loss=1.407, discriminator_fake_loss=1.36, generator_loss=27.35, generator_mel_loss=19.41, generator_kl_loss=1.372, generator_dur_loss=1.764, generator_adv_loss=1.834, generator_feat_match_loss=2.973, over 66.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.407, discriminator_fake_loss=1.36, generator_loss=27.35, generator_mel_loss=19.41, generator_kl_loss=1.372, generator_dur_loss=1.764, generator_adv_loss=1.834, generator_feat_match_loss=2.973, over 66.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:39:10,055 INFO [train.py:527] (4/6) Epoch 126, batch 50, global_batch_idx: 15550, batch size: 62, loss[discriminator_loss=2.707, discriminator_real_loss=1.316, discriminator_fake_loss=1.391, generator_loss=28.06, generator_mel_loss=19.31, generator_kl_loss=1.638, generator_dur_loss=1.699, generator_adv_loss=1.807, generator_feat_match_loss=3.604, over 62.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.4, discriminator_fake_loss=1.36, generator_loss=27.14, generator_mel_loss=19.04, generator_kl_loss=1.411, generator_dur_loss=1.724, generator_adv_loss=1.895, generator_feat_match_loss=3.071, over 2906.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:41:27,707 INFO [train.py:527] (4/6) Epoch 126, batch 100, global_batch_idx: 15600, batch size: 77, loss[discriminator_loss=2.774, discriminator_real_loss=1.313, discriminator_fake_loss=1.461, generator_loss=26.25, generator_mel_loss=18.06, generator_kl_loss=1.341, generator_dur_loss=1.824, generator_adv_loss=2.234, generator_feat_match_loss=2.793, over 77.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.396, discriminator_fake_loss=1.349, generator_loss=27.2, generator_mel_loss=19.04, generator_kl_loss=1.403, generator_dur_loss=1.745, generator_adv_loss=1.906, generator_feat_match_loss=3.11, over 5778.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:41:27,709 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 12:41:36,595 INFO [train.py:591] (4/6) Epoch 126, validation: discriminator_loss=2.815, discriminator_real_loss=1.61, discriminator_fake_loss=1.205, generator_loss=25.88, generator_mel_loss=18.47, generator_kl_loss=1.162, generator_dur_loss=1.825, generator_adv_loss=2.079, generator_feat_match_loss=2.346, over 100.00 samples. +2024-03-12 12:41:36,596 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 12:42:40,366 INFO [train.py:919] (4/6) Start epoch 127 +2024-03-12 12:44:17,917 INFO [train.py:527] (4/6) Epoch 127, batch 26, global_batch_idx: 15650, batch size: 72, loss[discriminator_loss=2.763, discriminator_real_loss=1.443, discriminator_fake_loss=1.32, generator_loss=26.77, generator_mel_loss=18.75, generator_kl_loss=1.263, generator_dur_loss=1.783, generator_adv_loss=1.787, generator_feat_match_loss=3.183, over 72.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.397, discriminator_fake_loss=1.356, generator_loss=26.92, generator_mel_loss=18.86, generator_kl_loss=1.345, generator_dur_loss=1.749, generator_adv_loss=1.876, generator_feat_match_loss=3.088, over 1544.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:46:38,042 INFO [train.py:527] (4/6) Epoch 127, batch 76, global_batch_idx: 15700, batch size: 72, loss[discriminator_loss=2.686, discriminator_real_loss=1.406, discriminator_fake_loss=1.28, generator_loss=27.68, generator_mel_loss=18.85, generator_kl_loss=1.352, generator_dur_loss=1.787, generator_adv_loss=2.04, generator_feat_match_loss=3.648, over 72.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.388, discriminator_fake_loss=1.359, generator_loss=27.25, generator_mel_loss=18.96, generator_kl_loss=1.367, generator_dur_loss=1.763, generator_adv_loss=1.926, generator_feat_match_loss=3.228, over 4550.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:48:48,578 INFO [train.py:919] (4/6) Start epoch 128 +2024-03-12 12:49:17,633 INFO [train.py:527] (4/6) Epoch 128, batch 2, global_batch_idx: 15750, batch size: 80, loss[discriminator_loss=2.78, discriminator_real_loss=1.276, discriminator_fake_loss=1.504, generator_loss=27.95, generator_mel_loss=19.33, generator_kl_loss=1.301, generator_dur_loss=1.862, generator_adv_loss=2.019, generator_feat_match_loss=3.442, over 80.00 samples.], tot_loss[discriminator_loss=2.781, discriminator_real_loss=1.364, discriminator_fake_loss=1.417, generator_loss=27.26, generator_mel_loss=18.91, generator_kl_loss=1.302, generator_dur_loss=1.824, generator_adv_loss=1.899, generator_feat_match_loss=3.321, over 207.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:51:35,818 INFO [train.py:527] (4/6) Epoch 128, batch 52, global_batch_idx: 15800, batch size: 60, loss[discriminator_loss=2.831, discriminator_real_loss=1.443, discriminator_fake_loss=1.388, generator_loss=27.07, generator_mel_loss=19.02, generator_kl_loss=1.423, generator_dur_loss=1.759, generator_adv_loss=1.895, generator_feat_match_loss=2.969, over 60.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.377, discriminator_fake_loss=1.365, generator_loss=27.49, generator_mel_loss=19.17, generator_kl_loss=1.389, generator_dur_loss=1.77, generator_adv_loss=1.91, generator_feat_match_loss=3.252, over 2990.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:51:35,819 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 12:51:44,036 INFO [train.py:591] (4/6) Epoch 128, validation: discriminator_loss=2.787, discriminator_real_loss=1.483, discriminator_fake_loss=1.303, generator_loss=26.23, generator_mel_loss=19.01, generator_kl_loss=1.189, generator_dur_loss=1.831, generator_adv_loss=1.855, generator_feat_match_loss=2.347, over 100.00 samples. +2024-03-12 12:51:44,038 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 12:54:00,543 INFO [train.py:527] (4/6) Epoch 128, batch 102, global_batch_idx: 15850, batch size: 53, loss[discriminator_loss=2.749, discriminator_real_loss=1.475, discriminator_fake_loss=1.274, generator_loss=27.28, generator_mel_loss=19.12, generator_kl_loss=1.396, generator_dur_loss=1.757, generator_adv_loss=1.894, generator_feat_match_loss=3.11, over 53.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.389, discriminator_fake_loss=1.355, generator_loss=27.38, generator_mel_loss=19.11, generator_kl_loss=1.383, generator_dur_loss=1.773, generator_adv_loss=1.907, generator_feat_match_loss=3.205, over 5801.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:55:02,021 INFO [train.py:919] (4/6) Start epoch 129 +2024-03-12 12:56:44,543 INFO [train.py:527] (4/6) Epoch 129, batch 28, global_batch_idx: 15900, batch size: 74, loss[discriminator_loss=2.763, discriminator_real_loss=1.403, discriminator_fake_loss=1.361, generator_loss=27.23, generator_mel_loss=18.98, generator_kl_loss=1.324, generator_dur_loss=1.794, generator_adv_loss=1.91, generator_feat_match_loss=3.217, over 74.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.389, discriminator_fake_loss=1.363, generator_loss=27.22, generator_mel_loss=19.03, generator_kl_loss=1.386, generator_dur_loss=1.795, generator_adv_loss=1.888, generator_feat_match_loss=3.126, over 1798.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:59:02,162 INFO [train.py:527] (4/6) Epoch 129, batch 78, global_batch_idx: 15950, batch size: 64, loss[discriminator_loss=2.786, discriminator_real_loss=1.421, discriminator_fake_loss=1.365, generator_loss=26.56, generator_mel_loss=18.76, generator_kl_loss=1.238, generator_dur_loss=1.851, generator_adv_loss=1.725, generator_feat_match_loss=2.989, over 64.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.396, discriminator_fake_loss=1.36, generator_loss=27.24, generator_mel_loss=19.02, generator_kl_loss=1.386, generator_dur_loss=1.783, generator_adv_loss=1.907, generator_feat_match_loss=3.145, over 4356.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:01:07,481 INFO [train.py:919] (4/6) Start epoch 130 +2024-03-12 13:01:42,819 INFO [train.py:527] (4/6) Epoch 130, batch 4, global_batch_idx: 16000, batch size: 72, loss[discriminator_loss=2.705, discriminator_real_loss=1.327, discriminator_fake_loss=1.378, generator_loss=27.21, generator_mel_loss=18.72, generator_kl_loss=1.408, generator_dur_loss=1.829, generator_adv_loss=1.89, generator_feat_match_loss=3.365, over 72.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.371, discriminator_fake_loss=1.365, generator_loss=27.74, generator_mel_loss=19.28, generator_kl_loss=1.454, generator_dur_loss=1.809, generator_adv_loss=1.894, generator_feat_match_loss=3.304, over 298.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:01:42,822 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 13:01:50,755 INFO [train.py:591] (4/6) Epoch 130, validation: discriminator_loss=2.706, discriminator_real_loss=1.423, discriminator_fake_loss=1.283, generator_loss=26.49, generator_mel_loss=19.13, generator_kl_loss=1.206, generator_dur_loss=1.832, generator_adv_loss=1.842, generator_feat_match_loss=2.484, over 100.00 samples. +2024-03-12 13:01:50,757 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 13:04:09,451 INFO [train.py:527] (4/6) Epoch 130, batch 54, global_batch_idx: 16050, batch size: 68, loss[discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=27.53, generator_mel_loss=19.39, generator_kl_loss=1.282, generator_dur_loss=1.794, generator_adv_loss=1.854, generator_feat_match_loss=3.207, over 68.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.39, discriminator_fake_loss=1.361, generator_loss=27.24, generator_mel_loss=19.01, generator_kl_loss=1.359, generator_dur_loss=1.794, generator_adv_loss=1.902, generator_feat_match_loss=3.175, over 3320.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:06:28,547 INFO [train.py:527] (4/6) Epoch 130, batch 104, global_batch_idx: 16100, batch size: 36, loss[discriminator_loss=2.794, discriminator_real_loss=1.522, discriminator_fake_loss=1.272, generator_loss=27.36, generator_mel_loss=19.57, generator_kl_loss=1.472, generator_dur_loss=1.68, generator_adv_loss=1.729, generator_feat_match_loss=2.912, over 36.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.395, discriminator_fake_loss=1.359, generator_loss=27.22, generator_mel_loss=19.01, generator_kl_loss=1.365, generator_dur_loss=1.788, generator_adv_loss=1.903, generator_feat_match_loss=3.158, over 6283.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:07:19,624 INFO [train.py:919] (4/6) Start epoch 131 +2024-03-12 13:09:08,205 INFO [train.py:527] (4/6) Epoch 131, batch 30, global_batch_idx: 16150, batch size: 61, loss[discriminator_loss=2.765, discriminator_real_loss=1.389, discriminator_fake_loss=1.375, generator_loss=27.08, generator_mel_loss=18.84, generator_kl_loss=1.255, generator_dur_loss=1.738, generator_adv_loss=1.855, generator_feat_match_loss=3.392, over 61.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.4, discriminator_fake_loss=1.345, generator_loss=27.12, generator_mel_loss=18.88, generator_kl_loss=1.368, generator_dur_loss=1.761, generator_adv_loss=1.909, generator_feat_match_loss=3.204, over 1749.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:11:30,181 INFO [train.py:527] (4/6) Epoch 131, batch 80, global_batch_idx: 16200, batch size: 58, loss[discriminator_loss=2.704, discriminator_real_loss=1.455, discriminator_fake_loss=1.249, generator_loss=26.88, generator_mel_loss=18.19, generator_kl_loss=1.453, generator_dur_loss=1.706, generator_adv_loss=2.029, generator_feat_match_loss=3.503, over 58.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.393, discriminator_fake_loss=1.347, generator_loss=27.15, generator_mel_loss=18.92, generator_kl_loss=1.39, generator_dur_loss=1.744, generator_adv_loss=1.913, generator_feat_match_loss=3.184, over 4583.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:11:30,183 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 13:11:39,151 INFO [train.py:591] (4/6) Epoch 131, validation: discriminator_loss=2.756, discriminator_real_loss=1.528, discriminator_fake_loss=1.229, generator_loss=27.04, generator_mel_loss=19.35, generator_kl_loss=1.247, generator_dur_loss=1.804, generator_adv_loss=1.937, generator_feat_match_loss=2.705, over 100.00 samples. +2024-03-12 13:11:39,151 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 13:13:34,527 INFO [train.py:919] (4/6) Start epoch 132 +2024-03-12 13:14:15,390 INFO [train.py:527] (4/6) Epoch 132, batch 6, global_batch_idx: 16250, batch size: 31, loss[discriminator_loss=2.887, discriminator_real_loss=1.661, discriminator_fake_loss=1.226, generator_loss=27.92, generator_mel_loss=19.29, generator_kl_loss=1.683, generator_dur_loss=1.574, generator_adv_loss=1.952, generator_feat_match_loss=3.423, over 31.00 samples.], tot_loss[discriminator_loss=2.873, discriminator_real_loss=1.583, discriminator_fake_loss=1.29, generator_loss=28.09, generator_mel_loss=19.14, generator_kl_loss=1.395, generator_dur_loss=1.717, generator_adv_loss=2.281, generator_feat_match_loss=3.556, over 339.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:16:32,893 INFO [train.py:527] (4/6) Epoch 132, batch 56, global_batch_idx: 16300, batch size: 58, loss[discriminator_loss=2.831, discriminator_real_loss=1.477, discriminator_fake_loss=1.354, generator_loss=27.65, generator_mel_loss=19.54, generator_kl_loss=1.368, generator_dur_loss=1.742, generator_adv_loss=1.655, generator_feat_match_loss=3.345, over 58.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.42, discriminator_fake_loss=1.348, generator_loss=27.41, generator_mel_loss=19.09, generator_kl_loss=1.382, generator_dur_loss=1.739, generator_adv_loss=1.988, generator_feat_match_loss=3.206, over 3129.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:18:53,760 INFO [train.py:527] (4/6) Epoch 132, batch 106, global_batch_idx: 16350, batch size: 44, loss[discriminator_loss=2.685, discriminator_real_loss=1.384, discriminator_fake_loss=1.301, generator_loss=27.81, generator_mel_loss=18.98, generator_kl_loss=1.585, generator_dur_loss=1.676, generator_adv_loss=1.915, generator_feat_match_loss=3.654, over 44.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.41, discriminator_fake_loss=1.359, generator_loss=27.36, generator_mel_loss=19.1, generator_kl_loss=1.382, generator_dur_loss=1.748, generator_adv_loss=1.947, generator_feat_match_loss=3.184, over 5875.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:19:43,185 INFO [train.py:919] (4/6) Start epoch 133 +2024-03-12 13:21:35,334 INFO [train.py:527] (4/6) Epoch 133, batch 32, global_batch_idx: 16400, batch size: 80, loss[discriminator_loss=2.735, discriminator_real_loss=1.421, discriminator_fake_loss=1.314, generator_loss=27.08, generator_mel_loss=19.02, generator_kl_loss=1.286, generator_dur_loss=1.847, generator_adv_loss=1.706, generator_feat_match_loss=3.225, over 80.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.394, discriminator_fake_loss=1.342, generator_loss=27.36, generator_mel_loss=19.02, generator_kl_loss=1.42, generator_dur_loss=1.731, generator_adv_loss=1.921, generator_feat_match_loss=3.276, over 1759.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:21:35,336 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 13:21:43,787 INFO [train.py:591] (4/6) Epoch 133, validation: discriminator_loss=2.789, discriminator_real_loss=1.338, discriminator_fake_loss=1.45, generator_loss=25.78, generator_mel_loss=19.04, generator_kl_loss=1.105, generator_dur_loss=1.795, generator_adv_loss=1.623, generator_feat_match_loss=2.221, over 100.00 samples. +2024-03-12 13:21:43,788 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 13:24:02,162 INFO [train.py:527] (4/6) Epoch 133, batch 82, global_batch_idx: 16450, batch size: 96, loss[discriminator_loss=2.753, discriminator_real_loss=1.469, discriminator_fake_loss=1.284, generator_loss=26.82, generator_mel_loss=18.43, generator_kl_loss=1.289, generator_dur_loss=1.809, generator_adv_loss=2.05, generator_feat_match_loss=3.247, over 96.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.39, discriminator_fake_loss=1.362, generator_loss=27.26, generator_mel_loss=18.96, generator_kl_loss=1.375, generator_dur_loss=1.763, generator_adv_loss=1.906, generator_feat_match_loss=3.257, over 4991.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:25:57,304 INFO [train.py:919] (4/6) Start epoch 134 +2024-03-12 13:26:43,179 INFO [train.py:527] (4/6) Epoch 134, batch 8, global_batch_idx: 16500, batch size: 64, loss[discriminator_loss=2.738, discriminator_real_loss=1.355, discriminator_fake_loss=1.383, generator_loss=27.84, generator_mel_loss=19.47, generator_kl_loss=1.386, generator_dur_loss=1.765, generator_adv_loss=1.95, generator_feat_match_loss=3.264, over 64.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.433, discriminator_fake_loss=1.331, generator_loss=27.32, generator_mel_loss=19.12, generator_kl_loss=1.358, generator_dur_loss=1.758, generator_adv_loss=1.985, generator_feat_match_loss=3.095, over 524.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:29:01,176 INFO [train.py:527] (4/6) Epoch 134, batch 58, global_batch_idx: 16550, batch size: 77, loss[discriminator_loss=2.737, discriminator_real_loss=1.413, discriminator_fake_loss=1.324, generator_loss=27.12, generator_mel_loss=18.76, generator_kl_loss=1.343, generator_dur_loss=1.779, generator_adv_loss=1.924, generator_feat_match_loss=3.314, over 77.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.395, discriminator_fake_loss=1.351, generator_loss=27.42, generator_mel_loss=19.13, generator_kl_loss=1.377, generator_dur_loss=1.753, generator_adv_loss=1.921, generator_feat_match_loss=3.238, over 3312.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:31:20,821 INFO [train.py:527] (4/6) Epoch 134, batch 108, global_batch_idx: 16600, batch size: 72, loss[discriminator_loss=2.657, discriminator_real_loss=1.333, discriminator_fake_loss=1.324, generator_loss=28.03, generator_mel_loss=19.29, generator_kl_loss=1.357, generator_dur_loss=1.828, generator_adv_loss=1.997, generator_feat_match_loss=3.561, over 72.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.387, discriminator_fake_loss=1.349, generator_loss=27.4, generator_mel_loss=19.09, generator_kl_loss=1.384, generator_dur_loss=1.754, generator_adv_loss=1.927, generator_feat_match_loss=3.246, over 6193.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:31:20,822 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 13:31:29,813 INFO [train.py:591] (4/6) Epoch 134, validation: discriminator_loss=2.726, discriminator_real_loss=1.442, discriminator_fake_loss=1.284, generator_loss=26.65, generator_mel_loss=19.52, generator_kl_loss=1.061, generator_dur_loss=1.825, generator_adv_loss=1.899, generator_feat_match_loss=2.339, over 100.00 samples. +2024-03-12 13:31:29,814 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 13:32:11,655 INFO [train.py:919] (4/6) Start epoch 135 +2024-03-12 13:34:10,395 INFO [train.py:527] (4/6) Epoch 135, batch 34, global_batch_idx: 16650, batch size: 70, loss[discriminator_loss=2.697, discriminator_real_loss=1.398, discriminator_fake_loss=1.299, generator_loss=26.88, generator_mel_loss=18.71, generator_kl_loss=1.281, generator_dur_loss=1.8, generator_adv_loss=2.025, generator_feat_match_loss=3.064, over 70.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.383, discriminator_fake_loss=1.353, generator_loss=27.23, generator_mel_loss=18.88, generator_kl_loss=1.378, generator_dur_loss=1.768, generator_adv_loss=1.926, generator_feat_match_loss=3.279, over 1936.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:36:29,229 INFO [train.py:527] (4/6) Epoch 135, batch 84, global_batch_idx: 16700, batch size: 39, loss[discriminator_loss=2.674, discriminator_real_loss=1.455, discriminator_fake_loss=1.219, generator_loss=27.13, generator_mel_loss=18.96, generator_kl_loss=1.589, generator_dur_loss=1.632, generator_adv_loss=1.945, generator_feat_match_loss=3.007, over 39.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.398, discriminator_fake_loss=1.352, generator_loss=27.43, generator_mel_loss=18.94, generator_kl_loss=1.379, generator_dur_loss=1.762, generator_adv_loss=1.999, generator_feat_match_loss=3.343, over 4995.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:38:16,257 INFO [train.py:919] (4/6) Start epoch 136 +2024-03-12 13:39:10,267 INFO [train.py:527] (4/6) Epoch 136, batch 10, global_batch_idx: 16750, batch size: 42, loss[discriminator_loss=2.71, discriminator_real_loss=1.349, discriminator_fake_loss=1.361, generator_loss=26.96, generator_mel_loss=18.54, generator_kl_loss=1.319, generator_dur_loss=1.703, generator_adv_loss=2.133, generator_feat_match_loss=3.261, over 42.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.381, discriminator_fake_loss=1.341, generator_loss=26.87, generator_mel_loss=18.76, generator_kl_loss=1.32, generator_dur_loss=1.753, generator_adv_loss=1.925, generator_feat_match_loss=3.108, over 623.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:41:29,489 INFO [train.py:527] (4/6) Epoch 136, batch 60, global_batch_idx: 16800, batch size: 77, loss[discriminator_loss=2.734, discriminator_real_loss=1.475, discriminator_fake_loss=1.259, generator_loss=26.42, generator_mel_loss=18.53, generator_kl_loss=1.237, generator_dur_loss=1.809, generator_adv_loss=1.767, generator_feat_match_loss=3.077, over 77.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.387, discriminator_fake_loss=1.36, generator_loss=27.04, generator_mel_loss=18.9, generator_kl_loss=1.36, generator_dur_loss=1.742, generator_adv_loss=1.901, generator_feat_match_loss=3.141, over 3392.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:41:29,490 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 13:41:37,383 INFO [train.py:591] (4/6) Epoch 136, validation: discriminator_loss=2.743, discriminator_real_loss=1.405, discriminator_fake_loss=1.338, generator_loss=26.94, generator_mel_loss=19.67, generator_kl_loss=1.201, generator_dur_loss=1.773, generator_adv_loss=1.724, generator_feat_match_loss=2.572, over 100.00 samples. +2024-03-12 13:41:37,384 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 13:43:55,310 INFO [train.py:527] (4/6) Epoch 136, batch 110, global_batch_idx: 16850, batch size: 42, loss[discriminator_loss=2.824, discriminator_real_loss=1.391, discriminator_fake_loss=1.434, generator_loss=26.65, generator_mel_loss=18.32, generator_kl_loss=1.406, generator_dur_loss=1.662, generator_adv_loss=2.133, generator_feat_match_loss=3.126, over 42.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.393, discriminator_fake_loss=1.356, generator_loss=27.12, generator_mel_loss=18.96, generator_kl_loss=1.38, generator_dur_loss=1.745, generator_adv_loss=1.897, generator_feat_match_loss=3.141, over 6031.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:44:31,105 INFO [train.py:919] (4/6) Start epoch 137 +2024-03-12 13:46:36,216 INFO [train.py:527] (4/6) Epoch 137, batch 36, global_batch_idx: 16900, batch size: 45, loss[discriminator_loss=2.745, discriminator_real_loss=1.39, discriminator_fake_loss=1.355, generator_loss=28.57, generator_mel_loss=19.72, generator_kl_loss=1.662, generator_dur_loss=1.644, generator_adv_loss=2.01, generator_feat_match_loss=3.541, over 45.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.382, discriminator_fake_loss=1.356, generator_loss=27.24, generator_mel_loss=19.01, generator_kl_loss=1.394, generator_dur_loss=1.766, generator_adv_loss=1.889, generator_feat_match_loss=3.18, over 2018.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:48:57,990 INFO [train.py:527] (4/6) Epoch 137, batch 86, global_batch_idx: 16950, batch size: 74, loss[discriminator_loss=2.72, discriminator_real_loss=1.351, discriminator_fake_loss=1.368, generator_loss=26.17, generator_mel_loss=18.16, generator_kl_loss=1.319, generator_dur_loss=1.81, generator_adv_loss=1.867, generator_feat_match_loss=3.014, over 74.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.384, discriminator_fake_loss=1.353, generator_loss=27.37, generator_mel_loss=19.02, generator_kl_loss=1.383, generator_dur_loss=1.758, generator_adv_loss=1.931, generator_feat_match_loss=3.277, over 4846.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:50:39,869 INFO [train.py:919] (4/6) Start epoch 138 +2024-03-12 13:51:34,803 INFO [train.py:527] (4/6) Epoch 138, batch 12, global_batch_idx: 17000, batch size: 39, loss[discriminator_loss=2.753, discriminator_real_loss=1.439, discriminator_fake_loss=1.314, generator_loss=27.21, generator_mel_loss=19.31, generator_kl_loss=1.334, generator_dur_loss=1.741, generator_adv_loss=1.799, generator_feat_match_loss=3.026, over 39.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.388, discriminator_fake_loss=1.344, generator_loss=27.01, generator_mel_loss=18.85, generator_kl_loss=1.326, generator_dur_loss=1.808, generator_adv_loss=1.889, generator_feat_match_loss=3.137, over 770.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:51:34,806 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 13:51:42,684 INFO [train.py:591] (4/6) Epoch 138, validation: discriminator_loss=2.777, discriminator_real_loss=1.404, discriminator_fake_loss=1.373, generator_loss=26.47, generator_mel_loss=19.28, generator_kl_loss=1.093, generator_dur_loss=1.828, generator_adv_loss=1.725, generator_feat_match_loss=2.551, over 100.00 samples. +2024-03-12 13:51:42,685 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 13:54:03,209 INFO [train.py:527] (4/6) Epoch 138, batch 62, global_batch_idx: 17050, batch size: 88, loss[discriminator_loss=2.717, discriminator_real_loss=1.327, discriminator_fake_loss=1.39, generator_loss=27.57, generator_mel_loss=18.97, generator_kl_loss=1.337, generator_dur_loss=1.831, generator_adv_loss=1.807, generator_feat_match_loss=3.626, over 88.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.38, discriminator_fake_loss=1.347, generator_loss=27.2, generator_mel_loss=18.93, generator_kl_loss=1.354, generator_dur_loss=1.782, generator_adv_loss=1.906, generator_feat_match_loss=3.227, over 3877.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:56:19,472 INFO [train.py:527] (4/6) Epoch 138, batch 112, global_batch_idx: 17100, batch size: 48, loss[discriminator_loss=2.699, discriminator_real_loss=1.332, discriminator_fake_loss=1.367, generator_loss=26.79, generator_mel_loss=18.66, generator_kl_loss=1.426, generator_dur_loss=1.677, generator_adv_loss=1.872, generator_feat_match_loss=3.16, over 48.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.386, discriminator_fake_loss=1.352, generator_loss=27.22, generator_mel_loss=18.96, generator_kl_loss=1.377, generator_dur_loss=1.774, generator_adv_loss=1.897, generator_feat_match_loss=3.214, over 6575.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:56:50,833 INFO [train.py:919] (4/6) Start epoch 139 +2024-03-12 13:59:01,231 INFO [train.py:527] (4/6) Epoch 139, batch 38, global_batch_idx: 17150, batch size: 45, loss[discriminator_loss=2.874, discriminator_real_loss=1.636, discriminator_fake_loss=1.238, generator_loss=25.71, generator_mel_loss=17.9, generator_kl_loss=1.366, generator_dur_loss=1.677, generator_adv_loss=1.879, generator_feat_match_loss=2.884, over 45.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.405, discriminator_fake_loss=1.363, generator_loss=27.59, generator_mel_loss=19.07, generator_kl_loss=1.402, generator_dur_loss=1.757, generator_adv_loss=2.005, generator_feat_match_loss=3.356, over 2180.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:01:21,603 INFO [train.py:527] (4/6) Epoch 139, batch 88, global_batch_idx: 17200, batch size: 64, loss[discriminator_loss=2.679, discriminator_real_loss=1.376, discriminator_fake_loss=1.303, generator_loss=27.45, generator_mel_loss=19.13, generator_kl_loss=1.477, generator_dur_loss=1.718, generator_adv_loss=1.981, generator_feat_match_loss=3.145, over 64.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.398, discriminator_fake_loss=1.353, generator_loss=27.41, generator_mel_loss=19.01, generator_kl_loss=1.39, generator_dur_loss=1.771, generator_adv_loss=1.949, generator_feat_match_loss=3.29, over 5087.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:01:21,605 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 14:01:29,860 INFO [train.py:591] (4/6) Epoch 139, validation: discriminator_loss=2.667, discriminator_real_loss=1.392, discriminator_fake_loss=1.275, generator_loss=26.29, generator_mel_loss=18.75, generator_kl_loss=1.098, generator_dur_loss=1.795, generator_adv_loss=1.943, generator_feat_match_loss=2.702, over 100.00 samples. +2024-03-12 14:01:29,861 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 14:03:08,316 INFO [train.py:919] (4/6) Start epoch 140 +2024-03-12 14:04:12,711 INFO [train.py:527] (4/6) Epoch 140, batch 14, global_batch_idx: 17250, batch size: 25, loss[discriminator_loss=2.732, discriminator_real_loss=1.415, discriminator_fake_loss=1.318, generator_loss=28.53, generator_mel_loss=20.05, generator_kl_loss=1.733, generator_dur_loss=1.603, generator_adv_loss=1.829, generator_feat_match_loss=3.319, over 25.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.412, discriminator_fake_loss=1.357, generator_loss=27.02, generator_mel_loss=18.9, generator_kl_loss=1.371, generator_dur_loss=1.777, generator_adv_loss=1.876, generator_feat_match_loss=3.093, over 913.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:06:31,488 INFO [train.py:527] (4/6) Epoch 140, batch 64, global_batch_idx: 17300, batch size: 52, loss[discriminator_loss=2.718, discriminator_real_loss=1.404, discriminator_fake_loss=1.314, generator_loss=27.55, generator_mel_loss=19.22, generator_kl_loss=1.429, generator_dur_loss=1.694, generator_adv_loss=1.86, generator_feat_match_loss=3.35, over 52.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.395, discriminator_fake_loss=1.355, generator_loss=27.27, generator_mel_loss=19.01, generator_kl_loss=1.396, generator_dur_loss=1.75, generator_adv_loss=1.892, generator_feat_match_loss=3.224, over 3736.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:08:49,291 INFO [train.py:527] (4/6) Epoch 140, batch 114, global_batch_idx: 17350, batch size: 53, loss[discriminator_loss=2.762, discriminator_real_loss=1.411, discriminator_fake_loss=1.351, generator_loss=26.71, generator_mel_loss=18.77, generator_kl_loss=1.241, generator_dur_loss=1.688, generator_adv_loss=2.127, generator_feat_match_loss=2.883, over 53.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.395, discriminator_fake_loss=1.357, generator_loss=27.29, generator_mel_loss=19.02, generator_kl_loss=1.388, generator_dur_loss=1.747, generator_adv_loss=1.897, generator_feat_match_loss=3.233, over 6444.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:09:14,781 INFO [train.py:919] (4/6) Start epoch 141 +2024-03-12 14:11:31,978 INFO [train.py:527] (4/6) Epoch 141, batch 40, global_batch_idx: 17400, batch size: 48, loss[discriminator_loss=2.721, discriminator_real_loss=1.289, discriminator_fake_loss=1.431, generator_loss=27.39, generator_mel_loss=19.13, generator_kl_loss=1.528, generator_dur_loss=1.701, generator_adv_loss=1.911, generator_feat_match_loss=3.119, over 48.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.64, generator_mel_loss=19.22, generator_kl_loss=1.41, generator_dur_loss=1.729, generator_adv_loss=1.936, generator_feat_match_loss=3.348, over 1985.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:11:31,980 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 14:11:40,076 INFO [train.py:591] (4/6) Epoch 141, validation: discriminator_loss=2.773, discriminator_real_loss=1.432, discriminator_fake_loss=1.341, generator_loss=26.01, generator_mel_loss=18.65, generator_kl_loss=1.195, generator_dur_loss=1.798, generator_adv_loss=1.86, generator_feat_match_loss=2.51, over 100.00 samples. +2024-03-12 14:11:40,077 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 14:13:59,383 INFO [train.py:527] (4/6) Epoch 141, batch 90, global_batch_idx: 17450, batch size: 50, loss[discriminator_loss=2.715, discriminator_real_loss=1.395, discriminator_fake_loss=1.32, generator_loss=27.35, generator_mel_loss=19.18, generator_kl_loss=1.385, generator_dur_loss=1.703, generator_adv_loss=1.896, generator_feat_match_loss=3.187, over 50.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.394, discriminator_fake_loss=1.346, generator_loss=27.52, generator_mel_loss=19.17, generator_kl_loss=1.409, generator_dur_loss=1.734, generator_adv_loss=1.913, generator_feat_match_loss=3.302, over 4707.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:15:33,449 INFO [train.py:919] (4/6) Start epoch 142 +2024-03-12 14:16:41,486 INFO [train.py:527] (4/6) Epoch 142, batch 16, global_batch_idx: 17500, batch size: 74, loss[discriminator_loss=2.76, discriminator_real_loss=1.365, discriminator_fake_loss=1.395, generator_loss=27.25, generator_mel_loss=19.16, generator_kl_loss=1.269, generator_dur_loss=1.781, generator_adv_loss=1.864, generator_feat_match_loss=3.175, over 74.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.434, discriminator_fake_loss=1.335, generator_loss=27.14, generator_mel_loss=18.92, generator_kl_loss=1.418, generator_dur_loss=1.75, generator_adv_loss=1.896, generator_feat_match_loss=3.154, over 956.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:19:00,016 INFO [train.py:527] (4/6) Epoch 142, batch 66, global_batch_idx: 17550, batch size: 36, loss[discriminator_loss=2.683, discriminator_real_loss=1.418, discriminator_fake_loss=1.265, generator_loss=27.67, generator_mel_loss=19.12, generator_kl_loss=1.494, generator_dur_loss=1.706, generator_adv_loss=1.924, generator_feat_match_loss=3.43, over 36.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.403, discriminator_fake_loss=1.35, generator_loss=27.19, generator_mel_loss=18.91, generator_kl_loss=1.398, generator_dur_loss=1.754, generator_adv_loss=1.9, generator_feat_match_loss=3.23, over 3750.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:21:16,224 INFO [train.py:527] (4/6) Epoch 142, batch 116, global_batch_idx: 17600, batch size: 48, loss[discriminator_loss=3.232, discriminator_real_loss=1.615, discriminator_fake_loss=1.617, generator_loss=26.97, generator_mel_loss=18.94, generator_kl_loss=1.361, generator_dur_loss=1.717, generator_adv_loss=1.82, generator_feat_match_loss=3.134, over 48.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.398, discriminator_fake_loss=1.358, generator_loss=27.3, generator_mel_loss=18.98, generator_kl_loss=1.396, generator_dur_loss=1.75, generator_adv_loss=1.906, generator_feat_match_loss=3.26, over 6448.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:21:16,225 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 14:21:25,032 INFO [train.py:591] (4/6) Epoch 142, validation: discriminator_loss=3.002, discriminator_real_loss=1.685, discriminator_fake_loss=1.318, generator_loss=27, generator_mel_loss=19.09, generator_kl_loss=1.229, generator_dur_loss=1.787, generator_adv_loss=1.925, generator_feat_match_loss=2.968, over 100.00 samples. +2024-03-12 14:21:25,033 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 14:21:45,397 INFO [train.py:919] (4/6) Start epoch 143 +2024-03-12 14:24:07,022 INFO [train.py:527] (4/6) Epoch 143, batch 42, global_batch_idx: 17650, batch size: 39, loss[discriminator_loss=2.76, discriminator_real_loss=1.5, discriminator_fake_loss=1.26, generator_loss=27.87, generator_mel_loss=19.21, generator_kl_loss=1.517, generator_dur_loss=1.639, generator_adv_loss=2.013, generator_feat_match_loss=3.494, over 39.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.394, discriminator_fake_loss=1.345, generator_loss=27.3, generator_mel_loss=18.91, generator_kl_loss=1.418, generator_dur_loss=1.724, generator_adv_loss=1.943, generator_feat_match_loss=3.304, over 2327.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:26:24,703 INFO [train.py:527] (4/6) Epoch 143, batch 92, global_batch_idx: 17700, batch size: 72, loss[discriminator_loss=2.771, discriminator_real_loss=1.392, discriminator_fake_loss=1.379, generator_loss=27.13, generator_mel_loss=18.82, generator_kl_loss=1.489, generator_dur_loss=1.735, generator_adv_loss=1.928, generator_feat_match_loss=3.164, over 72.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.392, discriminator_fake_loss=1.345, generator_loss=27.32, generator_mel_loss=18.94, generator_kl_loss=1.401, generator_dur_loss=1.733, generator_adv_loss=1.929, generator_feat_match_loss=3.311, over 5113.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:27:50,844 INFO [train.py:919] (4/6) Start epoch 144 +2024-03-12 14:29:03,994 INFO [train.py:527] (4/6) Epoch 144, batch 18, global_batch_idx: 17750, batch size: 61, loss[discriminator_loss=2.91, discriminator_real_loss=1.706, discriminator_fake_loss=1.204, generator_loss=26.21, generator_mel_loss=18.6, generator_kl_loss=1.425, generator_dur_loss=1.732, generator_adv_loss=1.537, generator_feat_match_loss=2.919, over 61.00 samples.], tot_loss[discriminator_loss=2.773, discriminator_real_loss=1.414, discriminator_fake_loss=1.358, generator_loss=27.02, generator_mel_loss=18.78, generator_kl_loss=1.384, generator_dur_loss=1.747, generator_adv_loss=1.901, generator_feat_match_loss=3.212, over 1054.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:31:23,700 INFO [train.py:527] (4/6) Epoch 144, batch 68, global_batch_idx: 17800, batch size: 52, loss[discriminator_loss=2.748, discriminator_real_loss=1.364, discriminator_fake_loss=1.383, generator_loss=26.02, generator_mel_loss=18.18, generator_kl_loss=1.411, generator_dur_loss=1.706, generator_adv_loss=1.796, generator_feat_match_loss=2.922, over 52.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.39, discriminator_fake_loss=1.358, generator_loss=27.29, generator_mel_loss=18.91, generator_kl_loss=1.409, generator_dur_loss=1.751, generator_adv_loss=1.921, generator_feat_match_loss=3.295, over 3656.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:31:23,701 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 14:31:31,868 INFO [train.py:591] (4/6) Epoch 144, validation: discriminator_loss=2.728, discriminator_real_loss=1.3, discriminator_fake_loss=1.429, generator_loss=26.78, generator_mel_loss=19.42, generator_kl_loss=1.208, generator_dur_loss=1.821, generator_adv_loss=1.719, generator_feat_match_loss=2.614, over 100.00 samples. +2024-03-12 14:31:31,869 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 14:33:51,163 INFO [train.py:527] (4/6) Epoch 144, batch 118, global_batch_idx: 17850, batch size: 83, loss[discriminator_loss=2.765, discriminator_real_loss=1.334, discriminator_fake_loss=1.432, generator_loss=27.8, generator_mel_loss=19.16, generator_kl_loss=1.31, generator_dur_loss=1.874, generator_adv_loss=1.906, generator_feat_match_loss=3.556, over 83.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.39, discriminator_fake_loss=1.358, generator_loss=27.28, generator_mel_loss=18.93, generator_kl_loss=1.394, generator_dur_loss=1.769, generator_adv_loss=1.911, generator_feat_match_loss=3.275, over 6613.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:34:06,417 INFO [train.py:919] (4/6) Start epoch 145 +2024-03-12 14:36:30,852 INFO [train.py:527] (4/6) Epoch 145, batch 44, global_batch_idx: 17900, batch size: 61, loss[discriminator_loss=2.714, discriminator_real_loss=1.392, discriminator_fake_loss=1.321, generator_loss=27.25, generator_mel_loss=19.24, generator_kl_loss=1.37, generator_dur_loss=1.733, generator_adv_loss=1.907, generator_feat_match_loss=3.004, over 61.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.4, discriminator_fake_loss=1.349, generator_loss=27.3, generator_mel_loss=19, generator_kl_loss=1.404, generator_dur_loss=1.766, generator_adv_loss=1.906, generator_feat_match_loss=3.228, over 2724.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:38:48,767 INFO [train.py:527] (4/6) Epoch 145, batch 94, global_batch_idx: 17950, batch size: 15, loss[discriminator_loss=2.657, discriminator_real_loss=1.414, discriminator_fake_loss=1.243, generator_loss=29.01, generator_mel_loss=19.46, generator_kl_loss=1.684, generator_dur_loss=1.631, generator_adv_loss=1.97, generator_feat_match_loss=4.268, over 15.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.396, discriminator_fake_loss=1.357, generator_loss=27.25, generator_mel_loss=18.95, generator_kl_loss=1.403, generator_dur_loss=1.76, generator_adv_loss=1.897, generator_feat_match_loss=3.234, over 5284.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:40:11,154 INFO [train.py:919] (4/6) Start epoch 146 +2024-03-12 14:41:30,515 INFO [train.py:527] (4/6) Epoch 146, batch 20, global_batch_idx: 18000, batch size: 58, loss[discriminator_loss=2.796, discriminator_real_loss=1.443, discriminator_fake_loss=1.352, generator_loss=26.21, generator_mel_loss=18.35, generator_kl_loss=1.449, generator_dur_loss=1.717, generator_adv_loss=1.72, generator_feat_match_loss=2.969, over 58.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.383, discriminator_fake_loss=1.348, generator_loss=27.38, generator_mel_loss=19.01, generator_kl_loss=1.359, generator_dur_loss=1.757, generator_adv_loss=1.924, generator_feat_match_loss=3.337, over 1308.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:41:30,516 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 14:41:38,562 INFO [train.py:591] (4/6) Epoch 146, validation: discriminator_loss=2.748, discriminator_real_loss=1.38, discriminator_fake_loss=1.368, generator_loss=26.46, generator_mel_loss=19.02, generator_kl_loss=1.269, generator_dur_loss=1.793, generator_adv_loss=1.78, generator_feat_match_loss=2.597, over 100.00 samples. +2024-03-12 14:41:38,563 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 14:43:56,247 INFO [train.py:527] (4/6) Epoch 146, batch 70, global_batch_idx: 18050, batch size: 47, loss[discriminator_loss=2.71, discriminator_real_loss=1.473, discriminator_fake_loss=1.237, generator_loss=28.25, generator_mel_loss=19.43, generator_kl_loss=1.54, generator_dur_loss=1.701, generator_adv_loss=1.916, generator_feat_match_loss=3.66, over 47.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.382, discriminator_fake_loss=1.346, generator_loss=27.38, generator_mel_loss=18.94, generator_kl_loss=1.388, generator_dur_loss=1.77, generator_adv_loss=1.92, generator_feat_match_loss=3.366, over 4240.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:46:14,837 INFO [train.py:527] (4/6) Epoch 146, batch 120, global_batch_idx: 18100, batch size: 59, loss[discriminator_loss=2.774, discriminator_real_loss=1.296, discriminator_fake_loss=1.478, generator_loss=28.19, generator_mel_loss=19.6, generator_kl_loss=1.378, generator_dur_loss=1.753, generator_adv_loss=2.028, generator_feat_match_loss=3.426, over 59.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.381, discriminator_fake_loss=1.349, generator_loss=27.41, generator_mel_loss=18.95, generator_kl_loss=1.39, generator_dur_loss=1.775, generator_adv_loss=1.925, generator_feat_match_loss=3.365, over 7059.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:46:24,405 INFO [train.py:919] (4/6) Start epoch 147 +2024-03-12 14:48:55,208 INFO [train.py:527] (4/6) Epoch 147, batch 46, global_batch_idx: 18150, batch size: 31, loss[discriminator_loss=2.716, discriminator_real_loss=1.394, discriminator_fake_loss=1.321, generator_loss=27.18, generator_mel_loss=18.81, generator_kl_loss=1.624, generator_dur_loss=1.616, generator_adv_loss=2.031, generator_feat_match_loss=3.105, over 31.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.391, discriminator_fake_loss=1.343, generator_loss=27.39, generator_mel_loss=18.94, generator_kl_loss=1.385, generator_dur_loss=1.77, generator_adv_loss=1.928, generator_feat_match_loss=3.364, over 2797.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:51:14,678 INFO [train.py:527] (4/6) Epoch 147, batch 96, global_batch_idx: 18200, batch size: 77, loss[discriminator_loss=2.722, discriminator_real_loss=1.318, discriminator_fake_loss=1.404, generator_loss=27.1, generator_mel_loss=18.73, generator_kl_loss=1.289, generator_dur_loss=1.812, generator_adv_loss=1.89, generator_feat_match_loss=3.375, over 77.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.388, discriminator_fake_loss=1.349, generator_loss=27.4, generator_mel_loss=18.95, generator_kl_loss=1.382, generator_dur_loss=1.761, generator_adv_loss=1.924, generator_feat_match_loss=3.385, over 5738.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:51:14,679 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 14:51:22,538 INFO [train.py:591] (4/6) Epoch 147, validation: discriminator_loss=2.8, discriminator_real_loss=1.475, discriminator_fake_loss=1.325, generator_loss=26.11, generator_mel_loss=18.68, generator_kl_loss=1.205, generator_dur_loss=1.791, generator_adv_loss=1.852, generator_feat_match_loss=2.585, over 100.00 samples. +2024-03-12 14:51:22,539 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 14:52:35,260 INFO [train.py:919] (4/6) Start epoch 148 +2024-03-12 14:54:01,862 INFO [train.py:527] (4/6) Epoch 148, batch 22, global_batch_idx: 18250, batch size: 13, loss[discriminator_loss=2.73, discriminator_real_loss=1.301, discriminator_fake_loss=1.43, generator_loss=28.74, generator_mel_loss=19.79, generator_kl_loss=2.047, generator_dur_loss=1.631, generator_adv_loss=1.786, generator_feat_match_loss=3.491, over 13.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.384, discriminator_fake_loss=1.376, generator_loss=27.1, generator_mel_loss=18.75, generator_kl_loss=1.369, generator_dur_loss=1.759, generator_adv_loss=1.969, generator_feat_match_loss=3.26, over 1290.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:56:18,658 INFO [train.py:527] (4/6) Epoch 148, batch 72, global_batch_idx: 18300, batch size: 52, loss[discriminator_loss=2.7, discriminator_real_loss=1.423, discriminator_fake_loss=1.278, generator_loss=28.37, generator_mel_loss=19.6, generator_kl_loss=1.516, generator_dur_loss=1.751, generator_adv_loss=1.974, generator_feat_match_loss=3.53, over 52.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.387, discriminator_fake_loss=1.349, generator_loss=27.3, generator_mel_loss=18.91, generator_kl_loss=1.392, generator_dur_loss=1.756, generator_adv_loss=1.936, generator_feat_match_loss=3.314, over 3976.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:58:38,514 INFO [train.py:527] (4/6) Epoch 148, batch 122, global_batch_idx: 18350, batch size: 64, loss[discriminator_loss=2.767, discriminator_real_loss=1.481, discriminator_fake_loss=1.286, generator_loss=26.31, generator_mel_loss=18.89, generator_kl_loss=1.324, generator_dur_loss=1.772, generator_adv_loss=1.745, generator_feat_match_loss=2.574, over 64.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.386, discriminator_fake_loss=1.354, generator_loss=27.35, generator_mel_loss=18.95, generator_kl_loss=1.385, generator_dur_loss=1.761, generator_adv_loss=1.926, generator_feat_match_loss=3.331, over 6937.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:58:43,734 INFO [train.py:919] (4/6) Start epoch 149 +2024-03-12 15:01:23,283 INFO [train.py:527] (4/6) Epoch 149, batch 48, global_batch_idx: 18400, batch size: 70, loss[discriminator_loss=2.764, discriminator_real_loss=1.389, discriminator_fake_loss=1.374, generator_loss=27.81, generator_mel_loss=19.09, generator_kl_loss=1.403, generator_dur_loss=1.776, generator_adv_loss=2.004, generator_feat_match_loss=3.537, over 70.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.39, discriminator_fake_loss=1.352, generator_loss=27.37, generator_mel_loss=18.96, generator_kl_loss=1.388, generator_dur_loss=1.755, generator_adv_loss=1.927, generator_feat_match_loss=3.339, over 2826.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:01:23,285 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 15:01:31,250 INFO [train.py:591] (4/6) Epoch 149, validation: discriminator_loss=2.775, discriminator_real_loss=1.531, discriminator_fake_loss=1.244, generator_loss=26.8, generator_mel_loss=19.07, generator_kl_loss=1.13, generator_dur_loss=1.803, generator_adv_loss=2.019, generator_feat_match_loss=2.784, over 100.00 samples. +2024-03-12 15:01:31,251 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 15:03:52,627 INFO [train.py:527] (4/6) Epoch 149, batch 98, global_batch_idx: 18450, batch size: 70, loss[discriminator_loss=2.836, discriminator_real_loss=1.481, discriminator_fake_loss=1.355, generator_loss=27.2, generator_mel_loss=18.85, generator_kl_loss=1.346, generator_dur_loss=1.8, generator_adv_loss=1.672, generator_feat_match_loss=3.539, over 70.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.398, discriminator_fake_loss=1.35, generator_loss=27.51, generator_mel_loss=19.05, generator_kl_loss=1.407, generator_dur_loss=1.753, generator_adv_loss=1.924, generator_feat_match_loss=3.375, over 5422.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:05:02,755 INFO [train.py:919] (4/6) Start epoch 150 +2024-03-12 15:06:33,934 INFO [train.py:527] (4/6) Epoch 150, batch 24, global_batch_idx: 18500, batch size: 80, loss[discriminator_loss=2.723, discriminator_real_loss=1.442, discriminator_fake_loss=1.281, generator_loss=26.4, generator_mel_loss=18.15, generator_kl_loss=1.423, generator_dur_loss=1.851, generator_adv_loss=1.795, generator_feat_match_loss=3.181, over 80.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.386, discriminator_fake_loss=1.339, generator_loss=27.45, generator_mel_loss=18.93, generator_kl_loss=1.4, generator_dur_loss=1.765, generator_adv_loss=1.94, generator_feat_match_loss=3.408, over 1411.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:08:53,858 INFO [train.py:527] (4/6) Epoch 150, batch 74, global_batch_idx: 18550, batch size: 31, loss[discriminator_loss=2.786, discriminator_real_loss=1.307, discriminator_fake_loss=1.48, generator_loss=28.06, generator_mel_loss=19.81, generator_kl_loss=1.591, generator_dur_loss=1.61, generator_adv_loss=1.932, generator_feat_match_loss=3.117, over 31.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.394, discriminator_fake_loss=1.349, generator_loss=27.38, generator_mel_loss=18.92, generator_kl_loss=1.394, generator_dur_loss=1.773, generator_adv_loss=1.925, generator_feat_match_loss=3.367, over 4283.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:11:08,727 INFO [train.py:919] (4/6) Start epoch 151 +2024-03-12 15:11:33,499 INFO [train.py:527] (4/6) Epoch 151, batch 0, global_batch_idx: 18600, batch size: 88, loss[discriminator_loss=2.701, discriminator_real_loss=1.277, discriminator_fake_loss=1.424, generator_loss=27.88, generator_mel_loss=19.17, generator_kl_loss=1.303, generator_dur_loss=1.816, generator_adv_loss=1.961, generator_feat_match_loss=3.63, over 88.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.277, discriminator_fake_loss=1.424, generator_loss=27.88, generator_mel_loss=19.17, generator_kl_loss=1.303, generator_dur_loss=1.816, generator_adv_loss=1.961, generator_feat_match_loss=3.63, over 88.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:11:33,503 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 15:11:41,536 INFO [train.py:591] (4/6) Epoch 151, validation: discriminator_loss=2.735, discriminator_real_loss=1.467, discriminator_fake_loss=1.268, generator_loss=26.89, generator_mel_loss=19.14, generator_kl_loss=1.258, generator_dur_loss=1.801, generator_adv_loss=1.948, generator_feat_match_loss=2.737, over 100.00 samples. +2024-03-12 15:11:41,538 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 15:13:59,572 INFO [train.py:527] (4/6) Epoch 151, batch 50, global_batch_idx: 18650, batch size: 52, loss[discriminator_loss=2.637, discriminator_real_loss=1.265, discriminator_fake_loss=1.372, generator_loss=27.94, generator_mel_loss=18.91, generator_kl_loss=1.424, generator_dur_loss=1.689, generator_adv_loss=2.097, generator_feat_match_loss=3.828, over 52.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.387, discriminator_fake_loss=1.347, generator_loss=27.34, generator_mel_loss=18.85, generator_kl_loss=1.367, generator_dur_loss=1.774, generator_adv_loss=1.937, generator_feat_match_loss=3.418, over 3135.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:16:18,410 INFO [train.py:527] (4/6) Epoch 151, batch 100, global_batch_idx: 18700, batch size: 62, loss[discriminator_loss=2.702, discriminator_real_loss=1.325, discriminator_fake_loss=1.377, generator_loss=27.03, generator_mel_loss=18.45, generator_kl_loss=1.456, generator_dur_loss=1.732, generator_adv_loss=1.994, generator_feat_match_loss=3.394, over 62.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.399, discriminator_fake_loss=1.345, generator_loss=27.29, generator_mel_loss=18.83, generator_kl_loss=1.388, generator_dur_loss=1.774, generator_adv_loss=1.937, generator_feat_match_loss=3.355, over 5964.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:17:24,223 INFO [train.py:919] (4/6) Start epoch 152 +2024-03-12 15:19:01,256 INFO [train.py:527] (4/6) Epoch 152, batch 26, global_batch_idx: 18750, batch size: 55, loss[discriminator_loss=2.684, discriminator_real_loss=1.421, discriminator_fake_loss=1.263, generator_loss=26.3, generator_mel_loss=18.34, generator_kl_loss=1.299, generator_dur_loss=1.722, generator_adv_loss=1.891, generator_feat_match_loss=3.054, over 55.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.387, discriminator_fake_loss=1.364, generator_loss=27.28, generator_mel_loss=18.89, generator_kl_loss=1.331, generator_dur_loss=1.771, generator_adv_loss=1.902, generator_feat_match_loss=3.381, over 1724.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:21:20,263 INFO [train.py:527] (4/6) Epoch 152, batch 76, global_batch_idx: 18800, batch size: 77, loss[discriminator_loss=2.739, discriminator_real_loss=1.35, discriminator_fake_loss=1.389, generator_loss=27.35, generator_mel_loss=18.89, generator_kl_loss=1.381, generator_dur_loss=1.851, generator_adv_loss=2.027, generator_feat_match_loss=3.193, over 77.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.388, discriminator_fake_loss=1.356, generator_loss=27.34, generator_mel_loss=18.91, generator_kl_loss=1.361, generator_dur_loss=1.766, generator_adv_loss=1.919, generator_feat_match_loss=3.386, over 4655.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:21:20,265 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 15:21:28,346 INFO [train.py:591] (4/6) Epoch 152, validation: discriminator_loss=2.778, discriminator_real_loss=1.529, discriminator_fake_loss=1.249, generator_loss=26.45, generator_mel_loss=18.82, generator_kl_loss=1.153, generator_dur_loss=1.825, generator_adv_loss=2.006, generator_feat_match_loss=2.646, over 100.00 samples. +2024-03-12 15:21:28,347 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 15:23:37,281 INFO [train.py:919] (4/6) Start epoch 153 +2024-03-12 15:24:09,008 INFO [train.py:527] (4/6) Epoch 153, batch 2, global_batch_idx: 18850, batch size: 96, loss[discriminator_loss=2.624, discriminator_real_loss=1.338, discriminator_fake_loss=1.285, generator_loss=27.64, generator_mel_loss=18.8, generator_kl_loss=1.327, generator_dur_loss=1.917, generator_adv_loss=1.94, generator_feat_match_loss=3.655, over 96.00 samples.], tot_loss[discriminator_loss=2.656, discriminator_real_loss=1.312, discriminator_fake_loss=1.344, generator_loss=27.99, generator_mel_loss=19, generator_kl_loss=1.418, generator_dur_loss=1.844, generator_adv_loss=2.044, generator_feat_match_loss=3.685, over 177.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:26:29,810 INFO [train.py:527] (4/6) Epoch 153, batch 52, global_batch_idx: 18900, batch size: 59, loss[discriminator_loss=2.676, discriminator_real_loss=1.42, discriminator_fake_loss=1.256, generator_loss=27.23, generator_mel_loss=18.73, generator_kl_loss=1.278, generator_dur_loss=1.699, generator_adv_loss=1.96, generator_feat_match_loss=3.568, over 59.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.381, discriminator_fake_loss=1.351, generator_loss=27.52, generator_mel_loss=18.97, generator_kl_loss=1.4, generator_dur_loss=1.755, generator_adv_loss=1.962, generator_feat_match_loss=3.439, over 2944.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:28:46,066 INFO [train.py:527] (4/6) Epoch 153, batch 102, global_batch_idx: 18950, batch size: 59, loss[discriminator_loss=2.698, discriminator_real_loss=1.344, discriminator_fake_loss=1.354, generator_loss=25.98, generator_mel_loss=18.15, generator_kl_loss=1.329, generator_dur_loss=1.711, generator_adv_loss=1.884, generator_feat_match_loss=2.913, over 59.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.385, discriminator_fake_loss=1.347, generator_loss=27.39, generator_mel_loss=18.91, generator_kl_loss=1.378, generator_dur_loss=1.764, generator_adv_loss=1.939, generator_feat_match_loss=3.4, over 5940.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:29:46,607 INFO [train.py:919] (4/6) Start epoch 154 +2024-03-12 15:31:26,273 INFO [train.py:527] (4/6) Epoch 154, batch 28, global_batch_idx: 19000, batch size: 83, loss[discriminator_loss=2.681, discriminator_real_loss=1.355, discriminator_fake_loss=1.326, generator_loss=27.1, generator_mel_loss=18.96, generator_kl_loss=1.254, generator_dur_loss=1.773, generator_adv_loss=1.844, generator_feat_match_loss=3.266, over 83.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.395, discriminator_fake_loss=1.343, generator_loss=27.2, generator_mel_loss=18.92, generator_kl_loss=1.401, generator_dur_loss=1.707, generator_adv_loss=1.89, generator_feat_match_loss=3.277, over 1614.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:31:26,275 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 15:31:34,130 INFO [train.py:591] (4/6) Epoch 154, validation: discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=26.3, generator_mel_loss=18.72, generator_kl_loss=1.243, generator_dur_loss=1.74, generator_adv_loss=1.839, generator_feat_match_loss=2.755, over 100.00 samples. +2024-03-12 15:31:34,131 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 15:33:52,969 INFO [train.py:527] (4/6) Epoch 154, batch 78, global_batch_idx: 19050, batch size: 31, loss[discriminator_loss=3.007, discriminator_real_loss=1.78, discriminator_fake_loss=1.227, generator_loss=27.65, generator_mel_loss=19.54, generator_kl_loss=1.74, generator_dur_loss=1.688, generator_adv_loss=1.691, generator_feat_match_loss=2.994, over 31.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.394, discriminator_fake_loss=1.348, generator_loss=27.26, generator_mel_loss=18.93, generator_kl_loss=1.388, generator_dur_loss=1.725, generator_adv_loss=1.911, generator_feat_match_loss=3.308, over 4398.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:36:01,038 INFO [train.py:919] (4/6) Start epoch 155 +2024-03-12 15:36:35,978 INFO [train.py:527] (4/6) Epoch 155, batch 4, global_batch_idx: 19100, batch size: 64, loss[discriminator_loss=2.717, discriminator_real_loss=1.392, discriminator_fake_loss=1.325, generator_loss=27.79, generator_mel_loss=19.24, generator_kl_loss=1.261, generator_dur_loss=1.748, generator_adv_loss=2.048, generator_feat_match_loss=3.486, over 64.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.391, discriminator_fake_loss=1.363, generator_loss=27.12, generator_mel_loss=18.71, generator_kl_loss=1.31, generator_dur_loss=1.784, generator_adv_loss=1.927, generator_feat_match_loss=3.392, over 322.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:38:53,489 INFO [train.py:527] (4/6) Epoch 155, batch 54, global_batch_idx: 19150, batch size: 58, loss[discriminator_loss=2.673, discriminator_real_loss=1.409, discriminator_fake_loss=1.264, generator_loss=27.04, generator_mel_loss=18.73, generator_kl_loss=1.308, generator_dur_loss=1.747, generator_adv_loss=1.897, generator_feat_match_loss=3.364, over 58.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.38, discriminator_fake_loss=1.354, generator_loss=27.37, generator_mel_loss=18.93, generator_kl_loss=1.373, generator_dur_loss=1.761, generator_adv_loss=1.917, generator_feat_match_loss=3.383, over 3203.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:41:13,977 INFO [train.py:527] (4/6) Epoch 155, batch 104, global_batch_idx: 19200, batch size: 62, loss[discriminator_loss=2.704, discriminator_real_loss=1.351, discriminator_fake_loss=1.353, generator_loss=27.52, generator_mel_loss=18.86, generator_kl_loss=1.295, generator_dur_loss=1.766, generator_adv_loss=2.014, generator_feat_match_loss=3.581, over 62.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.381, discriminator_fake_loss=1.353, generator_loss=27.42, generator_mel_loss=18.92, generator_kl_loss=1.386, generator_dur_loss=1.758, generator_adv_loss=1.938, generator_feat_match_loss=3.419, over 5816.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:41:13,978 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 15:41:22,940 INFO [train.py:591] (4/6) Epoch 155, validation: discriminator_loss=2.767, discriminator_real_loss=1.508, discriminator_fake_loss=1.259, generator_loss=26.42, generator_mel_loss=18.97, generator_kl_loss=1.178, generator_dur_loss=1.82, generator_adv_loss=1.914, generator_feat_match_loss=2.536, over 100.00 samples. +2024-03-12 15:41:22,941 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 15:42:17,871 INFO [train.py:919] (4/6) Start epoch 156 +2024-03-12 15:44:06,862 INFO [train.py:527] (4/6) Epoch 156, batch 30, global_batch_idx: 19250, batch size: 56, loss[discriminator_loss=2.712, discriminator_real_loss=1.336, discriminator_fake_loss=1.376, generator_loss=27.51, generator_mel_loss=18.97, generator_kl_loss=1.506, generator_dur_loss=1.648, generator_adv_loss=1.952, generator_feat_match_loss=3.431, over 56.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.36, discriminator_fake_loss=1.35, generator_loss=27.38, generator_mel_loss=18.81, generator_kl_loss=1.389, generator_dur_loss=1.751, generator_adv_loss=1.936, generator_feat_match_loss=3.494, over 1871.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:46:28,132 INFO [train.py:527] (4/6) Epoch 156, batch 80, global_batch_idx: 19300, batch size: 83, loss[discriminator_loss=2.731, discriminator_real_loss=1.424, discriminator_fake_loss=1.307, generator_loss=26.78, generator_mel_loss=18.34, generator_kl_loss=1.454, generator_dur_loss=1.784, generator_adv_loss=1.849, generator_feat_match_loss=3.351, over 83.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.377, discriminator_fake_loss=1.351, generator_loss=27.41, generator_mel_loss=18.87, generator_kl_loss=1.39, generator_dur_loss=1.737, generator_adv_loss=1.942, generator_feat_match_loss=3.47, over 4763.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:48:24,552 INFO [train.py:919] (4/6) Start epoch 157 +2024-03-12 15:49:07,914 INFO [train.py:527] (4/6) Epoch 157, batch 6, global_batch_idx: 19350, batch size: 26, loss[discriminator_loss=2.755, discriminator_real_loss=1.31, discriminator_fake_loss=1.445, generator_loss=28.77, generator_mel_loss=19.79, generator_kl_loss=1.525, generator_dur_loss=1.607, generator_adv_loss=2.038, generator_feat_match_loss=3.805, over 26.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.345, discriminator_fake_loss=1.413, generator_loss=27.45, generator_mel_loss=18.82, generator_kl_loss=1.382, generator_dur_loss=1.777, generator_adv_loss=1.87, generator_feat_match_loss=3.61, over 439.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:51:26,049 INFO [train.py:527] (4/6) Epoch 157, batch 56, global_batch_idx: 19400, batch size: 55, loss[discriminator_loss=2.705, discriminator_real_loss=1.382, discriminator_fake_loss=1.322, generator_loss=27.3, generator_mel_loss=18.73, generator_kl_loss=1.42, generator_dur_loss=1.683, generator_adv_loss=2.031, generator_feat_match_loss=3.437, over 55.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.401, discriminator_fake_loss=1.347, generator_loss=27.64, generator_mel_loss=19.05, generator_kl_loss=1.395, generator_dur_loss=1.743, generator_adv_loss=1.963, generator_feat_match_loss=3.487, over 3071.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:51:26,050 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 15:51:34,074 INFO [train.py:591] (4/6) Epoch 157, validation: discriminator_loss=2.749, discriminator_real_loss=1.516, discriminator_fake_loss=1.233, generator_loss=26.44, generator_mel_loss=19.06, generator_kl_loss=1.206, generator_dur_loss=1.765, generator_adv_loss=1.966, generator_feat_match_loss=2.451, over 100.00 samples. +2024-03-12 15:51:34,074 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 15:53:51,967 INFO [train.py:527] (4/6) Epoch 157, batch 106, global_batch_idx: 19450, batch size: 83, loss[discriminator_loss=2.655, discriminator_real_loss=1.301, discriminator_fake_loss=1.354, generator_loss=28.3, generator_mel_loss=19.33, generator_kl_loss=1.511, generator_dur_loss=1.826, generator_adv_loss=1.85, generator_feat_match_loss=3.786, over 83.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.406, discriminator_fake_loss=1.351, generator_loss=27.5, generator_mel_loss=19.02, generator_kl_loss=1.388, generator_dur_loss=1.746, generator_adv_loss=1.957, generator_feat_match_loss=3.398, over 5958.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:54:42,936 INFO [train.py:919] (4/6) Start epoch 158 +2024-03-12 15:56:35,734 INFO [train.py:527] (4/6) Epoch 158, batch 32, global_batch_idx: 19500, batch size: 74, loss[discriminator_loss=2.717, discriminator_real_loss=1.326, discriminator_fake_loss=1.392, generator_loss=27.88, generator_mel_loss=19.15, generator_kl_loss=1.461, generator_dur_loss=1.826, generator_adv_loss=1.957, generator_feat_match_loss=3.486, over 74.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.394, discriminator_fake_loss=1.344, generator_loss=27.15, generator_mel_loss=18.79, generator_kl_loss=1.388, generator_dur_loss=1.762, generator_adv_loss=1.896, generator_feat_match_loss=3.313, over 1952.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:58:57,672 INFO [train.py:527] (4/6) Epoch 158, batch 82, global_batch_idx: 19550, batch size: 74, loss[discriminator_loss=2.745, discriminator_real_loss=1.295, discriminator_fake_loss=1.45, generator_loss=27.84, generator_mel_loss=19.19, generator_kl_loss=1.224, generator_dur_loss=1.839, generator_adv_loss=2.086, generator_feat_match_loss=3.493, over 74.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.395, discriminator_fake_loss=1.346, generator_loss=27.22, generator_mel_loss=18.83, generator_kl_loss=1.389, generator_dur_loss=1.77, generator_adv_loss=1.91, generator_feat_match_loss=3.313, over 4826.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:00:50,311 INFO [train.py:919] (4/6) Start epoch 159 +2024-03-12 16:01:36,546 INFO [train.py:527] (4/6) Epoch 159, batch 8, global_batch_idx: 19600, batch size: 70, loss[discriminator_loss=2.682, discriminator_real_loss=1.435, discriminator_fake_loss=1.247, generator_loss=27.91, generator_mel_loss=19.12, generator_kl_loss=1.285, generator_dur_loss=1.755, generator_adv_loss=2.03, generator_feat_match_loss=3.728, over 70.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.375, discriminator_fake_loss=1.337, generator_loss=27.49, generator_mel_loss=18.93, generator_kl_loss=1.416, generator_dur_loss=1.751, generator_adv_loss=1.926, generator_feat_match_loss=3.462, over 505.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:01:36,557 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 16:01:44,546 INFO [train.py:591] (4/6) Epoch 159, validation: discriminator_loss=2.698, discriminator_real_loss=1.452, discriminator_fake_loss=1.247, generator_loss=27.56, generator_mel_loss=19.76, generator_kl_loss=1.168, generator_dur_loss=1.787, generator_adv_loss=1.922, generator_feat_match_loss=2.921, over 100.00 samples. +2024-03-12 16:01:44,548 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 16:04:06,605 INFO [train.py:527] (4/6) Epoch 159, batch 58, global_batch_idx: 19650, batch size: 56, loss[discriminator_loss=2.75, discriminator_real_loss=1.433, discriminator_fake_loss=1.317, generator_loss=26.93, generator_mel_loss=18.47, generator_kl_loss=1.473, generator_dur_loss=1.692, generator_adv_loss=1.921, generator_feat_match_loss=3.37, over 56.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.389, discriminator_fake_loss=1.355, generator_loss=27.53, generator_mel_loss=18.99, generator_kl_loss=1.408, generator_dur_loss=1.747, generator_adv_loss=1.928, generator_feat_match_loss=3.45, over 3252.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:06:25,382 INFO [train.py:527] (4/6) Epoch 159, batch 108, global_batch_idx: 19700, batch size: 47, loss[discriminator_loss=2.717, discriminator_real_loss=1.243, discriminator_fake_loss=1.474, generator_loss=28.88, generator_mel_loss=19.43, generator_kl_loss=1.521, generator_dur_loss=1.697, generator_adv_loss=2.182, generator_feat_match_loss=4.054, over 47.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.384, discriminator_fake_loss=1.353, generator_loss=27.49, generator_mel_loss=18.97, generator_kl_loss=1.415, generator_dur_loss=1.742, generator_adv_loss=1.932, generator_feat_match_loss=3.427, over 5950.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:07:05,377 INFO [train.py:919] (4/6) Start epoch 160 +2024-03-12 16:09:05,140 INFO [train.py:527] (4/6) Epoch 160, batch 34, global_batch_idx: 19750, batch size: 52, loss[discriminator_loss=2.74, discriminator_real_loss=1.441, discriminator_fake_loss=1.299, generator_loss=26.94, generator_mel_loss=18.55, generator_kl_loss=1.554, generator_dur_loss=1.696, generator_adv_loss=1.891, generator_feat_match_loss=3.253, over 52.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.408, discriminator_fake_loss=1.343, generator_loss=27.13, generator_mel_loss=18.7, generator_kl_loss=1.393, generator_dur_loss=1.755, generator_adv_loss=1.944, generator_feat_match_loss=3.333, over 1960.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:11:24,979 INFO [train.py:527] (4/6) Epoch 160, batch 84, global_batch_idx: 19800, batch size: 39, loss[discriminator_loss=2.758, discriminator_real_loss=1.31, discriminator_fake_loss=1.448, generator_loss=27.54, generator_mel_loss=18.68, generator_kl_loss=1.695, generator_dur_loss=1.673, generator_adv_loss=1.812, generator_feat_match_loss=3.686, over 39.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.395, discriminator_fake_loss=1.349, generator_loss=27.2, generator_mel_loss=18.76, generator_kl_loss=1.395, generator_dur_loss=1.76, generator_adv_loss=1.917, generator_feat_match_loss=3.37, over 4871.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:11:24,981 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 16:11:32,894 INFO [train.py:591] (4/6) Epoch 160, validation: discriminator_loss=2.75, discriminator_real_loss=1.446, discriminator_fake_loss=1.304, generator_loss=26.95, generator_mel_loss=19.15, generator_kl_loss=1.266, generator_dur_loss=1.828, generator_adv_loss=1.832, generator_feat_match_loss=2.876, over 100.00 samples. +2024-03-12 16:11:32,895 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 16:13:23,262 INFO [train.py:919] (4/6) Start epoch 161 +2024-03-12 16:14:14,943 INFO [train.py:527] (4/6) Epoch 161, batch 10, global_batch_idx: 19850, batch size: 50, loss[discriminator_loss=2.771, discriminator_real_loss=1.465, discriminator_fake_loss=1.306, generator_loss=28.04, generator_mel_loss=19.69, generator_kl_loss=1.525, generator_dur_loss=1.619, generator_adv_loss=1.917, generator_feat_match_loss=3.295, over 50.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.407, discriminator_fake_loss=1.345, generator_loss=27.38, generator_mel_loss=18.99, generator_kl_loss=1.424, generator_dur_loss=1.726, generator_adv_loss=1.941, generator_feat_match_loss=3.297, over 564.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:16:33,447 INFO [train.py:527] (4/6) Epoch 161, batch 60, global_batch_idx: 19900, batch size: 55, loss[discriminator_loss=2.752, discriminator_real_loss=1.354, discriminator_fake_loss=1.398, generator_loss=27.59, generator_mel_loss=18.98, generator_kl_loss=1.342, generator_dur_loss=1.699, generator_adv_loss=1.986, generator_feat_match_loss=3.587, over 55.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.393, discriminator_fake_loss=1.349, generator_loss=27.45, generator_mel_loss=18.96, generator_kl_loss=1.405, generator_dur_loss=1.746, generator_adv_loss=1.925, generator_feat_match_loss=3.415, over 3250.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:18:52,705 INFO [train.py:527] (4/6) Epoch 161, batch 110, global_batch_idx: 19950, batch size: 56, loss[discriminator_loss=2.762, discriminator_real_loss=1.335, discriminator_fake_loss=1.427, generator_loss=26.82, generator_mel_loss=18.75, generator_kl_loss=1.343, generator_dur_loss=1.696, generator_adv_loss=2.146, generator_feat_match_loss=2.888, over 56.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.397, discriminator_fake_loss=1.351, generator_loss=27.38, generator_mel_loss=18.91, generator_kl_loss=1.384, generator_dur_loss=1.754, generator_adv_loss=1.917, generator_feat_match_loss=3.405, over 6027.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:19:29,501 INFO [train.py:919] (4/6) Start epoch 162 +2024-03-12 16:21:31,349 INFO [train.py:527] (4/6) Epoch 162, batch 36, global_batch_idx: 20000, batch size: 62, loss[discriminator_loss=2.722, discriminator_real_loss=1.38, discriminator_fake_loss=1.342, generator_loss=27.41, generator_mel_loss=18.93, generator_kl_loss=1.515, generator_dur_loss=1.721, generator_adv_loss=1.781, generator_feat_match_loss=3.462, over 62.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.398, discriminator_fake_loss=1.348, generator_loss=27.13, generator_mel_loss=18.71, generator_kl_loss=1.396, generator_dur_loss=1.76, generator_adv_loss=1.92, generator_feat_match_loss=3.341, over 2131.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:21:31,351 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 16:21:39,252 INFO [train.py:591] (4/6) Epoch 162, validation: discriminator_loss=2.779, discriminator_real_loss=1.425, discriminator_fake_loss=1.354, generator_loss=26.49, generator_mel_loss=19.13, generator_kl_loss=1.198, generator_dur_loss=1.781, generator_adv_loss=1.756, generator_feat_match_loss=2.623, over 100.00 samples. +2024-03-12 16:21:39,253 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 16:23:56,701 INFO [train.py:527] (4/6) Epoch 162, batch 86, global_batch_idx: 20050, batch size: 61, loss[discriminator_loss=2.794, discriminator_real_loss=1.303, discriminator_fake_loss=1.491, generator_loss=27.07, generator_mel_loss=18.62, generator_kl_loss=1.284, generator_dur_loss=1.734, generator_adv_loss=2.052, generator_feat_match_loss=3.38, over 61.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.392, discriminator_fake_loss=1.349, generator_loss=27.33, generator_mel_loss=18.83, generator_kl_loss=1.398, generator_dur_loss=1.753, generator_adv_loss=1.926, generator_feat_match_loss=3.426, over 5072.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:25:43,812 INFO [train.py:919] (4/6) Start epoch 163 +2024-03-12 16:26:42,829 INFO [train.py:527] (4/6) Epoch 163, batch 12, global_batch_idx: 20100, batch size: 55, loss[discriminator_loss=2.713, discriminator_real_loss=1.417, discriminator_fake_loss=1.296, generator_loss=27.89, generator_mel_loss=19.45, generator_kl_loss=1.332, generator_dur_loss=1.671, generator_adv_loss=2.134, generator_feat_match_loss=3.304, over 55.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.348, generator_loss=27.24, generator_mel_loss=18.77, generator_kl_loss=1.332, generator_dur_loss=1.764, generator_adv_loss=1.941, generator_feat_match_loss=3.429, over 789.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:29:01,045 INFO [train.py:527] (4/6) Epoch 163, batch 62, global_batch_idx: 20150, batch size: 31, loss[discriminator_loss=2.7, discriminator_real_loss=1.362, discriminator_fake_loss=1.338, generator_loss=27.25, generator_mel_loss=18.72, generator_kl_loss=1.655, generator_dur_loss=1.593, generator_adv_loss=2.003, generator_feat_match_loss=3.274, over 31.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.389, discriminator_fake_loss=1.353, generator_loss=27.35, generator_mel_loss=18.91, generator_kl_loss=1.376, generator_dur_loss=1.759, generator_adv_loss=1.908, generator_feat_match_loss=3.396, over 3621.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:31:20,178 INFO [train.py:527] (4/6) Epoch 163, batch 112, global_batch_idx: 20200, batch size: 31, loss[discriminator_loss=2.886, discriminator_real_loss=1.495, discriminator_fake_loss=1.39, generator_loss=26.7, generator_mel_loss=18.76, generator_kl_loss=1.453, generator_dur_loss=1.668, generator_adv_loss=1.956, generator_feat_match_loss=2.857, over 31.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.389, discriminator_fake_loss=1.354, generator_loss=27.53, generator_mel_loss=18.92, generator_kl_loss=1.385, generator_dur_loss=1.752, generator_adv_loss=1.961, generator_feat_match_loss=3.513, over 6631.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:31:20,179 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 16:31:29,124 INFO [train.py:591] (4/6) Epoch 163, validation: discriminator_loss=2.982, discriminator_real_loss=1.568, discriminator_fake_loss=1.414, generator_loss=26.57, generator_mel_loss=19.42, generator_kl_loss=1.199, generator_dur_loss=1.781, generator_adv_loss=1.785, generator_feat_match_loss=2.386, over 100.00 samples. +2024-03-12 16:31:29,125 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 16:31:59,340 INFO [train.py:919] (4/6) Start epoch 164 +2024-03-12 16:34:11,309 INFO [train.py:527] (4/6) Epoch 164, batch 38, global_batch_idx: 20250, batch size: 66, loss[discriminator_loss=2.731, discriminator_real_loss=1.442, discriminator_fake_loss=1.289, generator_loss=27.37, generator_mel_loss=18.9, generator_kl_loss=1.277, generator_dur_loss=1.758, generator_adv_loss=1.845, generator_feat_match_loss=3.589, over 66.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.404, discriminator_fake_loss=1.343, generator_loss=27.31, generator_mel_loss=18.8, generator_kl_loss=1.398, generator_dur_loss=1.759, generator_adv_loss=1.937, generator_feat_match_loss=3.42, over 2266.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:36:29,676 INFO [train.py:527] (4/6) Epoch 164, batch 88, global_batch_idx: 20300, batch size: 47, loss[discriminator_loss=2.735, discriminator_real_loss=1.411, discriminator_fake_loss=1.324, generator_loss=27.92, generator_mel_loss=19.21, generator_kl_loss=1.559, generator_dur_loss=1.658, generator_adv_loss=1.834, generator_feat_match_loss=3.663, over 47.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.395, discriminator_fake_loss=1.344, generator_loss=27.29, generator_mel_loss=18.78, generator_kl_loss=1.384, generator_dur_loss=1.753, generator_adv_loss=1.93, generator_feat_match_loss=3.446, over 5155.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:38:06,007 INFO [train.py:919] (4/6) Start epoch 165 +2024-03-12 16:39:10,387 INFO [train.py:527] (4/6) Epoch 165, batch 14, global_batch_idx: 20350, batch size: 55, loss[discriminator_loss=2.698, discriminator_real_loss=1.21, discriminator_fake_loss=1.488, generator_loss=28.4, generator_mel_loss=19.58, generator_kl_loss=1.337, generator_dur_loss=1.733, generator_adv_loss=1.89, generator_feat_match_loss=3.862, over 55.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.368, discriminator_fake_loss=1.367, generator_loss=27.4, generator_mel_loss=18.77, generator_kl_loss=1.376, generator_dur_loss=1.745, generator_adv_loss=1.936, generator_feat_match_loss=3.567, over 857.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:41:30,732 INFO [train.py:527] (4/6) Epoch 165, batch 64, global_batch_idx: 20400, batch size: 62, loss[discriminator_loss=2.716, discriminator_real_loss=1.384, discriminator_fake_loss=1.332, generator_loss=27.24, generator_mel_loss=18.96, generator_kl_loss=1.361, generator_dur_loss=1.74, generator_adv_loss=1.817, generator_feat_match_loss=3.363, over 62.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.394, discriminator_fake_loss=1.357, generator_loss=27.23, generator_mel_loss=18.76, generator_kl_loss=1.383, generator_dur_loss=1.741, generator_adv_loss=1.917, generator_feat_match_loss=3.429, over 3905.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:41:30,734 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 16:41:38,610 INFO [train.py:591] (4/6) Epoch 165, validation: discriminator_loss=2.729, discriminator_real_loss=1.357, discriminator_fake_loss=1.372, generator_loss=26.09, generator_mel_loss=18.77, generator_kl_loss=1.186, generator_dur_loss=1.781, generator_adv_loss=1.756, generator_feat_match_loss=2.6, over 100.00 samples. +2024-03-12 16:41:38,611 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 16:43:59,052 INFO [train.py:527] (4/6) Epoch 165, batch 114, global_batch_idx: 20450, batch size: 74, loss[discriminator_loss=2.726, discriminator_real_loss=1.379, discriminator_fake_loss=1.347, generator_loss=27.26, generator_mel_loss=18.72, generator_kl_loss=1.323, generator_dur_loss=1.845, generator_adv_loss=1.797, generator_feat_match_loss=3.579, over 74.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.395, discriminator_fake_loss=1.355, generator_loss=27.25, generator_mel_loss=18.79, generator_kl_loss=1.395, generator_dur_loss=1.741, generator_adv_loss=1.918, generator_feat_match_loss=3.409, over 6636.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:44:25,403 INFO [train.py:919] (4/6) Start epoch 166 +2024-03-12 16:46:40,192 INFO [train.py:527] (4/6) Epoch 166, batch 40, global_batch_idx: 20500, batch size: 45, loss[discriminator_loss=2.777, discriminator_real_loss=1.437, discriminator_fake_loss=1.34, generator_loss=28.47, generator_mel_loss=20.06, generator_kl_loss=1.472, generator_dur_loss=1.662, generator_adv_loss=1.763, generator_feat_match_loss=3.512, over 45.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.375, discriminator_fake_loss=1.359, generator_loss=27.35, generator_mel_loss=18.88, generator_kl_loss=1.396, generator_dur_loss=1.74, generator_adv_loss=1.905, generator_feat_match_loss=3.428, over 2302.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:48:59,965 INFO [train.py:527] (4/6) Epoch 166, batch 90, global_batch_idx: 20550, batch size: 70, loss[discriminator_loss=2.775, discriminator_real_loss=1.431, discriminator_fake_loss=1.344, generator_loss=26.76, generator_mel_loss=18.83, generator_kl_loss=1.356, generator_dur_loss=1.75, generator_adv_loss=1.915, generator_feat_match_loss=2.912, over 70.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.386, discriminator_fake_loss=1.355, generator_loss=27.26, generator_mel_loss=18.85, generator_kl_loss=1.385, generator_dur_loss=1.716, generator_adv_loss=1.903, generator_feat_match_loss=3.406, over 5311.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:50:32,200 INFO [train.py:919] (4/6) Start epoch 167 +2024-03-12 16:51:41,443 INFO [train.py:527] (4/6) Epoch 167, batch 16, global_batch_idx: 20600, batch size: 25, loss[discriminator_loss=2.704, discriminator_real_loss=1.459, discriminator_fake_loss=1.245, generator_loss=29.11, generator_mel_loss=19.93, generator_kl_loss=1.868, generator_dur_loss=1.554, generator_adv_loss=1.85, generator_feat_match_loss=3.911, over 25.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.386, discriminator_fake_loss=1.356, generator_loss=27.62, generator_mel_loss=18.91, generator_kl_loss=1.471, generator_dur_loss=1.68, generator_adv_loss=1.93, generator_feat_match_loss=3.63, over 759.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:51:41,445 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 16:51:49,247 INFO [train.py:591] (4/6) Epoch 167, validation: discriminator_loss=2.73, discriminator_real_loss=1.293, discriminator_fake_loss=1.436, generator_loss=27.39, generator_mel_loss=19.63, generator_kl_loss=1.25, generator_dur_loss=1.775, generator_adv_loss=1.671, generator_feat_match_loss=3.066, over 100.00 samples. +2024-03-12 16:51:49,248 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 16:54:07,850 INFO [train.py:527] (4/6) Epoch 167, batch 66, global_batch_idx: 20650, batch size: 36, loss[discriminator_loss=2.713, discriminator_real_loss=1.453, discriminator_fake_loss=1.26, generator_loss=26.6, generator_mel_loss=18.71, generator_kl_loss=1.403, generator_dur_loss=1.678, generator_adv_loss=2.021, generator_feat_match_loss=2.794, over 36.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.388, discriminator_fake_loss=1.356, generator_loss=27.46, generator_mel_loss=18.88, generator_kl_loss=1.412, generator_dur_loss=1.725, generator_adv_loss=1.92, generator_feat_match_loss=3.528, over 3473.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:56:25,103 INFO [train.py:527] (4/6) Epoch 167, batch 116, global_batch_idx: 20700, batch size: 83, loss[discriminator_loss=2.628, discriminator_real_loss=1.356, discriminator_fake_loss=1.272, generator_loss=27.24, generator_mel_loss=18.7, generator_kl_loss=1.225, generator_dur_loss=1.792, generator_adv_loss=1.744, generator_feat_match_loss=3.788, over 83.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.387, discriminator_fake_loss=1.35, generator_loss=27.47, generator_mel_loss=18.88, generator_kl_loss=1.412, generator_dur_loss=1.728, generator_adv_loss=1.924, generator_feat_match_loss=3.524, over 6183.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:56:46,912 INFO [train.py:919] (4/6) Start epoch 168 +2024-03-12 16:59:06,614 INFO [train.py:527] (4/6) Epoch 168, batch 42, global_batch_idx: 20750, batch size: 45, loss[discriminator_loss=2.782, discriminator_real_loss=1.37, discriminator_fake_loss=1.412, generator_loss=29.03, generator_mel_loss=20.02, generator_kl_loss=1.626, generator_dur_loss=1.678, generator_adv_loss=2.003, generator_feat_match_loss=3.704, over 45.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.397, discriminator_fake_loss=1.359, generator_loss=27.46, generator_mel_loss=18.91, generator_kl_loss=1.435, generator_dur_loss=1.712, generator_adv_loss=1.927, generator_feat_match_loss=3.476, over 2138.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:01:25,291 INFO [train.py:527] (4/6) Epoch 168, batch 92, global_batch_idx: 20800, batch size: 52, loss[discriminator_loss=2.729, discriminator_real_loss=1.453, discriminator_fake_loss=1.276, generator_loss=27.55, generator_mel_loss=19.21, generator_kl_loss=1.315, generator_dur_loss=1.749, generator_adv_loss=1.879, generator_feat_match_loss=3.395, over 52.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.395, discriminator_fake_loss=1.348, generator_loss=27.38, generator_mel_loss=18.86, generator_kl_loss=1.405, generator_dur_loss=1.73, generator_adv_loss=1.917, generator_feat_match_loss=3.472, over 5040.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:01:25,292 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 17:01:33,559 INFO [train.py:591] (4/6) Epoch 168, validation: discriminator_loss=2.724, discriminator_real_loss=1.436, discriminator_fake_loss=1.287, generator_loss=26.81, generator_mel_loss=19.08, generator_kl_loss=1.096, generator_dur_loss=1.803, generator_adv_loss=1.849, generator_feat_match_loss=2.988, over 100.00 samples. +2024-03-12 17:01:33,560 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 17:03:00,031 INFO [train.py:919] (4/6) Start epoch 169 +2024-03-12 17:04:15,939 INFO [train.py:527] (4/6) Epoch 169, batch 18, global_batch_idx: 20850, batch size: 66, loss[discriminator_loss=2.805, discriminator_real_loss=1.385, discriminator_fake_loss=1.42, generator_loss=26.75, generator_mel_loss=18.71, generator_kl_loss=1.341, generator_dur_loss=1.724, generator_adv_loss=1.843, generator_feat_match_loss=3.132, over 66.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.363, discriminator_fake_loss=1.339, generator_loss=27.82, generator_mel_loss=18.79, generator_kl_loss=1.372, generator_dur_loss=1.732, generator_adv_loss=2.12, generator_feat_match_loss=3.813, over 1096.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:06:32,637 INFO [train.py:527] (4/6) Epoch 169, batch 68, global_batch_idx: 20900, batch size: 44, loss[discriminator_loss=2.67, discriminator_real_loss=1.319, discriminator_fake_loss=1.351, generator_loss=27.02, generator_mel_loss=18.31, generator_kl_loss=1.51, generator_dur_loss=1.643, generator_adv_loss=1.861, generator_feat_match_loss=3.701, over 44.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.381, discriminator_fake_loss=1.339, generator_loss=27.53, generator_mel_loss=18.81, generator_kl_loss=1.379, generator_dur_loss=1.733, generator_adv_loss=1.984, generator_feat_match_loss=3.622, over 3892.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:08:52,726 INFO [train.py:527] (4/6) Epoch 169, batch 118, global_batch_idx: 20950, batch size: 26, loss[discriminator_loss=2.648, discriminator_real_loss=1.377, discriminator_fake_loss=1.271, generator_loss=28.92, generator_mel_loss=20.19, generator_kl_loss=1.544, generator_dur_loss=1.57, generator_adv_loss=1.831, generator_feat_match_loss=3.782, over 26.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.391, discriminator_fake_loss=1.337, generator_loss=27.42, generator_mel_loss=18.8, generator_kl_loss=1.376, generator_dur_loss=1.744, generator_adv_loss=1.953, generator_feat_match_loss=3.55, over 6759.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:09:08,609 INFO [train.py:919] (4/6) Start epoch 170 +2024-03-12 17:11:33,893 INFO [train.py:527] (4/6) Epoch 170, batch 44, global_batch_idx: 21000, batch size: 36, loss[discriminator_loss=2.718, discriminator_real_loss=1.464, discriminator_fake_loss=1.254, generator_loss=26.8, generator_mel_loss=18.26, generator_kl_loss=1.429, generator_dur_loss=1.704, generator_adv_loss=1.736, generator_feat_match_loss=3.669, over 36.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.391, discriminator_fake_loss=1.352, generator_loss=27.36, generator_mel_loss=18.84, generator_kl_loss=1.364, generator_dur_loss=1.764, generator_adv_loss=1.906, generator_feat_match_loss=3.479, over 2705.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:11:33,895 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 17:11:41,705 INFO [train.py:591] (4/6) Epoch 170, validation: discriminator_loss=2.708, discriminator_real_loss=1.313, discriminator_fake_loss=1.394, generator_loss=26.42, generator_mel_loss=18.87, generator_kl_loss=1.098, generator_dur_loss=1.825, generator_adv_loss=1.715, generator_feat_match_loss=2.916, over 100.00 samples. +2024-03-12 17:11:41,706 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 17:14:00,465 INFO [train.py:527] (4/6) Epoch 170, batch 94, global_batch_idx: 21050, batch size: 52, loss[discriminator_loss=2.728, discriminator_real_loss=1.366, discriminator_fake_loss=1.363, generator_loss=28.4, generator_mel_loss=19.77, generator_kl_loss=1.512, generator_dur_loss=1.666, generator_adv_loss=1.85, generator_feat_match_loss=3.6, over 52.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.392, discriminator_fake_loss=1.351, generator_loss=27.38, generator_mel_loss=18.87, generator_kl_loss=1.363, generator_dur_loss=1.765, generator_adv_loss=1.909, generator_feat_match_loss=3.475, over 5600.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:15:20,651 INFO [train.py:919] (4/6) Start epoch 171 +2024-03-12 17:16:42,790 INFO [train.py:527] (4/6) Epoch 171, batch 20, global_batch_idx: 21100, batch size: 77, loss[discriminator_loss=2.762, discriminator_real_loss=1.49, discriminator_fake_loss=1.272, generator_loss=27.44, generator_mel_loss=18.89, generator_kl_loss=1.288, generator_dur_loss=1.878, generator_adv_loss=1.935, generator_feat_match_loss=3.45, over 77.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.391, discriminator_fake_loss=1.339, generator_loss=27.59, generator_mel_loss=18.96, generator_kl_loss=1.409, generator_dur_loss=1.77, generator_adv_loss=1.923, generator_feat_match_loss=3.519, over 1183.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:19:03,184 INFO [train.py:527] (4/6) Epoch 171, batch 70, global_batch_idx: 21150, batch size: 39, loss[discriminator_loss=2.788, discriminator_real_loss=1.509, discriminator_fake_loss=1.279, generator_loss=27.29, generator_mel_loss=18.69, generator_kl_loss=1.583, generator_dur_loss=1.669, generator_adv_loss=2.04, generator_feat_match_loss=3.309, over 39.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=27.42, generator_mel_loss=18.85, generator_kl_loss=1.392, generator_dur_loss=1.758, generator_adv_loss=1.922, generator_feat_match_loss=3.496, over 4125.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:21:20,459 INFO [train.py:527] (4/6) Epoch 171, batch 120, global_batch_idx: 21200, batch size: 47, loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=26.99, generator_mel_loss=18.37, generator_kl_loss=1.527, generator_dur_loss=1.666, generator_adv_loss=1.982, generator_feat_match_loss=3.444, over 47.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.4, discriminator_fake_loss=1.347, generator_loss=27.43, generator_mel_loss=18.8, generator_kl_loss=1.393, generator_dur_loss=1.757, generator_adv_loss=1.963, generator_feat_match_loss=3.517, over 6922.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:21:20,460 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 17:21:29,265 INFO [train.py:591] (4/6) Epoch 171, validation: discriminator_loss=2.765, discriminator_real_loss=1.541, discriminator_fake_loss=1.224, generator_loss=26.33, generator_mel_loss=18.74, generator_kl_loss=1.2, generator_dur_loss=1.815, generator_adv_loss=1.967, generator_feat_match_loss=2.605, over 100.00 samples. +2024-03-12 17:21:29,266 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 17:21:38,943 INFO [train.py:919] (4/6) Start epoch 172 +2024-03-12 17:24:09,803 INFO [train.py:527] (4/6) Epoch 172, batch 46, global_batch_idx: 21250, batch size: 45, loss[discriminator_loss=2.708, discriminator_real_loss=1.375, discriminator_fake_loss=1.332, generator_loss=27.31, generator_mel_loss=18.88, generator_kl_loss=1.449, generator_dur_loss=1.703, generator_adv_loss=1.836, generator_feat_match_loss=3.444, over 45.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.392, discriminator_fake_loss=1.349, generator_loss=27.19, generator_mel_loss=18.73, generator_kl_loss=1.361, generator_dur_loss=1.77, generator_adv_loss=1.897, generator_feat_match_loss=3.44, over 2643.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:26:25,303 INFO [train.py:527] (4/6) Epoch 172, batch 96, global_batch_idx: 21300, batch size: 70, loss[discriminator_loss=2.701, discriminator_real_loss=1.317, discriminator_fake_loss=1.384, generator_loss=27.04, generator_mel_loss=18.71, generator_kl_loss=1.25, generator_dur_loss=1.815, generator_adv_loss=2.029, generator_feat_match_loss=3.238, over 70.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.387, discriminator_fake_loss=1.345, generator_loss=27.25, generator_mel_loss=18.76, generator_kl_loss=1.369, generator_dur_loss=1.768, generator_adv_loss=1.907, generator_feat_match_loss=3.444, over 5362.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:27:42,796 INFO [train.py:919] (4/6) Start epoch 173 +2024-03-12 17:29:08,004 INFO [train.py:527] (4/6) Epoch 173, batch 22, global_batch_idx: 21350, batch size: 64, loss[discriminator_loss=2.77, discriminator_real_loss=1.44, discriminator_fake_loss=1.33, generator_loss=27.63, generator_mel_loss=19.35, generator_kl_loss=1.477, generator_dur_loss=1.808, generator_adv_loss=1.814, generator_feat_match_loss=3.189, over 64.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.39, discriminator_fake_loss=1.351, generator_loss=27.41, generator_mel_loss=18.86, generator_kl_loss=1.4, generator_dur_loss=1.784, generator_adv_loss=1.93, generator_feat_match_loss=3.437, over 1386.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:31:29,511 INFO [train.py:527] (4/6) Epoch 173, batch 72, global_batch_idx: 21400, batch size: 53, loss[discriminator_loss=2.63, discriminator_real_loss=1.385, discriminator_fake_loss=1.245, generator_loss=27.79, generator_mel_loss=19.2, generator_kl_loss=1.486, generator_dur_loss=1.692, generator_adv_loss=1.8, generator_feat_match_loss=3.614, over 53.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.395, discriminator_fake_loss=1.35, generator_loss=27.3, generator_mel_loss=18.81, generator_kl_loss=1.379, generator_dur_loss=1.78, generator_adv_loss=1.909, generator_feat_match_loss=3.419, over 4270.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:31:29,512 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 17:31:37,517 INFO [train.py:591] (4/6) Epoch 173, validation: discriminator_loss=2.743, discriminator_real_loss=1.333, discriminator_fake_loss=1.41, generator_loss=26.61, generator_mel_loss=19, generator_kl_loss=1.281, generator_dur_loss=1.815, generator_adv_loss=1.698, generator_feat_match_loss=2.808, over 100.00 samples. +2024-03-12 17:31:37,518 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 17:33:54,589 INFO [train.py:527] (4/6) Epoch 173, batch 122, global_batch_idx: 21450, batch size: 58, loss[discriminator_loss=2.786, discriminator_real_loss=1.499, discriminator_fake_loss=1.286, generator_loss=27.2, generator_mel_loss=18.82, generator_kl_loss=1.403, generator_dur_loss=1.727, generator_adv_loss=1.816, generator_feat_match_loss=3.44, over 58.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.392, discriminator_fake_loss=1.35, generator_loss=27.3, generator_mel_loss=18.77, generator_kl_loss=1.372, generator_dur_loss=1.777, generator_adv_loss=1.92, generator_feat_match_loss=3.458, over 7147.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:33:59,723 INFO [train.py:919] (4/6) Start epoch 174 +2024-03-12 17:36:37,022 INFO [train.py:527] (4/6) Epoch 174, batch 48, global_batch_idx: 21500, batch size: 68, loss[discriminator_loss=2.722, discriminator_real_loss=1.367, discriminator_fake_loss=1.355, generator_loss=27.14, generator_mel_loss=18.67, generator_kl_loss=1.265, generator_dur_loss=1.758, generator_adv_loss=1.876, generator_feat_match_loss=3.568, over 68.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.394, discriminator_fake_loss=1.35, generator_loss=27.3, generator_mel_loss=18.8, generator_kl_loss=1.402, generator_dur_loss=1.748, generator_adv_loss=1.908, generator_feat_match_loss=3.441, over 2804.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:38:55,037 INFO [train.py:527] (4/6) Epoch 174, batch 98, global_batch_idx: 21550, batch size: 77, loss[discriminator_loss=2.786, discriminator_real_loss=1.47, discriminator_fake_loss=1.317, generator_loss=26.17, generator_mel_loss=18.13, generator_kl_loss=1.283, generator_dur_loss=1.847, generator_adv_loss=1.79, generator_feat_match_loss=3.116, over 77.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.396, discriminator_fake_loss=1.354, generator_loss=27.23, generator_mel_loss=18.72, generator_kl_loss=1.386, generator_dur_loss=1.76, generator_adv_loss=1.914, generator_feat_match_loss=3.445, over 5732.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:40:01,784 INFO [train.py:919] (4/6) Start epoch 175 +2024-03-12 17:41:32,583 INFO [train.py:527] (4/6) Epoch 175, batch 24, global_batch_idx: 21600, batch size: 36, loss[discriminator_loss=2.805, discriminator_real_loss=1.364, discriminator_fake_loss=1.441, generator_loss=26, generator_mel_loss=17.7, generator_kl_loss=1.25, generator_dur_loss=1.791, generator_adv_loss=2.152, generator_feat_match_loss=3.099, over 36.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.382, discriminator_fake_loss=1.36, generator_loss=27.38, generator_mel_loss=18.81, generator_kl_loss=1.373, generator_dur_loss=1.784, generator_adv_loss=1.897, generator_feat_match_loss=3.511, over 1486.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:41:32,585 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 17:41:40,573 INFO [train.py:591] (4/6) Epoch 175, validation: discriminator_loss=2.808, discriminator_real_loss=1.562, discriminator_fake_loss=1.246, generator_loss=26.82, generator_mel_loss=18.91, generator_kl_loss=1.191, generator_dur_loss=1.83, generator_adv_loss=2.054, generator_feat_match_loss=2.835, over 100.00 samples. +2024-03-12 17:41:40,574 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 17:44:02,814 INFO [train.py:527] (4/6) Epoch 175, batch 74, global_batch_idx: 21650, batch size: 64, loss[discriminator_loss=2.772, discriminator_real_loss=1.445, discriminator_fake_loss=1.326, generator_loss=26.71, generator_mel_loss=18.66, generator_kl_loss=1.379, generator_dur_loss=1.79, generator_adv_loss=1.71, generator_feat_match_loss=3.169, over 64.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.391, discriminator_fake_loss=1.349, generator_loss=27.25, generator_mel_loss=18.75, generator_kl_loss=1.371, generator_dur_loss=1.771, generator_adv_loss=1.904, generator_feat_match_loss=3.459, over 4532.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:46:17,957 INFO [train.py:919] (4/6) Start epoch 176 +2024-03-12 17:46:42,003 INFO [train.py:527] (4/6) Epoch 176, batch 0, global_batch_idx: 21700, batch size: 45, loss[discriminator_loss=2.792, discriminator_real_loss=1.351, discriminator_fake_loss=1.44, generator_loss=27.65, generator_mel_loss=18.66, generator_kl_loss=1.557, generator_dur_loss=1.659, generator_adv_loss=2.067, generator_feat_match_loss=3.705, over 45.00 samples.], tot_loss[discriminator_loss=2.792, discriminator_real_loss=1.351, discriminator_fake_loss=1.44, generator_loss=27.65, generator_mel_loss=18.66, generator_kl_loss=1.557, generator_dur_loss=1.659, generator_adv_loss=2.067, generator_feat_match_loss=3.705, over 45.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:49:00,880 INFO [train.py:527] (4/6) Epoch 176, batch 50, global_batch_idx: 21750, batch size: 53, loss[discriminator_loss=2.755, discriminator_real_loss=1.458, discriminator_fake_loss=1.297, generator_loss=27.03, generator_mel_loss=18.59, generator_kl_loss=1.569, generator_dur_loss=1.708, generator_adv_loss=1.968, generator_feat_match_loss=3.201, over 53.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.389, discriminator_fake_loss=1.358, generator_loss=27.42, generator_mel_loss=18.78, generator_kl_loss=1.419, generator_dur_loss=1.738, generator_adv_loss=1.933, generator_feat_match_loss=3.55, over 2641.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:51:22,145 INFO [train.py:527] (4/6) Epoch 176, batch 100, global_batch_idx: 21800, batch size: 25, loss[discriminator_loss=2.914, discriminator_real_loss=1.525, discriminator_fake_loss=1.389, generator_loss=28.83, generator_mel_loss=20.18, generator_kl_loss=1.686, generator_dur_loss=1.543, generator_adv_loss=1.924, generator_feat_match_loss=3.505, over 25.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.388, discriminator_fake_loss=1.364, generator_loss=27.35, generator_mel_loss=18.77, generator_kl_loss=1.405, generator_dur_loss=1.753, generator_adv_loss=1.922, generator_feat_match_loss=3.498, over 5471.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:51:22,147 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 17:51:31,010 INFO [train.py:591] (4/6) Epoch 176, validation: discriminator_loss=2.772, discriminator_real_loss=1.518, discriminator_fake_loss=1.254, generator_loss=26.16, generator_mel_loss=18.47, generator_kl_loss=1.18, generator_dur_loss=1.799, generator_adv_loss=1.881, generator_feat_match_loss=2.832, over 100.00 samples. +2024-03-12 17:51:31,011 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 17:52:33,380 INFO [train.py:919] (4/6) Start epoch 177 +2024-03-12 17:54:10,058 INFO [train.py:527] (4/6) Epoch 177, batch 26, global_batch_idx: 21850, batch size: 42, loss[discriminator_loss=2.665, discriminator_real_loss=1.262, discriminator_fake_loss=1.403, generator_loss=28.09, generator_mel_loss=19.02, generator_kl_loss=1.606, generator_dur_loss=1.666, generator_adv_loss=1.948, generator_feat_match_loss=3.853, over 42.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.399, discriminator_fake_loss=1.346, generator_loss=27.3, generator_mel_loss=18.69, generator_kl_loss=1.386, generator_dur_loss=1.751, generator_adv_loss=1.942, generator_feat_match_loss=3.535, over 1473.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:56:28,985 INFO [train.py:527] (4/6) Epoch 177, batch 76, global_batch_idx: 21900, batch size: 25, loss[discriminator_loss=2.75, discriminator_real_loss=1.408, discriminator_fake_loss=1.341, generator_loss=27.13, generator_mel_loss=18.25, generator_kl_loss=1.774, generator_dur_loss=1.552, generator_adv_loss=2.04, generator_feat_match_loss=3.506, over 25.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.395, discriminator_fake_loss=1.351, generator_loss=27.28, generator_mel_loss=18.7, generator_kl_loss=1.397, generator_dur_loss=1.747, generator_adv_loss=1.916, generator_feat_match_loss=3.514, over 4203.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:58:41,197 INFO [train.py:919] (4/6) Start epoch 178 +2024-03-12 17:59:09,264 INFO [train.py:527] (4/6) Epoch 178, batch 2, global_batch_idx: 21950, batch size: 31, loss[discriminator_loss=2.71, discriminator_real_loss=1.385, discriminator_fake_loss=1.325, generator_loss=26.65, generator_mel_loss=18.66, generator_kl_loss=1.548, generator_dur_loss=1.627, generator_adv_loss=1.867, generator_feat_match_loss=2.942, over 31.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.401, discriminator_fake_loss=1.33, generator_loss=27.06, generator_mel_loss=18.72, generator_kl_loss=1.434, generator_dur_loss=1.681, generator_adv_loss=1.892, generator_feat_match_loss=3.34, over 126.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:01:30,756 INFO [train.py:527] (4/6) Epoch 178, batch 52, global_batch_idx: 22000, batch size: 68, loss[discriminator_loss=2.721, discriminator_real_loss=1.397, discriminator_fake_loss=1.324, generator_loss=27.15, generator_mel_loss=18.53, generator_kl_loss=1.294, generator_dur_loss=1.804, generator_adv_loss=1.817, generator_feat_match_loss=3.712, over 68.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.382, discriminator_fake_loss=1.345, generator_loss=27.36, generator_mel_loss=18.71, generator_kl_loss=1.402, generator_dur_loss=1.745, generator_adv_loss=1.912, generator_feat_match_loss=3.594, over 2901.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:01:30,757 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 18:01:38,780 INFO [train.py:591] (4/6) Epoch 178, validation: discriminator_loss=2.709, discriminator_real_loss=1.303, discriminator_fake_loss=1.406, generator_loss=26.32, generator_mel_loss=18.65, generator_kl_loss=1.19, generator_dur_loss=1.805, generator_adv_loss=1.767, generator_feat_match_loss=2.913, over 100.00 samples. +2024-03-12 18:01:38,781 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 18:03:57,051 INFO [train.py:527] (4/6) Epoch 178, batch 102, global_batch_idx: 22050, batch size: 31, loss[discriminator_loss=2.755, discriminator_real_loss=1.426, discriminator_fake_loss=1.329, generator_loss=28.31, generator_mel_loss=19.53, generator_kl_loss=1.656, generator_dur_loss=1.599, generator_adv_loss=1.746, generator_feat_match_loss=3.775, over 31.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=27.28, generator_mel_loss=18.68, generator_kl_loss=1.395, generator_dur_loss=1.752, generator_adv_loss=1.918, generator_feat_match_loss=3.538, over 5889.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:04:55,592 INFO [train.py:919] (4/6) Start epoch 179 +2024-03-12 18:06:36,867 INFO [train.py:527] (4/6) Epoch 179, batch 28, global_batch_idx: 22100, batch size: 45, loss[discriminator_loss=2.779, discriminator_real_loss=1.534, discriminator_fake_loss=1.245, generator_loss=26.76, generator_mel_loss=18.62, generator_kl_loss=1.369, generator_dur_loss=1.715, generator_adv_loss=1.759, generator_feat_match_loss=3.292, over 45.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.374, discriminator_fake_loss=1.365, generator_loss=27.52, generator_mel_loss=18.83, generator_kl_loss=1.41, generator_dur_loss=1.761, generator_adv_loss=1.947, generator_feat_match_loss=3.572, over 1615.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:08:55,040 INFO [train.py:527] (4/6) Epoch 179, batch 78, global_batch_idx: 22150, batch size: 77, loss[discriminator_loss=2.694, discriminator_real_loss=1.366, discriminator_fake_loss=1.328, generator_loss=27.1, generator_mel_loss=18.66, generator_kl_loss=1.323, generator_dur_loss=1.841, generator_adv_loss=1.831, generator_feat_match_loss=3.437, over 77.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.384, discriminator_fake_loss=1.351, generator_loss=27.47, generator_mel_loss=18.81, generator_kl_loss=1.404, generator_dur_loss=1.76, generator_adv_loss=1.943, generator_feat_match_loss=3.559, over 4413.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:11:01,787 INFO [train.py:919] (4/6) Start epoch 180 +2024-03-12 18:11:35,653 INFO [train.py:527] (4/6) Epoch 180, batch 4, global_batch_idx: 22200, batch size: 50, loss[discriminator_loss=2.759, discriminator_real_loss=1.422, discriminator_fake_loss=1.337, generator_loss=27.37, generator_mel_loss=18.79, generator_kl_loss=1.554, generator_dur_loss=1.639, generator_adv_loss=1.784, generator_feat_match_loss=3.607, over 50.00 samples.], tot_loss[discriminator_loss=2.798, discriminator_real_loss=1.442, discriminator_fake_loss=1.357, generator_loss=27.31, generator_mel_loss=19.05, generator_kl_loss=1.42, generator_dur_loss=1.721, generator_adv_loss=1.808, generator_feat_match_loss=3.305, over 265.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:11:35,656 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 18:11:43,426 INFO [train.py:591] (4/6) Epoch 180, validation: discriminator_loss=2.791, discriminator_real_loss=1.35, discriminator_fake_loss=1.44, generator_loss=25.83, generator_mel_loss=18.47, generator_kl_loss=1.269, generator_dur_loss=1.814, generator_adv_loss=1.689, generator_feat_match_loss=2.591, over 100.00 samples. +2024-03-12 18:11:43,428 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 18:14:02,376 INFO [train.py:527] (4/6) Epoch 180, batch 54, global_batch_idx: 22250, batch size: 66, loss[discriminator_loss=2.764, discriminator_real_loss=1.38, discriminator_fake_loss=1.384, generator_loss=27.17, generator_mel_loss=18.38, generator_kl_loss=1.398, generator_dur_loss=1.807, generator_adv_loss=1.989, generator_feat_match_loss=3.593, over 66.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.395, discriminator_fake_loss=1.344, generator_loss=27.39, generator_mel_loss=18.78, generator_kl_loss=1.398, generator_dur_loss=1.744, generator_adv_loss=1.915, generator_feat_match_loss=3.551, over 3109.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:16:22,221 INFO [train.py:527] (4/6) Epoch 180, batch 104, global_batch_idx: 22300, batch size: 72, loss[discriminator_loss=2.755, discriminator_real_loss=1.357, discriminator_fake_loss=1.398, generator_loss=27.6, generator_mel_loss=18.81, generator_kl_loss=1.333, generator_dur_loss=1.812, generator_adv_loss=1.884, generator_feat_match_loss=3.77, over 72.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.388, discriminator_fake_loss=1.353, generator_loss=27.34, generator_mel_loss=18.74, generator_kl_loss=1.388, generator_dur_loss=1.753, generator_adv_loss=1.914, generator_feat_match_loss=3.542, over 6174.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:17:12,983 INFO [train.py:919] (4/6) Start epoch 181 +2024-03-12 18:19:03,209 INFO [train.py:527] (4/6) Epoch 181, batch 30, global_batch_idx: 22350, batch size: 88, loss[discriminator_loss=2.771, discriminator_real_loss=1.497, discriminator_fake_loss=1.274, generator_loss=27.16, generator_mel_loss=18.67, generator_kl_loss=1.087, generator_dur_loss=1.851, generator_adv_loss=2.013, generator_feat_match_loss=3.536, over 88.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.376, discriminator_fake_loss=1.358, generator_loss=27.48, generator_mel_loss=18.81, generator_kl_loss=1.368, generator_dur_loss=1.795, generator_adv_loss=1.93, generator_feat_match_loss=3.581, over 1981.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:21:23,401 INFO [train.py:527] (4/6) Epoch 181, batch 80, global_batch_idx: 22400, batch size: 83, loss[discriminator_loss=2.832, discriminator_real_loss=1.461, discriminator_fake_loss=1.371, generator_loss=26.69, generator_mel_loss=18.45, generator_kl_loss=1.345, generator_dur_loss=1.854, generator_adv_loss=1.939, generator_feat_match_loss=3.105, over 83.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.389, discriminator_fake_loss=1.354, generator_loss=27.46, generator_mel_loss=18.76, generator_kl_loss=1.379, generator_dur_loss=1.78, generator_adv_loss=1.945, generator_feat_match_loss=3.594, over 4759.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:21:23,403 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 18:21:31,724 INFO [train.py:591] (4/6) Epoch 181, validation: discriminator_loss=2.819, discriminator_real_loss=1.557, discriminator_fake_loss=1.263, generator_loss=26.69, generator_mel_loss=18.85, generator_kl_loss=1.198, generator_dur_loss=1.781, generator_adv_loss=1.944, generator_feat_match_loss=2.917, over 100.00 samples. +2024-03-12 18:21:31,725 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 18:23:28,370 INFO [train.py:919] (4/6) Start epoch 182 +2024-03-12 18:24:07,833 INFO [train.py:527] (4/6) Epoch 182, batch 6, global_batch_idx: 22450, batch size: 68, loss[discriminator_loss=2.846, discriminator_real_loss=1.53, discriminator_fake_loss=1.316, generator_loss=27.48, generator_mel_loss=19.33, generator_kl_loss=1.342, generator_dur_loss=1.821, generator_adv_loss=1.731, generator_feat_match_loss=3.262, over 68.00 samples.], tot_loss[discriminator_loss=2.782, discriminator_real_loss=1.42, discriminator_fake_loss=1.363, generator_loss=27.34, generator_mel_loss=18.83, generator_kl_loss=1.332, generator_dur_loss=1.786, generator_adv_loss=1.875, generator_feat_match_loss=3.518, over 473.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:26:28,661 INFO [train.py:527] (4/6) Epoch 182, batch 56, global_batch_idx: 22500, batch size: 61, loss[discriminator_loss=2.748, discriminator_real_loss=1.492, discriminator_fake_loss=1.255, generator_loss=27.22, generator_mel_loss=18.53, generator_kl_loss=1.327, generator_dur_loss=1.777, generator_adv_loss=1.948, generator_feat_match_loss=3.636, over 61.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.409, discriminator_fake_loss=1.356, generator_loss=27.27, generator_mel_loss=18.75, generator_kl_loss=1.377, generator_dur_loss=1.742, generator_adv_loss=1.908, generator_feat_match_loss=3.493, over 3195.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:28:47,550 INFO [train.py:527] (4/6) Epoch 182, batch 106, global_batch_idx: 22550, batch size: 96, loss[discriminator_loss=2.723, discriminator_real_loss=1.386, discriminator_fake_loss=1.336, generator_loss=27, generator_mel_loss=18.55, generator_kl_loss=1.285, generator_dur_loss=1.926, generator_adv_loss=1.801, generator_feat_match_loss=3.438, over 96.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.401, discriminator_fake_loss=1.353, generator_loss=27.29, generator_mel_loss=18.72, generator_kl_loss=1.37, generator_dur_loss=1.758, generator_adv_loss=1.919, generator_feat_match_loss=3.524, over 6078.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:29:34,506 INFO [train.py:919] (4/6) Start epoch 183 +2024-03-12 18:31:27,930 INFO [train.py:527] (4/6) Epoch 183, batch 32, global_batch_idx: 22600, batch size: 61, loss[discriminator_loss=2.752, discriminator_real_loss=1.484, discriminator_fake_loss=1.267, generator_loss=27.4, generator_mel_loss=18.5, generator_kl_loss=1.452, generator_dur_loss=1.807, generator_adv_loss=1.898, generator_feat_match_loss=3.744, over 61.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.408, discriminator_fake_loss=1.337, generator_loss=27.27, generator_mel_loss=18.67, generator_kl_loss=1.394, generator_dur_loss=1.765, generator_adv_loss=1.913, generator_feat_match_loss=3.525, over 1885.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:31:27,931 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 18:31:35,883 INFO [train.py:591] (4/6) Epoch 183, validation: discriminator_loss=2.735, discriminator_real_loss=1.413, discriminator_fake_loss=1.323, generator_loss=27.1, generator_mel_loss=19, generator_kl_loss=1.172, generator_dur_loss=1.819, generator_adv_loss=1.838, generator_feat_match_loss=3.275, over 100.00 samples. +2024-03-12 18:31:35,884 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 18:33:56,383 INFO [train.py:527] (4/6) Epoch 183, batch 82, global_batch_idx: 22650, batch size: 42, loss[discriminator_loss=2.733, discriminator_real_loss=1.343, discriminator_fake_loss=1.39, generator_loss=27.72, generator_mel_loss=18.84, generator_kl_loss=1.487, generator_dur_loss=1.702, generator_adv_loss=2.001, generator_feat_match_loss=3.69, over 42.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.397, discriminator_fake_loss=1.343, generator_loss=27.39, generator_mel_loss=18.74, generator_kl_loss=1.389, generator_dur_loss=1.763, generator_adv_loss=1.922, generator_feat_match_loss=3.574, over 4771.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:35:50,098 INFO [train.py:919] (4/6) Start epoch 184 +2024-03-12 18:36:35,622 INFO [train.py:527] (4/6) Epoch 184, batch 8, global_batch_idx: 22700, batch size: 58, loss[discriminator_loss=2.742, discriminator_real_loss=1.368, discriminator_fake_loss=1.374, generator_loss=27.77, generator_mel_loss=18.67, generator_kl_loss=1.506, generator_dur_loss=1.797, generator_adv_loss=1.897, generator_feat_match_loss=3.906, over 58.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.398, discriminator_fake_loss=1.352, generator_loss=27.25, generator_mel_loss=18.56, generator_kl_loss=1.473, generator_dur_loss=1.774, generator_adv_loss=1.898, generator_feat_match_loss=3.543, over 513.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:38:51,505 INFO [train.py:527] (4/6) Epoch 184, batch 58, global_batch_idx: 22750, batch size: 50, loss[discriminator_loss=2.709, discriminator_real_loss=1.323, discriminator_fake_loss=1.387, generator_loss=27.83, generator_mel_loss=18.68, generator_kl_loss=1.433, generator_dur_loss=1.634, generator_adv_loss=2.105, generator_feat_match_loss=3.973, over 50.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.383, discriminator_fake_loss=1.363, generator_loss=27.4, generator_mel_loss=18.74, generator_kl_loss=1.419, generator_dur_loss=1.744, generator_adv_loss=1.937, generator_feat_match_loss=3.556, over 3225.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:41:09,959 INFO [train.py:527] (4/6) Epoch 184, batch 108, global_batch_idx: 22800, batch size: 60, loss[discriminator_loss=2.8, discriminator_real_loss=1.365, discriminator_fake_loss=1.435, generator_loss=27.41, generator_mel_loss=19.28, generator_kl_loss=1.295, generator_dur_loss=1.774, generator_adv_loss=2.111, generator_feat_match_loss=2.941, over 60.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.387, discriminator_fake_loss=1.354, generator_loss=27.31, generator_mel_loss=18.7, generator_kl_loss=1.413, generator_dur_loss=1.75, generator_adv_loss=1.925, generator_feat_match_loss=3.528, over 5964.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:41:09,961 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 18:41:18,936 INFO [train.py:591] (4/6) Epoch 184, validation: discriminator_loss=2.867, discriminator_real_loss=1.662, discriminator_fake_loss=1.205, generator_loss=26.73, generator_mel_loss=19.04, generator_kl_loss=1.196, generator_dur_loss=1.823, generator_adv_loss=2.106, generator_feat_match_loss=2.571, over 100.00 samples. +2024-03-12 18:41:18,937 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 18:42:02,313 INFO [train.py:919] (4/6) Start epoch 185 +2024-03-12 18:44:00,242 INFO [train.py:527] (4/6) Epoch 185, batch 34, global_batch_idx: 22850, batch size: 55, loss[discriminator_loss=2.702, discriminator_real_loss=1.458, discriminator_fake_loss=1.245, generator_loss=26.78, generator_mel_loss=18.2, generator_kl_loss=1.319, generator_dur_loss=1.67, generator_adv_loss=1.961, generator_feat_match_loss=3.637, over 55.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.406, discriminator_fake_loss=1.351, generator_loss=27.23, generator_mel_loss=18.7, generator_kl_loss=1.372, generator_dur_loss=1.772, generator_adv_loss=1.914, generator_feat_match_loss=3.481, over 2065.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:46:18,264 INFO [train.py:527] (4/6) Epoch 185, batch 84, global_batch_idx: 22900, batch size: 58, loss[discriminator_loss=2.671, discriminator_real_loss=1.39, discriminator_fake_loss=1.281, generator_loss=27.68, generator_mel_loss=18.82, generator_kl_loss=1.338, generator_dur_loss=1.754, generator_adv_loss=1.778, generator_feat_match_loss=3.984, over 58.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.398, discriminator_fake_loss=1.355, generator_loss=27.25, generator_mel_loss=18.69, generator_kl_loss=1.374, generator_dur_loss=1.77, generator_adv_loss=1.9, generator_feat_match_loss=3.522, over 5014.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:48:09,340 INFO [train.py:919] (4/6) Start epoch 186 +2024-03-12 18:49:00,468 INFO [train.py:527] (4/6) Epoch 186, batch 10, global_batch_idx: 22950, batch size: 80, loss[discriminator_loss=2.684, discriminator_real_loss=1.433, discriminator_fake_loss=1.251, generator_loss=28.1, generator_mel_loss=18.68, generator_kl_loss=1.326, generator_dur_loss=1.794, generator_adv_loss=2.319, generator_feat_match_loss=3.981, over 80.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.378, discriminator_fake_loss=1.372, generator_loss=27.34, generator_mel_loss=18.68, generator_kl_loss=1.371, generator_dur_loss=1.748, generator_adv_loss=1.981, generator_feat_match_loss=3.561, over 594.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:51:17,375 INFO [train.py:527] (4/6) Epoch 186, batch 60, global_batch_idx: 23000, batch size: 16, loss[discriminator_loss=2.781, discriminator_real_loss=1.513, discriminator_fake_loss=1.267, generator_loss=29.7, generator_mel_loss=20.79, generator_kl_loss=1.761, generator_dur_loss=1.634, generator_adv_loss=1.917, generator_feat_match_loss=3.597, over 16.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.392, discriminator_fake_loss=1.35, generator_loss=27.38, generator_mel_loss=18.72, generator_kl_loss=1.401, generator_dur_loss=1.755, generator_adv_loss=1.935, generator_feat_match_loss=3.574, over 3318.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:51:17,376 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 18:51:25,374 INFO [train.py:591] (4/6) Epoch 186, validation: discriminator_loss=2.794, discriminator_real_loss=1.451, discriminator_fake_loss=1.344, generator_loss=26.57, generator_mel_loss=18.9, generator_kl_loss=1.241, generator_dur_loss=1.796, generator_adv_loss=1.81, generator_feat_match_loss=2.816, over 100.00 samples. +2024-03-12 18:51:25,375 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 18:53:42,534 INFO [train.py:527] (4/6) Epoch 186, batch 110, global_batch_idx: 23050, batch size: 64, loss[discriminator_loss=2.772, discriminator_real_loss=1.431, discriminator_fake_loss=1.341, generator_loss=27.38, generator_mel_loss=18.79, generator_kl_loss=1.454, generator_dur_loss=1.752, generator_adv_loss=1.839, generator_feat_match_loss=3.544, over 64.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.39, discriminator_fake_loss=1.354, generator_loss=27.32, generator_mel_loss=18.72, generator_kl_loss=1.39, generator_dur_loss=1.76, generator_adv_loss=1.923, generator_feat_match_loss=3.533, over 6212.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:54:20,265 INFO [train.py:919] (4/6) Start epoch 187 +2024-03-12 18:56:22,575 INFO [train.py:527] (4/6) Epoch 187, batch 36, global_batch_idx: 23100, batch size: 42, loss[discriminator_loss=2.699, discriminator_real_loss=1.269, discriminator_fake_loss=1.43, generator_loss=28.98, generator_mel_loss=19.6, generator_kl_loss=1.459, generator_dur_loss=1.704, generator_adv_loss=2.051, generator_feat_match_loss=4.162, over 42.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.385, discriminator_fake_loss=1.344, generator_loss=27.43, generator_mel_loss=18.74, generator_kl_loss=1.403, generator_dur_loss=1.73, generator_adv_loss=1.953, generator_feat_match_loss=3.604, over 2004.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:58:38,484 INFO [train.py:527] (4/6) Epoch 187, batch 86, global_batch_idx: 23150, batch size: 61, loss[discriminator_loss=2.742, discriminator_real_loss=1.377, discriminator_fake_loss=1.365, generator_loss=27.72, generator_mel_loss=18.61, generator_kl_loss=1.295, generator_dur_loss=1.752, generator_adv_loss=2.031, generator_feat_match_loss=4.025, over 61.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.389, discriminator_fake_loss=1.345, generator_loss=27.27, generator_mel_loss=18.66, generator_kl_loss=1.384, generator_dur_loss=1.748, generator_adv_loss=1.921, generator_feat_match_loss=3.566, over 4844.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:00:22,995 INFO [train.py:919] (4/6) Start epoch 188 +2024-03-12 19:01:22,032 INFO [train.py:527] (4/6) Epoch 188, batch 12, global_batch_idx: 23200, batch size: 66, loss[discriminator_loss=2.797, discriminator_real_loss=1.378, discriminator_fake_loss=1.419, generator_loss=27.59, generator_mel_loss=18.56, generator_kl_loss=1.396, generator_dur_loss=1.816, generator_adv_loss=1.888, generator_feat_match_loss=3.928, over 66.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.395, discriminator_fake_loss=1.367, generator_loss=27.3, generator_mel_loss=18.69, generator_kl_loss=1.37, generator_dur_loss=1.775, generator_adv_loss=1.901, generator_feat_match_loss=3.563, over 855.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:01:22,034 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 19:01:30,004 INFO [train.py:591] (4/6) Epoch 188, validation: discriminator_loss=2.763, discriminator_real_loss=1.442, discriminator_fake_loss=1.321, generator_loss=26.05, generator_mel_loss=18.39, generator_kl_loss=1.334, generator_dur_loss=1.8, generator_adv_loss=1.876, generator_feat_match_loss=2.644, over 100.00 samples. +2024-03-12 19:01:30,005 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 19:03:48,368 INFO [train.py:527] (4/6) Epoch 188, batch 62, global_batch_idx: 23250, batch size: 72, loss[discriminator_loss=2.757, discriminator_real_loss=1.321, discriminator_fake_loss=1.436, generator_loss=26.77, generator_mel_loss=18.41, generator_kl_loss=1.33, generator_dur_loss=1.802, generator_adv_loss=1.846, generator_feat_match_loss=3.377, over 72.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.393, discriminator_fake_loss=1.351, generator_loss=27.25, generator_mel_loss=18.63, generator_kl_loss=1.392, generator_dur_loss=1.755, generator_adv_loss=1.913, generator_feat_match_loss=3.552, over 3771.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:06:09,551 INFO [train.py:527] (4/6) Epoch 188, batch 112, global_batch_idx: 23300, batch size: 70, loss[discriminator_loss=2.764, discriminator_real_loss=1.23, discriminator_fake_loss=1.534, generator_loss=27.67, generator_mel_loss=18.69, generator_kl_loss=1.275, generator_dur_loss=1.794, generator_adv_loss=2.016, generator_feat_match_loss=3.895, over 70.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.385, discriminator_fake_loss=1.355, generator_loss=27.37, generator_mel_loss=18.7, generator_kl_loss=1.4, generator_dur_loss=1.758, generator_adv_loss=1.918, generator_feat_match_loss=3.595, over 6536.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:06:41,733 INFO [train.py:919] (4/6) Start epoch 189 +2024-03-12 19:08:53,134 INFO [train.py:527] (4/6) Epoch 189, batch 38, global_batch_idx: 23350, batch size: 70, loss[discriminator_loss=2.736, discriminator_real_loss=1.437, discriminator_fake_loss=1.299, generator_loss=27.75, generator_mel_loss=18.94, generator_kl_loss=1.378, generator_dur_loss=1.799, generator_adv_loss=1.878, generator_feat_match_loss=3.753, over 70.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.418, discriminator_fake_loss=1.343, generator_loss=27.15, generator_mel_loss=18.61, generator_kl_loss=1.372, generator_dur_loss=1.784, generator_adv_loss=1.916, generator_feat_match_loss=3.469, over 2302.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:11:15,696 INFO [train.py:527] (4/6) Epoch 189, batch 88, global_batch_idx: 23400, batch size: 96, loss[discriminator_loss=2.762, discriminator_real_loss=1.401, discriminator_fake_loss=1.361, generator_loss=26.85, generator_mel_loss=18.42, generator_kl_loss=1.253, generator_dur_loss=1.895, generator_adv_loss=1.967, generator_feat_match_loss=3.317, over 96.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.397, discriminator_fake_loss=1.349, generator_loss=27.2, generator_mel_loss=18.63, generator_kl_loss=1.379, generator_dur_loss=1.775, generator_adv_loss=1.91, generator_feat_match_loss=3.505, over 5302.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:11:15,698 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 19:11:23,871 INFO [train.py:591] (4/6) Epoch 189, validation: discriminator_loss=2.761, discriminator_real_loss=1.482, discriminator_fake_loss=1.279, generator_loss=27.2, generator_mel_loss=18.92, generator_kl_loss=1.165, generator_dur_loss=1.831, generator_adv_loss=1.969, generator_feat_match_loss=3.312, over 100.00 samples. +2024-03-12 19:11:23,872 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 19:12:58,281 INFO [train.py:919] (4/6) Start epoch 190 +2024-03-12 19:14:01,625 INFO [train.py:527] (4/6) Epoch 190, batch 14, global_batch_idx: 23450, batch size: 72, loss[discriminator_loss=2.823, discriminator_real_loss=1.375, discriminator_fake_loss=1.448, generator_loss=27.55, generator_mel_loss=18.74, generator_kl_loss=1.336, generator_dur_loss=1.845, generator_adv_loss=2.008, generator_feat_match_loss=3.62, over 72.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.41, discriminator_fake_loss=1.353, generator_loss=27.27, generator_mel_loss=18.62, generator_kl_loss=1.352, generator_dur_loss=1.784, generator_adv_loss=1.976, generator_feat_match_loss=3.541, over 913.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:16:20,309 INFO [train.py:527] (4/6) Epoch 190, batch 64, global_batch_idx: 23500, batch size: 25, loss[discriminator_loss=2.725, discriminator_real_loss=1.487, discriminator_fake_loss=1.238, generator_loss=27.46, generator_mel_loss=18.83, generator_kl_loss=1.56, generator_dur_loss=1.641, generator_adv_loss=1.99, generator_feat_match_loss=3.437, over 25.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.397, discriminator_fake_loss=1.347, generator_loss=27.38, generator_mel_loss=18.72, generator_kl_loss=1.385, generator_dur_loss=1.749, generator_adv_loss=1.934, generator_feat_match_loss=3.595, over 3539.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:18:39,700 INFO [train.py:527] (4/6) Epoch 190, batch 114, global_batch_idx: 23550, batch size: 52, loss[discriminator_loss=2.791, discriminator_real_loss=1.423, discriminator_fake_loss=1.368, generator_loss=26.98, generator_mel_loss=18.25, generator_kl_loss=1.535, generator_dur_loss=1.72, generator_adv_loss=1.86, generator_feat_match_loss=3.614, over 52.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.393, discriminator_fake_loss=1.352, generator_loss=27.36, generator_mel_loss=18.71, generator_kl_loss=1.383, generator_dur_loss=1.762, generator_adv_loss=1.92, generator_feat_match_loss=3.593, over 6478.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:19:04,168 INFO [train.py:919] (4/6) Start epoch 191 +2024-03-12 19:21:21,792 INFO [train.py:527] (4/6) Epoch 191, batch 40, global_batch_idx: 23600, batch size: 64, loss[discriminator_loss=2.779, discriminator_real_loss=1.349, discriminator_fake_loss=1.43, generator_loss=26.41, generator_mel_loss=17.96, generator_kl_loss=1.319, generator_dur_loss=1.754, generator_adv_loss=2.074, generator_feat_match_loss=3.308, over 64.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.404, discriminator_fake_loss=1.34, generator_loss=27.3, generator_mel_loss=18.69, generator_kl_loss=1.428, generator_dur_loss=1.69, generator_adv_loss=1.928, generator_feat_match_loss=3.563, over 2290.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:21:21,793 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 19:21:29,539 INFO [train.py:591] (4/6) Epoch 191, validation: discriminator_loss=2.788, discriminator_real_loss=1.5, discriminator_fake_loss=1.288, generator_loss=26.94, generator_mel_loss=18.69, generator_kl_loss=1.259, generator_dur_loss=1.796, generator_adv_loss=2.027, generator_feat_match_loss=3.167, over 100.00 samples. +2024-03-12 19:21:29,540 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 19:23:48,629 INFO [train.py:527] (4/6) Epoch 191, batch 90, global_batch_idx: 23650, batch size: 45, loss[discriminator_loss=2.745, discriminator_real_loss=1.489, discriminator_fake_loss=1.256, generator_loss=28.4, generator_mel_loss=19.15, generator_kl_loss=1.663, generator_dur_loss=1.695, generator_adv_loss=1.924, generator_feat_match_loss=3.966, over 45.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.399, discriminator_fake_loss=1.346, generator_loss=27.31, generator_mel_loss=18.67, generator_kl_loss=1.405, generator_dur_loss=1.725, generator_adv_loss=1.916, generator_feat_match_loss=3.587, over 5204.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:25:17,525 INFO [train.py:919] (4/6) Start epoch 192 +2024-03-12 19:26:26,497 INFO [train.py:527] (4/6) Epoch 192, batch 16, global_batch_idx: 23700, batch size: 39, loss[discriminator_loss=2.648, discriminator_real_loss=1.311, discriminator_fake_loss=1.338, generator_loss=27.77, generator_mel_loss=18.42, generator_kl_loss=1.481, generator_dur_loss=1.727, generator_adv_loss=2.075, generator_feat_match_loss=4.065, over 39.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.379, discriminator_fake_loss=1.354, generator_loss=27.17, generator_mel_loss=18.56, generator_kl_loss=1.352, generator_dur_loss=1.79, generator_adv_loss=1.926, generator_feat_match_loss=3.535, over 997.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:28:41,797 INFO [train.py:527] (4/6) Epoch 192, batch 66, global_batch_idx: 23750, batch size: 68, loss[discriminator_loss=2.744, discriminator_real_loss=1.438, discriminator_fake_loss=1.307, generator_loss=27.16, generator_mel_loss=18.69, generator_kl_loss=1.235, generator_dur_loss=1.812, generator_adv_loss=1.865, generator_feat_match_loss=3.553, over 68.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.383, discriminator_fake_loss=1.354, generator_loss=27.33, generator_mel_loss=18.68, generator_kl_loss=1.384, generator_dur_loss=1.761, generator_adv_loss=1.916, generator_feat_match_loss=3.597, over 3640.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:31:01,571 INFO [train.py:527] (4/6) Epoch 192, batch 116, global_batch_idx: 23800, batch size: 48, loss[discriminator_loss=2.738, discriminator_real_loss=1.459, discriminator_fake_loss=1.28, generator_loss=26.73, generator_mel_loss=18.22, generator_kl_loss=1.472, generator_dur_loss=1.664, generator_adv_loss=1.818, generator_feat_match_loss=3.552, over 48.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.386, discriminator_fake_loss=1.355, generator_loss=27.38, generator_mel_loss=18.68, generator_kl_loss=1.384, generator_dur_loss=1.766, generator_adv_loss=1.915, generator_feat_match_loss=3.638, over 6631.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:31:01,572 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 19:31:10,675 INFO [train.py:591] (4/6) Epoch 192, validation: discriminator_loss=2.816, discriminator_real_loss=1.43, discriminator_fake_loss=1.386, generator_loss=25.78, generator_mel_loss=18.34, generator_kl_loss=1.284, generator_dur_loss=1.825, generator_adv_loss=1.748, generator_feat_match_loss=2.581, over 100.00 samples. +2024-03-12 19:31:10,676 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 19:31:32,630 INFO [train.py:919] (4/6) Start epoch 193 +2024-03-12 19:33:52,750 INFO [train.py:527] (4/6) Epoch 193, batch 42, global_batch_idx: 23850, batch size: 39, loss[discriminator_loss=2.737, discriminator_real_loss=1.443, discriminator_fake_loss=1.293, generator_loss=28.01, generator_mel_loss=19.14, generator_kl_loss=1.334, generator_dur_loss=1.744, generator_adv_loss=1.843, generator_feat_match_loss=3.957, over 39.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.393, discriminator_fake_loss=1.349, generator_loss=27.32, generator_mel_loss=18.61, generator_kl_loss=1.399, generator_dur_loss=1.747, generator_adv_loss=1.92, generator_feat_match_loss=3.639, over 2396.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:36:10,067 INFO [train.py:527] (4/6) Epoch 193, batch 92, global_batch_idx: 23900, batch size: 56, loss[discriminator_loss=2.747, discriminator_real_loss=1.35, discriminator_fake_loss=1.397, generator_loss=27.38, generator_mel_loss=18.76, generator_kl_loss=1.294, generator_dur_loss=1.732, generator_adv_loss=1.839, generator_feat_match_loss=3.75, over 56.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.389, discriminator_fake_loss=1.346, generator_loss=27.29, generator_mel_loss=18.61, generator_kl_loss=1.392, generator_dur_loss=1.751, generator_adv_loss=1.917, generator_feat_match_loss=3.622, over 5312.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:37:38,823 INFO [train.py:919] (4/6) Start epoch 194 +2024-03-12 19:38:53,508 INFO [train.py:527] (4/6) Epoch 194, batch 18, global_batch_idx: 23950, batch size: 59, loss[discriminator_loss=2.84, discriminator_real_loss=1.337, discriminator_fake_loss=1.503, generator_loss=26.96, generator_mel_loss=18.57, generator_kl_loss=1.462, generator_dur_loss=1.697, generator_adv_loss=1.983, generator_feat_match_loss=3.25, over 59.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.381, discriminator_fake_loss=1.361, generator_loss=27.54, generator_mel_loss=18.78, generator_kl_loss=1.432, generator_dur_loss=1.719, generator_adv_loss=1.928, generator_feat_match_loss=3.679, over 1007.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:41:16,323 INFO [train.py:527] (4/6) Epoch 194, batch 68, global_batch_idx: 24000, batch size: 36, loss[discriminator_loss=2.837, discriminator_real_loss=1.445, discriminator_fake_loss=1.392, generator_loss=25.93, generator_mel_loss=17.81, generator_kl_loss=1.524, generator_dur_loss=1.657, generator_adv_loss=1.954, generator_feat_match_loss=2.988, over 36.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.398, discriminator_fake_loss=1.348, generator_loss=27.44, generator_mel_loss=18.7, generator_kl_loss=1.406, generator_dur_loss=1.732, generator_adv_loss=1.942, generator_feat_match_loss=3.659, over 3767.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:41:16,324 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 19:41:24,224 INFO [train.py:591] (4/6) Epoch 194, validation: discriminator_loss=2.733, discriminator_real_loss=1.463, discriminator_fake_loss=1.27, generator_loss=26.68, generator_mel_loss=18.65, generator_kl_loss=1.177, generator_dur_loss=1.78, generator_adv_loss=2.005, generator_feat_match_loss=3.069, over 100.00 samples. +2024-03-12 19:41:24,225 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 19:43:40,243 INFO [train.py:527] (4/6) Epoch 194, batch 118, global_batch_idx: 24050, batch size: 62, loss[discriminator_loss=2.801, discriminator_real_loss=1.532, discriminator_fake_loss=1.269, generator_loss=27.15, generator_mel_loss=18.85, generator_kl_loss=1.349, generator_dur_loss=1.728, generator_adv_loss=1.948, generator_feat_match_loss=3.277, over 62.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.389, discriminator_fake_loss=1.348, generator_loss=27.41, generator_mel_loss=18.67, generator_kl_loss=1.4, generator_dur_loss=1.734, generator_adv_loss=1.927, generator_feat_match_loss=3.674, over 6696.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:43:56,243 INFO [train.py:919] (4/6) Start epoch 195 +2024-03-12 19:46:20,728 INFO [train.py:527] (4/6) Epoch 195, batch 44, global_batch_idx: 24100, batch size: 74, loss[discriminator_loss=2.69, discriminator_real_loss=1.348, discriminator_fake_loss=1.342, generator_loss=27.76, generator_mel_loss=19.06, generator_kl_loss=1.307, generator_dur_loss=1.814, generator_adv_loss=1.864, generator_feat_match_loss=3.717, over 74.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.389, discriminator_fake_loss=1.345, generator_loss=27.54, generator_mel_loss=18.7, generator_kl_loss=1.395, generator_dur_loss=1.722, generator_adv_loss=2.023, generator_feat_match_loss=3.695, over 2499.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:48:38,952 INFO [train.py:527] (4/6) Epoch 195, batch 94, global_batch_idx: 24150, batch size: 55, loss[discriminator_loss=2.757, discriminator_real_loss=1.365, discriminator_fake_loss=1.392, generator_loss=27.67, generator_mel_loss=18.85, generator_kl_loss=1.376, generator_dur_loss=1.646, generator_adv_loss=1.893, generator_feat_match_loss=3.906, over 55.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.391, discriminator_fake_loss=1.345, generator_loss=27.51, generator_mel_loss=18.7, generator_kl_loss=1.385, generator_dur_loss=1.747, generator_adv_loss=1.969, generator_feat_match_loss=3.713, over 5617.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:50:03,717 INFO [train.py:919] (4/6) Start epoch 196 +2024-03-12 19:51:22,222 INFO [train.py:527] (4/6) Epoch 196, batch 20, global_batch_idx: 24200, batch size: 61, loss[discriminator_loss=2.715, discriminator_real_loss=1.333, discriminator_fake_loss=1.383, generator_loss=27.94, generator_mel_loss=19.06, generator_kl_loss=1.305, generator_dur_loss=1.716, generator_adv_loss=1.907, generator_feat_match_loss=3.956, over 61.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.402, discriminator_fake_loss=1.354, generator_loss=27.38, generator_mel_loss=18.72, generator_kl_loss=1.399, generator_dur_loss=1.759, generator_adv_loss=1.906, generator_feat_match_loss=3.598, over 1197.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:51:22,223 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 19:51:30,420 INFO [train.py:591] (4/6) Epoch 196, validation: discriminator_loss=2.765, discriminator_real_loss=1.547, discriminator_fake_loss=1.218, generator_loss=26.23, generator_mel_loss=18.75, generator_kl_loss=1.178, generator_dur_loss=1.825, generator_adv_loss=1.943, generator_feat_match_loss=2.539, over 100.00 samples. +2024-03-12 19:51:30,421 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 19:53:49,926 INFO [train.py:527] (4/6) Epoch 196, batch 70, global_batch_idx: 24250, batch size: 61, loss[discriminator_loss=2.761, discriminator_real_loss=1.383, discriminator_fake_loss=1.379, generator_loss=26.93, generator_mel_loss=18.51, generator_kl_loss=1.373, generator_dur_loss=1.776, generator_adv_loss=1.983, generator_feat_match_loss=3.293, over 61.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.396, discriminator_fake_loss=1.345, generator_loss=27.25, generator_mel_loss=18.59, generator_kl_loss=1.387, generator_dur_loss=1.769, generator_adv_loss=1.903, generator_feat_match_loss=3.601, over 4154.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:56:06,092 INFO [train.py:527] (4/6) Epoch 196, batch 120, global_batch_idx: 24300, batch size: 17, loss[discriminator_loss=2.573, discriminator_real_loss=1.263, discriminator_fake_loss=1.31, generator_loss=30.55, generator_mel_loss=20.72, generator_kl_loss=1.632, generator_dur_loss=1.599, generator_adv_loss=2.106, generator_feat_match_loss=4.489, over 17.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.392, discriminator_fake_loss=1.344, generator_loss=27.35, generator_mel_loss=18.64, generator_kl_loss=1.393, generator_dur_loss=1.768, generator_adv_loss=1.91, generator_feat_match_loss=3.637, over 6898.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:56:15,243 INFO [train.py:919] (4/6) Start epoch 197 +2024-03-12 19:58:46,769 INFO [train.py:527] (4/6) Epoch 197, batch 46, global_batch_idx: 24350, batch size: 80, loss[discriminator_loss=2.708, discriminator_real_loss=1.366, discriminator_fake_loss=1.342, generator_loss=27.95, generator_mel_loss=18.91, generator_kl_loss=1.425, generator_dur_loss=1.811, generator_adv_loss=1.903, generator_feat_match_loss=3.912, over 80.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.387, discriminator_fake_loss=1.35, generator_loss=27.41, generator_mel_loss=18.72, generator_kl_loss=1.393, generator_dur_loss=1.758, generator_adv_loss=1.939, generator_feat_match_loss=3.598, over 2836.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:01:06,565 INFO [train.py:527] (4/6) Epoch 197, batch 96, global_batch_idx: 24400, batch size: 47, loss[discriminator_loss=2.712, discriminator_real_loss=1.307, discriminator_fake_loss=1.406, generator_loss=27.62, generator_mel_loss=18.75, generator_kl_loss=1.64, generator_dur_loss=1.614, generator_adv_loss=1.978, generator_feat_match_loss=3.637, over 47.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.389, discriminator_fake_loss=1.351, generator_loss=27.36, generator_mel_loss=18.7, generator_kl_loss=1.389, generator_dur_loss=1.748, generator_adv_loss=1.924, generator_feat_match_loss=3.603, over 5649.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:01:06,566 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 20:01:15,297 INFO [train.py:591] (4/6) Epoch 197, validation: discriminator_loss=2.775, discriminator_real_loss=1.452, discriminator_fake_loss=1.324, generator_loss=25.87, generator_mel_loss=18.32, generator_kl_loss=1.216, generator_dur_loss=1.813, generator_adv_loss=1.908, generator_feat_match_loss=2.619, over 100.00 samples. +2024-03-12 20:01:15,297 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 20:02:32,848 INFO [train.py:919] (4/6) Start epoch 198 +2024-03-12 20:03:57,300 INFO [train.py:527] (4/6) Epoch 198, batch 22, global_batch_idx: 24450, batch size: 56, loss[discriminator_loss=2.718, discriminator_real_loss=1.224, discriminator_fake_loss=1.494, generator_loss=28.35, generator_mel_loss=19.03, generator_kl_loss=1.394, generator_dur_loss=1.716, generator_adv_loss=2.013, generator_feat_match_loss=4.197, over 56.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.342, generator_loss=27.49, generator_mel_loss=18.7, generator_kl_loss=1.388, generator_dur_loss=1.72, generator_adv_loss=1.935, generator_feat_match_loss=3.737, over 1184.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:06:18,075 INFO [train.py:527] (4/6) Epoch 198, batch 72, global_batch_idx: 24500, batch size: 66, loss[discriminator_loss=2.789, discriminator_real_loss=1.391, discriminator_fake_loss=1.398, generator_loss=28.41, generator_mel_loss=19.17, generator_kl_loss=1.601, generator_dur_loss=1.766, generator_adv_loss=1.873, generator_feat_match_loss=3.994, over 66.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.393, discriminator_fake_loss=1.344, generator_loss=27.39, generator_mel_loss=18.68, generator_kl_loss=1.391, generator_dur_loss=1.736, generator_adv_loss=1.916, generator_feat_match_loss=3.666, over 3974.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:08:36,502 INFO [train.py:527] (4/6) Epoch 198, batch 122, global_batch_idx: 24550, batch size: 56, loss[discriminator_loss=2.834, discriminator_real_loss=1.324, discriminator_fake_loss=1.51, generator_loss=26.24, generator_mel_loss=17.94, generator_kl_loss=1.352, generator_dur_loss=1.751, generator_adv_loss=1.929, generator_feat_match_loss=3.263, over 56.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.393, discriminator_fake_loss=1.346, generator_loss=27.35, generator_mel_loss=18.65, generator_kl_loss=1.388, generator_dur_loss=1.742, generator_adv_loss=1.919, generator_feat_match_loss=3.654, over 6822.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:08:41,435 INFO [train.py:919] (4/6) Start epoch 199 +2024-03-12 20:11:17,839 INFO [train.py:527] (4/6) Epoch 199, batch 48, global_batch_idx: 24600, batch size: 66, loss[discriminator_loss=2.764, discriminator_real_loss=1.349, discriminator_fake_loss=1.415, generator_loss=27.24, generator_mel_loss=18.71, generator_kl_loss=1.391, generator_dur_loss=1.782, generator_adv_loss=1.767, generator_feat_match_loss=3.591, over 66.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.393, discriminator_fake_loss=1.35, generator_loss=27.37, generator_mel_loss=18.68, generator_kl_loss=1.386, generator_dur_loss=1.757, generator_adv_loss=1.907, generator_feat_match_loss=3.637, over 2871.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:11:17,841 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 20:11:25,592 INFO [train.py:591] (4/6) Epoch 199, validation: discriminator_loss=2.783, discriminator_real_loss=1.337, discriminator_fake_loss=1.447, generator_loss=26.96, generator_mel_loss=19, generator_kl_loss=1.197, generator_dur_loss=1.821, generator_adv_loss=1.71, generator_feat_match_loss=3.232, over 100.00 samples. +2024-03-12 20:11:25,593 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 20:13:45,115 INFO [train.py:527] (4/6) Epoch 199, batch 98, global_batch_idx: 24650, batch size: 52, loss[discriminator_loss=2.772, discriminator_real_loss=1.456, discriminator_fake_loss=1.316, generator_loss=26.71, generator_mel_loss=18.13, generator_kl_loss=1.682, generator_dur_loss=1.663, generator_adv_loss=1.796, generator_feat_match_loss=3.433, over 52.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=27.41, generator_mel_loss=18.68, generator_kl_loss=1.39, generator_dur_loss=1.752, generator_adv_loss=1.916, generator_feat_match_loss=3.668, over 5739.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:14:55,547 INFO [train.py:919] (4/6) Start epoch 200 +2024-03-12 20:16:26,067 INFO [train.py:527] (4/6) Epoch 200, batch 24, global_batch_idx: 24700, batch size: 50, loss[discriminator_loss=2.811, discriminator_real_loss=1.334, discriminator_fake_loss=1.477, generator_loss=25.8, generator_mel_loss=17.75, generator_kl_loss=1.474, generator_dur_loss=1.681, generator_adv_loss=1.954, generator_feat_match_loss=2.939, over 50.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.397, discriminator_fake_loss=1.351, generator_loss=27.15, generator_mel_loss=18.54, generator_kl_loss=1.386, generator_dur_loss=1.733, generator_adv_loss=1.904, generator_feat_match_loss=3.588, over 1455.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:18:45,089 INFO [train.py:527] (4/6) Epoch 200, batch 74, global_batch_idx: 24750, batch size: 70, loss[discriminator_loss=2.712, discriminator_real_loss=1.407, discriminator_fake_loss=1.305, generator_loss=27.36, generator_mel_loss=18.75, generator_kl_loss=1.291, generator_dur_loss=1.749, generator_adv_loss=1.809, generator_feat_match_loss=3.755, over 70.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.398, discriminator_fake_loss=1.348, generator_loss=27.25, generator_mel_loss=18.63, generator_kl_loss=1.394, generator_dur_loss=1.731, generator_adv_loss=1.908, generator_feat_match_loss=3.589, over 4282.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:21:02,988 INFO [train.py:919] (4/6) Start epoch 201 +2024-03-12 20:21:28,019 INFO [train.py:527] (4/6) Epoch 201, batch 0, global_batch_idx: 24800, batch size: 47, loss[discriminator_loss=2.678, discriminator_real_loss=1.333, discriminator_fake_loss=1.345, generator_loss=27.64, generator_mel_loss=18.31, generator_kl_loss=1.411, generator_dur_loss=1.615, generator_adv_loss=1.907, generator_feat_match_loss=4.395, over 47.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.333, discriminator_fake_loss=1.345, generator_loss=27.64, generator_mel_loss=18.31, generator_kl_loss=1.411, generator_dur_loss=1.615, generator_adv_loss=1.907, generator_feat_match_loss=4.395, over 47.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:21:28,021 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 20:21:35,805 INFO [train.py:591] (4/6) Epoch 201, validation: discriminator_loss=2.747, discriminator_real_loss=1.414, discriminator_fake_loss=1.333, generator_loss=26.54, generator_mel_loss=19.03, generator_kl_loss=1.132, generator_dur_loss=1.801, generator_adv_loss=1.8, generator_feat_match_loss=2.77, over 100.00 samples. +2024-03-12 20:21:35,807 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 20:23:53,753 INFO [train.py:527] (4/6) Epoch 201, batch 50, global_batch_idx: 24850, batch size: 36, loss[discriminator_loss=2.731, discriminator_real_loss=1.375, discriminator_fake_loss=1.356, generator_loss=27.57, generator_mel_loss=18.98, generator_kl_loss=1.649, generator_dur_loss=1.727, generator_adv_loss=1.922, generator_feat_match_loss=3.295, over 36.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.388, discriminator_fake_loss=1.351, generator_loss=27.47, generator_mel_loss=18.72, generator_kl_loss=1.393, generator_dur_loss=1.742, generator_adv_loss=1.9, generator_feat_match_loss=3.716, over 2859.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:26:11,201 INFO [train.py:527] (4/6) Epoch 201, batch 100, global_batch_idx: 24900, batch size: 58, loss[discriminator_loss=2.695, discriminator_real_loss=1.348, discriminator_fake_loss=1.347, generator_loss=27.05, generator_mel_loss=18.21, generator_kl_loss=1.377, generator_dur_loss=1.729, generator_adv_loss=2.063, generator_feat_match_loss=3.668, over 58.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.381, discriminator_fake_loss=1.353, generator_loss=27.5, generator_mel_loss=18.7, generator_kl_loss=1.39, generator_dur_loss=1.741, generator_adv_loss=1.934, generator_feat_match_loss=3.728, over 5714.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:27:18,197 INFO [train.py:919] (4/6) Start epoch 202 +2024-03-12 20:28:54,308 INFO [train.py:527] (4/6) Epoch 202, batch 26, global_batch_idx: 24950, batch size: 47, loss[discriminator_loss=2.76, discriminator_real_loss=1.437, discriminator_fake_loss=1.323, generator_loss=27.44, generator_mel_loss=18.7, generator_kl_loss=1.594, generator_dur_loss=1.652, generator_adv_loss=1.743, generator_feat_match_loss=3.755, over 47.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.375, discriminator_fake_loss=1.358, generator_loss=27.38, generator_mel_loss=18.6, generator_kl_loss=1.389, generator_dur_loss=1.752, generator_adv_loss=1.929, generator_feat_match_loss=3.714, over 1558.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:31:15,259 INFO [train.py:527] (4/6) Epoch 202, batch 76, global_batch_idx: 25000, batch size: 39, loss[discriminator_loss=2.77, discriminator_real_loss=1.37, discriminator_fake_loss=1.4, generator_loss=28.05, generator_mel_loss=18.63, generator_kl_loss=1.457, generator_dur_loss=1.701, generator_adv_loss=2.047, generator_feat_match_loss=4.212, over 39.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.378, discriminator_fake_loss=1.358, generator_loss=27.33, generator_mel_loss=18.61, generator_kl_loss=1.397, generator_dur_loss=1.753, generator_adv_loss=1.914, generator_feat_match_loss=3.658, over 4361.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:31:15,261 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 20:31:23,179 INFO [train.py:591] (4/6) Epoch 202, validation: discriminator_loss=2.801, discriminator_real_loss=1.557, discriminator_fake_loss=1.244, generator_loss=26.27, generator_mel_loss=18.44, generator_kl_loss=1.31, generator_dur_loss=1.826, generator_adv_loss=1.946, generator_feat_match_loss=2.748, over 100.00 samples. +2024-03-12 20:31:23,179 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 20:33:34,008 INFO [train.py:919] (4/6) Start epoch 203 +2024-03-12 20:34:05,348 INFO [train.py:527] (4/6) Epoch 203, batch 2, global_batch_idx: 25050, batch size: 42, loss[discriminator_loss=2.761, discriminator_real_loss=1.287, discriminator_fake_loss=1.475, generator_loss=27.49, generator_mel_loss=18.68, generator_kl_loss=1.431, generator_dur_loss=1.681, generator_adv_loss=2.048, generator_feat_match_loss=3.643, over 42.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.387, discriminator_fake_loss=1.353, generator_loss=28.14, generator_mel_loss=19.19, generator_kl_loss=1.381, generator_dur_loss=1.7, generator_adv_loss=1.988, generator_feat_match_loss=3.88, over 137.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:36:24,690 INFO [train.py:527] (4/6) Epoch 203, batch 52, global_batch_idx: 25100, batch size: 80, loss[discriminator_loss=2.692, discriminator_real_loss=1.311, discriminator_fake_loss=1.381, generator_loss=28.31, generator_mel_loss=18.91, generator_kl_loss=1.329, generator_dur_loss=1.848, generator_adv_loss=2.079, generator_feat_match_loss=4.136, over 80.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.382, discriminator_fake_loss=1.345, generator_loss=27.26, generator_mel_loss=18.54, generator_kl_loss=1.386, generator_dur_loss=1.756, generator_adv_loss=1.929, generator_feat_match_loss=3.65, over 3142.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:38:42,964 INFO [train.py:527] (4/6) Epoch 203, batch 102, global_batch_idx: 25150, batch size: 31, loss[discriminator_loss=2.764, discriminator_real_loss=1.367, discriminator_fake_loss=1.398, generator_loss=27, generator_mel_loss=18.73, generator_kl_loss=1.469, generator_dur_loss=1.593, generator_adv_loss=1.908, generator_feat_match_loss=3.303, over 31.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.388, discriminator_fake_loss=1.346, generator_loss=27.4, generator_mel_loss=18.63, generator_kl_loss=1.392, generator_dur_loss=1.743, generator_adv_loss=1.947, generator_feat_match_loss=3.694, over 5820.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:39:42,910 INFO [train.py:919] (4/6) Start epoch 204 +2024-03-12 20:41:27,044 INFO [train.py:527] (4/6) Epoch 204, batch 28, global_batch_idx: 25200, batch size: 53, loss[discriminator_loss=2.744, discriminator_real_loss=1.354, discriminator_fake_loss=1.39, generator_loss=27.14, generator_mel_loss=18.34, generator_kl_loss=1.395, generator_dur_loss=1.662, generator_adv_loss=2.077, generator_feat_match_loss=3.662, over 53.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.375, discriminator_fake_loss=1.354, generator_loss=27.44, generator_mel_loss=18.65, generator_kl_loss=1.391, generator_dur_loss=1.752, generator_adv_loss=1.902, generator_feat_match_loss=3.742, over 1676.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:41:27,046 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 20:41:34,969 INFO [train.py:591] (4/6) Epoch 204, validation: discriminator_loss=2.807, discriminator_real_loss=1.546, discriminator_fake_loss=1.261, generator_loss=26.54, generator_mel_loss=18.68, generator_kl_loss=1.063, generator_dur_loss=1.807, generator_adv_loss=1.974, generator_feat_match_loss=3.013, over 100.00 samples. +2024-03-12 20:41:34,971 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 20:43:55,073 INFO [train.py:527] (4/6) Epoch 204, batch 78, global_batch_idx: 25250, batch size: 47, loss[discriminator_loss=2.766, discriminator_real_loss=1.357, discriminator_fake_loss=1.41, generator_loss=28.5, generator_mel_loss=19.57, generator_kl_loss=1.55, generator_dur_loss=1.771, generator_adv_loss=1.855, generator_feat_match_loss=3.758, over 47.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.384, discriminator_fake_loss=1.356, generator_loss=27.4, generator_mel_loss=18.65, generator_kl_loss=1.401, generator_dur_loss=1.746, generator_adv_loss=1.904, generator_feat_match_loss=3.701, over 4374.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:45:59,377 INFO [train.py:919] (4/6) Start epoch 205 +2024-03-12 20:46:34,556 INFO [train.py:527] (4/6) Epoch 205, batch 4, global_batch_idx: 25300, batch size: 88, loss[discriminator_loss=2.837, discriminator_real_loss=1.436, discriminator_fake_loss=1.4, generator_loss=26.52, generator_mel_loss=18.14, generator_kl_loss=1.299, generator_dur_loss=1.827, generator_adv_loss=1.803, generator_feat_match_loss=3.445, over 88.00 samples.], tot_loss[discriminator_loss=2.773, discriminator_real_loss=1.409, discriminator_fake_loss=1.364, generator_loss=26.95, generator_mel_loss=18.41, generator_kl_loss=1.321, generator_dur_loss=1.816, generator_adv_loss=1.896, generator_feat_match_loss=3.501, over 306.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:48:55,180 INFO [train.py:527] (4/6) Epoch 205, batch 54, global_batch_idx: 25350, batch size: 59, loss[discriminator_loss=2.64, discriminator_real_loss=1.497, discriminator_fake_loss=1.142, generator_loss=28.42, generator_mel_loss=18.54, generator_kl_loss=1.425, generator_dur_loss=1.697, generator_adv_loss=2.213, generator_feat_match_loss=4.548, over 59.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.395, discriminator_fake_loss=1.361, generator_loss=27.74, generator_mel_loss=18.62, generator_kl_loss=1.39, generator_dur_loss=1.757, generator_adv_loss=2.022, generator_feat_match_loss=3.956, over 3130.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:51:12,543 INFO [train.py:527] (4/6) Epoch 205, batch 104, global_batch_idx: 25400, batch size: 45, loss[discriminator_loss=2.764, discriminator_real_loss=1.264, discriminator_fake_loss=1.501, generator_loss=27.29, generator_mel_loss=18.58, generator_kl_loss=1.463, generator_dur_loss=1.675, generator_adv_loss=1.916, generator_feat_match_loss=3.66, over 45.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.399, discriminator_fake_loss=1.349, generator_loss=27.57, generator_mel_loss=18.64, generator_kl_loss=1.381, generator_dur_loss=1.754, generator_adv_loss=1.99, generator_feat_match_loss=3.801, over 6077.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:51:12,544 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 20:51:21,445 INFO [train.py:591] (4/6) Epoch 205, validation: discriminator_loss=2.768, discriminator_real_loss=1.479, discriminator_fake_loss=1.289, generator_loss=26.36, generator_mel_loss=18.63, generator_kl_loss=1.039, generator_dur_loss=1.821, generator_adv_loss=1.939, generator_feat_match_loss=2.932, over 100.00 samples. +2024-03-12 20:51:21,446 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 20:52:14,030 INFO [train.py:919] (4/6) Start epoch 206 +2024-03-12 20:54:02,055 INFO [train.py:527] (4/6) Epoch 206, batch 30, global_batch_idx: 25450, batch size: 36, loss[discriminator_loss=2.763, discriminator_real_loss=1.407, discriminator_fake_loss=1.357, generator_loss=26.73, generator_mel_loss=18.3, generator_kl_loss=1.523, generator_dur_loss=1.735, generator_adv_loss=1.93, generator_feat_match_loss=3.244, over 36.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.398, discriminator_fake_loss=1.343, generator_loss=27.43, generator_mel_loss=18.69, generator_kl_loss=1.363, generator_dur_loss=1.795, generator_adv_loss=1.903, generator_feat_match_loss=3.683, over 1864.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:56:21,010 INFO [train.py:527] (4/6) Epoch 206, batch 80, global_batch_idx: 25500, batch size: 72, loss[discriminator_loss=2.703, discriminator_real_loss=1.478, discriminator_fake_loss=1.225, generator_loss=27.2, generator_mel_loss=18.71, generator_kl_loss=1.238, generator_dur_loss=1.837, generator_adv_loss=1.813, generator_feat_match_loss=3.596, over 72.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.387, discriminator_fake_loss=1.342, generator_loss=27.41, generator_mel_loss=18.68, generator_kl_loss=1.365, generator_dur_loss=1.778, generator_adv_loss=1.904, generator_feat_match_loss=3.684, over 4759.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:58:19,399 INFO [train.py:919] (4/6) Start epoch 207 +2024-03-12 20:58:57,489 INFO [train.py:527] (4/6) Epoch 207, batch 6, global_batch_idx: 25550, batch size: 60, loss[discriminator_loss=2.78, discriminator_real_loss=1.488, discriminator_fake_loss=1.292, generator_loss=26.6, generator_mel_loss=18.39, generator_kl_loss=1.409, generator_dur_loss=1.762, generator_adv_loss=1.801, generator_feat_match_loss=3.238, over 60.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.395, discriminator_fake_loss=1.357, generator_loss=27.43, generator_mel_loss=18.77, generator_kl_loss=1.432, generator_dur_loss=1.756, generator_adv_loss=1.91, generator_feat_match_loss=3.565, over 410.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:01:18,424 INFO [train.py:527] (4/6) Epoch 207, batch 56, global_batch_idx: 25600, batch size: 48, loss[discriminator_loss=2.898, discriminator_real_loss=1.458, discriminator_fake_loss=1.44, generator_loss=27.16, generator_mel_loss=18.6, generator_kl_loss=1.515, generator_dur_loss=1.712, generator_adv_loss=2.148, generator_feat_match_loss=3.186, over 48.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.374, discriminator_fake_loss=1.362, generator_loss=27.5, generator_mel_loss=18.66, generator_kl_loss=1.375, generator_dur_loss=1.783, generator_adv_loss=1.92, generator_feat_match_loss=3.758, over 3461.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:01:18,425 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 21:01:26,533 INFO [train.py:591] (4/6) Epoch 207, validation: discriminator_loss=2.717, discriminator_real_loss=1.586, discriminator_fake_loss=1.131, generator_loss=27.03, generator_mel_loss=18.54, generator_kl_loss=1.148, generator_dur_loss=1.846, generator_adv_loss=2.142, generator_feat_match_loss=3.347, over 100.00 samples. +2024-03-12 21:01:26,534 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 21:03:49,610 INFO [train.py:527] (4/6) Epoch 207, batch 106, global_batch_idx: 25650, batch size: 48, loss[discriminator_loss=2.894, discriminator_real_loss=1.686, discriminator_fake_loss=1.209, generator_loss=27.1, generator_mel_loss=18.55, generator_kl_loss=1.529, generator_dur_loss=1.675, generator_adv_loss=1.684, generator_feat_match_loss=3.665, over 48.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.44, generator_mel_loss=18.65, generator_kl_loss=1.363, generator_dur_loss=1.78, generator_adv_loss=1.928, generator_feat_match_loss=3.72, over 6374.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:04:33,313 INFO [train.py:919] (4/6) Start epoch 208 +2024-03-12 21:06:25,004 INFO [train.py:527] (4/6) Epoch 208, batch 32, global_batch_idx: 25700, batch size: 70, loss[discriminator_loss=2.738, discriminator_real_loss=1.383, discriminator_fake_loss=1.355, generator_loss=27.64, generator_mel_loss=18.69, generator_kl_loss=1.397, generator_dur_loss=1.8, generator_adv_loss=1.908, generator_feat_match_loss=3.843, over 70.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.388, discriminator_fake_loss=1.331, generator_loss=27.55, generator_mel_loss=18.73, generator_kl_loss=1.358, generator_dur_loss=1.755, generator_adv_loss=1.92, generator_feat_match_loss=3.787, over 1756.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:08:44,931 INFO [train.py:527] (4/6) Epoch 208, batch 82, global_batch_idx: 25750, batch size: 52, loss[discriminator_loss=2.681, discriminator_real_loss=1.33, discriminator_fake_loss=1.351, generator_loss=27.03, generator_mel_loss=18.33, generator_kl_loss=1.431, generator_dur_loss=1.681, generator_adv_loss=1.811, generator_feat_match_loss=3.772, over 52.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=27.47, generator_mel_loss=18.66, generator_kl_loss=1.382, generator_dur_loss=1.764, generator_adv_loss=1.915, generator_feat_match_loss=3.753, over 4558.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:10:39,546 INFO [train.py:919] (4/6) Start epoch 209 +2024-03-12 21:11:24,732 INFO [train.py:527] (4/6) Epoch 209, batch 8, global_batch_idx: 25800, batch size: 68, loss[discriminator_loss=2.749, discriminator_real_loss=1.491, discriminator_fake_loss=1.258, generator_loss=26.84, generator_mel_loss=18.42, generator_kl_loss=1.326, generator_dur_loss=1.791, generator_adv_loss=1.869, generator_feat_match_loss=3.435, over 68.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.411, discriminator_fake_loss=1.334, generator_loss=27.16, generator_mel_loss=18.49, generator_kl_loss=1.417, generator_dur_loss=1.729, generator_adv_loss=1.912, generator_feat_match_loss=3.614, over 508.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:11:24,735 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 21:11:32,763 INFO [train.py:591] (4/6) Epoch 209, validation: discriminator_loss=2.782, discriminator_real_loss=1.439, discriminator_fake_loss=1.343, generator_loss=26, generator_mel_loss=18.5, generator_kl_loss=1.201, generator_dur_loss=1.815, generator_adv_loss=1.769, generator_feat_match_loss=2.715, over 100.00 samples. +2024-03-12 21:11:32,765 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 21:13:50,551 INFO [train.py:527] (4/6) Epoch 209, batch 58, global_batch_idx: 25850, batch size: 31, loss[discriminator_loss=2.694, discriminator_real_loss=1.232, discriminator_fake_loss=1.462, generator_loss=28.54, generator_mel_loss=19.25, generator_kl_loss=1.583, generator_dur_loss=1.592, generator_adv_loss=2.083, generator_feat_match_loss=4.028, over 31.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.394, discriminator_fake_loss=1.346, generator_loss=27.32, generator_mel_loss=18.56, generator_kl_loss=1.388, generator_dur_loss=1.739, generator_adv_loss=1.918, generator_feat_match_loss=3.72, over 3362.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:16:09,427 INFO [train.py:527] (4/6) Epoch 209, batch 108, global_batch_idx: 25900, batch size: 14, loss[discriminator_loss=2.667, discriminator_real_loss=1.345, discriminator_fake_loss=1.322, generator_loss=29.72, generator_mel_loss=20.14, generator_kl_loss=1.791, generator_dur_loss=1.627, generator_adv_loss=1.947, generator_feat_match_loss=4.222, over 14.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.392, discriminator_fake_loss=1.348, generator_loss=27.31, generator_mel_loss=18.57, generator_kl_loss=1.386, generator_dur_loss=1.736, generator_adv_loss=1.921, generator_feat_match_loss=3.694, over 6286.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:16:51,512 INFO [train.py:919] (4/6) Start epoch 210 +2024-03-12 21:18:49,184 INFO [train.py:527] (4/6) Epoch 210, batch 34, global_batch_idx: 25950, batch size: 25, loss[discriminator_loss=2.703, discriminator_real_loss=1.326, discriminator_fake_loss=1.377, generator_loss=29.79, generator_mel_loss=20.41, generator_kl_loss=1.568, generator_dur_loss=1.564, generator_adv_loss=2.031, generator_feat_match_loss=4.214, over 25.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.389, discriminator_fake_loss=1.339, generator_loss=27.35, generator_mel_loss=18.6, generator_kl_loss=1.391, generator_dur_loss=1.728, generator_adv_loss=1.92, generator_feat_match_loss=3.719, over 1861.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:21:09,210 INFO [train.py:527] (4/6) Epoch 210, batch 84, global_batch_idx: 26000, batch size: 45, loss[discriminator_loss=2.707, discriminator_real_loss=1.464, discriminator_fake_loss=1.243, generator_loss=26.54, generator_mel_loss=17.97, generator_kl_loss=1.483, generator_dur_loss=1.653, generator_adv_loss=1.827, generator_feat_match_loss=3.6, over 45.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.388, discriminator_fake_loss=1.34, generator_loss=27.36, generator_mel_loss=18.58, generator_kl_loss=1.386, generator_dur_loss=1.737, generator_adv_loss=1.921, generator_feat_match_loss=3.733, over 4693.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:21:09,211 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 21:21:17,006 INFO [train.py:591] (4/6) Epoch 210, validation: discriminator_loss=2.839, discriminator_real_loss=1.406, discriminator_fake_loss=1.433, generator_loss=26.29, generator_mel_loss=18.73, generator_kl_loss=1.079, generator_dur_loss=1.807, generator_adv_loss=1.765, generator_feat_match_loss=2.917, over 100.00 samples. +2024-03-12 21:21:17,007 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 21:23:06,356 INFO [train.py:919] (4/6) Start epoch 211 +2024-03-12 21:23:58,793 INFO [train.py:527] (4/6) Epoch 211, batch 10, global_batch_idx: 26050, batch size: 45, loss[discriminator_loss=2.679, discriminator_real_loss=1.366, discriminator_fake_loss=1.313, generator_loss=28.65, generator_mel_loss=19.52, generator_kl_loss=1.667, generator_dur_loss=1.639, generator_adv_loss=1.797, generator_feat_match_loss=4.031, over 45.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.373, discriminator_fake_loss=1.369, generator_loss=27.82, generator_mel_loss=18.86, generator_kl_loss=1.407, generator_dur_loss=1.73, generator_adv_loss=1.898, generator_feat_match_loss=3.932, over 646.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:26:18,178 INFO [train.py:527] (4/6) Epoch 211, batch 60, global_batch_idx: 26100, batch size: 59, loss[discriminator_loss=2.719, discriminator_real_loss=1.422, discriminator_fake_loss=1.297, generator_loss=27.77, generator_mel_loss=18.69, generator_kl_loss=1.45, generator_dur_loss=1.722, generator_adv_loss=1.926, generator_feat_match_loss=3.986, over 59.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.388, discriminator_fake_loss=1.343, generator_loss=27.46, generator_mel_loss=18.65, generator_kl_loss=1.382, generator_dur_loss=1.726, generator_adv_loss=1.916, generator_feat_match_loss=3.787, over 3563.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:28:36,086 INFO [train.py:527] (4/6) Epoch 211, batch 110, global_batch_idx: 26150, batch size: 16, loss[discriminator_loss=2.682, discriminator_real_loss=1.388, discriminator_fake_loss=1.294, generator_loss=27.83, generator_mel_loss=18.53, generator_kl_loss=1.709, generator_dur_loss=1.539, generator_adv_loss=1.88, generator_feat_match_loss=4.171, over 16.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.392, discriminator_fake_loss=1.348, generator_loss=27.35, generator_mel_loss=18.6, generator_kl_loss=1.373, generator_dur_loss=1.737, generator_adv_loss=1.91, generator_feat_match_loss=3.733, over 6473.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:29:13,349 INFO [train.py:919] (4/6) Start epoch 212 +2024-03-12 21:31:21,579 INFO [train.py:527] (4/6) Epoch 212, batch 36, global_batch_idx: 26200, batch size: 50, loss[discriminator_loss=2.665, discriminator_real_loss=1.205, discriminator_fake_loss=1.46, generator_loss=27.39, generator_mel_loss=18.59, generator_kl_loss=1.23, generator_dur_loss=1.686, generator_adv_loss=2.094, generator_feat_match_loss=3.785, over 50.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.38, discriminator_fake_loss=1.347, generator_loss=27.39, generator_mel_loss=18.55, generator_kl_loss=1.349, generator_dur_loss=1.778, generator_adv_loss=1.956, generator_feat_match_loss=3.756, over 2188.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:31:21,581 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 21:31:29,922 INFO [train.py:591] (4/6) Epoch 212, validation: discriminator_loss=2.765, discriminator_real_loss=1.38, discriminator_fake_loss=1.384, generator_loss=26.31, generator_mel_loss=18.38, generator_kl_loss=1.243, generator_dur_loss=1.806, generator_adv_loss=1.88, generator_feat_match_loss=3.006, over 100.00 samples. +2024-03-12 21:31:29,923 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 21:33:49,090 INFO [train.py:527] (4/6) Epoch 212, batch 86, global_batch_idx: 26250, batch size: 61, loss[discriminator_loss=2.76, discriminator_real_loss=1.363, discriminator_fake_loss=1.397, generator_loss=27.95, generator_mel_loss=19.06, generator_kl_loss=1.324, generator_dur_loss=1.759, generator_adv_loss=1.998, generator_feat_match_loss=3.809, over 61.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.344, generator_loss=27.47, generator_mel_loss=18.6, generator_kl_loss=1.365, generator_dur_loss=1.76, generator_adv_loss=1.951, generator_feat_match_loss=3.794, over 4913.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:35:33,361 INFO [train.py:919] (4/6) Start epoch 213 +2024-03-12 21:36:29,152 INFO [train.py:527] (4/6) Epoch 213, batch 12, global_batch_idx: 26300, batch size: 36, loss[discriminator_loss=2.664, discriminator_real_loss=1.356, discriminator_fake_loss=1.308, generator_loss=28.39, generator_mel_loss=18.91, generator_kl_loss=1.571, generator_dur_loss=1.713, generator_adv_loss=1.971, generator_feat_match_loss=4.226, over 36.00 samples.], tot_loss[discriminator_loss=2.794, discriminator_real_loss=1.433, discriminator_fake_loss=1.361, generator_loss=27.06, generator_mel_loss=18.52, generator_kl_loss=1.391, generator_dur_loss=1.743, generator_adv_loss=1.894, generator_feat_match_loss=3.511, over 701.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:38:49,993 INFO [train.py:527] (4/6) Epoch 213, batch 62, global_batch_idx: 26350, batch size: 74, loss[discriminator_loss=2.747, discriminator_real_loss=1.451, discriminator_fake_loss=1.296, generator_loss=27.03, generator_mel_loss=18.51, generator_kl_loss=1.253, generator_dur_loss=1.849, generator_adv_loss=1.911, generator_feat_match_loss=3.507, over 74.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.411, discriminator_fake_loss=1.355, generator_loss=27.4, generator_mel_loss=18.62, generator_kl_loss=1.373, generator_dur_loss=1.758, generator_adv_loss=1.93, generator_feat_match_loss=3.711, over 3467.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:41:08,803 INFO [train.py:527] (4/6) Epoch 213, batch 112, global_batch_idx: 26400, batch size: 39, loss[discriminator_loss=2.828, discriminator_real_loss=1.517, discriminator_fake_loss=1.31, generator_loss=27.22, generator_mel_loss=18.58, generator_kl_loss=1.511, generator_dur_loss=1.684, generator_adv_loss=1.829, generator_feat_match_loss=3.621, over 39.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.404, discriminator_fake_loss=1.35, generator_loss=27.43, generator_mel_loss=18.63, generator_kl_loss=1.381, generator_dur_loss=1.758, generator_adv_loss=1.932, generator_feat_match_loss=3.734, over 6158.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:41:08,804 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 21:41:17,869 INFO [train.py:591] (4/6) Epoch 213, validation: discriminator_loss=2.801, discriminator_real_loss=1.44, discriminator_fake_loss=1.36, generator_loss=26.31, generator_mel_loss=18.64, generator_kl_loss=1.191, generator_dur_loss=1.82, generator_adv_loss=1.78, generator_feat_match_loss=2.883, over 100.00 samples. +2024-03-12 21:41:17,870 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 21:41:48,995 INFO [train.py:919] (4/6) Start epoch 214 +2024-03-12 21:43:55,355 INFO [train.py:527] (4/6) Epoch 214, batch 38, global_batch_idx: 26450, batch size: 55, loss[discriminator_loss=2.771, discriminator_real_loss=1.587, discriminator_fake_loss=1.184, generator_loss=26.94, generator_mel_loss=18.7, generator_kl_loss=1.352, generator_dur_loss=1.743, generator_adv_loss=1.617, generator_feat_match_loss=3.526, over 55.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.39, discriminator_fake_loss=1.346, generator_loss=27.41, generator_mel_loss=18.66, generator_kl_loss=1.378, generator_dur_loss=1.759, generator_adv_loss=1.924, generator_feat_match_loss=3.683, over 2182.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:46:16,891 INFO [train.py:527] (4/6) Epoch 214, batch 88, global_batch_idx: 26500, batch size: 74, loss[discriminator_loss=2.751, discriminator_real_loss=1.506, discriminator_fake_loss=1.246, generator_loss=27.01, generator_mel_loss=18.66, generator_kl_loss=1.358, generator_dur_loss=1.809, generator_adv_loss=1.803, generator_feat_match_loss=3.382, over 74.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.382, discriminator_fake_loss=1.356, generator_loss=27.3, generator_mel_loss=18.59, generator_kl_loss=1.368, generator_dur_loss=1.761, generator_adv_loss=1.911, generator_feat_match_loss=3.668, over 5118.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:47:54,566 INFO [train.py:919] (4/6) Start epoch 215 +2024-03-12 21:48:58,709 INFO [train.py:527] (4/6) Epoch 215, batch 14, global_batch_idx: 26550, batch size: 25, loss[discriminator_loss=2.715, discriminator_real_loss=1.298, discriminator_fake_loss=1.417, generator_loss=28.32, generator_mel_loss=19.3, generator_kl_loss=1.434, generator_dur_loss=1.601, generator_adv_loss=2.101, generator_feat_match_loss=3.88, over 25.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.376, discriminator_fake_loss=1.361, generator_loss=27.59, generator_mel_loss=18.76, generator_kl_loss=1.38, generator_dur_loss=1.758, generator_adv_loss=1.908, generator_feat_match_loss=3.777, over 860.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:51:17,895 INFO [train.py:527] (4/6) Epoch 215, batch 64, global_batch_idx: 26600, batch size: 48, loss[discriminator_loss=2.78, discriminator_real_loss=1.433, discriminator_fake_loss=1.347, generator_loss=27.39, generator_mel_loss=19.23, generator_kl_loss=1.452, generator_dur_loss=1.715, generator_adv_loss=1.781, generator_feat_match_loss=3.208, over 48.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=27.39, generator_mel_loss=18.57, generator_kl_loss=1.359, generator_dur_loss=1.779, generator_adv_loss=1.946, generator_feat_match_loss=3.74, over 3936.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:51:17,897 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 21:51:25,980 INFO [train.py:591] (4/6) Epoch 215, validation: discriminator_loss=2.771, discriminator_real_loss=1.38, discriminator_fake_loss=1.391, generator_loss=26, generator_mel_loss=18.38, generator_kl_loss=1.159, generator_dur_loss=1.825, generator_adv_loss=1.779, generator_feat_match_loss=2.863, over 100.00 samples. +2024-03-12 21:51:25,981 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 21:53:43,264 INFO [train.py:527] (4/6) Epoch 215, batch 114, global_batch_idx: 26650, batch size: 16, loss[discriminator_loss=2.712, discriminator_real_loss=1.422, discriminator_fake_loss=1.29, generator_loss=28.12, generator_mel_loss=19.39, generator_kl_loss=1.758, generator_dur_loss=1.589, generator_adv_loss=1.899, generator_feat_match_loss=3.483, over 16.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=27.45, generator_mel_loss=18.61, generator_kl_loss=1.382, generator_dur_loss=1.761, generator_adv_loss=1.933, generator_feat_match_loss=3.761, over 6561.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:54:09,181 INFO [train.py:919] (4/6) Start epoch 216 +2024-03-12 21:56:25,340 INFO [train.py:527] (4/6) Epoch 216, batch 40, global_batch_idx: 26700, batch size: 53, loss[discriminator_loss=2.678, discriminator_real_loss=1.38, discriminator_fake_loss=1.298, generator_loss=26.92, generator_mel_loss=18.05, generator_kl_loss=1.288, generator_dur_loss=1.667, generator_adv_loss=1.871, generator_feat_match_loss=4.044, over 53.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.384, discriminator_fake_loss=1.348, generator_loss=27.27, generator_mel_loss=18.48, generator_kl_loss=1.356, generator_dur_loss=1.777, generator_adv_loss=1.912, generator_feat_match_loss=3.735, over 2494.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:58:46,846 INFO [train.py:527] (4/6) Epoch 216, batch 90, global_batch_idx: 26750, batch size: 50, loss[discriminator_loss=2.791, discriminator_real_loss=1.45, discriminator_fake_loss=1.341, generator_loss=26.29, generator_mel_loss=18.02, generator_kl_loss=1.476, generator_dur_loss=1.692, generator_adv_loss=1.931, generator_feat_match_loss=3.175, over 50.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.386, discriminator_fake_loss=1.348, generator_loss=27.34, generator_mel_loss=18.51, generator_kl_loss=1.367, generator_dur_loss=1.771, generator_adv_loss=1.937, generator_feat_match_loss=3.753, over 5522.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:00:15,222 INFO [train.py:919] (4/6) Start epoch 217 +2024-03-12 22:01:21,268 INFO [train.py:527] (4/6) Epoch 217, batch 16, global_batch_idx: 26800, batch size: 66, loss[discriminator_loss=2.815, discriminator_real_loss=1.388, discriminator_fake_loss=1.426, generator_loss=27.63, generator_mel_loss=18.93, generator_kl_loss=1.34, generator_dur_loss=1.803, generator_adv_loss=2.111, generator_feat_match_loss=3.448, over 66.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.407, discriminator_fake_loss=1.35, generator_loss=27.41, generator_mel_loss=18.67, generator_kl_loss=1.376, generator_dur_loss=1.754, generator_adv_loss=1.924, generator_feat_match_loss=3.681, over 982.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:01:21,269 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 22:01:29,068 INFO [train.py:591] (4/6) Epoch 217, validation: discriminator_loss=2.836, discriminator_real_loss=1.53, discriminator_fake_loss=1.306, generator_loss=26.29, generator_mel_loss=18.49, generator_kl_loss=1.131, generator_dur_loss=1.814, generator_adv_loss=1.997, generator_feat_match_loss=2.857, over 100.00 samples. +2024-03-12 22:01:29,069 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 22:03:48,072 INFO [train.py:527] (4/6) Epoch 217, batch 66, global_batch_idx: 26850, batch size: 25, loss[discriminator_loss=2.739, discriminator_real_loss=1.42, discriminator_fake_loss=1.319, generator_loss=28.08, generator_mel_loss=18.84, generator_kl_loss=1.756, generator_dur_loss=1.608, generator_adv_loss=1.967, generator_feat_match_loss=3.906, over 25.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.393, discriminator_fake_loss=1.356, generator_loss=27.35, generator_mel_loss=18.61, generator_kl_loss=1.391, generator_dur_loss=1.751, generator_adv_loss=1.91, generator_feat_match_loss=3.691, over 3872.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:06:09,855 INFO [train.py:527] (4/6) Epoch 217, batch 116, global_batch_idx: 26900, batch size: 55, loss[discriminator_loss=2.755, discriminator_real_loss=1.456, discriminator_fake_loss=1.298, generator_loss=26.44, generator_mel_loss=18.29, generator_kl_loss=1.362, generator_dur_loss=1.664, generator_adv_loss=1.833, generator_feat_match_loss=3.297, over 55.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.394, discriminator_fake_loss=1.358, generator_loss=27.37, generator_mel_loss=18.61, generator_kl_loss=1.405, generator_dur_loss=1.741, generator_adv_loss=1.905, generator_feat_match_loss=3.708, over 6419.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:06:32,819 INFO [train.py:919] (4/6) Start epoch 218 +2024-03-12 22:08:55,592 INFO [train.py:527] (4/6) Epoch 218, batch 42, global_batch_idx: 26950, batch size: 80, loss[discriminator_loss=2.745, discriminator_real_loss=1.37, discriminator_fake_loss=1.375, generator_loss=27.45, generator_mel_loss=18.63, generator_kl_loss=1.336, generator_dur_loss=1.808, generator_adv_loss=2.057, generator_feat_match_loss=3.625, over 80.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.382, discriminator_fake_loss=1.346, generator_loss=27.44, generator_mel_loss=18.55, generator_kl_loss=1.401, generator_dur_loss=1.747, generator_adv_loss=1.921, generator_feat_match_loss=3.821, over 2258.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:11:14,170 INFO [train.py:527] (4/6) Epoch 218, batch 92, global_batch_idx: 27000, batch size: 64, loss[discriminator_loss=2.801, discriminator_real_loss=1.512, discriminator_fake_loss=1.289, generator_loss=27.19, generator_mel_loss=18.45, generator_kl_loss=1.217, generator_dur_loss=1.767, generator_adv_loss=1.808, generator_feat_match_loss=3.949, over 64.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.387, discriminator_fake_loss=1.348, generator_loss=27.39, generator_mel_loss=18.55, generator_kl_loss=1.392, generator_dur_loss=1.748, generator_adv_loss=1.912, generator_feat_match_loss=3.782, over 5129.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:11:14,171 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 22:11:22,207 INFO [train.py:591] (4/6) Epoch 218, validation: discriminator_loss=2.822, discriminator_real_loss=1.38, discriminator_fake_loss=1.442, generator_loss=26.12, generator_mel_loss=18.68, generator_kl_loss=1.194, generator_dur_loss=1.799, generator_adv_loss=1.661, generator_feat_match_loss=2.793, over 100.00 samples. +2024-03-12 22:11:22,208 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 22:12:47,020 INFO [train.py:919] (4/6) Start epoch 219 +2024-03-12 22:14:00,065 INFO [train.py:527] (4/6) Epoch 219, batch 18, global_batch_idx: 27050, batch size: 49, loss[discriminator_loss=2.703, discriminator_real_loss=1.527, discriminator_fake_loss=1.176, generator_loss=27.05, generator_mel_loss=18.39, generator_kl_loss=1.464, generator_dur_loss=1.67, generator_adv_loss=2.017, generator_feat_match_loss=3.505, over 49.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.396, discriminator_fake_loss=1.343, generator_loss=28.29, generator_mel_loss=18.65, generator_kl_loss=1.401, generator_dur_loss=1.781, generator_adv_loss=2.162, generator_feat_match_loss=4.296, over 1144.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:16:19,132 INFO [train.py:527] (4/6) Epoch 219, batch 68, global_batch_idx: 27100, batch size: 55, loss[discriminator_loss=2.692, discriminator_real_loss=1.352, discriminator_fake_loss=1.34, generator_loss=26.86, generator_mel_loss=18.37, generator_kl_loss=1.389, generator_dur_loss=1.674, generator_adv_loss=1.835, generator_feat_match_loss=3.599, over 55.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.392, discriminator_fake_loss=1.343, generator_loss=27.57, generator_mel_loss=18.61, generator_kl_loss=1.38, generator_dur_loss=1.776, generator_adv_loss=1.987, generator_feat_match_loss=3.818, over 3954.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:18:34,756 INFO [train.py:527] (4/6) Epoch 219, batch 118, global_batch_idx: 27150, batch size: 26, loss[discriminator_loss=2.561, discriminator_real_loss=1.247, discriminator_fake_loss=1.314, generator_loss=28.4, generator_mel_loss=18.65, generator_kl_loss=1.79, generator_dur_loss=1.562, generator_adv_loss=2, generator_feat_match_loss=4.398, over 26.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.388, discriminator_fake_loss=1.343, generator_loss=27.51, generator_mel_loss=18.59, generator_kl_loss=1.391, generator_dur_loss=1.761, generator_adv_loss=1.958, generator_feat_match_loss=3.811, over 6675.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:18:49,848 INFO [train.py:919] (4/6) Start epoch 220 +2024-03-12 22:21:10,712 INFO [train.py:527] (4/6) Epoch 220, batch 44, global_batch_idx: 27200, batch size: 15, loss[discriminator_loss=2.673, discriminator_real_loss=1.45, discriminator_fake_loss=1.223, generator_loss=29.1, generator_mel_loss=19.42, generator_kl_loss=1.676, generator_dur_loss=1.611, generator_adv_loss=2.06, generator_feat_match_loss=4.333, over 15.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=27.52, generator_mel_loss=18.63, generator_kl_loss=1.353, generator_dur_loss=1.756, generator_adv_loss=1.929, generator_feat_match_loss=3.845, over 2689.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:21:10,713 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 22:21:18,865 INFO [train.py:591] (4/6) Epoch 220, validation: discriminator_loss=2.776, discriminator_real_loss=1.508, discriminator_fake_loss=1.268, generator_loss=26.93, generator_mel_loss=18.91, generator_kl_loss=1.249, generator_dur_loss=1.824, generator_adv_loss=1.972, generator_feat_match_loss=2.972, over 100.00 samples. +2024-03-12 22:21:18,867 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 22:23:37,441 INFO [train.py:527] (4/6) Epoch 220, batch 94, global_batch_idx: 27250, batch size: 45, loss[discriminator_loss=2.72, discriminator_real_loss=1.271, discriminator_fake_loss=1.449, generator_loss=28.05, generator_mel_loss=18.96, generator_kl_loss=1.574, generator_dur_loss=1.648, generator_adv_loss=1.972, generator_feat_match_loss=3.892, over 45.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.381, discriminator_fake_loss=1.348, generator_loss=27.51, generator_mel_loss=18.62, generator_kl_loss=1.382, generator_dur_loss=1.748, generator_adv_loss=1.925, generator_feat_match_loss=3.834, over 5514.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:25:02,057 INFO [train.py:919] (4/6) Start epoch 221 +2024-03-12 22:26:20,685 INFO [train.py:527] (4/6) Epoch 221, batch 20, global_batch_idx: 27300, batch size: 55, loss[discriminator_loss=2.744, discriminator_real_loss=1.478, discriminator_fake_loss=1.267, generator_loss=27.56, generator_mel_loss=19.08, generator_kl_loss=1.434, generator_dur_loss=1.751, generator_adv_loss=1.846, generator_feat_match_loss=3.448, over 55.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.373, discriminator_fake_loss=1.352, generator_loss=27.63, generator_mel_loss=18.7, generator_kl_loss=1.397, generator_dur_loss=1.765, generator_adv_loss=1.905, generator_feat_match_loss=3.864, over 1238.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:28:38,534 INFO [train.py:527] (4/6) Epoch 221, batch 70, global_batch_idx: 27350, batch size: 55, loss[discriminator_loss=2.724, discriminator_real_loss=1.324, discriminator_fake_loss=1.4, generator_loss=27.02, generator_mel_loss=18.11, generator_kl_loss=1.403, generator_dur_loss=1.68, generator_adv_loss=2.059, generator_feat_match_loss=3.765, over 55.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.384, discriminator_fake_loss=1.349, generator_loss=27.65, generator_mel_loss=18.69, generator_kl_loss=1.404, generator_dur_loss=1.759, generator_adv_loss=1.928, generator_feat_match_loss=3.863, over 3894.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:30:57,926 INFO [train.py:527] (4/6) Epoch 221, batch 120, global_batch_idx: 27400, batch size: 96, loss[discriminator_loss=2.714, discriminator_real_loss=1.323, discriminator_fake_loss=1.391, generator_loss=27.44, generator_mel_loss=18.64, generator_kl_loss=1.17, generator_dur_loss=1.874, generator_adv_loss=1.916, generator_feat_match_loss=3.847, over 96.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.386, discriminator_fake_loss=1.353, generator_loss=27.53, generator_mel_loss=18.63, generator_kl_loss=1.392, generator_dur_loss=1.768, generator_adv_loss=1.923, generator_feat_match_loss=3.82, over 6698.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:30:57,927 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 22:31:06,763 INFO [train.py:591] (4/6) Epoch 221, validation: discriminator_loss=2.749, discriminator_real_loss=1.474, discriminator_fake_loss=1.275, generator_loss=26.81, generator_mel_loss=18.73, generator_kl_loss=1.234, generator_dur_loss=1.804, generator_adv_loss=1.867, generator_feat_match_loss=3.184, over 100.00 samples. +2024-03-12 22:31:06,764 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 22:31:17,710 INFO [train.py:919] (4/6) Start epoch 222 +2024-03-12 22:33:52,078 INFO [train.py:527] (4/6) Epoch 222, batch 46, global_batch_idx: 27450, batch size: 88, loss[discriminator_loss=2.74, discriminator_real_loss=1.325, discriminator_fake_loss=1.415, generator_loss=27.84, generator_mel_loss=18.57, generator_kl_loss=1.247, generator_dur_loss=1.818, generator_adv_loss=2.024, generator_feat_match_loss=4.181, over 88.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.385, discriminator_fake_loss=1.347, generator_loss=27.29, generator_mel_loss=18.5, generator_kl_loss=1.347, generator_dur_loss=1.75, generator_adv_loss=1.92, generator_feat_match_loss=3.772, over 2878.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:36:11,900 INFO [train.py:527] (4/6) Epoch 222, batch 96, global_batch_idx: 27500, batch size: 55, loss[discriminator_loss=2.746, discriminator_real_loss=1.381, discriminator_fake_loss=1.365, generator_loss=27.79, generator_mel_loss=18.86, generator_kl_loss=1.444, generator_dur_loss=1.717, generator_adv_loss=1.933, generator_feat_match_loss=3.841, over 55.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.388, discriminator_fake_loss=1.355, generator_loss=27.27, generator_mel_loss=18.5, generator_kl_loss=1.361, generator_dur_loss=1.766, generator_adv_loss=1.908, generator_feat_match_loss=3.734, over 5783.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:37:23,626 INFO [train.py:919] (4/6) Start epoch 223 +2024-03-12 22:38:48,896 INFO [train.py:527] (4/6) Epoch 223, batch 22, global_batch_idx: 27550, batch size: 48, loss[discriminator_loss=2.801, discriminator_real_loss=1.423, discriminator_fake_loss=1.378, generator_loss=27.65, generator_mel_loss=18.89, generator_kl_loss=1.315, generator_dur_loss=1.686, generator_adv_loss=1.947, generator_feat_match_loss=3.809, over 48.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.378, discriminator_fake_loss=1.354, generator_loss=27.48, generator_mel_loss=18.65, generator_kl_loss=1.415, generator_dur_loss=1.707, generator_adv_loss=1.911, generator_feat_match_loss=3.788, over 1128.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:41:08,828 INFO [train.py:527] (4/6) Epoch 223, batch 72, global_batch_idx: 27600, batch size: 59, loss[discriminator_loss=2.778, discriminator_real_loss=1.444, discriminator_fake_loss=1.335, generator_loss=27.72, generator_mel_loss=18.81, generator_kl_loss=1.352, generator_dur_loss=1.718, generator_adv_loss=1.885, generator_feat_match_loss=3.953, over 59.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.381, discriminator_fake_loss=1.35, generator_loss=27.53, generator_mel_loss=18.6, generator_kl_loss=1.388, generator_dur_loss=1.74, generator_adv_loss=1.926, generator_feat_match_loss=3.88, over 4116.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:41:08,830 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 22:41:16,883 INFO [train.py:591] (4/6) Epoch 223, validation: discriminator_loss=2.848, discriminator_real_loss=1.505, discriminator_fake_loss=1.342, generator_loss=27.03, generator_mel_loss=19.04, generator_kl_loss=1.2, generator_dur_loss=1.809, generator_adv_loss=1.849, generator_feat_match_loss=3.136, over 100.00 samples. +2024-03-12 22:41:16,884 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 22:43:34,934 INFO [train.py:527] (4/6) Epoch 223, batch 122, global_batch_idx: 27650, batch size: 15, loss[discriminator_loss=2.757, discriminator_real_loss=1.484, discriminator_fake_loss=1.273, generator_loss=30.54, generator_mel_loss=21.99, generator_kl_loss=1.656, generator_dur_loss=1.602, generator_adv_loss=2.012, generator_feat_match_loss=3.282, over 15.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.385, discriminator_fake_loss=1.353, generator_loss=27.48, generator_mel_loss=18.58, generator_kl_loss=1.393, generator_dur_loss=1.739, generator_adv_loss=1.919, generator_feat_match_loss=3.846, over 6808.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:43:40,097 INFO [train.py:919] (4/6) Start epoch 224 +2024-03-12 22:46:18,875 INFO [train.py:527] (4/6) Epoch 224, batch 48, global_batch_idx: 27700, batch size: 42, loss[discriminator_loss=2.768, discriminator_real_loss=1.447, discriminator_fake_loss=1.321, generator_loss=27.48, generator_mel_loss=18.86, generator_kl_loss=1.437, generator_dur_loss=1.706, generator_adv_loss=1.858, generator_feat_match_loss=3.612, over 42.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.388, discriminator_fake_loss=1.347, generator_loss=27.55, generator_mel_loss=18.61, generator_kl_loss=1.38, generator_dur_loss=1.754, generator_adv_loss=1.943, generator_feat_match_loss=3.863, over 2916.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:48:41,928 INFO [train.py:527] (4/6) Epoch 224, batch 98, global_batch_idx: 27750, batch size: 74, loss[discriminator_loss=2.775, discriminator_real_loss=1.301, discriminator_fake_loss=1.475, generator_loss=26.96, generator_mel_loss=18.22, generator_kl_loss=1.28, generator_dur_loss=1.767, generator_adv_loss=1.941, generator_feat_match_loss=3.76, over 74.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.39, discriminator_fake_loss=1.343, generator_loss=27.37, generator_mel_loss=18.52, generator_kl_loss=1.359, generator_dur_loss=1.76, generator_adv_loss=1.929, generator_feat_match_loss=3.802, over 5972.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:49:49,836 INFO [train.py:919] (4/6) Start epoch 225 +2024-03-12 22:51:18,670 INFO [train.py:527] (4/6) Epoch 225, batch 24, global_batch_idx: 27800, batch size: 61, loss[discriminator_loss=2.787, discriminator_real_loss=1.432, discriminator_fake_loss=1.355, generator_loss=26.63, generator_mel_loss=18.24, generator_kl_loss=1.248, generator_dur_loss=1.757, generator_adv_loss=1.847, generator_feat_match_loss=3.53, over 61.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.393, discriminator_fake_loss=1.353, generator_loss=27.45, generator_mel_loss=18.58, generator_kl_loss=1.371, generator_dur_loss=1.741, generator_adv_loss=1.944, generator_feat_match_loss=3.816, over 1329.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 22:51:18,671 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 22:51:26,433 INFO [train.py:591] (4/6) Epoch 225, validation: discriminator_loss=2.695, discriminator_real_loss=1.385, discriminator_fake_loss=1.31, generator_loss=26.14, generator_mel_loss=18.37, generator_kl_loss=1.122, generator_dur_loss=1.82, generator_adv_loss=1.82, generator_feat_match_loss=3.004, over 100.00 samples. +2024-03-12 22:51:26,434 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 22:53:48,639 INFO [train.py:527] (4/6) Epoch 225, batch 74, global_batch_idx: 27850, batch size: 88, loss[discriminator_loss=2.988, discriminator_real_loss=1.402, discriminator_fake_loss=1.586, generator_loss=27.02, generator_mel_loss=18.62, generator_kl_loss=1.234, generator_dur_loss=1.878, generator_adv_loss=1.844, generator_feat_match_loss=3.443, over 88.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.379, discriminator_fake_loss=1.363, generator_loss=27.6, generator_mel_loss=18.57, generator_kl_loss=1.366, generator_dur_loss=1.768, generator_adv_loss=1.987, generator_feat_match_loss=3.908, over 4477.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 22:56:01,177 INFO [train.py:919] (4/6) Start epoch 226 +2024-03-12 22:56:24,024 INFO [train.py:527] (4/6) Epoch 226, batch 0, global_batch_idx: 27900, batch size: 66, loss[discriminator_loss=2.726, discriminator_real_loss=1.398, discriminator_fake_loss=1.327, generator_loss=26.58, generator_mel_loss=18.26, generator_kl_loss=1.422, generator_dur_loss=1.763, generator_adv_loss=1.857, generator_feat_match_loss=3.279, over 66.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.398, discriminator_fake_loss=1.327, generator_loss=26.58, generator_mel_loss=18.26, generator_kl_loss=1.422, generator_dur_loss=1.763, generator_adv_loss=1.857, generator_feat_match_loss=3.279, over 66.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 22:58:40,624 INFO [train.py:527] (4/6) Epoch 226, batch 50, global_batch_idx: 27950, batch size: 39, loss[discriminator_loss=2.745, discriminator_real_loss=1.465, discriminator_fake_loss=1.28, generator_loss=27.13, generator_mel_loss=18.38, generator_kl_loss=1.455, generator_dur_loss=1.632, generator_adv_loss=1.886, generator_feat_match_loss=3.774, over 39.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.391, discriminator_fake_loss=1.35, generator_loss=27.42, generator_mel_loss=18.58, generator_kl_loss=1.422, generator_dur_loss=1.742, generator_adv_loss=1.915, generator_feat_match_loss=3.761, over 2752.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:00:58,266 INFO [train.py:527] (4/6) Epoch 226, batch 100, global_batch_idx: 28000, batch size: 64, loss[discriminator_loss=2.772, discriminator_real_loss=1.376, discriminator_fake_loss=1.396, generator_loss=26.77, generator_mel_loss=18.23, generator_kl_loss=1.393, generator_dur_loss=1.754, generator_adv_loss=1.965, generator_feat_match_loss=3.42, over 64.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.388, discriminator_fake_loss=1.358, generator_loss=27.32, generator_mel_loss=18.5, generator_kl_loss=1.389, generator_dur_loss=1.766, generator_adv_loss=1.909, generator_feat_match_loss=3.755, over 5808.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:00:58,268 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 23:01:07,117 INFO [train.py:591] (4/6) Epoch 226, validation: discriminator_loss=2.759, discriminator_real_loss=1.512, discriminator_fake_loss=1.247, generator_loss=26.25, generator_mel_loss=18.5, generator_kl_loss=1.084, generator_dur_loss=1.847, generator_adv_loss=1.931, generator_feat_match_loss=2.893, over 100.00 samples. +2024-03-12 23:01:07,118 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 23:02:12,559 INFO [train.py:919] (4/6) Start epoch 227 +2024-03-12 23:03:46,708 INFO [train.py:527] (4/6) Epoch 227, batch 26, global_batch_idx: 28050, batch size: 31, loss[discriminator_loss=2.722, discriminator_real_loss=1.465, discriminator_fake_loss=1.258, generator_loss=27.51, generator_mel_loss=18.71, generator_kl_loss=1.455, generator_dur_loss=1.702, generator_adv_loss=1.847, generator_feat_match_loss=3.794, over 31.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.392, discriminator_fake_loss=1.338, generator_loss=27.56, generator_mel_loss=18.61, generator_kl_loss=1.422, generator_dur_loss=1.756, generator_adv_loss=1.906, generator_feat_match_loss=3.87, over 1502.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:06:09,283 INFO [train.py:527] (4/6) Epoch 227, batch 76, global_batch_idx: 28100, batch size: 74, loss[discriminator_loss=2.758, discriminator_real_loss=1.28, discriminator_fake_loss=1.478, generator_loss=27.77, generator_mel_loss=18.5, generator_kl_loss=1.437, generator_dur_loss=1.816, generator_adv_loss=1.946, generator_feat_match_loss=4.074, over 74.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.378, discriminator_fake_loss=1.353, generator_loss=27.42, generator_mel_loss=18.55, generator_kl_loss=1.386, generator_dur_loss=1.775, generator_adv_loss=1.911, generator_feat_match_loss=3.792, over 4513.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:08:18,175 INFO [train.py:919] (4/6) Start epoch 228 +2024-03-12 23:08:47,034 INFO [train.py:527] (4/6) Epoch 228, batch 2, global_batch_idx: 28150, batch size: 36, loss[discriminator_loss=2.604, discriminator_real_loss=1.325, discriminator_fake_loss=1.28, generator_loss=28.97, generator_mel_loss=19.35, generator_kl_loss=1.563, generator_dur_loss=1.631, generator_adv_loss=1.964, generator_feat_match_loss=4.459, over 36.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.387, discriminator_fake_loss=1.29, generator_loss=27.69, generator_mel_loss=18.83, generator_kl_loss=1.443, generator_dur_loss=1.755, generator_adv_loss=1.976, generator_feat_match_loss=3.686, over 163.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:11:05,121 INFO [train.py:527] (4/6) Epoch 228, batch 52, global_batch_idx: 28200, batch size: 74, loss[discriminator_loss=2.72, discriminator_real_loss=1.36, discriminator_fake_loss=1.36, generator_loss=27.67, generator_mel_loss=18.7, generator_kl_loss=1.316, generator_dur_loss=1.812, generator_adv_loss=2.045, generator_feat_match_loss=3.794, over 74.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.385, discriminator_fake_loss=1.343, generator_loss=27.49, generator_mel_loss=18.61, generator_kl_loss=1.36, generator_dur_loss=1.763, generator_adv_loss=1.922, generator_feat_match_loss=3.832, over 2964.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:11:05,122 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 23:11:13,162 INFO [train.py:591] (4/6) Epoch 228, validation: discriminator_loss=2.724, discriminator_real_loss=1.485, discriminator_fake_loss=1.239, generator_loss=27.09, generator_mel_loss=19.16, generator_kl_loss=1.311, generator_dur_loss=1.835, generator_adv_loss=1.944, generator_feat_match_loss=2.845, over 100.00 samples. +2024-03-12 23:11:13,163 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 23:13:35,224 INFO [train.py:527] (4/6) Epoch 228, batch 102, global_batch_idx: 28250, batch size: 31, loss[discriminator_loss=2.834, discriminator_real_loss=1.386, discriminator_fake_loss=1.448, generator_loss=27.66, generator_mel_loss=18.92, generator_kl_loss=1.451, generator_dur_loss=1.705, generator_adv_loss=1.93, generator_feat_match_loss=3.652, over 31.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.383, discriminator_fake_loss=1.352, generator_loss=27.52, generator_mel_loss=18.62, generator_kl_loss=1.374, generator_dur_loss=1.775, generator_adv_loss=1.918, generator_feat_match_loss=3.837, over 6022.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:14:38,414 INFO [train.py:919] (4/6) Start epoch 229 +2024-03-12 23:16:23,390 INFO [train.py:527] (4/6) Epoch 229, batch 28, global_batch_idx: 28300, batch size: 83, loss[discriminator_loss=2.65, discriminator_real_loss=1.223, discriminator_fake_loss=1.427, generator_loss=28.36, generator_mel_loss=19.02, generator_kl_loss=1.324, generator_dur_loss=1.803, generator_adv_loss=2.115, generator_feat_match_loss=4.096, over 83.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.371, discriminator_fake_loss=1.354, generator_loss=27.59, generator_mel_loss=18.66, generator_kl_loss=1.388, generator_dur_loss=1.754, generator_adv_loss=1.932, generator_feat_match_loss=3.852, over 1648.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:18:44,375 INFO [train.py:527] (4/6) Epoch 229, batch 78, global_batch_idx: 28350, batch size: 61, loss[discriminator_loss=2.775, discriminator_real_loss=1.418, discriminator_fake_loss=1.357, generator_loss=27.33, generator_mel_loss=18.45, generator_kl_loss=1.49, generator_dur_loss=1.749, generator_adv_loss=1.867, generator_feat_match_loss=3.769, over 61.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.383, discriminator_fake_loss=1.344, generator_loss=27.6, generator_mel_loss=18.64, generator_kl_loss=1.39, generator_dur_loss=1.751, generator_adv_loss=1.95, generator_feat_match_loss=3.866, over 4431.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:20:53,512 INFO [train.py:919] (4/6) Start epoch 230 +2024-03-12 23:21:31,178 INFO [train.py:527] (4/6) Epoch 230, batch 4, global_batch_idx: 28400, batch size: 47, loss[discriminator_loss=2.72, discriminator_real_loss=1.492, discriminator_fake_loss=1.228, generator_loss=26.83, generator_mel_loss=18.12, generator_kl_loss=1.389, generator_dur_loss=1.65, generator_adv_loss=1.961, generator_feat_match_loss=3.706, over 47.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.424, discriminator_fake_loss=1.337, generator_loss=27.05, generator_mel_loss=18.33, generator_kl_loss=1.397, generator_dur_loss=1.731, generator_adv_loss=1.918, generator_feat_match_loss=3.673, over 300.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:21:31,184 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 23:21:39,255 INFO [train.py:591] (4/6) Epoch 230, validation: discriminator_loss=2.768, discriminator_real_loss=1.443, discriminator_fake_loss=1.324, generator_loss=25.82, generator_mel_loss=18.11, generator_kl_loss=1.236, generator_dur_loss=1.816, generator_adv_loss=1.875, generator_feat_match_loss=2.781, over 100.00 samples. +2024-03-12 23:21:39,258 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 23:24:02,404 INFO [train.py:527] (4/6) Epoch 230, batch 54, global_batch_idx: 28450, batch size: 62, loss[discriminator_loss=2.697, discriminator_real_loss=1.403, discriminator_fake_loss=1.293, generator_loss=28.18, generator_mel_loss=19.07, generator_kl_loss=1.486, generator_dur_loss=1.757, generator_adv_loss=1.91, generator_feat_match_loss=3.954, over 62.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.394, discriminator_fake_loss=1.34, generator_loss=27.26, generator_mel_loss=18.47, generator_kl_loss=1.373, generator_dur_loss=1.757, generator_adv_loss=1.91, generator_feat_match_loss=3.753, over 3227.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:26:23,509 INFO [train.py:527] (4/6) Epoch 230, batch 104, global_batch_idx: 28500, batch size: 64, loss[discriminator_loss=2.728, discriminator_real_loss=1.423, discriminator_fake_loss=1.304, generator_loss=27.64, generator_mel_loss=19.02, generator_kl_loss=1.281, generator_dur_loss=1.749, generator_adv_loss=1.784, generator_feat_match_loss=3.805, over 64.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.387, discriminator_fake_loss=1.347, generator_loss=27.32, generator_mel_loss=18.5, generator_kl_loss=1.375, generator_dur_loss=1.749, generator_adv_loss=1.908, generator_feat_match_loss=3.788, over 5988.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:27:17,460 INFO [train.py:919] (4/6) Start epoch 231 +2024-03-12 23:29:07,521 INFO [train.py:527] (4/6) Epoch 231, batch 30, global_batch_idx: 28550, batch size: 88, loss[discriminator_loss=2.756, discriminator_real_loss=1.331, discriminator_fake_loss=1.424, generator_loss=27.6, generator_mel_loss=18.7, generator_kl_loss=1.333, generator_dur_loss=1.857, generator_adv_loss=1.888, generator_feat_match_loss=3.82, over 88.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.385, discriminator_fake_loss=1.339, generator_loss=27.42, generator_mel_loss=18.5, generator_kl_loss=1.415, generator_dur_loss=1.753, generator_adv_loss=1.917, generator_feat_match_loss=3.831, over 1720.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:31:26,452 INFO [train.py:527] (4/6) Epoch 231, batch 80, global_batch_idx: 28600, batch size: 53, loss[discriminator_loss=2.713, discriminator_real_loss=1.442, discriminator_fake_loss=1.27, generator_loss=28.5, generator_mel_loss=19.26, generator_kl_loss=1.486, generator_dur_loss=1.649, generator_adv_loss=1.917, generator_feat_match_loss=4.193, over 53.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.391, discriminator_fake_loss=1.344, generator_loss=27.48, generator_mel_loss=18.57, generator_kl_loss=1.415, generator_dur_loss=1.746, generator_adv_loss=1.919, generator_feat_match_loss=3.837, over 4538.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:31:26,453 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 23:31:34,637 INFO [train.py:591] (4/6) Epoch 231, validation: discriminator_loss=2.722, discriminator_real_loss=1.423, discriminator_fake_loss=1.299, generator_loss=26.46, generator_mel_loss=18.53, generator_kl_loss=1.149, generator_dur_loss=1.8, generator_adv_loss=1.853, generator_feat_match_loss=3.125, over 100.00 samples. +2024-03-12 23:31:34,637 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 23:33:37,090 INFO [train.py:919] (4/6) Start epoch 232 +2024-03-12 23:34:17,899 INFO [train.py:527] (4/6) Epoch 232, batch 6, global_batch_idx: 28650, batch size: 15, loss[discriminator_loss=2.703, discriminator_real_loss=1.306, discriminator_fake_loss=1.397, generator_loss=28.94, generator_mel_loss=18.71, generator_kl_loss=1.772, generator_dur_loss=1.629, generator_adv_loss=2.03, generator_feat_match_loss=4.792, over 15.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.349, discriminator_fake_loss=1.357, generator_loss=27.7, generator_mel_loss=18.57, generator_kl_loss=1.479, generator_dur_loss=1.757, generator_adv_loss=1.946, generator_feat_match_loss=3.945, over 323.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:36:36,767 INFO [train.py:527] (4/6) Epoch 232, batch 56, global_batch_idx: 28700, batch size: 83, loss[discriminator_loss=2.665, discriminator_real_loss=1.313, discriminator_fake_loss=1.352, generator_loss=27.16, generator_mel_loss=18.31, generator_kl_loss=1.253, generator_dur_loss=1.731, generator_adv_loss=1.941, generator_feat_match_loss=3.926, over 83.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.395, discriminator_fake_loss=1.344, generator_loss=27.41, generator_mel_loss=18.49, generator_kl_loss=1.379, generator_dur_loss=1.771, generator_adv_loss=1.94, generator_feat_match_loss=3.822, over 3544.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:38:57,984 INFO [train.py:527] (4/6) Epoch 232, batch 106, global_batch_idx: 28750, batch size: 72, loss[discriminator_loss=2.785, discriminator_real_loss=1.362, discriminator_fake_loss=1.422, generator_loss=26.98, generator_mel_loss=18.26, generator_kl_loss=1.283, generator_dur_loss=1.831, generator_adv_loss=1.867, generator_feat_match_loss=3.736, over 72.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.392, discriminator_fake_loss=1.347, generator_loss=27.39, generator_mel_loss=18.49, generator_kl_loss=1.391, generator_dur_loss=1.753, generator_adv_loss=1.934, generator_feat_match_loss=3.821, over 6534.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:39:48,420 INFO [train.py:919] (4/6) Start epoch 233 +2024-03-12 23:41:41,838 INFO [train.py:527] (4/6) Epoch 233, batch 32, global_batch_idx: 28800, batch size: 56, loss[discriminator_loss=2.706, discriminator_real_loss=1.407, discriminator_fake_loss=1.299, generator_loss=28.1, generator_mel_loss=19.34, generator_kl_loss=1.364, generator_dur_loss=1.712, generator_adv_loss=1.841, generator_feat_match_loss=3.849, over 56.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.388, discriminator_fake_loss=1.346, generator_loss=27.42, generator_mel_loss=18.52, generator_kl_loss=1.373, generator_dur_loss=1.726, generator_adv_loss=1.939, generator_feat_match_loss=3.862, over 1884.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:41:41,841 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 23:41:49,971 INFO [train.py:591] (4/6) Epoch 233, validation: discriminator_loss=2.74, discriminator_real_loss=1.359, discriminator_fake_loss=1.382, generator_loss=26.48, generator_mel_loss=18.5, generator_kl_loss=1.116, generator_dur_loss=1.783, generator_adv_loss=1.798, generator_feat_match_loss=3.277, over 100.00 samples. +2024-03-12 23:41:49,972 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 23:44:12,365 INFO [train.py:527] (4/6) Epoch 233, batch 82, global_batch_idx: 28850, batch size: 62, loss[discriminator_loss=2.701, discriminator_real_loss=1.42, discriminator_fake_loss=1.281, generator_loss=27.93, generator_mel_loss=18.76, generator_kl_loss=1.41, generator_dur_loss=1.753, generator_adv_loss=2.054, generator_feat_match_loss=3.957, over 62.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.379, discriminator_fake_loss=1.357, generator_loss=27.47, generator_mel_loss=18.53, generator_kl_loss=1.401, generator_dur_loss=1.726, generator_adv_loss=1.93, generator_feat_match_loss=3.881, over 4631.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:46:10,077 INFO [train.py:919] (4/6) Start epoch 234 +2024-03-12 23:46:57,693 INFO [train.py:527] (4/6) Epoch 234, batch 8, global_batch_idx: 28900, batch size: 52, loss[discriminator_loss=2.797, discriminator_real_loss=1.429, discriminator_fake_loss=1.368, generator_loss=26.93, generator_mel_loss=18.3, generator_kl_loss=1.491, generator_dur_loss=1.73, generator_adv_loss=1.706, generator_feat_match_loss=3.71, over 52.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.381, discriminator_fake_loss=1.348, generator_loss=27.32, generator_mel_loss=18.42, generator_kl_loss=1.383, generator_dur_loss=1.769, generator_adv_loss=1.923, generator_feat_match_loss=3.825, over 539.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:49:18,763 INFO [train.py:527] (4/6) Epoch 234, batch 58, global_batch_idx: 28950, batch size: 42, loss[discriminator_loss=2.646, discriminator_real_loss=1.298, discriminator_fake_loss=1.348, generator_loss=29.01, generator_mel_loss=19.3, generator_kl_loss=1.589, generator_dur_loss=1.724, generator_adv_loss=2.049, generator_feat_match_loss=4.353, over 42.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.391, discriminator_fake_loss=1.347, generator_loss=27.46, generator_mel_loss=18.54, generator_kl_loss=1.379, generator_dur_loss=1.772, generator_adv_loss=1.932, generator_feat_match_loss=3.829, over 3342.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:51:40,416 INFO [train.py:527] (4/6) Epoch 234, batch 108, global_batch_idx: 29000, batch size: 64, loss[discriminator_loss=2.764, discriminator_real_loss=1.475, discriminator_fake_loss=1.29, generator_loss=27.11, generator_mel_loss=18.37, generator_kl_loss=1.392, generator_dur_loss=1.761, generator_adv_loss=1.937, generator_feat_match_loss=3.646, over 64.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.39, discriminator_fake_loss=1.348, generator_loss=27.39, generator_mel_loss=18.48, generator_kl_loss=1.376, generator_dur_loss=1.773, generator_adv_loss=1.925, generator_feat_match_loss=3.84, over 6405.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:51:40,417 INFO [train.py:581] (4/6) Computing validation loss +2024-03-12 23:51:49,175 INFO [train.py:591] (4/6) Epoch 234, validation: discriminator_loss=2.773, discriminator_real_loss=1.465, discriminator_fake_loss=1.308, generator_loss=26.51, generator_mel_loss=18.66, generator_kl_loss=1.164, generator_dur_loss=1.825, generator_adv_loss=1.845, generator_feat_match_loss=3.016, over 100.00 samples. +2024-03-12 23:51:49,177 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-12 23:52:33,619 INFO [train.py:919] (4/6) Start epoch 235 +2024-03-12 23:54:37,218 INFO [train.py:527] (4/6) Epoch 235, batch 34, global_batch_idx: 29050, batch size: 53, loss[discriminator_loss=2.677, discriminator_real_loss=1.257, discriminator_fake_loss=1.42, generator_loss=27.1, generator_mel_loss=18.05, generator_kl_loss=1.428, generator_dur_loss=1.712, generator_adv_loss=1.963, generator_feat_match_loss=3.945, over 53.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.389, discriminator_fake_loss=1.35, generator_loss=27.27, generator_mel_loss=18.42, generator_kl_loss=1.37, generator_dur_loss=1.749, generator_adv_loss=1.934, generator_feat_match_loss=3.797, over 2115.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:57:01,643 INFO [train.py:527] (4/6) Epoch 235, batch 84, global_batch_idx: 29100, batch size: 15, loss[discriminator_loss=2.798, discriminator_real_loss=1.373, discriminator_fake_loss=1.425, generator_loss=29.01, generator_mel_loss=19.78, generator_kl_loss=1.68, generator_dur_loss=1.632, generator_adv_loss=1.814, generator_feat_match_loss=4.104, over 15.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.39, discriminator_fake_loss=1.35, generator_loss=27.37, generator_mel_loss=18.47, generator_kl_loss=1.387, generator_dur_loss=1.753, generator_adv_loss=1.932, generator_feat_match_loss=3.828, over 4730.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:58:51,176 INFO [train.py:919] (4/6) Start epoch 236 +2024-03-12 23:59:45,833 INFO [train.py:527] (4/6) Epoch 236, batch 10, global_batch_idx: 29150, batch size: 53, loss[discriminator_loss=2.697, discriminator_real_loss=1.337, discriminator_fake_loss=1.36, generator_loss=27.5, generator_mel_loss=18.42, generator_kl_loss=1.338, generator_dur_loss=1.687, generator_adv_loss=1.922, generator_feat_match_loss=4.131, over 53.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.378, discriminator_fake_loss=1.349, generator_loss=27.61, generator_mel_loss=18.71, generator_kl_loss=1.425, generator_dur_loss=1.733, generator_adv_loss=1.905, generator_feat_match_loss=3.842, over 585.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:02:09,642 INFO [train.py:527] (4/6) Epoch 236, batch 60, global_batch_idx: 29200, batch size: 64, loss[discriminator_loss=2.749, discriminator_real_loss=1.272, discriminator_fake_loss=1.477, generator_loss=27.44, generator_mel_loss=18.39, generator_kl_loss=1.429, generator_dur_loss=1.779, generator_adv_loss=2.008, generator_feat_match_loss=3.841, over 64.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.386, discriminator_fake_loss=1.345, generator_loss=27.42, generator_mel_loss=18.48, generator_kl_loss=1.39, generator_dur_loss=1.776, generator_adv_loss=1.91, generator_feat_match_loss=3.868, over 3530.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:02:09,644 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 00:02:17,718 INFO [train.py:591] (4/6) Epoch 236, validation: discriminator_loss=2.744, discriminator_real_loss=1.492, discriminator_fake_loss=1.252, generator_loss=26.21, generator_mel_loss=18.1, generator_kl_loss=1.199, generator_dur_loss=1.843, generator_adv_loss=1.976, generator_feat_match_loss=3.094, over 100.00 samples. +2024-03-13 00:02:17,719 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 00:04:37,558 INFO [train.py:527] (4/6) Epoch 236, batch 110, global_batch_idx: 29250, batch size: 25, loss[discriminator_loss=2.724, discriminator_real_loss=1.314, discriminator_fake_loss=1.41, generator_loss=28.74, generator_mel_loss=18.93, generator_kl_loss=1.618, generator_dur_loss=1.588, generator_adv_loss=2.035, generator_feat_match_loss=4.567, over 25.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.385, discriminator_fake_loss=1.346, generator_loss=27.46, generator_mel_loss=18.48, generator_kl_loss=1.401, generator_dur_loss=1.768, generator_adv_loss=1.917, generator_feat_match_loss=3.893, over 6289.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:05:15,818 INFO [train.py:919] (4/6) Start epoch 237 +2024-03-13 00:07:22,687 INFO [train.py:527] (4/6) Epoch 237, batch 36, global_batch_idx: 29300, batch size: 39, loss[discriminator_loss=2.675, discriminator_real_loss=1.322, discriminator_fake_loss=1.353, generator_loss=28.31, generator_mel_loss=19, generator_kl_loss=1.564, generator_dur_loss=1.687, generator_adv_loss=1.844, generator_feat_match_loss=4.223, over 39.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.384, discriminator_fake_loss=1.35, generator_loss=27.39, generator_mel_loss=18.5, generator_kl_loss=1.38, generator_dur_loss=1.765, generator_adv_loss=1.934, generator_feat_match_loss=3.816, over 2023.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:09:43,774 INFO [train.py:527] (4/6) Epoch 237, batch 86, global_batch_idx: 29350, batch size: 72, loss[discriminator_loss=2.732, discriminator_real_loss=1.373, discriminator_fake_loss=1.36, generator_loss=27.69, generator_mel_loss=18.33, generator_kl_loss=1.49, generator_dur_loss=1.812, generator_adv_loss=1.902, generator_feat_match_loss=4.16, over 72.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.382, discriminator_fake_loss=1.355, generator_loss=27.42, generator_mel_loss=18.5, generator_kl_loss=1.386, generator_dur_loss=1.778, generator_adv_loss=1.923, generator_feat_match_loss=3.83, over 5049.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:11:31,551 INFO [train.py:919] (4/6) Start epoch 238 +2024-03-13 00:12:28,585 INFO [train.py:527] (4/6) Epoch 238, batch 12, global_batch_idx: 29400, batch size: 53, loss[discriminator_loss=2.726, discriminator_real_loss=1.417, discriminator_fake_loss=1.309, generator_loss=27.04, generator_mel_loss=18.24, generator_kl_loss=1.43, generator_dur_loss=1.752, generator_adv_loss=1.896, generator_feat_match_loss=3.728, over 53.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.36, generator_mel_loss=18.47, generator_kl_loss=1.421, generator_dur_loss=1.785, generator_adv_loss=1.906, generator_feat_match_loss=3.778, over 748.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:12:28,587 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 00:12:36,531 INFO [train.py:591] (4/6) Epoch 238, validation: discriminator_loss=2.752, discriminator_real_loss=1.42, discriminator_fake_loss=1.332, generator_loss=26.55, generator_mel_loss=18.81, generator_kl_loss=1.148, generator_dur_loss=1.852, generator_adv_loss=1.926, generator_feat_match_loss=2.812, over 100.00 samples. +2024-03-13 00:12:36,532 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 00:14:57,918 INFO [train.py:527] (4/6) Epoch 238, batch 62, global_batch_idx: 29450, batch size: 70, loss[discriminator_loss=2.788, discriminator_real_loss=1.403, discriminator_fake_loss=1.385, generator_loss=27.79, generator_mel_loss=18.63, generator_kl_loss=1.273, generator_dur_loss=1.835, generator_adv_loss=1.943, generator_feat_match_loss=4.111, over 70.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.387, discriminator_fake_loss=1.346, generator_loss=27.46, generator_mel_loss=18.47, generator_kl_loss=1.387, generator_dur_loss=1.788, generator_adv_loss=1.925, generator_feat_match_loss=3.89, over 3638.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:17:20,267 INFO [train.py:527] (4/6) Epoch 238, batch 112, global_batch_idx: 29500, batch size: 25, loss[discriminator_loss=2.699, discriminator_real_loss=1.419, discriminator_fake_loss=1.28, generator_loss=30.32, generator_mel_loss=20.15, generator_kl_loss=1.661, generator_dur_loss=1.625, generator_adv_loss=2.004, generator_feat_match_loss=4.877, over 25.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.388, discriminator_fake_loss=1.351, generator_loss=27.41, generator_mel_loss=18.44, generator_kl_loss=1.386, generator_dur_loss=1.785, generator_adv_loss=1.928, generator_feat_match_loss=3.869, over 6532.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:17:51,097 INFO [train.py:919] (4/6) Start epoch 239 +2024-03-13 00:20:03,145 INFO [train.py:527] (4/6) Epoch 239, batch 38, global_batch_idx: 29550, batch size: 50, loss[discriminator_loss=2.791, discriminator_real_loss=1.301, discriminator_fake_loss=1.49, generator_loss=27.14, generator_mel_loss=18.55, generator_kl_loss=1.403, generator_dur_loss=1.749, generator_adv_loss=1.95, generator_feat_match_loss=3.486, over 50.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.391, discriminator_fake_loss=1.357, generator_loss=27.29, generator_mel_loss=18.46, generator_kl_loss=1.357, generator_dur_loss=1.777, generator_adv_loss=1.911, generator_feat_match_loss=3.782, over 2347.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:22:24,260 INFO [train.py:527] (4/6) Epoch 239, batch 88, global_batch_idx: 29600, batch size: 61, loss[discriminator_loss=2.692, discriminator_real_loss=1.326, discriminator_fake_loss=1.367, generator_loss=27.41, generator_mel_loss=18.48, generator_kl_loss=1.491, generator_dur_loss=1.76, generator_adv_loss=1.988, generator_feat_match_loss=3.69, over 61.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.392, discriminator_fake_loss=1.351, generator_loss=27.28, generator_mel_loss=18.43, generator_kl_loss=1.383, generator_dur_loss=1.77, generator_adv_loss=1.912, generator_feat_match_loss=3.789, over 5212.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:22:24,261 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 00:22:33,352 INFO [train.py:591] (4/6) Epoch 239, validation: discriminator_loss=2.695, discriminator_real_loss=1.44, discriminator_fake_loss=1.255, generator_loss=26.48, generator_mel_loss=18.34, generator_kl_loss=1.229, generator_dur_loss=1.854, generator_adv_loss=1.96, generator_feat_match_loss=3.105, over 100.00 samples. +2024-03-13 00:22:33,353 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 00:24:14,714 INFO [train.py:919] (4/6) Start epoch 240 +2024-03-13 00:25:20,244 INFO [train.py:527] (4/6) Epoch 240, batch 14, global_batch_idx: 29650, batch size: 44, loss[discriminator_loss=2.67, discriminator_real_loss=1.289, discriminator_fake_loss=1.381, generator_loss=27.04, generator_mel_loss=18.23, generator_kl_loss=1.392, generator_dur_loss=1.684, generator_adv_loss=1.946, generator_feat_match_loss=3.789, over 44.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.381, discriminator_fake_loss=1.335, generator_loss=27.4, generator_mel_loss=18.49, generator_kl_loss=1.362, generator_dur_loss=1.765, generator_adv_loss=1.906, generator_feat_match_loss=3.87, over 902.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:27:41,147 INFO [train.py:527] (4/6) Epoch 240, batch 64, global_batch_idx: 29700, batch size: 83, loss[discriminator_loss=2.709, discriminator_real_loss=1.302, discriminator_fake_loss=1.407, generator_loss=27.43, generator_mel_loss=18.46, generator_kl_loss=1.369, generator_dur_loss=1.862, generator_adv_loss=1.973, generator_feat_match_loss=3.761, over 83.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=27.34, generator_mel_loss=18.4, generator_kl_loss=1.382, generator_dur_loss=1.772, generator_adv_loss=1.907, generator_feat_match_loss=3.874, over 3919.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:30:00,413 INFO [train.py:527] (4/6) Epoch 240, batch 114, global_batch_idx: 29750, batch size: 42, loss[discriminator_loss=2.696, discriminator_real_loss=1.338, discriminator_fake_loss=1.358, generator_loss=27.82, generator_mel_loss=18.33, generator_kl_loss=1.333, generator_dur_loss=1.717, generator_adv_loss=2.07, generator_feat_match_loss=4.367, over 42.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.383, discriminator_fake_loss=1.348, generator_loss=27.4, generator_mel_loss=18.46, generator_kl_loss=1.384, generator_dur_loss=1.768, generator_adv_loss=1.915, generator_feat_match_loss=3.874, over 6824.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:30:28,667 INFO [train.py:919] (4/6) Start epoch 241 +2024-03-13 00:32:44,526 INFO [train.py:527] (4/6) Epoch 241, batch 40, global_batch_idx: 29800, batch size: 66, loss[discriminator_loss=2.693, discriminator_real_loss=1.366, discriminator_fake_loss=1.326, generator_loss=26.37, generator_mel_loss=17.79, generator_kl_loss=1.426, generator_dur_loss=1.79, generator_adv_loss=2.126, generator_feat_match_loss=3.244, over 66.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.376, discriminator_fake_loss=1.355, generator_loss=27.61, generator_mel_loss=18.55, generator_kl_loss=1.412, generator_dur_loss=1.773, generator_adv_loss=1.948, generator_feat_match_loss=3.92, over 2320.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:32:44,528 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 00:32:52,458 INFO [train.py:591] (4/6) Epoch 241, validation: discriminator_loss=2.611, discriminator_real_loss=1.419, discriminator_fake_loss=1.192, generator_loss=26.98, generator_mel_loss=18.69, generator_kl_loss=1.14, generator_dur_loss=1.838, generator_adv_loss=2.076, generator_feat_match_loss=3.241, over 100.00 samples. +2024-03-13 00:32:52,459 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 00:35:13,495 INFO [train.py:527] (4/6) Epoch 241, batch 90, global_batch_idx: 29850, batch size: 45, loss[discriminator_loss=2.704, discriminator_real_loss=1.468, discriminator_fake_loss=1.235, generator_loss=27.34, generator_mel_loss=18.52, generator_kl_loss=1.331, generator_dur_loss=1.689, generator_adv_loss=1.896, generator_feat_match_loss=3.911, over 45.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.391, discriminator_fake_loss=1.345, generator_loss=27.43, generator_mel_loss=18.46, generator_kl_loss=1.387, generator_dur_loss=1.779, generator_adv_loss=1.94, generator_feat_match_loss=3.864, over 5319.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:36:43,223 INFO [train.py:919] (4/6) Start epoch 242 +2024-03-13 00:37:54,043 INFO [train.py:527] (4/6) Epoch 242, batch 16, global_batch_idx: 29900, batch size: 64, loss[discriminator_loss=2.724, discriminator_real_loss=1.346, discriminator_fake_loss=1.378, generator_loss=27.16, generator_mel_loss=18.33, generator_kl_loss=1.346, generator_dur_loss=1.827, generator_adv_loss=1.948, generator_feat_match_loss=3.7, over 64.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.372, discriminator_fake_loss=1.333, generator_loss=27.5, generator_mel_loss=18.48, generator_kl_loss=1.422, generator_dur_loss=1.744, generator_adv_loss=1.932, generator_feat_match_loss=3.916, over 949.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:40:15,187 INFO [train.py:527] (4/6) Epoch 242, batch 66, global_batch_idx: 29950, batch size: 80, loss[discriminator_loss=2.772, discriminator_real_loss=1.378, discriminator_fake_loss=1.394, generator_loss=26.91, generator_mel_loss=18.24, generator_kl_loss=1.264, generator_dur_loss=1.85, generator_adv_loss=2.139, generator_feat_match_loss=3.414, over 80.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=27.48, generator_mel_loss=18.52, generator_kl_loss=1.412, generator_dur_loss=1.759, generator_adv_loss=1.935, generator_feat_match_loss=3.855, over 3714.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:42:32,677 INFO [train.py:527] (4/6) Epoch 242, batch 116, global_batch_idx: 30000, batch size: 56, loss[discriminator_loss=2.766, discriminator_real_loss=1.414, discriminator_fake_loss=1.352, generator_loss=26.95, generator_mel_loss=18.1, generator_kl_loss=1.339, generator_dur_loss=1.706, generator_adv_loss=2.055, generator_feat_match_loss=3.749, over 56.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.385, discriminator_fake_loss=1.346, generator_loss=27.41, generator_mel_loss=18.47, generator_kl_loss=1.394, generator_dur_loss=1.76, generator_adv_loss=1.924, generator_feat_match_loss=3.864, over 6720.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:42:32,678 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 00:42:41,231 INFO [train.py:591] (4/6) Epoch 242, validation: discriminator_loss=2.756, discriminator_real_loss=1.482, discriminator_fake_loss=1.275, generator_loss=27.11, generator_mel_loss=18.56, generator_kl_loss=1.247, generator_dur_loss=1.811, generator_adv_loss=1.991, generator_feat_match_loss=3.501, over 100.00 samples. +2024-03-13 00:42:41,232 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 00:43:01,470 INFO [train.py:919] (4/6) Start epoch 243 +2024-03-13 00:45:22,473 INFO [train.py:527] (4/6) Epoch 243, batch 42, global_batch_idx: 30050, batch size: 42, loss[discriminator_loss=2.763, discriminator_real_loss=1.488, discriminator_fake_loss=1.275, generator_loss=27.74, generator_mel_loss=18.74, generator_kl_loss=1.368, generator_dur_loss=1.728, generator_adv_loss=1.818, generator_feat_match_loss=4.089, over 42.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.377, discriminator_fake_loss=1.354, generator_loss=27.46, generator_mel_loss=18.46, generator_kl_loss=1.378, generator_dur_loss=1.765, generator_adv_loss=1.931, generator_feat_match_loss=3.924, over 2569.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:47:41,903 INFO [train.py:527] (4/6) Epoch 243, batch 92, global_batch_idx: 30100, batch size: 59, loss[discriminator_loss=2.724, discriminator_real_loss=1.405, discriminator_fake_loss=1.319, generator_loss=27.93, generator_mel_loss=18.88, generator_kl_loss=1.324, generator_dur_loss=1.78, generator_adv_loss=1.9, generator_feat_match_loss=4.041, over 59.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.381, discriminator_fake_loss=1.347, generator_loss=27.5, generator_mel_loss=18.47, generator_kl_loss=1.376, generator_dur_loss=1.766, generator_adv_loss=1.948, generator_feat_match_loss=3.944, over 5584.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:49:08,327 INFO [train.py:919] (4/6) Start epoch 244 +2024-03-13 00:50:23,913 INFO [train.py:527] (4/6) Epoch 244, batch 18, global_batch_idx: 30150, batch size: 77, loss[discriminator_loss=2.732, discriminator_real_loss=1.255, discriminator_fake_loss=1.477, generator_loss=27.48, generator_mel_loss=18.38, generator_kl_loss=1.192, generator_dur_loss=1.842, generator_adv_loss=2.04, generator_feat_match_loss=4.02, over 77.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=27.6, generator_mel_loss=18.6, generator_kl_loss=1.403, generator_dur_loss=1.774, generator_adv_loss=1.91, generator_feat_match_loss=3.918, over 1026.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:52:43,017 INFO [train.py:527] (4/6) Epoch 244, batch 68, global_batch_idx: 30200, batch size: 12, loss[discriminator_loss=2.822, discriminator_real_loss=1.445, discriminator_fake_loss=1.378, generator_loss=28.15, generator_mel_loss=18.41, generator_kl_loss=1.836, generator_dur_loss=1.702, generator_adv_loss=2.046, generator_feat_match_loss=4.158, over 12.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.384, discriminator_fake_loss=1.347, generator_loss=27.4, generator_mel_loss=18.48, generator_kl_loss=1.405, generator_dur_loss=1.78, generator_adv_loss=1.906, generator_feat_match_loss=3.827, over 3855.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:52:43,018 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 00:52:50,820 INFO [train.py:591] (4/6) Epoch 244, validation: discriminator_loss=2.754, discriminator_real_loss=1.48, discriminator_fake_loss=1.274, generator_loss=25.97, generator_mel_loss=18.17, generator_kl_loss=1.073, generator_dur_loss=1.844, generator_adv_loss=1.922, generator_feat_match_loss=2.962, over 100.00 samples. +2024-03-13 00:52:50,821 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 00:55:09,596 INFO [train.py:527] (4/6) Epoch 244, batch 118, global_batch_idx: 30250, batch size: 64, loss[discriminator_loss=2.746, discriminator_real_loss=1.287, discriminator_fake_loss=1.459, generator_loss=27.22, generator_mel_loss=18.44, generator_kl_loss=1.444, generator_dur_loss=1.806, generator_adv_loss=1.885, generator_feat_match_loss=3.641, over 64.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.385, discriminator_fake_loss=1.343, generator_loss=27.5, generator_mel_loss=18.51, generator_kl_loss=1.401, generator_dur_loss=1.781, generator_adv_loss=1.926, generator_feat_match_loss=3.889, over 6669.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:55:24,239 INFO [train.py:919] (4/6) Start epoch 245 +2024-03-13 00:57:48,083 INFO [train.py:527] (4/6) Epoch 245, batch 44, global_batch_idx: 30300, batch size: 52, loss[discriminator_loss=2.746, discriminator_real_loss=1.397, discriminator_fake_loss=1.349, generator_loss=27.32, generator_mel_loss=18.28, generator_kl_loss=1.458, generator_dur_loss=1.726, generator_adv_loss=1.921, generator_feat_match_loss=3.937, over 52.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.386, discriminator_fake_loss=1.363, generator_loss=27.34, generator_mel_loss=18.43, generator_kl_loss=1.38, generator_dur_loss=1.791, generator_adv_loss=1.899, generator_feat_match_loss=3.84, over 2758.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:00:05,246 INFO [train.py:527] (4/6) Epoch 245, batch 94, global_batch_idx: 30350, batch size: 45, loss[discriminator_loss=2.749, discriminator_real_loss=1.372, discriminator_fake_loss=1.376, generator_loss=27.7, generator_mel_loss=18.78, generator_kl_loss=1.552, generator_dur_loss=1.712, generator_adv_loss=2.05, generator_feat_match_loss=3.602, over 45.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=27.24, generator_mel_loss=18.36, generator_kl_loss=1.392, generator_dur_loss=1.777, generator_adv_loss=1.905, generator_feat_match_loss=3.8, over 5501.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:01:29,098 INFO [train.py:919] (4/6) Start epoch 246 +2024-03-13 01:02:46,392 INFO [train.py:527] (4/6) Epoch 246, batch 20, global_batch_idx: 30400, batch size: 88, loss[discriminator_loss=2.813, discriminator_real_loss=1.454, discriminator_fake_loss=1.359, generator_loss=27.5, generator_mel_loss=18.41, generator_kl_loss=1.492, generator_dur_loss=1.834, generator_adv_loss=1.771, generator_feat_match_loss=3.987, over 88.00 samples.], tot_loss[discriminator_loss=2.771, discriminator_real_loss=1.409, discriminator_fake_loss=1.362, generator_loss=27.37, generator_mel_loss=18.43, generator_kl_loss=1.382, generator_dur_loss=1.788, generator_adv_loss=1.919, generator_feat_match_loss=3.853, over 1253.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:02:46,394 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 01:02:54,160 INFO [train.py:591] (4/6) Epoch 246, validation: discriminator_loss=2.815, discriminator_real_loss=1.444, discriminator_fake_loss=1.371, generator_loss=26.77, generator_mel_loss=18.83, generator_kl_loss=1.235, generator_dur_loss=1.858, generator_adv_loss=1.815, generator_feat_match_loss=3.026, over 100.00 samples. +2024-03-13 01:02:54,161 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 01:05:15,248 INFO [train.py:527] (4/6) Epoch 246, batch 70, global_batch_idx: 30450, batch size: 47, loss[discriminator_loss=2.759, discriminator_real_loss=1.424, discriminator_fake_loss=1.334, generator_loss=26.7, generator_mel_loss=18.02, generator_kl_loss=1.445, generator_dur_loss=1.678, generator_adv_loss=1.968, generator_feat_match_loss=3.591, over 47.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.394, discriminator_fake_loss=1.358, generator_loss=27.33, generator_mel_loss=18.38, generator_kl_loss=1.401, generator_dur_loss=1.782, generator_adv_loss=1.914, generator_feat_match_loss=3.856, over 4164.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:07:31,702 INFO [train.py:527] (4/6) Epoch 246, batch 120, global_batch_idx: 30500, batch size: 83, loss[discriminator_loss=2.709, discriminator_real_loss=1.389, discriminator_fake_loss=1.32, generator_loss=28.16, generator_mel_loss=18.76, generator_kl_loss=1.397, generator_dur_loss=1.874, generator_adv_loss=1.938, generator_feat_match_loss=4.195, over 83.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.387, discriminator_fake_loss=1.354, generator_loss=27.41, generator_mel_loss=18.45, generator_kl_loss=1.403, generator_dur_loss=1.773, generator_adv_loss=1.917, generator_feat_match_loss=3.873, over 6946.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:07:41,159 INFO [train.py:919] (4/6) Start epoch 247 +2024-03-13 01:10:17,039 INFO [train.py:527] (4/6) Epoch 247, batch 46, global_batch_idx: 30550, batch size: 58, loss[discriminator_loss=2.723, discriminator_real_loss=1.402, discriminator_fake_loss=1.321, generator_loss=27.62, generator_mel_loss=18.72, generator_kl_loss=1.381, generator_dur_loss=1.731, generator_adv_loss=2.042, generator_feat_match_loss=3.752, over 58.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.393, discriminator_fake_loss=1.345, generator_loss=27.63, generator_mel_loss=18.59, generator_kl_loss=1.381, generator_dur_loss=1.791, generator_adv_loss=1.926, generator_feat_match_loss=3.94, over 2659.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:12:34,223 INFO [train.py:527] (4/6) Epoch 247, batch 96, global_batch_idx: 30600, batch size: 66, loss[discriminator_loss=2.769, discriminator_real_loss=1.43, discriminator_fake_loss=1.339, generator_loss=27.53, generator_mel_loss=18.93, generator_kl_loss=1.395, generator_dur_loss=1.825, generator_adv_loss=1.717, generator_feat_match_loss=3.654, over 66.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.393, discriminator_fake_loss=1.349, generator_loss=27.55, generator_mel_loss=18.54, generator_kl_loss=1.385, generator_dur_loss=1.783, generator_adv_loss=1.928, generator_feat_match_loss=3.914, over 5521.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:12:34,225 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 01:12:43,015 INFO [train.py:591] (4/6) Epoch 247, validation: discriminator_loss=2.79, discriminator_real_loss=1.374, discriminator_fake_loss=1.416, generator_loss=25.86, generator_mel_loss=18.28, generator_kl_loss=1.187, generator_dur_loss=1.851, generator_adv_loss=1.691, generator_feat_match_loss=2.855, over 100.00 samples. +2024-03-13 01:12:43,016 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 01:13:59,469 INFO [train.py:919] (4/6) Start epoch 248 +2024-03-13 01:15:24,459 INFO [train.py:527] (4/6) Epoch 248, batch 22, global_batch_idx: 30650, batch size: 61, loss[discriminator_loss=2.682, discriminator_real_loss=1.341, discriminator_fake_loss=1.341, generator_loss=26.61, generator_mel_loss=17.93, generator_kl_loss=1.285, generator_dur_loss=1.728, generator_adv_loss=1.908, generator_feat_match_loss=3.759, over 61.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.384, discriminator_fake_loss=1.332, generator_loss=27.38, generator_mel_loss=18.43, generator_kl_loss=1.4, generator_dur_loss=1.724, generator_adv_loss=1.944, generator_feat_match_loss=3.881, over 1178.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:17:42,694 INFO [train.py:527] (4/6) Epoch 248, batch 72, global_batch_idx: 30700, batch size: 50, loss[discriminator_loss=2.721, discriminator_real_loss=1.401, discriminator_fake_loss=1.32, generator_loss=27.35, generator_mel_loss=18.3, generator_kl_loss=1.534, generator_dur_loss=1.709, generator_adv_loss=1.913, generator_feat_match_loss=3.893, over 50.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.38, discriminator_fake_loss=1.343, generator_loss=27.42, generator_mel_loss=18.4, generator_kl_loss=1.414, generator_dur_loss=1.753, generator_adv_loss=1.927, generator_feat_match_loss=3.918, over 3776.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:20:00,067 INFO [train.py:527] (4/6) Epoch 248, batch 122, global_batch_idx: 30750, batch size: 64, loss[discriminator_loss=2.783, discriminator_real_loss=1.44, discriminator_fake_loss=1.344, generator_loss=27.7, generator_mel_loss=18.59, generator_kl_loss=1.389, generator_dur_loss=1.754, generator_adv_loss=2.009, generator_feat_match_loss=3.958, over 64.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=27.47, generator_mel_loss=18.44, generator_kl_loss=1.408, generator_dur_loss=1.758, generator_adv_loss=1.921, generator_feat_match_loss=3.941, over 6536.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:20:05,809 INFO [train.py:919] (4/6) Start epoch 249 +2024-03-13 01:22:42,469 INFO [train.py:527] (4/6) Epoch 249, batch 48, global_batch_idx: 30800, batch size: 70, loss[discriminator_loss=2.752, discriminator_real_loss=1.418, discriminator_fake_loss=1.334, generator_loss=27.03, generator_mel_loss=18.1, generator_kl_loss=1.438, generator_dur_loss=1.75, generator_adv_loss=1.99, generator_feat_match_loss=3.759, over 70.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.381, discriminator_fake_loss=1.354, generator_loss=27.46, generator_mel_loss=18.51, generator_kl_loss=1.364, generator_dur_loss=1.756, generator_adv_loss=1.897, generator_feat_match_loss=3.939, over 2876.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:22:42,471 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 01:22:50,507 INFO [train.py:591] (4/6) Epoch 249, validation: discriminator_loss=2.733, discriminator_real_loss=1.498, discriminator_fake_loss=1.234, generator_loss=26.64, generator_mel_loss=18.71, generator_kl_loss=1.198, generator_dur_loss=1.834, generator_adv_loss=1.961, generator_feat_match_loss=2.934, over 100.00 samples. +2024-03-13 01:22:50,508 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 01:25:08,810 INFO [train.py:527] (4/6) Epoch 249, batch 98, global_batch_idx: 30850, batch size: 53, loss[discriminator_loss=2.751, discriminator_real_loss=1.384, discriminator_fake_loss=1.367, generator_loss=27.12, generator_mel_loss=18.13, generator_kl_loss=1.333, generator_dur_loss=1.72, generator_adv_loss=1.946, generator_feat_match_loss=3.997, over 53.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.377, discriminator_fake_loss=1.359, generator_loss=27.45, generator_mel_loss=18.49, generator_kl_loss=1.364, generator_dur_loss=1.777, generator_adv_loss=1.9, generator_feat_match_loss=3.928, over 5988.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:26:20,190 INFO [train.py:919] (4/6) Start epoch 250 +2024-03-13 01:27:49,321 INFO [train.py:527] (4/6) Epoch 250, batch 24, global_batch_idx: 30900, batch size: 58, loss[discriminator_loss=2.679, discriminator_real_loss=1.413, discriminator_fake_loss=1.267, generator_loss=27.45, generator_mel_loss=18.52, generator_kl_loss=1.412, generator_dur_loss=1.677, generator_adv_loss=1.876, generator_feat_match_loss=3.963, over 58.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.388, discriminator_fake_loss=1.348, generator_loss=27.38, generator_mel_loss=18.44, generator_kl_loss=1.398, generator_dur_loss=1.766, generator_adv_loss=1.912, generator_feat_match_loss=3.869, over 1468.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:30:07,474 INFO [train.py:527] (4/6) Epoch 250, batch 74, global_batch_idx: 30950, batch size: 88, loss[discriminator_loss=2.757, discriminator_real_loss=1.448, discriminator_fake_loss=1.309, generator_loss=27.09, generator_mel_loss=18.07, generator_kl_loss=1.426, generator_dur_loss=1.831, generator_adv_loss=1.897, generator_feat_match_loss=3.865, over 88.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.391, discriminator_fake_loss=1.349, generator_loss=27.37, generator_mel_loss=18.45, generator_kl_loss=1.397, generator_dur_loss=1.751, generator_adv_loss=1.917, generator_feat_match_loss=3.856, over 4265.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:32:28,039 INFO [train.py:919] (4/6) Start epoch 251 +2024-03-13 01:32:48,842 INFO [train.py:527] (4/6) Epoch 251, batch 0, global_batch_idx: 31000, batch size: 70, loss[discriminator_loss=2.783, discriminator_real_loss=1.461, discriminator_fake_loss=1.322, generator_loss=26.41, generator_mel_loss=18.11, generator_kl_loss=1.077, generator_dur_loss=1.827, generator_adv_loss=1.876, generator_feat_match_loss=3.518, over 70.00 samples.], tot_loss[discriminator_loss=2.783, discriminator_real_loss=1.461, discriminator_fake_loss=1.322, generator_loss=26.41, generator_mel_loss=18.11, generator_kl_loss=1.077, generator_dur_loss=1.827, generator_adv_loss=1.876, generator_feat_match_loss=3.518, over 70.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:32:48,848 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 01:32:56,660 INFO [train.py:591] (4/6) Epoch 251, validation: discriminator_loss=2.724, discriminator_real_loss=1.416, discriminator_fake_loss=1.308, generator_loss=26.43, generator_mel_loss=18.63, generator_kl_loss=1.232, generator_dur_loss=1.815, generator_adv_loss=1.85, generator_feat_match_loss=2.905, over 100.00 samples. +2024-03-13 01:32:56,663 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 01:35:15,853 INFO [train.py:527] (4/6) Epoch 251, batch 50, global_batch_idx: 31050, batch size: 31, loss[discriminator_loss=2.697, discriminator_real_loss=1.398, discriminator_fake_loss=1.299, generator_loss=29.84, generator_mel_loss=19.47, generator_kl_loss=1.779, generator_dur_loss=1.571, generator_adv_loss=1.952, generator_feat_match_loss=5.066, over 31.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=27.49, generator_mel_loss=18.41, generator_kl_loss=1.395, generator_dur_loss=1.745, generator_adv_loss=1.923, generator_feat_match_loss=4.021, over 2852.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:37:37,505 INFO [train.py:527] (4/6) Epoch 251, batch 100, global_batch_idx: 31100, batch size: 55, loss[discriminator_loss=2.809, discriminator_real_loss=1.402, discriminator_fake_loss=1.407, generator_loss=27.75, generator_mel_loss=18.94, generator_kl_loss=1.401, generator_dur_loss=1.711, generator_adv_loss=1.943, generator_feat_match_loss=3.756, over 55.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.382, discriminator_fake_loss=1.349, generator_loss=27.5, generator_mel_loss=18.48, generator_kl_loss=1.395, generator_dur_loss=1.757, generator_adv_loss=1.909, generator_feat_match_loss=3.954, over 6053.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:38:39,183 INFO [train.py:919] (4/6) Start epoch 252 +2024-03-13 01:40:18,029 INFO [train.py:527] (4/6) Epoch 252, batch 26, global_batch_idx: 31150, batch size: 53, loss[discriminator_loss=2.819, discriminator_real_loss=1.432, discriminator_fake_loss=1.387, generator_loss=27.31, generator_mel_loss=18.21, generator_kl_loss=1.569, generator_dur_loss=1.746, generator_adv_loss=1.953, generator_feat_match_loss=3.832, over 53.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.367, discriminator_fake_loss=1.354, generator_loss=27.5, generator_mel_loss=18.37, generator_kl_loss=1.439, generator_dur_loss=1.754, generator_adv_loss=1.922, generator_feat_match_loss=4.011, over 1592.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:42:36,522 INFO [train.py:527] (4/6) Epoch 252, batch 76, global_batch_idx: 31200, batch size: 36, loss[discriminator_loss=2.843, discriminator_real_loss=1.429, discriminator_fake_loss=1.414, generator_loss=27.15, generator_mel_loss=18.72, generator_kl_loss=1.305, generator_dur_loss=1.762, generator_adv_loss=2.016, generator_feat_match_loss=3.345, over 36.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.374, discriminator_fake_loss=1.354, generator_loss=27.41, generator_mel_loss=18.38, generator_kl_loss=1.397, generator_dur_loss=1.751, generator_adv_loss=1.921, generator_feat_match_loss=3.961, over 4302.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:42:36,523 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 01:42:44,415 INFO [train.py:591] (4/6) Epoch 252, validation: discriminator_loss=2.785, discriminator_real_loss=1.56, discriminator_fake_loss=1.224, generator_loss=26.5, generator_mel_loss=18.6, generator_kl_loss=1.167, generator_dur_loss=1.826, generator_adv_loss=1.996, generator_feat_match_loss=2.911, over 100.00 samples. +2024-03-13 01:42:44,416 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 01:44:54,086 INFO [train.py:919] (4/6) Start epoch 253 +2024-03-13 01:45:20,444 INFO [train.py:527] (4/6) Epoch 253, batch 2, global_batch_idx: 31250, batch size: 44, loss[discriminator_loss=2.669, discriminator_real_loss=1.28, discriminator_fake_loss=1.389, generator_loss=27.98, generator_mel_loss=18.7, generator_kl_loss=1.57, generator_dur_loss=1.698, generator_adv_loss=1.86, generator_feat_match_loss=4.146, over 44.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.321, discriminator_fake_loss=1.388, generator_loss=27.38, generator_mel_loss=18.48, generator_kl_loss=1.467, generator_dur_loss=1.738, generator_adv_loss=1.865, generator_feat_match_loss=3.827, over 162.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:47:37,752 INFO [train.py:527] (4/6) Epoch 253, batch 52, global_batch_idx: 31300, batch size: 47, loss[discriminator_loss=2.711, discriminator_real_loss=1.437, discriminator_fake_loss=1.274, generator_loss=27.02, generator_mel_loss=18.11, generator_kl_loss=1.415, generator_dur_loss=1.72, generator_adv_loss=2.075, generator_feat_match_loss=3.703, over 47.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.378, discriminator_fake_loss=1.354, generator_loss=27.74, generator_mel_loss=18.5, generator_kl_loss=1.4, generator_dur_loss=1.754, generator_adv_loss=1.992, generator_feat_match_loss=4.093, over 2932.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:49:55,109 INFO [train.py:527] (4/6) Epoch 253, batch 102, global_batch_idx: 31350, batch size: 56, loss[discriminator_loss=2.757, discriminator_real_loss=1.429, discriminator_fake_loss=1.328, generator_loss=27.8, generator_mel_loss=18.63, generator_kl_loss=1.441, generator_dur_loss=1.694, generator_adv_loss=1.914, generator_feat_match_loss=4.116, over 56.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.375, discriminator_fake_loss=1.35, generator_loss=27.6, generator_mel_loss=18.48, generator_kl_loss=1.39, generator_dur_loss=1.752, generator_adv_loss=1.948, generator_feat_match_loss=4.035, over 5844.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:50:56,568 INFO [train.py:919] (4/6) Start epoch 254 +2024-03-13 01:52:38,349 INFO [train.py:527] (4/6) Epoch 254, batch 28, global_batch_idx: 31400, batch size: 15, loss[discriminator_loss=2.725, discriminator_real_loss=1.315, discriminator_fake_loss=1.409, generator_loss=27.68, generator_mel_loss=18.5, generator_kl_loss=1.531, generator_dur_loss=1.616, generator_adv_loss=2.065, generator_feat_match_loss=3.974, over 15.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.382, discriminator_fake_loss=1.362, generator_loss=27.49, generator_mel_loss=18.52, generator_kl_loss=1.372, generator_dur_loss=1.763, generator_adv_loss=1.894, generator_feat_match_loss=3.94, over 1672.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:52:38,350 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 01:52:46,338 INFO [train.py:591] (4/6) Epoch 254, validation: discriminator_loss=2.75, discriminator_real_loss=1.459, discriminator_fake_loss=1.292, generator_loss=26.99, generator_mel_loss=18.55, generator_kl_loss=1.213, generator_dur_loss=1.832, generator_adv_loss=1.963, generator_feat_match_loss=3.434, over 100.00 samples. +2024-03-13 01:52:46,339 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 01:55:03,463 INFO [train.py:527] (4/6) Epoch 254, batch 78, global_batch_idx: 31450, batch size: 74, loss[discriminator_loss=2.761, discriminator_real_loss=1.331, discriminator_fake_loss=1.43, generator_loss=27.21, generator_mel_loss=18.06, generator_kl_loss=1.334, generator_dur_loss=1.822, generator_adv_loss=1.794, generator_feat_match_loss=4.199, over 74.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.387, discriminator_fake_loss=1.353, generator_loss=27.45, generator_mel_loss=18.46, generator_kl_loss=1.376, generator_dur_loss=1.758, generator_adv_loss=1.904, generator_feat_match_loss=3.96, over 4376.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:57:12,586 INFO [train.py:919] (4/6) Start epoch 255 +2024-03-13 01:57:47,778 INFO [train.py:527] (4/6) Epoch 255, batch 4, global_batch_idx: 31500, batch size: 61, loss[discriminator_loss=2.784, discriminator_real_loss=1.464, discriminator_fake_loss=1.32, generator_loss=26.97, generator_mel_loss=18.12, generator_kl_loss=1.582, generator_dur_loss=1.748, generator_adv_loss=1.912, generator_feat_match_loss=3.616, over 61.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.404, discriminator_fake_loss=1.313, generator_loss=27.82, generator_mel_loss=18.65, generator_kl_loss=1.46, generator_dur_loss=1.744, generator_adv_loss=1.96, generator_feat_match_loss=4.006, over 311.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:00:05,953 INFO [train.py:527] (4/6) Epoch 255, batch 54, global_batch_idx: 31550, batch size: 70, loss[discriminator_loss=2.683, discriminator_real_loss=1.395, discriminator_fake_loss=1.289, generator_loss=28.52, generator_mel_loss=18.88, generator_kl_loss=1.285, generator_dur_loss=1.773, generator_adv_loss=1.852, generator_feat_match_loss=4.732, over 70.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.378, discriminator_fake_loss=1.343, generator_loss=27.57, generator_mel_loss=18.48, generator_kl_loss=1.419, generator_dur_loss=1.749, generator_adv_loss=1.922, generator_feat_match_loss=4, over 2978.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:02:24,274 INFO [train.py:527] (4/6) Epoch 255, batch 104, global_batch_idx: 31600, batch size: 31, loss[discriminator_loss=2.8, discriminator_real_loss=1.47, discriminator_fake_loss=1.33, generator_loss=27.49, generator_mel_loss=18.31, generator_kl_loss=1.553, generator_dur_loss=1.629, generator_adv_loss=1.905, generator_feat_match_loss=4.093, over 31.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.347, generator_loss=27.72, generator_mel_loss=18.5, generator_kl_loss=1.415, generator_dur_loss=1.749, generator_adv_loss=1.964, generator_feat_match_loss=4.09, over 5756.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:02:24,276 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 02:02:33,142 INFO [train.py:591] (4/6) Epoch 255, validation: discriminator_loss=2.723, discriminator_real_loss=1.394, discriminator_fake_loss=1.329, generator_loss=26.85, generator_mel_loss=18.73, generator_kl_loss=1.242, generator_dur_loss=1.828, generator_adv_loss=1.886, generator_feat_match_loss=3.165, over 100.00 samples. +2024-03-13 02:02:33,143 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 02:03:26,711 INFO [train.py:919] (4/6) Start epoch 256 +2024-03-13 02:05:13,777 INFO [train.py:527] (4/6) Epoch 256, batch 30, global_batch_idx: 31650, batch size: 72, loss[discriminator_loss=2.818, discriminator_real_loss=1.602, discriminator_fake_loss=1.216, generator_loss=27.04, generator_mel_loss=18.09, generator_kl_loss=1.395, generator_dur_loss=1.81, generator_adv_loss=1.792, generator_feat_match_loss=3.961, over 72.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.398, discriminator_fake_loss=1.35, generator_loss=27.42, generator_mel_loss=18.43, generator_kl_loss=1.366, generator_dur_loss=1.764, generator_adv_loss=1.916, generator_feat_match_loss=3.95, over 1772.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:07:33,011 INFO [train.py:527] (4/6) Epoch 256, batch 80, global_batch_idx: 31700, batch size: 31, loss[discriminator_loss=2.635, discriminator_real_loss=1.498, discriminator_fake_loss=1.137, generator_loss=28.47, generator_mel_loss=18.83, generator_kl_loss=1.405, generator_dur_loss=1.706, generator_adv_loss=2.029, generator_feat_match_loss=4.508, over 31.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.392, discriminator_fake_loss=1.352, generator_loss=27.42, generator_mel_loss=18.45, generator_kl_loss=1.386, generator_dur_loss=1.761, generator_adv_loss=1.904, generator_feat_match_loss=3.921, over 4545.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:09:31,583 INFO [train.py:919] (4/6) Start epoch 257 +2024-03-13 02:10:12,028 INFO [train.py:527] (4/6) Epoch 257, batch 6, global_batch_idx: 31750, batch size: 72, loss[discriminator_loss=2.739, discriminator_real_loss=1.419, discriminator_fake_loss=1.32, generator_loss=27.12, generator_mel_loss=18.5, generator_kl_loss=1.344, generator_dur_loss=1.785, generator_adv_loss=1.856, generator_feat_match_loss=3.634, over 72.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.392, discriminator_fake_loss=1.364, generator_loss=27.16, generator_mel_loss=18.41, generator_kl_loss=1.42, generator_dur_loss=1.7, generator_adv_loss=1.907, generator_feat_match_loss=3.728, over 307.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:12:30,552 INFO [train.py:527] (4/6) Epoch 257, batch 56, global_batch_idx: 31800, batch size: 88, loss[discriminator_loss=2.688, discriminator_real_loss=1.278, discriminator_fake_loss=1.41, generator_loss=28.18, generator_mel_loss=18.48, generator_kl_loss=1.368, generator_dur_loss=1.836, generator_adv_loss=1.933, generator_feat_match_loss=4.561, over 88.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.381, discriminator_fake_loss=1.356, generator_loss=27.31, generator_mel_loss=18.42, generator_kl_loss=1.385, generator_dur_loss=1.751, generator_adv_loss=1.893, generator_feat_match_loss=3.869, over 3129.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:12:30,554 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 02:12:38,372 INFO [train.py:591] (4/6) Epoch 257, validation: discriminator_loss=2.759, discriminator_real_loss=1.537, discriminator_fake_loss=1.221, generator_loss=26.49, generator_mel_loss=18.51, generator_kl_loss=1.253, generator_dur_loss=1.822, generator_adv_loss=1.931, generator_feat_match_loss=2.969, over 100.00 samples. +2024-03-13 02:12:38,373 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 02:14:57,214 INFO [train.py:527] (4/6) Epoch 257, batch 106, global_batch_idx: 31850, batch size: 47, loss[discriminator_loss=2.7, discriminator_real_loss=1.29, discriminator_fake_loss=1.41, generator_loss=27.59, generator_mel_loss=18.46, generator_kl_loss=1.455, generator_dur_loss=1.654, generator_adv_loss=1.891, generator_feat_match_loss=4.133, over 47.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.36, generator_mel_loss=18.43, generator_kl_loss=1.401, generator_dur_loss=1.741, generator_adv_loss=1.901, generator_feat_match_loss=3.885, over 5779.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:15:43,885 INFO [train.py:919] (4/6) Start epoch 258 +2024-03-13 02:17:39,478 INFO [train.py:527] (4/6) Epoch 258, batch 32, global_batch_idx: 31900, batch size: 66, loss[discriminator_loss=2.744, discriminator_real_loss=1.442, discriminator_fake_loss=1.302, generator_loss=27.01, generator_mel_loss=18.3, generator_kl_loss=1.377, generator_dur_loss=1.802, generator_adv_loss=1.855, generator_feat_match_loss=3.677, over 66.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.389, discriminator_fake_loss=1.353, generator_loss=27.35, generator_mel_loss=18.47, generator_kl_loss=1.375, generator_dur_loss=1.767, generator_adv_loss=1.888, generator_feat_match_loss=3.849, over 2000.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:19:57,434 INFO [train.py:527] (4/6) Epoch 258, batch 82, global_batch_idx: 31950, batch size: 61, loss[discriminator_loss=2.747, discriminator_real_loss=1.417, discriminator_fake_loss=1.329, generator_loss=28.03, generator_mel_loss=18.63, generator_kl_loss=1.4, generator_dur_loss=1.705, generator_adv_loss=1.965, generator_feat_match_loss=4.333, over 61.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.47, generator_mel_loss=18.47, generator_kl_loss=1.386, generator_dur_loss=1.767, generator_adv_loss=1.929, generator_feat_match_loss=3.919, over 4784.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:21:51,807 INFO [train.py:919] (4/6) Start epoch 259 +2024-03-13 02:22:39,483 INFO [train.py:527] (4/6) Epoch 259, batch 8, global_batch_idx: 32000, batch size: 45, loss[discriminator_loss=2.859, discriminator_real_loss=1.533, discriminator_fake_loss=1.326, generator_loss=27.28, generator_mel_loss=18.57, generator_kl_loss=1.467, generator_dur_loss=1.695, generator_adv_loss=1.789, generator_feat_match_loss=3.759, over 45.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.397, discriminator_fake_loss=1.346, generator_loss=27.46, generator_mel_loss=18.24, generator_kl_loss=1.346, generator_dur_loss=1.813, generator_adv_loss=1.933, generator_feat_match_loss=4.128, over 596.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:22:39,486 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 02:22:47,455 INFO [train.py:591] (4/6) Epoch 259, validation: discriminator_loss=2.777, discriminator_real_loss=1.429, discriminator_fake_loss=1.348, generator_loss=26.66, generator_mel_loss=18.57, generator_kl_loss=1.241, generator_dur_loss=1.835, generator_adv_loss=1.784, generator_feat_match_loss=3.231, over 100.00 samples. +2024-03-13 02:22:47,457 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 02:25:08,184 INFO [train.py:527] (4/6) Epoch 259, batch 58, global_batch_idx: 32050, batch size: 50, loss[discriminator_loss=2.706, discriminator_real_loss=1.398, discriminator_fake_loss=1.308, generator_loss=27.34, generator_mel_loss=18.43, generator_kl_loss=1.382, generator_dur_loss=1.705, generator_adv_loss=1.847, generator_feat_match_loss=3.975, over 50.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.389, discriminator_fake_loss=1.35, generator_loss=27.55, generator_mel_loss=18.45, generator_kl_loss=1.395, generator_dur_loss=1.775, generator_adv_loss=1.917, generator_feat_match_loss=4.011, over 3404.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:27:26,575 INFO [train.py:527] (4/6) Epoch 259, batch 108, global_batch_idx: 32100, batch size: 55, loss[discriminator_loss=2.704, discriminator_real_loss=1.389, discriminator_fake_loss=1.315, generator_loss=28.35, generator_mel_loss=19.05, generator_kl_loss=1.393, generator_dur_loss=1.684, generator_adv_loss=1.939, generator_feat_match_loss=4.289, over 55.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.387, discriminator_fake_loss=1.352, generator_loss=27.53, generator_mel_loss=18.46, generator_kl_loss=1.388, generator_dur_loss=1.773, generator_adv_loss=1.913, generator_feat_match_loss=4.002, over 6265.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:28:09,811 INFO [train.py:919] (4/6) Start epoch 260 +2024-03-13 02:30:09,130 INFO [train.py:527] (4/6) Epoch 260, batch 34, global_batch_idx: 32150, batch size: 66, loss[discriminator_loss=2.74, discriminator_real_loss=1.422, discriminator_fake_loss=1.318, generator_loss=26.52, generator_mel_loss=18.21, generator_kl_loss=1.376, generator_dur_loss=1.776, generator_adv_loss=1.862, generator_feat_match_loss=3.302, over 66.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.373, discriminator_fake_loss=1.352, generator_loss=27.46, generator_mel_loss=18.43, generator_kl_loss=1.404, generator_dur_loss=1.773, generator_adv_loss=1.91, generator_feat_match_loss=3.942, over 2008.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:32:29,166 INFO [train.py:527] (4/6) Epoch 260, batch 84, global_batch_idx: 32200, batch size: 31, loss[discriminator_loss=2.591, discriminator_real_loss=1.255, discriminator_fake_loss=1.336, generator_loss=29.16, generator_mel_loss=18.84, generator_kl_loss=1.548, generator_dur_loss=1.629, generator_adv_loss=2.179, generator_feat_match_loss=4.973, over 31.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.386, discriminator_fake_loss=1.353, generator_loss=27.43, generator_mel_loss=18.41, generator_kl_loss=1.394, generator_dur_loss=1.779, generator_adv_loss=1.913, generator_feat_match_loss=3.929, over 5041.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:32:29,168 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 02:32:37,975 INFO [train.py:591] (4/6) Epoch 260, validation: discriminator_loss=2.713, discriminator_real_loss=1.453, discriminator_fake_loss=1.26, generator_loss=27.27, generator_mel_loss=18.88, generator_kl_loss=1.229, generator_dur_loss=1.83, generator_adv_loss=2.043, generator_feat_match_loss=3.287, over 100.00 samples. +2024-03-13 02:32:37,976 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 02:34:26,936 INFO [train.py:919] (4/6) Start epoch 261 +2024-03-13 02:35:21,371 INFO [train.py:527] (4/6) Epoch 261, batch 10, global_batch_idx: 32250, batch size: 88, loss[discriminator_loss=2.796, discriminator_real_loss=1.312, discriminator_fake_loss=1.484, generator_loss=27.04, generator_mel_loss=18.06, generator_kl_loss=1.382, generator_dur_loss=1.867, generator_adv_loss=1.953, generator_feat_match_loss=3.78, over 88.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.4, discriminator_fake_loss=1.368, generator_loss=27.51, generator_mel_loss=18.55, generator_kl_loss=1.362, generator_dur_loss=1.773, generator_adv_loss=1.91, generator_feat_match_loss=3.918, over 718.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:37:37,276 INFO [train.py:527] (4/6) Epoch 261, batch 60, global_batch_idx: 32300, batch size: 45, loss[discriminator_loss=2.777, discriminator_real_loss=1.473, discriminator_fake_loss=1.304, generator_loss=26.97, generator_mel_loss=18.14, generator_kl_loss=1.444, generator_dur_loss=1.687, generator_adv_loss=2.013, generator_feat_match_loss=3.68, over 45.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.383, discriminator_fake_loss=1.358, generator_loss=27.46, generator_mel_loss=18.46, generator_kl_loss=1.384, generator_dur_loss=1.747, generator_adv_loss=1.922, generator_feat_match_loss=3.946, over 3328.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:39:54,438 INFO [train.py:527] (4/6) Epoch 261, batch 110, global_batch_idx: 32350, batch size: 74, loss[discriminator_loss=2.704, discriminator_real_loss=1.373, discriminator_fake_loss=1.332, generator_loss=27.43, generator_mel_loss=18.31, generator_kl_loss=1.407, generator_dur_loss=1.808, generator_adv_loss=2.041, generator_feat_match_loss=3.865, over 74.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.385, discriminator_fake_loss=1.356, generator_loss=27.47, generator_mel_loss=18.45, generator_kl_loss=1.405, generator_dur_loss=1.75, generator_adv_loss=1.921, generator_feat_match_loss=3.945, over 6105.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:40:30,064 INFO [train.py:919] (4/6) Start epoch 262 +2024-03-13 02:42:32,855 INFO [train.py:527] (4/6) Epoch 262, batch 36, global_batch_idx: 32400, batch size: 42, loss[discriminator_loss=2.684, discriminator_real_loss=1.441, discriminator_fake_loss=1.244, generator_loss=27.89, generator_mel_loss=19.14, generator_kl_loss=1.366, generator_dur_loss=1.775, generator_adv_loss=1.946, generator_feat_match_loss=3.669, over 42.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.405, discriminator_fake_loss=1.332, generator_loss=27.47, generator_mel_loss=18.45, generator_kl_loss=1.424, generator_dur_loss=1.761, generator_adv_loss=1.926, generator_feat_match_loss=3.916, over 2038.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:42:32,857 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 02:42:41,013 INFO [train.py:591] (4/6) Epoch 262, validation: discriminator_loss=2.761, discriminator_real_loss=1.36, discriminator_fake_loss=1.401, generator_loss=26.68, generator_mel_loss=18.58, generator_kl_loss=1.141, generator_dur_loss=1.828, generator_adv_loss=1.766, generator_feat_match_loss=3.362, over 100.00 samples. +2024-03-13 02:42:41,014 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 02:45:03,107 INFO [train.py:527] (4/6) Epoch 262, batch 86, global_batch_idx: 32450, batch size: 95, loss[discriminator_loss=2.683, discriminator_real_loss=1.278, discriminator_fake_loss=1.405, generator_loss=28.04, generator_mel_loss=18.48, generator_kl_loss=1.33, generator_dur_loss=1.844, generator_adv_loss=1.999, generator_feat_match_loss=4.389, over 95.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.388, discriminator_fake_loss=1.338, generator_loss=27.52, generator_mel_loss=18.42, generator_kl_loss=1.426, generator_dur_loss=1.754, generator_adv_loss=1.945, generator_feat_match_loss=3.977, over 4787.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:46:48,042 INFO [train.py:919] (4/6) Start epoch 263 +2024-03-13 02:47:44,482 INFO [train.py:527] (4/6) Epoch 263, batch 12, global_batch_idx: 32500, batch size: 62, loss[discriminator_loss=2.802, discriminator_real_loss=1.506, discriminator_fake_loss=1.296, generator_loss=25.9, generator_mel_loss=17.71, generator_kl_loss=1.232, generator_dur_loss=1.758, generator_adv_loss=1.869, generator_feat_match_loss=3.333, over 62.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.355, discriminator_fake_loss=1.352, generator_loss=27.53, generator_mel_loss=18.45, generator_kl_loss=1.397, generator_dur_loss=1.731, generator_adv_loss=1.915, generator_feat_match_loss=4.037, over 707.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:50:03,534 INFO [train.py:527] (4/6) Epoch 263, batch 62, global_batch_idx: 32550, batch size: 66, loss[discriminator_loss=2.718, discriminator_real_loss=1.441, discriminator_fake_loss=1.277, generator_loss=25.84, generator_mel_loss=17.58, generator_kl_loss=1.278, generator_dur_loss=1.777, generator_adv_loss=1.722, generator_feat_match_loss=3.479, over 66.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.382, discriminator_fake_loss=1.346, generator_loss=27.55, generator_mel_loss=18.46, generator_kl_loss=1.414, generator_dur_loss=1.761, generator_adv_loss=1.927, generator_feat_match_loss=3.988, over 3607.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:52:22,821 INFO [train.py:527] (4/6) Epoch 263, batch 112, global_batch_idx: 32600, batch size: 55, loss[discriminator_loss=2.741, discriminator_real_loss=1.485, discriminator_fake_loss=1.255, generator_loss=27.87, generator_mel_loss=18.75, generator_kl_loss=1.374, generator_dur_loss=1.741, generator_adv_loss=1.937, generator_feat_match_loss=4.07, over 55.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.385, discriminator_fake_loss=1.349, generator_loss=27.48, generator_mel_loss=18.41, generator_kl_loss=1.407, generator_dur_loss=1.762, generator_adv_loss=1.916, generator_feat_match_loss=3.985, over 6423.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:52:22,822 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 02:52:31,660 INFO [train.py:591] (4/6) Epoch 263, validation: discriminator_loss=2.759, discriminator_real_loss=1.445, discriminator_fake_loss=1.314, generator_loss=26.63, generator_mel_loss=18.66, generator_kl_loss=1.182, generator_dur_loss=1.837, generator_adv_loss=1.862, generator_feat_match_loss=3.083, over 100.00 samples. +2024-03-13 02:52:31,661 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 02:53:03,912 INFO [train.py:919] (4/6) Start epoch 264 +2024-03-13 02:55:15,604 INFO [train.py:527] (4/6) Epoch 264, batch 38, global_batch_idx: 32650, batch size: 48, loss[discriminator_loss=2.753, discriminator_real_loss=1.402, discriminator_fake_loss=1.351, generator_loss=28.23, generator_mel_loss=18.81, generator_kl_loss=1.608, generator_dur_loss=1.645, generator_adv_loss=2, generator_feat_match_loss=4.168, over 48.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.387, discriminator_fake_loss=1.358, generator_loss=27.44, generator_mel_loss=18.36, generator_kl_loss=1.444, generator_dur_loss=1.753, generator_adv_loss=1.926, generator_feat_match_loss=3.958, over 2208.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:57:36,659 INFO [train.py:527] (4/6) Epoch 264, batch 88, global_batch_idx: 32700, batch size: 47, loss[discriminator_loss=2.784, discriminator_real_loss=1.487, discriminator_fake_loss=1.297, generator_loss=26.56, generator_mel_loss=17.91, generator_kl_loss=1.46, generator_dur_loss=1.667, generator_adv_loss=1.824, generator_feat_match_loss=3.705, over 47.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.389, discriminator_fake_loss=1.353, generator_loss=27.4, generator_mel_loss=18.36, generator_kl_loss=1.419, generator_dur_loss=1.752, generator_adv_loss=1.922, generator_feat_match_loss=3.95, over 4963.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:59:12,953 INFO [train.py:919] (4/6) Start epoch 265 +2024-03-13 03:00:17,350 INFO [train.py:527] (4/6) Epoch 265, batch 14, global_batch_idx: 32750, batch size: 50, loss[discriminator_loss=2.692, discriminator_real_loss=1.344, discriminator_fake_loss=1.348, generator_loss=27.36, generator_mel_loss=18.19, generator_kl_loss=1.449, generator_dur_loss=1.687, generator_adv_loss=1.909, generator_feat_match_loss=4.128, over 50.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.389, discriminator_fake_loss=1.358, generator_loss=27.43, generator_mel_loss=18.54, generator_kl_loss=1.366, generator_dur_loss=1.768, generator_adv_loss=1.866, generator_feat_match_loss=3.895, over 937.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 03:02:39,888 INFO [train.py:527] (4/6) Epoch 265, batch 64, global_batch_idx: 32800, batch size: 31, loss[discriminator_loss=2.77, discriminator_real_loss=1.358, discriminator_fake_loss=1.412, generator_loss=28.2, generator_mel_loss=19.28, generator_kl_loss=1.582, generator_dur_loss=1.656, generator_adv_loss=1.838, generator_feat_match_loss=3.842, over 31.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.382, discriminator_fake_loss=1.357, generator_loss=27.4, generator_mel_loss=18.45, generator_kl_loss=1.359, generator_dur_loss=1.762, generator_adv_loss=1.911, generator_feat_match_loss=3.922, over 3906.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 03:02:39,890 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 03:02:48,050 INFO [train.py:591] (4/6) Epoch 265, validation: discriminator_loss=2.754, discriminator_real_loss=1.464, discriminator_fake_loss=1.291, generator_loss=26.78, generator_mel_loss=18.76, generator_kl_loss=1.124, generator_dur_loss=1.824, generator_adv_loss=1.835, generator_feat_match_loss=3.235, over 100.00 samples. +2024-03-13 03:02:48,050 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 03:05:03,328 INFO [train.py:527] (4/6) Epoch 265, batch 114, global_batch_idx: 32850, batch size: 77, loss[discriminator_loss=2.706, discriminator_real_loss=1.383, discriminator_fake_loss=1.323, generator_loss=27.24, generator_mel_loss=18.22, generator_kl_loss=1.302, generator_dur_loss=1.852, generator_adv_loss=1.862, generator_feat_match_loss=4.002, over 77.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.382, discriminator_fake_loss=1.354, generator_loss=27.49, generator_mel_loss=18.47, generator_kl_loss=1.379, generator_dur_loss=1.758, generator_adv_loss=1.915, generator_feat_match_loss=3.968, over 6569.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 03:05:29,196 INFO [train.py:919] (4/6) Start epoch 266 +2024-03-13 03:07:43,990 INFO [train.py:527] (4/6) Epoch 266, batch 40, global_batch_idx: 32900, batch size: 47, loss[discriminator_loss=2.743, discriminator_real_loss=1.405, discriminator_fake_loss=1.338, generator_loss=27.85, generator_mel_loss=18.59, generator_kl_loss=1.637, generator_dur_loss=1.679, generator_adv_loss=1.837, generator_feat_match_loss=4.108, over 47.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.387, discriminator_fake_loss=1.337, generator_loss=27.59, generator_mel_loss=18.47, generator_kl_loss=1.411, generator_dur_loss=1.739, generator_adv_loss=1.936, generator_feat_match_loss=4.037, over 2283.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:10:05,547 INFO [train.py:527] (4/6) Epoch 266, batch 90, global_batch_idx: 32950, batch size: 96, loss[discriminator_loss=2.774, discriminator_real_loss=1.477, discriminator_fake_loss=1.297, generator_loss=28, generator_mel_loss=18.21, generator_kl_loss=1.447, generator_dur_loss=1.87, generator_adv_loss=2.095, generator_feat_match_loss=4.378, over 96.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.388, discriminator_fake_loss=1.346, generator_loss=27.55, generator_mel_loss=18.43, generator_kl_loss=1.405, generator_dur_loss=1.758, generator_adv_loss=1.934, generator_feat_match_loss=4.023, over 5407.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:11:35,732 INFO [train.py:919] (4/6) Start epoch 267 +2024-03-13 03:12:46,533 INFO [train.py:527] (4/6) Epoch 267, batch 16, global_batch_idx: 33000, batch size: 53, loss[discriminator_loss=2.704, discriminator_real_loss=1.323, discriminator_fake_loss=1.381, generator_loss=28.66, generator_mel_loss=18.88, generator_kl_loss=1.412, generator_dur_loss=1.692, generator_adv_loss=1.877, generator_feat_match_loss=4.799, over 53.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.373, discriminator_fake_loss=1.358, generator_loss=27.62, generator_mel_loss=18.54, generator_kl_loss=1.373, generator_dur_loss=1.767, generator_adv_loss=1.921, generator_feat_match_loss=4.025, over 976.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:12:46,535 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 03:12:54,394 INFO [train.py:591] (4/6) Epoch 267, validation: discriminator_loss=2.75, discriminator_real_loss=1.458, discriminator_fake_loss=1.292, generator_loss=26.14, generator_mel_loss=18.44, generator_kl_loss=1.151, generator_dur_loss=1.833, generator_adv_loss=1.903, generator_feat_match_loss=2.817, over 100.00 samples. +2024-03-13 03:12:54,395 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 03:15:11,679 INFO [train.py:527] (4/6) Epoch 267, batch 66, global_batch_idx: 33050, batch size: 62, loss[discriminator_loss=2.736, discriminator_real_loss=1.373, discriminator_fake_loss=1.362, generator_loss=28.09, generator_mel_loss=18.9, generator_kl_loss=1.398, generator_dur_loss=1.767, generator_adv_loss=1.972, generator_feat_match_loss=4.053, over 62.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=27.5, generator_mel_loss=18.4, generator_kl_loss=1.383, generator_dur_loss=1.765, generator_adv_loss=1.919, generator_feat_match_loss=4.028, over 3750.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:17:28,109 INFO [train.py:527] (4/6) Epoch 267, batch 116, global_batch_idx: 33100, batch size: 31, loss[discriminator_loss=2.627, discriminator_real_loss=1.338, discriminator_fake_loss=1.289, generator_loss=28.49, generator_mel_loss=19.19, generator_kl_loss=1.448, generator_dur_loss=1.683, generator_adv_loss=2.024, generator_feat_match_loss=4.142, over 31.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.54, generator_mel_loss=18.41, generator_kl_loss=1.4, generator_dur_loss=1.754, generator_adv_loss=1.923, generator_feat_match_loss=4.053, over 6411.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:17:51,122 INFO [train.py:919] (4/6) Start epoch 268 +2024-03-13 03:20:12,502 INFO [train.py:527] (4/6) Epoch 268, batch 42, global_batch_idx: 33150, batch size: 66, loss[discriminator_loss=2.772, discriminator_real_loss=1.324, discriminator_fake_loss=1.448, generator_loss=28.02, generator_mel_loss=18.28, generator_kl_loss=1.407, generator_dur_loss=1.813, generator_adv_loss=2.094, generator_feat_match_loss=4.428, over 66.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.378, discriminator_fake_loss=1.335, generator_loss=27.69, generator_mel_loss=18.44, generator_kl_loss=1.408, generator_dur_loss=1.759, generator_adv_loss=1.977, generator_feat_match_loss=4.11, over 2423.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:22:32,523 INFO [train.py:527] (4/6) Epoch 268, batch 92, global_batch_idx: 33200, batch size: 96, loss[discriminator_loss=2.655, discriminator_real_loss=1.389, discriminator_fake_loss=1.265, generator_loss=27.59, generator_mel_loss=18.24, generator_kl_loss=1.349, generator_dur_loss=1.902, generator_adv_loss=1.918, generator_feat_match_loss=4.185, over 96.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.38, discriminator_fake_loss=1.342, generator_loss=27.6, generator_mel_loss=18.4, generator_kl_loss=1.39, generator_dur_loss=1.775, generator_adv_loss=1.944, generator_feat_match_loss=4.086, over 5504.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:22:32,525 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 03:22:40,300 INFO [train.py:591] (4/6) Epoch 268, validation: discriminator_loss=2.728, discriminator_real_loss=1.388, discriminator_fake_loss=1.34, generator_loss=26.18, generator_mel_loss=18.21, generator_kl_loss=1.207, generator_dur_loss=1.82, generator_adv_loss=1.833, generator_feat_match_loss=3.11, over 100.00 samples. +2024-03-13 03:22:40,301 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 03:24:07,616 INFO [train.py:919] (4/6) Start epoch 269 +2024-03-13 03:25:22,430 INFO [train.py:527] (4/6) Epoch 269, batch 18, global_batch_idx: 33250, batch size: 25, loss[discriminator_loss=2.7, discriminator_real_loss=1.338, discriminator_fake_loss=1.363, generator_loss=27.32, generator_mel_loss=18.03, generator_kl_loss=1.631, generator_dur_loss=1.596, generator_adv_loss=1.9, generator_feat_match_loss=4.156, over 25.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.377, discriminator_fake_loss=1.36, generator_loss=27.3, generator_mel_loss=18.22, generator_kl_loss=1.371, generator_dur_loss=1.754, generator_adv_loss=1.91, generator_feat_match_loss=4.047, over 996.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:27:42,958 INFO [train.py:527] (4/6) Epoch 269, batch 68, global_batch_idx: 33300, batch size: 68, loss[discriminator_loss=2.708, discriminator_real_loss=1.383, discriminator_fake_loss=1.325, generator_loss=26.85, generator_mel_loss=18.1, generator_kl_loss=1.308, generator_dur_loss=1.809, generator_adv_loss=1.921, generator_feat_match_loss=3.714, over 68.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.388, discriminator_fake_loss=1.344, generator_loss=27.46, generator_mel_loss=18.34, generator_kl_loss=1.369, generator_dur_loss=1.766, generator_adv_loss=1.94, generator_feat_match_loss=4.037, over 3928.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:30:02,498 INFO [train.py:527] (4/6) Epoch 269, batch 118, global_batch_idx: 33350, batch size: 66, loss[discriminator_loss=2.745, discriminator_real_loss=1.42, discriminator_fake_loss=1.324, generator_loss=26.91, generator_mel_loss=18.04, generator_kl_loss=1.297, generator_dur_loss=1.809, generator_adv_loss=1.844, generator_feat_match_loss=3.917, over 66.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.386, discriminator_fake_loss=1.348, generator_loss=27.51, generator_mel_loss=18.39, generator_kl_loss=1.376, generator_dur_loss=1.764, generator_adv_loss=1.935, generator_feat_match_loss=4.047, over 6677.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:30:18,771 INFO [train.py:919] (4/6) Start epoch 270 +2024-03-13 03:32:45,242 INFO [train.py:527] (4/6) Epoch 270, batch 44, global_batch_idx: 33400, batch size: 80, loss[discriminator_loss=2.676, discriminator_real_loss=1.38, discriminator_fake_loss=1.295, generator_loss=27.71, generator_mel_loss=18.4, generator_kl_loss=1.371, generator_dur_loss=1.732, generator_adv_loss=2, generator_feat_match_loss=4.21, over 80.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.385, discriminator_fake_loss=1.352, generator_loss=27.44, generator_mel_loss=18.39, generator_kl_loss=1.378, generator_dur_loss=1.755, generator_adv_loss=1.916, generator_feat_match_loss=3.995, over 2729.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:32:45,244 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 03:32:53,132 INFO [train.py:591] (4/6) Epoch 270, validation: discriminator_loss=2.652, discriminator_real_loss=1.411, discriminator_fake_loss=1.241, generator_loss=26.71, generator_mel_loss=18.7, generator_kl_loss=1.252, generator_dur_loss=1.777, generator_adv_loss=1.922, generator_feat_match_loss=3.056, over 100.00 samples. +2024-03-13 03:32:53,133 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 03:35:15,399 INFO [train.py:527] (4/6) Epoch 270, batch 94, global_batch_idx: 33450, batch size: 59, loss[discriminator_loss=2.769, discriminator_real_loss=1.436, discriminator_fake_loss=1.332, generator_loss=27.26, generator_mel_loss=18.32, generator_kl_loss=1.408, generator_dur_loss=1.769, generator_adv_loss=1.964, generator_feat_match_loss=3.802, over 59.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.387, discriminator_fake_loss=1.346, generator_loss=27.44, generator_mel_loss=18.38, generator_kl_loss=1.383, generator_dur_loss=1.745, generator_adv_loss=1.937, generator_feat_match_loss=3.992, over 5550.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:36:34,484 INFO [train.py:919] (4/6) Start epoch 271 +2024-03-13 03:37:54,896 INFO [train.py:527] (4/6) Epoch 271, batch 20, global_batch_idx: 33500, batch size: 83, loss[discriminator_loss=2.753, discriminator_real_loss=1.444, discriminator_fake_loss=1.309, generator_loss=27.53, generator_mel_loss=18.32, generator_kl_loss=1.501, generator_dur_loss=1.78, generator_adv_loss=1.889, generator_feat_match_loss=4.05, over 83.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.389, discriminator_fake_loss=1.332, generator_loss=27.39, generator_mel_loss=18.27, generator_kl_loss=1.417, generator_dur_loss=1.756, generator_adv_loss=1.937, generator_feat_match_loss=4.013, over 1287.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:40:09,439 INFO [train.py:527] (4/6) Epoch 271, batch 70, global_batch_idx: 33550, batch size: 64, loss[discriminator_loss=2.708, discriminator_real_loss=1.246, discriminator_fake_loss=1.462, generator_loss=28.5, generator_mel_loss=18.8, generator_kl_loss=1.468, generator_dur_loss=1.716, generator_adv_loss=2.016, generator_feat_match_loss=4.506, over 64.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=27.53, generator_mel_loss=18.42, generator_kl_loss=1.414, generator_dur_loss=1.743, generator_adv_loss=1.914, generator_feat_match_loss=4.039, over 4070.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:42:31,181 INFO [train.py:527] (4/6) Epoch 271, batch 120, global_batch_idx: 33600, batch size: 88, loss[discriminator_loss=2.785, discriminator_real_loss=1.325, discriminator_fake_loss=1.46, generator_loss=27.55, generator_mel_loss=18.16, generator_kl_loss=1.364, generator_dur_loss=1.885, generator_adv_loss=2.113, generator_feat_match_loss=4.03, over 88.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.382, discriminator_fake_loss=1.348, generator_loss=27.49, generator_mel_loss=18.38, generator_kl_loss=1.4, generator_dur_loss=1.744, generator_adv_loss=1.917, generator_feat_match_loss=4.048, over 6895.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:42:31,183 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 03:42:40,082 INFO [train.py:591] (4/6) Epoch 271, validation: discriminator_loss=2.862, discriminator_real_loss=1.643, discriminator_fake_loss=1.219, generator_loss=26.49, generator_mel_loss=18.31, generator_kl_loss=1.227, generator_dur_loss=1.8, generator_adv_loss=2.089, generator_feat_match_loss=3.065, over 100.00 samples. +2024-03-13 03:42:40,083 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 03:42:49,663 INFO [train.py:919] (4/6) Start epoch 272 +2024-03-13 03:45:20,027 INFO [train.py:527] (4/6) Epoch 272, batch 46, global_batch_idx: 33650, batch size: 70, loss[discriminator_loss=2.821, discriminator_real_loss=1.562, discriminator_fake_loss=1.259, generator_loss=26.27, generator_mel_loss=18.01, generator_kl_loss=1.406, generator_dur_loss=1.798, generator_adv_loss=1.767, generator_feat_match_loss=3.295, over 70.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.397, discriminator_fake_loss=1.338, generator_loss=27.28, generator_mel_loss=18.28, generator_kl_loss=1.399, generator_dur_loss=1.766, generator_adv_loss=1.925, generator_feat_match_loss=3.914, over 2647.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:47:40,141 INFO [train.py:527] (4/6) Epoch 272, batch 96, global_batch_idx: 33700, batch size: 62, loss[discriminator_loss=2.713, discriminator_real_loss=1.319, discriminator_fake_loss=1.394, generator_loss=27.87, generator_mel_loss=18.38, generator_kl_loss=1.422, generator_dur_loss=1.752, generator_adv_loss=2.001, generator_feat_match_loss=4.316, over 62.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.393, discriminator_fake_loss=1.343, generator_loss=27.39, generator_mel_loss=18.3, generator_kl_loss=1.396, generator_dur_loss=1.777, generator_adv_loss=1.923, generator_feat_match_loss=3.993, over 5602.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:48:57,161 INFO [train.py:919] (4/6) Start epoch 273 +2024-03-13 03:50:21,938 INFO [train.py:527] (4/6) Epoch 273, batch 22, global_batch_idx: 33750, batch size: 36, loss[discriminator_loss=2.733, discriminator_real_loss=1.392, discriminator_fake_loss=1.341, generator_loss=27.45, generator_mel_loss=18.63, generator_kl_loss=1.52, generator_dur_loss=1.717, generator_adv_loss=1.822, generator_feat_match_loss=3.769, over 36.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.389, discriminator_fake_loss=1.342, generator_loss=27.63, generator_mel_loss=18.43, generator_kl_loss=1.395, generator_dur_loss=1.782, generator_adv_loss=1.929, generator_feat_match_loss=4.088, over 1352.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:52:43,488 INFO [train.py:527] (4/6) Epoch 273, batch 72, global_batch_idx: 33800, batch size: 56, loss[discriminator_loss=2.754, discriminator_real_loss=1.408, discriminator_fake_loss=1.346, generator_loss=27.02, generator_mel_loss=17.88, generator_kl_loss=1.366, generator_dur_loss=1.747, generator_adv_loss=1.786, generator_feat_match_loss=4.239, over 56.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.386, discriminator_fake_loss=1.344, generator_loss=27.6, generator_mel_loss=18.45, generator_kl_loss=1.371, generator_dur_loss=1.787, generator_adv_loss=1.926, generator_feat_match_loss=4.07, over 4247.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:52:43,489 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 03:52:51,232 INFO [train.py:591] (4/6) Epoch 273, validation: discriminator_loss=2.808, discriminator_real_loss=1.398, discriminator_fake_loss=1.41, generator_loss=26.31, generator_mel_loss=18.41, generator_kl_loss=1.102, generator_dur_loss=1.851, generator_adv_loss=1.741, generator_feat_match_loss=3.2, over 100.00 samples. +2024-03-13 03:52:51,233 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 03:55:07,338 INFO [train.py:527] (4/6) Epoch 273, batch 122, global_batch_idx: 33850, batch size: 59, loss[discriminator_loss=2.714, discriminator_real_loss=1.337, discriminator_fake_loss=1.377, generator_loss=27.56, generator_mel_loss=18.33, generator_kl_loss=1.46, generator_dur_loss=1.736, generator_adv_loss=1.848, generator_feat_match_loss=4.183, over 59.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.381, discriminator_fake_loss=1.342, generator_loss=27.63, generator_mel_loss=18.46, generator_kl_loss=1.388, generator_dur_loss=1.782, generator_adv_loss=1.925, generator_feat_match_loss=4.07, over 7011.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:55:12,181 INFO [train.py:919] (4/6) Start epoch 274 +2024-03-13 03:57:46,967 INFO [train.py:527] (4/6) Epoch 274, batch 48, global_batch_idx: 33900, batch size: 17, loss[discriminator_loss=2.718, discriminator_real_loss=1.372, discriminator_fake_loss=1.346, generator_loss=27.6, generator_mel_loss=17.8, generator_kl_loss=1.582, generator_dur_loss=1.665, generator_adv_loss=2.03, generator_feat_match_loss=4.524, over 17.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.385, discriminator_fake_loss=1.344, generator_loss=27.43, generator_mel_loss=18.3, generator_kl_loss=1.378, generator_dur_loss=1.796, generator_adv_loss=1.926, generator_feat_match_loss=4.029, over 2785.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:00:05,810 INFO [train.py:527] (4/6) Epoch 274, batch 98, global_batch_idx: 33950, batch size: 56, loss[discriminator_loss=2.695, discriminator_real_loss=1.319, discriminator_fake_loss=1.376, generator_loss=27.02, generator_mel_loss=17.82, generator_kl_loss=1.139, generator_dur_loss=1.82, generator_adv_loss=1.868, generator_feat_match_loss=4.369, over 56.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.382, discriminator_fake_loss=1.346, generator_loss=27.54, generator_mel_loss=18.35, generator_kl_loss=1.383, generator_dur_loss=1.795, generator_adv_loss=1.93, generator_feat_match_loss=4.078, over 5655.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:01:18,256 INFO [train.py:919] (4/6) Start epoch 275 +2024-03-13 04:02:49,752 INFO [train.py:527] (4/6) Epoch 275, batch 24, global_batch_idx: 34000, batch size: 16, loss[discriminator_loss=2.702, discriminator_real_loss=1.277, discriminator_fake_loss=1.424, generator_loss=30.6, generator_mel_loss=19.67, generator_kl_loss=1.817, generator_dur_loss=1.622, generator_adv_loss=2.04, generator_feat_match_loss=5.454, over 16.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.378, discriminator_fake_loss=1.344, generator_loss=27.37, generator_mel_loss=18.31, generator_kl_loss=1.362, generator_dur_loss=1.792, generator_adv_loss=1.906, generator_feat_match_loss=4.003, over 1478.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:02:49,753 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 04:02:57,797 INFO [train.py:591] (4/6) Epoch 275, validation: discriminator_loss=2.829, discriminator_real_loss=1.515, discriminator_fake_loss=1.314, generator_loss=26.55, generator_mel_loss=18.36, generator_kl_loss=1.205, generator_dur_loss=1.815, generator_adv_loss=1.905, generator_feat_match_loss=3.262, over 100.00 samples. +2024-03-13 04:02:57,799 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 04:05:16,022 INFO [train.py:527] (4/6) Epoch 275, batch 74, global_batch_idx: 34050, batch size: 96, loss[discriminator_loss=2.73, discriminator_real_loss=1.456, discriminator_fake_loss=1.274, generator_loss=27.14, generator_mel_loss=18.37, generator_kl_loss=1.114, generator_dur_loss=1.884, generator_adv_loss=1.817, generator_feat_match_loss=3.955, over 96.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.405, discriminator_fake_loss=1.347, generator_loss=27.52, generator_mel_loss=18.33, generator_kl_loss=1.374, generator_dur_loss=1.781, generator_adv_loss=1.967, generator_feat_match_loss=4.073, over 4317.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:07:32,024 INFO [train.py:919] (4/6) Start epoch 276 +2024-03-13 04:07:54,854 INFO [train.py:527] (4/6) Epoch 276, batch 0, global_batch_idx: 34100, batch size: 39, loss[discriminator_loss=2.679, discriminator_real_loss=1.411, discriminator_fake_loss=1.268, generator_loss=27.7, generator_mel_loss=18.4, generator_kl_loss=1.521, generator_dur_loss=1.648, generator_adv_loss=1.769, generator_feat_match_loss=4.369, over 39.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.411, discriminator_fake_loss=1.268, generator_loss=27.7, generator_mel_loss=18.4, generator_kl_loss=1.521, generator_dur_loss=1.648, generator_adv_loss=1.769, generator_feat_match_loss=4.369, over 39.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:10:12,680 INFO [train.py:527] (4/6) Epoch 276, batch 50, global_batch_idx: 34150, batch size: 68, loss[discriminator_loss=2.728, discriminator_real_loss=1.392, discriminator_fake_loss=1.336, generator_loss=27.78, generator_mel_loss=18.65, generator_kl_loss=1.536, generator_dur_loss=1.796, generator_adv_loss=1.716, generator_feat_match_loss=4.085, over 68.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.389, discriminator_fake_loss=1.352, generator_loss=27.38, generator_mel_loss=18.36, generator_kl_loss=1.388, generator_dur_loss=1.775, generator_adv_loss=1.901, generator_feat_match_loss=3.953, over 2901.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:12:31,953 INFO [train.py:527] (4/6) Epoch 276, batch 100, global_batch_idx: 34200, batch size: 58, loss[discriminator_loss=2.771, discriminator_real_loss=1.493, discriminator_fake_loss=1.278, generator_loss=26.99, generator_mel_loss=18.23, generator_kl_loss=1.44, generator_dur_loss=1.762, generator_adv_loss=1.947, generator_feat_match_loss=3.609, over 58.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.394, discriminator_fake_loss=1.348, generator_loss=27.33, generator_mel_loss=18.32, generator_kl_loss=1.396, generator_dur_loss=1.772, generator_adv_loss=1.906, generator_feat_match_loss=3.936, over 5755.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:12:31,954 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 04:12:41,116 INFO [train.py:591] (4/6) Epoch 276, validation: discriminator_loss=2.776, discriminator_real_loss=1.46, discriminator_fake_loss=1.316, generator_loss=26.89, generator_mel_loss=18.73, generator_kl_loss=1.25, generator_dur_loss=1.828, generator_adv_loss=1.894, generator_feat_match_loss=3.185, over 100.00 samples. +2024-03-13 04:12:41,117 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 04:13:45,819 INFO [train.py:919] (4/6) Start epoch 277 +2024-03-13 04:15:22,377 INFO [train.py:527] (4/6) Epoch 277, batch 26, global_batch_idx: 34250, batch size: 36, loss[discriminator_loss=2.696, discriminator_real_loss=1.418, discriminator_fake_loss=1.277, generator_loss=27.76, generator_mel_loss=18.66, generator_kl_loss=1.394, generator_dur_loss=1.735, generator_adv_loss=2.082, generator_feat_match_loss=3.889, over 36.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.379, discriminator_fake_loss=1.339, generator_loss=27.55, generator_mel_loss=18.4, generator_kl_loss=1.421, generator_dur_loss=1.767, generator_adv_loss=1.919, generator_feat_match_loss=4.047, over 1537.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:17:41,365 INFO [train.py:527] (4/6) Epoch 277, batch 76, global_batch_idx: 34300, batch size: 74, loss[discriminator_loss=2.75, discriminator_real_loss=1.438, discriminator_fake_loss=1.312, generator_loss=27.01, generator_mel_loss=18.17, generator_kl_loss=1.455, generator_dur_loss=1.828, generator_adv_loss=1.798, generator_feat_match_loss=3.755, over 74.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.384, discriminator_fake_loss=1.35, generator_loss=27.64, generator_mel_loss=18.5, generator_kl_loss=1.432, generator_dur_loss=1.764, generator_adv_loss=1.915, generator_feat_match_loss=4.031, over 4326.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:19:55,771 INFO [train.py:919] (4/6) Start epoch 278 +2024-03-13 04:20:25,034 INFO [train.py:527] (4/6) Epoch 278, batch 2, global_batch_idx: 34350, batch size: 36, loss[discriminator_loss=2.764, discriminator_real_loss=1.386, discriminator_fake_loss=1.378, generator_loss=28.73, generator_mel_loss=18.98, generator_kl_loss=1.626, generator_dur_loss=1.664, generator_adv_loss=2.068, generator_feat_match_loss=4.389, over 36.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.37, discriminator_fake_loss=1.352, generator_loss=28.67, generator_mel_loss=18.95, generator_kl_loss=1.54, generator_dur_loss=1.716, generator_adv_loss=1.964, generator_feat_match_loss=4.506, over 124.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:22:44,423 INFO [train.py:527] (4/6) Epoch 278, batch 52, global_batch_idx: 34400, batch size: 55, loss[discriminator_loss=2.734, discriminator_real_loss=1.366, discriminator_fake_loss=1.368, generator_loss=27.01, generator_mel_loss=18.03, generator_kl_loss=1.416, generator_dur_loss=1.722, generator_adv_loss=1.867, generator_feat_match_loss=3.976, over 55.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.385, discriminator_fake_loss=1.349, generator_loss=27.68, generator_mel_loss=18.46, generator_kl_loss=1.403, generator_dur_loss=1.759, generator_adv_loss=1.968, generator_feat_match_loss=4.09, over 2938.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:22:44,424 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 04:22:52,580 INFO [train.py:591] (4/6) Epoch 278, validation: discriminator_loss=2.754, discriminator_real_loss=1.362, discriminator_fake_loss=1.392, generator_loss=26.71, generator_mel_loss=18.73, generator_kl_loss=1.163, generator_dur_loss=1.848, generator_adv_loss=1.733, generator_feat_match_loss=3.236, over 100.00 samples. +2024-03-13 04:22:52,582 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 04:25:11,133 INFO [train.py:527] (4/6) Epoch 278, batch 102, global_batch_idx: 34450, batch size: 66, loss[discriminator_loss=2.715, discriminator_real_loss=1.313, discriminator_fake_loss=1.402, generator_loss=27.77, generator_mel_loss=18.32, generator_kl_loss=1.388, generator_dur_loss=1.871, generator_adv_loss=2.029, generator_feat_match_loss=4.155, over 66.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.384, discriminator_fake_loss=1.35, generator_loss=27.57, generator_mel_loss=18.39, generator_kl_loss=1.391, generator_dur_loss=1.77, generator_adv_loss=1.949, generator_feat_match_loss=4.064, over 5830.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:26:11,175 INFO [train.py:919] (4/6) Start epoch 279 +2024-03-13 04:27:52,033 INFO [train.py:527] (4/6) Epoch 279, batch 28, global_batch_idx: 34500, batch size: 58, loss[discriminator_loss=2.777, discriminator_real_loss=1.507, discriminator_fake_loss=1.27, generator_loss=28.56, generator_mel_loss=19.09, generator_kl_loss=1.357, generator_dur_loss=1.734, generator_adv_loss=2.03, generator_feat_match_loss=4.35, over 58.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.381, discriminator_fake_loss=1.342, generator_loss=27.52, generator_mel_loss=18.33, generator_kl_loss=1.396, generator_dur_loss=1.752, generator_adv_loss=1.942, generator_feat_match_loss=4.1, over 1541.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:30:10,934 INFO [train.py:527] (4/6) Epoch 279, batch 78, global_batch_idx: 34550, batch size: 64, loss[discriminator_loss=2.771, discriminator_real_loss=1.367, discriminator_fake_loss=1.404, generator_loss=27.78, generator_mel_loss=18.67, generator_kl_loss=1.448, generator_dur_loss=1.772, generator_adv_loss=1.835, generator_feat_match_loss=4.047, over 64.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.381, discriminator_fake_loss=1.343, generator_loss=27.51, generator_mel_loss=18.37, generator_kl_loss=1.401, generator_dur_loss=1.754, generator_adv_loss=1.932, generator_feat_match_loss=4.051, over 4277.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:32:16,643 INFO [train.py:919] (4/6) Start epoch 280 +2024-03-13 04:32:52,431 INFO [train.py:527] (4/6) Epoch 280, batch 4, global_batch_idx: 34600, batch size: 70, loss[discriminator_loss=2.713, discriminator_real_loss=1.385, discriminator_fake_loss=1.327, generator_loss=27.24, generator_mel_loss=18.14, generator_kl_loss=1.391, generator_dur_loss=1.777, generator_adv_loss=1.868, generator_feat_match_loss=4.07, over 70.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.351, discriminator_fake_loss=1.347, generator_loss=27.93, generator_mel_loss=18.65, generator_kl_loss=1.446, generator_dur_loss=1.728, generator_adv_loss=1.905, generator_feat_match_loss=4.201, over 258.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:32:52,434 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 04:33:00,130 INFO [train.py:591] (4/6) Epoch 280, validation: discriminator_loss=2.782, discriminator_real_loss=1.365, discriminator_fake_loss=1.417, generator_loss=26.63, generator_mel_loss=18.46, generator_kl_loss=1.26, generator_dur_loss=1.827, generator_adv_loss=1.759, generator_feat_match_loss=3.332, over 100.00 samples. +2024-03-13 04:33:00,133 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 04:35:18,235 INFO [train.py:527] (4/6) Epoch 280, batch 54, global_batch_idx: 34650, batch size: 96, loss[discriminator_loss=2.752, discriminator_real_loss=1.389, discriminator_fake_loss=1.363, generator_loss=27.24, generator_mel_loss=18.13, generator_kl_loss=1.43, generator_dur_loss=1.881, generator_adv_loss=1.996, generator_feat_match_loss=3.797, over 96.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.383, discriminator_fake_loss=1.357, generator_loss=27.55, generator_mel_loss=18.39, generator_kl_loss=1.42, generator_dur_loss=1.753, generator_adv_loss=1.921, generator_feat_match_loss=4.073, over 2985.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:37:37,914 INFO [train.py:527] (4/6) Epoch 280, batch 104, global_batch_idx: 34700, batch size: 96, loss[discriminator_loss=2.742, discriminator_real_loss=1.367, discriminator_fake_loss=1.375, generator_loss=26.95, generator_mel_loss=17.93, generator_kl_loss=1.319, generator_dur_loss=1.909, generator_adv_loss=1.847, generator_feat_match_loss=3.951, over 96.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.385, discriminator_fake_loss=1.354, generator_loss=27.49, generator_mel_loss=18.36, generator_kl_loss=1.423, generator_dur_loss=1.766, generator_adv_loss=1.917, generator_feat_match_loss=4.032, over 5905.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:38:30,731 INFO [train.py:919] (4/6) Start epoch 281 +2024-03-13 04:40:20,052 INFO [train.py:527] (4/6) Epoch 281, batch 30, global_batch_idx: 34750, batch size: 77, loss[discriminator_loss=2.752, discriminator_real_loss=1.465, discriminator_fake_loss=1.287, generator_loss=27.42, generator_mel_loss=18.25, generator_kl_loss=1.29, generator_dur_loss=1.827, generator_adv_loss=1.886, generator_feat_match_loss=4.167, over 77.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.386, discriminator_fake_loss=1.339, generator_loss=27.29, generator_mel_loss=18.23, generator_kl_loss=1.355, generator_dur_loss=1.771, generator_adv_loss=1.926, generator_feat_match_loss=4.006, over 1862.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:42:40,476 INFO [train.py:527] (4/6) Epoch 281, batch 80, global_batch_idx: 34800, batch size: 50, loss[discriminator_loss=2.702, discriminator_real_loss=1.433, discriminator_fake_loss=1.269, generator_loss=27.88, generator_mel_loss=18.69, generator_kl_loss=1.325, generator_dur_loss=1.737, generator_adv_loss=1.963, generator_feat_match_loss=4.173, over 50.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.39, discriminator_fake_loss=1.341, generator_loss=27.43, generator_mel_loss=18.28, generator_kl_loss=1.395, generator_dur_loss=1.772, generator_adv_loss=1.933, generator_feat_match_loss=4.047, over 4721.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:42:40,477 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 04:42:48,869 INFO [train.py:591] (4/6) Epoch 281, validation: discriminator_loss=2.807, discriminator_real_loss=1.512, discriminator_fake_loss=1.295, generator_loss=26.8, generator_mel_loss=18.6, generator_kl_loss=1.13, generator_dur_loss=1.832, generator_adv_loss=1.896, generator_feat_match_loss=3.337, over 100.00 samples. +2024-03-13 04:42:48,870 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 04:44:47,426 INFO [train.py:919] (4/6) Start epoch 282 +2024-03-13 04:45:27,844 INFO [train.py:527] (4/6) Epoch 282, batch 6, global_batch_idx: 34850, batch size: 55, loss[discriminator_loss=2.718, discriminator_real_loss=1.388, discriminator_fake_loss=1.33, generator_loss=27.11, generator_mel_loss=17.96, generator_kl_loss=1.451, generator_dur_loss=1.681, generator_adv_loss=2.075, generator_feat_match_loss=3.937, over 55.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.373, discriminator_fake_loss=1.332, generator_loss=27.79, generator_mel_loss=18.42, generator_kl_loss=1.451, generator_dur_loss=1.75, generator_adv_loss=1.943, generator_feat_match_loss=4.223, over 399.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:47:45,434 INFO [train.py:527] (4/6) Epoch 282, batch 56, global_batch_idx: 34900, batch size: 55, loss[discriminator_loss=2.769, discriminator_real_loss=1.389, discriminator_fake_loss=1.38, generator_loss=27.59, generator_mel_loss=18.48, generator_kl_loss=1.302, generator_dur_loss=1.78, generator_adv_loss=1.881, generator_feat_match_loss=4.143, over 55.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=27.66, generator_mel_loss=18.42, generator_kl_loss=1.383, generator_dur_loss=1.764, generator_adv_loss=1.927, generator_feat_match_loss=4.171, over 3352.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:50:06,634 INFO [train.py:527] (4/6) Epoch 282, batch 106, global_batch_idx: 34950, batch size: 88, loss[discriminator_loss=2.801, discriminator_real_loss=1.444, discriminator_fake_loss=1.358, generator_loss=26.79, generator_mel_loss=18.17, generator_kl_loss=1.316, generator_dur_loss=1.877, generator_adv_loss=1.745, generator_feat_match_loss=3.68, over 88.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.383, discriminator_fake_loss=1.347, generator_loss=27.59, generator_mel_loss=18.39, generator_kl_loss=1.379, generator_dur_loss=1.767, generator_adv_loss=1.936, generator_feat_match_loss=4.116, over 6323.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:50:53,513 INFO [train.py:919] (4/6) Start epoch 283 +2024-03-13 04:52:47,961 INFO [train.py:527] (4/6) Epoch 283, batch 32, global_batch_idx: 35000, batch size: 47, loss[discriminator_loss=2.758, discriminator_real_loss=1.382, discriminator_fake_loss=1.376, generator_loss=28.34, generator_mel_loss=19.08, generator_kl_loss=1.409, generator_dur_loss=1.671, generator_adv_loss=1.88, generator_feat_match_loss=4.298, over 47.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.398, discriminator_fake_loss=1.337, generator_loss=27.74, generator_mel_loss=18.5, generator_kl_loss=1.431, generator_dur_loss=1.751, generator_adv_loss=1.928, generator_feat_match_loss=4.124, over 1673.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:52:47,963 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 04:52:56,062 INFO [train.py:591] (4/6) Epoch 283, validation: discriminator_loss=2.796, discriminator_real_loss=1.416, discriminator_fake_loss=1.38, generator_loss=26.57, generator_mel_loss=18.39, generator_kl_loss=1.123, generator_dur_loss=1.821, generator_adv_loss=1.839, generator_feat_match_loss=3.391, over 100.00 samples. +2024-03-13 04:52:56,063 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 04:55:17,678 INFO [train.py:527] (4/6) Epoch 283, batch 82, global_batch_idx: 35050, batch size: 83, loss[discriminator_loss=2.704, discriminator_real_loss=1.409, discriminator_fake_loss=1.295, generator_loss=27, generator_mel_loss=18.24, generator_kl_loss=1.292, generator_dur_loss=1.862, generator_adv_loss=1.889, generator_feat_match_loss=3.724, over 83.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.389, discriminator_fake_loss=1.339, generator_loss=27.58, generator_mel_loss=18.36, generator_kl_loss=1.404, generator_dur_loss=1.765, generator_adv_loss=1.937, generator_feat_match_loss=4.114, over 4623.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:57:08,513 INFO [train.py:919] (4/6) Start epoch 284 +2024-03-13 04:57:56,570 INFO [train.py:527] (4/6) Epoch 284, batch 8, global_batch_idx: 35100, batch size: 47, loss[discriminator_loss=2.779, discriminator_real_loss=1.274, discriminator_fake_loss=1.505, generator_loss=27.81, generator_mel_loss=18.44, generator_kl_loss=1.48, generator_dur_loss=1.647, generator_adv_loss=1.93, generator_feat_match_loss=4.317, over 47.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.375, discriminator_fake_loss=1.349, generator_loss=28.12, generator_mel_loss=18.63, generator_kl_loss=1.427, generator_dur_loss=1.729, generator_adv_loss=1.938, generator_feat_match_loss=4.387, over 444.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:00:13,739 INFO [train.py:527] (4/6) Epoch 284, batch 58, global_batch_idx: 35150, batch size: 50, loss[discriminator_loss=2.468, discriminator_real_loss=1.147, discriminator_fake_loss=1.321, generator_loss=28.59, generator_mel_loss=18.53, generator_kl_loss=1.441, generator_dur_loss=1.696, generator_adv_loss=2.337, generator_feat_match_loss=4.582, over 50.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.378, discriminator_fake_loss=1.346, generator_loss=27.93, generator_mel_loss=18.52, generator_kl_loss=1.425, generator_dur_loss=1.741, generator_adv_loss=1.969, generator_feat_match_loss=4.278, over 3060.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:02:32,486 INFO [train.py:527] (4/6) Epoch 284, batch 108, global_batch_idx: 35200, batch size: 48, loss[discriminator_loss=2.781, discriminator_real_loss=1.302, discriminator_fake_loss=1.479, generator_loss=27.13, generator_mel_loss=18.41, generator_kl_loss=1.347, generator_dur_loss=1.71, generator_adv_loss=1.899, generator_feat_match_loss=3.765, over 48.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.383, discriminator_fake_loss=1.342, generator_loss=27.72, generator_mel_loss=18.43, generator_kl_loss=1.4, generator_dur_loss=1.753, generator_adv_loss=1.97, generator_feat_match_loss=4.168, over 6020.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:02:32,488 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 05:02:41,050 INFO [train.py:591] (4/6) Epoch 284, validation: discriminator_loss=2.771, discriminator_real_loss=1.492, discriminator_fake_loss=1.28, generator_loss=25.9, generator_mel_loss=17.74, generator_kl_loss=1.18, generator_dur_loss=1.807, generator_adv_loss=1.92, generator_feat_match_loss=3.253, over 100.00 samples. +2024-03-13 05:02:41,051 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 05:03:24,076 INFO [train.py:919] (4/6) Start epoch 285 +2024-03-13 05:05:22,038 INFO [train.py:527] (4/6) Epoch 285, batch 34, global_batch_idx: 35250, batch size: 58, loss[discriminator_loss=2.802, discriminator_real_loss=1.515, discriminator_fake_loss=1.288, generator_loss=25.53, generator_mel_loss=17.69, generator_kl_loss=1.227, generator_dur_loss=1.778, generator_adv_loss=1.786, generator_feat_match_loss=3.047, over 58.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.383, discriminator_fake_loss=1.355, generator_loss=27.54, generator_mel_loss=18.37, generator_kl_loss=1.416, generator_dur_loss=1.756, generator_adv_loss=1.923, generator_feat_match_loss=4.073, over 2139.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:07:39,962 INFO [train.py:527] (4/6) Epoch 285, batch 84, global_batch_idx: 35300, batch size: 68, loss[discriminator_loss=2.724, discriminator_real_loss=1.342, discriminator_fake_loss=1.382, generator_loss=27.54, generator_mel_loss=18.43, generator_kl_loss=1.446, generator_dur_loss=1.773, generator_adv_loss=1.897, generator_feat_match_loss=3.989, over 68.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.384, discriminator_fake_loss=1.348, generator_loss=27.58, generator_mel_loss=18.41, generator_kl_loss=1.426, generator_dur_loss=1.748, generator_adv_loss=1.917, generator_feat_match_loss=4.085, over 4897.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:09:32,842 INFO [train.py:919] (4/6) Start epoch 286 +2024-03-13 05:10:24,752 INFO [train.py:527] (4/6) Epoch 286, batch 10, global_batch_idx: 35350, batch size: 56, loss[discriminator_loss=2.794, discriminator_real_loss=1.545, discriminator_fake_loss=1.25, generator_loss=27.09, generator_mel_loss=18.26, generator_kl_loss=1.606, generator_dur_loss=1.729, generator_adv_loss=1.827, generator_feat_match_loss=3.661, over 56.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.386, discriminator_fake_loss=1.352, generator_loss=27.51, generator_mel_loss=18.42, generator_kl_loss=1.407, generator_dur_loss=1.789, generator_adv_loss=1.89, generator_feat_match_loss=4.006, over 641.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:12:44,935 INFO [train.py:527] (4/6) Epoch 286, batch 60, global_batch_idx: 35400, batch size: 68, loss[discriminator_loss=2.726, discriminator_real_loss=1.33, discriminator_fake_loss=1.396, generator_loss=27.8, generator_mel_loss=18.62, generator_kl_loss=1.389, generator_dur_loss=1.798, generator_adv_loss=1.931, generator_feat_match_loss=4.057, over 68.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.376, discriminator_fake_loss=1.355, generator_loss=27.42, generator_mel_loss=18.27, generator_kl_loss=1.377, generator_dur_loss=1.792, generator_adv_loss=1.917, generator_feat_match_loss=4.072, over 3808.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:12:44,937 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 05:12:52,912 INFO [train.py:591] (4/6) Epoch 286, validation: discriminator_loss=2.799, discriminator_real_loss=1.48, discriminator_fake_loss=1.319, generator_loss=26.04, generator_mel_loss=18.04, generator_kl_loss=1.163, generator_dur_loss=1.825, generator_adv_loss=1.883, generator_feat_match_loss=3.125, over 100.00 samples. +2024-03-13 05:12:52,913 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 05:15:11,565 INFO [train.py:527] (4/6) Epoch 286, batch 110, global_batch_idx: 35450, batch size: 96, loss[discriminator_loss=2.736, discriminator_real_loss=1.391, discriminator_fake_loss=1.345, generator_loss=26.25, generator_mel_loss=17.51, generator_kl_loss=1.245, generator_dur_loss=1.828, generator_adv_loss=1.837, generator_feat_match_loss=3.822, over 96.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.377, discriminator_fake_loss=1.351, generator_loss=27.48, generator_mel_loss=18.3, generator_kl_loss=1.395, generator_dur_loss=1.778, generator_adv_loss=1.921, generator_feat_match_loss=4.088, over 6762.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:15:49,582 INFO [train.py:919] (4/6) Start epoch 287 +2024-03-13 05:17:54,316 INFO [train.py:527] (4/6) Epoch 287, batch 36, global_batch_idx: 35500, batch size: 44, loss[discriminator_loss=2.728, discriminator_real_loss=1.362, discriminator_fake_loss=1.366, generator_loss=27.34, generator_mel_loss=18.01, generator_kl_loss=1.606, generator_dur_loss=1.666, generator_adv_loss=1.824, generator_feat_match_loss=4.233, over 44.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=27.54, generator_mel_loss=18.33, generator_kl_loss=1.444, generator_dur_loss=1.727, generator_adv_loss=1.927, generator_feat_match_loss=4.116, over 1945.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:20:15,399 INFO [train.py:527] (4/6) Epoch 287, batch 86, global_batch_idx: 35550, batch size: 56, loss[discriminator_loss=2.734, discriminator_real_loss=1.425, discriminator_fake_loss=1.31, generator_loss=26.94, generator_mel_loss=18.05, generator_kl_loss=1.423, generator_dur_loss=1.69, generator_adv_loss=1.963, generator_feat_match_loss=3.816, over 56.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=27.56, generator_mel_loss=18.33, generator_kl_loss=1.414, generator_dur_loss=1.746, generator_adv_loss=1.931, generator_feat_match_loss=4.139, over 4885.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:21:56,419 INFO [train.py:919] (4/6) Start epoch 288 +2024-03-13 05:22:55,031 INFO [train.py:527] (4/6) Epoch 288, batch 12, global_batch_idx: 35600, batch size: 59, loss[discriminator_loss=2.739, discriminator_real_loss=1.348, discriminator_fake_loss=1.391, generator_loss=27.84, generator_mel_loss=18.65, generator_kl_loss=1.377, generator_dur_loss=1.75, generator_adv_loss=2.016, generator_feat_match_loss=4.048, over 59.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.389, discriminator_fake_loss=1.346, generator_loss=27.65, generator_mel_loss=18.44, generator_kl_loss=1.44, generator_dur_loss=1.734, generator_adv_loss=1.929, generator_feat_match_loss=4.105, over 755.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:22:55,034 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 05:23:03,117 INFO [train.py:591] (4/6) Epoch 288, validation: discriminator_loss=2.768, discriminator_real_loss=1.54, discriminator_fake_loss=1.228, generator_loss=27.05, generator_mel_loss=18.75, generator_kl_loss=1.322, generator_dur_loss=1.823, generator_adv_loss=2, generator_feat_match_loss=3.158, over 100.00 samples. +2024-03-13 05:23:03,118 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 05:25:20,676 INFO [train.py:527] (4/6) Epoch 288, batch 62, global_batch_idx: 35650, batch size: 45, loss[discriminator_loss=2.72, discriminator_real_loss=1.407, discriminator_fake_loss=1.312, generator_loss=26.02, generator_mel_loss=17.31, generator_kl_loss=1.633, generator_dur_loss=1.657, generator_adv_loss=1.761, generator_feat_match_loss=3.659, over 45.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=27.5, generator_mel_loss=18.32, generator_kl_loss=1.394, generator_dur_loss=1.749, generator_adv_loss=1.914, generator_feat_match_loss=4.126, over 3539.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:27:39,578 INFO [train.py:527] (4/6) Epoch 288, batch 112, global_batch_idx: 35700, batch size: 36, loss[discriminator_loss=2.736, discriminator_real_loss=1.434, discriminator_fake_loss=1.302, generator_loss=26.58, generator_mel_loss=17.84, generator_kl_loss=1.47, generator_dur_loss=1.721, generator_adv_loss=1.945, generator_feat_match_loss=3.608, over 36.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.382, discriminator_fake_loss=1.348, generator_loss=27.53, generator_mel_loss=18.31, generator_kl_loss=1.397, generator_dur_loss=1.755, generator_adv_loss=1.93, generator_feat_match_loss=4.133, over 6455.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:28:10,668 INFO [train.py:919] (4/6) Start epoch 289 +2024-03-13 05:30:22,435 INFO [train.py:527] (4/6) Epoch 289, batch 38, global_batch_idx: 35750, batch size: 15, loss[discriminator_loss=2.826, discriminator_real_loss=1.557, discriminator_fake_loss=1.27, generator_loss=29.94, generator_mel_loss=20.08, generator_kl_loss=1.799, generator_dur_loss=1.554, generator_adv_loss=1.904, generator_feat_match_loss=4.608, over 15.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.374, discriminator_fake_loss=1.355, generator_loss=27.57, generator_mel_loss=18.34, generator_kl_loss=1.435, generator_dur_loss=1.781, generator_adv_loss=1.929, generator_feat_match_loss=4.086, over 2202.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:32:42,132 INFO [train.py:527] (4/6) Epoch 289, batch 88, global_batch_idx: 35800, batch size: 74, loss[discriminator_loss=2.687, discriminator_real_loss=1.32, discriminator_fake_loss=1.367, generator_loss=28.85, generator_mel_loss=18.83, generator_kl_loss=1.288, generator_dur_loss=1.799, generator_adv_loss=2.077, generator_feat_match_loss=4.858, over 74.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.38, discriminator_fake_loss=1.352, generator_loss=27.59, generator_mel_loss=18.36, generator_kl_loss=1.408, generator_dur_loss=1.779, generator_adv_loss=1.93, generator_feat_match_loss=4.109, over 5086.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:32:42,134 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 05:32:50,895 INFO [train.py:591] (4/6) Epoch 289, validation: discriminator_loss=2.816, discriminator_real_loss=1.512, discriminator_fake_loss=1.304, generator_loss=25.95, generator_mel_loss=18.07, generator_kl_loss=1.23, generator_dur_loss=1.839, generator_adv_loss=1.954, generator_feat_match_loss=2.858, over 100.00 samples. +2024-03-13 05:32:50,896 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 05:34:27,296 INFO [train.py:919] (4/6) Start epoch 290 +2024-03-13 05:35:31,911 INFO [train.py:527] (4/6) Epoch 290, batch 14, global_batch_idx: 35850, batch size: 70, loss[discriminator_loss=2.664, discriminator_real_loss=1.399, discriminator_fake_loss=1.265, generator_loss=28.02, generator_mel_loss=18.95, generator_kl_loss=1.34, generator_dur_loss=1.792, generator_adv_loss=1.922, generator_feat_match_loss=4.015, over 70.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.383, discriminator_fake_loss=1.347, generator_loss=27.64, generator_mel_loss=18.52, generator_kl_loss=1.372, generator_dur_loss=1.764, generator_adv_loss=1.92, generator_feat_match_loss=4.062, over 867.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:37:53,294 INFO [train.py:527] (4/6) Epoch 290, batch 64, global_batch_idx: 35900, batch size: 36, loss[discriminator_loss=2.741, discriminator_real_loss=1.37, discriminator_fake_loss=1.372, generator_loss=27.08, generator_mel_loss=18.29, generator_kl_loss=1.591, generator_dur_loss=1.636, generator_adv_loss=1.758, generator_feat_match_loss=3.8, over 36.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.381, discriminator_fake_loss=1.347, generator_loss=27.71, generator_mel_loss=18.43, generator_kl_loss=1.395, generator_dur_loss=1.754, generator_adv_loss=1.945, generator_feat_match_loss=4.189, over 3933.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:40:10,186 INFO [train.py:527] (4/6) Epoch 290, batch 114, global_batch_idx: 35950, batch size: 47, loss[discriminator_loss=2.875, discriminator_real_loss=1.685, discriminator_fake_loss=1.19, generator_loss=26.36, generator_mel_loss=18, generator_kl_loss=1.256, generator_dur_loss=1.672, generator_adv_loss=1.829, generator_feat_match_loss=3.603, over 47.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.379, discriminator_fake_loss=1.347, generator_loss=27.71, generator_mel_loss=18.42, generator_kl_loss=1.396, generator_dur_loss=1.754, generator_adv_loss=1.937, generator_feat_match_loss=4.198, over 6852.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:40:36,091 INFO [train.py:919] (4/6) Start epoch 291 +2024-03-13 05:42:51,886 INFO [train.py:527] (4/6) Epoch 291, batch 40, global_batch_idx: 36000, batch size: 83, loss[discriminator_loss=2.721, discriminator_real_loss=1.334, discriminator_fake_loss=1.387, generator_loss=27.14, generator_mel_loss=18.28, generator_kl_loss=1.307, generator_dur_loss=1.843, generator_adv_loss=1.839, generator_feat_match_loss=3.873, over 83.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=27.7, generator_mel_loss=18.41, generator_kl_loss=1.396, generator_dur_loss=1.766, generator_adv_loss=1.932, generator_feat_match_loss=4.196, over 2451.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:42:51,888 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 05:42:59,876 INFO [train.py:591] (4/6) Epoch 291, validation: discriminator_loss=2.702, discriminator_real_loss=1.352, discriminator_fake_loss=1.351, generator_loss=26.77, generator_mel_loss=18.71, generator_kl_loss=1.263, generator_dur_loss=1.81, generator_adv_loss=1.784, generator_feat_match_loss=3.208, over 100.00 samples. +2024-03-13 05:42:59,877 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 05:45:19,118 INFO [train.py:527] (4/6) Epoch 291, batch 90, global_batch_idx: 36050, batch size: 88, loss[discriminator_loss=2.717, discriminator_real_loss=1.361, discriminator_fake_loss=1.357, generator_loss=27.53, generator_mel_loss=18.24, generator_kl_loss=1.32, generator_dur_loss=1.841, generator_adv_loss=1.951, generator_feat_match_loss=4.184, over 88.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.378, discriminator_fake_loss=1.345, generator_loss=27.66, generator_mel_loss=18.4, generator_kl_loss=1.398, generator_dur_loss=1.753, generator_adv_loss=1.928, generator_feat_match_loss=4.186, over 5353.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:46:50,710 INFO [train.py:919] (4/6) Start epoch 292 +2024-03-13 05:47:55,709 INFO [train.py:527] (4/6) Epoch 292, batch 16, global_batch_idx: 36100, batch size: 16, loss[discriminator_loss=2.692, discriminator_real_loss=1.343, discriminator_fake_loss=1.349, generator_loss=29.57, generator_mel_loss=19.21, generator_kl_loss=1.775, generator_dur_loss=1.589, generator_adv_loss=1.995, generator_feat_match_loss=4.996, over 16.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.362, discriminator_fake_loss=1.36, generator_loss=27.58, generator_mel_loss=18.29, generator_kl_loss=1.422, generator_dur_loss=1.76, generator_adv_loss=1.94, generator_feat_match_loss=4.17, over 928.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:50:13,916 INFO [train.py:527] (4/6) Epoch 292, batch 66, global_batch_idx: 36150, batch size: 95, loss[discriminator_loss=2.731, discriminator_real_loss=1.352, discriminator_fake_loss=1.379, generator_loss=27.2, generator_mel_loss=18.24, generator_kl_loss=1.255, generator_dur_loss=1.91, generator_adv_loss=1.821, generator_feat_match_loss=3.97, over 95.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=27.65, generator_mel_loss=18.37, generator_kl_loss=1.408, generator_dur_loss=1.756, generator_adv_loss=1.951, generator_feat_match_loss=4.168, over 3687.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:52:34,956 INFO [train.py:527] (4/6) Epoch 292, batch 116, global_batch_idx: 36200, batch size: 72, loss[discriminator_loss=2.677, discriminator_real_loss=1.311, discriminator_fake_loss=1.367, generator_loss=26.78, generator_mel_loss=17.79, generator_kl_loss=1.372, generator_dur_loss=1.842, generator_adv_loss=1.838, generator_feat_match_loss=3.948, over 72.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=27.59, generator_mel_loss=18.32, generator_kl_loss=1.397, generator_dur_loss=1.765, generator_adv_loss=1.94, generator_feat_match_loss=4.165, over 6616.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:52:34,957 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 05:52:43,683 INFO [train.py:591] (4/6) Epoch 292, validation: discriminator_loss=2.741, discriminator_real_loss=1.413, discriminator_fake_loss=1.328, generator_loss=26.7, generator_mel_loss=18.29, generator_kl_loss=1.206, generator_dur_loss=1.825, generator_adv_loss=1.835, generator_feat_match_loss=3.55, over 100.00 samples. +2024-03-13 05:52:43,684 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 05:53:04,580 INFO [train.py:919] (4/6) Start epoch 293 +2024-03-13 05:55:21,908 INFO [train.py:527] (4/6) Epoch 293, batch 42, global_batch_idx: 36250, batch size: 80, loss[discriminator_loss=2.76, discriminator_real_loss=1.354, discriminator_fake_loss=1.406, generator_loss=27.32, generator_mel_loss=18.14, generator_kl_loss=1.321, generator_dur_loss=1.842, generator_adv_loss=1.982, generator_feat_match_loss=4.036, over 80.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.382, discriminator_fake_loss=1.342, generator_loss=27.51, generator_mel_loss=18.29, generator_kl_loss=1.415, generator_dur_loss=1.753, generator_adv_loss=1.917, generator_feat_match_loss=4.136, over 2423.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:57:42,459 INFO [train.py:527] (4/6) Epoch 293, batch 92, global_batch_idx: 36300, batch size: 72, loss[discriminator_loss=2.699, discriminator_real_loss=1.309, discriminator_fake_loss=1.39, generator_loss=28.48, generator_mel_loss=18.71, generator_kl_loss=1.437, generator_dur_loss=1.809, generator_adv_loss=1.969, generator_feat_match_loss=4.554, over 72.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=27.64, generator_mel_loss=18.36, generator_kl_loss=1.419, generator_dur_loss=1.759, generator_adv_loss=1.917, generator_feat_match_loss=4.181, over 5408.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:59:11,347 INFO [train.py:919] (4/6) Start epoch 294 +2024-03-13 06:00:24,393 INFO [train.py:527] (4/6) Epoch 294, batch 18, global_batch_idx: 36350, batch size: 50, loss[discriminator_loss=2.726, discriminator_real_loss=1.347, discriminator_fake_loss=1.38, generator_loss=27.48, generator_mel_loss=18.6, generator_kl_loss=1.226, generator_dur_loss=1.669, generator_adv_loss=1.974, generator_feat_match_loss=4.007, over 50.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.385, discriminator_fake_loss=1.345, generator_loss=27.42, generator_mel_loss=18.19, generator_kl_loss=1.378, generator_dur_loss=1.747, generator_adv_loss=1.915, generator_feat_match_loss=4.193, over 1153.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:02:44,226 INFO [train.py:527] (4/6) Epoch 294, batch 68, global_batch_idx: 36400, batch size: 77, loss[discriminator_loss=2.748, discriminator_real_loss=1.457, discriminator_fake_loss=1.292, generator_loss=27.29, generator_mel_loss=18.26, generator_kl_loss=1.308, generator_dur_loss=1.835, generator_adv_loss=1.888, generator_feat_match_loss=3.995, over 77.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.376, discriminator_fake_loss=1.352, generator_loss=27.61, generator_mel_loss=18.29, generator_kl_loss=1.371, generator_dur_loss=1.766, generator_adv_loss=1.961, generator_feat_match_loss=4.214, over 4317.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:02:44,227 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 06:02:52,555 INFO [train.py:591] (4/6) Epoch 294, validation: discriminator_loss=2.832, discriminator_real_loss=1.44, discriminator_fake_loss=1.392, generator_loss=27.03, generator_mel_loss=18.63, generator_kl_loss=1.28, generator_dur_loss=1.816, generator_adv_loss=1.795, generator_feat_match_loss=3.515, over 100.00 samples. +2024-03-13 06:02:52,556 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 06:05:11,231 INFO [train.py:527] (4/6) Epoch 294, batch 118, global_batch_idx: 36450, batch size: 25, loss[discriminator_loss=2.843, discriminator_real_loss=1.549, discriminator_fake_loss=1.294, generator_loss=27.89, generator_mel_loss=19.56, generator_kl_loss=1.6, generator_dur_loss=1.633, generator_adv_loss=1.917, generator_feat_match_loss=3.184, over 25.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.377, discriminator_fake_loss=1.35, generator_loss=27.54, generator_mel_loss=18.26, generator_kl_loss=1.367, generator_dur_loss=1.77, generator_adv_loss=1.949, generator_feat_match_loss=4.189, over 7413.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:05:25,010 INFO [train.py:919] (4/6) Start epoch 295 +2024-03-13 06:07:52,678 INFO [train.py:527] (4/6) Epoch 295, batch 44, global_batch_idx: 36500, batch size: 45, loss[discriminator_loss=2.698, discriminator_real_loss=1.333, discriminator_fake_loss=1.366, generator_loss=26.98, generator_mel_loss=18.01, generator_kl_loss=1.437, generator_dur_loss=1.693, generator_adv_loss=1.997, generator_feat_match_loss=3.852, over 45.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.382, discriminator_fake_loss=1.346, generator_loss=27.44, generator_mel_loss=18.22, generator_kl_loss=1.417, generator_dur_loss=1.759, generator_adv_loss=1.908, generator_feat_match_loss=4.131, over 2508.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:10:12,724 INFO [train.py:527] (4/6) Epoch 295, batch 94, global_batch_idx: 36550, batch size: 66, loss[discriminator_loss=2.733, discriminator_real_loss=1.414, discriminator_fake_loss=1.32, generator_loss=28.01, generator_mel_loss=18.47, generator_kl_loss=1.224, generator_dur_loss=1.806, generator_adv_loss=1.962, generator_feat_match_loss=4.554, over 66.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.383, discriminator_fake_loss=1.346, generator_loss=27.49, generator_mel_loss=18.25, generator_kl_loss=1.417, generator_dur_loss=1.766, generator_adv_loss=1.914, generator_feat_match_loss=4.143, over 5414.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:11:31,688 INFO [train.py:919] (4/6) Start epoch 296 +2024-03-13 06:12:52,834 INFO [train.py:527] (4/6) Epoch 296, batch 20, global_batch_idx: 36600, batch size: 61, loss[discriminator_loss=2.75, discriminator_real_loss=1.306, discriminator_fake_loss=1.444, generator_loss=27.28, generator_mel_loss=18.29, generator_kl_loss=1.357, generator_dur_loss=1.741, generator_adv_loss=1.846, generator_feat_match_loss=4.049, over 61.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=27.63, generator_mel_loss=18.34, generator_kl_loss=1.399, generator_dur_loss=1.784, generator_adv_loss=1.929, generator_feat_match_loss=4.183, over 1249.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:12:52,835 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 06:13:00,869 INFO [train.py:591] (4/6) Epoch 296, validation: discriminator_loss=2.754, discriminator_real_loss=1.435, discriminator_fake_loss=1.319, generator_loss=26.94, generator_mel_loss=18.58, generator_kl_loss=1.185, generator_dur_loss=1.837, generator_adv_loss=1.825, generator_feat_match_loss=3.513, over 100.00 samples. +2024-03-13 06:13:00,870 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 06:15:21,449 INFO [train.py:527] (4/6) Epoch 296, batch 70, global_batch_idx: 36650, batch size: 42, loss[discriminator_loss=2.71, discriminator_real_loss=1.402, discriminator_fake_loss=1.308, generator_loss=27.56, generator_mel_loss=18.22, generator_kl_loss=1.597, generator_dur_loss=1.699, generator_adv_loss=1.799, generator_feat_match_loss=4.243, over 42.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=27.59, generator_mel_loss=18.32, generator_kl_loss=1.408, generator_dur_loss=1.773, generator_adv_loss=1.923, generator_feat_match_loss=4.159, over 4070.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:17:34,813 INFO [train.py:527] (4/6) Epoch 296, batch 120, global_batch_idx: 36700, batch size: 74, loss[discriminator_loss=2.738, discriminator_real_loss=1.378, discriminator_fake_loss=1.36, generator_loss=27.58, generator_mel_loss=18.44, generator_kl_loss=1.243, generator_dur_loss=1.828, generator_adv_loss=1.896, generator_feat_match_loss=4.174, over 74.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.376, discriminator_fake_loss=1.349, generator_loss=27.63, generator_mel_loss=18.34, generator_kl_loss=1.407, generator_dur_loss=1.775, generator_adv_loss=1.919, generator_feat_match_loss=4.179, over 7031.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:17:44,973 INFO [train.py:919] (4/6) Start epoch 297 +2024-03-13 06:20:16,568 INFO [train.py:527] (4/6) Epoch 297, batch 46, global_batch_idx: 36750, batch size: 77, loss[discriminator_loss=2.728, discriminator_real_loss=1.425, discriminator_fake_loss=1.303, generator_loss=27.24, generator_mel_loss=18.3, generator_kl_loss=1.34, generator_dur_loss=1.885, generator_adv_loss=1.871, generator_feat_match_loss=3.851, over 77.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.403, discriminator_fake_loss=1.347, generator_loss=27.57, generator_mel_loss=18.36, generator_kl_loss=1.395, generator_dur_loss=1.771, generator_adv_loss=1.926, generator_feat_match_loss=4.114, over 2764.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:22:36,398 INFO [train.py:527] (4/6) Epoch 297, batch 96, global_batch_idx: 36800, batch size: 56, loss[discriminator_loss=2.724, discriminator_real_loss=1.374, discriminator_fake_loss=1.351, generator_loss=27.93, generator_mel_loss=18.64, generator_kl_loss=1.36, generator_dur_loss=1.746, generator_adv_loss=1.972, generator_feat_match_loss=4.212, over 56.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.39, discriminator_fake_loss=1.345, generator_loss=27.61, generator_mel_loss=18.36, generator_kl_loss=1.381, generator_dur_loss=1.783, generator_adv_loss=1.947, generator_feat_match_loss=4.147, over 5635.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:22:36,399 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 06:22:45,238 INFO [train.py:591] (4/6) Epoch 297, validation: discriminator_loss=2.785, discriminator_real_loss=1.471, discriminator_fake_loss=1.315, generator_loss=26.04, generator_mel_loss=18.26, generator_kl_loss=1.083, generator_dur_loss=1.846, generator_adv_loss=1.844, generator_feat_match_loss=3.012, over 100.00 samples. +2024-03-13 06:22:45,239 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 06:24:00,748 INFO [train.py:919] (4/6) Start epoch 298 +2024-03-13 06:25:24,675 INFO [train.py:527] (4/6) Epoch 298, batch 22, global_batch_idx: 36850, batch size: 77, loss[discriminator_loss=2.773, discriminator_real_loss=1.415, discriminator_fake_loss=1.358, generator_loss=27.38, generator_mel_loss=18.22, generator_kl_loss=1.286, generator_dur_loss=1.843, generator_adv_loss=1.933, generator_feat_match_loss=4.095, over 77.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.352, generator_loss=27.47, generator_mel_loss=18.3, generator_kl_loss=1.361, generator_dur_loss=1.787, generator_adv_loss=1.921, generator_feat_match_loss=4.105, over 1410.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:27:44,428 INFO [train.py:527] (4/6) Epoch 298, batch 72, global_batch_idx: 36900, batch size: 56, loss[discriminator_loss=2.777, discriminator_real_loss=1.421, discriminator_fake_loss=1.356, generator_loss=27.07, generator_mel_loss=18.06, generator_kl_loss=1.439, generator_dur_loss=1.715, generator_adv_loss=1.754, generator_feat_match_loss=4.106, over 56.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=27.58, generator_mel_loss=18.32, generator_kl_loss=1.391, generator_dur_loss=1.766, generator_adv_loss=1.932, generator_feat_match_loss=4.175, over 4295.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:30:01,625 INFO [train.py:527] (4/6) Epoch 298, batch 122, global_batch_idx: 36950, batch size: 66, loss[discriminator_loss=2.756, discriminator_real_loss=1.4, discriminator_fake_loss=1.356, generator_loss=27.72, generator_mel_loss=18.46, generator_kl_loss=1.575, generator_dur_loss=1.785, generator_adv_loss=1.868, generator_feat_match_loss=4.038, over 66.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.383, discriminator_fake_loss=1.348, generator_loss=27.61, generator_mel_loss=18.34, generator_kl_loss=1.4, generator_dur_loss=1.767, generator_adv_loss=1.928, generator_feat_match_loss=4.178, over 7213.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:30:06,770 INFO [train.py:919] (4/6) Start epoch 299 +2024-03-13 06:32:45,655 INFO [train.py:527] (4/6) Epoch 299, batch 48, global_batch_idx: 37000, batch size: 52, loss[discriminator_loss=2.742, discriminator_real_loss=1.422, discriminator_fake_loss=1.321, generator_loss=26.44, generator_mel_loss=17.81, generator_kl_loss=1.322, generator_dur_loss=1.703, generator_adv_loss=1.87, generator_feat_match_loss=3.733, over 52.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.377, discriminator_fake_loss=1.351, generator_loss=27.39, generator_mel_loss=18.19, generator_kl_loss=1.408, generator_dur_loss=1.752, generator_adv_loss=1.932, generator_feat_match_loss=4.109, over 2674.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:32:45,656 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 06:32:53,799 INFO [train.py:591] (4/6) Epoch 299, validation: discriminator_loss=2.772, discriminator_real_loss=1.425, discriminator_fake_loss=1.347, generator_loss=26.89, generator_mel_loss=18.68, generator_kl_loss=1.164, generator_dur_loss=1.826, generator_adv_loss=1.825, generator_feat_match_loss=3.392, over 100.00 samples. +2024-03-13 06:32:53,800 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 06:35:15,123 INFO [train.py:527] (4/6) Epoch 299, batch 98, global_batch_idx: 37050, batch size: 58, loss[discriminator_loss=2.723, discriminator_real_loss=1.45, discriminator_fake_loss=1.272, generator_loss=26.54, generator_mel_loss=17.84, generator_kl_loss=1.406, generator_dur_loss=1.73, generator_adv_loss=1.913, generator_feat_match_loss=3.648, over 58.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.378, discriminator_fake_loss=1.347, generator_loss=27.48, generator_mel_loss=18.23, generator_kl_loss=1.393, generator_dur_loss=1.76, generator_adv_loss=1.934, generator_feat_match_loss=4.169, over 5658.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:36:23,860 INFO [train.py:919] (4/6) Start epoch 300 +2024-03-13 06:37:53,654 INFO [train.py:527] (4/6) Epoch 300, batch 24, global_batch_idx: 37100, batch size: 62, loss[discriminator_loss=2.766, discriminator_real_loss=1.392, discriminator_fake_loss=1.373, generator_loss=26.95, generator_mel_loss=18.25, generator_kl_loss=1.405, generator_dur_loss=1.776, generator_adv_loss=1.881, generator_feat_match_loss=3.633, over 62.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.34, generator_loss=27.73, generator_mel_loss=18.3, generator_kl_loss=1.387, generator_dur_loss=1.759, generator_adv_loss=2.001, generator_feat_match_loss=4.289, over 1457.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:40:10,445 INFO [train.py:527] (4/6) Epoch 300, batch 74, global_batch_idx: 37150, batch size: 68, loss[discriminator_loss=2.823, discriminator_real_loss=1.499, discriminator_fake_loss=1.323, generator_loss=27.91, generator_mel_loss=18.37, generator_kl_loss=1.385, generator_dur_loss=1.798, generator_adv_loss=1.678, generator_feat_match_loss=4.685, over 68.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.378, discriminator_fake_loss=1.344, generator_loss=27.63, generator_mel_loss=18.29, generator_kl_loss=1.401, generator_dur_loss=1.756, generator_adv_loss=1.944, generator_feat_match_loss=4.234, over 4148.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:42:27,104 INFO [train.py:919] (4/6) Start epoch 301 +2024-03-13 06:42:51,351 INFO [train.py:527] (4/6) Epoch 301, batch 0, global_batch_idx: 37200, batch size: 55, loss[discriminator_loss=2.638, discriminator_real_loss=1.366, discriminator_fake_loss=1.271, generator_loss=27.69, generator_mel_loss=18.22, generator_kl_loss=1.357, generator_dur_loss=1.685, generator_adv_loss=2.041, generator_feat_match_loss=4.383, over 55.00 samples.], tot_loss[discriminator_loss=2.638, discriminator_real_loss=1.366, discriminator_fake_loss=1.271, generator_loss=27.69, generator_mel_loss=18.22, generator_kl_loss=1.357, generator_dur_loss=1.685, generator_adv_loss=2.041, generator_feat_match_loss=4.383, over 55.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:42:51,354 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 06:42:59,303 INFO [train.py:591] (4/6) Epoch 301, validation: discriminator_loss=2.769, discriminator_real_loss=1.55, discriminator_fake_loss=1.219, generator_loss=26.62, generator_mel_loss=18.4, generator_kl_loss=1.283, generator_dur_loss=1.834, generator_adv_loss=1.981, generator_feat_match_loss=3.116, over 100.00 samples. +2024-03-13 06:42:59,305 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 06:45:19,203 INFO [train.py:527] (4/6) Epoch 301, batch 50, global_batch_idx: 37250, batch size: 61, loss[discriminator_loss=2.717, discriminator_real_loss=1.406, discriminator_fake_loss=1.311, generator_loss=27.74, generator_mel_loss=18.13, generator_kl_loss=1.371, generator_dur_loss=1.767, generator_adv_loss=1.926, generator_feat_match_loss=4.542, over 61.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.38, discriminator_fake_loss=1.341, generator_loss=27.64, generator_mel_loss=18.3, generator_kl_loss=1.39, generator_dur_loss=1.785, generator_adv_loss=1.929, generator_feat_match_loss=4.24, over 3165.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:47:38,490 INFO [train.py:527] (4/6) Epoch 301, batch 100, global_batch_idx: 37300, batch size: 45, loss[discriminator_loss=2.729, discriminator_real_loss=1.353, discriminator_fake_loss=1.376, generator_loss=27.44, generator_mel_loss=18.27, generator_kl_loss=1.502, generator_dur_loss=1.713, generator_adv_loss=1.997, generator_feat_match_loss=3.958, over 45.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=27.54, generator_mel_loss=18.24, generator_kl_loss=1.399, generator_dur_loss=1.78, generator_adv_loss=1.925, generator_feat_match_loss=4.189, over 6200.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:48:41,522 INFO [train.py:919] (4/6) Start epoch 302 +2024-03-13 06:50:16,554 INFO [train.py:527] (4/6) Epoch 302, batch 26, global_batch_idx: 37350, batch size: 80, loss[discriminator_loss=2.771, discriminator_real_loss=1.464, discriminator_fake_loss=1.307, generator_loss=26.38, generator_mel_loss=17.49, generator_kl_loss=1.327, generator_dur_loss=1.809, generator_adv_loss=1.882, generator_feat_match_loss=3.871, over 80.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.396, discriminator_fake_loss=1.342, generator_loss=27.41, generator_mel_loss=18.25, generator_kl_loss=1.406, generator_dur_loss=1.749, generator_adv_loss=1.922, generator_feat_match_loss=4.086, over 1538.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:52:36,103 INFO [train.py:527] (4/6) Epoch 302, batch 76, global_batch_idx: 37400, batch size: 77, loss[discriminator_loss=2.793, discriminator_real_loss=1.457, discriminator_fake_loss=1.336, generator_loss=26.93, generator_mel_loss=18.01, generator_kl_loss=1.349, generator_dur_loss=1.847, generator_adv_loss=1.7, generator_feat_match_loss=4.02, over 77.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.387, discriminator_fake_loss=1.347, generator_loss=27.54, generator_mel_loss=18.28, generator_kl_loss=1.41, generator_dur_loss=1.762, generator_adv_loss=1.926, generator_feat_match_loss=4.162, over 4349.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:52:36,105 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 06:52:44,012 INFO [train.py:591] (4/6) Epoch 302, validation: discriminator_loss=2.744, discriminator_real_loss=1.354, discriminator_fake_loss=1.39, generator_loss=26.66, generator_mel_loss=18.47, generator_kl_loss=1.251, generator_dur_loss=1.85, generator_adv_loss=1.778, generator_feat_match_loss=3.304, over 100.00 samples. +2024-03-13 06:52:44,013 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 06:54:55,139 INFO [train.py:919] (4/6) Start epoch 303 +2024-03-13 06:55:25,384 INFO [train.py:527] (4/6) Epoch 303, batch 2, global_batch_idx: 37450, batch size: 83, loss[discriminator_loss=3.025, discriminator_real_loss=1.205, discriminator_fake_loss=1.82, generator_loss=28, generator_mel_loss=18.33, generator_kl_loss=1.364, generator_dur_loss=1.853, generator_adv_loss=1.811, generator_feat_match_loss=4.642, over 83.00 samples.], tot_loss[discriminator_loss=2.912, discriminator_real_loss=1.359, discriminator_fake_loss=1.553, generator_loss=27.67, generator_mel_loss=17.96, generator_kl_loss=1.425, generator_dur_loss=1.811, generator_adv_loss=2.019, generator_feat_match_loss=4.458, over 227.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:57:42,811 INFO [train.py:527] (4/6) Epoch 303, batch 52, global_batch_idx: 37500, batch size: 36, loss[discriminator_loss=2.736, discriminator_real_loss=1.38, discriminator_fake_loss=1.356, generator_loss=28.37, generator_mel_loss=18.35, generator_kl_loss=1.619, generator_dur_loss=1.707, generator_adv_loss=2.208, generator_feat_match_loss=4.489, over 36.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.378, discriminator_fake_loss=1.36, generator_loss=27.44, generator_mel_loss=18.16, generator_kl_loss=1.414, generator_dur_loss=1.748, generator_adv_loss=1.943, generator_feat_match_loss=4.175, over 2958.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:00:01,166 INFO [train.py:527] (4/6) Epoch 303, batch 102, global_batch_idx: 37550, batch size: 62, loss[discriminator_loss=2.787, discriminator_real_loss=1.472, discriminator_fake_loss=1.315, generator_loss=27.51, generator_mel_loss=18.44, generator_kl_loss=1.35, generator_dur_loss=1.767, generator_adv_loss=1.813, generator_feat_match_loss=4.14, over 62.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.381, discriminator_fake_loss=1.355, generator_loss=27.53, generator_mel_loss=18.26, generator_kl_loss=1.41, generator_dur_loss=1.762, generator_adv_loss=1.953, generator_feat_match_loss=4.145, over 5896.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:01:00,408 INFO [train.py:919] (4/6) Start epoch 304 +2024-03-13 07:02:43,392 INFO [train.py:527] (4/6) Epoch 304, batch 28, global_batch_idx: 37600, batch size: 16, loss[discriminator_loss=2.696, discriminator_real_loss=1.25, discriminator_fake_loss=1.447, generator_loss=29.82, generator_mel_loss=19.42, generator_kl_loss=1.755, generator_dur_loss=1.57, generator_adv_loss=2.218, generator_feat_match_loss=4.86, over 16.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.386, discriminator_fake_loss=1.342, generator_loss=27.61, generator_mel_loss=18.29, generator_kl_loss=1.386, generator_dur_loss=1.778, generator_adv_loss=1.919, generator_feat_match_loss=4.236, over 1679.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:02:43,393 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 07:02:51,456 INFO [train.py:591] (4/6) Epoch 304, validation: discriminator_loss=2.808, discriminator_real_loss=1.577, discriminator_fake_loss=1.231, generator_loss=27.06, generator_mel_loss=18.74, generator_kl_loss=1.14, generator_dur_loss=1.848, generator_adv_loss=2.087, generator_feat_match_loss=3.25, over 100.00 samples. +2024-03-13 07:02:51,457 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 07:05:09,906 INFO [train.py:527] (4/6) Epoch 304, batch 78, global_batch_idx: 37650, batch size: 88, loss[discriminator_loss=2.7, discriminator_real_loss=1.455, discriminator_fake_loss=1.245, generator_loss=26.89, generator_mel_loss=17.81, generator_kl_loss=1.168, generator_dur_loss=1.852, generator_adv_loss=1.841, generator_feat_match_loss=4.225, over 88.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.387, discriminator_fake_loss=1.344, generator_loss=27.61, generator_mel_loss=18.27, generator_kl_loss=1.399, generator_dur_loss=1.768, generator_adv_loss=1.922, generator_feat_match_loss=4.245, over 4484.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:07:11,853 INFO [train.py:919] (4/6) Start epoch 305 +2024-03-13 07:07:47,431 INFO [train.py:527] (4/6) Epoch 305, batch 4, global_batch_idx: 37700, batch size: 48, loss[discriminator_loss=2.73, discriminator_real_loss=1.321, discriminator_fake_loss=1.408, generator_loss=27.16, generator_mel_loss=18.35, generator_kl_loss=1.405, generator_dur_loss=1.716, generator_adv_loss=1.857, generator_feat_match_loss=3.834, over 48.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.38, discriminator_fake_loss=1.357, generator_loss=27.2, generator_mel_loss=18.13, generator_kl_loss=1.347, generator_dur_loss=1.814, generator_adv_loss=1.886, generator_feat_match_loss=4.027, over 345.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:10:05,714 INFO [train.py:527] (4/6) Epoch 305, batch 54, global_batch_idx: 37750, batch size: 59, loss[discriminator_loss=2.759, discriminator_real_loss=1.469, discriminator_fake_loss=1.29, generator_loss=27.53, generator_mel_loss=18.11, generator_kl_loss=1.444, generator_dur_loss=1.768, generator_adv_loss=1.704, generator_feat_match_loss=4.5, over 59.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=27.4, generator_mel_loss=18.17, generator_kl_loss=1.416, generator_dur_loss=1.788, generator_adv_loss=1.91, generator_feat_match_loss=4.119, over 3118.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:12:24,870 INFO [train.py:527] (4/6) Epoch 305, batch 104, global_batch_idx: 37800, batch size: 53, loss[discriminator_loss=2.764, discriminator_real_loss=1.385, discriminator_fake_loss=1.378, generator_loss=27.78, generator_mel_loss=18.75, generator_kl_loss=1.29, generator_dur_loss=1.718, generator_adv_loss=1.902, generator_feat_match_loss=4.124, over 53.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.381, discriminator_fake_loss=1.35, generator_loss=27.49, generator_mel_loss=18.24, generator_kl_loss=1.41, generator_dur_loss=1.786, generator_adv_loss=1.915, generator_feat_match_loss=4.133, over 5904.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:12:24,871 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 07:12:33,606 INFO [train.py:591] (4/6) Epoch 305, validation: discriminator_loss=2.747, discriminator_real_loss=1.47, discriminator_fake_loss=1.277, generator_loss=26.84, generator_mel_loss=18.33, generator_kl_loss=1.252, generator_dur_loss=1.839, generator_adv_loss=1.93, generator_feat_match_loss=3.486, over 100.00 samples. +2024-03-13 07:12:33,606 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 07:13:27,288 INFO [train.py:919] (4/6) Start epoch 306 +2024-03-13 07:15:15,023 INFO [train.py:527] (4/6) Epoch 306, batch 30, global_batch_idx: 37850, batch size: 52, loss[discriminator_loss=2.757, discriminator_real_loss=1.211, discriminator_fake_loss=1.545, generator_loss=28.1, generator_mel_loss=18.89, generator_kl_loss=1.417, generator_dur_loss=1.756, generator_adv_loss=1.855, generator_feat_match_loss=4.183, over 52.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.382, discriminator_fake_loss=1.346, generator_loss=27.57, generator_mel_loss=18.23, generator_kl_loss=1.41, generator_dur_loss=1.775, generator_adv_loss=1.93, generator_feat_match_loss=4.225, over 1696.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:17:33,457 INFO [train.py:527] (4/6) Epoch 306, batch 80, global_batch_idx: 37900, batch size: 72, loss[discriminator_loss=2.776, discriminator_real_loss=1.294, discriminator_fake_loss=1.482, generator_loss=27.85, generator_mel_loss=18.44, generator_kl_loss=1.358, generator_dur_loss=1.82, generator_adv_loss=1.908, generator_feat_match_loss=4.321, over 72.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.384, discriminator_fake_loss=1.347, generator_loss=27.63, generator_mel_loss=18.28, generator_kl_loss=1.399, generator_dur_loss=1.775, generator_adv_loss=1.959, generator_feat_match_loss=4.22, over 4633.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:19:33,159 INFO [train.py:919] (4/6) Start epoch 307 +2024-03-13 07:20:14,521 INFO [train.py:527] (4/6) Epoch 307, batch 6, global_batch_idx: 37950, batch size: 80, loss[discriminator_loss=2.739, discriminator_real_loss=1.334, discriminator_fake_loss=1.405, generator_loss=26.84, generator_mel_loss=17.97, generator_kl_loss=1.204, generator_dur_loss=1.86, generator_adv_loss=1.904, generator_feat_match_loss=3.903, over 80.00 samples.], tot_loss[discriminator_loss=2.781, discriminator_real_loss=1.414, discriminator_fake_loss=1.367, generator_loss=27.64, generator_mel_loss=18.43, generator_kl_loss=1.35, generator_dur_loss=1.806, generator_adv_loss=1.907, generator_feat_match_loss=4.14, over 486.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:22:34,447 INFO [train.py:527] (4/6) Epoch 307, batch 56, global_batch_idx: 38000, batch size: 72, loss[discriminator_loss=2.684, discriminator_real_loss=1.341, discriminator_fake_loss=1.343, generator_loss=26.04, generator_mel_loss=17.36, generator_kl_loss=1.286, generator_dur_loss=1.852, generator_adv_loss=1.767, generator_feat_match_loss=3.773, over 72.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.389, discriminator_fake_loss=1.346, generator_loss=27.49, generator_mel_loss=18.28, generator_kl_loss=1.385, generator_dur_loss=1.763, generator_adv_loss=1.926, generator_feat_match_loss=4.136, over 3020.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:22:34,448 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 07:22:42,559 INFO [train.py:591] (4/6) Epoch 307, validation: discriminator_loss=2.697, discriminator_real_loss=1.295, discriminator_fake_loss=1.402, generator_loss=26.5, generator_mel_loss=18.36, generator_kl_loss=1.187, generator_dur_loss=1.84, generator_adv_loss=1.76, generator_feat_match_loss=3.359, over 100.00 samples. +2024-03-13 07:22:42,560 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 07:25:00,920 INFO [train.py:527] (4/6) Epoch 307, batch 106, global_batch_idx: 38050, batch size: 62, loss[discriminator_loss=2.747, discriminator_real_loss=1.328, discriminator_fake_loss=1.418, generator_loss=26.71, generator_mel_loss=18.28, generator_kl_loss=1.338, generator_dur_loss=1.741, generator_adv_loss=2.004, generator_feat_match_loss=3.346, over 62.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.382, discriminator_fake_loss=1.346, generator_loss=27.53, generator_mel_loss=18.25, generator_kl_loss=1.391, generator_dur_loss=1.769, generator_adv_loss=1.928, generator_feat_match_loss=4.183, over 6044.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:25:48,085 INFO [train.py:919] (4/6) Start epoch 308 +2024-03-13 07:27:39,176 INFO [train.py:527] (4/6) Epoch 308, batch 32, global_batch_idx: 38100, batch size: 25, loss[discriminator_loss=2.773, discriminator_real_loss=1.511, discriminator_fake_loss=1.262, generator_loss=27.45, generator_mel_loss=18.39, generator_kl_loss=1.673, generator_dur_loss=1.641, generator_adv_loss=1.863, generator_feat_match_loss=3.882, over 25.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.401, discriminator_fake_loss=1.352, generator_loss=27.67, generator_mel_loss=18.37, generator_kl_loss=1.415, generator_dur_loss=1.759, generator_adv_loss=1.928, generator_feat_match_loss=4.192, over 1868.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:29:59,266 INFO [train.py:527] (4/6) Epoch 308, batch 82, global_batch_idx: 38150, batch size: 74, loss[discriminator_loss=2.709, discriminator_real_loss=1.366, discriminator_fake_loss=1.343, generator_loss=27.82, generator_mel_loss=18.18, generator_kl_loss=1.33, generator_dur_loss=1.849, generator_adv_loss=1.984, generator_feat_match_loss=4.47, over 74.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.383, discriminator_fake_loss=1.351, generator_loss=27.67, generator_mel_loss=18.35, generator_kl_loss=1.411, generator_dur_loss=1.769, generator_adv_loss=1.933, generator_feat_match_loss=4.212, over 4689.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:31:53,508 INFO [train.py:919] (4/6) Start epoch 309 +2024-03-13 07:32:39,926 INFO [train.py:527] (4/6) Epoch 309, batch 8, global_batch_idx: 38200, batch size: 25, loss[discriminator_loss=2.728, discriminator_real_loss=1.327, discriminator_fake_loss=1.401, generator_loss=29.57, generator_mel_loss=19.81, generator_kl_loss=1.828, generator_dur_loss=1.585, generator_adv_loss=1.956, generator_feat_match_loss=4.391, over 25.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.353, discriminator_fake_loss=1.355, generator_loss=28.06, generator_mel_loss=18.52, generator_kl_loss=1.439, generator_dur_loss=1.751, generator_adv_loss=1.956, generator_feat_match_loss=4.394, over 469.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:32:39,931 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 07:32:47,771 INFO [train.py:591] (4/6) Epoch 309, validation: discriminator_loss=2.804, discriminator_real_loss=1.554, discriminator_fake_loss=1.251, generator_loss=26.52, generator_mel_loss=18.44, generator_kl_loss=1.251, generator_dur_loss=1.822, generator_adv_loss=1.96, generator_feat_match_loss=3.042, over 100.00 samples. +2024-03-13 07:32:47,773 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 07:35:05,267 INFO [train.py:527] (4/6) Epoch 309, batch 58, global_batch_idx: 38250, batch size: 62, loss[discriminator_loss=2.679, discriminator_real_loss=1.344, discriminator_fake_loss=1.335, generator_loss=28.22, generator_mel_loss=18.65, generator_kl_loss=1.414, generator_dur_loss=1.727, generator_adv_loss=1.896, generator_feat_match_loss=4.531, over 62.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.376, discriminator_fake_loss=1.358, generator_loss=27.71, generator_mel_loss=18.34, generator_kl_loss=1.438, generator_dur_loss=1.754, generator_adv_loss=1.958, generator_feat_match_loss=4.215, over 3149.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:37:22,826 INFO [train.py:527] (4/6) Epoch 309, batch 108, global_batch_idx: 38300, batch size: 61, loss[discriminator_loss=2.742, discriminator_real_loss=1.369, discriminator_fake_loss=1.374, generator_loss=27.58, generator_mel_loss=18.24, generator_kl_loss=1.483, generator_dur_loss=1.739, generator_adv_loss=2.016, generator_feat_match_loss=4.097, over 61.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.375, discriminator_fake_loss=1.353, generator_loss=27.63, generator_mel_loss=18.3, generator_kl_loss=1.439, generator_dur_loss=1.762, generator_adv_loss=1.94, generator_feat_match_loss=4.197, over 5976.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:38:08,178 INFO [train.py:919] (4/6) Start epoch 310 +2024-03-13 07:40:05,816 INFO [train.py:527] (4/6) Epoch 310, batch 34, global_batch_idx: 38350, batch size: 64, loss[discriminator_loss=2.756, discriminator_real_loss=1.297, discriminator_fake_loss=1.459, generator_loss=28, generator_mel_loss=18.64, generator_kl_loss=1.31, generator_dur_loss=1.779, generator_adv_loss=2.122, generator_feat_match_loss=4.148, over 64.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=27.73, generator_mel_loss=18.3, generator_kl_loss=1.424, generator_dur_loss=1.774, generator_adv_loss=1.934, generator_feat_match_loss=4.295, over 1943.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:42:24,812 INFO [train.py:527] (4/6) Epoch 310, batch 84, global_batch_idx: 38400, batch size: 47, loss[discriminator_loss=2.736, discriminator_real_loss=1.398, discriminator_fake_loss=1.338, generator_loss=28.29, generator_mel_loss=18.43, generator_kl_loss=1.576, generator_dur_loss=1.699, generator_adv_loss=1.999, generator_feat_match_loss=4.588, over 47.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.379, discriminator_fake_loss=1.348, generator_loss=27.67, generator_mel_loss=18.31, generator_kl_loss=1.409, generator_dur_loss=1.78, generator_adv_loss=1.93, generator_feat_match_loss=4.244, over 4863.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:42:24,813 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 07:42:33,707 INFO [train.py:591] (4/6) Epoch 310, validation: discriminator_loss=2.728, discriminator_real_loss=1.448, discriminator_fake_loss=1.28, generator_loss=26.99, generator_mel_loss=18.42, generator_kl_loss=1.213, generator_dur_loss=1.819, generator_adv_loss=1.958, generator_feat_match_loss=3.579, over 100.00 samples. +2024-03-13 07:42:33,708 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 07:44:23,005 INFO [train.py:919] (4/6) Start epoch 311 +2024-03-13 07:45:16,107 INFO [train.py:527] (4/6) Epoch 311, batch 10, global_batch_idx: 38450, batch size: 74, loss[discriminator_loss=2.78, discriminator_real_loss=1.495, discriminator_fake_loss=1.286, generator_loss=26.1, generator_mel_loss=17.61, generator_kl_loss=1.149, generator_dur_loss=1.835, generator_adv_loss=1.866, generator_feat_match_loss=3.638, over 74.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.382, discriminator_fake_loss=1.349, generator_loss=27.29, generator_mel_loss=18.09, generator_kl_loss=1.328, generator_dur_loss=1.784, generator_adv_loss=1.947, generator_feat_match_loss=4.138, over 718.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:47:35,306 INFO [train.py:527] (4/6) Epoch 311, batch 60, global_batch_idx: 38500, batch size: 44, loss[discriminator_loss=2.656, discriminator_real_loss=1.377, discriminator_fake_loss=1.279, generator_loss=28.21, generator_mel_loss=18.75, generator_kl_loss=1.448, generator_dur_loss=1.692, generator_adv_loss=1.891, generator_feat_match_loss=4.429, over 44.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.387, discriminator_fake_loss=1.343, generator_loss=27.49, generator_mel_loss=18.21, generator_kl_loss=1.399, generator_dur_loss=1.755, generator_adv_loss=1.928, generator_feat_match_loss=4.196, over 3570.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:49:52,811 INFO [train.py:527] (4/6) Epoch 311, batch 110, global_batch_idx: 38550, batch size: 53, loss[discriminator_loss=2.723, discriminator_real_loss=1.364, discriminator_fake_loss=1.358, generator_loss=27.33, generator_mel_loss=18.16, generator_kl_loss=1.418, generator_dur_loss=1.733, generator_adv_loss=1.823, generator_feat_match_loss=4.199, over 53.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.385, discriminator_fake_loss=1.343, generator_loss=27.51, generator_mel_loss=18.22, generator_kl_loss=1.407, generator_dur_loss=1.749, generator_adv_loss=1.926, generator_feat_match_loss=4.206, over 6413.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:50:29,057 INFO [train.py:919] (4/6) Start epoch 312 +2024-03-13 07:52:36,860 INFO [train.py:527] (4/6) Epoch 312, batch 36, global_batch_idx: 38600, batch size: 42, loss[discriminator_loss=2.844, discriminator_real_loss=1.373, discriminator_fake_loss=1.472, generator_loss=28.3, generator_mel_loss=18.4, generator_kl_loss=1.759, generator_dur_loss=1.626, generator_adv_loss=1.996, generator_feat_match_loss=4.518, over 42.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=27.72, generator_mel_loss=18.36, generator_kl_loss=1.42, generator_dur_loss=1.761, generator_adv_loss=1.888, generator_feat_match_loss=4.29, over 2188.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:52:36,861 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 07:52:44,818 INFO [train.py:591] (4/6) Epoch 312, validation: discriminator_loss=2.855, discriminator_real_loss=1.551, discriminator_fake_loss=1.303, generator_loss=26.99, generator_mel_loss=18.56, generator_kl_loss=1.204, generator_dur_loss=1.823, generator_adv_loss=1.996, generator_feat_match_loss=3.411, over 100.00 samples. +2024-03-13 07:52:44,819 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 07:55:03,769 INFO [train.py:527] (4/6) Epoch 312, batch 86, global_batch_idx: 38650, batch size: 66, loss[discriminator_loss=2.731, discriminator_real_loss=1.382, discriminator_fake_loss=1.349, generator_loss=27.4, generator_mel_loss=18.12, generator_kl_loss=1.499, generator_dur_loss=1.777, generator_adv_loss=1.96, generator_feat_match_loss=4.035, over 66.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.65, generator_mel_loss=18.34, generator_kl_loss=1.412, generator_dur_loss=1.766, generator_adv_loss=1.9, generator_feat_match_loss=4.233, over 5167.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:56:43,089 INFO [train.py:919] (4/6) Start epoch 313 +2024-03-13 07:57:41,500 INFO [train.py:527] (4/6) Epoch 313, batch 12, global_batch_idx: 38700, batch size: 58, loss[discriminator_loss=2.713, discriminator_real_loss=1.34, discriminator_fake_loss=1.373, generator_loss=27.21, generator_mel_loss=18.47, generator_kl_loss=1.244, generator_dur_loss=1.732, generator_adv_loss=2.036, generator_feat_match_loss=3.729, over 58.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.39, discriminator_fake_loss=1.361, generator_loss=27.39, generator_mel_loss=18.25, generator_kl_loss=1.415, generator_dur_loss=1.762, generator_adv_loss=1.897, generator_feat_match_loss=4.065, over 746.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:00:02,081 INFO [train.py:527] (4/6) Epoch 313, batch 62, global_batch_idx: 38750, batch size: 83, loss[discriminator_loss=2.684, discriminator_real_loss=1.286, discriminator_fake_loss=1.398, generator_loss=27.83, generator_mel_loss=18.39, generator_kl_loss=1.378, generator_dur_loss=1.844, generator_adv_loss=1.958, generator_feat_match_loss=4.263, over 83.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.37, discriminator_fake_loss=1.351, generator_loss=27.5, generator_mel_loss=18.23, generator_kl_loss=1.38, generator_dur_loss=1.776, generator_adv_loss=1.918, generator_feat_match_loss=4.198, over 3843.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:02:21,381 INFO [train.py:527] (4/6) Epoch 313, batch 112, global_batch_idx: 38800, batch size: 47, loss[discriminator_loss=2.716, discriminator_real_loss=1.365, discriminator_fake_loss=1.351, generator_loss=28.51, generator_mel_loss=18.46, generator_kl_loss=1.473, generator_dur_loss=1.713, generator_adv_loss=1.961, generator_feat_match_loss=4.904, over 47.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=27.63, generator_mel_loss=18.29, generator_kl_loss=1.395, generator_dur_loss=1.768, generator_adv_loss=1.923, generator_feat_match_loss=4.253, over 6640.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:02:21,383 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 08:02:30,293 INFO [train.py:591] (4/6) Epoch 313, validation: discriminator_loss=2.775, discriminator_real_loss=1.461, discriminator_fake_loss=1.314, generator_loss=26.44, generator_mel_loss=18.39, generator_kl_loss=1.12, generator_dur_loss=1.834, generator_adv_loss=1.867, generator_feat_match_loss=3.229, over 100.00 samples. +2024-03-13 08:02:30,294 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 08:03:00,453 INFO [train.py:919] (4/6) Start epoch 314 +2024-03-13 08:05:10,061 INFO [train.py:527] (4/6) Epoch 314, batch 38, global_batch_idx: 38850, batch size: 53, loss[discriminator_loss=2.822, discriminator_real_loss=1.361, discriminator_fake_loss=1.462, generator_loss=27.08, generator_mel_loss=17.69, generator_kl_loss=1.379, generator_dur_loss=1.726, generator_adv_loss=2.082, generator_feat_match_loss=4.207, over 53.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.381, discriminator_fake_loss=1.354, generator_loss=27.4, generator_mel_loss=18.14, generator_kl_loss=1.391, generator_dur_loss=1.765, generator_adv_loss=1.941, generator_feat_match_loss=4.159, over 2274.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:07:30,828 INFO [train.py:527] (4/6) Epoch 314, batch 88, global_batch_idx: 38900, batch size: 62, loss[discriminator_loss=2.706, discriminator_real_loss=1.352, discriminator_fake_loss=1.355, generator_loss=27.5, generator_mel_loss=17.85, generator_kl_loss=1.616, generator_dur_loss=1.707, generator_adv_loss=1.922, generator_feat_match_loss=4.403, over 62.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.378, discriminator_fake_loss=1.352, generator_loss=27.53, generator_mel_loss=18.21, generator_kl_loss=1.402, generator_dur_loss=1.752, generator_adv_loss=1.931, generator_feat_match_loss=4.237, over 5127.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:09:07,610 INFO [train.py:919] (4/6) Start epoch 315 +2024-03-13 08:10:09,723 INFO [train.py:527] (4/6) Epoch 315, batch 14, global_batch_idx: 38950, batch size: 64, loss[discriminator_loss=2.74, discriminator_real_loss=1.325, discriminator_fake_loss=1.415, generator_loss=28.15, generator_mel_loss=18.67, generator_kl_loss=1.303, generator_dur_loss=1.777, generator_adv_loss=1.87, generator_feat_match_loss=4.534, over 64.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.35, discriminator_fake_loss=1.383, generator_loss=27.74, generator_mel_loss=18.33, generator_kl_loss=1.467, generator_dur_loss=1.778, generator_adv_loss=1.914, generator_feat_match_loss=4.254, over 837.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:12:31,527 INFO [train.py:527] (4/6) Epoch 315, batch 64, global_batch_idx: 39000, batch size: 74, loss[discriminator_loss=2.694, discriminator_real_loss=1.37, discriminator_fake_loss=1.324, generator_loss=27.4, generator_mel_loss=18.18, generator_kl_loss=1.389, generator_dur_loss=1.824, generator_adv_loss=1.864, generator_feat_match_loss=4.142, over 74.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.37, discriminator_fake_loss=1.349, generator_loss=27.73, generator_mel_loss=18.28, generator_kl_loss=1.411, generator_dur_loss=1.768, generator_adv_loss=1.959, generator_feat_match_loss=4.315, over 3717.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:12:31,529 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 08:12:39,668 INFO [train.py:591] (4/6) Epoch 315, validation: discriminator_loss=2.758, discriminator_real_loss=1.362, discriminator_fake_loss=1.396, generator_loss=25.86, generator_mel_loss=17.96, generator_kl_loss=1.163, generator_dur_loss=1.846, generator_adv_loss=1.792, generator_feat_match_loss=3.098, over 100.00 samples. +2024-03-13 08:12:39,669 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 08:14:57,046 INFO [train.py:527] (4/6) Epoch 315, batch 114, global_batch_idx: 39050, batch size: 68, loss[discriminator_loss=2.762, discriminator_real_loss=1.368, discriminator_fake_loss=1.394, generator_loss=27.54, generator_mel_loss=18.21, generator_kl_loss=1.362, generator_dur_loss=1.821, generator_adv_loss=2.038, generator_feat_match_loss=4.108, over 68.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.371, discriminator_fake_loss=1.354, generator_loss=27.71, generator_mel_loss=18.28, generator_kl_loss=1.413, generator_dur_loss=1.773, generator_adv_loss=1.943, generator_feat_match_loss=4.305, over 6603.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:15:23,650 INFO [train.py:919] (4/6) Start epoch 316 +2024-03-13 08:17:41,139 INFO [train.py:527] (4/6) Epoch 316, batch 40, global_batch_idx: 39100, batch size: 58, loss[discriminator_loss=2.718, discriminator_real_loss=1.417, discriminator_fake_loss=1.301, generator_loss=28.45, generator_mel_loss=18.77, generator_kl_loss=1.388, generator_dur_loss=1.769, generator_adv_loss=1.876, generator_feat_match_loss=4.648, over 58.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.381, discriminator_fake_loss=1.348, generator_loss=27.71, generator_mel_loss=18.37, generator_kl_loss=1.39, generator_dur_loss=1.774, generator_adv_loss=1.921, generator_feat_match_loss=4.256, over 2387.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:19:58,082 INFO [train.py:527] (4/6) Epoch 316, batch 90, global_batch_idx: 39150, batch size: 77, loss[discriminator_loss=2.738, discriminator_real_loss=1.388, discriminator_fake_loss=1.35, generator_loss=27.14, generator_mel_loss=17.82, generator_kl_loss=1.304, generator_dur_loss=1.772, generator_adv_loss=1.887, generator_feat_match_loss=4.357, over 77.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=27.67, generator_mel_loss=18.31, generator_kl_loss=1.386, generator_dur_loss=1.766, generator_adv_loss=1.926, generator_feat_match_loss=4.283, over 5368.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:21:30,129 INFO [train.py:919] (4/6) Start epoch 317 +2024-03-13 08:22:39,677 INFO [train.py:527] (4/6) Epoch 317, batch 16, global_batch_idx: 39200, batch size: 66, loss[discriminator_loss=2.741, discriminator_real_loss=1.502, discriminator_fake_loss=1.239, generator_loss=27.07, generator_mel_loss=17.94, generator_kl_loss=1.327, generator_dur_loss=1.754, generator_adv_loss=1.877, generator_feat_match_loss=4.176, over 66.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.381, discriminator_fake_loss=1.335, generator_loss=27.74, generator_mel_loss=18.35, generator_kl_loss=1.363, generator_dur_loss=1.783, generator_adv_loss=1.975, generator_feat_match_loss=4.263, over 1139.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:22:39,679 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 08:22:47,858 INFO [train.py:591] (4/6) Epoch 317, validation: discriminator_loss=2.8, discriminator_real_loss=1.399, discriminator_fake_loss=1.401, generator_loss=25.76, generator_mel_loss=18.18, generator_kl_loss=1.179, generator_dur_loss=1.814, generator_adv_loss=1.727, generator_feat_match_loss=2.859, over 100.00 samples. +2024-03-13 08:22:47,858 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 08:25:08,344 INFO [train.py:527] (4/6) Epoch 317, batch 66, global_batch_idx: 39250, batch size: 74, loss[discriminator_loss=2.781, discriminator_real_loss=1.458, discriminator_fake_loss=1.324, generator_loss=26.84, generator_mel_loss=17.81, generator_kl_loss=1.29, generator_dur_loss=1.767, generator_adv_loss=1.732, generator_feat_match_loss=4.241, over 74.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.385, discriminator_fake_loss=1.344, generator_loss=27.59, generator_mel_loss=18.26, generator_kl_loss=1.388, generator_dur_loss=1.762, generator_adv_loss=1.942, generator_feat_match_loss=4.244, over 3993.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:27:27,162 INFO [train.py:527] (4/6) Epoch 317, batch 116, global_batch_idx: 39300, batch size: 16, loss[discriminator_loss=2.585, discriminator_real_loss=1.282, discriminator_fake_loss=1.303, generator_loss=31.37, generator_mel_loss=20.76, generator_kl_loss=1.859, generator_dur_loss=1.617, generator_adv_loss=2.192, generator_feat_match_loss=4.945, over 16.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.383, discriminator_fake_loss=1.349, generator_loss=27.59, generator_mel_loss=18.24, generator_kl_loss=1.4, generator_dur_loss=1.757, generator_adv_loss=1.932, generator_feat_match_loss=4.254, over 6730.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:27:47,636 INFO [train.py:919] (4/6) Start epoch 318 +2024-03-13 08:30:05,497 INFO [train.py:527] (4/6) Epoch 318, batch 42, global_batch_idx: 39350, batch size: 39, loss[discriminator_loss=2.605, discriminator_real_loss=1.325, discriminator_fake_loss=1.28, generator_loss=28.96, generator_mel_loss=18.51, generator_kl_loss=1.558, generator_dur_loss=1.717, generator_adv_loss=2.325, generator_feat_match_loss=4.845, over 39.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.369, discriminator_fake_loss=1.348, generator_loss=27.73, generator_mel_loss=18.21, generator_kl_loss=1.394, generator_dur_loss=1.768, generator_adv_loss=1.99, generator_feat_match_loss=4.369, over 2456.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:32:24,872 INFO [train.py:527] (4/6) Epoch 318, batch 92, global_batch_idx: 39400, batch size: 47, loss[discriminator_loss=2.688, discriminator_real_loss=1.397, discriminator_fake_loss=1.29, generator_loss=29.15, generator_mel_loss=19.04, generator_kl_loss=1.45, generator_dur_loss=1.678, generator_adv_loss=2.128, generator_feat_match_loss=4.854, over 47.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.385, discriminator_fake_loss=1.34, generator_loss=27.6, generator_mel_loss=18.19, generator_kl_loss=1.4, generator_dur_loss=1.76, generator_adv_loss=1.969, generator_feat_match_loss=4.278, over 5286.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:32:24,873 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 08:32:33,727 INFO [train.py:591] (4/6) Epoch 318, validation: discriminator_loss=2.765, discriminator_real_loss=1.53, discriminator_fake_loss=1.235, generator_loss=27.36, generator_mel_loss=18.41, generator_kl_loss=1.17, generator_dur_loss=1.838, generator_adv_loss=2.086, generator_feat_match_loss=3.85, over 100.00 samples. +2024-03-13 08:32:33,727 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 08:34:00,649 INFO [train.py:919] (4/6) Start epoch 319 +2024-03-13 08:35:17,686 INFO [train.py:527] (4/6) Epoch 319, batch 18, global_batch_idx: 39450, batch size: 56, loss[discriminator_loss=2.692, discriminator_real_loss=1.348, discriminator_fake_loss=1.344, generator_loss=27.31, generator_mel_loss=18.1, generator_kl_loss=1.614, generator_dur_loss=1.758, generator_adv_loss=1.977, generator_feat_match_loss=3.856, over 56.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.387, discriminator_fake_loss=1.352, generator_loss=27.85, generator_mel_loss=18.37, generator_kl_loss=1.385, generator_dur_loss=1.772, generator_adv_loss=1.924, generator_feat_match_loss=4.395, over 1149.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:37:36,126 INFO [train.py:527] (4/6) Epoch 319, batch 68, global_batch_idx: 39500, batch size: 44, loss[discriminator_loss=2.695, discriminator_real_loss=1.384, discriminator_fake_loss=1.311, generator_loss=27.23, generator_mel_loss=17.99, generator_kl_loss=1.523, generator_dur_loss=1.694, generator_adv_loss=1.926, generator_feat_match_loss=4.093, over 44.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.377, discriminator_fake_loss=1.354, generator_loss=27.75, generator_mel_loss=18.31, generator_kl_loss=1.397, generator_dur_loss=1.778, generator_adv_loss=1.924, generator_feat_match_loss=4.334, over 4076.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:39:53,012 INFO [train.py:527] (4/6) Epoch 319, batch 118, global_batch_idx: 39550, batch size: 68, loss[discriminator_loss=2.713, discriminator_real_loss=1.272, discriminator_fake_loss=1.441, generator_loss=28.26, generator_mel_loss=18.04, generator_kl_loss=1.368, generator_dur_loss=1.795, generator_adv_loss=2.306, generator_feat_match_loss=4.755, over 68.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.377, discriminator_fake_loss=1.356, generator_loss=27.72, generator_mel_loss=18.31, generator_kl_loss=1.399, generator_dur_loss=1.775, generator_adv_loss=1.927, generator_feat_match_loss=4.32, over 6867.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:40:09,460 INFO [train.py:919] (4/6) Start epoch 320 +2024-03-13 08:42:30,487 INFO [train.py:527] (4/6) Epoch 320, batch 44, global_batch_idx: 39600, batch size: 58, loss[discriminator_loss=2.74, discriminator_real_loss=1.33, discriminator_fake_loss=1.411, generator_loss=28.54, generator_mel_loss=18.73, generator_kl_loss=1.582, generator_dur_loss=1.728, generator_adv_loss=1.956, generator_feat_match_loss=4.546, over 58.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.374, discriminator_fake_loss=1.347, generator_loss=27.64, generator_mel_loss=18.17, generator_kl_loss=1.406, generator_dur_loss=1.765, generator_adv_loss=1.951, generator_feat_match_loss=4.346, over 2699.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:42:30,488 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 08:42:38,338 INFO [train.py:591] (4/6) Epoch 320, validation: discriminator_loss=2.767, discriminator_real_loss=1.523, discriminator_fake_loss=1.243, generator_loss=26.73, generator_mel_loss=18.11, generator_kl_loss=1.136, generator_dur_loss=1.827, generator_adv_loss=1.956, generator_feat_match_loss=3.705, over 100.00 samples. +2024-03-13 08:42:38,339 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 08:44:58,484 INFO [train.py:527] (4/6) Epoch 320, batch 94, global_batch_idx: 39650, batch size: 58, loss[discriminator_loss=2.68, discriminator_real_loss=1.29, discriminator_fake_loss=1.39, generator_loss=28.38, generator_mel_loss=18.52, generator_kl_loss=1.464, generator_dur_loss=1.762, generator_adv_loss=2.011, generator_feat_match_loss=4.618, over 58.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.375, discriminator_fake_loss=1.349, generator_loss=27.68, generator_mel_loss=18.21, generator_kl_loss=1.409, generator_dur_loss=1.772, generator_adv_loss=1.948, generator_feat_match_loss=4.333, over 5696.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:46:19,917 INFO [train.py:919] (4/6) Start epoch 321 +2024-03-13 08:47:37,948 INFO [train.py:527] (4/6) Epoch 321, batch 20, global_batch_idx: 39700, batch size: 45, loss[discriminator_loss=2.725, discriminator_real_loss=1.391, discriminator_fake_loss=1.334, generator_loss=27.55, generator_mel_loss=18.3, generator_kl_loss=1.541, generator_dur_loss=1.721, generator_adv_loss=1.815, generator_feat_match_loss=4.176, over 45.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.387, discriminator_fake_loss=1.35, generator_loss=27.52, generator_mel_loss=18.26, generator_kl_loss=1.446, generator_dur_loss=1.741, generator_adv_loss=1.906, generator_feat_match_loss=4.168, over 1187.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:49:55,683 INFO [train.py:527] (4/6) Epoch 321, batch 70, global_batch_idx: 39750, batch size: 55, loss[discriminator_loss=2.719, discriminator_real_loss=1.36, discriminator_fake_loss=1.359, generator_loss=28.1, generator_mel_loss=18.2, generator_kl_loss=1.493, generator_dur_loss=1.724, generator_adv_loss=2.033, generator_feat_match_loss=4.644, over 55.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.379, discriminator_fake_loss=1.351, generator_loss=27.7, generator_mel_loss=18.31, generator_kl_loss=1.445, generator_dur_loss=1.746, generator_adv_loss=1.926, generator_feat_match_loss=4.271, over 3859.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:52:15,309 INFO [train.py:527] (4/6) Epoch 321, batch 120, global_batch_idx: 39800, batch size: 12, loss[discriminator_loss=2.792, discriminator_real_loss=1.519, discriminator_fake_loss=1.273, generator_loss=28.88, generator_mel_loss=18.59, generator_kl_loss=1.977, generator_dur_loss=1.57, generator_adv_loss=1.953, generator_feat_match_loss=4.791, over 12.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.379, discriminator_fake_loss=1.35, generator_loss=27.71, generator_mel_loss=18.3, generator_kl_loss=1.434, generator_dur_loss=1.755, generator_adv_loss=1.933, generator_feat_match_loss=4.293, over 6556.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:52:15,310 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 08:52:24,263 INFO [train.py:591] (4/6) Epoch 321, validation: discriminator_loss=2.749, discriminator_real_loss=1.382, discriminator_fake_loss=1.367, generator_loss=27.2, generator_mel_loss=18.48, generator_kl_loss=1.231, generator_dur_loss=1.847, generator_adv_loss=1.814, generator_feat_match_loss=3.827, over 100.00 samples. +2024-03-13 08:52:24,264 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 08:52:34,738 INFO [train.py:919] (4/6) Start epoch 322 +2024-03-13 08:55:05,025 INFO [train.py:527] (4/6) Epoch 322, batch 46, global_batch_idx: 39850, batch size: 42, loss[discriminator_loss=2.718, discriminator_real_loss=1.342, discriminator_fake_loss=1.376, generator_loss=27.18, generator_mel_loss=18.1, generator_kl_loss=1.448, generator_dur_loss=1.716, generator_adv_loss=2.067, generator_feat_match_loss=3.849, over 42.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.389, discriminator_fake_loss=1.341, generator_loss=27.57, generator_mel_loss=18.21, generator_kl_loss=1.363, generator_dur_loss=1.787, generator_adv_loss=1.934, generator_feat_match_loss=4.274, over 2846.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:57:20,914 INFO [train.py:527] (4/6) Epoch 322, batch 96, global_batch_idx: 39900, batch size: 70, loss[discriminator_loss=2.71, discriminator_real_loss=1.301, discriminator_fake_loss=1.409, generator_loss=27.22, generator_mel_loss=18.24, generator_kl_loss=1.344, generator_dur_loss=1.795, generator_adv_loss=1.99, generator_feat_match_loss=3.857, over 70.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.383, discriminator_fake_loss=1.344, generator_loss=27.63, generator_mel_loss=18.27, generator_kl_loss=1.381, generator_dur_loss=1.773, generator_adv_loss=1.93, generator_feat_match_loss=4.274, over 5660.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:58:37,509 INFO [train.py:919] (4/6) Start epoch 323 +2024-03-13 09:00:00,632 INFO [train.py:527] (4/6) Epoch 323, batch 22, global_batch_idx: 39950, batch size: 64, loss[discriminator_loss=2.689, discriminator_real_loss=1.42, discriminator_fake_loss=1.269, generator_loss=27.53, generator_mel_loss=18.23, generator_kl_loss=1.388, generator_dur_loss=1.726, generator_adv_loss=1.896, generator_feat_match_loss=4.291, over 64.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.394, discriminator_fake_loss=1.333, generator_loss=27.61, generator_mel_loss=18.3, generator_kl_loss=1.428, generator_dur_loss=1.708, generator_adv_loss=1.955, generator_feat_match_loss=4.219, over 1267.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:02:19,770 INFO [train.py:527] (4/6) Epoch 323, batch 72, global_batch_idx: 40000, batch size: 89, loss[discriminator_loss=2.683, discriminator_real_loss=1.305, discriminator_fake_loss=1.377, generator_loss=28.16, generator_mel_loss=18.46, generator_kl_loss=1.503, generator_dur_loss=1.815, generator_adv_loss=1.845, generator_feat_match_loss=4.546, over 89.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.383, discriminator_fake_loss=1.345, generator_loss=27.68, generator_mel_loss=18.35, generator_kl_loss=1.407, generator_dur_loss=1.737, generator_adv_loss=1.935, generator_feat_match_loss=4.246, over 4117.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:02:19,772 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 09:02:27,807 INFO [train.py:591] (4/6) Epoch 323, validation: discriminator_loss=2.763, discriminator_real_loss=1.41, discriminator_fake_loss=1.353, generator_loss=27.02, generator_mel_loss=18.64, generator_kl_loss=1.197, generator_dur_loss=1.831, generator_adv_loss=1.786, generator_feat_match_loss=3.572, over 100.00 samples. +2024-03-13 09:02:27,807 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 09:04:45,699 INFO [train.py:527] (4/6) Epoch 323, batch 122, global_batch_idx: 40050, batch size: 39, loss[discriminator_loss=2.688, discriminator_real_loss=1.382, discriminator_fake_loss=1.306, generator_loss=27.18, generator_mel_loss=17.81, generator_kl_loss=1.559, generator_dur_loss=1.756, generator_adv_loss=2.008, generator_feat_match_loss=4.044, over 39.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.378, discriminator_fake_loss=1.346, generator_loss=27.69, generator_mel_loss=18.33, generator_kl_loss=1.413, generator_dur_loss=1.743, generator_adv_loss=1.931, generator_feat_match_loss=4.275, over 6713.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:04:50,974 INFO [train.py:919] (4/6) Start epoch 324 +2024-03-13 09:07:28,019 INFO [train.py:527] (4/6) Epoch 324, batch 48, global_batch_idx: 40100, batch size: 58, loss[discriminator_loss=2.768, discriminator_real_loss=1.289, discriminator_fake_loss=1.479, generator_loss=27.56, generator_mel_loss=18.16, generator_kl_loss=1.443, generator_dur_loss=1.787, generator_adv_loss=2.061, generator_feat_match_loss=4.106, over 58.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.377, discriminator_fake_loss=1.338, generator_loss=27.69, generator_mel_loss=18.29, generator_kl_loss=1.415, generator_dur_loss=1.763, generator_adv_loss=1.943, generator_feat_match_loss=4.276, over 2772.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:09:47,750 INFO [train.py:527] (4/6) Epoch 324, batch 98, global_batch_idx: 40150, batch size: 80, loss[discriminator_loss=2.67, discriminator_real_loss=1.343, discriminator_fake_loss=1.327, generator_loss=27.28, generator_mel_loss=17.98, generator_kl_loss=1.276, generator_dur_loss=1.822, generator_adv_loss=1.927, generator_feat_match_loss=4.275, over 80.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=27.62, generator_mel_loss=18.23, generator_kl_loss=1.397, generator_dur_loss=1.769, generator_adv_loss=1.931, generator_feat_match_loss=4.289, over 5779.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:10:56,812 INFO [train.py:919] (4/6) Start epoch 325 +2024-03-13 09:12:27,284 INFO [train.py:527] (4/6) Epoch 325, batch 24, global_batch_idx: 40200, batch size: 58, loss[discriminator_loss=2.747, discriminator_real_loss=1.391, discriminator_fake_loss=1.356, generator_loss=27.48, generator_mel_loss=18.5, generator_kl_loss=1.385, generator_dur_loss=1.787, generator_adv_loss=1.842, generator_feat_match_loss=3.967, over 58.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.376, discriminator_fake_loss=1.35, generator_loss=27.82, generator_mel_loss=18.4, generator_kl_loss=1.421, generator_dur_loss=1.777, generator_adv_loss=1.911, generator_feat_match_loss=4.313, over 1410.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:12:27,285 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 09:12:35,412 INFO [train.py:591] (4/6) Epoch 325, validation: discriminator_loss=2.748, discriminator_real_loss=1.363, discriminator_fake_loss=1.385, generator_loss=25.94, generator_mel_loss=18.11, generator_kl_loss=1.265, generator_dur_loss=1.831, generator_adv_loss=1.778, generator_feat_match_loss=2.957, over 100.00 samples. +2024-03-13 09:12:35,413 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 09:14:54,048 INFO [train.py:527] (4/6) Epoch 325, batch 74, global_batch_idx: 40250, batch size: 36, loss[discriminator_loss=2.808, discriminator_real_loss=1.333, discriminator_fake_loss=1.475, generator_loss=27.71, generator_mel_loss=18.33, generator_kl_loss=1.548, generator_dur_loss=1.762, generator_adv_loss=1.872, generator_feat_match_loss=4.207, over 36.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.378, discriminator_fake_loss=1.349, generator_loss=27.66, generator_mel_loss=18.32, generator_kl_loss=1.421, generator_dur_loss=1.758, generator_adv_loss=1.915, generator_feat_match_loss=4.244, over 4211.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:17:11,509 INFO [train.py:919] (4/6) Start epoch 326 +2024-03-13 09:17:34,883 INFO [train.py:527] (4/6) Epoch 326, batch 0, global_batch_idx: 40300, batch size: 58, loss[discriminator_loss=2.609, discriminator_real_loss=1.289, discriminator_fake_loss=1.321, generator_loss=27.48, generator_mel_loss=17.92, generator_kl_loss=1.38, generator_dur_loss=1.763, generator_adv_loss=2.11, generator_feat_match_loss=4.304, over 58.00 samples.], tot_loss[discriminator_loss=2.609, discriminator_real_loss=1.289, discriminator_fake_loss=1.321, generator_loss=27.48, generator_mel_loss=17.92, generator_kl_loss=1.38, generator_dur_loss=1.763, generator_adv_loss=2.11, generator_feat_match_loss=4.304, over 58.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:19:53,414 INFO [train.py:527] (4/6) Epoch 326, batch 50, global_batch_idx: 40350, batch size: 53, loss[discriminator_loss=2.696, discriminator_real_loss=1.434, discriminator_fake_loss=1.262, generator_loss=28.29, generator_mel_loss=18.46, generator_kl_loss=1.386, generator_dur_loss=1.704, generator_adv_loss=1.854, generator_feat_match_loss=4.886, over 53.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.379, discriminator_fake_loss=1.333, generator_loss=27.71, generator_mel_loss=18.25, generator_kl_loss=1.391, generator_dur_loss=1.763, generator_adv_loss=1.951, generator_feat_match_loss=4.356, over 2862.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:22:12,711 INFO [train.py:527] (4/6) Epoch 326, batch 100, global_batch_idx: 40400, batch size: 53, loss[discriminator_loss=2.731, discriminator_real_loss=1.495, discriminator_fake_loss=1.236, generator_loss=27.61, generator_mel_loss=18.23, generator_kl_loss=1.547, generator_dur_loss=1.662, generator_adv_loss=1.83, generator_feat_match_loss=4.338, over 53.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.374, discriminator_fake_loss=1.338, generator_loss=27.67, generator_mel_loss=18.22, generator_kl_loss=1.402, generator_dur_loss=1.764, generator_adv_loss=1.938, generator_feat_match_loss=4.348, over 5841.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:22:12,713 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 09:22:21,415 INFO [train.py:591] (4/6) Epoch 326, validation: discriminator_loss=2.746, discriminator_real_loss=1.398, discriminator_fake_loss=1.347, generator_loss=26.39, generator_mel_loss=18.21, generator_kl_loss=1.223, generator_dur_loss=1.815, generator_adv_loss=1.736, generator_feat_match_loss=3.409, over 100.00 samples. +2024-03-13 09:22:21,416 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 09:23:27,148 INFO [train.py:919] (4/6) Start epoch 327 +2024-03-13 09:25:03,108 INFO [train.py:527] (4/6) Epoch 327, batch 26, global_batch_idx: 40450, batch size: 61, loss[discriminator_loss=2.724, discriminator_real_loss=1.311, discriminator_fake_loss=1.413, generator_loss=27.49, generator_mel_loss=18.16, generator_kl_loss=1.475, generator_dur_loss=1.762, generator_adv_loss=2.043, generator_feat_match_loss=4.053, over 61.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.381, discriminator_fake_loss=1.335, generator_loss=27.75, generator_mel_loss=18.26, generator_kl_loss=1.417, generator_dur_loss=1.753, generator_adv_loss=1.957, generator_feat_match_loss=4.357, over 1501.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:27:22,162 INFO [train.py:527] (4/6) Epoch 327, batch 76, global_batch_idx: 40500, batch size: 53, loss[discriminator_loss=2.78, discriminator_real_loss=1.344, discriminator_fake_loss=1.436, generator_loss=27.47, generator_mel_loss=17.99, generator_kl_loss=1.486, generator_dur_loss=1.707, generator_adv_loss=2.095, generator_feat_match_loss=4.194, over 53.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=27.73, generator_mel_loss=18.3, generator_kl_loss=1.399, generator_dur_loss=1.764, generator_adv_loss=1.939, generator_feat_match_loss=4.332, over 4467.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:29:34,317 INFO [train.py:919] (4/6) Start epoch 328 +2024-03-13 09:30:05,271 INFO [train.py:527] (4/6) Epoch 328, batch 2, global_batch_idx: 40550, batch size: 62, loss[discriminator_loss=2.772, discriminator_real_loss=1.403, discriminator_fake_loss=1.369, generator_loss=27.72, generator_mel_loss=18.46, generator_kl_loss=1.425, generator_dur_loss=1.777, generator_adv_loss=1.953, generator_feat_match_loss=4.107, over 62.00 samples.], tot_loss[discriminator_loss=2.773, discriminator_real_loss=1.426, discriminator_fake_loss=1.347, generator_loss=27.85, generator_mel_loss=18.49, generator_kl_loss=1.471, generator_dur_loss=1.716, generator_adv_loss=1.947, generator_feat_match_loss=4.223, over 140.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:32:23,453 INFO [train.py:527] (4/6) Epoch 328, batch 52, global_batch_idx: 40600, batch size: 72, loss[discriminator_loss=2.759, discriminator_real_loss=1.425, discriminator_fake_loss=1.334, generator_loss=27.56, generator_mel_loss=17.95, generator_kl_loss=1.355, generator_dur_loss=1.838, generator_adv_loss=1.919, generator_feat_match_loss=4.498, over 72.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.385, discriminator_fake_loss=1.35, generator_loss=27.77, generator_mel_loss=18.31, generator_kl_loss=1.424, generator_dur_loss=1.772, generator_adv_loss=1.927, generator_feat_match_loss=4.339, over 3012.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:32:23,454 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 09:32:31,661 INFO [train.py:591] (4/6) Epoch 328, validation: discriminator_loss=2.788, discriminator_real_loss=1.456, discriminator_fake_loss=1.332, generator_loss=27.04, generator_mel_loss=18.69, generator_kl_loss=1.18, generator_dur_loss=1.834, generator_adv_loss=1.859, generator_feat_match_loss=3.474, over 100.00 samples. +2024-03-13 09:32:31,662 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 09:34:49,848 INFO [train.py:527] (4/6) Epoch 328, batch 102, global_batch_idx: 40650, batch size: 16, loss[discriminator_loss=2.816, discriminator_real_loss=1.422, discriminator_fake_loss=1.395, generator_loss=27.71, generator_mel_loss=18.34, generator_kl_loss=1.814, generator_dur_loss=1.619, generator_adv_loss=1.864, generator_feat_match_loss=4.071, over 16.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.38, discriminator_fake_loss=1.351, generator_loss=27.74, generator_mel_loss=18.26, generator_kl_loss=1.42, generator_dur_loss=1.766, generator_adv_loss=1.926, generator_feat_match_loss=4.361, over 5758.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:35:48,926 INFO [train.py:919] (4/6) Start epoch 329 +2024-03-13 09:37:29,845 INFO [train.py:527] (4/6) Epoch 329, batch 28, global_batch_idx: 40700, batch size: 45, loss[discriminator_loss=2.742, discriminator_real_loss=1.487, discriminator_fake_loss=1.255, generator_loss=27.57, generator_mel_loss=18.43, generator_kl_loss=1.46, generator_dur_loss=1.694, generator_adv_loss=1.756, generator_feat_match_loss=4.229, over 45.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=27.51, generator_mel_loss=18.14, generator_kl_loss=1.428, generator_dur_loss=1.741, generator_adv_loss=1.917, generator_feat_match_loss=4.289, over 1516.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:39:49,155 INFO [train.py:527] (4/6) Epoch 329, batch 78, global_batch_idx: 40750, batch size: 83, loss[discriminator_loss=2.71, discriminator_real_loss=1.41, discriminator_fake_loss=1.3, generator_loss=27.5, generator_mel_loss=18.29, generator_kl_loss=1.32, generator_dur_loss=1.871, generator_adv_loss=1.774, generator_feat_match_loss=4.247, over 83.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.379, discriminator_fake_loss=1.349, generator_loss=27.66, generator_mel_loss=18.24, generator_kl_loss=1.39, generator_dur_loss=1.753, generator_adv_loss=1.921, generator_feat_match_loss=4.352, over 4386.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:41:52,064 INFO [train.py:919] (4/6) Start epoch 330 +2024-03-13 09:42:27,624 INFO [train.py:527] (4/6) Epoch 330, batch 4, global_batch_idx: 40800, batch size: 80, loss[discriminator_loss=2.77, discriminator_real_loss=1.325, discriminator_fake_loss=1.446, generator_loss=27.23, generator_mel_loss=18.1, generator_kl_loss=1.354, generator_dur_loss=1.831, generator_adv_loss=1.904, generator_feat_match_loss=4.045, over 80.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.348, discriminator_fake_loss=1.384, generator_loss=27.41, generator_mel_loss=18.22, generator_kl_loss=1.426, generator_dur_loss=1.753, generator_adv_loss=1.876, generator_feat_match_loss=4.136, over 284.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:42:27,627 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 09:42:35,303 INFO [train.py:591] (4/6) Epoch 330, validation: discriminator_loss=2.748, discriminator_real_loss=1.445, discriminator_fake_loss=1.304, generator_loss=26.97, generator_mel_loss=18.55, generator_kl_loss=1.265, generator_dur_loss=1.82, generator_adv_loss=1.899, generator_feat_match_loss=3.435, over 100.00 samples. +2024-03-13 09:42:35,305 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 09:44:59,977 INFO [train.py:527] (4/6) Epoch 330, batch 54, global_batch_idx: 40850, batch size: 39, loss[discriminator_loss=2.801, discriminator_real_loss=1.221, discriminator_fake_loss=1.58, generator_loss=27.95, generator_mel_loss=18.99, generator_kl_loss=1.505, generator_dur_loss=1.691, generator_adv_loss=1.901, generator_feat_match_loss=3.858, over 39.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.37, discriminator_fake_loss=1.349, generator_loss=27.79, generator_mel_loss=18.31, generator_kl_loss=1.411, generator_dur_loss=1.774, generator_adv_loss=1.927, generator_feat_match_loss=4.361, over 3176.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:47:18,860 INFO [train.py:527] (4/6) Epoch 330, batch 104, global_batch_idx: 40900, batch size: 36, loss[discriminator_loss=2.732, discriminator_real_loss=1.429, discriminator_fake_loss=1.302, generator_loss=27.79, generator_mel_loss=18.2, generator_kl_loss=1.318, generator_dur_loss=1.743, generator_adv_loss=2.004, generator_feat_match_loss=4.52, over 36.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.377, discriminator_fake_loss=1.347, generator_loss=27.72, generator_mel_loss=18.3, generator_kl_loss=1.396, generator_dur_loss=1.764, generator_adv_loss=1.932, generator_feat_match_loss=4.337, over 5981.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:48:10,974 INFO [train.py:919] (4/6) Start epoch 331 +2024-03-13 09:49:59,680 INFO [train.py:527] (4/6) Epoch 331, batch 30, global_batch_idx: 40950, batch size: 56, loss[discriminator_loss=2.663, discriminator_real_loss=1.358, discriminator_fake_loss=1.305, generator_loss=27.92, generator_mel_loss=18.04, generator_kl_loss=1.421, generator_dur_loss=1.768, generator_adv_loss=2.013, generator_feat_match_loss=4.676, over 56.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.38, discriminator_fake_loss=1.359, generator_loss=27.64, generator_mel_loss=18.21, generator_kl_loss=1.382, generator_dur_loss=1.775, generator_adv_loss=1.926, generator_feat_match_loss=4.346, over 1876.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:52:17,464 INFO [train.py:527] (4/6) Epoch 331, batch 80, global_batch_idx: 41000, batch size: 74, loss[discriminator_loss=2.722, discriminator_real_loss=1.279, discriminator_fake_loss=1.443, generator_loss=27.33, generator_mel_loss=18.1, generator_kl_loss=1.294, generator_dur_loss=1.832, generator_adv_loss=1.922, generator_feat_match_loss=4.178, over 74.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.381, discriminator_fake_loss=1.357, generator_loss=27.55, generator_mel_loss=18.21, generator_kl_loss=1.383, generator_dur_loss=1.771, generator_adv_loss=1.927, generator_feat_match_loss=4.26, over 4819.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:52:17,466 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 09:52:26,154 INFO [train.py:591] (4/6) Epoch 331, validation: discriminator_loss=2.744, discriminator_real_loss=1.409, discriminator_fake_loss=1.336, generator_loss=26.9, generator_mel_loss=18.54, generator_kl_loss=1.258, generator_dur_loss=1.821, generator_adv_loss=1.844, generator_feat_match_loss=3.44, over 100.00 samples. +2024-03-13 09:52:26,155 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 09:54:26,898 INFO [train.py:919] (4/6) Start epoch 332 +2024-03-13 09:55:07,464 INFO [train.py:527] (4/6) Epoch 332, batch 6, global_batch_idx: 41050, batch size: 36, loss[discriminator_loss=2.697, discriminator_real_loss=1.418, discriminator_fake_loss=1.279, generator_loss=27.79, generator_mel_loss=17.78, generator_kl_loss=1.725, generator_dur_loss=1.695, generator_adv_loss=2.017, generator_feat_match_loss=4.578, over 36.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.346, discriminator_fake_loss=1.357, generator_loss=27.71, generator_mel_loss=18.09, generator_kl_loss=1.467, generator_dur_loss=1.745, generator_adv_loss=1.938, generator_feat_match_loss=4.469, over 402.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:57:25,723 INFO [train.py:527] (4/6) Epoch 332, batch 56, global_batch_idx: 41100, batch size: 47, loss[discriminator_loss=2.706, discriminator_real_loss=1.326, discriminator_fake_loss=1.379, generator_loss=27.34, generator_mel_loss=18, generator_kl_loss=1.363, generator_dur_loss=1.699, generator_adv_loss=1.872, generator_feat_match_loss=4.409, over 47.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.382, discriminator_fake_loss=1.344, generator_loss=27.71, generator_mel_loss=18.22, generator_kl_loss=1.44, generator_dur_loss=1.755, generator_adv_loss=1.93, generator_feat_match_loss=4.361, over 3184.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:59:44,635 INFO [train.py:527] (4/6) Epoch 332, batch 106, global_batch_idx: 41150, batch size: 39, loss[discriminator_loss=2.61, discriminator_real_loss=1.314, discriminator_fake_loss=1.296, generator_loss=28.84, generator_mel_loss=18.83, generator_kl_loss=1.487, generator_dur_loss=1.696, generator_adv_loss=1.987, generator_feat_match_loss=4.835, over 39.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.383, discriminator_fake_loss=1.344, generator_loss=27.68, generator_mel_loss=18.21, generator_kl_loss=1.426, generator_dur_loss=1.757, generator_adv_loss=1.93, generator_feat_match_loss=4.357, over 6018.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:00:33,646 INFO [train.py:919] (4/6) Start epoch 333 +2024-03-13 10:02:27,562 INFO [train.py:527] (4/6) Epoch 333, batch 32, global_batch_idx: 41200, batch size: 48, loss[discriminator_loss=2.703, discriminator_real_loss=1.285, discriminator_fake_loss=1.418, generator_loss=28.38, generator_mel_loss=18.39, generator_kl_loss=1.534, generator_dur_loss=1.707, generator_adv_loss=1.989, generator_feat_match_loss=4.761, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=27.76, generator_mel_loss=18.23, generator_kl_loss=1.385, generator_dur_loss=1.77, generator_adv_loss=1.932, generator_feat_match_loss=4.44, over 1962.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:02:27,564 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 10:02:35,683 INFO [train.py:591] (4/6) Epoch 333, validation: discriminator_loss=2.759, discriminator_real_loss=1.473, discriminator_fake_loss=1.286, generator_loss=26.73, generator_mel_loss=18.49, generator_kl_loss=1.272, generator_dur_loss=1.809, generator_adv_loss=1.928, generator_feat_match_loss=3.235, over 100.00 samples. +2024-03-13 10:02:35,684 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 10:04:55,345 INFO [train.py:527] (4/6) Epoch 333, batch 82, global_batch_idx: 41250, batch size: 47, loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=28.02, generator_mel_loss=18.31, generator_kl_loss=1.475, generator_dur_loss=1.712, generator_adv_loss=1.748, generator_feat_match_loss=4.777, over 47.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.382, discriminator_fake_loss=1.344, generator_loss=27.78, generator_mel_loss=18.28, generator_kl_loss=1.406, generator_dur_loss=1.757, generator_adv_loss=1.93, generator_feat_match_loss=4.401, over 4754.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:06:51,431 INFO [train.py:919] (4/6) Start epoch 334 +2024-03-13 10:07:38,863 INFO [train.py:527] (4/6) Epoch 334, batch 8, global_batch_idx: 41300, batch size: 42, loss[discriminator_loss=2.697, discriminator_real_loss=1.335, discriminator_fake_loss=1.362, generator_loss=28.42, generator_mel_loss=18.43, generator_kl_loss=1.651, generator_dur_loss=1.68, generator_adv_loss=1.795, generator_feat_match_loss=4.867, over 42.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.391, discriminator_fake_loss=1.33, generator_loss=27.82, generator_mel_loss=18.31, generator_kl_loss=1.395, generator_dur_loss=1.773, generator_adv_loss=1.971, generator_feat_match_loss=4.371, over 516.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:09:55,677 INFO [train.py:527] (4/6) Epoch 334, batch 58, global_batch_idx: 41350, batch size: 68, loss[discriminator_loss=2.754, discriminator_real_loss=1.361, discriminator_fake_loss=1.393, generator_loss=27.15, generator_mel_loss=17.98, generator_kl_loss=1.389, generator_dur_loss=1.83, generator_adv_loss=1.728, generator_feat_match_loss=4.218, over 68.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.383, discriminator_fake_loss=1.345, generator_loss=27.72, generator_mel_loss=18.24, generator_kl_loss=1.409, generator_dur_loss=1.775, generator_adv_loss=1.926, generator_feat_match_loss=4.373, over 3477.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:12:16,037 INFO [train.py:527] (4/6) Epoch 334, batch 108, global_batch_idx: 41400, batch size: 83, loss[discriminator_loss=2.701, discriminator_real_loss=1.438, discriminator_fake_loss=1.263, generator_loss=27.12, generator_mel_loss=17.84, generator_kl_loss=1.366, generator_dur_loss=1.844, generator_adv_loss=1.741, generator_feat_match_loss=4.327, over 83.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.383, discriminator_fake_loss=1.342, generator_loss=27.71, generator_mel_loss=18.23, generator_kl_loss=1.412, generator_dur_loss=1.777, generator_adv_loss=1.931, generator_feat_match_loss=4.36, over 6353.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:12:16,039 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 10:12:24,660 INFO [train.py:591] (4/6) Epoch 334, validation: discriminator_loss=2.834, discriminator_real_loss=1.331, discriminator_fake_loss=1.503, generator_loss=27.15, generator_mel_loss=18.92, generator_kl_loss=1.164, generator_dur_loss=1.829, generator_adv_loss=1.652, generator_feat_match_loss=3.582, over 100.00 samples. +2024-03-13 10:12:24,661 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 10:13:07,739 INFO [train.py:919] (4/6) Start epoch 335 +2024-03-13 10:15:06,079 INFO [train.py:527] (4/6) Epoch 335, batch 34, global_batch_idx: 41450, batch size: 72, loss[discriminator_loss=2.737, discriminator_real_loss=1.472, discriminator_fake_loss=1.265, generator_loss=27.02, generator_mel_loss=17.6, generator_kl_loss=1.504, generator_dur_loss=1.812, generator_adv_loss=2.176, generator_feat_match_loss=3.92, over 72.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.376, discriminator_fake_loss=1.35, generator_loss=27.54, generator_mel_loss=18.05, generator_kl_loss=1.438, generator_dur_loss=1.774, generator_adv_loss=1.939, generator_feat_match_loss=4.34, over 2037.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:17:24,562 INFO [train.py:527] (4/6) Epoch 335, batch 84, global_batch_idx: 41500, batch size: 52, loss[discriminator_loss=2.761, discriminator_real_loss=1.404, discriminator_fake_loss=1.357, generator_loss=27.9, generator_mel_loss=18.38, generator_kl_loss=1.467, generator_dur_loss=1.741, generator_adv_loss=1.981, generator_feat_match_loss=4.329, over 52.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=27.72, generator_mel_loss=18.17, generator_kl_loss=1.435, generator_dur_loss=1.761, generator_adv_loss=1.949, generator_feat_match_loss=4.4, over 4762.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:19:14,253 INFO [train.py:919] (4/6) Start epoch 336 +2024-03-13 10:20:06,647 INFO [train.py:527] (4/6) Epoch 336, batch 10, global_batch_idx: 41550, batch size: 68, loss[discriminator_loss=2.746, discriminator_real_loss=1.335, discriminator_fake_loss=1.41, generator_loss=26.86, generator_mel_loss=17.95, generator_kl_loss=1.484, generator_dur_loss=1.793, generator_adv_loss=1.888, generator_feat_match_loss=3.752, over 68.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.386, discriminator_fake_loss=1.344, generator_loss=27.59, generator_mel_loss=18.3, generator_kl_loss=1.419, generator_dur_loss=1.767, generator_adv_loss=1.902, generator_feat_match_loss=4.206, over 650.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:22:23,987 INFO [train.py:527] (4/6) Epoch 336, batch 60, global_batch_idx: 41600, batch size: 44, loss[discriminator_loss=2.691, discriminator_real_loss=1.262, discriminator_fake_loss=1.428, generator_loss=28.82, generator_mel_loss=18.86, generator_kl_loss=1.466, generator_dur_loss=1.708, generator_adv_loss=2.04, generator_feat_match_loss=4.742, over 44.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.71, generator_mel_loss=18.26, generator_kl_loss=1.417, generator_dur_loss=1.771, generator_adv_loss=1.927, generator_feat_match_loss=4.34, over 3554.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:22:23,988 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 10:22:32,326 INFO [train.py:591] (4/6) Epoch 336, validation: discriminator_loss=2.743, discriminator_real_loss=1.444, discriminator_fake_loss=1.299, generator_loss=26.76, generator_mel_loss=18.56, generator_kl_loss=1.235, generator_dur_loss=1.829, generator_adv_loss=1.946, generator_feat_match_loss=3.193, over 100.00 samples. +2024-03-13 10:22:32,326 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 10:24:50,777 INFO [train.py:527] (4/6) Epoch 336, batch 110, global_batch_idx: 41650, batch size: 39, loss[discriminator_loss=2.731, discriminator_real_loss=1.377, discriminator_fake_loss=1.354, generator_loss=28.49, generator_mel_loss=18.65, generator_kl_loss=1.627, generator_dur_loss=1.668, generator_adv_loss=1.913, generator_feat_match_loss=4.63, over 39.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=27.77, generator_mel_loss=18.26, generator_kl_loss=1.412, generator_dur_loss=1.77, generator_adv_loss=1.947, generator_feat_match_loss=4.382, over 6407.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:25:28,186 INFO [train.py:919] (4/6) Start epoch 337 +2024-03-13 10:27:32,911 INFO [train.py:527] (4/6) Epoch 337, batch 36, global_batch_idx: 41700, batch size: 80, loss[discriminator_loss=2.757, discriminator_real_loss=1.368, discriminator_fake_loss=1.389, generator_loss=28.06, generator_mel_loss=18.56, generator_kl_loss=1.314, generator_dur_loss=1.839, generator_adv_loss=1.886, generator_feat_match_loss=4.452, over 80.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.77, generator_mel_loss=18.25, generator_kl_loss=1.41, generator_dur_loss=1.78, generator_adv_loss=1.912, generator_feat_match_loss=4.415, over 2299.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:29:48,322 INFO [train.py:527] (4/6) Epoch 337, batch 86, global_batch_idx: 41750, batch size: 47, loss[discriminator_loss=2.807, discriminator_real_loss=1.433, discriminator_fake_loss=1.374, generator_loss=27.48, generator_mel_loss=18.07, generator_kl_loss=1.503, generator_dur_loss=1.689, generator_adv_loss=1.929, generator_feat_match_loss=4.287, over 47.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.38, discriminator_fake_loss=1.344, generator_loss=27.75, generator_mel_loss=18.24, generator_kl_loss=1.413, generator_dur_loss=1.772, generator_adv_loss=1.923, generator_feat_match_loss=4.397, over 5116.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:31:33,205 INFO [train.py:919] (4/6) Start epoch 338 +2024-03-13 10:32:32,772 INFO [train.py:527] (4/6) Epoch 338, batch 12, global_batch_idx: 41800, batch size: 70, loss[discriminator_loss=2.728, discriminator_real_loss=1.411, discriminator_fake_loss=1.318, generator_loss=27.84, generator_mel_loss=18.15, generator_kl_loss=1.395, generator_dur_loss=1.797, generator_adv_loss=2.003, generator_feat_match_loss=4.491, over 70.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.366, discriminator_fake_loss=1.348, generator_loss=28.06, generator_mel_loss=18.28, generator_kl_loss=1.381, generator_dur_loss=1.803, generator_adv_loss=1.935, generator_feat_match_loss=4.66, over 862.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:32:32,774 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 10:32:40,997 INFO [train.py:591] (4/6) Epoch 338, validation: discriminator_loss=2.734, discriminator_real_loss=1.466, discriminator_fake_loss=1.268, generator_loss=26.89, generator_mel_loss=18.59, generator_kl_loss=1.281, generator_dur_loss=1.832, generator_adv_loss=1.936, generator_feat_match_loss=3.255, over 100.00 samples. +2024-03-13 10:32:40,998 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 10:34:58,346 INFO [train.py:527] (4/6) Epoch 338, batch 62, global_batch_idx: 41850, batch size: 53, loss[discriminator_loss=2.655, discriminator_real_loss=1.395, discriminator_fake_loss=1.26, generator_loss=27.13, generator_mel_loss=17.79, generator_kl_loss=1.413, generator_dur_loss=1.749, generator_adv_loss=1.995, generator_feat_match_loss=4.186, over 53.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.377, discriminator_fake_loss=1.339, generator_loss=27.79, generator_mel_loss=18.24, generator_kl_loss=1.404, generator_dur_loss=1.776, generator_adv_loss=1.936, generator_feat_match_loss=4.425, over 3616.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:37:18,480 INFO [train.py:527] (4/6) Epoch 338, batch 112, global_batch_idx: 41900, batch size: 50, loss[discriminator_loss=2.722, discriminator_real_loss=1.312, discriminator_fake_loss=1.41, generator_loss=27.36, generator_mel_loss=18.12, generator_kl_loss=1.454, generator_dur_loss=1.682, generator_adv_loss=2.03, generator_feat_match_loss=4.077, over 50.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=27.73, generator_mel_loss=18.21, generator_kl_loss=1.401, generator_dur_loss=1.782, generator_adv_loss=1.939, generator_feat_match_loss=4.398, over 6675.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:37:47,191 INFO [train.py:919] (4/6) Start epoch 339 +2024-03-13 10:39:57,971 INFO [train.py:527] (4/6) Epoch 339, batch 38, global_batch_idx: 41950, batch size: 47, loss[discriminator_loss=2.719, discriminator_real_loss=1.417, discriminator_fake_loss=1.302, generator_loss=27.67, generator_mel_loss=18.03, generator_kl_loss=1.629, generator_dur_loss=1.736, generator_adv_loss=1.921, generator_feat_match_loss=4.346, over 47.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=27.72, generator_mel_loss=18.28, generator_kl_loss=1.422, generator_dur_loss=1.77, generator_adv_loss=1.922, generator_feat_match_loss=4.324, over 2220.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:42:14,519 INFO [train.py:527] (4/6) Epoch 339, batch 88, global_batch_idx: 42000, batch size: 16, loss[discriminator_loss=2.478, discriminator_real_loss=1.173, discriminator_fake_loss=1.304, generator_loss=30.8, generator_mel_loss=19.29, generator_kl_loss=1.821, generator_dur_loss=1.576, generator_adv_loss=2.179, generator_feat_match_loss=5.938, over 16.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.379, discriminator_fake_loss=1.344, generator_loss=27.82, generator_mel_loss=18.31, generator_kl_loss=1.433, generator_dur_loss=1.77, generator_adv_loss=1.93, generator_feat_match_loss=4.383, over 4960.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:42:14,520 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 10:42:23,425 INFO [train.py:591] (4/6) Epoch 339, validation: discriminator_loss=2.779, discriminator_real_loss=1.482, discriminator_fake_loss=1.298, generator_loss=27.57, generator_mel_loss=18.6, generator_kl_loss=1.219, generator_dur_loss=1.837, generator_adv_loss=2.026, generator_feat_match_loss=3.893, over 100.00 samples. +2024-03-13 10:42:23,426 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 10:44:02,313 INFO [train.py:919] (4/6) Start epoch 340 +2024-03-13 10:45:05,848 INFO [train.py:527] (4/6) Epoch 340, batch 14, global_batch_idx: 42050, batch size: 62, loss[discriminator_loss=2.685, discriminator_real_loss=1.349, discriminator_fake_loss=1.336, generator_loss=27.55, generator_mel_loss=18.35, generator_kl_loss=1.369, generator_dur_loss=1.778, generator_adv_loss=1.933, generator_feat_match_loss=4.127, over 62.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.386, discriminator_fake_loss=1.346, generator_loss=27.46, generator_mel_loss=18.15, generator_kl_loss=1.347, generator_dur_loss=1.764, generator_adv_loss=1.915, generator_feat_match_loss=4.282, over 848.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:47:26,164 INFO [train.py:527] (4/6) Epoch 340, batch 64, global_batch_idx: 42100, batch size: 39, loss[discriminator_loss=2.671, discriminator_real_loss=1.353, discriminator_fake_loss=1.319, generator_loss=27.17, generator_mel_loss=18.24, generator_kl_loss=1.478, generator_dur_loss=1.694, generator_adv_loss=1.97, generator_feat_match_loss=3.797, over 39.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.383, discriminator_fake_loss=1.343, generator_loss=27.56, generator_mel_loss=18.2, generator_kl_loss=1.388, generator_dur_loss=1.768, generator_adv_loss=1.923, generator_feat_match_loss=4.285, over 3839.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:49:42,722 INFO [train.py:527] (4/6) Epoch 340, batch 114, global_batch_idx: 42150, batch size: 53, loss[discriminator_loss=2.693, discriminator_real_loss=1.434, discriminator_fake_loss=1.26, generator_loss=27.6, generator_mel_loss=18.26, generator_kl_loss=1.546, generator_dur_loss=1.711, generator_adv_loss=1.898, generator_feat_match_loss=4.183, over 53.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.386, discriminator_fake_loss=1.343, generator_loss=27.76, generator_mel_loss=18.25, generator_kl_loss=1.398, generator_dur_loss=1.766, generator_adv_loss=1.964, generator_feat_match_loss=4.387, over 6662.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:50:08,978 INFO [train.py:919] (4/6) Start epoch 341 +2024-03-13 10:52:27,832 INFO [train.py:527] (4/6) Epoch 341, batch 40, global_batch_idx: 42200, batch size: 52, loss[discriminator_loss=2.7, discriminator_real_loss=1.318, discriminator_fake_loss=1.382, generator_loss=28.23, generator_mel_loss=18.67, generator_kl_loss=1.589, generator_dur_loss=1.65, generator_adv_loss=1.842, generator_feat_match_loss=4.475, over 52.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.378, discriminator_fake_loss=1.347, generator_loss=27.66, generator_mel_loss=18.23, generator_kl_loss=1.401, generator_dur_loss=1.743, generator_adv_loss=1.921, generator_feat_match_loss=4.367, over 2297.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:52:27,834 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 10:52:35,859 INFO [train.py:591] (4/6) Epoch 341, validation: discriminator_loss=2.714, discriminator_real_loss=1.35, discriminator_fake_loss=1.364, generator_loss=26.04, generator_mel_loss=18.07, generator_kl_loss=1.259, generator_dur_loss=1.82, generator_adv_loss=1.815, generator_feat_match_loss=3.073, over 100.00 samples. +2024-03-13 10:52:35,860 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 10:54:54,152 INFO [train.py:527] (4/6) Epoch 341, batch 90, global_batch_idx: 42250, batch size: 72, loss[discriminator_loss=2.669, discriminator_real_loss=1.335, discriminator_fake_loss=1.334, generator_loss=27.42, generator_mel_loss=17.92, generator_kl_loss=1.348, generator_dur_loss=1.857, generator_adv_loss=2.033, generator_feat_match_loss=4.259, over 72.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.379, discriminator_fake_loss=1.348, generator_loss=27.6, generator_mel_loss=18.19, generator_kl_loss=1.411, generator_dur_loss=1.751, generator_adv_loss=1.922, generator_feat_match_loss=4.325, over 5018.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:56:23,723 INFO [train.py:919] (4/6) Start epoch 342 +2024-03-13 10:57:33,209 INFO [train.py:527] (4/6) Epoch 342, batch 16, global_batch_idx: 42300, batch size: 68, loss[discriminator_loss=2.718, discriminator_real_loss=1.482, discriminator_fake_loss=1.236, generator_loss=27.1, generator_mel_loss=18.04, generator_kl_loss=1.426, generator_dur_loss=1.787, generator_adv_loss=1.782, generator_feat_match_loss=4.066, over 68.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.379, discriminator_fake_loss=1.34, generator_loss=27.65, generator_mel_loss=18.13, generator_kl_loss=1.389, generator_dur_loss=1.791, generator_adv_loss=1.919, generator_feat_match_loss=4.42, over 1097.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:59:51,303 INFO [train.py:527] (4/6) Epoch 342, batch 66, global_batch_idx: 42350, batch size: 80, loss[discriminator_loss=2.742, discriminator_real_loss=1.404, discriminator_fake_loss=1.338, generator_loss=27.47, generator_mel_loss=18.06, generator_kl_loss=1.214, generator_dur_loss=1.816, generator_adv_loss=1.987, generator_feat_match_loss=4.394, over 80.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.38, discriminator_fake_loss=1.338, generator_loss=27.67, generator_mel_loss=18.14, generator_kl_loss=1.4, generator_dur_loss=1.767, generator_adv_loss=1.941, generator_feat_match_loss=4.422, over 3846.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:02:09,674 INFO [train.py:527] (4/6) Epoch 342, batch 116, global_batch_idx: 42400, batch size: 83, loss[discriminator_loss=2.667, discriminator_real_loss=1.345, discriminator_fake_loss=1.322, generator_loss=28.04, generator_mel_loss=18.16, generator_kl_loss=1.36, generator_dur_loss=1.822, generator_adv_loss=2.034, generator_feat_match_loss=4.665, over 83.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=27.7, generator_mel_loss=18.19, generator_kl_loss=1.411, generator_dur_loss=1.761, generator_adv_loss=1.936, generator_feat_match_loss=4.4, over 6574.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:02:09,675 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 11:02:18,384 INFO [train.py:591] (4/6) Epoch 342, validation: discriminator_loss=2.732, discriminator_real_loss=1.465, discriminator_fake_loss=1.266, generator_loss=27.05, generator_mel_loss=18.36, generator_kl_loss=1.188, generator_dur_loss=1.828, generator_adv_loss=1.955, generator_feat_match_loss=3.718, over 100.00 samples. +2024-03-13 11:02:18,385 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 11:02:38,732 INFO [train.py:919] (4/6) Start epoch 343 +2024-03-13 11:05:00,082 INFO [train.py:527] (4/6) Epoch 343, batch 42, global_batch_idx: 42450, batch size: 77, loss[discriminator_loss=2.739, discriminator_real_loss=1.4, discriminator_fake_loss=1.34, generator_loss=27.65, generator_mel_loss=18.15, generator_kl_loss=1.435, generator_dur_loss=1.836, generator_adv_loss=1.996, generator_feat_match_loss=4.241, over 77.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=27.87, generator_mel_loss=18.29, generator_kl_loss=1.432, generator_dur_loss=1.764, generator_adv_loss=1.934, generator_feat_match_loss=4.452, over 2373.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:07:19,471 INFO [train.py:527] (4/6) Epoch 343, batch 92, global_batch_idx: 42500, batch size: 56, loss[discriminator_loss=2.748, discriminator_real_loss=1.364, discriminator_fake_loss=1.384, generator_loss=28.05, generator_mel_loss=18.41, generator_kl_loss=1.463, generator_dur_loss=1.71, generator_adv_loss=1.956, generator_feat_match_loss=4.508, over 56.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.86, generator_mel_loss=18.27, generator_kl_loss=1.424, generator_dur_loss=1.772, generator_adv_loss=1.93, generator_feat_match_loss=4.465, over 5291.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:08:48,078 INFO [train.py:919] (4/6) Start epoch 344 +2024-03-13 11:10:02,100 INFO [train.py:527] (4/6) Epoch 344, batch 18, global_batch_idx: 42550, batch size: 47, loss[discriminator_loss=2.67, discriminator_real_loss=1.427, discriminator_fake_loss=1.243, generator_loss=26.97, generator_mel_loss=17.88, generator_kl_loss=1.349, generator_dur_loss=1.704, generator_adv_loss=1.798, generator_feat_match_loss=4.247, over 47.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.392, discriminator_fake_loss=1.329, generator_loss=27.69, generator_mel_loss=18.22, generator_kl_loss=1.39, generator_dur_loss=1.765, generator_adv_loss=1.938, generator_feat_match_loss=4.376, over 1076.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:12:19,881 INFO [train.py:527] (4/6) Epoch 344, batch 68, global_batch_idx: 42600, batch size: 42, loss[discriminator_loss=2.725, discriminator_real_loss=1.347, discriminator_fake_loss=1.378, generator_loss=28.45, generator_mel_loss=18.65, generator_kl_loss=1.441, generator_dur_loss=1.729, generator_adv_loss=1.847, generator_feat_match_loss=4.789, over 42.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.38, discriminator_fake_loss=1.341, generator_loss=27.74, generator_mel_loss=18.24, generator_kl_loss=1.388, generator_dur_loss=1.757, generator_adv_loss=1.936, generator_feat_match_loss=4.421, over 3980.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:12:19,882 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 11:12:27,977 INFO [train.py:591] (4/6) Epoch 344, validation: discriminator_loss=2.744, discriminator_real_loss=1.406, discriminator_fake_loss=1.338, generator_loss=26.36, generator_mel_loss=18.54, generator_kl_loss=1.174, generator_dur_loss=1.827, generator_adv_loss=1.746, generator_feat_match_loss=3.079, over 100.00 samples. +2024-03-13 11:12:27,978 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 11:14:44,642 INFO [train.py:527] (4/6) Epoch 344, batch 118, global_batch_idx: 42650, batch size: 74, loss[discriminator_loss=2.716, discriminator_real_loss=1.437, discriminator_fake_loss=1.279, generator_loss=27.47, generator_mel_loss=18.18, generator_kl_loss=1.308, generator_dur_loss=1.79, generator_adv_loss=1.768, generator_feat_match_loss=4.419, over 74.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.345, generator_loss=27.8, generator_mel_loss=18.25, generator_kl_loss=1.396, generator_dur_loss=1.76, generator_adv_loss=1.949, generator_feat_match_loss=4.451, over 6755.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:15:00,893 INFO [train.py:919] (4/6) Start epoch 345 +2024-03-13 11:17:24,204 INFO [train.py:527] (4/6) Epoch 345, batch 44, global_batch_idx: 42700, batch size: 80, loss[discriminator_loss=2.754, discriminator_real_loss=1.475, discriminator_fake_loss=1.279, generator_loss=26.93, generator_mel_loss=17.86, generator_kl_loss=1.269, generator_dur_loss=1.843, generator_adv_loss=1.806, generator_feat_match_loss=4.147, over 80.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.384, discriminator_fake_loss=1.332, generator_loss=27.64, generator_mel_loss=18.14, generator_kl_loss=1.413, generator_dur_loss=1.759, generator_adv_loss=1.934, generator_feat_match_loss=4.389, over 2461.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:19:44,561 INFO [train.py:527] (4/6) Epoch 345, batch 94, global_batch_idx: 42750, batch size: 62, loss[discriminator_loss=2.681, discriminator_real_loss=1.385, discriminator_fake_loss=1.296, generator_loss=27.44, generator_mel_loss=17.82, generator_kl_loss=1.439, generator_dur_loss=1.755, generator_adv_loss=2.081, generator_feat_match_loss=4.35, over 62.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.383, discriminator_fake_loss=1.338, generator_loss=27.7, generator_mel_loss=18.17, generator_kl_loss=1.422, generator_dur_loss=1.768, generator_adv_loss=1.941, generator_feat_match_loss=4.4, over 5157.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:21:03,770 INFO [train.py:919] (4/6) Start epoch 346 +2024-03-13 11:22:25,974 INFO [train.py:527] (4/6) Epoch 346, batch 20, global_batch_idx: 42800, batch size: 61, loss[discriminator_loss=2.69, discriminator_real_loss=1.325, discriminator_fake_loss=1.365, generator_loss=28.06, generator_mel_loss=18.39, generator_kl_loss=1.333, generator_dur_loss=1.749, generator_adv_loss=2.073, generator_feat_match_loss=4.517, over 61.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.38, discriminator_fake_loss=1.346, generator_loss=27.94, generator_mel_loss=18.42, generator_kl_loss=1.434, generator_dur_loss=1.773, generator_adv_loss=1.938, generator_feat_match_loss=4.384, over 1221.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:22:25,975 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 11:22:34,312 INFO [train.py:591] (4/6) Epoch 346, validation: discriminator_loss=2.746, discriminator_real_loss=1.451, discriminator_fake_loss=1.295, generator_loss=26.76, generator_mel_loss=18.2, generator_kl_loss=1.215, generator_dur_loss=1.834, generator_adv_loss=1.972, generator_feat_match_loss=3.542, over 100.00 samples. +2024-03-13 11:22:34,313 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 11:24:53,430 INFO [train.py:527] (4/6) Epoch 346, batch 70, global_batch_idx: 42850, batch size: 31, loss[discriminator_loss=2.735, discriminator_real_loss=1.365, discriminator_fake_loss=1.37, generator_loss=27.89, generator_mel_loss=18.04, generator_kl_loss=1.733, generator_dur_loss=1.589, generator_adv_loss=2.154, generator_feat_match_loss=4.375, over 31.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.376, discriminator_fake_loss=1.347, generator_loss=27.84, generator_mel_loss=18.25, generator_kl_loss=1.407, generator_dur_loss=1.768, generator_adv_loss=1.944, generator_feat_match_loss=4.474, over 4168.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:27:09,831 INFO [train.py:527] (4/6) Epoch 346, batch 120, global_batch_idx: 42900, batch size: 96, loss[discriminator_loss=2.689, discriminator_real_loss=1.353, discriminator_fake_loss=1.337, generator_loss=26.73, generator_mel_loss=17.48, generator_kl_loss=1.448, generator_dur_loss=1.831, generator_adv_loss=1.906, generator_feat_match_loss=4.068, over 96.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.382, discriminator_fake_loss=1.347, generator_loss=27.74, generator_mel_loss=18.23, generator_kl_loss=1.422, generator_dur_loss=1.762, generator_adv_loss=1.936, generator_feat_match_loss=4.391, over 6985.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:27:21,119 INFO [train.py:919] (4/6) Start epoch 347 +2024-03-13 11:29:52,551 INFO [train.py:527] (4/6) Epoch 347, batch 46, global_batch_idx: 42950, batch size: 77, loss[discriminator_loss=2.853, discriminator_real_loss=1.337, discriminator_fake_loss=1.516, generator_loss=27.53, generator_mel_loss=18.16, generator_kl_loss=1.4, generator_dur_loss=1.82, generator_adv_loss=2.125, generator_feat_match_loss=4.021, over 77.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.344, generator_loss=27.79, generator_mel_loss=18.23, generator_kl_loss=1.442, generator_dur_loss=1.735, generator_adv_loss=1.95, generator_feat_match_loss=4.429, over 2596.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:32:12,755 INFO [train.py:527] (4/6) Epoch 347, batch 96, global_batch_idx: 43000, batch size: 88, loss[discriminator_loss=2.773, discriminator_real_loss=1.413, discriminator_fake_loss=1.361, generator_loss=28.03, generator_mel_loss=18.5, generator_kl_loss=1.292, generator_dur_loss=1.824, generator_adv_loss=2.229, generator_feat_match_loss=4.19, over 88.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=27.77, generator_mel_loss=18.24, generator_kl_loss=1.431, generator_dur_loss=1.738, generator_adv_loss=1.941, generator_feat_match_loss=4.423, over 5361.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:32:12,757 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 11:32:21,587 INFO [train.py:591] (4/6) Epoch 347, validation: discriminator_loss=2.924, discriminator_real_loss=1.778, discriminator_fake_loss=1.146, generator_loss=26.77, generator_mel_loss=18.36, generator_kl_loss=1.268, generator_dur_loss=1.8, generator_adv_loss=2.294, generator_feat_match_loss=3.051, over 100.00 samples. +2024-03-13 11:32:21,588 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 11:33:37,248 INFO [train.py:919] (4/6) Start epoch 348 +2024-03-13 11:35:02,269 INFO [train.py:527] (4/6) Epoch 348, batch 22, global_batch_idx: 43050, batch size: 50, loss[discriminator_loss=2.752, discriminator_real_loss=1.363, discriminator_fake_loss=1.388, generator_loss=26.79, generator_mel_loss=17.99, generator_kl_loss=1.404, generator_dur_loss=1.761, generator_adv_loss=1.842, generator_feat_match_loss=3.789, over 50.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.366, discriminator_fake_loss=1.352, generator_loss=27.63, generator_mel_loss=18.12, generator_kl_loss=1.391, generator_dur_loss=1.785, generator_adv_loss=1.929, generator_feat_match_loss=4.405, over 1445.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:37:23,109 INFO [train.py:527] (4/6) Epoch 348, batch 72, global_batch_idx: 43100, batch size: 64, loss[discriminator_loss=2.772, discriminator_real_loss=1.4, discriminator_fake_loss=1.372, generator_loss=27.42, generator_mel_loss=18.42, generator_kl_loss=1.38, generator_dur_loss=1.784, generator_adv_loss=1.841, generator_feat_match_loss=3.995, over 64.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.369, discriminator_fake_loss=1.345, generator_loss=27.76, generator_mel_loss=18.19, generator_kl_loss=1.415, generator_dur_loss=1.766, generator_adv_loss=1.947, generator_feat_match_loss=4.44, over 4255.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:39:39,587 INFO [train.py:527] (4/6) Epoch 348, batch 122, global_batch_idx: 43150, batch size: 83, loss[discriminator_loss=2.769, discriminator_real_loss=1.428, discriminator_fake_loss=1.341, generator_loss=26.96, generator_mel_loss=18.11, generator_kl_loss=1.201, generator_dur_loss=1.854, generator_adv_loss=1.823, generator_feat_match_loss=3.97, over 83.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.73, generator_mel_loss=18.22, generator_kl_loss=1.416, generator_dur_loss=1.763, generator_adv_loss=1.937, generator_feat_match_loss=4.39, over 6963.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:39:44,746 INFO [train.py:919] (4/6) Start epoch 349 +2024-03-13 11:42:24,481 INFO [train.py:527] (4/6) Epoch 349, batch 48, global_batch_idx: 43200, batch size: 64, loss[discriminator_loss=2.71, discriminator_real_loss=1.338, discriminator_fake_loss=1.371, generator_loss=27.47, generator_mel_loss=17.6, generator_kl_loss=1.3, generator_dur_loss=1.691, generator_adv_loss=2.131, generator_feat_match_loss=4.749, over 64.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.385, discriminator_fake_loss=1.341, generator_loss=27.6, generator_mel_loss=18.12, generator_kl_loss=1.389, generator_dur_loss=1.772, generator_adv_loss=1.937, generator_feat_match_loss=4.38, over 3009.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:42:24,482 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 11:42:32,517 INFO [train.py:591] (4/6) Epoch 349, validation: discriminator_loss=2.758, discriminator_real_loss=1.504, discriminator_fake_loss=1.254, generator_loss=26.15, generator_mel_loss=18.04, generator_kl_loss=1.188, generator_dur_loss=1.785, generator_adv_loss=1.958, generator_feat_match_loss=3.174, over 100.00 samples. +2024-03-13 11:42:32,518 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 11:44:52,641 INFO [train.py:527] (4/6) Epoch 349, batch 98, global_batch_idx: 43250, batch size: 52, loss[discriminator_loss=2.768, discriminator_real_loss=1.481, discriminator_fake_loss=1.286, generator_loss=27.42, generator_mel_loss=18.49, generator_kl_loss=1.399, generator_dur_loss=1.684, generator_adv_loss=1.818, generator_feat_match_loss=4.024, over 52.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.387, discriminator_fake_loss=1.342, generator_loss=27.62, generator_mel_loss=18.17, generator_kl_loss=1.403, generator_dur_loss=1.757, generator_adv_loss=1.937, generator_feat_match_loss=4.351, over 5889.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:46:04,074 INFO [train.py:919] (4/6) Start epoch 350 +2024-03-13 11:47:37,319 INFO [train.py:527] (4/6) Epoch 350, batch 24, global_batch_idx: 43300, batch size: 50, loss[discriminator_loss=2.79, discriminator_real_loss=1.545, discriminator_fake_loss=1.245, generator_loss=27.07, generator_mel_loss=18.19, generator_kl_loss=1.505, generator_dur_loss=1.697, generator_adv_loss=1.701, generator_feat_match_loss=3.976, over 50.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.381, discriminator_fake_loss=1.333, generator_loss=27.8, generator_mel_loss=18.26, generator_kl_loss=1.39, generator_dur_loss=1.755, generator_adv_loss=1.945, generator_feat_match_loss=4.448, over 1409.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:49:58,905 INFO [train.py:527] (4/6) Epoch 350, batch 74, global_batch_idx: 43350, batch size: 68, loss[discriminator_loss=2.736, discriminator_real_loss=1.357, discriminator_fake_loss=1.38, generator_loss=27.47, generator_mel_loss=17.87, generator_kl_loss=1.462, generator_dur_loss=1.811, generator_adv_loss=1.969, generator_feat_match_loss=4.364, over 68.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.371, discriminator_fake_loss=1.353, generator_loss=27.78, generator_mel_loss=18.23, generator_kl_loss=1.422, generator_dur_loss=1.744, generator_adv_loss=1.943, generator_feat_match_loss=4.44, over 4060.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:52:16,760 INFO [train.py:919] (4/6) Start epoch 351 +2024-03-13 11:52:41,686 INFO [train.py:527] (4/6) Epoch 351, batch 0, global_batch_idx: 43400, batch size: 68, loss[discriminator_loss=2.76, discriminator_real_loss=1.321, discriminator_fake_loss=1.439, generator_loss=28.5, generator_mel_loss=18.64, generator_kl_loss=1.4, generator_dur_loss=1.755, generator_adv_loss=1.997, generator_feat_match_loss=4.71, over 68.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.321, discriminator_fake_loss=1.439, generator_loss=28.5, generator_mel_loss=18.64, generator_kl_loss=1.4, generator_dur_loss=1.755, generator_adv_loss=1.997, generator_feat_match_loss=4.71, over 68.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:52:41,689 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 11:52:49,646 INFO [train.py:591] (4/6) Epoch 351, validation: discriminator_loss=2.765, discriminator_real_loss=1.506, discriminator_fake_loss=1.26, generator_loss=26.69, generator_mel_loss=18.17, generator_kl_loss=1.219, generator_dur_loss=1.83, generator_adv_loss=1.985, generator_feat_match_loss=3.495, over 100.00 samples. +2024-03-13 11:52:49,649 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 11:55:12,192 INFO [train.py:527] (4/6) Epoch 351, batch 50, global_batch_idx: 43450, batch size: 42, loss[discriminator_loss=2.72, discriminator_real_loss=1.373, discriminator_fake_loss=1.348, generator_loss=27.81, generator_mel_loss=18.33, generator_kl_loss=1.339, generator_dur_loss=1.672, generator_adv_loss=1.879, generator_feat_match_loss=4.586, over 42.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.389, discriminator_fake_loss=1.345, generator_loss=27.6, generator_mel_loss=18.13, generator_kl_loss=1.402, generator_dur_loss=1.767, generator_adv_loss=1.932, generator_feat_match_loss=4.373, over 2979.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:57:34,522 INFO [train.py:527] (4/6) Epoch 351, batch 100, global_batch_idx: 43500, batch size: 36, loss[discriminator_loss=2.752, discriminator_real_loss=1.297, discriminator_fake_loss=1.455, generator_loss=28, generator_mel_loss=18.54, generator_kl_loss=1.639, generator_dur_loss=1.646, generator_adv_loss=1.871, generator_feat_match_loss=4.301, over 36.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.381, discriminator_fake_loss=1.344, generator_loss=27.65, generator_mel_loss=18.15, generator_kl_loss=1.397, generator_dur_loss=1.774, generator_adv_loss=1.928, generator_feat_match_loss=4.403, over 6096.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:58:40,009 INFO [train.py:919] (4/6) Start epoch 352 +2024-03-13 12:00:19,246 INFO [train.py:527] (4/6) Epoch 352, batch 26, global_batch_idx: 43550, batch size: 80, loss[discriminator_loss=2.783, discriminator_real_loss=1.491, discriminator_fake_loss=1.291, generator_loss=26.1, generator_mel_loss=17.64, generator_kl_loss=1.274, generator_dur_loss=1.829, generator_adv_loss=1.794, generator_feat_match_loss=3.567, over 80.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.386, discriminator_fake_loss=1.345, generator_loss=27.66, generator_mel_loss=18.12, generator_kl_loss=1.425, generator_dur_loss=1.747, generator_adv_loss=1.943, generator_feat_match_loss=4.428, over 1450.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:02:40,125 INFO [train.py:527] (4/6) Epoch 352, batch 76, global_batch_idx: 43600, batch size: 74, loss[discriminator_loss=2.657, discriminator_real_loss=1.314, discriminator_fake_loss=1.343, generator_loss=28.22, generator_mel_loss=18.46, generator_kl_loss=1.413, generator_dur_loss=1.827, generator_adv_loss=1.975, generator_feat_match_loss=4.549, over 74.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.388, discriminator_fake_loss=1.346, generator_loss=27.61, generator_mel_loss=18.1, generator_kl_loss=1.425, generator_dur_loss=1.759, generator_adv_loss=1.929, generator_feat_match_loss=4.401, over 4353.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:02:40,127 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 12:02:48,184 INFO [train.py:591] (4/6) Epoch 352, validation: discriminator_loss=2.794, discriminator_real_loss=1.464, discriminator_fake_loss=1.33, generator_loss=26.55, generator_mel_loss=18.12, generator_kl_loss=1.187, generator_dur_loss=1.813, generator_adv_loss=1.878, generator_feat_match_loss=3.552, over 100.00 samples. +2024-03-13 12:02:48,185 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 12:05:03,835 INFO [train.py:919] (4/6) Start epoch 353 +2024-03-13 12:05:34,535 INFO [train.py:527] (4/6) Epoch 353, batch 2, global_batch_idx: 43650, batch size: 74, loss[discriminator_loss=2.716, discriminator_real_loss=1.3, discriminator_fake_loss=1.415, generator_loss=27.77, generator_mel_loss=18.27, generator_kl_loss=1.408, generator_dur_loss=1.84, generator_adv_loss=1.975, generator_feat_match_loss=4.27, over 74.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.368, discriminator_fake_loss=1.354, generator_loss=27.7, generator_mel_loss=18.13, generator_kl_loss=1.448, generator_dur_loss=1.828, generator_adv_loss=1.9, generator_feat_match_loss=4.394, over 214.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:07:55,202 INFO [train.py:527] (4/6) Epoch 353, batch 52, global_batch_idx: 43700, batch size: 58, loss[discriminator_loss=2.676, discriminator_real_loss=1.329, discriminator_fake_loss=1.346, generator_loss=27.86, generator_mel_loss=18.23, generator_kl_loss=1.442, generator_dur_loss=1.734, generator_adv_loss=2.072, generator_feat_match_loss=4.386, over 58.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.384, discriminator_fake_loss=1.348, generator_loss=27.66, generator_mel_loss=18.11, generator_kl_loss=1.412, generator_dur_loss=1.782, generator_adv_loss=1.92, generator_feat_match_loss=4.431, over 3078.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:10:16,658 INFO [train.py:527] (4/6) Epoch 353, batch 102, global_batch_idx: 43750, batch size: 47, loss[discriminator_loss=2.739, discriminator_real_loss=1.395, discriminator_fake_loss=1.343, generator_loss=26.84, generator_mel_loss=17.6, generator_kl_loss=1.494, generator_dur_loss=1.658, generator_adv_loss=1.989, generator_feat_match_loss=4.095, over 47.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.384, discriminator_fake_loss=1.345, generator_loss=27.69, generator_mel_loss=18.12, generator_kl_loss=1.421, generator_dur_loss=1.78, generator_adv_loss=1.928, generator_feat_match_loss=4.437, over 5965.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:11:16,297 INFO [train.py:919] (4/6) Start epoch 354 +2024-03-13 12:13:02,946 INFO [train.py:527] (4/6) Epoch 354, batch 28, global_batch_idx: 43800, batch size: 39, loss[discriminator_loss=2.719, discriminator_real_loss=1.351, discriminator_fake_loss=1.368, generator_loss=28.52, generator_mel_loss=18.74, generator_kl_loss=1.497, generator_dur_loss=1.7, generator_adv_loss=1.943, generator_feat_match_loss=4.636, over 39.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.381, discriminator_fake_loss=1.331, generator_loss=27.71, generator_mel_loss=18.11, generator_kl_loss=1.388, generator_dur_loss=1.791, generator_adv_loss=1.949, generator_feat_match_loss=4.472, over 1670.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:13:02,947 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 12:13:11,068 INFO [train.py:591] (4/6) Epoch 354, validation: discriminator_loss=2.739, discriminator_real_loss=1.403, discriminator_fake_loss=1.335, generator_loss=26.52, generator_mel_loss=18.09, generator_kl_loss=1.258, generator_dur_loss=1.833, generator_adv_loss=1.868, generator_feat_match_loss=3.472, over 100.00 samples. +2024-03-13 12:13:11,069 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 12:15:30,813 INFO [train.py:527] (4/6) Epoch 354, batch 78, global_batch_idx: 43850, batch size: 36, loss[discriminator_loss=2.658, discriminator_real_loss=1.39, discriminator_fake_loss=1.268, generator_loss=28.45, generator_mel_loss=18.65, generator_kl_loss=1.531, generator_dur_loss=1.696, generator_adv_loss=1.967, generator_feat_match_loss=4.604, over 36.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.376, discriminator_fake_loss=1.342, generator_loss=27.79, generator_mel_loss=18.17, generator_kl_loss=1.423, generator_dur_loss=1.782, generator_adv_loss=1.94, generator_feat_match_loss=4.473, over 4630.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:17:39,069 INFO [train.py:919] (4/6) Start epoch 355 +2024-03-13 12:18:15,302 INFO [train.py:527] (4/6) Epoch 355, batch 4, global_batch_idx: 43900, batch size: 39, loss[discriminator_loss=2.674, discriminator_real_loss=1.233, discriminator_fake_loss=1.441, generator_loss=28.68, generator_mel_loss=18.62, generator_kl_loss=1.764, generator_dur_loss=1.66, generator_adv_loss=1.988, generator_feat_match_loss=4.652, over 39.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.397, discriminator_fake_loss=1.3, generator_loss=27.58, generator_mel_loss=18.13, generator_kl_loss=1.469, generator_dur_loss=1.789, generator_adv_loss=1.915, generator_feat_match_loss=4.272, over 318.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:20:37,032 INFO [train.py:527] (4/6) Epoch 355, batch 54, global_batch_idx: 43950, batch size: 83, loss[discriminator_loss=2.691, discriminator_real_loss=1.262, discriminator_fake_loss=1.429, generator_loss=28.05, generator_mel_loss=18.46, generator_kl_loss=1.375, generator_dur_loss=1.863, generator_adv_loss=1.788, generator_feat_match_loss=4.565, over 83.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.374, discriminator_fake_loss=1.344, generator_loss=27.92, generator_mel_loss=18.23, generator_kl_loss=1.45, generator_dur_loss=1.751, generator_adv_loss=1.929, generator_feat_match_loss=4.562, over 2964.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:23:01,825 INFO [train.py:527] (4/6) Epoch 355, batch 104, global_batch_idx: 44000, batch size: 70, loss[discriminator_loss=2.771, discriminator_real_loss=1.298, discriminator_fake_loss=1.473, generator_loss=27.18, generator_mel_loss=17.82, generator_kl_loss=1.392, generator_dur_loss=1.798, generator_adv_loss=2.038, generator_feat_match_loss=4.131, over 70.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.38, discriminator_fake_loss=1.348, generator_loss=27.77, generator_mel_loss=18.16, generator_kl_loss=1.429, generator_dur_loss=1.77, generator_adv_loss=1.94, generator_feat_match_loss=4.467, over 6088.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:23:01,827 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 12:23:10,645 INFO [train.py:591] (4/6) Epoch 355, validation: discriminator_loss=2.771, discriminator_real_loss=1.539, discriminator_fake_loss=1.233, generator_loss=26.44, generator_mel_loss=17.91, generator_kl_loss=1.252, generator_dur_loss=1.84, generator_adv_loss=2.006, generator_feat_match_loss=3.429, over 100.00 samples. +2024-03-13 12:23:10,646 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 12:24:02,061 INFO [train.py:919] (4/6) Start epoch 356 +2024-03-13 12:25:51,541 INFO [train.py:527] (4/6) Epoch 356, batch 30, global_batch_idx: 44050, batch size: 66, loss[discriminator_loss=2.757, discriminator_real_loss=1.358, discriminator_fake_loss=1.399, generator_loss=28.13, generator_mel_loss=18.29, generator_kl_loss=1.467, generator_dur_loss=1.793, generator_adv_loss=1.767, generator_feat_match_loss=4.811, over 66.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.386, discriminator_fake_loss=1.351, generator_loss=27.61, generator_mel_loss=18.11, generator_kl_loss=1.423, generator_dur_loss=1.748, generator_adv_loss=1.928, generator_feat_match_loss=4.4, over 1589.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:28:09,442 INFO [train.py:527] (4/6) Epoch 356, batch 80, global_batch_idx: 44100, batch size: 66, loss[discriminator_loss=2.817, discriminator_real_loss=1.501, discriminator_fake_loss=1.317, generator_loss=27.13, generator_mel_loss=17.96, generator_kl_loss=1.327, generator_dur_loss=1.751, generator_adv_loss=1.747, generator_feat_match_loss=4.339, over 66.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=27.62, generator_mel_loss=18.12, generator_kl_loss=1.421, generator_dur_loss=1.75, generator_adv_loss=1.923, generator_feat_match_loss=4.408, over 4278.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:30:10,587 INFO [train.py:919] (4/6) Start epoch 357 +2024-03-13 12:30:50,320 INFO [train.py:527] (4/6) Epoch 357, batch 6, global_batch_idx: 44150, batch size: 31, loss[discriminator_loss=2.818, discriminator_real_loss=1.561, discriminator_fake_loss=1.257, generator_loss=25.95, generator_mel_loss=17.36, generator_kl_loss=1.322, generator_dur_loss=1.622, generator_adv_loss=1.863, generator_feat_match_loss=3.776, over 31.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.394, discriminator_fake_loss=1.359, generator_loss=27.08, generator_mel_loss=17.96, generator_kl_loss=1.384, generator_dur_loss=1.735, generator_adv_loss=1.933, generator_feat_match_loss=4.07, over 379.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:33:13,616 INFO [train.py:527] (4/6) Epoch 357, batch 56, global_batch_idx: 44200, batch size: 56, loss[discriminator_loss=2.701, discriminator_real_loss=1.292, discriminator_fake_loss=1.409, generator_loss=28.58, generator_mel_loss=18.25, generator_kl_loss=1.548, generator_dur_loss=1.703, generator_adv_loss=2.052, generator_feat_match_loss=5.03, over 56.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.37, discriminator_fake_loss=1.355, generator_loss=27.76, generator_mel_loss=18.18, generator_kl_loss=1.403, generator_dur_loss=1.76, generator_adv_loss=1.921, generator_feat_match_loss=4.496, over 3197.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:33:13,617 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 12:33:21,457 INFO [train.py:591] (4/6) Epoch 357, validation: discriminator_loss=2.821, discriminator_real_loss=1.547, discriminator_fake_loss=1.274, generator_loss=26.95, generator_mel_loss=18.57, generator_kl_loss=1.318, generator_dur_loss=1.801, generator_adv_loss=1.969, generator_feat_match_loss=3.285, over 100.00 samples. +2024-03-13 12:33:21,458 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 12:35:40,655 INFO [train.py:527] (4/6) Epoch 357, batch 106, global_batch_idx: 44250, batch size: 53, loss[discriminator_loss=2.755, discriminator_real_loss=1.266, discriminator_fake_loss=1.488, generator_loss=27.87, generator_mel_loss=17.95, generator_kl_loss=1.503, generator_dur_loss=1.743, generator_adv_loss=2.207, generator_feat_match_loss=4.461, over 53.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.373, discriminator_fake_loss=1.354, generator_loss=27.7, generator_mel_loss=18.15, generator_kl_loss=1.415, generator_dur_loss=1.756, generator_adv_loss=1.918, generator_feat_match_loss=4.459, over 5893.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:36:29,253 INFO [train.py:919] (4/6) Start epoch 358 +2024-03-13 12:38:26,627 INFO [train.py:527] (4/6) Epoch 358, batch 32, global_batch_idx: 44300, batch size: 66, loss[discriminator_loss=2.733, discriminator_real_loss=1.436, discriminator_fake_loss=1.297, generator_loss=27.59, generator_mel_loss=18.18, generator_kl_loss=1.302, generator_dur_loss=1.795, generator_adv_loss=1.963, generator_feat_match_loss=4.357, over 66.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.376, discriminator_fake_loss=1.34, generator_loss=27.92, generator_mel_loss=18.26, generator_kl_loss=1.447, generator_dur_loss=1.734, generator_adv_loss=1.931, generator_feat_match_loss=4.542, over 1637.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:40:47,947 INFO [train.py:527] (4/6) Epoch 358, batch 82, global_batch_idx: 44350, batch size: 72, loss[discriminator_loss=2.733, discriminator_real_loss=1.342, discriminator_fake_loss=1.391, generator_loss=27.71, generator_mel_loss=18.07, generator_kl_loss=1.457, generator_dur_loss=1.775, generator_adv_loss=1.949, generator_feat_match_loss=4.452, over 72.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.67, generator_mel_loss=18.11, generator_kl_loss=1.44, generator_dur_loss=1.742, generator_adv_loss=1.928, generator_feat_match_loss=4.446, over 4274.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:42:43,789 INFO [train.py:919] (4/6) Start epoch 359 +2024-03-13 12:43:29,799 INFO [train.py:527] (4/6) Epoch 359, batch 8, global_batch_idx: 44400, batch size: 39, loss[discriminator_loss=2.691, discriminator_real_loss=1.267, discriminator_fake_loss=1.424, generator_loss=28.25, generator_mel_loss=18.24, generator_kl_loss=1.431, generator_dur_loss=1.676, generator_adv_loss=2.018, generator_feat_match_loss=4.889, over 39.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.376, discriminator_fake_loss=1.34, generator_loss=28.08, generator_mel_loss=18.22, generator_kl_loss=1.438, generator_dur_loss=1.711, generator_adv_loss=1.942, generator_feat_match_loss=4.762, over 449.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:43:29,802 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 12:43:37,892 INFO [train.py:591] (4/6) Epoch 359, validation: discriminator_loss=2.795, discriminator_real_loss=1.503, discriminator_fake_loss=1.292, generator_loss=27.33, generator_mel_loss=18.38, generator_kl_loss=1.276, generator_dur_loss=1.823, generator_adv_loss=1.984, generator_feat_match_loss=3.863, over 100.00 samples. +2024-03-13 12:43:37,894 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 12:45:59,728 INFO [train.py:527] (4/6) Epoch 359, batch 58, global_batch_idx: 44450, batch size: 66, loss[discriminator_loss=2.678, discriminator_real_loss=1.349, discriminator_fake_loss=1.329, generator_loss=27.02, generator_mel_loss=17.87, generator_kl_loss=1.339, generator_dur_loss=1.782, generator_adv_loss=1.884, generator_feat_match_loss=4.146, over 66.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=27.7, generator_mel_loss=18.17, generator_kl_loss=1.395, generator_dur_loss=1.747, generator_adv_loss=1.931, generator_feat_match_loss=4.456, over 3439.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:48:21,824 INFO [train.py:527] (4/6) Epoch 359, batch 108, global_batch_idx: 44500, batch size: 25, loss[discriminator_loss=2.773, discriminator_real_loss=1.536, discriminator_fake_loss=1.237, generator_loss=26.35, generator_mel_loss=18.08, generator_kl_loss=1.523, generator_dur_loss=1.605, generator_adv_loss=1.9, generator_feat_match_loss=3.246, over 25.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.374, discriminator_fake_loss=1.344, generator_loss=27.7, generator_mel_loss=18.16, generator_kl_loss=1.408, generator_dur_loss=1.743, generator_adv_loss=1.932, generator_feat_match_loss=4.456, over 6117.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:49:04,165 INFO [train.py:919] (4/6) Start epoch 360 +2024-03-13 12:51:04,503 INFO [train.py:527] (4/6) Epoch 360, batch 34, global_batch_idx: 44550, batch size: 66, loss[discriminator_loss=2.747, discriminator_real_loss=1.396, discriminator_fake_loss=1.351, generator_loss=27.21, generator_mel_loss=17.69, generator_kl_loss=1.288, generator_dur_loss=1.773, generator_adv_loss=1.873, generator_feat_match_loss=4.58, over 66.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.377, discriminator_fake_loss=1.338, generator_loss=27.6, generator_mel_loss=18.08, generator_kl_loss=1.388, generator_dur_loss=1.759, generator_adv_loss=1.932, generator_feat_match_loss=4.435, over 2211.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:53:25,858 INFO [train.py:527] (4/6) Epoch 360, batch 84, global_batch_idx: 44600, batch size: 25, loss[discriminator_loss=2.737, discriminator_real_loss=1.512, discriminator_fake_loss=1.225, generator_loss=29.06, generator_mel_loss=19.05, generator_kl_loss=1.712, generator_dur_loss=1.521, generator_adv_loss=1.885, generator_feat_match_loss=4.892, over 25.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=27.73, generator_mel_loss=18.12, generator_kl_loss=1.402, generator_dur_loss=1.761, generator_adv_loss=1.933, generator_feat_match_loss=4.511, over 5152.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:53:25,859 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 12:53:34,614 INFO [train.py:591] (4/6) Epoch 360, validation: discriminator_loss=2.725, discriminator_real_loss=1.361, discriminator_fake_loss=1.365, generator_loss=26.64, generator_mel_loss=17.88, generator_kl_loss=1.22, generator_dur_loss=1.823, generator_adv_loss=1.872, generator_feat_match_loss=3.841, over 100.00 samples. +2024-03-13 12:53:34,615 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 12:55:25,004 INFO [train.py:919] (4/6) Start epoch 361 +2024-03-13 12:56:21,907 INFO [train.py:527] (4/6) Epoch 361, batch 10, global_batch_idx: 44650, batch size: 42, loss[discriminator_loss=2.748, discriminator_real_loss=1.344, discriminator_fake_loss=1.403, generator_loss=28.25, generator_mel_loss=18.55, generator_kl_loss=1.444, generator_dur_loss=1.675, generator_adv_loss=1.963, generator_feat_match_loss=4.622, over 42.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.378, discriminator_fake_loss=1.344, generator_loss=27.99, generator_mel_loss=18.3, generator_kl_loss=1.475, generator_dur_loss=1.732, generator_adv_loss=1.937, generator_feat_match_loss=4.546, over 576.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:58:43,179 INFO [train.py:527] (4/6) Epoch 361, batch 60, global_batch_idx: 44700, batch size: 80, loss[discriminator_loss=2.797, discriminator_real_loss=1.446, discriminator_fake_loss=1.351, generator_loss=27.71, generator_mel_loss=18.17, generator_kl_loss=1.427, generator_dur_loss=1.805, generator_adv_loss=2.023, generator_feat_match_loss=4.283, over 80.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.384, discriminator_fake_loss=1.343, generator_loss=27.83, generator_mel_loss=18.22, generator_kl_loss=1.442, generator_dur_loss=1.76, generator_adv_loss=1.932, generator_feat_match_loss=4.479, over 3489.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:01:03,665 INFO [train.py:527] (4/6) Epoch 361, batch 110, global_batch_idx: 44750, batch size: 39, loss[discriminator_loss=2.847, discriminator_real_loss=1.514, discriminator_fake_loss=1.333, generator_loss=27.27, generator_mel_loss=17.86, generator_kl_loss=1.566, generator_dur_loss=1.672, generator_adv_loss=1.818, generator_feat_match_loss=4.351, over 39.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.379, discriminator_fake_loss=1.345, generator_loss=27.79, generator_mel_loss=18.19, generator_kl_loss=1.418, generator_dur_loss=1.763, generator_adv_loss=1.933, generator_feat_match_loss=4.484, over 6482.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:01:41,328 INFO [train.py:919] (4/6) Start epoch 362 +2024-03-13 13:03:46,584 INFO [train.py:527] (4/6) Epoch 362, batch 36, global_batch_idx: 44800, batch size: 77, loss[discriminator_loss=2.69, discriminator_real_loss=1.384, discriminator_fake_loss=1.306, generator_loss=27.19, generator_mel_loss=17.86, generator_kl_loss=1.35, generator_dur_loss=1.801, generator_adv_loss=1.971, generator_feat_match_loss=4.204, over 77.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.377, discriminator_fake_loss=1.339, generator_loss=27.75, generator_mel_loss=18.16, generator_kl_loss=1.401, generator_dur_loss=1.758, generator_adv_loss=1.938, generator_feat_match_loss=4.492, over 2074.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:03:46,585 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 13:03:54,776 INFO [train.py:591] (4/6) Epoch 362, validation: discriminator_loss=2.702, discriminator_real_loss=1.446, discriminator_fake_loss=1.256, generator_loss=27.42, generator_mel_loss=18.5, generator_kl_loss=1.251, generator_dur_loss=1.811, generator_adv_loss=1.942, generator_feat_match_loss=3.917, over 100.00 samples. +2024-03-13 13:03:54,777 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 13:06:14,436 INFO [train.py:527] (4/6) Epoch 362, batch 86, global_batch_idx: 44850, batch size: 56, loss[discriminator_loss=2.751, discriminator_real_loss=1.309, discriminator_fake_loss=1.443, generator_loss=27.71, generator_mel_loss=18.18, generator_kl_loss=1.589, generator_dur_loss=1.718, generator_adv_loss=1.942, generator_feat_match_loss=4.279, over 56.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.365, discriminator_fake_loss=1.347, generator_loss=27.84, generator_mel_loss=18.19, generator_kl_loss=1.411, generator_dur_loss=1.74, generator_adv_loss=1.943, generator_feat_match_loss=4.56, over 4931.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:08:02,960 INFO [train.py:919] (4/6) Start epoch 363 +2024-03-13 13:09:03,562 INFO [train.py:527] (4/6) Epoch 363, batch 12, global_batch_idx: 44900, batch size: 13, loss[discriminator_loss=2.704, discriminator_real_loss=1.337, discriminator_fake_loss=1.367, generator_loss=28.79, generator_mel_loss=19.04, generator_kl_loss=1.76, generator_dur_loss=1.58, generator_adv_loss=1.956, generator_feat_match_loss=4.454, over 13.00 samples.], tot_loss[discriminator_loss=2.779, discriminator_real_loss=1.432, discriminator_fake_loss=1.347, generator_loss=27.51, generator_mel_loss=18.05, generator_kl_loss=1.444, generator_dur_loss=1.733, generator_adv_loss=1.957, generator_feat_match_loss=4.325, over 713.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:11:29,865 INFO [train.py:527] (4/6) Epoch 363, batch 62, global_batch_idx: 44950, batch size: 61, loss[discriminator_loss=2.748, discriminator_real_loss=1.393, discriminator_fake_loss=1.356, generator_loss=28.81, generator_mel_loss=18.52, generator_kl_loss=1.489, generator_dur_loss=1.68, generator_adv_loss=2.037, generator_feat_match_loss=5.086, over 61.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.388, discriminator_fake_loss=1.355, generator_loss=27.64, generator_mel_loss=18.13, generator_kl_loss=1.415, generator_dur_loss=1.733, generator_adv_loss=1.931, generator_feat_match_loss=4.438, over 3588.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:13:48,387 INFO [train.py:527] (4/6) Epoch 363, batch 112, global_batch_idx: 45000, batch size: 25, loss[discriminator_loss=2.716, discriminator_real_loss=1.404, discriminator_fake_loss=1.312, generator_loss=28.24, generator_mel_loss=18.92, generator_kl_loss=1.58, generator_dur_loss=1.569, generator_adv_loss=1.954, generator_feat_match_loss=4.219, over 25.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.381, discriminator_fake_loss=1.355, generator_loss=27.68, generator_mel_loss=18.14, generator_kl_loss=1.421, generator_dur_loss=1.734, generator_adv_loss=1.937, generator_feat_match_loss=4.441, over 6275.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:13:48,388 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 13:13:57,312 INFO [train.py:591] (4/6) Epoch 363, validation: discriminator_loss=2.769, discriminator_real_loss=1.475, discriminator_fake_loss=1.294, generator_loss=27.23, generator_mel_loss=18.81, generator_kl_loss=1.246, generator_dur_loss=1.825, generator_adv_loss=1.883, generator_feat_match_loss=3.464, over 100.00 samples. +2024-03-13 13:13:57,313 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 13:14:28,725 INFO [train.py:919] (4/6) Start epoch 364 +2024-03-13 13:16:41,275 INFO [train.py:527] (4/6) Epoch 364, batch 38, global_batch_idx: 45050, batch size: 80, loss[discriminator_loss=2.68, discriminator_real_loss=1.385, discriminator_fake_loss=1.295, generator_loss=27.59, generator_mel_loss=18.04, generator_kl_loss=1.399, generator_dur_loss=1.786, generator_adv_loss=1.974, generator_feat_match_loss=4.394, over 80.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.366, discriminator_fake_loss=1.347, generator_loss=27.84, generator_mel_loss=18.12, generator_kl_loss=1.457, generator_dur_loss=1.749, generator_adv_loss=1.965, generator_feat_match_loss=4.548, over 2229.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:19:04,715 INFO [train.py:527] (4/6) Epoch 364, batch 88, global_batch_idx: 45100, batch size: 61, loss[discriminator_loss=2.662, discriminator_real_loss=1.373, discriminator_fake_loss=1.288, generator_loss=27.91, generator_mel_loss=17.98, generator_kl_loss=1.396, generator_dur_loss=1.763, generator_adv_loss=2.072, generator_feat_match_loss=4.699, over 61.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.374, discriminator_fake_loss=1.338, generator_loss=27.75, generator_mel_loss=18.12, generator_kl_loss=1.417, generator_dur_loss=1.77, generator_adv_loss=1.947, generator_feat_match_loss=4.493, over 5223.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:20:42,690 INFO [train.py:919] (4/6) Start epoch 365 +2024-03-13 13:21:47,470 INFO [train.py:527] (4/6) Epoch 365, batch 14, global_batch_idx: 45150, batch size: 39, loss[discriminator_loss=2.774, discriminator_real_loss=1.479, discriminator_fake_loss=1.295, generator_loss=27.76, generator_mel_loss=18.16, generator_kl_loss=1.533, generator_dur_loss=1.707, generator_adv_loss=1.974, generator_feat_match_loss=4.385, over 39.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.354, discriminator_fake_loss=1.358, generator_loss=27.99, generator_mel_loss=18.31, generator_kl_loss=1.42, generator_dur_loss=1.779, generator_adv_loss=1.943, generator_feat_match_loss=4.532, over 912.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:24:06,082 INFO [train.py:527] (4/6) Epoch 365, batch 64, global_batch_idx: 45200, batch size: 68, loss[discriminator_loss=2.731, discriminator_real_loss=1.394, discriminator_fake_loss=1.336, generator_loss=27.13, generator_mel_loss=18.24, generator_kl_loss=1.245, generator_dur_loss=1.815, generator_adv_loss=2.029, generator_feat_match_loss=3.804, over 68.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.373, discriminator_fake_loss=1.348, generator_loss=27.81, generator_mel_loss=18.18, generator_kl_loss=1.416, generator_dur_loss=1.769, generator_adv_loss=1.943, generator_feat_match_loss=4.498, over 3730.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:24:06,083 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 13:24:14,059 INFO [train.py:591] (4/6) Epoch 365, validation: discriminator_loss=2.753, discriminator_real_loss=1.471, discriminator_fake_loss=1.282, generator_loss=27.2, generator_mel_loss=18.57, generator_kl_loss=1.177, generator_dur_loss=1.815, generator_adv_loss=1.984, generator_feat_match_loss=3.647, over 100.00 samples. +2024-03-13 13:24:14,060 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 13:26:35,946 INFO [train.py:527] (4/6) Epoch 365, batch 114, global_batch_idx: 45250, batch size: 58, loss[discriminator_loss=2.788, discriminator_real_loss=1.387, discriminator_fake_loss=1.401, generator_loss=27.65, generator_mel_loss=18.15, generator_kl_loss=1.484, generator_dur_loss=1.752, generator_adv_loss=1.719, generator_feat_match_loss=4.54, over 58.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.377, discriminator_fake_loss=1.348, generator_loss=27.87, generator_mel_loss=18.22, generator_kl_loss=1.417, generator_dur_loss=1.764, generator_adv_loss=1.938, generator_feat_match_loss=4.535, over 6481.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:27:02,987 INFO [train.py:919] (4/6) Start epoch 366 +2024-03-13 13:29:19,136 INFO [train.py:527] (4/6) Epoch 366, batch 40, global_batch_idx: 45300, batch size: 31, loss[discriminator_loss=2.744, discriminator_real_loss=1.485, discriminator_fake_loss=1.26, generator_loss=27.4, generator_mel_loss=17.77, generator_kl_loss=1.626, generator_dur_loss=1.634, generator_adv_loss=1.94, generator_feat_match_loss=4.436, over 31.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=27.84, generator_mel_loss=18.2, generator_kl_loss=1.394, generator_dur_loss=1.77, generator_adv_loss=1.934, generator_feat_match_loss=4.549, over 2282.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:31:38,438 INFO [train.py:527] (4/6) Epoch 366, batch 90, global_batch_idx: 45350, batch size: 58, loss[discriminator_loss=2.717, discriminator_real_loss=1.387, discriminator_fake_loss=1.331, generator_loss=27.89, generator_mel_loss=18.29, generator_kl_loss=1.367, generator_dur_loss=1.778, generator_adv_loss=1.95, generator_feat_match_loss=4.504, over 58.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.341, generator_loss=27.95, generator_mel_loss=18.24, generator_kl_loss=1.41, generator_dur_loss=1.764, generator_adv_loss=1.949, generator_feat_match_loss=4.586, over 4901.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:33:12,499 INFO [train.py:919] (4/6) Start epoch 367 +2024-03-13 13:34:23,829 INFO [train.py:527] (4/6) Epoch 367, batch 16, global_batch_idx: 45400, batch size: 55, loss[discriminator_loss=2.683, discriminator_real_loss=1.399, discriminator_fake_loss=1.284, generator_loss=28.1, generator_mel_loss=18.02, generator_kl_loss=1.407, generator_dur_loss=1.694, generator_adv_loss=2.037, generator_feat_match_loss=4.934, over 55.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.364, discriminator_fake_loss=1.343, generator_loss=27.8, generator_mel_loss=18.1, generator_kl_loss=1.401, generator_dur_loss=1.771, generator_adv_loss=1.946, generator_feat_match_loss=4.579, over 1004.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:34:23,831 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 13:34:31,913 INFO [train.py:591] (4/6) Epoch 367, validation: discriminator_loss=2.754, discriminator_real_loss=1.484, discriminator_fake_loss=1.269, generator_loss=26.96, generator_mel_loss=18.29, generator_kl_loss=1.187, generator_dur_loss=1.826, generator_adv_loss=1.993, generator_feat_match_loss=3.665, over 100.00 samples. +2024-03-13 13:34:31,914 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 13:36:53,524 INFO [train.py:527] (4/6) Epoch 367, batch 66, global_batch_idx: 45450, batch size: 88, loss[discriminator_loss=2.74, discriminator_real_loss=1.393, discriminator_fake_loss=1.347, generator_loss=27.14, generator_mel_loss=17.8, generator_kl_loss=1.302, generator_dur_loss=1.838, generator_adv_loss=1.929, generator_feat_match_loss=4.272, over 88.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.382, discriminator_fake_loss=1.344, generator_loss=27.82, generator_mel_loss=18.15, generator_kl_loss=1.414, generator_dur_loss=1.759, generator_adv_loss=1.944, generator_feat_match_loss=4.553, over 3721.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:39:09,394 INFO [train.py:527] (4/6) Epoch 367, batch 116, global_batch_idx: 45500, batch size: 56, loss[discriminator_loss=2.705, discriminator_real_loss=1.364, discriminator_fake_loss=1.34, generator_loss=27.69, generator_mel_loss=18.19, generator_kl_loss=1.379, generator_dur_loss=1.689, generator_adv_loss=1.75, generator_feat_match_loss=4.682, over 56.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.379, discriminator_fake_loss=1.345, generator_loss=27.73, generator_mel_loss=18.12, generator_kl_loss=1.413, generator_dur_loss=1.759, generator_adv_loss=1.939, generator_feat_match_loss=4.506, over 6503.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:39:32,622 INFO [train.py:919] (4/6) Start epoch 368 +2024-03-13 13:41:59,455 INFO [train.py:527] (4/6) Epoch 368, batch 42, global_batch_idx: 45550, batch size: 42, loss[discriminator_loss=2.729, discriminator_real_loss=1.399, discriminator_fake_loss=1.33, generator_loss=27.17, generator_mel_loss=18.04, generator_kl_loss=1.523, generator_dur_loss=1.714, generator_adv_loss=1.803, generator_feat_match_loss=4.084, over 42.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.37, discriminator_fake_loss=1.34, generator_loss=27.82, generator_mel_loss=18.17, generator_kl_loss=1.383, generator_dur_loss=1.777, generator_adv_loss=1.931, generator_feat_match_loss=4.551, over 2666.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:44:20,742 INFO [train.py:527] (4/6) Epoch 368, batch 92, global_batch_idx: 45600, batch size: 31, loss[discriminator_loss=2.675, discriminator_real_loss=1.334, discriminator_fake_loss=1.341, generator_loss=28.96, generator_mel_loss=18.97, generator_kl_loss=1.551, generator_dur_loss=1.633, generator_adv_loss=2.003, generator_feat_match_loss=4.8, over 31.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.375, discriminator_fake_loss=1.338, generator_loss=27.89, generator_mel_loss=18.22, generator_kl_loss=1.404, generator_dur_loss=1.76, generator_adv_loss=1.947, generator_feat_match_loss=4.555, over 5373.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:44:20,743 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 13:44:29,495 INFO [train.py:591] (4/6) Epoch 368, validation: discriminator_loss=2.755, discriminator_real_loss=1.457, discriminator_fake_loss=1.298, generator_loss=26.51, generator_mel_loss=18.05, generator_kl_loss=1.227, generator_dur_loss=1.792, generator_adv_loss=1.932, generator_feat_match_loss=3.517, over 100.00 samples. +2024-03-13 13:44:29,496 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 13:45:58,151 INFO [train.py:919] (4/6) Start epoch 369 +2024-03-13 13:47:12,363 INFO [train.py:527] (4/6) Epoch 369, batch 18, global_batch_idx: 45650, batch size: 64, loss[discriminator_loss=2.734, discriminator_real_loss=1.367, discriminator_fake_loss=1.367, generator_loss=27.43, generator_mel_loss=17.97, generator_kl_loss=1.307, generator_dur_loss=1.8, generator_adv_loss=1.968, generator_feat_match_loss=4.384, over 64.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=27.61, generator_mel_loss=18.13, generator_kl_loss=1.347, generator_dur_loss=1.785, generator_adv_loss=1.931, generator_feat_match_loss=4.421, over 1236.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:49:31,555 INFO [train.py:527] (4/6) Epoch 369, batch 68, global_batch_idx: 45700, batch size: 61, loss[discriminator_loss=2.639, discriminator_real_loss=1.384, discriminator_fake_loss=1.255, generator_loss=28.61, generator_mel_loss=18.53, generator_kl_loss=1.441, generator_dur_loss=1.728, generator_adv_loss=1.878, generator_feat_match_loss=5.032, over 61.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=27.68, generator_mel_loss=18.12, generator_kl_loss=1.407, generator_dur_loss=1.755, generator_adv_loss=1.928, generator_feat_match_loss=4.472, over 3924.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:51:54,962 INFO [train.py:527] (4/6) Epoch 369, batch 118, global_batch_idx: 45750, batch size: 15, loss[discriminator_loss=2.635, discriminator_real_loss=1.329, discriminator_fake_loss=1.306, generator_loss=29.76, generator_mel_loss=19.02, generator_kl_loss=1.957, generator_dur_loss=1.62, generator_adv_loss=2.108, generator_feat_match_loss=5.051, over 15.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.382, discriminator_fake_loss=1.344, generator_loss=27.72, generator_mel_loss=18.13, generator_kl_loss=1.419, generator_dur_loss=1.747, generator_adv_loss=1.936, generator_feat_match_loss=4.489, over 6640.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:52:11,126 INFO [train.py:919] (4/6) Start epoch 370 +2024-03-13 13:54:38,397 INFO [train.py:527] (4/6) Epoch 370, batch 44, global_batch_idx: 45800, batch size: 72, loss[discriminator_loss=2.692, discriminator_real_loss=1.319, discriminator_fake_loss=1.373, generator_loss=28.39, generator_mel_loss=18.4, generator_kl_loss=1.415, generator_dur_loss=1.819, generator_adv_loss=1.992, generator_feat_match_loss=4.758, over 72.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.378, discriminator_fake_loss=1.338, generator_loss=27.82, generator_mel_loss=18.16, generator_kl_loss=1.415, generator_dur_loss=1.741, generator_adv_loss=1.935, generator_feat_match_loss=4.568, over 2647.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:54:38,399 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 13:54:46,121 INFO [train.py:591] (4/6) Epoch 370, validation: discriminator_loss=2.784, discriminator_real_loss=1.52, discriminator_fake_loss=1.264, generator_loss=27.2, generator_mel_loss=18.68, generator_kl_loss=1.298, generator_dur_loss=1.818, generator_adv_loss=1.961, generator_feat_match_loss=3.441, over 100.00 samples. +2024-03-13 13:54:46,121 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 13:57:03,172 INFO [train.py:527] (4/6) Epoch 370, batch 94, global_batch_idx: 45850, batch size: 66, loss[discriminator_loss=2.768, discriminator_real_loss=1.445, discriminator_fake_loss=1.323, generator_loss=28.16, generator_mel_loss=18.57, generator_kl_loss=1.318, generator_dur_loss=1.781, generator_adv_loss=1.917, generator_feat_match_loss=4.575, over 66.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.376, discriminator_fake_loss=1.338, generator_loss=27.85, generator_mel_loss=18.18, generator_kl_loss=1.423, generator_dur_loss=1.742, generator_adv_loss=1.942, generator_feat_match_loss=4.565, over 5423.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:58:22,810 INFO [train.py:919] (4/6) Start epoch 371 +2024-03-13 13:59:42,308 INFO [train.py:527] (4/6) Epoch 371, batch 20, global_batch_idx: 45900, batch size: 45, loss[discriminator_loss=2.715, discriminator_real_loss=1.28, discriminator_fake_loss=1.436, generator_loss=28.81, generator_mel_loss=18.51, generator_kl_loss=1.458, generator_dur_loss=1.661, generator_adv_loss=2.03, generator_feat_match_loss=5.156, over 45.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.379, discriminator_fake_loss=1.34, generator_loss=27.77, generator_mel_loss=18.19, generator_kl_loss=1.362, generator_dur_loss=1.771, generator_adv_loss=1.933, generator_feat_match_loss=4.518, over 1293.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:02:03,631 INFO [train.py:527] (4/6) Epoch 371, batch 70, global_batch_idx: 45950, batch size: 55, loss[discriminator_loss=2.723, discriminator_real_loss=1.417, discriminator_fake_loss=1.306, generator_loss=27.4, generator_mel_loss=18.28, generator_kl_loss=1.319, generator_dur_loss=1.716, generator_adv_loss=1.961, generator_feat_match_loss=4.122, over 55.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.387, discriminator_fake_loss=1.344, generator_loss=27.67, generator_mel_loss=18.09, generator_kl_loss=1.373, generator_dur_loss=1.756, generator_adv_loss=1.948, generator_feat_match_loss=4.498, over 4442.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:04:17,147 INFO [train.py:527] (4/6) Epoch 371, batch 120, global_batch_idx: 46000, batch size: 47, loss[discriminator_loss=2.643, discriminator_real_loss=1.297, discriminator_fake_loss=1.345, generator_loss=28.04, generator_mel_loss=17.76, generator_kl_loss=1.58, generator_dur_loss=1.677, generator_adv_loss=1.985, generator_feat_match_loss=5.037, over 47.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.385, discriminator_fake_loss=1.343, generator_loss=27.77, generator_mel_loss=18.15, generator_kl_loss=1.405, generator_dur_loss=1.74, generator_adv_loss=1.942, generator_feat_match_loss=4.533, over 7206.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:04:17,149 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 14:04:26,085 INFO [train.py:591] (4/6) Epoch 371, validation: discriminator_loss=2.727, discriminator_real_loss=1.415, discriminator_fake_loss=1.312, generator_loss=27.89, generator_mel_loss=18.8, generator_kl_loss=1.346, generator_dur_loss=1.788, generator_adv_loss=1.931, generator_feat_match_loss=4.025, over 100.00 samples. +2024-03-13 14:04:26,086 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 14:04:35,515 INFO [train.py:919] (4/6) Start epoch 372 +2024-03-13 14:07:06,484 INFO [train.py:527] (4/6) Epoch 372, batch 46, global_batch_idx: 46050, batch size: 80, loss[discriminator_loss=2.681, discriminator_real_loss=1.402, discriminator_fake_loss=1.279, generator_loss=27.44, generator_mel_loss=17.85, generator_kl_loss=1.364, generator_dur_loss=1.775, generator_adv_loss=1.929, generator_feat_match_loss=4.523, over 80.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.371, discriminator_fake_loss=1.352, generator_loss=27.84, generator_mel_loss=18.23, generator_kl_loss=1.438, generator_dur_loss=1.726, generator_adv_loss=1.942, generator_feat_match_loss=4.5, over 2453.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:09:23,745 INFO [train.py:527] (4/6) Epoch 372, batch 96, global_batch_idx: 46100, batch size: 48, loss[discriminator_loss=2.737, discriminator_real_loss=1.353, discriminator_fake_loss=1.383, generator_loss=28.29, generator_mel_loss=18.59, generator_kl_loss=1.638, generator_dur_loss=1.769, generator_adv_loss=1.942, generator_feat_match_loss=4.356, over 48.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.379, discriminator_fake_loss=1.344, generator_loss=27.77, generator_mel_loss=18.17, generator_kl_loss=1.443, generator_dur_loss=1.731, generator_adv_loss=1.935, generator_feat_match_loss=4.494, over 5171.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:10:41,796 INFO [train.py:919] (4/6) Start epoch 373 +2024-03-13 14:12:07,450 INFO [train.py:527] (4/6) Epoch 373, batch 22, global_batch_idx: 46150, batch size: 74, loss[discriminator_loss=2.695, discriminator_real_loss=1.399, discriminator_fake_loss=1.296, generator_loss=27.12, generator_mel_loss=17.85, generator_kl_loss=1.238, generator_dur_loss=1.785, generator_adv_loss=1.957, generator_feat_match_loss=4.291, over 74.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.395, discriminator_fake_loss=1.362, generator_loss=27.82, generator_mel_loss=18.21, generator_kl_loss=1.405, generator_dur_loss=1.769, generator_adv_loss=1.968, generator_feat_match_loss=4.467, over 1342.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:14:27,951 INFO [train.py:527] (4/6) Epoch 373, batch 72, global_batch_idx: 46200, batch size: 44, loss[discriminator_loss=2.693, discriminator_real_loss=1.35, discriminator_fake_loss=1.343, generator_loss=28.67, generator_mel_loss=18.28, generator_kl_loss=1.531, generator_dur_loss=1.653, generator_adv_loss=1.988, generator_feat_match_loss=5.227, over 44.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.377, discriminator_fake_loss=1.348, generator_loss=27.77, generator_mel_loss=18.17, generator_kl_loss=1.391, generator_dur_loss=1.771, generator_adv_loss=1.948, generator_feat_match_loss=4.499, over 4357.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:14:27,952 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 14:14:36,415 INFO [train.py:591] (4/6) Epoch 373, validation: discriminator_loss=2.779, discriminator_real_loss=1.446, discriminator_fake_loss=1.333, generator_loss=27.44, generator_mel_loss=18.47, generator_kl_loss=1.349, generator_dur_loss=1.809, generator_adv_loss=1.9, generator_feat_match_loss=3.912, over 100.00 samples. +2024-03-13 14:14:36,415 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 14:16:53,650 INFO [train.py:527] (4/6) Epoch 373, batch 122, global_batch_idx: 46250, batch size: 25, loss[discriminator_loss=2.706, discriminator_real_loss=1.37, discriminator_fake_loss=1.336, generator_loss=29.55, generator_mel_loss=18.48, generator_kl_loss=1.673, generator_dur_loss=1.575, generator_adv_loss=2.14, generator_feat_match_loss=5.681, over 25.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.376, discriminator_fake_loss=1.347, generator_loss=27.79, generator_mel_loss=18.17, generator_kl_loss=1.402, generator_dur_loss=1.759, generator_adv_loss=1.948, generator_feat_match_loss=4.51, over 7098.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:16:57,635 INFO [train.py:919] (4/6) Start epoch 374 +2024-03-13 14:19:34,097 INFO [train.py:527] (4/6) Epoch 374, batch 48, global_batch_idx: 46300, batch size: 77, loss[discriminator_loss=2.75, discriminator_real_loss=1.469, discriminator_fake_loss=1.28, generator_loss=27.29, generator_mel_loss=17.95, generator_kl_loss=1.348, generator_dur_loss=1.816, generator_adv_loss=1.942, generator_feat_match_loss=4.239, over 77.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=27.86, generator_mel_loss=18.13, generator_kl_loss=1.444, generator_dur_loss=1.753, generator_adv_loss=1.94, generator_feat_match_loss=4.6, over 2674.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:21:53,551 INFO [train.py:527] (4/6) Epoch 374, batch 98, global_batch_idx: 46350, batch size: 56, loss[discriminator_loss=2.769, discriminator_real_loss=1.434, discriminator_fake_loss=1.334, generator_loss=28.37, generator_mel_loss=18.38, generator_kl_loss=1.425, generator_dur_loss=1.779, generator_adv_loss=1.935, generator_feat_match_loss=4.853, over 56.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.382, discriminator_fake_loss=1.34, generator_loss=27.74, generator_mel_loss=18.1, generator_kl_loss=1.436, generator_dur_loss=1.752, generator_adv_loss=1.934, generator_feat_match_loss=4.521, over 5456.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:23:03,678 INFO [train.py:919] (4/6) Start epoch 375 +2024-03-13 14:24:35,867 INFO [train.py:527] (4/6) Epoch 375, batch 24, global_batch_idx: 46400, batch size: 42, loss[discriminator_loss=2.685, discriminator_real_loss=1.397, discriminator_fake_loss=1.288, generator_loss=29.61, generator_mel_loss=19.33, generator_kl_loss=1.739, generator_dur_loss=1.679, generator_adv_loss=1.909, generator_feat_match_loss=4.953, over 42.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.396, discriminator_fake_loss=1.338, generator_loss=27.87, generator_mel_loss=18.26, generator_kl_loss=1.459, generator_dur_loss=1.741, generator_adv_loss=1.931, generator_feat_match_loss=4.476, over 1398.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:24:35,868 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 14:24:43,824 INFO [train.py:591] (4/6) Epoch 375, validation: discriminator_loss=2.756, discriminator_real_loss=1.434, discriminator_fake_loss=1.322, generator_loss=26.87, generator_mel_loss=18.2, generator_kl_loss=1.23, generator_dur_loss=1.825, generator_adv_loss=1.9, generator_feat_match_loss=3.715, over 100.00 samples. +2024-03-13 14:24:43,825 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 14:27:02,846 INFO [train.py:527] (4/6) Epoch 375, batch 74, global_batch_idx: 46450, batch size: 61, loss[discriminator_loss=2.658, discriminator_real_loss=1.319, discriminator_fake_loss=1.339, generator_loss=27.89, generator_mel_loss=18.28, generator_kl_loss=1.23, generator_dur_loss=1.78, generator_adv_loss=2.017, generator_feat_match_loss=4.586, over 61.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.89, generator_mel_loss=18.21, generator_kl_loss=1.421, generator_dur_loss=1.757, generator_adv_loss=1.93, generator_feat_match_loss=4.563, over 4114.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:29:21,229 INFO [train.py:919] (4/6) Start epoch 376 +2024-03-13 14:29:45,034 INFO [train.py:527] (4/6) Epoch 376, batch 0, global_batch_idx: 46500, batch size: 55, loss[discriminator_loss=2.677, discriminator_real_loss=1.377, discriminator_fake_loss=1.301, generator_loss=27.6, generator_mel_loss=18.28, generator_kl_loss=1.587, generator_dur_loss=1.759, generator_adv_loss=1.776, generator_feat_match_loss=4.199, over 55.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.377, discriminator_fake_loss=1.301, generator_loss=27.6, generator_mel_loss=18.28, generator_kl_loss=1.587, generator_dur_loss=1.759, generator_adv_loss=1.776, generator_feat_match_loss=4.199, over 55.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:32:04,778 INFO [train.py:527] (4/6) Epoch 376, batch 50, global_batch_idx: 46550, batch size: 31, loss[discriminator_loss=2.73, discriminator_real_loss=1.343, discriminator_fake_loss=1.388, generator_loss=28.04, generator_mel_loss=18.36, generator_kl_loss=1.688, generator_dur_loss=1.622, generator_adv_loss=2.06, generator_feat_match_loss=4.305, over 31.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=27.93, generator_mel_loss=18.17, generator_kl_loss=1.421, generator_dur_loss=1.751, generator_adv_loss=1.945, generator_feat_match_loss=4.643, over 2722.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:34:24,104 INFO [train.py:527] (4/6) Epoch 376, batch 100, global_batch_idx: 46600, batch size: 55, loss[discriminator_loss=2.764, discriminator_real_loss=1.323, discriminator_fake_loss=1.442, generator_loss=28.78, generator_mel_loss=18.84, generator_kl_loss=1.311, generator_dur_loss=1.729, generator_adv_loss=2.173, generator_feat_match_loss=4.733, over 55.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=28, generator_mel_loss=18.2, generator_kl_loss=1.428, generator_dur_loss=1.751, generator_adv_loss=1.966, generator_feat_match_loss=4.664, over 5496.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:34:24,105 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 14:34:32,873 INFO [train.py:591] (4/6) Epoch 376, validation: discriminator_loss=2.856, discriminator_real_loss=1.496, discriminator_fake_loss=1.36, generator_loss=26.78, generator_mel_loss=18.47, generator_kl_loss=1.237, generator_dur_loss=1.83, generator_adv_loss=1.915, generator_feat_match_loss=3.331, over 100.00 samples. +2024-03-13 14:34:32,874 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 14:35:34,063 INFO [train.py:919] (4/6) Start epoch 377 +2024-03-13 14:37:11,506 INFO [train.py:527] (4/6) Epoch 377, batch 26, global_batch_idx: 46650, batch size: 68, loss[discriminator_loss=2.735, discriminator_real_loss=1.371, discriminator_fake_loss=1.364, generator_loss=28.32, generator_mel_loss=18.38, generator_kl_loss=1.578, generator_dur_loss=1.79, generator_adv_loss=2.001, generator_feat_match_loss=4.575, over 68.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.381, discriminator_fake_loss=1.343, generator_loss=27.93, generator_mel_loss=18.27, generator_kl_loss=1.392, generator_dur_loss=1.769, generator_adv_loss=1.926, generator_feat_match_loss=4.573, over 1607.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:39:32,054 INFO [train.py:527] (4/6) Epoch 377, batch 76, global_batch_idx: 46700, batch size: 58, loss[discriminator_loss=2.738, discriminator_real_loss=1.374, discriminator_fake_loss=1.364, generator_loss=27.9, generator_mel_loss=18.15, generator_kl_loss=1.458, generator_dur_loss=1.724, generator_adv_loss=1.996, generator_feat_match_loss=4.579, over 58.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=27.86, generator_mel_loss=18.19, generator_kl_loss=1.386, generator_dur_loss=1.774, generator_adv_loss=1.932, generator_feat_match_loss=4.581, over 4599.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:41:41,336 INFO [train.py:919] (4/6) Start epoch 378 +2024-03-13 14:42:11,545 INFO [train.py:527] (4/6) Epoch 378, batch 2, global_batch_idx: 46750, batch size: 77, loss[discriminator_loss=2.714, discriminator_real_loss=1.416, discriminator_fake_loss=1.298, generator_loss=28.23, generator_mel_loss=18.29, generator_kl_loss=1.373, generator_dur_loss=1.826, generator_adv_loss=1.843, generator_feat_match_loss=4.901, over 77.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.388, discriminator_fake_loss=1.327, generator_loss=28.01, generator_mel_loss=18.15, generator_kl_loss=1.507, generator_dur_loss=1.771, generator_adv_loss=1.917, generator_feat_match_loss=4.667, over 153.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:44:34,240 INFO [train.py:527] (4/6) Epoch 378, batch 52, global_batch_idx: 46800, batch size: 62, loss[discriminator_loss=2.753, discriminator_real_loss=1.31, discriminator_fake_loss=1.443, generator_loss=28.03, generator_mel_loss=18.19, generator_kl_loss=1.47, generator_dur_loss=1.76, generator_adv_loss=1.825, generator_feat_match_loss=4.785, over 62.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=27.66, generator_mel_loss=18.09, generator_kl_loss=1.411, generator_dur_loss=1.767, generator_adv_loss=1.926, generator_feat_match_loss=4.462, over 3009.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:44:34,242 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 14:44:42,306 INFO [train.py:591] (4/6) Epoch 378, validation: discriminator_loss=2.745, discriminator_real_loss=1.341, discriminator_fake_loss=1.404, generator_loss=27.12, generator_mel_loss=18.6, generator_kl_loss=1.324, generator_dur_loss=1.814, generator_adv_loss=1.742, generator_feat_match_loss=3.636, over 100.00 samples. +2024-03-13 14:44:42,307 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 14:47:01,076 INFO [train.py:527] (4/6) Epoch 378, batch 102, global_batch_idx: 46850, batch size: 96, loss[discriminator_loss=2.685, discriminator_real_loss=1.325, discriminator_fake_loss=1.36, generator_loss=27.77, generator_mel_loss=18.06, generator_kl_loss=1.335, generator_dur_loss=1.856, generator_adv_loss=2.096, generator_feat_match_loss=4.421, over 96.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=27.78, generator_mel_loss=18.15, generator_kl_loss=1.413, generator_dur_loss=1.756, generator_adv_loss=1.941, generator_feat_match_loss=4.529, over 5741.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:47:56,277 INFO [train.py:919] (4/6) Start epoch 379 +2024-03-13 14:49:40,455 INFO [train.py:527] (4/6) Epoch 379, batch 28, global_batch_idx: 46900, batch size: 64, loss[discriminator_loss=2.68, discriminator_real_loss=1.423, discriminator_fake_loss=1.258, generator_loss=28.75, generator_mel_loss=18.62, generator_kl_loss=1.361, generator_dur_loss=1.805, generator_adv_loss=1.89, generator_feat_match_loss=5.068, over 64.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=27.91, generator_mel_loss=18.2, generator_kl_loss=1.387, generator_dur_loss=1.787, generator_adv_loss=1.928, generator_feat_match_loss=4.61, over 1935.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:51:57,953 INFO [train.py:527] (4/6) Epoch 379, batch 78, global_batch_idx: 46950, batch size: 50, loss[discriminator_loss=2.659, discriminator_real_loss=1.349, discriminator_fake_loss=1.311, generator_loss=28.41, generator_mel_loss=18.22, generator_kl_loss=1.49, generator_dur_loss=1.651, generator_adv_loss=1.978, generator_feat_match_loss=5.066, over 50.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=27.85, generator_mel_loss=18.17, generator_kl_loss=1.413, generator_dur_loss=1.763, generator_adv_loss=1.927, generator_feat_match_loss=4.576, over 4740.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:54:01,426 INFO [train.py:919] (4/6) Start epoch 380 +2024-03-13 14:54:36,685 INFO [train.py:527] (4/6) Epoch 380, batch 4, global_batch_idx: 47000, batch size: 55, loss[discriminator_loss=2.706, discriminator_real_loss=1.333, discriminator_fake_loss=1.373, generator_loss=28.55, generator_mel_loss=18.71, generator_kl_loss=1.498, generator_dur_loss=1.673, generator_adv_loss=2.043, generator_feat_match_loss=4.623, over 55.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.369, discriminator_fake_loss=1.345, generator_loss=27.91, generator_mel_loss=18.29, generator_kl_loss=1.42, generator_dur_loss=1.749, generator_adv_loss=1.948, generator_feat_match_loss=4.503, over 292.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:54:36,702 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 14:54:44,911 INFO [train.py:591] (4/6) Epoch 380, validation: discriminator_loss=2.783, discriminator_real_loss=1.544, discriminator_fake_loss=1.239, generator_loss=27.31, generator_mel_loss=18.45, generator_kl_loss=1.266, generator_dur_loss=1.819, generator_adv_loss=2.008, generator_feat_match_loss=3.769, over 100.00 samples. +2024-03-13 14:54:44,913 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 14:57:04,663 INFO [train.py:527] (4/6) Epoch 380, batch 54, global_batch_idx: 47050, batch size: 44, loss[discriminator_loss=2.69, discriminator_real_loss=1.335, discriminator_fake_loss=1.355, generator_loss=27.85, generator_mel_loss=17.64, generator_kl_loss=1.654, generator_dur_loss=1.714, generator_adv_loss=2.034, generator_feat_match_loss=4.81, over 44.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.382, discriminator_fake_loss=1.341, generator_loss=27.76, generator_mel_loss=18.11, generator_kl_loss=1.397, generator_dur_loss=1.773, generator_adv_loss=1.935, generator_feat_match_loss=4.553, over 3210.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:59:25,803 INFO [train.py:527] (4/6) Epoch 380, batch 104, global_batch_idx: 47100, batch size: 72, loss[discriminator_loss=2.649, discriminator_real_loss=1.357, discriminator_fake_loss=1.292, generator_loss=29.35, generator_mel_loss=18.77, generator_kl_loss=1.288, generator_dur_loss=1.857, generator_adv_loss=1.981, generator_feat_match_loss=5.448, over 72.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.378, discriminator_fake_loss=1.345, generator_loss=27.77, generator_mel_loss=18.1, generator_kl_loss=1.393, generator_dur_loss=1.772, generator_adv_loss=1.934, generator_feat_match_loss=4.568, over 6174.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:00:16,708 INFO [train.py:919] (4/6) Start epoch 381 +2024-03-13 15:02:04,240 INFO [train.py:527] (4/6) Epoch 381, batch 30, global_batch_idx: 47150, batch size: 44, loss[discriminator_loss=2.703, discriminator_real_loss=1.382, discriminator_fake_loss=1.321, generator_loss=27.74, generator_mel_loss=18.27, generator_kl_loss=1.51, generator_dur_loss=1.684, generator_adv_loss=1.909, generator_feat_match_loss=4.367, over 44.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.386, discriminator_fake_loss=1.356, generator_loss=27.66, generator_mel_loss=18.13, generator_kl_loss=1.382, generator_dur_loss=1.8, generator_adv_loss=1.92, generator_feat_match_loss=4.432, over 1883.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:04:25,743 INFO [train.py:527] (4/6) Epoch 381, batch 80, global_batch_idx: 47200, batch size: 68, loss[discriminator_loss=2.72, discriminator_real_loss=1.357, discriminator_fake_loss=1.363, generator_loss=27.48, generator_mel_loss=17.94, generator_kl_loss=1.216, generator_dur_loss=1.822, generator_adv_loss=1.881, generator_feat_match_loss=4.625, over 68.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.379, discriminator_fake_loss=1.35, generator_loss=27.81, generator_mel_loss=18.09, generator_kl_loss=1.384, generator_dur_loss=1.795, generator_adv_loss=1.975, generator_feat_match_loss=4.57, over 4979.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:04:25,744 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 15:04:34,804 INFO [train.py:591] (4/6) Epoch 381, validation: discriminator_loss=2.724, discriminator_real_loss=1.401, discriminator_fake_loss=1.323, generator_loss=26.88, generator_mel_loss=18.38, generator_kl_loss=1.162, generator_dur_loss=1.845, generator_adv_loss=1.823, generator_feat_match_loss=3.671, over 100.00 samples. +2024-03-13 15:04:34,805 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 15:06:34,083 INFO [train.py:919] (4/6) Start epoch 382 +2024-03-13 15:07:16,286 INFO [train.py:527] (4/6) Epoch 382, batch 6, global_batch_idx: 47250, batch size: 64, loss[discriminator_loss=2.647, discriminator_real_loss=1.324, discriminator_fake_loss=1.323, generator_loss=28, generator_mel_loss=17.93, generator_kl_loss=1.399, generator_dur_loss=1.822, generator_adv_loss=1.977, generator_feat_match_loss=4.871, over 64.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.35, discriminator_fake_loss=1.341, generator_loss=27.6, generator_mel_loss=17.99, generator_kl_loss=1.379, generator_dur_loss=1.763, generator_adv_loss=1.93, generator_feat_match_loss=4.541, over 365.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:09:36,768 INFO [train.py:527] (4/6) Epoch 382, batch 56, global_batch_idx: 47300, batch size: 48, loss[discriminator_loss=2.681, discriminator_real_loss=1.417, discriminator_fake_loss=1.265, generator_loss=29.08, generator_mel_loss=18.32, generator_kl_loss=1.539, generator_dur_loss=1.666, generator_adv_loss=1.804, generator_feat_match_loss=5.745, over 48.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.338, generator_loss=27.98, generator_mel_loss=18.12, generator_kl_loss=1.422, generator_dur_loss=1.776, generator_adv_loss=1.949, generator_feat_match_loss=4.707, over 3282.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:11:58,777 INFO [train.py:527] (4/6) Epoch 382, batch 106, global_batch_idx: 47350, batch size: 56, loss[discriminator_loss=2.757, discriminator_real_loss=1.445, discriminator_fake_loss=1.312, generator_loss=28.09, generator_mel_loss=18.26, generator_kl_loss=1.507, generator_dur_loss=1.732, generator_adv_loss=1.912, generator_feat_match_loss=4.67, over 56.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.368, discriminator_fake_loss=1.344, generator_loss=27.96, generator_mel_loss=18.14, generator_kl_loss=1.416, generator_dur_loss=1.777, generator_adv_loss=1.946, generator_feat_match_loss=4.679, over 6103.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:12:47,354 INFO [train.py:919] (4/6) Start epoch 383 +2024-03-13 15:14:45,968 INFO [train.py:527] (4/6) Epoch 383, batch 32, global_batch_idx: 47400, batch size: 55, loss[discriminator_loss=2.753, discriminator_real_loss=1.474, discriminator_fake_loss=1.279, generator_loss=28, generator_mel_loss=18.35, generator_kl_loss=1.603, generator_dur_loss=1.748, generator_adv_loss=1.895, generator_feat_match_loss=4.409, over 55.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.365, discriminator_fake_loss=1.355, generator_loss=27.84, generator_mel_loss=18.02, generator_kl_loss=1.403, generator_dur_loss=1.768, generator_adv_loss=1.942, generator_feat_match_loss=4.702, over 1875.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:14:45,969 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 15:14:54,036 INFO [train.py:591] (4/6) Epoch 383, validation: discriminator_loss=2.804, discriminator_real_loss=1.39, discriminator_fake_loss=1.414, generator_loss=27.16, generator_mel_loss=18.68, generator_kl_loss=1.291, generator_dur_loss=1.826, generator_adv_loss=1.773, generator_feat_match_loss=3.595, over 100.00 samples. +2024-03-13 15:14:54,036 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 15:17:15,691 INFO [train.py:527] (4/6) Epoch 383, batch 82, global_batch_idx: 47450, batch size: 42, loss[discriminator_loss=2.69, discriminator_real_loss=1.322, discriminator_fake_loss=1.368, generator_loss=28.37, generator_mel_loss=18.32, generator_kl_loss=1.507, generator_dur_loss=1.678, generator_adv_loss=2.064, generator_feat_match_loss=4.801, over 42.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.365, discriminator_fake_loss=1.356, generator_loss=27.82, generator_mel_loss=18.1, generator_kl_loss=1.408, generator_dur_loss=1.755, generator_adv_loss=1.938, generator_feat_match_loss=4.619, over 4720.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:19:12,278 INFO [train.py:919] (4/6) Start epoch 384 +2024-03-13 15:19:59,839 INFO [train.py:527] (4/6) Epoch 384, batch 8, global_batch_idx: 47500, batch size: 64, loss[discriminator_loss=2.735, discriminator_real_loss=1.429, discriminator_fake_loss=1.306, generator_loss=27.49, generator_mel_loss=17.82, generator_kl_loss=1.511, generator_dur_loss=1.768, generator_adv_loss=1.83, generator_feat_match_loss=4.562, over 64.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.371, discriminator_fake_loss=1.376, generator_loss=27.79, generator_mel_loss=18.08, generator_kl_loss=1.364, generator_dur_loss=1.789, generator_adv_loss=1.922, generator_feat_match_loss=4.635, over 613.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:22:23,474 INFO [train.py:527] (4/6) Epoch 384, batch 58, global_batch_idx: 47550, batch size: 50, loss[discriminator_loss=2.742, discriminator_real_loss=1.449, discriminator_fake_loss=1.293, generator_loss=27.65, generator_mel_loss=18.54, generator_kl_loss=1.476, generator_dur_loss=1.678, generator_adv_loss=1.932, generator_feat_match_loss=4.02, over 50.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.373, discriminator_fake_loss=1.348, generator_loss=27.82, generator_mel_loss=18.09, generator_kl_loss=1.423, generator_dur_loss=1.75, generator_adv_loss=1.939, generator_feat_match_loss=4.616, over 3407.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:24:42,670 INFO [train.py:527] (4/6) Epoch 384, batch 108, global_batch_idx: 47600, batch size: 13, loss[discriminator_loss=2.609, discriminator_real_loss=1.288, discriminator_fake_loss=1.321, generator_loss=29.25, generator_mel_loss=18.65, generator_kl_loss=1.387, generator_dur_loss=1.627, generator_adv_loss=2.068, generator_feat_match_loss=5.513, over 13.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=27.79, generator_mel_loss=18.11, generator_kl_loss=1.412, generator_dur_loss=1.741, generator_adv_loss=1.94, generator_feat_match_loss=4.596, over 6051.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:24:42,671 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 15:24:51,609 INFO [train.py:591] (4/6) Epoch 384, validation: discriminator_loss=2.74, discriminator_real_loss=1.424, discriminator_fake_loss=1.317, generator_loss=26.79, generator_mel_loss=18.36, generator_kl_loss=1.215, generator_dur_loss=1.759, generator_adv_loss=1.913, generator_feat_match_loss=3.538, over 100.00 samples. +2024-03-13 15:24:51,610 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 15:25:32,858 INFO [train.py:919] (4/6) Start epoch 385 +2024-03-13 15:27:32,035 INFO [train.py:527] (4/6) Epoch 385, batch 34, global_batch_idx: 47650, batch size: 25, loss[discriminator_loss=2.639, discriminator_real_loss=1.301, discriminator_fake_loss=1.338, generator_loss=29.66, generator_mel_loss=18.29, generator_kl_loss=1.658, generator_dur_loss=1.508, generator_adv_loss=2.052, generator_feat_match_loss=6.146, over 25.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.38, discriminator_fake_loss=1.338, generator_loss=27.88, generator_mel_loss=18.1, generator_kl_loss=1.44, generator_dur_loss=1.735, generator_adv_loss=1.95, generator_feat_match_loss=4.656, over 1945.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:29:55,422 INFO [train.py:527] (4/6) Epoch 385, batch 84, global_batch_idx: 47700, batch size: 88, loss[discriminator_loss=2.751, discriminator_real_loss=1.406, discriminator_fake_loss=1.346, generator_loss=27.99, generator_mel_loss=18.21, generator_kl_loss=1.286, generator_dur_loss=1.848, generator_adv_loss=1.837, generator_feat_match_loss=4.804, over 88.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.378, discriminator_fake_loss=1.344, generator_loss=27.85, generator_mel_loss=18.13, generator_kl_loss=1.416, generator_dur_loss=1.749, generator_adv_loss=1.944, generator_feat_match_loss=4.61, over 4874.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:31:46,421 INFO [train.py:919] (4/6) Start epoch 386 +2024-03-13 15:32:37,997 INFO [train.py:527] (4/6) Epoch 386, batch 10, global_batch_idx: 47750, batch size: 14, loss[discriminator_loss=2.772, discriminator_real_loss=1.217, discriminator_fake_loss=1.554, generator_loss=29.17, generator_mel_loss=18.97, generator_kl_loss=1.564, generator_dur_loss=1.691, generator_adv_loss=2.193, generator_feat_match_loss=4.744, over 14.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.354, discriminator_fake_loss=1.35, generator_loss=27.95, generator_mel_loss=18.12, generator_kl_loss=1.408, generator_dur_loss=1.771, generator_adv_loss=1.932, generator_feat_match_loss=4.72, over 596.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:34:59,605 INFO [train.py:527] (4/6) Epoch 386, batch 60, global_batch_idx: 47800, batch size: 68, loss[discriminator_loss=2.728, discriminator_real_loss=1.344, discriminator_fake_loss=1.384, generator_loss=28.06, generator_mel_loss=18.56, generator_kl_loss=1.427, generator_dur_loss=1.8, generator_adv_loss=1.861, generator_feat_match_loss=4.408, over 68.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=27.88, generator_mel_loss=18.16, generator_kl_loss=1.4, generator_dur_loss=1.76, generator_adv_loss=1.944, generator_feat_match_loss=4.616, over 3484.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:34:59,606 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 15:35:07,966 INFO [train.py:591] (4/6) Epoch 386, validation: discriminator_loss=2.777, discriminator_real_loss=1.402, discriminator_fake_loss=1.375, generator_loss=26.5, generator_mel_loss=18.08, generator_kl_loss=1.129, generator_dur_loss=1.808, generator_adv_loss=1.855, generator_feat_match_loss=3.628, over 100.00 samples. +2024-03-13 15:35:07,966 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 15:37:28,640 INFO [train.py:527] (4/6) Epoch 386, batch 110, global_batch_idx: 47850, batch size: 83, loss[discriminator_loss=2.711, discriminator_real_loss=1.377, discriminator_fake_loss=1.334, generator_loss=27.27, generator_mel_loss=17.78, generator_kl_loss=1.401, generator_dur_loss=1.782, generator_adv_loss=1.822, generator_feat_match_loss=4.491, over 83.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.81, generator_mel_loss=18.12, generator_kl_loss=1.413, generator_dur_loss=1.764, generator_adv_loss=1.935, generator_feat_match_loss=4.573, over 6518.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:38:05,305 INFO [train.py:919] (4/6) Start epoch 387 +2024-03-13 15:40:12,015 INFO [train.py:527] (4/6) Epoch 387, batch 36, global_batch_idx: 47900, batch size: 61, loss[discriminator_loss=2.716, discriminator_real_loss=1.356, discriminator_fake_loss=1.36, generator_loss=27.37, generator_mel_loss=17.96, generator_kl_loss=1.406, generator_dur_loss=1.726, generator_adv_loss=2.018, generator_feat_match_loss=4.258, over 61.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.388, discriminator_fake_loss=1.349, generator_loss=27.75, generator_mel_loss=18.11, generator_kl_loss=1.394, generator_dur_loss=1.763, generator_adv_loss=1.93, generator_feat_match_loss=4.559, over 2258.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:42:31,097 INFO [train.py:527] (4/6) Epoch 387, batch 86, global_batch_idx: 47950, batch size: 61, loss[discriminator_loss=2.724, discriminator_real_loss=1.259, discriminator_fake_loss=1.465, generator_loss=27.68, generator_mel_loss=17.74, generator_kl_loss=1.376, generator_dur_loss=1.783, generator_adv_loss=2.102, generator_feat_match_loss=4.684, over 61.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.378, discriminator_fake_loss=1.348, generator_loss=27.75, generator_mel_loss=18.06, generator_kl_loss=1.395, generator_dur_loss=1.761, generator_adv_loss=1.934, generator_feat_match_loss=4.597, over 5271.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:44:16,356 INFO [train.py:919] (4/6) Start epoch 388 +2024-03-13 15:45:14,000 INFO [train.py:527] (4/6) Epoch 388, batch 12, global_batch_idx: 48000, batch size: 55, loss[discriminator_loss=2.707, discriminator_real_loss=1.347, discriminator_fake_loss=1.36, generator_loss=28.3, generator_mel_loss=17.92, generator_kl_loss=1.559, generator_dur_loss=1.719, generator_adv_loss=2.068, generator_feat_match_loss=5.035, over 55.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.379, discriminator_fake_loss=1.35, generator_loss=27.82, generator_mel_loss=18.1, generator_kl_loss=1.435, generator_dur_loss=1.768, generator_adv_loss=1.932, generator_feat_match_loss=4.581, over 800.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:45:14,003 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 15:45:21,913 INFO [train.py:591] (4/6) Epoch 388, validation: discriminator_loss=2.76, discriminator_real_loss=1.511, discriminator_fake_loss=1.249, generator_loss=26.7, generator_mel_loss=18.05, generator_kl_loss=1.114, generator_dur_loss=1.821, generator_adv_loss=2.015, generator_feat_match_loss=3.705, over 100.00 samples. +2024-03-13 15:45:21,914 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 15:47:41,591 INFO [train.py:527] (4/6) Epoch 388, batch 62, global_batch_idx: 48050, batch size: 53, loss[discriminator_loss=2.653, discriminator_real_loss=1.35, discriminator_fake_loss=1.303, generator_loss=26.54, generator_mel_loss=17.62, generator_kl_loss=1.32, generator_dur_loss=1.741, generator_adv_loss=1.881, generator_feat_match_loss=3.975, over 53.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.377, discriminator_fake_loss=1.336, generator_loss=27.98, generator_mel_loss=18.16, generator_kl_loss=1.421, generator_dur_loss=1.765, generator_adv_loss=1.941, generator_feat_match_loss=4.691, over 3750.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:50:02,381 INFO [train.py:527] (4/6) Epoch 388, batch 112, global_batch_idx: 48100, batch size: 31, loss[discriminator_loss=2.723, discriminator_real_loss=1.44, discriminator_fake_loss=1.282, generator_loss=27.72, generator_mel_loss=18.4, generator_kl_loss=1.613, generator_dur_loss=1.637, generator_adv_loss=1.937, generator_feat_match_loss=4.135, over 31.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=27.95, generator_mel_loss=18.15, generator_kl_loss=1.423, generator_dur_loss=1.76, generator_adv_loss=1.943, generator_feat_match_loss=4.68, over 6337.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:50:35,222 INFO [train.py:919] (4/6) Start epoch 389 +2024-03-13 15:52:48,388 INFO [train.py:527] (4/6) Epoch 389, batch 38, global_batch_idx: 48150, batch size: 53, loss[discriminator_loss=2.701, discriminator_real_loss=1.376, discriminator_fake_loss=1.324, generator_loss=27.49, generator_mel_loss=17.8, generator_kl_loss=1.446, generator_dur_loss=1.701, generator_adv_loss=2.052, generator_feat_match_loss=4.5, over 53.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.374, discriminator_fake_loss=1.344, generator_loss=27.75, generator_mel_loss=18.1, generator_kl_loss=1.386, generator_dur_loss=1.755, generator_adv_loss=1.955, generator_feat_match_loss=4.551, over 2223.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:55:12,726 INFO [train.py:527] (4/6) Epoch 389, batch 88, global_batch_idx: 48200, batch size: 17, loss[discriminator_loss=2.772, discriminator_real_loss=1.559, discriminator_fake_loss=1.213, generator_loss=29.37, generator_mel_loss=18.56, generator_kl_loss=1.724, generator_dur_loss=1.588, generator_adv_loss=1.938, generator_feat_match_loss=5.56, over 17.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.371, discriminator_fake_loss=1.351, generator_loss=27.87, generator_mel_loss=18.12, generator_kl_loss=1.378, generator_dur_loss=1.779, generator_adv_loss=1.945, generator_feat_match_loss=4.643, over 5340.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:55:12,728 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 15:55:21,782 INFO [train.py:591] (4/6) Epoch 389, validation: discriminator_loss=2.788, discriminator_real_loss=1.392, discriminator_fake_loss=1.397, generator_loss=27.36, generator_mel_loss=18.79, generator_kl_loss=1.243, generator_dur_loss=1.835, generator_adv_loss=1.774, generator_feat_match_loss=3.719, over 100.00 samples. +2024-03-13 15:55:21,782 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 15:56:57,461 INFO [train.py:919] (4/6) Start epoch 390 +2024-03-13 15:58:02,125 INFO [train.py:527] (4/6) Epoch 390, batch 14, global_batch_idx: 48250, batch size: 96, loss[discriminator_loss=2.719, discriminator_real_loss=1.311, discriminator_fake_loss=1.409, generator_loss=27.51, generator_mel_loss=17.81, generator_kl_loss=1.313, generator_dur_loss=1.917, generator_adv_loss=2.018, generator_feat_match_loss=4.452, over 96.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.373, discriminator_fake_loss=1.355, generator_loss=27.74, generator_mel_loss=18.12, generator_kl_loss=1.397, generator_dur_loss=1.774, generator_adv_loss=1.923, generator_feat_match_loss=4.525, over 908.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 16:00:21,684 INFO [train.py:527] (4/6) Epoch 390, batch 64, global_batch_idx: 48300, batch size: 64, loss[discriminator_loss=2.673, discriminator_real_loss=1.431, discriminator_fake_loss=1.242, generator_loss=28.26, generator_mel_loss=18.26, generator_kl_loss=1.417, generator_dur_loss=1.825, generator_adv_loss=1.939, generator_feat_match_loss=4.819, over 64.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.369, discriminator_fake_loss=1.349, generator_loss=27.93, generator_mel_loss=18.16, generator_kl_loss=1.404, generator_dur_loss=1.749, generator_adv_loss=1.954, generator_feat_match_loss=4.666, over 3555.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 16:02:39,591 INFO [train.py:527] (4/6) Epoch 390, batch 114, global_batch_idx: 48350, batch size: 36, loss[discriminator_loss=2.779, discriminator_real_loss=1.464, discriminator_fake_loss=1.315, generator_loss=28.8, generator_mel_loss=18.93, generator_kl_loss=1.665, generator_dur_loss=1.753, generator_adv_loss=1.955, generator_feat_match_loss=4.503, over 36.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=27.92, generator_mel_loss=18.13, generator_kl_loss=1.412, generator_dur_loss=1.758, generator_adv_loss=1.964, generator_feat_match_loss=4.658, over 6226.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 16:03:05,576 INFO [train.py:919] (4/6) Start epoch 391 +2024-03-13 16:05:21,134 INFO [train.py:527] (4/6) Epoch 391, batch 40, global_batch_idx: 48400, batch size: 52, loss[discriminator_loss=2.736, discriminator_real_loss=1.28, discriminator_fake_loss=1.456, generator_loss=27.8, generator_mel_loss=17.78, generator_kl_loss=1.279, generator_dur_loss=1.671, generator_adv_loss=1.967, generator_feat_match_loss=5.094, over 52.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.379, discriminator_fake_loss=1.349, generator_loss=27.63, generator_mel_loss=18.03, generator_kl_loss=1.38, generator_dur_loss=1.769, generator_adv_loss=1.927, generator_feat_match_loss=4.526, over 2389.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:05:21,136 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 16:05:28,906 INFO [train.py:591] (4/6) Epoch 391, validation: discriminator_loss=2.794, discriminator_real_loss=1.506, discriminator_fake_loss=1.288, generator_loss=27.61, generator_mel_loss=18.79, generator_kl_loss=1.207, generator_dur_loss=1.81, generator_adv_loss=1.969, generator_feat_match_loss=3.84, over 100.00 samples. +2024-03-13 16:05:28,907 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 16:07:47,760 INFO [train.py:527] (4/6) Epoch 391, batch 90, global_batch_idx: 48450, batch size: 88, loss[discriminator_loss=2.744, discriminator_real_loss=1.461, discriminator_fake_loss=1.283, generator_loss=27.66, generator_mel_loss=18.01, generator_kl_loss=1.312, generator_dur_loss=1.834, generator_adv_loss=1.788, generator_feat_match_loss=4.72, over 88.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.38, discriminator_fake_loss=1.339, generator_loss=27.81, generator_mel_loss=18.09, generator_kl_loss=1.417, generator_dur_loss=1.756, generator_adv_loss=1.935, generator_feat_match_loss=4.603, over 4999.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:09:22,917 INFO [train.py:919] (4/6) Start epoch 392 +2024-03-13 16:10:32,254 INFO [train.py:527] (4/6) Epoch 392, batch 16, global_batch_idx: 48500, batch size: 55, loss[discriminator_loss=2.72, discriminator_real_loss=1.496, discriminator_fake_loss=1.224, generator_loss=27.55, generator_mel_loss=18.05, generator_kl_loss=1.47, generator_dur_loss=1.684, generator_adv_loss=1.873, generator_feat_match_loss=4.466, over 55.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=27.93, generator_mel_loss=18.1, generator_kl_loss=1.405, generator_dur_loss=1.774, generator_adv_loss=1.948, generator_feat_match_loss=4.706, over 1013.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:12:50,704 INFO [train.py:527] (4/6) Epoch 392, batch 66, global_batch_idx: 48550, batch size: 64, loss[discriminator_loss=2.744, discriminator_real_loss=1.356, discriminator_fake_loss=1.388, generator_loss=27.7, generator_mel_loss=18.02, generator_kl_loss=1.279, generator_dur_loss=1.821, generator_adv_loss=2.055, generator_feat_match_loss=4.52, over 64.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.338, generator_loss=27.87, generator_mel_loss=18.1, generator_kl_loss=1.44, generator_dur_loss=1.751, generator_adv_loss=1.95, generator_feat_match_loss=4.632, over 3721.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:15:11,513 INFO [train.py:527] (4/6) Epoch 392, batch 116, global_batch_idx: 48600, batch size: 59, loss[discriminator_loss=2.8, discriminator_real_loss=1.424, discriminator_fake_loss=1.376, generator_loss=27.42, generator_mel_loss=18.34, generator_kl_loss=1.412, generator_dur_loss=1.769, generator_adv_loss=1.872, generator_feat_match_loss=4.024, over 59.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.373, discriminator_fake_loss=1.339, generator_loss=27.96, generator_mel_loss=18.15, generator_kl_loss=1.423, generator_dur_loss=1.757, generator_adv_loss=1.949, generator_feat_match_loss=4.682, over 6789.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:15:11,514 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 16:15:20,601 INFO [train.py:591] (4/6) Epoch 392, validation: discriminator_loss=2.784, discriminator_real_loss=1.422, discriminator_fake_loss=1.362, generator_loss=26.41, generator_mel_loss=18.19, generator_kl_loss=1.158, generator_dur_loss=1.815, generator_adv_loss=1.784, generator_feat_match_loss=3.458, over 100.00 samples. +2024-03-13 16:15:20,602 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 16:15:41,014 INFO [train.py:919] (4/6) Start epoch 393 +2024-03-13 16:18:01,844 INFO [train.py:527] (4/6) Epoch 393, batch 42, global_batch_idx: 48650, batch size: 58, loss[discriminator_loss=2.67, discriminator_real_loss=1.379, discriminator_fake_loss=1.29, generator_loss=27.29, generator_mel_loss=18.01, generator_kl_loss=1.339, generator_dur_loss=1.761, generator_adv_loss=2.002, generator_feat_match_loss=4.183, over 58.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.375, discriminator_fake_loss=1.333, generator_loss=28.01, generator_mel_loss=18.15, generator_kl_loss=1.413, generator_dur_loss=1.753, generator_adv_loss=1.964, generator_feat_match_loss=4.725, over 2157.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:20:18,337 INFO [train.py:527] (4/6) Epoch 393, batch 92, global_batch_idx: 48700, batch size: 74, loss[discriminator_loss=2.727, discriminator_real_loss=1.344, discriminator_fake_loss=1.383, generator_loss=27.4, generator_mel_loss=18.06, generator_kl_loss=1.331, generator_dur_loss=1.834, generator_adv_loss=1.931, generator_feat_match_loss=4.246, over 74.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.375, discriminator_fake_loss=1.335, generator_loss=27.91, generator_mel_loss=18.1, generator_kl_loss=1.418, generator_dur_loss=1.743, generator_adv_loss=1.956, generator_feat_match_loss=4.69, over 4978.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:21:48,615 INFO [train.py:919] (4/6) Start epoch 394 +2024-03-13 16:23:03,810 INFO [train.py:527] (4/6) Epoch 394, batch 18, global_batch_idx: 48750, batch size: 58, loss[discriminator_loss=2.742, discriminator_real_loss=1.453, discriminator_fake_loss=1.289, generator_loss=28.02, generator_mel_loss=18.3, generator_kl_loss=1.514, generator_dur_loss=1.758, generator_adv_loss=1.903, generator_feat_match_loss=4.54, over 58.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.385, discriminator_fake_loss=1.33, generator_loss=27.67, generator_mel_loss=18.05, generator_kl_loss=1.432, generator_dur_loss=1.734, generator_adv_loss=1.94, generator_feat_match_loss=4.517, over 1082.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:25:26,162 INFO [train.py:527] (4/6) Epoch 394, batch 68, global_batch_idx: 48800, batch size: 68, loss[discriminator_loss=2.726, discriminator_real_loss=1.48, discriminator_fake_loss=1.247, generator_loss=27.61, generator_mel_loss=17.86, generator_kl_loss=1.398, generator_dur_loss=1.784, generator_adv_loss=1.921, generator_feat_match_loss=4.644, over 68.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.38, discriminator_fake_loss=1.334, generator_loss=27.88, generator_mel_loss=18.04, generator_kl_loss=1.402, generator_dur_loss=1.756, generator_adv_loss=1.96, generator_feat_match_loss=4.716, over 4033.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:25:26,164 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 16:25:34,363 INFO [train.py:591] (4/6) Epoch 394, validation: discriminator_loss=2.725, discriminator_real_loss=1.438, discriminator_fake_loss=1.287, generator_loss=26.54, generator_mel_loss=18.13, generator_kl_loss=1.228, generator_dur_loss=1.831, generator_adv_loss=1.875, generator_feat_match_loss=3.482, over 100.00 samples. +2024-03-13 16:25:34,364 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 16:27:50,264 INFO [train.py:527] (4/6) Epoch 394, batch 118, global_batch_idx: 48850, batch size: 55, loss[discriminator_loss=2.73, discriminator_real_loss=1.377, discriminator_fake_loss=1.353, generator_loss=27.71, generator_mel_loss=18.3, generator_kl_loss=1.346, generator_dur_loss=1.768, generator_adv_loss=1.895, generator_feat_match_loss=4.396, over 55.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=27.93, generator_mel_loss=18.09, generator_kl_loss=1.403, generator_dur_loss=1.756, generator_adv_loss=1.958, generator_feat_match_loss=4.722, over 6899.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:28:05,093 INFO [train.py:919] (4/6) Start epoch 395 +2024-03-13 16:30:31,070 INFO [train.py:527] (4/6) Epoch 395, batch 44, global_batch_idx: 48900, batch size: 42, loss[discriminator_loss=2.696, discriminator_real_loss=1.37, discriminator_fake_loss=1.327, generator_loss=29.23, generator_mel_loss=18.2, generator_kl_loss=1.594, generator_dur_loss=1.665, generator_adv_loss=2.077, generator_feat_match_loss=5.69, over 42.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.373, discriminator_fake_loss=1.348, generator_loss=27.93, generator_mel_loss=18.11, generator_kl_loss=1.416, generator_dur_loss=1.761, generator_adv_loss=1.935, generator_feat_match_loss=4.705, over 2576.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:32:51,092 INFO [train.py:527] (4/6) Epoch 395, batch 94, global_batch_idx: 48950, batch size: 16, loss[discriminator_loss=2.696, discriminator_real_loss=1.337, discriminator_fake_loss=1.359, generator_loss=30.04, generator_mel_loss=19.41, generator_kl_loss=1.838, generator_dur_loss=1.591, generator_adv_loss=2.145, generator_feat_match_loss=5.062, over 16.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.37, discriminator_fake_loss=1.347, generator_loss=27.99, generator_mel_loss=18.15, generator_kl_loss=1.42, generator_dur_loss=1.761, generator_adv_loss=1.936, generator_feat_match_loss=4.722, over 5562.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:34:12,619 INFO [train.py:919] (4/6) Start epoch 396 +2024-03-13 16:35:34,931 INFO [train.py:527] (4/6) Epoch 396, batch 20, global_batch_idx: 49000, batch size: 44, loss[discriminator_loss=2.761, discriminator_real_loss=1.441, discriminator_fake_loss=1.32, generator_loss=27.19, generator_mel_loss=17.64, generator_kl_loss=1.334, generator_dur_loss=1.72, generator_adv_loss=1.914, generator_feat_match_loss=4.588, over 44.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=27.75, generator_mel_loss=18.03, generator_kl_loss=1.392, generator_dur_loss=1.788, generator_adv_loss=1.935, generator_feat_match_loss=4.612, over 1236.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:35:34,933 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 16:35:43,002 INFO [train.py:591] (4/6) Epoch 396, validation: discriminator_loss=2.766, discriminator_real_loss=1.413, discriminator_fake_loss=1.353, generator_loss=27.43, generator_mel_loss=18.44, generator_kl_loss=1.212, generator_dur_loss=1.83, generator_adv_loss=1.883, generator_feat_match_loss=4.072, over 100.00 samples. +2024-03-13 16:35:43,003 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 16:37:59,756 INFO [train.py:527] (4/6) Epoch 396, batch 70, global_batch_idx: 49050, batch size: 88, loss[discriminator_loss=2.691, discriminator_real_loss=1.395, discriminator_fake_loss=1.296, generator_loss=28.11, generator_mel_loss=18.21, generator_kl_loss=1.301, generator_dur_loss=1.851, generator_adv_loss=1.883, generator_feat_match_loss=4.867, over 88.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.372, discriminator_fake_loss=1.347, generator_loss=27.9, generator_mel_loss=18.12, generator_kl_loss=1.419, generator_dur_loss=1.763, generator_adv_loss=1.935, generator_feat_match_loss=4.664, over 3903.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:40:17,428 INFO [train.py:527] (4/6) Epoch 396, batch 120, global_batch_idx: 49100, batch size: 56, loss[discriminator_loss=2.674, discriminator_real_loss=1.298, discriminator_fake_loss=1.375, generator_loss=29.08, generator_mel_loss=18.37, generator_kl_loss=1.413, generator_dur_loss=1.658, generator_adv_loss=2.205, generator_feat_match_loss=5.431, over 56.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.366, discriminator_fake_loss=1.347, generator_loss=28.04, generator_mel_loss=18.17, generator_kl_loss=1.419, generator_dur_loss=1.754, generator_adv_loss=1.952, generator_feat_match_loss=4.747, over 6637.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:40:26,661 INFO [train.py:919] (4/6) Start epoch 397 +2024-03-13 16:42:57,349 INFO [train.py:527] (4/6) Epoch 397, batch 46, global_batch_idx: 49150, batch size: 59, loss[discriminator_loss=2.698, discriminator_real_loss=1.392, discriminator_fake_loss=1.307, generator_loss=27.37, generator_mel_loss=18.29, generator_kl_loss=1.424, generator_dur_loss=1.682, generator_adv_loss=1.911, generator_feat_match_loss=4.059, over 59.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.382, discriminator_fake_loss=1.338, generator_loss=27.8, generator_mel_loss=18.06, generator_kl_loss=1.417, generator_dur_loss=1.744, generator_adv_loss=1.943, generator_feat_match_loss=4.634, over 2797.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:45:16,665 INFO [train.py:527] (4/6) Epoch 397, batch 96, global_batch_idx: 49200, batch size: 13, loss[discriminator_loss=2.781, discriminator_real_loss=1.489, discriminator_fake_loss=1.291, generator_loss=29.68, generator_mel_loss=18.69, generator_kl_loss=1.835, generator_dur_loss=1.553, generator_adv_loss=1.782, generator_feat_match_loss=5.816, over 13.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.94, generator_mel_loss=18.14, generator_kl_loss=1.423, generator_dur_loss=1.733, generator_adv_loss=1.94, generator_feat_match_loss=4.703, over 5688.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:45:16,667 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 16:45:25,872 INFO [train.py:591] (4/6) Epoch 397, validation: discriminator_loss=2.879, discriminator_real_loss=1.341, discriminator_fake_loss=1.538, generator_loss=26.4, generator_mel_loss=18.34, generator_kl_loss=1.162, generator_dur_loss=1.801, generator_adv_loss=1.615, generator_feat_match_loss=3.478, over 100.00 samples. +2024-03-13 16:45:25,873 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 16:46:39,731 INFO [train.py:919] (4/6) Start epoch 398 +2024-03-13 16:48:06,593 INFO [train.py:527] (4/6) Epoch 398, batch 22, global_batch_idx: 49250, batch size: 74, loss[discriminator_loss=2.745, discriminator_real_loss=1.47, discriminator_fake_loss=1.275, generator_loss=27.65, generator_mel_loss=18, generator_kl_loss=1.313, generator_dur_loss=1.748, generator_adv_loss=2.071, generator_feat_match_loss=4.519, over 74.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.372, discriminator_fake_loss=1.327, generator_loss=27.92, generator_mel_loss=18.14, generator_kl_loss=1.406, generator_dur_loss=1.737, generator_adv_loss=1.971, generator_feat_match_loss=4.673, over 1380.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:50:27,735 INFO [train.py:527] (4/6) Epoch 398, batch 72, global_batch_idx: 49300, batch size: 42, loss[discriminator_loss=2.724, discriminator_real_loss=1.383, discriminator_fake_loss=1.341, generator_loss=27.56, generator_mel_loss=18.16, generator_kl_loss=1.504, generator_dur_loss=1.625, generator_adv_loss=1.902, generator_feat_match_loss=4.373, over 42.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.382, discriminator_fake_loss=1.339, generator_loss=27.95, generator_mel_loss=18.18, generator_kl_loss=1.444, generator_dur_loss=1.713, generator_adv_loss=1.943, generator_feat_match_loss=4.667, over 4040.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:52:46,006 INFO [train.py:527] (4/6) Epoch 398, batch 122, global_batch_idx: 49350, batch size: 59, loss[discriminator_loss=2.765, discriminator_real_loss=1.437, discriminator_fake_loss=1.327, generator_loss=28.15, generator_mel_loss=18.58, generator_kl_loss=1.409, generator_dur_loss=1.697, generator_adv_loss=1.889, generator_feat_match_loss=4.573, over 59.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.382, discriminator_fake_loss=1.342, generator_loss=27.95, generator_mel_loss=18.2, generator_kl_loss=1.438, generator_dur_loss=1.715, generator_adv_loss=1.937, generator_feat_match_loss=4.66, over 6658.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:52:51,436 INFO [train.py:919] (4/6) Start epoch 399 +2024-03-13 16:55:32,719 INFO [train.py:527] (4/6) Epoch 399, batch 48, global_batch_idx: 49400, batch size: 66, loss[discriminator_loss=2.804, discriminator_real_loss=1.433, discriminator_fake_loss=1.371, generator_loss=27.74, generator_mel_loss=17.83, generator_kl_loss=1.461, generator_dur_loss=1.716, generator_adv_loss=1.768, generator_feat_match_loss=4.967, over 66.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.389, discriminator_fake_loss=1.344, generator_loss=27.83, generator_mel_loss=18.13, generator_kl_loss=1.452, generator_dur_loss=1.724, generator_adv_loss=1.937, generator_feat_match_loss=4.589, over 2776.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:55:32,720 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 16:55:40,681 INFO [train.py:591] (4/6) Epoch 399, validation: discriminator_loss=2.758, discriminator_real_loss=1.359, discriminator_fake_loss=1.399, generator_loss=26.71, generator_mel_loss=17.97, generator_kl_loss=1.191, generator_dur_loss=1.789, generator_adv_loss=1.772, generator_feat_match_loss=3.995, over 100.00 samples. +2024-03-13 16:55:40,681 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 16:58:02,358 INFO [train.py:527] (4/6) Epoch 399, batch 98, global_batch_idx: 49450, batch size: 70, loss[discriminator_loss=2.764, discriminator_real_loss=1.371, discriminator_fake_loss=1.393, generator_loss=27.01, generator_mel_loss=17.61, generator_kl_loss=1.318, generator_dur_loss=1.817, generator_adv_loss=1.956, generator_feat_match_loss=4.307, over 70.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.388, discriminator_fake_loss=1.343, generator_loss=27.81, generator_mel_loss=18.09, generator_kl_loss=1.432, generator_dur_loss=1.742, generator_adv_loss=1.951, generator_feat_match_loss=4.59, over 5669.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:59:12,850 INFO [train.py:919] (4/6) Start epoch 400 +2024-03-13 17:00:45,607 INFO [train.py:527] (4/6) Epoch 400, batch 24, global_batch_idx: 49500, batch size: 56, loss[discriminator_loss=2.714, discriminator_real_loss=1.439, discriminator_fake_loss=1.275, generator_loss=28.64, generator_mel_loss=18.53, generator_kl_loss=1.509, generator_dur_loss=1.656, generator_adv_loss=1.846, generator_feat_match_loss=5.102, over 56.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.379, discriminator_fake_loss=1.347, generator_loss=27.97, generator_mel_loss=18.21, generator_kl_loss=1.465, generator_dur_loss=1.76, generator_adv_loss=1.93, generator_feat_match_loss=4.604, over 1425.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:03:05,353 INFO [train.py:527] (4/6) Epoch 400, batch 74, global_batch_idx: 49550, batch size: 48, loss[discriminator_loss=2.688, discriminator_real_loss=1.341, discriminator_fake_loss=1.347, generator_loss=26.67, generator_mel_loss=17.27, generator_kl_loss=1.489, generator_dur_loss=1.742, generator_adv_loss=1.938, generator_feat_match_loss=4.226, over 48.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.371, discriminator_fake_loss=1.348, generator_loss=27.88, generator_mel_loss=18.14, generator_kl_loss=1.432, generator_dur_loss=1.766, generator_adv_loss=1.929, generator_feat_match_loss=4.607, over 4392.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:05:20,548 INFO [train.py:919] (4/6) Start epoch 401 +2024-03-13 17:05:44,943 INFO [train.py:527] (4/6) Epoch 401, batch 0, global_batch_idx: 49600, batch size: 48, loss[discriminator_loss=2.774, discriminator_real_loss=1.464, discriminator_fake_loss=1.31, generator_loss=27.96, generator_mel_loss=18.34, generator_kl_loss=1.427, generator_dur_loss=1.706, generator_adv_loss=1.819, generator_feat_match_loss=4.668, over 48.00 samples.], tot_loss[discriminator_loss=2.774, discriminator_real_loss=1.464, discriminator_fake_loss=1.31, generator_loss=27.96, generator_mel_loss=18.34, generator_kl_loss=1.427, generator_dur_loss=1.706, generator_adv_loss=1.819, generator_feat_match_loss=4.668, over 48.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:05:44,968 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 17:05:52,962 INFO [train.py:591] (4/6) Epoch 401, validation: discriminator_loss=2.752, discriminator_real_loss=1.332, discriminator_fake_loss=1.42, generator_loss=26.54, generator_mel_loss=18.37, generator_kl_loss=1.296, generator_dur_loss=1.826, generator_adv_loss=1.745, generator_feat_match_loss=3.304, over 100.00 samples. +2024-03-13 17:05:52,964 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 17:08:10,699 INFO [train.py:527] (4/6) Epoch 401, batch 50, global_batch_idx: 49650, batch size: 96, loss[discriminator_loss=2.677, discriminator_real_loss=1.313, discriminator_fake_loss=1.364, generator_loss=26.84, generator_mel_loss=17.35, generator_kl_loss=1.313, generator_dur_loss=1.9, generator_adv_loss=1.931, generator_feat_match_loss=4.349, over 96.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.369, discriminator_fake_loss=1.345, generator_loss=27.73, generator_mel_loss=18.02, generator_kl_loss=1.413, generator_dur_loss=1.749, generator_adv_loss=1.949, generator_feat_match_loss=4.6, over 2740.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:10:29,586 INFO [train.py:527] (4/6) Epoch 401, batch 100, global_batch_idx: 49700, batch size: 48, loss[discriminator_loss=2.656, discriminator_real_loss=1.385, discriminator_fake_loss=1.271, generator_loss=28.6, generator_mel_loss=18.5, generator_kl_loss=1.421, generator_dur_loss=1.676, generator_adv_loss=2.017, generator_feat_match_loss=4.985, over 48.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=27.82, generator_mel_loss=18.05, generator_kl_loss=1.414, generator_dur_loss=1.757, generator_adv_loss=1.944, generator_feat_match_loss=4.654, over 5592.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:11:35,787 INFO [train.py:919] (4/6) Start epoch 402 +2024-03-13 17:13:12,070 INFO [train.py:527] (4/6) Epoch 402, batch 26, global_batch_idx: 49750, batch size: 74, loss[discriminator_loss=2.82, discriminator_real_loss=1.576, discriminator_fake_loss=1.243, generator_loss=27.61, generator_mel_loss=18.32, generator_kl_loss=1.284, generator_dur_loss=1.8, generator_adv_loss=1.781, generator_feat_match_loss=4.425, over 74.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.354, discriminator_fake_loss=1.35, generator_loss=27.99, generator_mel_loss=18.11, generator_kl_loss=1.364, generator_dur_loss=1.782, generator_adv_loss=2.006, generator_feat_match_loss=4.729, over 1651.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:15:32,520 INFO [train.py:527] (4/6) Epoch 402, batch 76, global_batch_idx: 49800, batch size: 58, loss[discriminator_loss=2.74, discriminator_real_loss=1.454, discriminator_fake_loss=1.286, generator_loss=27.62, generator_mel_loss=18.13, generator_kl_loss=1.44, generator_dur_loss=1.779, generator_adv_loss=1.864, generator_feat_match_loss=4.405, over 58.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.374, discriminator_fake_loss=1.339, generator_loss=27.93, generator_mel_loss=18.09, generator_kl_loss=1.383, generator_dur_loss=1.779, generator_adv_loss=1.977, generator_feat_match_loss=4.701, over 4672.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:15:32,521 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 17:15:40,609 INFO [train.py:591] (4/6) Epoch 402, validation: discriminator_loss=2.792, discriminator_real_loss=1.35, discriminator_fake_loss=1.442, generator_loss=27.21, generator_mel_loss=18.53, generator_kl_loss=1.328, generator_dur_loss=1.833, generator_adv_loss=1.747, generator_feat_match_loss=3.771, over 100.00 samples. +2024-03-13 17:15:40,610 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 17:17:51,362 INFO [train.py:919] (4/6) Start epoch 403 +2024-03-13 17:18:19,579 INFO [train.py:527] (4/6) Epoch 403, batch 2, global_batch_idx: 49850, batch size: 44, loss[discriminator_loss=2.683, discriminator_real_loss=1.301, discriminator_fake_loss=1.382, generator_loss=28.35, generator_mel_loss=18.4, generator_kl_loss=1.469, generator_dur_loss=1.695, generator_adv_loss=1.91, generator_feat_match_loss=4.877, over 44.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.338, discriminator_fake_loss=1.33, generator_loss=27.94, generator_mel_loss=18.09, generator_kl_loss=1.417, generator_dur_loss=1.79, generator_adv_loss=1.942, generator_feat_match_loss=4.701, over 211.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:20:38,887 INFO [train.py:527] (4/6) Epoch 403, batch 52, global_batch_idx: 49900, batch size: 39, loss[discriminator_loss=2.786, discriminator_real_loss=1.455, discriminator_fake_loss=1.331, generator_loss=28.54, generator_mel_loss=18.54, generator_kl_loss=1.475, generator_dur_loss=1.677, generator_adv_loss=2.017, generator_feat_match_loss=4.823, over 39.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.378, discriminator_fake_loss=1.34, generator_loss=27.84, generator_mel_loss=18.05, generator_kl_loss=1.415, generator_dur_loss=1.766, generator_adv_loss=1.939, generator_feat_match_loss=4.667, over 3036.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:22:55,455 INFO [train.py:527] (4/6) Epoch 403, batch 102, global_batch_idx: 49950, batch size: 62, loss[discriminator_loss=2.696, discriminator_real_loss=1.376, discriminator_fake_loss=1.32, generator_loss=28.31, generator_mel_loss=17.87, generator_kl_loss=1.354, generator_dur_loss=1.757, generator_adv_loss=1.999, generator_feat_match_loss=5.321, over 62.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.377, discriminator_fake_loss=1.336, generator_loss=27.87, generator_mel_loss=18.09, generator_kl_loss=1.418, generator_dur_loss=1.764, generator_adv_loss=1.94, generator_feat_match_loss=4.651, over 5934.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:23:56,832 INFO [train.py:919] (4/6) Start epoch 404 +2024-03-13 17:25:39,405 INFO [train.py:527] (4/6) Epoch 404, batch 28, global_batch_idx: 50000, batch size: 31, loss[discriminator_loss=2.629, discriminator_real_loss=1.302, discriminator_fake_loss=1.328, generator_loss=28.91, generator_mel_loss=18.38, generator_kl_loss=1.688, generator_dur_loss=1.613, generator_adv_loss=1.958, generator_feat_match_loss=5.269, over 31.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.343, generator_loss=27.84, generator_mel_loss=18.06, generator_kl_loss=1.43, generator_dur_loss=1.759, generator_adv_loss=1.928, generator_feat_match_loss=4.662, over 1661.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:25:39,406 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 17:25:47,417 INFO [train.py:591] (4/6) Epoch 404, validation: discriminator_loss=2.75, discriminator_real_loss=1.455, discriminator_fake_loss=1.296, generator_loss=26.89, generator_mel_loss=18.19, generator_kl_loss=1.2, generator_dur_loss=1.796, generator_adv_loss=1.865, generator_feat_match_loss=3.839, over 100.00 samples. +2024-03-13 17:25:47,418 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 17:28:05,603 INFO [train.py:527] (4/6) Epoch 404, batch 78, global_batch_idx: 50050, batch size: 74, loss[discriminator_loss=2.661, discriminator_real_loss=1.364, discriminator_fake_loss=1.296, generator_loss=28.23, generator_mel_loss=18.43, generator_kl_loss=1.295, generator_dur_loss=1.713, generator_adv_loss=1.956, generator_feat_match_loss=4.835, over 74.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.367, discriminator_fake_loss=1.341, generator_loss=27.87, generator_mel_loss=18.09, generator_kl_loss=1.436, generator_dur_loss=1.736, generator_adv_loss=1.942, generator_feat_match_loss=4.666, over 4475.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:30:10,249 INFO [train.py:919] (4/6) Start epoch 405 +2024-03-13 17:30:44,000 INFO [train.py:527] (4/6) Epoch 405, batch 4, global_batch_idx: 50100, batch size: 83, loss[discriminator_loss=2.713, discriminator_real_loss=1.383, discriminator_fake_loss=1.329, generator_loss=28.21, generator_mel_loss=18.52, generator_kl_loss=1.3, generator_dur_loss=1.764, generator_adv_loss=1.913, generator_feat_match_loss=4.712, over 83.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.362, discriminator_fake_loss=1.344, generator_loss=27.89, generator_mel_loss=18.1, generator_kl_loss=1.394, generator_dur_loss=1.771, generator_adv_loss=1.939, generator_feat_match_loss=4.683, over 335.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:33:06,931 INFO [train.py:527] (4/6) Epoch 405, batch 54, global_batch_idx: 50150, batch size: 88, loss[discriminator_loss=2.694, discriminator_real_loss=1.351, discriminator_fake_loss=1.342, generator_loss=27.94, generator_mel_loss=18.02, generator_kl_loss=1.321, generator_dur_loss=1.856, generator_adv_loss=1.841, generator_feat_match_loss=4.898, over 88.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.369, discriminator_fake_loss=1.336, generator_loss=27.97, generator_mel_loss=18.12, generator_kl_loss=1.426, generator_dur_loss=1.752, generator_adv_loss=1.938, generator_feat_match_loss=4.734, over 3299.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:35:24,885 INFO [train.py:527] (4/6) Epoch 405, batch 104, global_batch_idx: 50200, batch size: 64, loss[discriminator_loss=2.745, discriminator_real_loss=1.405, discriminator_fake_loss=1.339, generator_loss=28.69, generator_mel_loss=18.47, generator_kl_loss=1.31, generator_dur_loss=1.764, generator_adv_loss=1.955, generator_feat_match_loss=5.192, over 64.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.371, discriminator_fake_loss=1.34, generator_loss=27.95, generator_mel_loss=18.11, generator_kl_loss=1.412, generator_dur_loss=1.751, generator_adv_loss=1.95, generator_feat_match_loss=4.729, over 6113.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:35:24,887 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 17:35:33,902 INFO [train.py:591] (4/6) Epoch 405, validation: discriminator_loss=2.778, discriminator_real_loss=1.448, discriminator_fake_loss=1.33, generator_loss=28.05, generator_mel_loss=18.97, generator_kl_loss=1.287, generator_dur_loss=1.812, generator_adv_loss=1.879, generator_feat_match_loss=4.106, over 100.00 samples. +2024-03-13 17:35:33,902 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 17:36:23,413 INFO [train.py:919] (4/6) Start epoch 406 +2024-03-13 17:38:09,539 INFO [train.py:527] (4/6) Epoch 406, batch 30, global_batch_idx: 50250, batch size: 31, loss[discriminator_loss=2.665, discriminator_real_loss=1.429, discriminator_fake_loss=1.236, generator_loss=28.53, generator_mel_loss=18.46, generator_kl_loss=1.433, generator_dur_loss=1.646, generator_adv_loss=2.129, generator_feat_match_loss=4.861, over 31.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=28.09, generator_mel_loss=18.26, generator_kl_loss=1.412, generator_dur_loss=1.739, generator_adv_loss=1.941, generator_feat_match_loss=4.741, over 1767.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:40:30,659 INFO [train.py:527] (4/6) Epoch 406, batch 80, global_batch_idx: 50300, batch size: 64, loss[discriminator_loss=2.706, discriminator_real_loss=1.425, discriminator_fake_loss=1.281, generator_loss=27.61, generator_mel_loss=18.02, generator_kl_loss=1.327, generator_dur_loss=1.727, generator_adv_loss=2.107, generator_feat_match_loss=4.429, over 64.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=27.91, generator_mel_loss=18.12, generator_kl_loss=1.385, generator_dur_loss=1.764, generator_adv_loss=1.948, generator_feat_match_loss=4.7, over 5026.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:42:26,712 INFO [train.py:919] (4/6) Start epoch 407 +2024-03-13 17:43:08,954 INFO [train.py:527] (4/6) Epoch 407, batch 6, global_batch_idx: 50350, batch size: 48, loss[discriminator_loss=2.701, discriminator_real_loss=1.437, discriminator_fake_loss=1.264, generator_loss=29.61, generator_mel_loss=18.51, generator_kl_loss=1.448, generator_dur_loss=1.644, generator_adv_loss=1.976, generator_feat_match_loss=6.029, over 48.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.403, discriminator_fake_loss=1.346, generator_loss=28.44, generator_mel_loss=18.28, generator_kl_loss=1.467, generator_dur_loss=1.708, generator_adv_loss=1.956, generator_feat_match_loss=5.033, over 322.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:45:26,381 INFO [train.py:527] (4/6) Epoch 407, batch 56, global_batch_idx: 50400, batch size: 80, loss[discriminator_loss=2.653, discriminator_real_loss=1.315, discriminator_fake_loss=1.338, generator_loss=27.65, generator_mel_loss=17.94, generator_kl_loss=1.283, generator_dur_loss=1.804, generator_adv_loss=2.005, generator_feat_match_loss=4.615, over 80.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.382, discriminator_fake_loss=1.34, generator_loss=27.91, generator_mel_loss=18.08, generator_kl_loss=1.426, generator_dur_loss=1.756, generator_adv_loss=1.975, generator_feat_match_loss=4.67, over 3105.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:45:26,383 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 17:45:34,375 INFO [train.py:591] (4/6) Epoch 407, validation: discriminator_loss=2.685, discriminator_real_loss=1.383, discriminator_fake_loss=1.302, generator_loss=27.62, generator_mel_loss=18.64, generator_kl_loss=1.114, generator_dur_loss=1.828, generator_adv_loss=1.979, generator_feat_match_loss=4.061, over 100.00 samples. +2024-03-13 17:45:34,376 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 17:47:53,298 INFO [train.py:527] (4/6) Epoch 407, batch 106, global_batch_idx: 50450, batch size: 47, loss[discriminator_loss=2.747, discriminator_real_loss=1.404, discriminator_fake_loss=1.343, generator_loss=27.33, generator_mel_loss=17.64, generator_kl_loss=1.372, generator_dur_loss=1.657, generator_adv_loss=1.854, generator_feat_match_loss=4.803, over 47.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.381, discriminator_fake_loss=1.335, generator_loss=27.91, generator_mel_loss=18.1, generator_kl_loss=1.423, generator_dur_loss=1.744, generator_adv_loss=1.96, generator_feat_match_loss=4.677, over 5814.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:48:41,097 INFO [train.py:919] (4/6) Start epoch 408 +2024-03-13 17:50:32,351 INFO [train.py:527] (4/6) Epoch 408, batch 32, global_batch_idx: 50500, batch size: 70, loss[discriminator_loss=2.702, discriminator_real_loss=1.372, discriminator_fake_loss=1.33, generator_loss=28.14, generator_mel_loss=18.17, generator_kl_loss=1.483, generator_dur_loss=1.798, generator_adv_loss=1.848, generator_feat_match_loss=4.84, over 70.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.79, generator_mel_loss=18.01, generator_kl_loss=1.393, generator_dur_loss=1.788, generator_adv_loss=1.926, generator_feat_match_loss=4.68, over 2078.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:52:51,967 INFO [train.py:527] (4/6) Epoch 408, batch 82, global_batch_idx: 50550, batch size: 60, loss[discriminator_loss=2.663, discriminator_real_loss=1.345, discriminator_fake_loss=1.317, generator_loss=28.07, generator_mel_loss=18.13, generator_kl_loss=1.286, generator_dur_loss=1.753, generator_adv_loss=1.958, generator_feat_match_loss=4.947, over 60.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.374, discriminator_fake_loss=1.346, generator_loss=27.98, generator_mel_loss=18.13, generator_kl_loss=1.404, generator_dur_loss=1.778, generator_adv_loss=1.941, generator_feat_match_loss=4.732, over 4928.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:54:46,777 INFO [train.py:919] (4/6) Start epoch 409 +2024-03-13 17:55:34,359 INFO [train.py:527] (4/6) Epoch 409, batch 8, global_batch_idx: 50600, batch size: 88, loss[discriminator_loss=2.703, discriminator_real_loss=1.26, discriminator_fake_loss=1.443, generator_loss=27.93, generator_mel_loss=18.13, generator_kl_loss=1.336, generator_dur_loss=1.859, generator_adv_loss=1.953, generator_feat_match_loss=4.649, over 88.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.34, discriminator_fake_loss=1.353, generator_loss=28.15, generator_mel_loss=18.28, generator_kl_loss=1.402, generator_dur_loss=1.797, generator_adv_loss=1.94, generator_feat_match_loss=4.731, over 504.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:55:34,363 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 17:55:42,243 INFO [train.py:591] (4/6) Epoch 409, validation: discriminator_loss=2.737, discriminator_real_loss=1.413, discriminator_fake_loss=1.323, generator_loss=27.31, generator_mel_loss=18.66, generator_kl_loss=1.288, generator_dur_loss=1.834, generator_adv_loss=1.885, generator_feat_match_loss=3.637, over 100.00 samples. +2024-03-13 17:55:42,245 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 17:58:01,429 INFO [train.py:527] (4/6) Epoch 409, batch 58, global_batch_idx: 50650, batch size: 48, loss[discriminator_loss=2.713, discriminator_real_loss=1.402, discriminator_fake_loss=1.311, generator_loss=27.51, generator_mel_loss=17.88, generator_kl_loss=1.575, generator_dur_loss=1.697, generator_adv_loss=1.996, generator_feat_match_loss=4.359, over 48.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.371, discriminator_fake_loss=1.341, generator_loss=27.99, generator_mel_loss=18.14, generator_kl_loss=1.425, generator_dur_loss=1.755, generator_adv_loss=1.951, generator_feat_match_loss=4.716, over 3173.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:00:17,676 INFO [train.py:527] (4/6) Epoch 409, batch 108, global_batch_idx: 50700, batch size: 74, loss[discriminator_loss=2.664, discriminator_real_loss=1.316, discriminator_fake_loss=1.348, generator_loss=27.92, generator_mel_loss=18.1, generator_kl_loss=1.073, generator_dur_loss=1.809, generator_adv_loss=1.947, generator_feat_match_loss=4.993, over 74.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=27.94, generator_mel_loss=18.11, generator_kl_loss=1.421, generator_dur_loss=1.754, generator_adv_loss=1.949, generator_feat_match_loss=4.71, over 5939.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:00:59,682 INFO [train.py:919] (4/6) Start epoch 410 +2024-03-13 18:02:59,008 INFO [train.py:527] (4/6) Epoch 410, batch 34, global_batch_idx: 50750, batch size: 25, loss[discriminator_loss=2.749, discriminator_real_loss=1.351, discriminator_fake_loss=1.398, generator_loss=28.6, generator_mel_loss=18.99, generator_kl_loss=1.738, generator_dur_loss=1.556, generator_adv_loss=2.002, generator_feat_match_loss=4.323, over 25.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=27.97, generator_mel_loss=18.19, generator_kl_loss=1.433, generator_dur_loss=1.701, generator_adv_loss=1.958, generator_feat_match_loss=4.691, over 1640.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:05:17,677 INFO [train.py:527] (4/6) Epoch 410, batch 84, global_batch_idx: 50800, batch size: 62, loss[discriminator_loss=2.793, discriminator_real_loss=1.533, discriminator_fake_loss=1.26, generator_loss=27.71, generator_mel_loss=18.05, generator_kl_loss=1.351, generator_dur_loss=1.734, generator_adv_loss=1.803, generator_feat_match_loss=4.776, over 62.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.382, discriminator_fake_loss=1.343, generator_loss=27.92, generator_mel_loss=18.11, generator_kl_loss=1.417, generator_dur_loss=1.735, generator_adv_loss=1.953, generator_feat_match_loss=4.704, over 4615.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:05:17,678 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 18:05:26,738 INFO [train.py:591] (4/6) Epoch 410, validation: discriminator_loss=2.764, discriminator_real_loss=1.383, discriminator_fake_loss=1.381, generator_loss=27.16, generator_mel_loss=18.4, generator_kl_loss=1.374, generator_dur_loss=1.794, generator_adv_loss=1.795, generator_feat_match_loss=3.795, over 100.00 samples. +2024-03-13 18:05:26,739 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 18:07:15,282 INFO [train.py:919] (4/6) Start epoch 411 +2024-03-13 18:08:06,452 INFO [train.py:527] (4/6) Epoch 411, batch 10, global_batch_idx: 50850, batch size: 44, loss[discriminator_loss=2.748, discriminator_real_loss=1.394, discriminator_fake_loss=1.354, generator_loss=26.85, generator_mel_loss=17.83, generator_kl_loss=1.47, generator_dur_loss=1.708, generator_adv_loss=2.076, generator_feat_match_loss=3.769, over 44.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.365, discriminator_fake_loss=1.345, generator_loss=27.9, generator_mel_loss=18.11, generator_kl_loss=1.454, generator_dur_loss=1.754, generator_adv_loss=1.942, generator_feat_match_loss=4.644, over 575.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:10:22,405 INFO [train.py:527] (4/6) Epoch 411, batch 60, global_batch_idx: 50900, batch size: 55, loss[discriminator_loss=2.701, discriminator_real_loss=1.359, discriminator_fake_loss=1.342, generator_loss=28.4, generator_mel_loss=18.47, generator_kl_loss=1.388, generator_dur_loss=1.69, generator_adv_loss=1.944, generator_feat_match_loss=4.904, over 55.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.37, discriminator_fake_loss=1.345, generator_loss=27.94, generator_mel_loss=18.16, generator_kl_loss=1.396, generator_dur_loss=1.751, generator_adv_loss=1.941, generator_feat_match_loss=4.687, over 3301.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:12:40,553 INFO [train.py:527] (4/6) Epoch 411, batch 110, global_batch_idx: 50950, batch size: 72, loss[discriminator_loss=2.719, discriminator_real_loss=1.42, discriminator_fake_loss=1.299, generator_loss=28.8, generator_mel_loss=18.81, generator_kl_loss=1.228, generator_dur_loss=1.787, generator_adv_loss=2.056, generator_feat_match_loss=4.919, over 72.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.371, discriminator_fake_loss=1.346, generator_loss=27.98, generator_mel_loss=18.15, generator_kl_loss=1.404, generator_dur_loss=1.742, generator_adv_loss=1.951, generator_feat_match_loss=4.725, over 6084.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:13:19,952 INFO [train.py:919] (4/6) Start epoch 412 +2024-03-13 18:15:23,133 INFO [train.py:527] (4/6) Epoch 412, batch 36, global_batch_idx: 51000, batch size: 80, loss[discriminator_loss=2.72, discriminator_real_loss=1.396, discriminator_fake_loss=1.325, generator_loss=27.2, generator_mel_loss=18.09, generator_kl_loss=1.259, generator_dur_loss=1.826, generator_adv_loss=1.821, generator_feat_match_loss=4.204, over 80.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.339, generator_loss=27.79, generator_mel_loss=18.01, generator_kl_loss=1.411, generator_dur_loss=1.761, generator_adv_loss=1.943, generator_feat_match_loss=4.67, over 2162.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:15:23,135 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 18:15:30,954 INFO [train.py:591] (4/6) Epoch 412, validation: discriminator_loss=2.748, discriminator_real_loss=1.361, discriminator_fake_loss=1.387, generator_loss=26.67, generator_mel_loss=18.35, generator_kl_loss=1.218, generator_dur_loss=1.81, generator_adv_loss=1.756, generator_feat_match_loss=3.537, over 100.00 samples. +2024-03-13 18:15:30,955 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 18:17:48,781 INFO [train.py:527] (4/6) Epoch 412, batch 86, global_batch_idx: 51050, batch size: 31, loss[discriminator_loss=2.816, discriminator_real_loss=1.368, discriminator_fake_loss=1.448, generator_loss=27.19, generator_mel_loss=18.06, generator_kl_loss=1.598, generator_dur_loss=1.66, generator_adv_loss=1.807, generator_feat_match_loss=4.064, over 31.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.375, discriminator_fake_loss=1.333, generator_loss=27.9, generator_mel_loss=18.1, generator_kl_loss=1.419, generator_dur_loss=1.756, generator_adv_loss=1.946, generator_feat_match_loss=4.683, over 4970.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:19:29,873 INFO [train.py:919] (4/6) Start epoch 413 +2024-03-13 18:20:26,271 INFO [train.py:527] (4/6) Epoch 413, batch 12, global_batch_idx: 51100, batch size: 55, loss[discriminator_loss=2.688, discriminator_real_loss=1.47, discriminator_fake_loss=1.218, generator_loss=27.59, generator_mel_loss=18.01, generator_kl_loss=1.402, generator_dur_loss=1.679, generator_adv_loss=2.018, generator_feat_match_loss=4.481, over 55.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.15, generator_mel_loss=18.14, generator_kl_loss=1.444, generator_dur_loss=1.729, generator_adv_loss=1.999, generator_feat_match_loss=4.842, over 757.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:22:47,480 INFO [train.py:527] (4/6) Epoch 413, batch 62, global_batch_idx: 51150, batch size: 42, loss[discriminator_loss=2.731, discriminator_real_loss=1.4, discriminator_fake_loss=1.331, generator_loss=27.7, generator_mel_loss=18.07, generator_kl_loss=1.56, generator_dur_loss=1.681, generator_adv_loss=1.89, generator_feat_match_loss=4.497, over 42.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.372, discriminator_fake_loss=1.333, generator_loss=27.97, generator_mel_loss=18.08, generator_kl_loss=1.422, generator_dur_loss=1.749, generator_adv_loss=1.964, generator_feat_match_loss=4.76, over 3687.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:25:05,886 INFO [train.py:527] (4/6) Epoch 413, batch 112, global_batch_idx: 51200, batch size: 74, loss[discriminator_loss=2.706, discriminator_real_loss=1.363, discriminator_fake_loss=1.344, generator_loss=28.18, generator_mel_loss=18.32, generator_kl_loss=1.384, generator_dur_loss=1.774, generator_adv_loss=2.043, generator_feat_match_loss=4.651, over 74.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.375, discriminator_fake_loss=1.337, generator_loss=27.96, generator_mel_loss=18.09, generator_kl_loss=1.426, generator_dur_loss=1.75, generator_adv_loss=1.959, generator_feat_match_loss=4.737, over 6435.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:25:05,888 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 18:25:14,605 INFO [train.py:591] (4/6) Epoch 413, validation: discriminator_loss=2.755, discriminator_real_loss=1.459, discriminator_fake_loss=1.296, generator_loss=27.05, generator_mel_loss=18.22, generator_kl_loss=1.273, generator_dur_loss=1.791, generator_adv_loss=1.897, generator_feat_match_loss=3.872, over 100.00 samples. +2024-03-13 18:25:14,606 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 18:25:43,940 INFO [train.py:919] (4/6) Start epoch 414 +2024-03-13 18:27:51,454 INFO [train.py:527] (4/6) Epoch 414, batch 38, global_batch_idx: 51250, batch size: 61, loss[discriminator_loss=2.766, discriminator_real_loss=1.434, discriminator_fake_loss=1.332, generator_loss=26.08, generator_mel_loss=17.68, generator_kl_loss=1.215, generator_dur_loss=1.713, generator_adv_loss=1.939, generator_feat_match_loss=3.533, over 61.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.06, generator_mel_loss=18.17, generator_kl_loss=1.427, generator_dur_loss=1.736, generator_adv_loss=1.944, generator_feat_match_loss=4.78, over 2143.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:30:07,717 INFO [train.py:527] (4/6) Epoch 414, batch 88, global_batch_idx: 51300, batch size: 96, loss[discriminator_loss=2.708, discriminator_real_loss=1.406, discriminator_fake_loss=1.303, generator_loss=27.79, generator_mel_loss=18.12, generator_kl_loss=1.31, generator_dur_loss=1.821, generator_adv_loss=1.845, generator_feat_match_loss=4.697, over 96.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.346, generator_loss=28.02, generator_mel_loss=18.13, generator_kl_loss=1.443, generator_dur_loss=1.735, generator_adv_loss=1.941, generator_feat_match_loss=4.776, over 4939.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:31:45,769 INFO [train.py:919] (4/6) Start epoch 415 +2024-03-13 18:32:48,724 INFO [train.py:527] (4/6) Epoch 415, batch 14, global_batch_idx: 51350, batch size: 59, loss[discriminator_loss=2.705, discriminator_real_loss=1.403, discriminator_fake_loss=1.303, generator_loss=28.22, generator_mel_loss=18.49, generator_kl_loss=1.313, generator_dur_loss=1.729, generator_adv_loss=1.942, generator_feat_match_loss=4.746, over 59.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.379, discriminator_fake_loss=1.337, generator_loss=27.94, generator_mel_loss=18.05, generator_kl_loss=1.422, generator_dur_loss=1.733, generator_adv_loss=1.96, generator_feat_match_loss=4.775, over 836.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:35:05,781 INFO [train.py:527] (4/6) Epoch 415, batch 64, global_batch_idx: 51400, batch size: 47, loss[discriminator_loss=2.711, discriminator_real_loss=1.333, discriminator_fake_loss=1.379, generator_loss=28.14, generator_mel_loss=18.14, generator_kl_loss=1.373, generator_dur_loss=1.676, generator_adv_loss=1.97, generator_feat_match_loss=4.976, over 47.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.337, generator_loss=28.01, generator_mel_loss=18.06, generator_kl_loss=1.444, generator_dur_loss=1.742, generator_adv_loss=1.94, generator_feat_match_loss=4.828, over 3542.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:35:05,783 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 18:35:13,671 INFO [train.py:591] (4/6) Epoch 415, validation: discriminator_loss=2.738, discriminator_real_loss=1.5, discriminator_fake_loss=1.239, generator_loss=25.83, generator_mel_loss=17.34, generator_kl_loss=1.242, generator_dur_loss=1.823, generator_adv_loss=1.931, generator_feat_match_loss=3.496, over 100.00 samples. +2024-03-13 18:35:13,672 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 18:37:33,150 INFO [train.py:527] (4/6) Epoch 415, batch 114, global_batch_idx: 51450, batch size: 31, loss[discriminator_loss=2.675, discriminator_real_loss=1.39, discriminator_fake_loss=1.285, generator_loss=28.03, generator_mel_loss=18.11, generator_kl_loss=1.596, generator_dur_loss=1.655, generator_adv_loss=1.869, generator_feat_match_loss=4.803, over 31.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.01, generator_mel_loss=18.07, generator_kl_loss=1.442, generator_dur_loss=1.747, generator_adv_loss=1.94, generator_feat_match_loss=4.817, over 6217.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:38:00,916 INFO [train.py:919] (4/6) Start epoch 416 +2024-03-13 18:40:16,396 INFO [train.py:527] (4/6) Epoch 416, batch 40, global_batch_idx: 51500, batch size: 80, loss[discriminator_loss=2.713, discriminator_real_loss=1.295, discriminator_fake_loss=1.418, generator_loss=27.85, generator_mel_loss=17.82, generator_kl_loss=1.236, generator_dur_loss=1.856, generator_adv_loss=2.309, generator_feat_match_loss=4.632, over 80.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.357, discriminator_fake_loss=1.346, generator_loss=27.95, generator_mel_loss=18.07, generator_kl_loss=1.421, generator_dur_loss=1.78, generator_adv_loss=1.949, generator_feat_match_loss=4.722, over 2498.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:42:34,120 INFO [train.py:527] (4/6) Epoch 416, batch 90, global_batch_idx: 51550, batch size: 58, loss[discriminator_loss=2.692, discriminator_real_loss=1.31, discriminator_fake_loss=1.382, generator_loss=27.84, generator_mel_loss=18.25, generator_kl_loss=1.367, generator_dur_loss=1.704, generator_adv_loss=1.917, generator_feat_match_loss=4.596, over 58.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.362, discriminator_fake_loss=1.348, generator_loss=27.94, generator_mel_loss=18.07, generator_kl_loss=1.427, generator_dur_loss=1.763, generator_adv_loss=1.94, generator_feat_match_loss=4.745, over 5184.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:44:03,643 INFO [train.py:919] (4/6) Start epoch 417 +2024-03-13 18:45:10,681 INFO [train.py:527] (4/6) Epoch 417, batch 16, global_batch_idx: 51600, batch size: 88, loss[discriminator_loss=2.734, discriminator_real_loss=1.409, discriminator_fake_loss=1.324, generator_loss=27.43, generator_mel_loss=17.88, generator_kl_loss=1.239, generator_dur_loss=1.858, generator_adv_loss=1.899, generator_feat_match_loss=4.561, over 88.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.395, discriminator_fake_loss=1.35, generator_loss=27.74, generator_mel_loss=18.07, generator_kl_loss=1.386, generator_dur_loss=1.754, generator_adv_loss=1.91, generator_feat_match_loss=4.621, over 932.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:45:10,683 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 18:45:18,452 INFO [train.py:591] (4/6) Epoch 417, validation: discriminator_loss=2.71, discriminator_real_loss=1.355, discriminator_fake_loss=1.354, generator_loss=26.6, generator_mel_loss=18.32, generator_kl_loss=1.193, generator_dur_loss=1.823, generator_adv_loss=1.818, generator_feat_match_loss=3.452, over 100.00 samples. +2024-03-13 18:45:18,453 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 18:47:36,706 INFO [train.py:527] (4/6) Epoch 417, batch 66, global_batch_idx: 51650, batch size: 50, loss[discriminator_loss=2.675, discriminator_real_loss=1.338, discriminator_fake_loss=1.337, generator_loss=27.42, generator_mel_loss=17.82, generator_kl_loss=1.476, generator_dur_loss=1.712, generator_adv_loss=1.989, generator_feat_match_loss=4.416, over 50.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.374, discriminator_fake_loss=1.341, generator_loss=27.87, generator_mel_loss=18.03, generator_kl_loss=1.409, generator_dur_loss=1.752, generator_adv_loss=1.941, generator_feat_match_loss=4.735, over 3860.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:49:56,186 INFO [train.py:527] (4/6) Epoch 417, batch 116, global_batch_idx: 51700, batch size: 52, loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.338, generator_loss=28.24, generator_mel_loss=18.35, generator_kl_loss=1.562, generator_dur_loss=1.734, generator_adv_loss=1.988, generator_feat_match_loss=4.61, over 52.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.372, discriminator_fake_loss=1.339, generator_loss=27.93, generator_mel_loss=18.07, generator_kl_loss=1.414, generator_dur_loss=1.759, generator_adv_loss=1.938, generator_feat_match_loss=4.74, over 6886.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:50:18,590 INFO [train.py:919] (4/6) Start epoch 418 +2024-03-13 18:52:38,441 INFO [train.py:527] (4/6) Epoch 418, batch 42, global_batch_idx: 51750, batch size: 68, loss[discriminator_loss=2.687, discriminator_real_loss=1.365, discriminator_fake_loss=1.322, generator_loss=27.1, generator_mel_loss=17.93, generator_kl_loss=1.432, generator_dur_loss=1.759, generator_adv_loss=1.978, generator_feat_match_loss=3.999, over 68.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.367, discriminator_fake_loss=1.343, generator_loss=28.02, generator_mel_loss=18.13, generator_kl_loss=1.437, generator_dur_loss=1.761, generator_adv_loss=1.95, generator_feat_match_loss=4.74, over 2311.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:54:54,637 INFO [train.py:527] (4/6) Epoch 418, batch 92, global_batch_idx: 51800, batch size: 31, loss[discriminator_loss=2.697, discriminator_real_loss=1.386, discriminator_fake_loss=1.311, generator_loss=28.24, generator_mel_loss=18.64, generator_kl_loss=1.677, generator_dur_loss=1.612, generator_adv_loss=2.168, generator_feat_match_loss=4.139, over 31.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.03, generator_mel_loss=18.14, generator_kl_loss=1.439, generator_dur_loss=1.75, generator_adv_loss=1.953, generator_feat_match_loss=4.746, over 5032.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:54:54,639 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 18:55:03,510 INFO [train.py:591] (4/6) Epoch 418, validation: discriminator_loss=2.779, discriminator_real_loss=1.581, discriminator_fake_loss=1.198, generator_loss=27.26, generator_mel_loss=18.42, generator_kl_loss=1.248, generator_dur_loss=1.792, generator_adv_loss=2.023, generator_feat_match_loss=3.77, over 100.00 samples. +2024-03-13 18:55:03,511 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 18:56:31,486 INFO [train.py:919] (4/6) Start epoch 419 +2024-03-13 18:57:48,376 INFO [train.py:527] (4/6) Epoch 419, batch 18, global_batch_idx: 51850, batch size: 80, loss[discriminator_loss=2.689, discriminator_real_loss=1.407, discriminator_fake_loss=1.282, generator_loss=28.12, generator_mel_loss=18.15, generator_kl_loss=1.31, generator_dur_loss=1.826, generator_adv_loss=1.915, generator_feat_match_loss=4.91, over 80.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.392, discriminator_fake_loss=1.339, generator_loss=27.97, generator_mel_loss=18.19, generator_kl_loss=1.435, generator_dur_loss=1.726, generator_adv_loss=1.941, generator_feat_match_loss=4.682, over 996.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:00:10,351 INFO [train.py:527] (4/6) Epoch 419, batch 68, global_batch_idx: 51900, batch size: 56, loss[discriminator_loss=2.696, discriminator_real_loss=1.341, discriminator_fake_loss=1.354, generator_loss=28.37, generator_mel_loss=17.89, generator_kl_loss=1.454, generator_dur_loss=1.755, generator_adv_loss=2.052, generator_feat_match_loss=5.219, over 56.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.373, discriminator_fake_loss=1.352, generator_loss=27.95, generator_mel_loss=18.07, generator_kl_loss=1.433, generator_dur_loss=1.755, generator_adv_loss=1.951, generator_feat_match_loss=4.742, over 4016.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:02:22,551 INFO [train.py:527] (4/6) Epoch 419, batch 118, global_batch_idx: 51950, batch size: 77, loss[discriminator_loss=2.696, discriminator_real_loss=1.353, discriminator_fake_loss=1.342, generator_loss=27.54, generator_mel_loss=17.96, generator_kl_loss=1.418, generator_dur_loss=1.836, generator_adv_loss=2.027, generator_feat_match_loss=4.299, over 77.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.372, discriminator_fake_loss=1.347, generator_loss=27.97, generator_mel_loss=18.11, generator_kl_loss=1.426, generator_dur_loss=1.755, generator_adv_loss=1.954, generator_feat_match_loss=4.73, over 6831.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:02:38,035 INFO [train.py:919] (4/6) Start epoch 420 +2024-03-13 19:05:05,344 INFO [train.py:527] (4/6) Epoch 420, batch 44, global_batch_idx: 52000, batch size: 68, loss[discriminator_loss=2.759, discriminator_real_loss=1.436, discriminator_fake_loss=1.323, generator_loss=26.94, generator_mel_loss=17.71, generator_kl_loss=1.37, generator_dur_loss=1.817, generator_adv_loss=1.955, generator_feat_match_loss=4.092, over 68.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.374, discriminator_fake_loss=1.337, generator_loss=28.05, generator_mel_loss=18.1, generator_kl_loss=1.413, generator_dur_loss=1.771, generator_adv_loss=1.943, generator_feat_match_loss=4.826, over 2673.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:05:05,345 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 19:05:13,469 INFO [train.py:591] (4/6) Epoch 420, validation: discriminator_loss=2.774, discriminator_real_loss=1.481, discriminator_fake_loss=1.293, generator_loss=27.64, generator_mel_loss=18.58, generator_kl_loss=1.209, generator_dur_loss=1.812, generator_adv_loss=1.936, generator_feat_match_loss=4.108, over 100.00 samples. +2024-03-13 19:05:13,470 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 19:07:32,963 INFO [train.py:527] (4/6) Epoch 420, batch 94, global_batch_idx: 52050, batch size: 70, loss[discriminator_loss=2.697, discriminator_real_loss=1.489, discriminator_fake_loss=1.208, generator_loss=27.75, generator_mel_loss=17.72, generator_kl_loss=1.319, generator_dur_loss=1.826, generator_adv_loss=2.05, generator_feat_match_loss=4.829, over 70.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.37, discriminator_fake_loss=1.335, generator_loss=28.03, generator_mel_loss=18.08, generator_kl_loss=1.414, generator_dur_loss=1.759, generator_adv_loss=1.949, generator_feat_match_loss=4.823, over 5460.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:08:53,626 INFO [train.py:919] (4/6) Start epoch 421 +2024-03-13 19:10:15,604 INFO [train.py:527] (4/6) Epoch 421, batch 20, global_batch_idx: 52100, batch size: 77, loss[discriminator_loss=2.733, discriminator_real_loss=1.349, discriminator_fake_loss=1.384, generator_loss=27.34, generator_mel_loss=17.69, generator_kl_loss=1.375, generator_dur_loss=1.839, generator_adv_loss=2.017, generator_feat_match_loss=4.419, over 77.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=28.02, generator_mel_loss=18.04, generator_kl_loss=1.428, generator_dur_loss=1.768, generator_adv_loss=1.948, generator_feat_match_loss=4.834, over 1214.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:12:34,666 INFO [train.py:527] (4/6) Epoch 421, batch 70, global_batch_idx: 52150, batch size: 74, loss[discriminator_loss=2.727, discriminator_real_loss=1.43, discriminator_fake_loss=1.297, generator_loss=27.82, generator_mel_loss=18.26, generator_kl_loss=1.132, generator_dur_loss=1.794, generator_adv_loss=1.874, generator_feat_match_loss=4.762, over 74.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.372, discriminator_fake_loss=1.339, generator_loss=27.95, generator_mel_loss=18.01, generator_kl_loss=1.41, generator_dur_loss=1.77, generator_adv_loss=1.951, generator_feat_match_loss=4.813, over 4138.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:14:48,341 INFO [train.py:527] (4/6) Epoch 421, batch 120, global_batch_idx: 52200, batch size: 16, loss[discriminator_loss=2.712, discriminator_real_loss=1.26, discriminator_fake_loss=1.452, generator_loss=29.46, generator_mel_loss=18.34, generator_kl_loss=2.023, generator_dur_loss=1.592, generator_adv_loss=2.012, generator_feat_match_loss=5.491, over 16.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=27.97, generator_mel_loss=18.07, generator_kl_loss=1.424, generator_dur_loss=1.762, generator_adv_loss=1.948, generator_feat_match_loss=4.773, over 6784.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:14:48,343 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 19:14:57,113 INFO [train.py:591] (4/6) Epoch 421, validation: discriminator_loss=2.767, discriminator_real_loss=1.497, discriminator_fake_loss=1.27, generator_loss=27.06, generator_mel_loss=18.58, generator_kl_loss=1.219, generator_dur_loss=1.818, generator_adv_loss=1.896, generator_feat_match_loss=3.554, over 100.00 samples. +2024-03-13 19:14:57,113 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 19:15:07,096 INFO [train.py:919] (4/6) Start epoch 422 +2024-03-13 19:17:39,936 INFO [train.py:527] (4/6) Epoch 422, batch 46, global_batch_idx: 52250, batch size: 53, loss[discriminator_loss=2.747, discriminator_real_loss=1.344, discriminator_fake_loss=1.402, generator_loss=28.08, generator_mel_loss=18.15, generator_kl_loss=1.441, generator_dur_loss=1.701, generator_adv_loss=1.844, generator_feat_match_loss=4.942, over 53.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.344, generator_loss=27.87, generator_mel_loss=18.06, generator_kl_loss=1.374, generator_dur_loss=1.77, generator_adv_loss=1.954, generator_feat_match_loss=4.721, over 2882.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:19:59,210 INFO [train.py:527] (4/6) Epoch 422, batch 96, global_batch_idx: 52300, batch size: 72, loss[discriminator_loss=2.717, discriminator_real_loss=1.292, discriminator_fake_loss=1.425, generator_loss=28.24, generator_mel_loss=18.36, generator_kl_loss=1.35, generator_dur_loss=1.814, generator_adv_loss=1.993, generator_feat_match_loss=4.718, over 72.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=27.85, generator_mel_loss=18, generator_kl_loss=1.398, generator_dur_loss=1.768, generator_adv_loss=1.948, generator_feat_match_loss=4.74, over 5697.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:21:13,372 INFO [train.py:919] (4/6) Start epoch 423 +2024-03-13 19:22:39,183 INFO [train.py:527] (4/6) Epoch 423, batch 22, global_batch_idx: 52350, batch size: 47, loss[discriminator_loss=2.682, discriminator_real_loss=1.38, discriminator_fake_loss=1.302, generator_loss=28.03, generator_mel_loss=17.89, generator_kl_loss=1.532, generator_dur_loss=1.675, generator_adv_loss=1.865, generator_feat_match_loss=5.072, over 47.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.364, discriminator_fake_loss=1.337, generator_loss=28.02, generator_mel_loss=17.98, generator_kl_loss=1.418, generator_dur_loss=1.789, generator_adv_loss=1.966, generator_feat_match_loss=4.858, over 1378.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:24:57,158 INFO [train.py:527] (4/6) Epoch 423, batch 72, global_batch_idx: 52400, batch size: 58, loss[discriminator_loss=2.809, discriminator_real_loss=1.301, discriminator_fake_loss=1.508, generator_loss=28.35, generator_mel_loss=18.19, generator_kl_loss=1.527, generator_dur_loss=1.773, generator_adv_loss=2.241, generator_feat_match_loss=4.622, over 58.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.373, discriminator_fake_loss=1.333, generator_loss=27.97, generator_mel_loss=18.01, generator_kl_loss=1.422, generator_dur_loss=1.767, generator_adv_loss=1.968, generator_feat_match_loss=4.799, over 4280.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:24:57,160 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 19:25:05,341 INFO [train.py:591] (4/6) Epoch 423, validation: discriminator_loss=2.891, discriminator_real_loss=1.675, discriminator_fake_loss=1.215, generator_loss=27.09, generator_mel_loss=17.91, generator_kl_loss=1.23, generator_dur_loss=1.832, generator_adv_loss=2.277, generator_feat_match_loss=3.847, over 100.00 samples. +2024-03-13 19:25:05,342 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 19:27:23,340 INFO [train.py:527] (4/6) Epoch 423, batch 122, global_batch_idx: 52450, batch size: 15, loss[discriminator_loss=2.644, discriminator_real_loss=1.286, discriminator_fake_loss=1.358, generator_loss=30.01, generator_mel_loss=19.3, generator_kl_loss=1.875, generator_dur_loss=1.609, generator_adv_loss=1.912, generator_feat_match_loss=5.316, over 15.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.373, discriminator_fake_loss=1.333, generator_loss=27.98, generator_mel_loss=18.02, generator_kl_loss=1.422, generator_dur_loss=1.764, generator_adv_loss=1.967, generator_feat_match_loss=4.803, over 7093.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:27:28,015 INFO [train.py:919] (4/6) Start epoch 424 +2024-03-13 19:30:06,157 INFO [train.py:527] (4/6) Epoch 424, batch 48, global_batch_idx: 52500, batch size: 36, loss[discriminator_loss=2.703, discriminator_real_loss=1.341, discriminator_fake_loss=1.362, generator_loss=28.49, generator_mel_loss=18.12, generator_kl_loss=1.577, generator_dur_loss=1.691, generator_adv_loss=2.059, generator_feat_match_loss=5.049, over 36.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.377, discriminator_fake_loss=1.338, generator_loss=27.93, generator_mel_loss=18.04, generator_kl_loss=1.419, generator_dur_loss=1.747, generator_adv_loss=1.951, generator_feat_match_loss=4.778, over 2772.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:32:24,305 INFO [train.py:527] (4/6) Epoch 424, batch 98, global_batch_idx: 52550, batch size: 66, loss[discriminator_loss=2.805, discriminator_real_loss=1.415, discriminator_fake_loss=1.39, generator_loss=28.01, generator_mel_loss=18.15, generator_kl_loss=1.338, generator_dur_loss=1.783, generator_adv_loss=1.98, generator_feat_match_loss=4.757, over 66.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.38, discriminator_fake_loss=1.339, generator_loss=27.97, generator_mel_loss=18.07, generator_kl_loss=1.436, generator_dur_loss=1.739, generator_adv_loss=1.958, generator_feat_match_loss=4.767, over 5521.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:33:33,066 INFO [train.py:919] (4/6) Start epoch 425 +2024-03-13 19:35:02,168 INFO [train.py:527] (4/6) Epoch 425, batch 24, global_batch_idx: 52600, batch size: 39, loss[discriminator_loss=2.687, discriminator_real_loss=1.351, discriminator_fake_loss=1.336, generator_loss=28.22, generator_mel_loss=18.02, generator_kl_loss=1.592, generator_dur_loss=1.723, generator_adv_loss=2.062, generator_feat_match_loss=4.823, over 39.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.367, discriminator_fake_loss=1.339, generator_loss=27.81, generator_mel_loss=17.95, generator_kl_loss=1.435, generator_dur_loss=1.759, generator_adv_loss=1.935, generator_feat_match_loss=4.735, over 1473.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:35:02,170 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 19:35:10,064 INFO [train.py:591] (4/6) Epoch 425, validation: discriminator_loss=2.718, discriminator_real_loss=1.424, discriminator_fake_loss=1.294, generator_loss=27.08, generator_mel_loss=18.35, generator_kl_loss=1.196, generator_dur_loss=1.825, generator_adv_loss=1.974, generator_feat_match_loss=3.734, over 100.00 samples. +2024-03-13 19:35:10,065 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 19:37:30,461 INFO [train.py:527] (4/6) Epoch 425, batch 74, global_batch_idx: 52650, batch size: 68, loss[discriminator_loss=2.669, discriminator_real_loss=1.408, discriminator_fake_loss=1.261, generator_loss=28.96, generator_mel_loss=18.19, generator_kl_loss=1.309, generator_dur_loss=1.777, generator_adv_loss=2.136, generator_feat_match_loss=5.559, over 68.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.368, discriminator_fake_loss=1.349, generator_loss=27.89, generator_mel_loss=18, generator_kl_loss=1.405, generator_dur_loss=1.774, generator_adv_loss=1.953, generator_feat_match_loss=4.757, over 4646.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:39:45,220 INFO [train.py:919] (4/6) Start epoch 426 +2024-03-13 19:40:10,312 INFO [train.py:527] (4/6) Epoch 426, batch 0, global_batch_idx: 52700, batch size: 48, loss[discriminator_loss=2.736, discriminator_real_loss=1.36, discriminator_fake_loss=1.375, generator_loss=27.61, generator_mel_loss=17.94, generator_kl_loss=1.538, generator_dur_loss=1.713, generator_adv_loss=1.803, generator_feat_match_loss=4.619, over 48.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.36, discriminator_fake_loss=1.375, generator_loss=27.61, generator_mel_loss=17.94, generator_kl_loss=1.538, generator_dur_loss=1.713, generator_adv_loss=1.803, generator_feat_match_loss=4.619, over 48.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:42:32,620 INFO [train.py:527] (4/6) Epoch 426, batch 50, global_batch_idx: 52750, batch size: 56, loss[discriminator_loss=2.73, discriminator_real_loss=1.444, discriminator_fake_loss=1.286, generator_loss=28.05, generator_mel_loss=18.21, generator_kl_loss=1.492, generator_dur_loss=1.743, generator_adv_loss=1.953, generator_feat_match_loss=4.659, over 56.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.387, discriminator_fake_loss=1.346, generator_loss=27.95, generator_mel_loss=18.1, generator_kl_loss=1.405, generator_dur_loss=1.779, generator_adv_loss=1.945, generator_feat_match_loss=4.721, over 3067.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:44:48,302 INFO [train.py:527] (4/6) Epoch 426, batch 100, global_batch_idx: 52800, batch size: 48, loss[discriminator_loss=2.706, discriminator_real_loss=1.33, discriminator_fake_loss=1.376, generator_loss=27.18, generator_mel_loss=17.79, generator_kl_loss=1.405, generator_dur_loss=1.686, generator_adv_loss=2.025, generator_feat_match_loss=4.273, over 48.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.379, discriminator_fake_loss=1.343, generator_loss=28.01, generator_mel_loss=18.13, generator_kl_loss=1.421, generator_dur_loss=1.762, generator_adv_loss=1.944, generator_feat_match_loss=4.751, over 5751.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:44:48,303 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 19:44:57,102 INFO [train.py:591] (4/6) Epoch 426, validation: discriminator_loss=2.772, discriminator_real_loss=1.461, discriminator_fake_loss=1.311, generator_loss=27.14, generator_mel_loss=18.28, generator_kl_loss=1.227, generator_dur_loss=1.818, generator_adv_loss=1.978, generator_feat_match_loss=3.839, over 100.00 samples. +2024-03-13 19:44:57,103 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 19:46:03,020 INFO [train.py:919] (4/6) Start epoch 427 +2024-03-13 19:47:37,255 INFO [train.py:527] (4/6) Epoch 427, batch 26, global_batch_idx: 52850, batch size: 80, loss[discriminator_loss=2.729, discriminator_real_loss=1.338, discriminator_fake_loss=1.391, generator_loss=27.37, generator_mel_loss=17.68, generator_kl_loss=1.193, generator_dur_loss=1.828, generator_adv_loss=1.85, generator_feat_match_loss=4.818, over 80.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.371, discriminator_fake_loss=1.345, generator_loss=28.22, generator_mel_loss=18.21, generator_kl_loss=1.408, generator_dur_loss=1.744, generator_adv_loss=1.958, generator_feat_match_loss=4.902, over 1440.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:49:57,046 INFO [train.py:527] (4/6) Epoch 427, batch 76, global_batch_idx: 52900, batch size: 68, loss[discriminator_loss=2.732, discriminator_real_loss=1.366, discriminator_fake_loss=1.366, generator_loss=27.44, generator_mel_loss=17.83, generator_kl_loss=1.159, generator_dur_loss=1.802, generator_adv_loss=2.018, generator_feat_match_loss=4.631, over 68.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=27.97, generator_mel_loss=18.05, generator_kl_loss=1.397, generator_dur_loss=1.749, generator_adv_loss=1.971, generator_feat_match_loss=4.797, over 4468.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:52:09,608 INFO [train.py:919] (4/6) Start epoch 428 +2024-03-13 19:52:39,500 INFO [train.py:527] (4/6) Epoch 428, batch 2, global_batch_idx: 52950, batch size: 15, loss[discriminator_loss=2.829, discriminator_real_loss=1.299, discriminator_fake_loss=1.53, generator_loss=27.86, generator_mel_loss=17.77, generator_kl_loss=1.665, generator_dur_loss=1.549, generator_adv_loss=1.92, generator_feat_match_loss=4.952, over 15.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.424, discriminator_fake_loss=1.332, generator_loss=27.74, generator_mel_loss=18.26, generator_kl_loss=1.422, generator_dur_loss=1.761, generator_adv_loss=1.855, generator_feat_match_loss=4.441, over 159.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:54:59,002 INFO [train.py:527] (4/6) Epoch 428, batch 52, global_batch_idx: 53000, batch size: 50, loss[discriminator_loss=2.723, discriminator_real_loss=1.336, discriminator_fake_loss=1.387, generator_loss=29.2, generator_mel_loss=18.36, generator_kl_loss=1.657, generator_dur_loss=1.657, generator_adv_loss=2.093, generator_feat_match_loss=5.437, over 50.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.38, discriminator_fake_loss=1.34, generator_loss=28, generator_mel_loss=18.14, generator_kl_loss=1.429, generator_dur_loss=1.732, generator_adv_loss=1.927, generator_feat_match_loss=4.778, over 2866.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:54:59,003 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 19:55:07,117 INFO [train.py:591] (4/6) Epoch 428, validation: discriminator_loss=2.731, discriminator_real_loss=1.543, discriminator_fake_loss=1.188, generator_loss=27.28, generator_mel_loss=18.15, generator_kl_loss=1.172, generator_dur_loss=1.804, generator_adv_loss=2.059, generator_feat_match_loss=4.093, over 100.00 samples. +2024-03-13 19:55:07,118 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 19:57:24,478 INFO [train.py:527] (4/6) Epoch 428, batch 102, global_batch_idx: 53050, batch size: 66, loss[discriminator_loss=2.696, discriminator_real_loss=1.351, discriminator_fake_loss=1.345, generator_loss=27.77, generator_mel_loss=17.89, generator_kl_loss=1.318, generator_dur_loss=1.74, generator_adv_loss=1.989, generator_feat_match_loss=4.827, over 66.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.342, generator_loss=28.03, generator_mel_loss=18.11, generator_kl_loss=1.434, generator_dur_loss=1.746, generator_adv_loss=1.934, generator_feat_match_loss=4.805, over 5864.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:58:22,319 INFO [train.py:919] (4/6) Start epoch 429 +2024-03-13 20:00:04,596 INFO [train.py:527] (4/6) Epoch 429, batch 28, global_batch_idx: 53100, batch size: 48, loss[discriminator_loss=2.71, discriminator_real_loss=1.444, discriminator_fake_loss=1.266, generator_loss=26.95, generator_mel_loss=17.62, generator_kl_loss=1.383, generator_dur_loss=1.695, generator_adv_loss=2.12, generator_feat_match_loss=4.132, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.383, discriminator_fake_loss=1.331, generator_loss=27.96, generator_mel_loss=18.05, generator_kl_loss=1.43, generator_dur_loss=1.75, generator_adv_loss=1.969, generator_feat_match_loss=4.753, over 1521.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:02:26,052 INFO [train.py:527] (4/6) Epoch 429, batch 78, global_batch_idx: 53150, batch size: 68, loss[discriminator_loss=2.69, discriminator_real_loss=1.437, discriminator_fake_loss=1.253, generator_loss=27.51, generator_mel_loss=17.84, generator_kl_loss=1.228, generator_dur_loss=1.791, generator_adv_loss=1.995, generator_feat_match_loss=4.656, over 68.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.377, discriminator_fake_loss=1.335, generator_loss=27.99, generator_mel_loss=18.05, generator_kl_loss=1.414, generator_dur_loss=1.766, generator_adv_loss=1.95, generator_feat_match_loss=4.809, over 4447.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:04:26,337 INFO [train.py:919] (4/6) Start epoch 430 +2024-03-13 20:05:00,161 INFO [train.py:527] (4/6) Epoch 430, batch 4, global_batch_idx: 53200, batch size: 31, loss[discriminator_loss=2.604, discriminator_real_loss=1.266, discriminator_fake_loss=1.338, generator_loss=29.71, generator_mel_loss=18.8, generator_kl_loss=1.648, generator_dur_loss=1.589, generator_adv_loss=1.946, generator_feat_match_loss=5.724, over 31.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.352, discriminator_fake_loss=1.332, generator_loss=28.17, generator_mel_loss=18.15, generator_kl_loss=1.488, generator_dur_loss=1.691, generator_adv_loss=1.956, generator_feat_match_loss=4.877, over 225.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:05:00,163 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 20:05:08,014 INFO [train.py:591] (4/6) Epoch 430, validation: discriminator_loss=2.768, discriminator_real_loss=1.384, discriminator_fake_loss=1.383, generator_loss=27.81, generator_mel_loss=19.1, generator_kl_loss=1.206, generator_dur_loss=1.806, generator_adv_loss=1.75, generator_feat_match_loss=3.952, over 100.00 samples. +2024-03-13 20:05:08,016 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 20:07:29,632 INFO [train.py:527] (4/6) Epoch 430, batch 54, global_batch_idx: 53250, batch size: 83, loss[discriminator_loss=2.653, discriminator_real_loss=1.305, discriminator_fake_loss=1.348, generator_loss=27.46, generator_mel_loss=17.49, generator_kl_loss=1.334, generator_dur_loss=1.839, generator_adv_loss=2.008, generator_feat_match_loss=4.788, over 83.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=27.9, generator_mel_loss=18.02, generator_kl_loss=1.431, generator_dur_loss=1.759, generator_adv_loss=1.949, generator_feat_match_loss=4.738, over 3194.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:09:48,908 INFO [train.py:527] (4/6) Epoch 430, batch 104, global_batch_idx: 53300, batch size: 36, loss[discriminator_loss=2.678, discriminator_real_loss=1.381, discriminator_fake_loss=1.297, generator_loss=29.3, generator_mel_loss=18.46, generator_kl_loss=1.551, generator_dur_loss=1.721, generator_adv_loss=1.963, generator_feat_match_loss=5.597, over 36.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.372, discriminator_fake_loss=1.34, generator_loss=27.98, generator_mel_loss=18.04, generator_kl_loss=1.425, generator_dur_loss=1.761, generator_adv_loss=1.96, generator_feat_match_loss=4.794, over 5989.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:10:42,053 INFO [train.py:919] (4/6) Start epoch 431 +2024-03-13 20:12:27,675 INFO [train.py:527] (4/6) Epoch 431, batch 30, global_batch_idx: 53350, batch size: 62, loss[discriminator_loss=2.74, discriminator_real_loss=1.384, discriminator_fake_loss=1.356, generator_loss=28.05, generator_mel_loss=18.31, generator_kl_loss=1.389, generator_dur_loss=1.751, generator_adv_loss=1.873, generator_feat_match_loss=4.72, over 62.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.37, discriminator_fake_loss=1.347, generator_loss=27.92, generator_mel_loss=18.04, generator_kl_loss=1.407, generator_dur_loss=1.771, generator_adv_loss=1.944, generator_feat_match_loss=4.761, over 1863.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:14:43,608 INFO [train.py:527] (4/6) Epoch 431, batch 80, global_batch_idx: 53400, batch size: 59, loss[discriminator_loss=2.747, discriminator_real_loss=1.368, discriminator_fake_loss=1.379, generator_loss=27.48, generator_mel_loss=18.18, generator_kl_loss=1.369, generator_dur_loss=1.773, generator_adv_loss=1.946, generator_feat_match_loss=4.213, over 59.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.342, generator_loss=28, generator_mel_loss=18.06, generator_kl_loss=1.416, generator_dur_loss=1.763, generator_adv_loss=1.955, generator_feat_match_loss=4.802, over 4664.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:14:43,609 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 20:14:51,579 INFO [train.py:591] (4/6) Epoch 431, validation: discriminator_loss=2.781, discriminator_real_loss=1.454, discriminator_fake_loss=1.327, generator_loss=26.56, generator_mel_loss=17.71, generator_kl_loss=1.241, generator_dur_loss=1.836, generator_adv_loss=1.938, generator_feat_match_loss=3.835, over 100.00 samples. +2024-03-13 20:14:51,580 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 20:16:53,831 INFO [train.py:919] (4/6) Start epoch 432 +2024-03-13 20:17:34,596 INFO [train.py:527] (4/6) Epoch 432, batch 6, global_batch_idx: 53450, batch size: 74, loss[discriminator_loss=2.719, discriminator_real_loss=1.361, discriminator_fake_loss=1.358, generator_loss=27.73, generator_mel_loss=17.95, generator_kl_loss=1.414, generator_dur_loss=1.825, generator_adv_loss=1.912, generator_feat_match_loss=4.628, over 74.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.337, discriminator_fake_loss=1.378, generator_loss=28.01, generator_mel_loss=17.95, generator_kl_loss=1.36, generator_dur_loss=1.825, generator_adv_loss=1.931, generator_feat_match_loss=4.944, over 519.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:19:56,466 INFO [train.py:527] (4/6) Epoch 432, batch 56, global_batch_idx: 53500, batch size: 36, loss[discriminator_loss=2.675, discriminator_real_loss=1.444, discriminator_fake_loss=1.231, generator_loss=28.47, generator_mel_loss=18.38, generator_kl_loss=1.453, generator_dur_loss=1.722, generator_adv_loss=1.965, generator_feat_match_loss=4.942, over 36.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.377, discriminator_fake_loss=1.344, generator_loss=28.11, generator_mel_loss=18.1, generator_kl_loss=1.428, generator_dur_loss=1.768, generator_adv_loss=1.961, generator_feat_match_loss=4.855, over 3201.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:22:15,140 INFO [train.py:527] (4/6) Epoch 432, batch 106, global_batch_idx: 53550, batch size: 52, loss[discriminator_loss=2.833, discriminator_real_loss=1.588, discriminator_fake_loss=1.245, generator_loss=27.22, generator_mel_loss=17.94, generator_kl_loss=1.393, generator_dur_loss=1.687, generator_adv_loss=1.776, generator_feat_match_loss=4.419, over 52.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.374, discriminator_fake_loss=1.341, generator_loss=28.12, generator_mel_loss=18.09, generator_kl_loss=1.417, generator_dur_loss=1.762, generator_adv_loss=1.957, generator_feat_match_loss=4.892, over 6039.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:23:01,773 INFO [train.py:919] (4/6) Start epoch 433 +2024-03-13 20:24:55,035 INFO [train.py:527] (4/6) Epoch 433, batch 32, global_batch_idx: 53600, batch size: 39, loss[discriminator_loss=2.688, discriminator_real_loss=1.369, discriminator_fake_loss=1.32, generator_loss=28.9, generator_mel_loss=18.45, generator_kl_loss=1.726, generator_dur_loss=1.649, generator_adv_loss=2.039, generator_feat_match_loss=5.037, over 39.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.361, discriminator_fake_loss=1.346, generator_loss=28.13, generator_mel_loss=18.13, generator_kl_loss=1.42, generator_dur_loss=1.763, generator_adv_loss=1.952, generator_feat_match_loss=4.866, over 1972.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:24:55,037 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 20:25:02,938 INFO [train.py:591] (4/6) Epoch 433, validation: discriminator_loss=2.762, discriminator_real_loss=1.486, discriminator_fake_loss=1.276, generator_loss=26.89, generator_mel_loss=17.98, generator_kl_loss=1.272, generator_dur_loss=1.838, generator_adv_loss=1.909, generator_feat_match_loss=3.895, over 100.00 samples. +2024-03-13 20:25:02,939 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 20:27:20,282 INFO [train.py:527] (4/6) Epoch 433, batch 82, global_batch_idx: 53650, batch size: 62, loss[discriminator_loss=2.617, discriminator_real_loss=1.322, discriminator_fake_loss=1.295, generator_loss=29.38, generator_mel_loss=18.37, generator_kl_loss=1.431, generator_dur_loss=1.774, generator_adv_loss=2.156, generator_feat_match_loss=5.653, over 62.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=28.06, generator_mel_loss=18.11, generator_kl_loss=1.419, generator_dur_loss=1.768, generator_adv_loss=1.953, generator_feat_match_loss=4.81, over 4894.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:29:12,903 INFO [train.py:919] (4/6) Start epoch 434 +2024-03-13 20:29:56,671 INFO [train.py:527] (4/6) Epoch 434, batch 8, global_batch_idx: 53700, batch size: 52, loss[discriminator_loss=2.672, discriminator_real_loss=1.392, discriminator_fake_loss=1.28, generator_loss=27.62, generator_mel_loss=18.01, generator_kl_loss=1.408, generator_dur_loss=1.721, generator_adv_loss=1.849, generator_feat_match_loss=4.64, over 52.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.365, discriminator_fake_loss=1.348, generator_loss=27.92, generator_mel_loss=18.13, generator_kl_loss=1.364, generator_dur_loss=1.751, generator_adv_loss=1.932, generator_feat_match_loss=4.748, over 599.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:32:14,640 INFO [train.py:527] (4/6) Epoch 434, batch 58, global_batch_idx: 53750, batch size: 88, loss[discriminator_loss=2.746, discriminator_real_loss=1.381, discriminator_fake_loss=1.366, generator_loss=27.19, generator_mel_loss=17.7, generator_kl_loss=1.235, generator_dur_loss=1.809, generator_adv_loss=1.915, generator_feat_match_loss=4.538, over 88.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.382, discriminator_fake_loss=1.345, generator_loss=27.92, generator_mel_loss=18.08, generator_kl_loss=1.396, generator_dur_loss=1.757, generator_adv_loss=1.94, generator_feat_match_loss=4.741, over 3467.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:34:33,478 INFO [train.py:527] (4/6) Epoch 434, batch 108, global_batch_idx: 53800, batch size: 64, loss[discriminator_loss=2.722, discriminator_real_loss=1.423, discriminator_fake_loss=1.299, generator_loss=28.49, generator_mel_loss=18.33, generator_kl_loss=1.254, generator_dur_loss=1.767, generator_adv_loss=1.915, generator_feat_match_loss=5.226, over 64.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.97, generator_mel_loss=18.08, generator_kl_loss=1.4, generator_dur_loss=1.757, generator_adv_loss=1.952, generator_feat_match_loss=4.783, over 6568.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:34:33,479 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 20:34:42,395 INFO [train.py:591] (4/6) Epoch 434, validation: discriminator_loss=2.76, discriminator_real_loss=1.447, discriminator_fake_loss=1.313, generator_loss=26.69, generator_mel_loss=18.37, generator_kl_loss=1.187, generator_dur_loss=1.837, generator_adv_loss=1.852, generator_feat_match_loss=3.445, over 100.00 samples. +2024-03-13 20:34:42,396 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 20:35:25,003 INFO [train.py:919] (4/6) Start epoch 435 +2024-03-13 20:37:25,051 INFO [train.py:527] (4/6) Epoch 435, batch 34, global_batch_idx: 53850, batch size: 77, loss[discriminator_loss=2.705, discriminator_real_loss=1.4, discriminator_fake_loss=1.304, generator_loss=27.41, generator_mel_loss=17.79, generator_kl_loss=1.184, generator_dur_loss=1.848, generator_adv_loss=1.919, generator_feat_match_loss=4.67, over 77.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.339, generator_loss=27.95, generator_mel_loss=18.06, generator_kl_loss=1.439, generator_dur_loss=1.748, generator_adv_loss=1.946, generator_feat_match_loss=4.756, over 1816.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:39:41,055 INFO [train.py:527] (4/6) Epoch 435, batch 84, global_batch_idx: 53900, batch size: 48, loss[discriminator_loss=2.753, discriminator_real_loss=1.434, discriminator_fake_loss=1.319, generator_loss=28.58, generator_mel_loss=18.51, generator_kl_loss=1.548, generator_dur_loss=1.705, generator_adv_loss=1.968, generator_feat_match_loss=4.85, over 48.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=27.97, generator_mel_loss=18.06, generator_kl_loss=1.421, generator_dur_loss=1.754, generator_adv_loss=1.952, generator_feat_match_loss=4.776, over 4721.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:41:32,055 INFO [train.py:919] (4/6) Start epoch 436 +2024-03-13 20:42:23,692 INFO [train.py:527] (4/6) Epoch 436, batch 10, global_batch_idx: 53950, batch size: 52, loss[discriminator_loss=2.698, discriminator_real_loss=1.373, discriminator_fake_loss=1.325, generator_loss=27.89, generator_mel_loss=17.76, generator_kl_loss=1.408, generator_dur_loss=1.698, generator_adv_loss=1.921, generator_feat_match_loss=5.11, over 52.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.386, discriminator_fake_loss=1.339, generator_loss=28.15, generator_mel_loss=18.18, generator_kl_loss=1.434, generator_dur_loss=1.724, generator_adv_loss=1.94, generator_feat_match_loss=4.871, over 582.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:44:40,797 INFO [train.py:527] (4/6) Epoch 436, batch 60, global_batch_idx: 54000, batch size: 64, loss[discriminator_loss=2.733, discriminator_real_loss=1.346, discriminator_fake_loss=1.387, generator_loss=27.82, generator_mel_loss=18.1, generator_kl_loss=1.475, generator_dur_loss=1.75, generator_adv_loss=1.982, generator_feat_match_loss=4.517, over 64.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.09, generator_mel_loss=18.12, generator_kl_loss=1.446, generator_dur_loss=1.734, generator_adv_loss=1.95, generator_feat_match_loss=4.844, over 3462.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:44:40,799 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 20:44:49,015 INFO [train.py:591] (4/6) Epoch 436, validation: discriminator_loss=2.777, discriminator_real_loss=1.482, discriminator_fake_loss=1.295, generator_loss=27.09, generator_mel_loss=18.27, generator_kl_loss=1.112, generator_dur_loss=1.792, generator_adv_loss=1.962, generator_feat_match_loss=3.957, over 100.00 samples. +2024-03-13 20:44:49,016 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 20:47:08,872 INFO [train.py:527] (4/6) Epoch 436, batch 110, global_batch_idx: 54050, batch size: 96, loss[discriminator_loss=2.7, discriminator_real_loss=1.305, discriminator_fake_loss=1.394, generator_loss=28.41, generator_mel_loss=18.19, generator_kl_loss=1.273, generator_dur_loss=1.859, generator_adv_loss=1.915, generator_feat_match_loss=5.172, over 96.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.09, generator_mel_loss=18.11, generator_kl_loss=1.434, generator_dur_loss=1.745, generator_adv_loss=1.947, generator_feat_match_loss=4.857, over 6269.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:47:45,259 INFO [train.py:919] (4/6) Start epoch 437 +2024-03-13 20:49:50,338 INFO [train.py:527] (4/6) Epoch 437, batch 36, global_batch_idx: 54100, batch size: 52, loss[discriminator_loss=2.7, discriminator_real_loss=1.357, discriminator_fake_loss=1.344, generator_loss=29.45, generator_mel_loss=18.85, generator_kl_loss=1.513, generator_dur_loss=1.692, generator_adv_loss=2.042, generator_feat_match_loss=5.354, over 52.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.368, discriminator_fake_loss=1.338, generator_loss=28.08, generator_mel_loss=17.99, generator_kl_loss=1.423, generator_dur_loss=1.77, generator_adv_loss=1.965, generator_feat_match_loss=4.924, over 1996.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:52:06,261 INFO [train.py:527] (4/6) Epoch 437, batch 86, global_batch_idx: 54150, batch size: 48, loss[discriminator_loss=2.532, discriminator_real_loss=1.292, discriminator_fake_loss=1.24, generator_loss=28.62, generator_mel_loss=18.12, generator_kl_loss=1.425, generator_dur_loss=1.712, generator_adv_loss=2.239, generator_feat_match_loss=5.129, over 48.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.368, discriminator_fake_loss=1.338, generator_loss=28.07, generator_mel_loss=18.03, generator_kl_loss=1.42, generator_dur_loss=1.756, generator_adv_loss=1.965, generator_feat_match_loss=4.896, over 4820.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:53:51,201 INFO [train.py:919] (4/6) Start epoch 438 +2024-03-13 20:54:48,966 INFO [train.py:527] (4/6) Epoch 438, batch 12, global_batch_idx: 54200, batch size: 50, loss[discriminator_loss=2.697, discriminator_real_loss=1.312, discriminator_fake_loss=1.385, generator_loss=28.32, generator_mel_loss=18.09, generator_kl_loss=1.383, generator_dur_loss=1.647, generator_adv_loss=1.926, generator_feat_match_loss=5.275, over 50.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.09, generator_mel_loss=18.1, generator_kl_loss=1.439, generator_dur_loss=1.715, generator_adv_loss=1.924, generator_feat_match_loss=4.912, over 686.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:54:48,968 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 20:54:56,887 INFO [train.py:591] (4/6) Epoch 438, validation: discriminator_loss=2.744, discriminator_real_loss=1.418, discriminator_fake_loss=1.327, generator_loss=27.28, generator_mel_loss=18.35, generator_kl_loss=1.23, generator_dur_loss=1.82, generator_adv_loss=1.905, generator_feat_match_loss=3.968, over 100.00 samples. +2024-03-13 20:54:56,888 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 20:57:16,536 INFO [train.py:527] (4/6) Epoch 438, batch 62, global_batch_idx: 54250, batch size: 36, loss[discriminator_loss=2.684, discriminator_real_loss=1.34, discriminator_fake_loss=1.344, generator_loss=27.9, generator_mel_loss=18.2, generator_kl_loss=1.631, generator_dur_loss=1.757, generator_adv_loss=1.954, generator_feat_match_loss=4.355, over 36.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.38, discriminator_fake_loss=1.344, generator_loss=27.94, generator_mel_loss=18.06, generator_kl_loss=1.421, generator_dur_loss=1.754, generator_adv_loss=1.937, generator_feat_match_loss=4.77, over 3535.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:59:33,000 INFO [train.py:527] (4/6) Epoch 438, batch 112, global_batch_idx: 54300, batch size: 96, loss[discriminator_loss=2.677, discriminator_real_loss=1.307, discriminator_fake_loss=1.37, generator_loss=28.12, generator_mel_loss=17.95, generator_kl_loss=1.28, generator_dur_loss=1.893, generator_adv_loss=2.11, generator_feat_match_loss=4.885, over 96.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.373, discriminator_fake_loss=1.348, generator_loss=27.94, generator_mel_loss=18.04, generator_kl_loss=1.406, generator_dur_loss=1.768, generator_adv_loss=1.936, generator_feat_match_loss=4.784, over 6733.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:00:05,177 INFO [train.py:919] (4/6) Start epoch 439 +2024-03-13 21:02:12,240 INFO [train.py:527] (4/6) Epoch 439, batch 38, global_batch_idx: 54350, batch size: 48, loss[discriminator_loss=2.715, discriminator_real_loss=1.419, discriminator_fake_loss=1.296, generator_loss=27.16, generator_mel_loss=17.46, generator_kl_loss=1.583, generator_dur_loss=1.74, generator_adv_loss=2.008, generator_feat_match_loss=4.374, over 48.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.366, discriminator_fake_loss=1.345, generator_loss=27.96, generator_mel_loss=18.05, generator_kl_loss=1.437, generator_dur_loss=1.765, generator_adv_loss=1.954, generator_feat_match_loss=4.753, over 2132.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:04:32,721 INFO [train.py:527] (4/6) Epoch 439, batch 88, global_batch_idx: 54400, batch size: 47, loss[discriminator_loss=2.748, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=29.09, generator_mel_loss=18.19, generator_kl_loss=1.57, generator_dur_loss=1.656, generator_adv_loss=2.019, generator_feat_match_loss=5.66, over 47.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=27.98, generator_mel_loss=18.08, generator_kl_loss=1.408, generator_dur_loss=1.771, generator_adv_loss=1.944, generator_feat_match_loss=4.778, over 5050.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:04:32,722 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 21:04:41,976 INFO [train.py:591] (4/6) Epoch 439, validation: discriminator_loss=2.799, discriminator_real_loss=1.513, discriminator_fake_loss=1.286, generator_loss=26.4, generator_mel_loss=18.01, generator_kl_loss=1.244, generator_dur_loss=1.829, generator_adv_loss=1.923, generator_feat_match_loss=3.392, over 100.00 samples. +2024-03-13 21:04:41,977 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 21:06:18,856 INFO [train.py:919] (4/6) Start epoch 440 +2024-03-13 21:07:20,396 INFO [train.py:527] (4/6) Epoch 440, batch 14, global_batch_idx: 54450, batch size: 55, loss[discriminator_loss=2.727, discriminator_real_loss=1.372, discriminator_fake_loss=1.356, generator_loss=28.9, generator_mel_loss=18.36, generator_kl_loss=1.466, generator_dur_loss=1.725, generator_adv_loss=2.186, generator_feat_match_loss=5.157, over 55.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.384, discriminator_fake_loss=1.326, generator_loss=28.37, generator_mel_loss=18.31, generator_kl_loss=1.456, generator_dur_loss=1.709, generator_adv_loss=1.966, generator_feat_match_loss=4.935, over 739.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:09:37,419 INFO [train.py:527] (4/6) Epoch 440, batch 64, global_batch_idx: 54500, batch size: 59, loss[discriminator_loss=2.784, discriminator_real_loss=1.467, discriminator_fake_loss=1.316, generator_loss=27.23, generator_mel_loss=17.94, generator_kl_loss=1.348, generator_dur_loss=1.788, generator_adv_loss=1.865, generator_feat_match_loss=4.292, over 59.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.391, discriminator_fake_loss=1.34, generator_loss=28.09, generator_mel_loss=18.14, generator_kl_loss=1.434, generator_dur_loss=1.731, generator_adv_loss=1.951, generator_feat_match_loss=4.838, over 3475.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:11:56,636 INFO [train.py:527] (4/6) Epoch 440, batch 114, global_batch_idx: 54550, batch size: 39, loss[discriminator_loss=2.797, discriminator_real_loss=1.428, discriminator_fake_loss=1.369, generator_loss=27.98, generator_mel_loss=18.38, generator_kl_loss=1.403, generator_dur_loss=1.744, generator_adv_loss=1.94, generator_feat_match_loss=4.512, over 39.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.381, discriminator_fake_loss=1.344, generator_loss=28.11, generator_mel_loss=18.15, generator_kl_loss=1.432, generator_dur_loss=1.737, generator_adv_loss=1.953, generator_feat_match_loss=4.839, over 6230.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:12:24,554 INFO [train.py:919] (4/6) Start epoch 441 +2024-03-13 21:14:37,143 INFO [train.py:527] (4/6) Epoch 441, batch 40, global_batch_idx: 54600, batch size: 55, loss[discriminator_loss=2.754, discriminator_real_loss=1.419, discriminator_fake_loss=1.336, generator_loss=27.93, generator_mel_loss=18.57, generator_kl_loss=1.373, generator_dur_loss=1.703, generator_adv_loss=1.817, generator_feat_match_loss=4.466, over 55.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.381, discriminator_fake_loss=1.337, generator_loss=28.03, generator_mel_loss=18.14, generator_kl_loss=1.426, generator_dur_loss=1.748, generator_adv_loss=1.947, generator_feat_match_loss=4.769, over 2293.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:14:37,145 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 21:14:45,258 INFO [train.py:591] (4/6) Epoch 441, validation: discriminator_loss=2.753, discriminator_real_loss=1.449, discriminator_fake_loss=1.304, generator_loss=26.88, generator_mel_loss=18.1, generator_kl_loss=1.25, generator_dur_loss=1.816, generator_adv_loss=1.859, generator_feat_match_loss=3.86, over 100.00 samples. +2024-03-13 21:14:45,259 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 21:17:05,691 INFO [train.py:527] (4/6) Epoch 441, batch 90, global_batch_idx: 54650, batch size: 15, loss[discriminator_loss=2.708, discriminator_real_loss=1.397, discriminator_fake_loss=1.311, generator_loss=29.65, generator_mel_loss=19.12, generator_kl_loss=1.752, generator_dur_loss=1.592, generator_adv_loss=2.124, generator_feat_match_loss=5.065, over 15.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.37, discriminator_fake_loss=1.346, generator_loss=28.11, generator_mel_loss=18.09, generator_kl_loss=1.419, generator_dur_loss=1.75, generator_adv_loss=1.954, generator_feat_match_loss=4.896, over 5106.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:18:39,287 INFO [train.py:919] (4/6) Start epoch 442 +2024-03-13 21:19:47,000 INFO [train.py:527] (4/6) Epoch 442, batch 16, global_batch_idx: 54700, batch size: 96, loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=28.49, generator_mel_loss=18.3, generator_kl_loss=1.337, generator_dur_loss=1.841, generator_adv_loss=1.906, generator_feat_match_loss=5.102, over 96.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.366, discriminator_fake_loss=1.365, generator_loss=28.05, generator_mel_loss=18.15, generator_kl_loss=1.39, generator_dur_loss=1.768, generator_adv_loss=1.962, generator_feat_match_loss=4.781, over 1086.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:22:08,458 INFO [train.py:527] (4/6) Epoch 442, batch 66, global_batch_idx: 54750, batch size: 88, loss[discriminator_loss=2.736, discriminator_real_loss=1.419, discriminator_fake_loss=1.317, generator_loss=27.97, generator_mel_loss=18.12, generator_kl_loss=1.243, generator_dur_loss=1.862, generator_adv_loss=1.934, generator_feat_match_loss=4.811, over 88.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.95, generator_mel_loss=18.05, generator_kl_loss=1.391, generator_dur_loss=1.765, generator_adv_loss=1.959, generator_feat_match_loss=4.788, over 4074.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:24:26,447 INFO [train.py:527] (4/6) Epoch 442, batch 116, global_batch_idx: 54800, batch size: 62, loss[discriminator_loss=2.738, discriminator_real_loss=1.414, discriminator_fake_loss=1.323, generator_loss=28.37, generator_mel_loss=18.47, generator_kl_loss=1.525, generator_dur_loss=1.777, generator_adv_loss=1.875, generator_feat_match_loss=4.724, over 62.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=28.01, generator_mel_loss=18.06, generator_kl_loss=1.406, generator_dur_loss=1.761, generator_adv_loss=1.952, generator_feat_match_loss=4.827, over 6845.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:24:26,449 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 21:24:35,318 INFO [train.py:591] (4/6) Epoch 442, validation: discriminator_loss=2.789, discriminator_real_loss=1.452, discriminator_fake_loss=1.337, generator_loss=27.65, generator_mel_loss=18.86, generator_kl_loss=1.358, generator_dur_loss=1.83, generator_adv_loss=1.862, generator_feat_match_loss=3.744, over 100.00 samples. +2024-03-13 21:24:35,319 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 21:24:53,837 INFO [train.py:919] (4/6) Start epoch 443 +2024-03-13 21:27:15,690 INFO [train.py:527] (4/6) Epoch 443, batch 42, global_batch_idx: 54850, batch size: 17, loss[discriminator_loss=2.66, discriminator_real_loss=1.188, discriminator_fake_loss=1.472, generator_loss=31.13, generator_mel_loss=18.82, generator_kl_loss=1.923, generator_dur_loss=1.561, generator_adv_loss=1.967, generator_feat_match_loss=6.859, over 17.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=28.04, generator_mel_loss=18.07, generator_kl_loss=1.403, generator_dur_loss=1.776, generator_adv_loss=1.942, generator_feat_match_loss=4.859, over 2578.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:29:35,336 INFO [train.py:527] (4/6) Epoch 443, batch 92, global_batch_idx: 54900, batch size: 58, loss[discriminator_loss=2.683, discriminator_real_loss=1.413, discriminator_fake_loss=1.27, generator_loss=27.55, generator_mel_loss=17.51, generator_kl_loss=1.564, generator_dur_loss=1.789, generator_adv_loss=2.126, generator_feat_match_loss=4.563, over 58.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.367, discriminator_fake_loss=1.343, generator_loss=28.25, generator_mel_loss=18.13, generator_kl_loss=1.408, generator_dur_loss=1.774, generator_adv_loss=1.994, generator_feat_match_loss=4.946, over 5369.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:30:59,499 INFO [train.py:919] (4/6) Start epoch 444 +2024-03-13 21:32:11,055 INFO [train.py:527] (4/6) Epoch 444, batch 18, global_batch_idx: 54950, batch size: 36, loss[discriminator_loss=2.842, discriminator_real_loss=1.41, discriminator_fake_loss=1.432, generator_loss=27.74, generator_mel_loss=18.27, generator_kl_loss=1.418, generator_dur_loss=1.699, generator_adv_loss=1.822, generator_feat_match_loss=4.527, over 36.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.366, discriminator_fake_loss=1.344, generator_loss=28.52, generator_mel_loss=18.25, generator_kl_loss=1.492, generator_dur_loss=1.742, generator_adv_loss=1.944, generator_feat_match_loss=5.088, over 922.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:34:31,960 INFO [train.py:527] (4/6) Epoch 444, batch 68, global_batch_idx: 55000, batch size: 56, loss[discriminator_loss=2.694, discriminator_real_loss=1.369, discriminator_fake_loss=1.325, generator_loss=28.77, generator_mel_loss=18.58, generator_kl_loss=1.414, generator_dur_loss=1.775, generator_adv_loss=1.903, generator_feat_match_loss=5.1, over 56.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.347, generator_loss=28.04, generator_mel_loss=18.09, generator_kl_loss=1.42, generator_dur_loss=1.768, generator_adv_loss=1.933, generator_feat_match_loss=4.824, over 4042.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:34:31,961 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 21:34:40,031 INFO [train.py:591] (4/6) Epoch 444, validation: discriminator_loss=2.77, discriminator_real_loss=1.474, discriminator_fake_loss=1.296, generator_loss=27.27, generator_mel_loss=18.42, generator_kl_loss=1.313, generator_dur_loss=1.839, generator_adv_loss=1.899, generator_feat_match_loss=3.804, over 100.00 samples. +2024-03-13 21:34:40,032 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 21:36:56,059 INFO [train.py:527] (4/6) Epoch 444, batch 118, global_batch_idx: 55050, batch size: 88, loss[discriminator_loss=2.708, discriminator_real_loss=1.406, discriminator_fake_loss=1.301, generator_loss=28.48, generator_mel_loss=18.35, generator_kl_loss=1.245, generator_dur_loss=1.874, generator_adv_loss=1.913, generator_feat_match_loss=5.105, over 88.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=28.05, generator_mel_loss=18.09, generator_kl_loss=1.415, generator_dur_loss=1.765, generator_adv_loss=1.944, generator_feat_match_loss=4.836, over 6948.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:37:11,051 INFO [train.py:919] (4/6) Start epoch 445 +2024-03-13 21:39:41,558 INFO [train.py:527] (4/6) Epoch 445, batch 44, global_batch_idx: 55100, batch size: 45, loss[discriminator_loss=2.702, discriminator_real_loss=1.307, discriminator_fake_loss=1.395, generator_loss=29.18, generator_mel_loss=18.6, generator_kl_loss=1.602, generator_dur_loss=1.626, generator_adv_loss=2.063, generator_feat_match_loss=5.291, over 45.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.36, discriminator_fake_loss=1.334, generator_loss=28.08, generator_mel_loss=18.14, generator_kl_loss=1.398, generator_dur_loss=1.783, generator_adv_loss=1.95, generator_feat_match_loss=4.807, over 2597.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:42:00,780 INFO [train.py:527] (4/6) Epoch 445, batch 94, global_batch_idx: 55150, batch size: 72, loss[discriminator_loss=2.703, discriminator_real_loss=1.377, discriminator_fake_loss=1.325, generator_loss=27.91, generator_mel_loss=18.21, generator_kl_loss=1.228, generator_dur_loss=1.836, generator_adv_loss=1.926, generator_feat_match_loss=4.713, over 72.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.371, discriminator_fake_loss=1.333, generator_loss=28.07, generator_mel_loss=18.11, generator_kl_loss=1.402, generator_dur_loss=1.779, generator_adv_loss=1.955, generator_feat_match_loss=4.822, over 5480.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:43:17,038 INFO [train.py:919] (4/6) Start epoch 446 +2024-03-13 21:44:33,590 INFO [train.py:527] (4/6) Epoch 446, batch 20, global_batch_idx: 55200, batch size: 31, loss[discriminator_loss=2.801, discriminator_real_loss=1.583, discriminator_fake_loss=1.218, generator_loss=27.94, generator_mel_loss=18.19, generator_kl_loss=1.601, generator_dur_loss=1.624, generator_adv_loss=1.923, generator_feat_match_loss=4.602, over 31.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.366, discriminator_fake_loss=1.355, generator_loss=28.26, generator_mel_loss=18.21, generator_kl_loss=1.404, generator_dur_loss=1.759, generator_adv_loss=1.962, generator_feat_match_loss=4.928, over 1198.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:44:33,591 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 21:44:41,674 INFO [train.py:591] (4/6) Epoch 446, validation: discriminator_loss=2.779, discriminator_real_loss=1.447, discriminator_fake_loss=1.332, generator_loss=26.72, generator_mel_loss=18.09, generator_kl_loss=1.257, generator_dur_loss=1.837, generator_adv_loss=1.85, generator_feat_match_loss=3.689, over 100.00 samples. +2024-03-13 21:44:41,674 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 21:46:59,750 INFO [train.py:527] (4/6) Epoch 446, batch 70, global_batch_idx: 55250, batch size: 44, loss[discriminator_loss=2.726, discriminator_real_loss=1.432, discriminator_fake_loss=1.294, generator_loss=29.32, generator_mel_loss=18.52, generator_kl_loss=1.709, generator_dur_loss=1.626, generator_adv_loss=1.887, generator_feat_match_loss=5.578, over 44.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.365, discriminator_fake_loss=1.348, generator_loss=28.14, generator_mel_loss=18.15, generator_kl_loss=1.403, generator_dur_loss=1.771, generator_adv_loss=1.952, generator_feat_match_loss=4.868, over 4059.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:49:19,348 INFO [train.py:527] (4/6) Epoch 446, batch 120, global_batch_idx: 55300, batch size: 15, loss[discriminator_loss=2.711, discriminator_real_loss=1.375, discriminator_fake_loss=1.336, generator_loss=27.58, generator_mel_loss=18.41, generator_kl_loss=1.708, generator_dur_loss=1.676, generator_adv_loss=1.973, generator_feat_match_loss=3.813, over 15.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.37, discriminator_fake_loss=1.346, generator_loss=28.17, generator_mel_loss=18.16, generator_kl_loss=1.406, generator_dur_loss=1.77, generator_adv_loss=1.957, generator_feat_match_loss=4.876, over 7014.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:49:29,310 INFO [train.py:919] (4/6) Start epoch 447 +2024-03-13 21:52:03,005 INFO [train.py:527] (4/6) Epoch 447, batch 46, global_batch_idx: 55350, batch size: 47, loss[discriminator_loss=2.636, discriminator_real_loss=1.333, discriminator_fake_loss=1.303, generator_loss=28.34, generator_mel_loss=18.22, generator_kl_loss=1.418, generator_dur_loss=1.707, generator_adv_loss=2.09, generator_feat_match_loss=4.907, over 47.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.37, discriminator_fake_loss=1.347, generator_loss=28.08, generator_mel_loss=18.08, generator_kl_loss=1.407, generator_dur_loss=1.771, generator_adv_loss=1.953, generator_feat_match_loss=4.864, over 2776.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:54:22,861 INFO [train.py:527] (4/6) Epoch 447, batch 96, global_batch_idx: 55400, batch size: 31, loss[discriminator_loss=2.735, discriminator_real_loss=1.359, discriminator_fake_loss=1.376, generator_loss=28.46, generator_mel_loss=18.57, generator_kl_loss=1.55, generator_dur_loss=1.612, generator_adv_loss=1.972, generator_feat_match_loss=4.757, over 31.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.372, discriminator_fake_loss=1.339, generator_loss=28.11, generator_mel_loss=18.09, generator_kl_loss=1.409, generator_dur_loss=1.766, generator_adv_loss=1.962, generator_feat_match_loss=4.89, over 5708.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:54:22,862 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 21:54:31,586 INFO [train.py:591] (4/6) Epoch 447, validation: discriminator_loss=2.762, discriminator_real_loss=1.484, discriminator_fake_loss=1.278, generator_loss=26.71, generator_mel_loss=18.1, generator_kl_loss=1.29, generator_dur_loss=1.837, generator_adv_loss=1.976, generator_feat_match_loss=3.501, over 100.00 samples. +2024-03-13 21:54:31,586 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 21:55:44,901 INFO [train.py:919] (4/6) Start epoch 448 +2024-03-13 21:57:09,240 INFO [train.py:527] (4/6) Epoch 448, batch 22, global_batch_idx: 55450, batch size: 68, loss[discriminator_loss=2.74, discriminator_real_loss=1.438, discriminator_fake_loss=1.302, generator_loss=27.98, generator_mel_loss=17.92, generator_kl_loss=1.435, generator_dur_loss=1.774, generator_adv_loss=1.898, generator_feat_match_loss=4.953, over 68.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.369, discriminator_fake_loss=1.348, generator_loss=28.06, generator_mel_loss=18.08, generator_kl_loss=1.384, generator_dur_loss=1.787, generator_adv_loss=1.944, generator_feat_match_loss=4.868, over 1412.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:59:27,773 INFO [train.py:527] (4/6) Epoch 448, batch 72, global_batch_idx: 55500, batch size: 66, loss[discriminator_loss=2.718, discriminator_real_loss=1.417, discriminator_fake_loss=1.301, generator_loss=28.24, generator_mel_loss=18.19, generator_kl_loss=1.347, generator_dur_loss=1.831, generator_adv_loss=1.976, generator_feat_match_loss=4.893, over 66.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.02, generator_mel_loss=18.03, generator_kl_loss=1.402, generator_dur_loss=1.775, generator_adv_loss=1.948, generator_feat_match_loss=4.872, over 4378.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:01:46,017 INFO [train.py:527] (4/6) Epoch 448, batch 122, global_batch_idx: 55550, batch size: 56, loss[discriminator_loss=2.738, discriminator_real_loss=1.397, discriminator_fake_loss=1.341, generator_loss=28.61, generator_mel_loss=18.12, generator_kl_loss=1.491, generator_dur_loss=1.762, generator_adv_loss=1.921, generator_feat_match_loss=5.312, over 56.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=28.08, generator_mel_loss=18.06, generator_kl_loss=1.409, generator_dur_loss=1.769, generator_adv_loss=1.954, generator_feat_match_loss=4.889, over 7266.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:01:51,092 INFO [train.py:919] (4/6) Start epoch 449 +2024-03-13 22:04:26,664 INFO [train.py:527] (4/6) Epoch 449, batch 48, global_batch_idx: 55600, batch size: 62, loss[discriminator_loss=2.68, discriminator_real_loss=1.315, discriminator_fake_loss=1.365, generator_loss=27.16, generator_mel_loss=17.53, generator_kl_loss=1.257, generator_dur_loss=1.743, generator_adv_loss=1.836, generator_feat_match_loss=4.793, over 62.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.379, discriminator_fake_loss=1.348, generator_loss=28.09, generator_mel_loss=18.11, generator_kl_loss=1.401, generator_dur_loss=1.758, generator_adv_loss=1.939, generator_feat_match_loss=4.876, over 2750.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:04:26,665 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 22:04:34,677 INFO [train.py:591] (4/6) Epoch 449, validation: discriminator_loss=2.757, discriminator_real_loss=1.339, discriminator_fake_loss=1.419, generator_loss=26.97, generator_mel_loss=18.16, generator_kl_loss=1.266, generator_dur_loss=1.819, generator_adv_loss=1.777, generator_feat_match_loss=3.945, over 100.00 samples. +2024-03-13 22:04:34,679 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 22:06:50,848 INFO [train.py:527] (4/6) Epoch 449, batch 98, global_batch_idx: 55650, batch size: 83, loss[discriminator_loss=2.671, discriminator_real_loss=1.396, discriminator_fake_loss=1.275, generator_loss=28.2, generator_mel_loss=18.11, generator_kl_loss=1.468, generator_dur_loss=1.814, generator_adv_loss=2.034, generator_feat_match_loss=4.778, over 83.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.374, discriminator_fake_loss=1.346, generator_loss=28.03, generator_mel_loss=18.09, generator_kl_loss=1.398, generator_dur_loss=1.751, generator_adv_loss=1.942, generator_feat_match_loss=4.855, over 5497.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:08:03,284 INFO [train.py:919] (4/6) Start epoch 450 +2024-03-13 22:09:34,084 INFO [train.py:527] (4/6) Epoch 450, batch 24, global_batch_idx: 55700, batch size: 13, loss[discriminator_loss=2.699, discriminator_real_loss=1.376, discriminator_fake_loss=1.322, generator_loss=29.39, generator_mel_loss=18.62, generator_kl_loss=2.035, generator_dur_loss=1.645, generator_adv_loss=1.925, generator_feat_match_loss=5.164, over 13.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.368, discriminator_fake_loss=1.341, generator_loss=27.95, generator_mel_loss=18.01, generator_kl_loss=1.415, generator_dur_loss=1.739, generator_adv_loss=1.949, generator_feat_match_loss=4.835, over 1422.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:11:52,361 INFO [train.py:527] (4/6) Epoch 450, batch 74, global_batch_idx: 55750, batch size: 12, loss[discriminator_loss=2.678, discriminator_real_loss=1.318, discriminator_fake_loss=1.36, generator_loss=28.54, generator_mel_loss=17.87, generator_kl_loss=1.949, generator_dur_loss=1.608, generator_adv_loss=1.942, generator_feat_match_loss=5.166, over 12.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.363, discriminator_fake_loss=1.343, generator_loss=28.13, generator_mel_loss=18.06, generator_kl_loss=1.405, generator_dur_loss=1.753, generator_adv_loss=1.973, generator_feat_match_loss=4.939, over 4260.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:14:07,726 INFO [train.py:919] (4/6) Start epoch 451 +2024-03-13 22:14:31,912 INFO [train.py:527] (4/6) Epoch 451, batch 0, global_batch_idx: 55800, batch size: 55, loss[discriminator_loss=2.723, discriminator_real_loss=1.315, discriminator_fake_loss=1.408, generator_loss=28.6, generator_mel_loss=18.54, generator_kl_loss=1.566, generator_dur_loss=1.701, generator_adv_loss=1.962, generator_feat_match_loss=4.829, over 55.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.315, discriminator_fake_loss=1.408, generator_loss=28.6, generator_mel_loss=18.54, generator_kl_loss=1.566, generator_dur_loss=1.701, generator_adv_loss=1.962, generator_feat_match_loss=4.829, over 55.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:14:31,915 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 22:14:39,768 INFO [train.py:591] (4/6) Epoch 451, validation: discriminator_loss=2.782, discriminator_real_loss=1.506, discriminator_fake_loss=1.276, generator_loss=26.31, generator_mel_loss=18.03, generator_kl_loss=1.253, generator_dur_loss=1.825, generator_adv_loss=1.882, generator_feat_match_loss=3.323, over 100.00 samples. +2024-03-13 22:14:39,770 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 22:16:57,334 INFO [train.py:527] (4/6) Epoch 451, batch 50, global_batch_idx: 55850, batch size: 48, loss[discriminator_loss=2.671, discriminator_real_loss=1.294, discriminator_fake_loss=1.377, generator_loss=30.54, generator_mel_loss=18.63, generator_kl_loss=1.558, generator_dur_loss=1.661, generator_adv_loss=2.084, generator_feat_match_loss=6.605, over 48.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=28.01, generator_mel_loss=18.08, generator_kl_loss=1.424, generator_dur_loss=1.75, generator_adv_loss=1.937, generator_feat_match_loss=4.825, over 2831.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:19:14,531 INFO [train.py:527] (4/6) Epoch 451, batch 100, global_batch_idx: 55900, batch size: 44, loss[discriminator_loss=2.702, discriminator_real_loss=1.358, discriminator_fake_loss=1.344, generator_loss=29.48, generator_mel_loss=18.36, generator_kl_loss=1.622, generator_dur_loss=1.697, generator_adv_loss=2.047, generator_feat_match_loss=5.756, over 44.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.381, discriminator_fake_loss=1.341, generator_loss=27.95, generator_mel_loss=18, generator_kl_loss=1.409, generator_dur_loss=1.758, generator_adv_loss=1.938, generator_feat_match_loss=4.836, over 5719.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:20:20,745 INFO [train.py:919] (4/6) Start epoch 452 +2024-03-13 22:21:57,284 INFO [train.py:527] (4/6) Epoch 452, batch 26, global_batch_idx: 55950, batch size: 52, loss[discriminator_loss=2.755, discriminator_real_loss=1.395, discriminator_fake_loss=1.36, generator_loss=28.04, generator_mel_loss=18.18, generator_kl_loss=1.423, generator_dur_loss=1.717, generator_adv_loss=1.954, generator_feat_match_loss=4.758, over 52.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.382, discriminator_fake_loss=1.342, generator_loss=28.16, generator_mel_loss=18.15, generator_kl_loss=1.437, generator_dur_loss=1.734, generator_adv_loss=1.968, generator_feat_match_loss=4.873, over 1468.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:24:17,356 INFO [train.py:527] (4/6) Epoch 452, batch 76, global_batch_idx: 56000, batch size: 80, loss[discriminator_loss=2.706, discriminator_real_loss=1.406, discriminator_fake_loss=1.3, generator_loss=28.27, generator_mel_loss=18.26, generator_kl_loss=1.31, generator_dur_loss=1.825, generator_adv_loss=2.033, generator_feat_match_loss=4.843, over 80.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.09, generator_mel_loss=18.11, generator_kl_loss=1.428, generator_dur_loss=1.745, generator_adv_loss=1.955, generator_feat_match_loss=4.858, over 4175.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:24:17,357 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 22:24:25,385 INFO [train.py:591] (4/6) Epoch 452, validation: discriminator_loss=2.778, discriminator_real_loss=1.488, discriminator_fake_loss=1.29, generator_loss=26.94, generator_mel_loss=18.1, generator_kl_loss=1.215, generator_dur_loss=1.844, generator_adv_loss=1.931, generator_feat_match_loss=3.847, over 100.00 samples. +2024-03-13 22:24:25,386 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 22:26:33,485 INFO [train.py:919] (4/6) Start epoch 453 +2024-03-13 22:27:01,562 INFO [train.py:527] (4/6) Epoch 453, batch 2, global_batch_idx: 56050, batch size: 42, loss[discriminator_loss=2.703, discriminator_real_loss=1.342, discriminator_fake_loss=1.361, generator_loss=28.21, generator_mel_loss=17.92, generator_kl_loss=1.561, generator_dur_loss=1.702, generator_adv_loss=2.01, generator_feat_match_loss=5.022, over 42.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.356, discriminator_fake_loss=1.338, generator_loss=28.72, generator_mel_loss=18.18, generator_kl_loss=1.581, generator_dur_loss=1.693, generator_adv_loss=1.999, generator_feat_match_loss=5.261, over 143.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:29:16,630 INFO [train.py:527] (4/6) Epoch 453, batch 52, global_batch_idx: 56100, batch size: 44, loss[discriminator_loss=2.754, discriminator_real_loss=1.383, discriminator_fake_loss=1.371, generator_loss=27.98, generator_mel_loss=18.15, generator_kl_loss=1.524, generator_dur_loss=1.723, generator_adv_loss=2.107, generator_feat_match_loss=4.48, over 44.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.347, generator_loss=28.1, generator_mel_loss=18.11, generator_kl_loss=1.453, generator_dur_loss=1.744, generator_adv_loss=1.949, generator_feat_match_loss=4.85, over 2765.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:31:35,321 INFO [train.py:527] (4/6) Epoch 453, batch 102, global_batch_idx: 56150, batch size: 61, loss[discriminator_loss=2.694, discriminator_real_loss=1.375, discriminator_fake_loss=1.319, generator_loss=28.85, generator_mel_loss=18.58, generator_kl_loss=1.606, generator_dur_loss=1.768, generator_adv_loss=2.032, generator_feat_match_loss=4.859, over 61.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.379, discriminator_fake_loss=1.348, generator_loss=27.96, generator_mel_loss=18.05, generator_kl_loss=1.43, generator_dur_loss=1.75, generator_adv_loss=1.943, generator_feat_match_loss=4.791, over 5759.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:32:35,652 INFO [train.py:919] (4/6) Start epoch 454 +2024-03-13 22:34:17,184 INFO [train.py:527] (4/6) Epoch 454, batch 28, global_batch_idx: 56200, batch size: 61, loss[discriminator_loss=2.817, discriminator_real_loss=1.364, discriminator_fake_loss=1.453, generator_loss=28.1, generator_mel_loss=17.88, generator_kl_loss=1.513, generator_dur_loss=1.704, generator_adv_loss=1.928, generator_feat_match_loss=5.08, over 61.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.371, discriminator_fake_loss=1.361, generator_loss=28.1, generator_mel_loss=18.04, generator_kl_loss=1.437, generator_dur_loss=1.763, generator_adv_loss=2.008, generator_feat_match_loss=4.85, over 1681.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:34:17,186 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 22:34:25,073 INFO [train.py:591] (4/6) Epoch 454, validation: discriminator_loss=2.785, discriminator_real_loss=1.482, discriminator_fake_loss=1.302, generator_loss=27.43, generator_mel_loss=18.39, generator_kl_loss=1.257, generator_dur_loss=1.828, generator_adv_loss=1.899, generator_feat_match_loss=4.065, over 100.00 samples. +2024-03-13 22:34:25,074 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 22:36:45,224 INFO [train.py:527] (4/6) Epoch 454, batch 78, global_batch_idx: 56250, batch size: 68, loss[discriminator_loss=2.716, discriminator_real_loss=1.401, discriminator_fake_loss=1.315, generator_loss=28.01, generator_mel_loss=18.06, generator_kl_loss=1.332, generator_dur_loss=1.806, generator_adv_loss=2.018, generator_feat_match_loss=4.801, over 68.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.383, discriminator_fake_loss=1.345, generator_loss=28.07, generator_mel_loss=18.07, generator_kl_loss=1.41, generator_dur_loss=1.77, generator_adv_loss=1.98, generator_feat_match_loss=4.842, over 4718.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:38:49,931 INFO [train.py:919] (4/6) Start epoch 455 +2024-03-13 22:39:24,143 INFO [train.py:527] (4/6) Epoch 455, batch 4, global_batch_idx: 56300, batch size: 39, loss[discriminator_loss=2.769, discriminator_real_loss=1.388, discriminator_fake_loss=1.381, generator_loss=27.39, generator_mel_loss=17.75, generator_kl_loss=1.511, generator_dur_loss=1.698, generator_adv_loss=1.974, generator_feat_match_loss=4.461, over 39.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.391, discriminator_fake_loss=1.331, generator_loss=28.35, generator_mel_loss=18.25, generator_kl_loss=1.479, generator_dur_loss=1.715, generator_adv_loss=1.954, generator_feat_match_loss=4.952, over 229.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:41:43,642 INFO [train.py:527] (4/6) Epoch 455, batch 54, global_batch_idx: 56350, batch size: 52, loss[discriminator_loss=2.703, discriminator_real_loss=1.304, discriminator_fake_loss=1.399, generator_loss=27.71, generator_mel_loss=18.08, generator_kl_loss=1.41, generator_dur_loss=1.729, generator_adv_loss=1.959, generator_feat_match_loss=4.537, over 52.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=28.03, generator_mel_loss=18.02, generator_kl_loss=1.414, generator_dur_loss=1.75, generator_adv_loss=1.939, generator_feat_match_loss=4.914, over 3209.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:44:02,027 INFO [train.py:527] (4/6) Epoch 455, batch 104, global_batch_idx: 56400, batch size: 42, loss[discriminator_loss=2.751, discriminator_real_loss=1.405, discriminator_fake_loss=1.346, generator_loss=28.47, generator_mel_loss=18.11, generator_kl_loss=1.567, generator_dur_loss=1.676, generator_adv_loss=2.088, generator_feat_match_loss=5.031, over 42.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.375, discriminator_fake_loss=1.344, generator_loss=28, generator_mel_loss=18.04, generator_kl_loss=1.412, generator_dur_loss=1.75, generator_adv_loss=1.949, generator_feat_match_loss=4.85, over 6141.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:44:02,029 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 22:44:11,040 INFO [train.py:591] (4/6) Epoch 455, validation: discriminator_loss=2.763, discriminator_real_loss=1.513, discriminator_fake_loss=1.25, generator_loss=27.47, generator_mel_loss=18.5, generator_kl_loss=1.143, generator_dur_loss=1.829, generator_adv_loss=2.035, generator_feat_match_loss=3.965, over 100.00 samples. +2024-03-13 22:44:11,041 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 22:45:04,844 INFO [train.py:919] (4/6) Start epoch 456 +2024-03-13 22:46:51,749 INFO [train.py:527] (4/6) Epoch 456, batch 30, global_batch_idx: 56450, batch size: 70, loss[discriminator_loss=2.759, discriminator_real_loss=1.402, discriminator_fake_loss=1.357, generator_loss=27.7, generator_mel_loss=17.84, generator_kl_loss=1.388, generator_dur_loss=1.737, generator_adv_loss=2.138, generator_feat_match_loss=4.598, over 70.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.04, generator_mel_loss=18.1, generator_kl_loss=1.428, generator_dur_loss=1.756, generator_adv_loss=1.948, generator_feat_match_loss=4.804, over 1585.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:49:11,319 INFO [train.py:527] (4/6) Epoch 456, batch 80, global_batch_idx: 56500, batch size: 58, loss[discriminator_loss=2.756, discriminator_real_loss=1.467, discriminator_fake_loss=1.289, generator_loss=27.95, generator_mel_loss=17.98, generator_kl_loss=1.516, generator_dur_loss=1.723, generator_adv_loss=1.973, generator_feat_match_loss=4.756, over 58.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.376, discriminator_fake_loss=1.336, generator_loss=27.95, generator_mel_loss=18.04, generator_kl_loss=1.4, generator_dur_loss=1.759, generator_adv_loss=1.953, generator_feat_match_loss=4.796, over 4458.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:51:12,091 INFO [train.py:919] (4/6) Start epoch 457 +2024-03-13 22:51:51,401 INFO [train.py:527] (4/6) Epoch 457, batch 6, global_batch_idx: 56550, batch size: 72, loss[discriminator_loss=2.708, discriminator_real_loss=1.37, discriminator_fake_loss=1.338, generator_loss=28.09, generator_mel_loss=18.06, generator_kl_loss=1.331, generator_dur_loss=1.833, generator_adv_loss=2.031, generator_feat_match_loss=4.833, over 72.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.374, discriminator_fake_loss=1.353, generator_loss=28.25, generator_mel_loss=18.19, generator_kl_loss=1.446, generator_dur_loss=1.777, generator_adv_loss=1.969, generator_feat_match_loss=4.861, over 413.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:54:13,887 INFO [train.py:527] (4/6) Epoch 457, batch 56, global_batch_idx: 56600, batch size: 62, loss[discriminator_loss=2.704, discriminator_real_loss=1.37, discriminator_fake_loss=1.334, generator_loss=27.77, generator_mel_loss=18.05, generator_kl_loss=1.426, generator_dur_loss=1.743, generator_adv_loss=1.949, generator_feat_match_loss=4.602, over 62.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.378, discriminator_fake_loss=1.343, generator_loss=28.1, generator_mel_loss=18.06, generator_kl_loss=1.417, generator_dur_loss=1.776, generator_adv_loss=1.958, generator_feat_match_loss=4.89, over 3468.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:54:13,889 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 22:54:21,932 INFO [train.py:591] (4/6) Epoch 457, validation: discriminator_loss=2.738, discriminator_real_loss=1.474, discriminator_fake_loss=1.264, generator_loss=26.53, generator_mel_loss=18.11, generator_kl_loss=1.237, generator_dur_loss=1.824, generator_adv_loss=1.89, generator_feat_match_loss=3.468, over 100.00 samples. +2024-03-13 22:54:21,933 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 22:56:41,907 INFO [train.py:527] (4/6) Epoch 457, batch 106, global_batch_idx: 56650, batch size: 47, loss[discriminator_loss=2.739, discriminator_real_loss=1.322, discriminator_fake_loss=1.416, generator_loss=28.58, generator_mel_loss=18.35, generator_kl_loss=1.503, generator_dur_loss=1.682, generator_adv_loss=2.003, generator_feat_match_loss=5.04, over 47.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.373, discriminator_fake_loss=1.347, generator_loss=28.16, generator_mel_loss=18.09, generator_kl_loss=1.413, generator_dur_loss=1.777, generator_adv_loss=1.954, generator_feat_match_loss=4.923, over 6480.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:57:26,369 INFO [train.py:919] (4/6) Start epoch 458 +2024-03-13 22:59:21,180 INFO [train.py:527] (4/6) Epoch 458, batch 32, global_batch_idx: 56700, batch size: 44, loss[discriminator_loss=2.704, discriminator_real_loss=1.421, discriminator_fake_loss=1.283, generator_loss=27.97, generator_mel_loss=17.96, generator_kl_loss=1.34, generator_dur_loss=1.707, generator_adv_loss=1.864, generator_feat_match_loss=5.094, over 44.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.369, discriminator_fake_loss=1.346, generator_loss=27.88, generator_mel_loss=17.98, generator_kl_loss=1.444, generator_dur_loss=1.754, generator_adv_loss=1.939, generator_feat_match_loss=4.759, over 1803.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:01:40,110 INFO [train.py:527] (4/6) Epoch 458, batch 82, global_batch_idx: 56750, batch size: 31, loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=29.05, generator_mel_loss=18.16, generator_kl_loss=1.548, generator_dur_loss=1.656, generator_adv_loss=2.007, generator_feat_match_loss=5.673, over 31.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=28.03, generator_mel_loss=18.05, generator_kl_loss=1.425, generator_dur_loss=1.751, generator_adv_loss=1.952, generator_feat_match_loss=4.844, over 4615.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:03:33,712 INFO [train.py:919] (4/6) Start epoch 459 +2024-03-13 23:04:20,574 INFO [train.py:527] (4/6) Epoch 459, batch 8, global_batch_idx: 56800, batch size: 47, loss[discriminator_loss=2.771, discriminator_real_loss=1.515, discriminator_fake_loss=1.256, generator_loss=28.52, generator_mel_loss=18.33, generator_kl_loss=1.503, generator_dur_loss=1.676, generator_adv_loss=1.854, generator_feat_match_loss=5.156, over 47.00 samples.], tot_loss[discriminator_loss=2.783, discriminator_real_loss=1.393, discriminator_fake_loss=1.39, generator_loss=27.69, generator_mel_loss=17.9, generator_kl_loss=1.472, generator_dur_loss=1.711, generator_adv_loss=1.954, generator_feat_match_loss=4.659, over 466.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:04:20,576 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 23:04:28,785 INFO [train.py:591] (4/6) Epoch 459, validation: discriminator_loss=2.756, discriminator_real_loss=1.415, discriminator_fake_loss=1.342, generator_loss=26.56, generator_mel_loss=18.05, generator_kl_loss=1.314, generator_dur_loss=1.787, generator_adv_loss=1.917, generator_feat_match_loss=3.492, over 100.00 samples. +2024-03-13 23:04:28,788 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 23:06:48,019 INFO [train.py:527] (4/6) Epoch 459, batch 58, global_batch_idx: 56850, batch size: 80, loss[discriminator_loss=2.8, discriminator_real_loss=1.34, discriminator_fake_loss=1.46, generator_loss=27.38, generator_mel_loss=17.72, generator_kl_loss=1.206, generator_dur_loss=1.797, generator_adv_loss=2.039, generator_feat_match_loss=4.621, over 80.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.344, generator_loss=27.94, generator_mel_loss=17.96, generator_kl_loss=1.431, generator_dur_loss=1.741, generator_adv_loss=1.943, generator_feat_match_loss=4.862, over 3300.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:09:07,398 INFO [train.py:527] (4/6) Epoch 459, batch 108, global_batch_idx: 56900, batch size: 36, loss[discriminator_loss=2.667, discriminator_real_loss=1.35, discriminator_fake_loss=1.316, generator_loss=28.14, generator_mel_loss=17.65, generator_kl_loss=1.438, generator_dur_loss=1.678, generator_adv_loss=2.077, generator_feat_match_loss=5.301, over 36.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.339, generator_loss=28.08, generator_mel_loss=18.02, generator_kl_loss=1.423, generator_dur_loss=1.747, generator_adv_loss=1.957, generator_feat_match_loss=4.927, over 6197.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:09:49,896 INFO [train.py:919] (4/6) Start epoch 460 +2024-03-13 23:11:48,774 INFO [train.py:527] (4/6) Epoch 460, batch 34, global_batch_idx: 56950, batch size: 96, loss[discriminator_loss=2.702, discriminator_real_loss=1.39, discriminator_fake_loss=1.312, generator_loss=27.34, generator_mel_loss=17.78, generator_kl_loss=1.325, generator_dur_loss=1.882, generator_adv_loss=1.851, generator_feat_match_loss=4.504, over 96.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.382, discriminator_fake_loss=1.334, generator_loss=28.15, generator_mel_loss=18.14, generator_kl_loss=1.421, generator_dur_loss=1.748, generator_adv_loss=1.946, generator_feat_match_loss=4.892, over 1923.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:14:09,560 INFO [train.py:527] (4/6) Epoch 460, batch 84, global_batch_idx: 57000, batch size: 74, loss[discriminator_loss=2.75, discriminator_real_loss=1.347, discriminator_fake_loss=1.403, generator_loss=27.65, generator_mel_loss=18.08, generator_kl_loss=1.405, generator_dur_loss=1.811, generator_adv_loss=1.984, generator_feat_match_loss=4.367, over 74.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=28.1, generator_mel_loss=18.09, generator_kl_loss=1.41, generator_dur_loss=1.757, generator_adv_loss=1.948, generator_feat_match_loss=4.892, over 4719.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:14:09,562 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 23:14:18,391 INFO [train.py:591] (4/6) Epoch 460, validation: discriminator_loss=2.79, discriminator_real_loss=1.545, discriminator_fake_loss=1.245, generator_loss=26.98, generator_mel_loss=18.17, generator_kl_loss=1.213, generator_dur_loss=1.823, generator_adv_loss=1.988, generator_feat_match_loss=3.778, over 100.00 samples. +2024-03-13 23:14:18,392 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 23:16:10,470 INFO [train.py:919] (4/6) Start epoch 461 +2024-03-13 23:17:01,944 INFO [train.py:527] (4/6) Epoch 461, batch 10, global_batch_idx: 57050, batch size: 45, loss[discriminator_loss=2.663, discriminator_real_loss=1.358, discriminator_fake_loss=1.305, generator_loss=30.06, generator_mel_loss=18.59, generator_kl_loss=1.611, generator_dur_loss=1.661, generator_adv_loss=1.915, generator_feat_match_loss=6.288, over 45.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.375, discriminator_fake_loss=1.32, generator_loss=28.44, generator_mel_loss=18.22, generator_kl_loss=1.452, generator_dur_loss=1.733, generator_adv_loss=1.945, generator_feat_match_loss=5.086, over 566.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:19:22,939 INFO [train.py:527] (4/6) Epoch 461, batch 60, global_batch_idx: 57100, batch size: 66, loss[discriminator_loss=2.748, discriminator_real_loss=1.345, discriminator_fake_loss=1.403, generator_loss=27.86, generator_mel_loss=17.92, generator_kl_loss=1.295, generator_dur_loss=1.745, generator_adv_loss=1.954, generator_feat_match_loss=4.94, over 66.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.365, discriminator_fake_loss=1.346, generator_loss=28.16, generator_mel_loss=18.1, generator_kl_loss=1.425, generator_dur_loss=1.735, generator_adv_loss=1.955, generator_feat_match_loss=4.949, over 3222.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:21:41,991 INFO [train.py:527] (4/6) Epoch 461, batch 110, global_batch_idx: 57150, batch size: 56, loss[discriminator_loss=2.7, discriminator_real_loss=1.358, discriminator_fake_loss=1.342, generator_loss=26.93, generator_mel_loss=17.63, generator_kl_loss=1.364, generator_dur_loss=1.76, generator_adv_loss=1.945, generator_feat_match_loss=4.23, over 56.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.346, generator_loss=28.08, generator_mel_loss=18.08, generator_kl_loss=1.418, generator_dur_loss=1.731, generator_adv_loss=1.95, generator_feat_match_loss=4.909, over 6062.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:22:19,106 INFO [train.py:919] (4/6) Start epoch 462 +2024-03-13 23:24:25,854 INFO [train.py:527] (4/6) Epoch 462, batch 36, global_batch_idx: 57200, batch size: 56, loss[discriminator_loss=2.692, discriminator_real_loss=1.269, discriminator_fake_loss=1.424, generator_loss=27.94, generator_mel_loss=17.9, generator_kl_loss=1.196, generator_dur_loss=1.672, generator_adv_loss=2.099, generator_feat_match_loss=5.076, over 56.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.393, discriminator_fake_loss=1.346, generator_loss=27.81, generator_mel_loss=17.99, generator_kl_loss=1.376, generator_dur_loss=1.744, generator_adv_loss=1.939, generator_feat_match_loss=4.768, over 2125.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:24:25,855 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 23:24:33,975 INFO [train.py:591] (4/6) Epoch 462, validation: discriminator_loss=2.77, discriminator_real_loss=1.451, discriminator_fake_loss=1.318, generator_loss=26.88, generator_mel_loss=18.14, generator_kl_loss=1.209, generator_dur_loss=1.803, generator_adv_loss=1.881, generator_feat_match_loss=3.847, over 100.00 samples. +2024-03-13 23:24:33,976 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 23:26:50,766 INFO [train.py:527] (4/6) Epoch 462, batch 86, global_batch_idx: 57250, batch size: 55, loss[discriminator_loss=2.736, discriminator_real_loss=1.423, discriminator_fake_loss=1.313, generator_loss=27.76, generator_mel_loss=17.9, generator_kl_loss=1.46, generator_dur_loss=1.719, generator_adv_loss=1.798, generator_feat_match_loss=4.879, over 55.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.386, discriminator_fake_loss=1.345, generator_loss=27.89, generator_mel_loss=18.01, generator_kl_loss=1.399, generator_dur_loss=1.751, generator_adv_loss=1.944, generator_feat_match_loss=4.792, over 4985.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:28:36,341 INFO [train.py:919] (4/6) Start epoch 463 +2024-03-13 23:29:31,939 INFO [train.py:527] (4/6) Epoch 463, batch 12, global_batch_idx: 57300, batch size: 25, loss[discriminator_loss=2.732, discriminator_real_loss=1.352, discriminator_fake_loss=1.38, generator_loss=28.96, generator_mel_loss=18.92, generator_kl_loss=1.659, generator_dur_loss=1.623, generator_adv_loss=1.807, generator_feat_match_loss=4.955, over 25.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.379, discriminator_fake_loss=1.365, generator_loss=27.93, generator_mel_loss=17.93, generator_kl_loss=1.407, generator_dur_loss=1.763, generator_adv_loss=1.908, generator_feat_match_loss=4.914, over 782.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:31:50,177 INFO [train.py:527] (4/6) Epoch 463, batch 62, global_batch_idx: 57350, batch size: 70, loss[discriminator_loss=2.722, discriminator_real_loss=1.278, discriminator_fake_loss=1.444, generator_loss=28.65, generator_mel_loss=18.25, generator_kl_loss=1.412, generator_dur_loss=1.804, generator_adv_loss=2.038, generator_feat_match_loss=5.143, over 70.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.378, discriminator_fake_loss=1.346, generator_loss=28.16, generator_mel_loss=18.08, generator_kl_loss=1.448, generator_dur_loss=1.741, generator_adv_loss=1.952, generator_feat_match_loss=4.937, over 3343.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:34:12,381 INFO [train.py:527] (4/6) Epoch 463, batch 112, global_batch_idx: 57400, batch size: 66, loss[discriminator_loss=2.666, discriminator_real_loss=1.273, discriminator_fake_loss=1.393, generator_loss=28.44, generator_mel_loss=18.2, generator_kl_loss=1.224, generator_dur_loss=1.712, generator_adv_loss=1.942, generator_feat_match_loss=5.357, over 66.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.373, discriminator_fake_loss=1.35, generator_loss=28.13, generator_mel_loss=18.09, generator_kl_loss=1.431, generator_dur_loss=1.747, generator_adv_loss=1.945, generator_feat_match_loss=4.919, over 6184.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:34:12,382 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 23:34:21,113 INFO [train.py:591] (4/6) Epoch 463, validation: discriminator_loss=2.716, discriminator_real_loss=1.355, discriminator_fake_loss=1.361, generator_loss=26.53, generator_mel_loss=17.99, generator_kl_loss=1.175, generator_dur_loss=1.819, generator_adv_loss=1.822, generator_feat_match_loss=3.73, over 100.00 samples. +2024-03-13 23:34:21,114 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 23:34:55,129 INFO [train.py:919] (4/6) Start epoch 464 +2024-03-13 23:37:06,110 INFO [train.py:527] (4/6) Epoch 464, batch 38, global_batch_idx: 57450, batch size: 72, loss[discriminator_loss=2.722, discriminator_real_loss=1.42, discriminator_fake_loss=1.302, generator_loss=28.22, generator_mel_loss=18.15, generator_kl_loss=1.406, generator_dur_loss=1.82, generator_adv_loss=1.834, generator_feat_match_loss=5.007, over 72.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.377, discriminator_fake_loss=1.335, generator_loss=28.01, generator_mel_loss=18.08, generator_kl_loss=1.449, generator_dur_loss=1.749, generator_adv_loss=1.954, generator_feat_match_loss=4.774, over 2062.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:39:24,457 INFO [train.py:527] (4/6) Epoch 464, batch 88, global_batch_idx: 57500, batch size: 48, loss[discriminator_loss=2.689, discriminator_real_loss=1.343, discriminator_fake_loss=1.345, generator_loss=28.63, generator_mel_loss=18.17, generator_kl_loss=1.489, generator_dur_loss=1.679, generator_adv_loss=1.828, generator_feat_match_loss=5.466, over 48.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=27.97, generator_mel_loss=18, generator_kl_loss=1.439, generator_dur_loss=1.76, generator_adv_loss=1.948, generator_feat_match_loss=4.818, over 4985.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:41:03,959 INFO [train.py:919] (4/6) Start epoch 465 +2024-03-13 23:42:06,792 INFO [train.py:527] (4/6) Epoch 465, batch 14, global_batch_idx: 57550, batch size: 74, loss[discriminator_loss=2.691, discriminator_real_loss=1.236, discriminator_fake_loss=1.454, generator_loss=28.35, generator_mel_loss=18.5, generator_kl_loss=1.34, generator_dur_loss=1.778, generator_adv_loss=1.994, generator_feat_match_loss=4.736, over 74.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.382, discriminator_fake_loss=1.345, generator_loss=27.83, generator_mel_loss=18.05, generator_kl_loss=1.392, generator_dur_loss=1.748, generator_adv_loss=1.949, generator_feat_match_loss=4.692, over 833.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:44:25,110 INFO [train.py:527] (4/6) Epoch 465, batch 64, global_batch_idx: 57600, batch size: 44, loss[discriminator_loss=2.687, discriminator_real_loss=1.347, discriminator_fake_loss=1.339, generator_loss=28.5, generator_mel_loss=18.67, generator_kl_loss=1.636, generator_dur_loss=1.717, generator_adv_loss=2.073, generator_feat_match_loss=4.41, over 44.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.37, discriminator_fake_loss=1.35, generator_loss=27.85, generator_mel_loss=17.95, generator_kl_loss=1.387, generator_dur_loss=1.749, generator_adv_loss=1.944, generator_feat_match_loss=4.817, over 3896.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:44:25,112 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 23:44:33,225 INFO [train.py:591] (4/6) Epoch 465, validation: discriminator_loss=2.722, discriminator_real_loss=1.484, discriminator_fake_loss=1.238, generator_loss=27.3, generator_mel_loss=18.26, generator_kl_loss=1.27, generator_dur_loss=1.798, generator_adv_loss=1.937, generator_feat_match_loss=4.031, over 100.00 samples. +2024-03-13 23:44:33,226 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 23:46:49,210 INFO [train.py:527] (4/6) Epoch 465, batch 114, global_batch_idx: 57650, batch size: 15, loss[discriminator_loss=2.787, discriminator_real_loss=1.437, discriminator_fake_loss=1.35, generator_loss=26.88, generator_mel_loss=17.64, generator_kl_loss=1.622, generator_dur_loss=1.565, generator_adv_loss=1.678, generator_feat_match_loss=4.377, over 15.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=27.89, generator_mel_loss=17.97, generator_kl_loss=1.397, generator_dur_loss=1.751, generator_adv_loss=1.944, generator_feat_match_loss=4.832, over 6780.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:47:16,264 INFO [train.py:919] (4/6) Start epoch 466 +2024-03-13 23:49:34,265 INFO [train.py:527] (4/6) Epoch 466, batch 40, global_batch_idx: 57700, batch size: 55, loss[discriminator_loss=2.737, discriminator_real_loss=1.428, discriminator_fake_loss=1.309, generator_loss=27.32, generator_mel_loss=17.86, generator_kl_loss=1.311, generator_dur_loss=1.748, generator_adv_loss=1.842, generator_feat_match_loss=4.563, over 55.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=28.09, generator_mel_loss=18, generator_kl_loss=1.46, generator_dur_loss=1.761, generator_adv_loss=1.952, generator_feat_match_loss=4.915, over 2193.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:51:52,868 INFO [train.py:527] (4/6) Epoch 466, batch 90, global_batch_idx: 57750, batch size: 72, loss[discriminator_loss=2.695, discriminator_real_loss=1.316, discriminator_fake_loss=1.379, generator_loss=28.11, generator_mel_loss=18.09, generator_kl_loss=1.334, generator_dur_loss=1.862, generator_adv_loss=1.988, generator_feat_match_loss=4.836, over 72.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.365, discriminator_fake_loss=1.345, generator_loss=28.13, generator_mel_loss=18.05, generator_kl_loss=1.434, generator_dur_loss=1.766, generator_adv_loss=1.948, generator_feat_match_loss=4.928, over 5035.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:53:26,183 INFO [train.py:919] (4/6) Start epoch 467 +2024-03-13 23:54:34,105 INFO [train.py:527] (4/6) Epoch 467, batch 16, global_batch_idx: 57800, batch size: 47, loss[discriminator_loss=2.747, discriminator_real_loss=1.407, discriminator_fake_loss=1.339, generator_loss=27.78, generator_mel_loss=18.14, generator_kl_loss=1.521, generator_dur_loss=1.681, generator_adv_loss=2.035, generator_feat_match_loss=4.403, over 47.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.374, discriminator_fake_loss=1.339, generator_loss=28.14, generator_mel_loss=18.15, generator_kl_loss=1.433, generator_dur_loss=1.736, generator_adv_loss=1.947, generator_feat_match_loss=4.866, over 887.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:54:34,107 INFO [train.py:581] (4/6) Computing validation loss +2024-03-13 23:54:42,161 INFO [train.py:591] (4/6) Epoch 467, validation: discriminator_loss=2.753, discriminator_real_loss=1.437, discriminator_fake_loss=1.316, generator_loss=27.38, generator_mel_loss=18.34, generator_kl_loss=1.376, generator_dur_loss=1.807, generator_adv_loss=1.921, generator_feat_match_loss=3.94, over 100.00 samples. +2024-03-13 23:54:42,162 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-13 23:57:00,031 INFO [train.py:527] (4/6) Epoch 467, batch 66, global_batch_idx: 57850, batch size: 31, loss[discriminator_loss=2.7, discriminator_real_loss=1.419, discriminator_fake_loss=1.281, generator_loss=28.67, generator_mel_loss=18.42, generator_kl_loss=1.442, generator_dur_loss=1.693, generator_adv_loss=2.016, generator_feat_match_loss=5.103, over 31.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=28.02, generator_mel_loss=18.01, generator_kl_loss=1.424, generator_dur_loss=1.756, generator_adv_loss=1.952, generator_feat_match_loss=4.879, over 3753.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:59:19,477 INFO [train.py:527] (4/6) Epoch 467, batch 116, global_batch_idx: 57900, batch size: 96, loss[discriminator_loss=2.74, discriminator_real_loss=1.42, discriminator_fake_loss=1.32, generator_loss=26.69, generator_mel_loss=17.35, generator_kl_loss=1.266, generator_dur_loss=1.868, generator_adv_loss=1.797, generator_feat_match_loss=4.409, over 96.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=27.97, generator_mel_loss=17.97, generator_kl_loss=1.416, generator_dur_loss=1.76, generator_adv_loss=1.945, generator_feat_match_loss=4.877, over 6693.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:59:41,761 INFO [train.py:919] (4/6) Start epoch 468 +2024-03-14 00:02:04,334 INFO [train.py:527] (4/6) Epoch 468, batch 42, global_batch_idx: 57950, batch size: 74, loss[discriminator_loss=2.676, discriminator_real_loss=1.443, discriminator_fake_loss=1.233, generator_loss=28.59, generator_mel_loss=18.36, generator_kl_loss=1.265, generator_dur_loss=1.827, generator_adv_loss=1.843, generator_feat_match_loss=5.301, over 74.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.37, discriminator_fake_loss=1.341, generator_loss=28.06, generator_mel_loss=17.93, generator_kl_loss=1.401, generator_dur_loss=1.766, generator_adv_loss=1.968, generator_feat_match_loss=4.99, over 2582.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:04:24,948 INFO [train.py:527] (4/6) Epoch 468, batch 92, global_batch_idx: 58000, batch size: 58, loss[discriminator_loss=2.672, discriminator_real_loss=1.346, discriminator_fake_loss=1.326, generator_loss=28.72, generator_mel_loss=18.14, generator_kl_loss=1.305, generator_dur_loss=1.768, generator_adv_loss=2.061, generator_feat_match_loss=5.437, over 58.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=27.99, generator_mel_loss=17.95, generator_kl_loss=1.392, generator_dur_loss=1.763, generator_adv_loss=1.96, generator_feat_match_loss=4.927, over 5582.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:04:24,950 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 00:04:34,035 INFO [train.py:591] (4/6) Epoch 468, validation: discriminator_loss=2.717, discriminator_real_loss=1.497, discriminator_fake_loss=1.219, generator_loss=28.21, generator_mel_loss=18.77, generator_kl_loss=1.172, generator_dur_loss=1.832, generator_adv_loss=2.018, generator_feat_match_loss=4.414, over 100.00 samples. +2024-03-14 00:04:34,036 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 00:05:57,170 INFO [train.py:919] (4/6) Start epoch 469 +2024-03-14 00:07:12,636 INFO [train.py:527] (4/6) Epoch 469, batch 18, global_batch_idx: 58050, batch size: 88, loss[discriminator_loss=2.806, discriminator_real_loss=1.358, discriminator_fake_loss=1.448, generator_loss=27.52, generator_mel_loss=17.66, generator_kl_loss=1.419, generator_dur_loss=1.859, generator_adv_loss=2.071, generator_feat_match_loss=4.506, over 88.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.369, discriminator_fake_loss=1.355, generator_loss=27.93, generator_mel_loss=18.02, generator_kl_loss=1.402, generator_dur_loss=1.762, generator_adv_loss=1.938, generator_feat_match_loss=4.802, over 1188.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:09:35,692 INFO [train.py:527] (4/6) Epoch 469, batch 68, global_batch_idx: 58100, batch size: 44, loss[discriminator_loss=2.717, discriminator_real_loss=1.36, discriminator_fake_loss=1.357, generator_loss=27.6, generator_mel_loss=17.6, generator_kl_loss=1.494, generator_dur_loss=1.698, generator_adv_loss=1.901, generator_feat_match_loss=4.912, over 44.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.371, discriminator_fake_loss=1.35, generator_loss=27.96, generator_mel_loss=17.98, generator_kl_loss=1.403, generator_dur_loss=1.762, generator_adv_loss=1.946, generator_feat_match_loss=4.87, over 3833.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:11:56,274 INFO [train.py:527] (4/6) Epoch 469, batch 118, global_batch_idx: 58150, batch size: 45, loss[discriminator_loss=2.722, discriminator_real_loss=1.34, discriminator_fake_loss=1.383, generator_loss=28.21, generator_mel_loss=18.06, generator_kl_loss=1.626, generator_dur_loss=1.667, generator_adv_loss=2.048, generator_feat_match_loss=4.813, over 45.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.347, generator_loss=28.02, generator_mel_loss=18, generator_kl_loss=1.409, generator_dur_loss=1.763, generator_adv_loss=1.938, generator_feat_match_loss=4.903, over 6570.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:12:09,786 INFO [train.py:919] (4/6) Start epoch 470 +2024-03-14 00:14:36,277 INFO [train.py:527] (4/6) Epoch 470, batch 44, global_batch_idx: 58200, batch size: 68, loss[discriminator_loss=2.67, discriminator_real_loss=1.36, discriminator_fake_loss=1.309, generator_loss=28.39, generator_mel_loss=17.81, generator_kl_loss=1.254, generator_dur_loss=1.813, generator_adv_loss=1.999, generator_feat_match_loss=5.514, over 68.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.368, discriminator_fake_loss=1.334, generator_loss=28.19, generator_mel_loss=18.04, generator_kl_loss=1.414, generator_dur_loss=1.767, generator_adv_loss=1.952, generator_feat_match_loss=5.016, over 2596.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:14:36,278 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 00:14:44,171 INFO [train.py:591] (4/6) Epoch 470, validation: discriminator_loss=2.713, discriminator_real_loss=1.448, discriminator_fake_loss=1.264, generator_loss=27.15, generator_mel_loss=18.23, generator_kl_loss=1.214, generator_dur_loss=1.836, generator_adv_loss=1.973, generator_feat_match_loss=3.9, over 100.00 samples. +2024-03-14 00:14:44,172 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 00:17:05,757 INFO [train.py:527] (4/6) Epoch 470, batch 94, global_batch_idx: 58250, batch size: 48, loss[discriminator_loss=2.77, discriminator_real_loss=1.558, discriminator_fake_loss=1.212, generator_loss=26.5, generator_mel_loss=17.42, generator_kl_loss=1.247, generator_dur_loss=1.745, generator_adv_loss=1.807, generator_feat_match_loss=4.28, over 48.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=28.09, generator_mel_loss=18, generator_kl_loss=1.406, generator_dur_loss=1.772, generator_adv_loss=1.95, generator_feat_match_loss=4.965, over 5485.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:18:32,261 INFO [train.py:919] (4/6) Start epoch 471 +2024-03-14 00:19:56,141 INFO [train.py:527] (4/6) Epoch 471, batch 20, global_batch_idx: 58300, batch size: 83, loss[discriminator_loss=2.719, discriminator_real_loss=1.39, discriminator_fake_loss=1.329, generator_loss=26.61, generator_mel_loss=17.47, generator_kl_loss=1.277, generator_dur_loss=1.873, generator_adv_loss=1.899, generator_feat_match_loss=4.095, over 83.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=27.95, generator_mel_loss=18.01, generator_kl_loss=1.392, generator_dur_loss=1.758, generator_adv_loss=1.961, generator_feat_match_loss=4.829, over 1141.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:22:19,634 INFO [train.py:527] (4/6) Epoch 471, batch 70, global_batch_idx: 58350, batch size: 31, loss[discriminator_loss=2.747, discriminator_real_loss=1.425, discriminator_fake_loss=1.322, generator_loss=29.06, generator_mel_loss=18.56, generator_kl_loss=1.587, generator_dur_loss=1.61, generator_adv_loss=2.007, generator_feat_match_loss=5.299, over 31.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.379, discriminator_fake_loss=1.342, generator_loss=28.04, generator_mel_loss=18.03, generator_kl_loss=1.408, generator_dur_loss=1.759, generator_adv_loss=1.956, generator_feat_match_loss=4.887, over 4048.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:24:37,664 INFO [train.py:527] (4/6) Epoch 471, batch 120, global_batch_idx: 58400, batch size: 58, loss[discriminator_loss=2.745, discriminator_real_loss=1.422, discriminator_fake_loss=1.323, generator_loss=28.03, generator_mel_loss=17.94, generator_kl_loss=1.519, generator_dur_loss=1.71, generator_adv_loss=1.817, generator_feat_match_loss=5.043, over 58.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.377, discriminator_fake_loss=1.343, generator_loss=28.08, generator_mel_loss=18.04, generator_kl_loss=1.415, generator_dur_loss=1.756, generator_adv_loss=1.945, generator_feat_match_loss=4.922, over 6781.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:24:37,666 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 00:24:46,222 INFO [train.py:591] (4/6) Epoch 471, validation: discriminator_loss=2.756, discriminator_real_loss=1.348, discriminator_fake_loss=1.408, generator_loss=26.82, generator_mel_loss=18.25, generator_kl_loss=1.196, generator_dur_loss=1.841, generator_adv_loss=1.775, generator_feat_match_loss=3.765, over 100.00 samples. +2024-03-14 00:24:46,223 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 00:24:55,196 INFO [train.py:919] (4/6) Start epoch 472 +2024-03-14 00:27:26,444 INFO [train.py:527] (4/6) Epoch 472, batch 46, global_batch_idx: 58450, batch size: 68, loss[discriminator_loss=2.649, discriminator_real_loss=1.208, discriminator_fake_loss=1.441, generator_loss=29.08, generator_mel_loss=18.29, generator_kl_loss=1.406, generator_dur_loss=1.727, generator_adv_loss=2.02, generator_feat_match_loss=5.637, over 68.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.368, discriminator_fake_loss=1.346, generator_loss=28.05, generator_mel_loss=18, generator_kl_loss=1.43, generator_dur_loss=1.736, generator_adv_loss=1.941, generator_feat_match_loss=4.949, over 2612.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:29:48,864 INFO [train.py:527] (4/6) Epoch 472, batch 96, global_batch_idx: 58500, batch size: 25, loss[discriminator_loss=2.672, discriminator_real_loss=1.276, discriminator_fake_loss=1.397, generator_loss=29.7, generator_mel_loss=19.58, generator_kl_loss=1.746, generator_dur_loss=1.551, generator_adv_loss=2.201, generator_feat_match_loss=4.619, over 25.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.376, discriminator_fake_loss=1.347, generator_loss=28.04, generator_mel_loss=18, generator_kl_loss=1.413, generator_dur_loss=1.743, generator_adv_loss=1.942, generator_feat_match_loss=4.938, over 5626.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:31:07,654 INFO [train.py:919] (4/6) Start epoch 473 +2024-03-14 00:32:34,887 INFO [train.py:527] (4/6) Epoch 473, batch 22, global_batch_idx: 58550, batch size: 48, loss[discriminator_loss=2.724, discriminator_real_loss=1.448, discriminator_fake_loss=1.276, generator_loss=27.7, generator_mel_loss=17.68, generator_kl_loss=1.51, generator_dur_loss=1.634, generator_adv_loss=1.897, generator_feat_match_loss=4.979, over 48.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.388, discriminator_fake_loss=1.339, generator_loss=28.16, generator_mel_loss=18.04, generator_kl_loss=1.382, generator_dur_loss=1.741, generator_adv_loss=1.964, generator_feat_match_loss=5.038, over 1359.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:34:57,174 INFO [train.py:527] (4/6) Epoch 473, batch 72, global_batch_idx: 58600, batch size: 62, loss[discriminator_loss=2.723, discriminator_real_loss=1.418, discriminator_fake_loss=1.306, generator_loss=28.11, generator_mel_loss=17.96, generator_kl_loss=1.553, generator_dur_loss=1.711, generator_adv_loss=1.789, generator_feat_match_loss=5.094, over 62.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.376, discriminator_fake_loss=1.342, generator_loss=28.17, generator_mel_loss=18.05, generator_kl_loss=1.422, generator_dur_loss=1.749, generator_adv_loss=1.971, generator_feat_match_loss=4.977, over 4166.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:34:57,175 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 00:35:05,376 INFO [train.py:591] (4/6) Epoch 473, validation: discriminator_loss=2.796, discriminator_real_loss=1.353, discriminator_fake_loss=1.442, generator_loss=26.19, generator_mel_loss=17.77, generator_kl_loss=1.184, generator_dur_loss=1.819, generator_adv_loss=1.691, generator_feat_match_loss=3.723, over 100.00 samples. +2024-03-14 00:35:05,377 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 00:37:25,753 INFO [train.py:527] (4/6) Epoch 473, batch 122, global_batch_idx: 58650, batch size: 66, loss[discriminator_loss=2.673, discriminator_real_loss=1.308, discriminator_fake_loss=1.364, generator_loss=28.63, generator_mel_loss=18.2, generator_kl_loss=1.294, generator_dur_loss=1.793, generator_adv_loss=2.039, generator_feat_match_loss=5.307, over 66.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=28.12, generator_mel_loss=18.03, generator_kl_loss=1.421, generator_dur_loss=1.752, generator_adv_loss=1.961, generator_feat_match_loss=4.95, over 6889.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:37:30,687 INFO [train.py:919] (4/6) Start epoch 474 +2024-03-14 00:40:13,552 INFO [train.py:527] (4/6) Epoch 474, batch 48, global_batch_idx: 58700, batch size: 52, loss[discriminator_loss=2.72, discriminator_real_loss=1.301, discriminator_fake_loss=1.419, generator_loss=28.29, generator_mel_loss=18.28, generator_kl_loss=1.371, generator_dur_loss=1.693, generator_adv_loss=2.041, generator_feat_match_loss=4.909, over 52.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.374, discriminator_fake_loss=1.337, generator_loss=28.02, generator_mel_loss=17.99, generator_kl_loss=1.408, generator_dur_loss=1.76, generator_adv_loss=1.941, generator_feat_match_loss=4.925, over 2862.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:42:34,243 INFO [train.py:527] (4/6) Epoch 474, batch 98, global_batch_idx: 58750, batch size: 48, loss[discriminator_loss=2.731, discriminator_real_loss=1.346, discriminator_fake_loss=1.385, generator_loss=27.91, generator_mel_loss=17.86, generator_kl_loss=1.519, generator_dur_loss=1.692, generator_adv_loss=2.001, generator_feat_match_loss=4.836, over 48.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.38, discriminator_fake_loss=1.337, generator_loss=27.99, generator_mel_loss=17.99, generator_kl_loss=1.425, generator_dur_loss=1.753, generator_adv_loss=1.938, generator_feat_match_loss=4.879, over 5622.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:43:48,321 INFO [train.py:919] (4/6) Start epoch 475 +2024-03-14 00:45:23,448 INFO [train.py:527] (4/6) Epoch 475, batch 24, global_batch_idx: 58800, batch size: 80, loss[discriminator_loss=2.669, discriminator_real_loss=1.338, discriminator_fake_loss=1.33, generator_loss=27.9, generator_mel_loss=18.12, generator_kl_loss=1.209, generator_dur_loss=1.813, generator_adv_loss=1.927, generator_feat_match_loss=4.826, over 80.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.364, discriminator_fake_loss=1.356, generator_loss=28.14, generator_mel_loss=18.06, generator_kl_loss=1.412, generator_dur_loss=1.765, generator_adv_loss=1.942, generator_feat_match_loss=4.96, over 1469.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:45:23,449 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 00:45:31,208 INFO [train.py:591] (4/6) Epoch 475, validation: discriminator_loss=2.769, discriminator_real_loss=1.347, discriminator_fake_loss=1.422, generator_loss=26.55, generator_mel_loss=18.26, generator_kl_loss=1.245, generator_dur_loss=1.828, generator_adv_loss=1.754, generator_feat_match_loss=3.463, over 100.00 samples. +2024-03-14 00:45:31,209 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 00:47:52,053 INFO [train.py:527] (4/6) Epoch 475, batch 74, global_batch_idx: 58850, batch size: 64, loss[discriminator_loss=2.712, discriminator_real_loss=1.411, discriminator_fake_loss=1.301, generator_loss=27.76, generator_mel_loss=17.57, generator_kl_loss=1.409, generator_dur_loss=1.81, generator_adv_loss=1.979, generator_feat_match_loss=4.986, over 64.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.376, discriminator_fake_loss=1.342, generator_loss=28.09, generator_mel_loss=18.04, generator_kl_loss=1.409, generator_dur_loss=1.752, generator_adv_loss=1.944, generator_feat_match_loss=4.949, over 4416.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:50:09,751 INFO [train.py:919] (4/6) Start epoch 476 +2024-03-14 00:50:34,904 INFO [train.py:527] (4/6) Epoch 476, batch 0, global_batch_idx: 58900, batch size: 39, loss[discriminator_loss=2.65, discriminator_real_loss=1.304, discriminator_fake_loss=1.346, generator_loss=29.27, generator_mel_loss=18.86, generator_kl_loss=1.621, generator_dur_loss=1.696, generator_adv_loss=1.999, generator_feat_match_loss=5.089, over 39.00 samples.], tot_loss[discriminator_loss=2.65, discriminator_real_loss=1.304, discriminator_fake_loss=1.346, generator_loss=29.27, generator_mel_loss=18.86, generator_kl_loss=1.621, generator_dur_loss=1.696, generator_adv_loss=1.999, generator_feat_match_loss=5.089, over 39.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:52:59,174 INFO [train.py:527] (4/6) Epoch 476, batch 50, global_batch_idx: 58950, batch size: 83, loss[discriminator_loss=2.666, discriminator_real_loss=1.358, discriminator_fake_loss=1.309, generator_loss=27.32, generator_mel_loss=17.67, generator_kl_loss=1.319, generator_dur_loss=1.828, generator_adv_loss=1.828, generator_feat_match_loss=4.679, over 83.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.366, discriminator_fake_loss=1.337, generator_loss=28.12, generator_mel_loss=18.03, generator_kl_loss=1.424, generator_dur_loss=1.762, generator_adv_loss=1.941, generator_feat_match_loss=4.963, over 2842.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:55:19,578 INFO [train.py:527] (4/6) Epoch 476, batch 100, global_batch_idx: 59000, batch size: 70, loss[discriminator_loss=2.775, discriminator_real_loss=1.494, discriminator_fake_loss=1.282, generator_loss=26.97, generator_mel_loss=17.39, generator_kl_loss=1.257, generator_dur_loss=1.753, generator_adv_loss=1.882, generator_feat_match_loss=4.683, over 70.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.16, generator_mel_loss=18.05, generator_kl_loss=1.4, generator_dur_loss=1.758, generator_adv_loss=1.971, generator_feat_match_loss=4.982, over 5756.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:55:19,579 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 00:55:28,425 INFO [train.py:591] (4/6) Epoch 476, validation: discriminator_loss=2.743, discriminator_real_loss=1.426, discriminator_fake_loss=1.317, generator_loss=26.56, generator_mel_loss=18.08, generator_kl_loss=1.197, generator_dur_loss=1.79, generator_adv_loss=1.856, generator_feat_match_loss=3.641, over 100.00 samples. +2024-03-14 00:55:28,426 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 00:56:31,378 INFO [train.py:919] (4/6) Start epoch 477 +2024-03-14 00:58:09,084 INFO [train.py:527] (4/6) Epoch 477, batch 26, global_batch_idx: 59050, batch size: 74, loss[discriminator_loss=2.737, discriminator_real_loss=1.415, discriminator_fake_loss=1.322, generator_loss=28.12, generator_mel_loss=17.72, generator_kl_loss=1.464, generator_dur_loss=1.745, generator_adv_loss=1.846, generator_feat_match_loss=5.338, over 74.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.1, generator_mel_loss=18.06, generator_kl_loss=1.421, generator_dur_loss=1.728, generator_adv_loss=1.94, generator_feat_match_loss=4.949, over 1422.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:00:33,354 INFO [train.py:527] (4/6) Epoch 477, batch 76, global_batch_idx: 59100, batch size: 59, loss[discriminator_loss=2.725, discriminator_real_loss=1.338, discriminator_fake_loss=1.387, generator_loss=29.08, generator_mel_loss=17.99, generator_kl_loss=1.555, generator_dur_loss=1.793, generator_adv_loss=2.039, generator_feat_match_loss=5.697, over 59.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=28.1, generator_mel_loss=18, generator_kl_loss=1.412, generator_dur_loss=1.757, generator_adv_loss=1.948, generator_feat_match_loss=4.977, over 4530.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:02:41,882 INFO [train.py:919] (4/6) Start epoch 478 +2024-03-14 01:03:10,921 INFO [train.py:527] (4/6) Epoch 478, batch 2, global_batch_idx: 59150, batch size: 52, loss[discriminator_loss=2.793, discriminator_real_loss=1.6, discriminator_fake_loss=1.193, generator_loss=29.47, generator_mel_loss=18.51, generator_kl_loss=1.45, generator_dur_loss=1.697, generator_adv_loss=2.001, generator_feat_match_loss=5.812, over 52.00 samples.], tot_loss[discriminator_loss=2.841, discriminator_real_loss=1.451, discriminator_fake_loss=1.391, generator_loss=28.45, generator_mel_loss=18.21, generator_kl_loss=1.423, generator_dur_loss=1.738, generator_adv_loss=2.031, generator_feat_match_loss=5.046, over 185.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:05:34,155 INFO [train.py:527] (4/6) Epoch 478, batch 52, global_batch_idx: 59200, batch size: 13, loss[discriminator_loss=2.651, discriminator_real_loss=1.331, discriminator_fake_loss=1.32, generator_loss=29.86, generator_mel_loss=19.35, generator_kl_loss=1.82, generator_dur_loss=1.688, generator_adv_loss=1.967, generator_feat_match_loss=5.037, over 13.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=28.11, generator_mel_loss=18.01, generator_kl_loss=1.426, generator_dur_loss=1.752, generator_adv_loss=1.948, generator_feat_match_loss=4.973, over 2952.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:05:34,157 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 01:05:41,915 INFO [train.py:591] (4/6) Epoch 478, validation: discriminator_loss=2.71, discriminator_real_loss=1.383, discriminator_fake_loss=1.327, generator_loss=27.1, generator_mel_loss=18.43, generator_kl_loss=1.199, generator_dur_loss=1.801, generator_adv_loss=1.869, generator_feat_match_loss=3.803, over 100.00 samples. +2024-03-14 01:05:41,916 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 01:08:05,605 INFO [train.py:527] (4/6) Epoch 478, batch 102, global_batch_idx: 59250, batch size: 72, loss[discriminator_loss=2.722, discriminator_real_loss=1.286, discriminator_fake_loss=1.437, generator_loss=28.53, generator_mel_loss=18.02, generator_kl_loss=1.474, generator_dur_loss=1.814, generator_adv_loss=1.93, generator_feat_match_loss=5.296, over 72.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=28.12, generator_mel_loss=18.01, generator_kl_loss=1.412, generator_dur_loss=1.765, generator_adv_loss=1.949, generator_feat_match_loss=4.992, over 5875.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:09:04,988 INFO [train.py:919] (4/6) Start epoch 479 +2024-03-14 01:10:51,248 INFO [train.py:527] (4/6) Epoch 479, batch 28, global_batch_idx: 59300, batch size: 55, loss[discriminator_loss=2.747, discriminator_real_loss=1.384, discriminator_fake_loss=1.363, generator_loss=27.96, generator_mel_loss=18.13, generator_kl_loss=1.478, generator_dur_loss=1.696, generator_adv_loss=2.036, generator_feat_match_loss=4.614, over 55.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.338, generator_loss=28.1, generator_mel_loss=17.97, generator_kl_loss=1.431, generator_dur_loss=1.758, generator_adv_loss=1.958, generator_feat_match_loss=4.984, over 1633.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:13:13,829 INFO [train.py:527] (4/6) Epoch 479, batch 78, global_batch_idx: 59350, batch size: 53, loss[discriminator_loss=2.739, discriminator_real_loss=1.413, discriminator_fake_loss=1.326, generator_loss=28.47, generator_mel_loss=18.5, generator_kl_loss=1.448, generator_dur_loss=1.759, generator_adv_loss=1.952, generator_feat_match_loss=4.815, over 53.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.339, generator_loss=28.11, generator_mel_loss=18.01, generator_kl_loss=1.416, generator_dur_loss=1.765, generator_adv_loss=1.958, generator_feat_match_loss=4.961, over 4495.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:15:20,615 INFO [train.py:919] (4/6) Start epoch 480 +2024-03-14 01:15:56,065 INFO [train.py:527] (4/6) Epoch 480, batch 4, global_batch_idx: 59400, batch size: 48, loss[discriminator_loss=2.776, discriminator_real_loss=1.405, discriminator_fake_loss=1.371, generator_loss=27.93, generator_mel_loss=18.27, generator_kl_loss=1.296, generator_dur_loss=1.709, generator_adv_loss=2.027, generator_feat_match_loss=4.628, over 48.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.381, discriminator_fake_loss=1.337, generator_loss=28.17, generator_mel_loss=18.07, generator_kl_loss=1.435, generator_dur_loss=1.723, generator_adv_loss=1.972, generator_feat_match_loss=4.965, over 239.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:15:56,086 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 01:16:03,714 INFO [train.py:591] (4/6) Epoch 480, validation: discriminator_loss=2.809, discriminator_real_loss=1.507, discriminator_fake_loss=1.302, generator_loss=27.04, generator_mel_loss=18.29, generator_kl_loss=1.284, generator_dur_loss=1.825, generator_adv_loss=1.928, generator_feat_match_loss=3.717, over 100.00 samples. +2024-03-14 01:16:03,716 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 01:18:25,422 INFO [train.py:527] (4/6) Epoch 480, batch 54, global_batch_idx: 59450, batch size: 25, loss[discriminator_loss=2.701, discriminator_real_loss=1.315, discriminator_fake_loss=1.386, generator_loss=29.26, generator_mel_loss=18.64, generator_kl_loss=1.559, generator_dur_loss=1.578, generator_adv_loss=2.045, generator_feat_match_loss=5.428, over 25.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.378, discriminator_fake_loss=1.347, generator_loss=28.07, generator_mel_loss=18.01, generator_kl_loss=1.4, generator_dur_loss=1.765, generator_adv_loss=1.965, generator_feat_match_loss=4.939, over 3262.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:20:46,648 INFO [train.py:527] (4/6) Epoch 480, batch 104, global_batch_idx: 59500, batch size: 55, loss[discriminator_loss=2.711, discriminator_real_loss=1.398, discriminator_fake_loss=1.313, generator_loss=27.21, generator_mel_loss=17.65, generator_kl_loss=1.591, generator_dur_loss=1.711, generator_adv_loss=1.902, generator_feat_match_loss=4.357, over 55.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.379, discriminator_fake_loss=1.343, generator_loss=28.07, generator_mel_loss=18.02, generator_kl_loss=1.413, generator_dur_loss=1.762, generator_adv_loss=1.96, generator_feat_match_loss=4.916, over 5997.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:21:42,211 INFO [train.py:919] (4/6) Start epoch 481 +2024-03-14 01:23:35,268 INFO [train.py:527] (4/6) Epoch 481, batch 30, global_batch_idx: 59550, batch size: 52, loss[discriminator_loss=2.692, discriminator_real_loss=1.294, discriminator_fake_loss=1.398, generator_loss=28.37, generator_mel_loss=17.92, generator_kl_loss=1.566, generator_dur_loss=1.645, generator_adv_loss=1.997, generator_feat_match_loss=5.244, over 52.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.14, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.77, generator_adv_loss=1.95, generator_feat_match_loss=4.995, over 1870.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:25:59,993 INFO [train.py:527] (4/6) Epoch 481, batch 80, global_batch_idx: 59600, batch size: 59, loss[discriminator_loss=2.733, discriminator_real_loss=1.389, discriminator_fake_loss=1.344, generator_loss=28.33, generator_mel_loss=17.92, generator_kl_loss=1.398, generator_dur_loss=1.754, generator_adv_loss=1.971, generator_feat_match_loss=5.286, over 59.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=28.13, generator_mel_loss=18.02, generator_kl_loss=1.396, generator_dur_loss=1.76, generator_adv_loss=1.955, generator_feat_match_loss=4.996, over 4876.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:25:59,994 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 01:26:08,543 INFO [train.py:591] (4/6) Epoch 481, validation: discriminator_loss=2.778, discriminator_real_loss=1.453, discriminator_fake_loss=1.325, generator_loss=26.79, generator_mel_loss=18.06, generator_kl_loss=1.274, generator_dur_loss=1.817, generator_adv_loss=1.858, generator_feat_match_loss=3.779, over 100.00 samples. +2024-03-14 01:26:08,544 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 01:28:08,649 INFO [train.py:919] (4/6) Start epoch 482 +2024-03-14 01:28:50,652 INFO [train.py:527] (4/6) Epoch 482, batch 6, global_batch_idx: 59650, batch size: 62, loss[discriminator_loss=2.679, discriminator_real_loss=1.382, discriminator_fake_loss=1.297, generator_loss=28.66, generator_mel_loss=18.38, generator_kl_loss=1.394, generator_dur_loss=1.74, generator_adv_loss=1.877, generator_feat_match_loss=5.271, over 62.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.372, discriminator_fake_loss=1.334, generator_loss=28.34, generator_mel_loss=18.11, generator_kl_loss=1.439, generator_dur_loss=1.714, generator_adv_loss=1.995, generator_feat_match_loss=5.08, over 376.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:31:11,729 INFO [train.py:527] (4/6) Epoch 482, batch 56, global_batch_idx: 59700, batch size: 77, loss[discriminator_loss=2.703, discriminator_real_loss=1.352, discriminator_fake_loss=1.351, generator_loss=27.58, generator_mel_loss=17.74, generator_kl_loss=1.323, generator_dur_loss=1.819, generator_adv_loss=1.973, generator_feat_match_loss=4.724, over 77.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=28.21, generator_mel_loss=18.08, generator_kl_loss=1.421, generator_dur_loss=1.746, generator_adv_loss=1.961, generator_feat_match_loss=5.004, over 3098.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:33:31,763 INFO [train.py:527] (4/6) Epoch 482, batch 106, global_batch_idx: 59750, batch size: 48, loss[discriminator_loss=2.695, discriminator_real_loss=1.4, discriminator_fake_loss=1.294, generator_loss=28.48, generator_mel_loss=18.05, generator_kl_loss=1.447, generator_dur_loss=1.672, generator_adv_loss=1.919, generator_feat_match_loss=5.387, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=28.17, generator_mel_loss=18.04, generator_kl_loss=1.423, generator_dur_loss=1.752, generator_adv_loss=1.946, generator_feat_match_loss=5.009, over 5877.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:34:23,801 INFO [train.py:919] (4/6) Start epoch 483 +2024-03-14 01:36:18,586 INFO [train.py:527] (4/6) Epoch 483, batch 32, global_batch_idx: 59800, batch size: 45, loss[discriminator_loss=2.675, discriminator_real_loss=1.319, discriminator_fake_loss=1.356, generator_loss=28.72, generator_mel_loss=18.32, generator_kl_loss=1.63, generator_dur_loss=1.668, generator_adv_loss=2.16, generator_feat_match_loss=4.95, over 45.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.16, generator_mel_loss=18, generator_kl_loss=1.427, generator_dur_loss=1.779, generator_adv_loss=1.964, generator_feat_match_loss=4.99, over 1910.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:36:18,588 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 01:36:26,621 INFO [train.py:591] (4/6) Epoch 483, validation: discriminator_loss=2.745, discriminator_real_loss=1.502, discriminator_fake_loss=1.242, generator_loss=26.89, generator_mel_loss=18.14, generator_kl_loss=1.281, generator_dur_loss=1.805, generator_adv_loss=1.944, generator_feat_match_loss=3.723, over 100.00 samples. +2024-03-14 01:36:26,622 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 01:38:50,014 INFO [train.py:527] (4/6) Epoch 483, batch 82, global_batch_idx: 59850, batch size: 48, loss[discriminator_loss=2.743, discriminator_real_loss=1.43, discriminator_fake_loss=1.313, generator_loss=27.9, generator_mel_loss=18.08, generator_kl_loss=1.466, generator_dur_loss=1.667, generator_adv_loss=1.947, generator_feat_match_loss=4.744, over 48.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.37, discriminator_fake_loss=1.339, generator_loss=28.2, generator_mel_loss=18.05, generator_kl_loss=1.422, generator_dur_loss=1.772, generator_adv_loss=1.959, generator_feat_match_loss=5.002, over 4848.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:40:45,722 INFO [train.py:919] (4/6) Start epoch 484 +2024-03-14 01:41:33,064 INFO [train.py:527] (4/6) Epoch 484, batch 8, global_batch_idx: 59900, batch size: 39, loss[discriminator_loss=2.692, discriminator_real_loss=1.427, discriminator_fake_loss=1.264, generator_loss=27.55, generator_mel_loss=17.98, generator_kl_loss=1.367, generator_dur_loss=1.712, generator_adv_loss=1.978, generator_feat_match_loss=4.512, over 39.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.386, discriminator_fake_loss=1.323, generator_loss=28.18, generator_mel_loss=18.09, generator_kl_loss=1.457, generator_dur_loss=1.743, generator_adv_loss=1.966, generator_feat_match_loss=4.926, over 475.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:43:54,262 INFO [train.py:527] (4/6) Epoch 484, batch 58, global_batch_idx: 59950, batch size: 68, loss[discriminator_loss=2.727, discriminator_real_loss=1.329, discriminator_fake_loss=1.398, generator_loss=28.21, generator_mel_loss=18.18, generator_kl_loss=1.279, generator_dur_loss=1.79, generator_adv_loss=2.147, generator_feat_match_loss=4.816, over 68.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.34, generator_loss=28.15, generator_mel_loss=18.02, generator_kl_loss=1.419, generator_dur_loss=1.767, generator_adv_loss=1.963, generator_feat_match_loss=4.987, over 3513.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:46:14,257 INFO [train.py:527] (4/6) Epoch 484, batch 108, global_batch_idx: 60000, batch size: 44, loss[discriminator_loss=2.697, discriminator_real_loss=1.354, discriminator_fake_loss=1.343, generator_loss=28.86, generator_mel_loss=18.12, generator_kl_loss=1.607, generator_dur_loss=1.686, generator_adv_loss=1.822, generator_feat_match_loss=5.623, over 44.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.376, discriminator_fake_loss=1.344, generator_loss=28.17, generator_mel_loss=18.03, generator_kl_loss=1.417, generator_dur_loss=1.761, generator_adv_loss=1.953, generator_feat_match_loss=5.012, over 6183.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:46:14,258 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 01:46:22,957 INFO [train.py:591] (4/6) Epoch 484, validation: discriminator_loss=2.761, discriminator_real_loss=1.313, discriminator_fake_loss=1.447, generator_loss=26.9, generator_mel_loss=18.08, generator_kl_loss=1.285, generator_dur_loss=1.83, generator_adv_loss=1.697, generator_feat_match_loss=4.007, over 100.00 samples. +2024-03-14 01:46:22,958 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 01:47:06,082 INFO [train.py:919] (4/6) Start epoch 485 +2024-03-14 01:49:07,750 INFO [train.py:527] (4/6) Epoch 485, batch 34, global_batch_idx: 60050, batch size: 15, loss[discriminator_loss=2.549, discriminator_real_loss=1.259, discriminator_fake_loss=1.29, generator_loss=30.38, generator_mel_loss=19.6, generator_kl_loss=1.833, generator_dur_loss=1.568, generator_adv_loss=2.212, generator_feat_match_loss=5.164, over 15.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=27.95, generator_mel_loss=17.94, generator_kl_loss=1.4, generator_dur_loss=1.755, generator_adv_loss=1.962, generator_feat_match_loss=4.89, over 2018.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:51:25,471 INFO [train.py:527] (4/6) Epoch 485, batch 84, global_batch_idx: 60100, batch size: 64, loss[discriminator_loss=2.785, discriminator_real_loss=1.426, discriminator_fake_loss=1.359, generator_loss=28.12, generator_mel_loss=17.92, generator_kl_loss=1.403, generator_dur_loss=1.774, generator_adv_loss=1.888, generator_feat_match_loss=5.135, over 64.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.369, discriminator_fake_loss=1.346, generator_loss=28.07, generator_mel_loss=18.01, generator_kl_loss=1.405, generator_dur_loss=1.766, generator_adv_loss=1.95, generator_feat_match_loss=4.946, over 4987.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:53:14,015 INFO [train.py:919] (4/6) Start epoch 486 +2024-03-14 01:54:02,997 INFO [train.py:527] (4/6) Epoch 486, batch 10, global_batch_idx: 60150, batch size: 42, loss[discriminator_loss=2.732, discriminator_real_loss=1.328, discriminator_fake_loss=1.404, generator_loss=29.66, generator_mel_loss=18.6, generator_kl_loss=1.554, generator_dur_loss=1.648, generator_adv_loss=1.87, generator_feat_match_loss=5.996, over 42.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.372, discriminator_fake_loss=1.355, generator_loss=28.49, generator_mel_loss=18.32, generator_kl_loss=1.44, generator_dur_loss=1.698, generator_adv_loss=1.957, generator_feat_match_loss=5.067, over 518.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:56:22,144 INFO [train.py:527] (4/6) Epoch 486, batch 60, global_batch_idx: 60200, batch size: 50, loss[discriminator_loss=2.705, discriminator_real_loss=1.323, discriminator_fake_loss=1.382, generator_loss=28.61, generator_mel_loss=18.53, generator_kl_loss=1.314, generator_dur_loss=1.723, generator_adv_loss=1.789, generator_feat_match_loss=5.248, over 50.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.373, discriminator_fake_loss=1.337, generator_loss=28.1, generator_mel_loss=18.04, generator_kl_loss=1.428, generator_dur_loss=1.728, generator_adv_loss=1.961, generator_feat_match_loss=4.944, over 3287.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:56:22,146 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 01:56:30,162 INFO [train.py:591] (4/6) Epoch 486, validation: discriminator_loss=2.72, discriminator_real_loss=1.342, discriminator_fake_loss=1.377, generator_loss=26.58, generator_mel_loss=18.11, generator_kl_loss=1.117, generator_dur_loss=1.813, generator_adv_loss=1.773, generator_feat_match_loss=3.764, over 100.00 samples. +2024-03-14 01:56:30,163 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 01:58:51,782 INFO [train.py:527] (4/6) Epoch 486, batch 110, global_batch_idx: 60250, batch size: 15, loss[discriminator_loss=2.659, discriminator_real_loss=1.26, discriminator_fake_loss=1.399, generator_loss=31.29, generator_mel_loss=19.01, generator_kl_loss=1.838, generator_dur_loss=1.601, generator_adv_loss=2.063, generator_feat_match_loss=6.769, over 15.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.373, discriminator_fake_loss=1.339, generator_loss=28.06, generator_mel_loss=17.99, generator_kl_loss=1.406, generator_dur_loss=1.755, generator_adv_loss=1.959, generator_feat_match_loss=4.952, over 6450.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:59:26,372 INFO [train.py:919] (4/6) Start epoch 487 +2024-03-14 02:01:30,024 INFO [train.py:527] (4/6) Epoch 487, batch 36, global_batch_idx: 60300, batch size: 45, loss[discriminator_loss=2.792, discriminator_real_loss=1.53, discriminator_fake_loss=1.262, generator_loss=27.85, generator_mel_loss=18.09, generator_kl_loss=1.491, generator_dur_loss=1.655, generator_adv_loss=1.935, generator_feat_match_loss=4.674, over 45.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.367, discriminator_fake_loss=1.346, generator_loss=28.06, generator_mel_loss=17.99, generator_kl_loss=1.431, generator_dur_loss=1.723, generator_adv_loss=1.944, generator_feat_match_loss=4.974, over 2018.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:03:49,133 INFO [train.py:527] (4/6) Epoch 487, batch 86, global_batch_idx: 60350, batch size: 47, loss[discriminator_loss=2.715, discriminator_real_loss=1.289, discriminator_fake_loss=1.426, generator_loss=27.91, generator_mel_loss=17.77, generator_kl_loss=1.397, generator_dur_loss=1.687, generator_adv_loss=2.016, generator_feat_match_loss=5.046, over 47.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.343, generator_loss=28.12, generator_mel_loss=18.02, generator_kl_loss=1.428, generator_dur_loss=1.728, generator_adv_loss=1.944, generator_feat_match_loss=5.004, over 4758.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:05:35,562 INFO [train.py:919] (4/6) Start epoch 488 +2024-03-14 02:06:34,154 INFO [train.py:527] (4/6) Epoch 488, batch 12, global_batch_idx: 60400, batch size: 55, loss[discriminator_loss=2.769, discriminator_real_loss=1.503, discriminator_fake_loss=1.266, generator_loss=28.21, generator_mel_loss=18.48, generator_kl_loss=1.222, generator_dur_loss=1.757, generator_adv_loss=1.849, generator_feat_match_loss=4.904, over 55.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=28.16, generator_mel_loss=18.07, generator_kl_loss=1.368, generator_dur_loss=1.773, generator_adv_loss=1.967, generator_feat_match_loss=4.991, over 763.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:06:34,157 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 02:06:42,166 INFO [train.py:591] (4/6) Epoch 488, validation: discriminator_loss=2.793, discriminator_real_loss=1.419, discriminator_fake_loss=1.374, generator_loss=27.17, generator_mel_loss=18.41, generator_kl_loss=1.266, generator_dur_loss=1.805, generator_adv_loss=1.847, generator_feat_match_loss=3.841, over 100.00 samples. +2024-03-14 02:06:42,167 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 02:09:02,872 INFO [train.py:527] (4/6) Epoch 488, batch 62, global_batch_idx: 60450, batch size: 64, loss[discriminator_loss=2.662, discriminator_real_loss=1.314, discriminator_fake_loss=1.348, generator_loss=28.4, generator_mel_loss=17.78, generator_kl_loss=1.468, generator_dur_loss=1.72, generator_adv_loss=1.964, generator_feat_match_loss=5.465, over 64.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.368, discriminator_fake_loss=1.347, generator_loss=28.11, generator_mel_loss=18.03, generator_kl_loss=1.413, generator_dur_loss=1.756, generator_adv_loss=1.945, generator_feat_match_loss=4.97, over 3477.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:11:20,144 INFO [train.py:527] (4/6) Epoch 488, batch 112, global_batch_idx: 60500, batch size: 36, loss[discriminator_loss=2.724, discriminator_real_loss=1.315, discriminator_fake_loss=1.409, generator_loss=29.5, generator_mel_loss=18.46, generator_kl_loss=1.674, generator_dur_loss=1.681, generator_adv_loss=2.039, generator_feat_match_loss=5.641, over 36.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.344, generator_loss=28.13, generator_mel_loss=18.02, generator_kl_loss=1.406, generator_dur_loss=1.758, generator_adv_loss=1.948, generator_feat_match_loss=5.004, over 6384.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:11:51,264 INFO [train.py:919] (4/6) Start epoch 489 +2024-03-14 02:14:00,769 INFO [train.py:527] (4/6) Epoch 489, batch 38, global_batch_idx: 60550, batch size: 39, loss[discriminator_loss=2.744, discriminator_real_loss=1.277, discriminator_fake_loss=1.466, generator_loss=29.2, generator_mel_loss=18.23, generator_kl_loss=1.601, generator_dur_loss=1.746, generator_adv_loss=1.825, generator_feat_match_loss=5.806, over 39.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.365, discriminator_fake_loss=1.348, generator_loss=28.14, generator_mel_loss=17.98, generator_kl_loss=1.417, generator_dur_loss=1.759, generator_adv_loss=1.992, generator_feat_match_loss=4.992, over 2222.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:16:20,636 INFO [train.py:527] (4/6) Epoch 489, batch 88, global_batch_idx: 60600, batch size: 83, loss[discriminator_loss=2.698, discriminator_real_loss=1.378, discriminator_fake_loss=1.32, generator_loss=28.42, generator_mel_loss=18.03, generator_kl_loss=1.42, generator_dur_loss=1.768, generator_adv_loss=2.016, generator_feat_match_loss=5.183, over 83.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.374, discriminator_fake_loss=1.337, generator_loss=28.06, generator_mel_loss=17.97, generator_kl_loss=1.411, generator_dur_loss=1.757, generator_adv_loss=1.971, generator_feat_match_loss=4.95, over 5093.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:16:20,638 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 02:16:29,500 INFO [train.py:591] (4/6) Epoch 489, validation: discriminator_loss=2.774, discriminator_real_loss=1.513, discriminator_fake_loss=1.261, generator_loss=27.55, generator_mel_loss=18.22, generator_kl_loss=1.264, generator_dur_loss=1.804, generator_adv_loss=1.977, generator_feat_match_loss=4.282, over 100.00 samples. +2024-03-14 02:16:29,500 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 02:18:06,644 INFO [train.py:919] (4/6) Start epoch 490 +2024-03-14 02:19:10,400 INFO [train.py:527] (4/6) Epoch 490, batch 14, global_batch_idx: 60650, batch size: 47, loss[discriminator_loss=2.709, discriminator_real_loss=1.342, discriminator_fake_loss=1.367, generator_loss=28.74, generator_mel_loss=18.35, generator_kl_loss=1.401, generator_dur_loss=1.648, generator_adv_loss=2.046, generator_feat_match_loss=5.29, over 47.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.356, discriminator_fake_loss=1.339, generator_loss=28.1, generator_mel_loss=18, generator_kl_loss=1.511, generator_dur_loss=1.705, generator_adv_loss=1.959, generator_feat_match_loss=4.92, over 731.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:21:27,334 INFO [train.py:527] (4/6) Epoch 490, batch 64, global_batch_idx: 60700, batch size: 96, loss[discriminator_loss=2.67, discriminator_real_loss=1.331, discriminator_fake_loss=1.339, generator_loss=28.47, generator_mel_loss=18.12, generator_kl_loss=1.381, generator_dur_loss=1.81, generator_adv_loss=2.006, generator_feat_match_loss=5.153, over 96.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.371, discriminator_fake_loss=1.334, generator_loss=28.15, generator_mel_loss=17.97, generator_kl_loss=1.447, generator_dur_loss=1.721, generator_adv_loss=1.988, generator_feat_match_loss=5.018, over 3495.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:23:47,037 INFO [train.py:527] (4/6) Epoch 490, batch 114, global_batch_idx: 60750, batch size: 56, loss[discriminator_loss=2.7, discriminator_real_loss=1.42, discriminator_fake_loss=1.279, generator_loss=28.04, generator_mel_loss=17.97, generator_kl_loss=1.455, generator_dur_loss=1.695, generator_adv_loss=1.828, generator_feat_match_loss=5.091, over 56.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.14, generator_mel_loss=17.97, generator_kl_loss=1.438, generator_dur_loss=1.725, generator_adv_loss=1.969, generator_feat_match_loss=5.04, over 6398.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:24:12,638 INFO [train.py:919] (4/6) Start epoch 491 +2024-03-14 02:26:29,236 INFO [train.py:527] (4/6) Epoch 491, batch 40, global_batch_idx: 60800, batch size: 48, loss[discriminator_loss=2.718, discriminator_real_loss=1.336, discriminator_fake_loss=1.382, generator_loss=28.89, generator_mel_loss=18.13, generator_kl_loss=1.483, generator_dur_loss=1.693, generator_adv_loss=2.025, generator_feat_match_loss=5.554, over 48.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=27.97, generator_mel_loss=17.97, generator_kl_loss=1.445, generator_dur_loss=1.734, generator_adv_loss=1.941, generator_feat_match_loss=4.876, over 2237.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:26:29,237 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 02:26:37,143 INFO [train.py:591] (4/6) Epoch 491, validation: discriminator_loss=2.776, discriminator_real_loss=1.472, discriminator_fake_loss=1.304, generator_loss=26.72, generator_mel_loss=17.81, generator_kl_loss=1.207, generator_dur_loss=1.834, generator_adv_loss=1.958, generator_feat_match_loss=3.919, over 100.00 samples. +2024-03-14 02:26:37,144 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 02:28:55,236 INFO [train.py:527] (4/6) Epoch 491, batch 90, global_batch_idx: 60850, batch size: 25, loss[discriminator_loss=2.638, discriminator_real_loss=1.376, discriminator_fake_loss=1.262, generator_loss=29.84, generator_mel_loss=18.68, generator_kl_loss=1.812, generator_dur_loss=1.55, generator_adv_loss=1.975, generator_feat_match_loss=5.82, over 25.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=28.03, generator_mel_loss=17.94, generator_kl_loss=1.417, generator_dur_loss=1.758, generator_adv_loss=1.945, generator_feat_match_loss=4.97, over 5289.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:30:28,301 INFO [train.py:919] (4/6) Start epoch 492 +2024-03-14 02:31:38,355 INFO [train.py:527] (4/6) Epoch 492, batch 16, global_batch_idx: 60900, batch size: 50, loss[discriminator_loss=2.74, discriminator_real_loss=1.449, discriminator_fake_loss=1.29, generator_loss=28.27, generator_mel_loss=18.26, generator_kl_loss=1.564, generator_dur_loss=1.643, generator_adv_loss=1.857, generator_feat_match_loss=4.939, over 50.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.378, discriminator_fake_loss=1.36, generator_loss=28.12, generator_mel_loss=18.05, generator_kl_loss=1.396, generator_dur_loss=1.767, generator_adv_loss=1.919, generator_feat_match_loss=4.985, over 1007.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:33:57,953 INFO [train.py:527] (4/6) Epoch 492, batch 66, global_batch_idx: 60950, batch size: 36, loss[discriminator_loss=2.787, discriminator_real_loss=1.527, discriminator_fake_loss=1.26, generator_loss=27.49, generator_mel_loss=17.94, generator_kl_loss=1.503, generator_dur_loss=1.651, generator_adv_loss=1.857, generator_feat_match_loss=4.539, over 36.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.378, discriminator_fake_loss=1.348, generator_loss=28.16, generator_mel_loss=18.07, generator_kl_loss=1.413, generator_dur_loss=1.758, generator_adv_loss=1.946, generator_feat_match_loss=4.979, over 3779.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:36:17,285 INFO [train.py:527] (4/6) Epoch 492, batch 116, global_batch_idx: 61000, batch size: 48, loss[discriminator_loss=2.709, discriminator_real_loss=1.357, discriminator_fake_loss=1.352, generator_loss=28.03, generator_mel_loss=17.53, generator_kl_loss=1.545, generator_dur_loss=1.762, generator_adv_loss=2.111, generator_feat_match_loss=5.085, over 48.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.379, discriminator_fake_loss=1.342, generator_loss=28.15, generator_mel_loss=18.03, generator_kl_loss=1.42, generator_dur_loss=1.761, generator_adv_loss=1.95, generator_feat_match_loss=4.991, over 6595.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:36:17,286 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 02:36:26,118 INFO [train.py:591] (4/6) Epoch 492, validation: discriminator_loss=2.795, discriminator_real_loss=1.56, discriminator_fake_loss=1.235, generator_loss=27.04, generator_mel_loss=18.32, generator_kl_loss=1.27, generator_dur_loss=1.83, generator_adv_loss=1.987, generator_feat_match_loss=3.637, over 100.00 samples. +2024-03-14 02:36:26,119 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 02:36:47,788 INFO [train.py:919] (4/6) Start epoch 493 +2024-03-14 02:39:07,085 INFO [train.py:527] (4/6) Epoch 493, batch 42, global_batch_idx: 61050, batch size: 70, loss[discriminator_loss=2.738, discriminator_real_loss=1.365, discriminator_fake_loss=1.373, generator_loss=27.94, generator_mel_loss=18.25, generator_kl_loss=1.35, generator_dur_loss=1.78, generator_adv_loss=2.001, generator_feat_match_loss=4.555, over 70.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.368, discriminator_fake_loss=1.348, generator_loss=28.28, generator_mel_loss=18.13, generator_kl_loss=1.476, generator_dur_loss=1.725, generator_adv_loss=1.951, generator_feat_match_loss=4.998, over 2150.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:41:26,054 INFO [train.py:527] (4/6) Epoch 493, batch 92, global_batch_idx: 61100, batch size: 58, loss[discriminator_loss=2.681, discriminator_real_loss=1.367, discriminator_fake_loss=1.314, generator_loss=28.09, generator_mel_loss=17.8, generator_kl_loss=1.419, generator_dur_loss=1.703, generator_adv_loss=2.046, generator_feat_match_loss=5.115, over 58.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.344, generator_loss=28.19, generator_mel_loss=18.04, generator_kl_loss=1.444, generator_dur_loss=1.729, generator_adv_loss=1.947, generator_feat_match_loss=5.023, over 4889.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:42:51,813 INFO [train.py:919] (4/6) Start epoch 494 +2024-03-14 02:44:05,531 INFO [train.py:527] (4/6) Epoch 494, batch 18, global_batch_idx: 61150, batch size: 50, loss[discriminator_loss=2.648, discriminator_real_loss=1.365, discriminator_fake_loss=1.282, generator_loss=28.98, generator_mel_loss=18.25, generator_kl_loss=1.568, generator_dur_loss=1.689, generator_adv_loss=1.967, generator_feat_match_loss=5.508, over 50.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.11, generator_mel_loss=17.95, generator_kl_loss=1.409, generator_dur_loss=1.729, generator_adv_loss=1.931, generator_feat_match_loss=5.081, over 1082.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:46:26,253 INFO [train.py:527] (4/6) Epoch 494, batch 68, global_batch_idx: 61200, batch size: 15, loss[discriminator_loss=2.622, discriminator_real_loss=1.413, discriminator_fake_loss=1.209, generator_loss=30.32, generator_mel_loss=18.84, generator_kl_loss=1.863, generator_dur_loss=1.521, generator_adv_loss=2.235, generator_feat_match_loss=5.865, over 15.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=28.02, generator_mel_loss=17.96, generator_kl_loss=1.424, generator_dur_loss=1.72, generator_adv_loss=1.944, generator_feat_match_loss=4.975, over 3704.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:46:26,254 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 02:46:34,379 INFO [train.py:591] (4/6) Epoch 494, validation: discriminator_loss=2.735, discriminator_real_loss=1.48, discriminator_fake_loss=1.256, generator_loss=26.8, generator_mel_loss=18.13, generator_kl_loss=1.329, generator_dur_loss=1.809, generator_adv_loss=1.932, generator_feat_match_loss=3.602, over 100.00 samples. +2024-03-14 02:46:34,380 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 02:48:51,416 INFO [train.py:527] (4/6) Epoch 494, batch 118, global_batch_idx: 61250, batch size: 56, loss[discriminator_loss=2.715, discriminator_real_loss=1.259, discriminator_fake_loss=1.456, generator_loss=29.65, generator_mel_loss=18.8, generator_kl_loss=1.465, generator_dur_loss=1.684, generator_adv_loss=1.896, generator_feat_match_loss=5.799, over 56.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=28.05, generator_mel_loss=17.96, generator_kl_loss=1.431, generator_dur_loss=1.727, generator_adv_loss=1.946, generator_feat_match_loss=4.987, over 6405.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:49:06,809 INFO [train.py:919] (4/6) Start epoch 495 +2024-03-14 02:51:31,734 INFO [train.py:527] (4/6) Epoch 495, batch 44, global_batch_idx: 61300, batch size: 45, loss[discriminator_loss=2.691, discriminator_real_loss=1.34, discriminator_fake_loss=1.351, generator_loss=28.63, generator_mel_loss=18.6, generator_kl_loss=1.535, generator_dur_loss=1.722, generator_adv_loss=1.872, generator_feat_match_loss=4.901, over 45.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.377, discriminator_fake_loss=1.335, generator_loss=27.95, generator_mel_loss=17.92, generator_kl_loss=1.411, generator_dur_loss=1.761, generator_adv_loss=1.948, generator_feat_match_loss=4.904, over 2654.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:53:52,218 INFO [train.py:527] (4/6) Epoch 495, batch 94, global_batch_idx: 61350, batch size: 83, loss[discriminator_loss=2.727, discriminator_real_loss=1.35, discriminator_fake_loss=1.377, generator_loss=28.16, generator_mel_loss=18.2, generator_kl_loss=1.425, generator_dur_loss=1.801, generator_adv_loss=1.923, generator_feat_match_loss=4.817, over 83.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.341, generator_loss=28.08, generator_mel_loss=17.97, generator_kl_loss=1.411, generator_dur_loss=1.757, generator_adv_loss=1.951, generator_feat_match_loss=4.983, over 5521.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:55:12,424 INFO [train.py:919] (4/6) Start epoch 496 +2024-03-14 02:56:33,657 INFO [train.py:527] (4/6) Epoch 496, batch 20, global_batch_idx: 61400, batch size: 59, loss[discriminator_loss=2.79, discriminator_real_loss=1.419, discriminator_fake_loss=1.371, generator_loss=26.62, generator_mel_loss=17.37, generator_kl_loss=1.431, generator_dur_loss=1.695, generator_adv_loss=2.023, generator_feat_match_loss=4.106, over 59.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.374, discriminator_fake_loss=1.348, generator_loss=28.28, generator_mel_loss=18.07, generator_kl_loss=1.405, generator_dur_loss=1.72, generator_adv_loss=1.949, generator_feat_match_loss=5.135, over 1220.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:56:33,659 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 02:56:41,777 INFO [train.py:591] (4/6) Epoch 496, validation: discriminator_loss=2.804, discriminator_real_loss=1.479, discriminator_fake_loss=1.325, generator_loss=26.64, generator_mel_loss=18.11, generator_kl_loss=1.165, generator_dur_loss=1.803, generator_adv_loss=1.929, generator_feat_match_loss=3.628, over 100.00 samples. +2024-03-14 02:56:41,778 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 02:59:00,484 INFO [train.py:527] (4/6) Epoch 496, batch 70, global_batch_idx: 61450, batch size: 45, loss[discriminator_loss=2.705, discriminator_real_loss=1.334, discriminator_fake_loss=1.371, generator_loss=28.58, generator_mel_loss=18.1, generator_kl_loss=1.614, generator_dur_loss=1.668, generator_adv_loss=1.847, generator_feat_match_loss=5.35, over 45.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.367, discriminator_fake_loss=1.345, generator_loss=28.26, generator_mel_loss=18.05, generator_kl_loss=1.458, generator_dur_loss=1.727, generator_adv_loss=1.943, generator_feat_match_loss=5.075, over 3747.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:01:19,048 INFO [train.py:527] (4/6) Epoch 496, batch 120, global_batch_idx: 61500, batch size: 17, loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=31.83, generator_mel_loss=20.15, generator_kl_loss=1.728, generator_dur_loss=1.57, generator_adv_loss=1.926, generator_feat_match_loss=6.455, over 17.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.18, generator_mel_loss=18.02, generator_kl_loss=1.44, generator_dur_loss=1.742, generator_adv_loss=1.945, generator_feat_match_loss=5.036, over 6733.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:01:29,314 INFO [train.py:919] (4/6) Start epoch 497 +2024-03-14 03:04:01,908 INFO [train.py:527] (4/6) Epoch 497, batch 46, global_batch_idx: 61550, batch size: 52, loss[discriminator_loss=2.677, discriminator_real_loss=1.324, discriminator_fake_loss=1.354, generator_loss=28.03, generator_mel_loss=18.01, generator_kl_loss=1.34, generator_dur_loss=1.714, generator_adv_loss=2.118, generator_feat_match_loss=4.844, over 52.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.368, discriminator_fake_loss=1.348, generator_loss=28.14, generator_mel_loss=18.02, generator_kl_loss=1.411, generator_dur_loss=1.764, generator_adv_loss=1.965, generator_feat_match_loss=4.974, over 2722.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:06:17,963 INFO [train.py:527] (4/6) Epoch 497, batch 96, global_batch_idx: 61600, batch size: 64, loss[discriminator_loss=2.723, discriminator_real_loss=1.356, discriminator_fake_loss=1.367, generator_loss=28.53, generator_mel_loss=18.21, generator_kl_loss=1.447, generator_dur_loss=1.77, generator_adv_loss=1.971, generator_feat_match_loss=5.131, over 64.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=28.2, generator_mel_loss=18.01, generator_kl_loss=1.41, generator_dur_loss=1.763, generator_adv_loss=1.963, generator_feat_match_loss=5.05, over 5547.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:06:17,965 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 03:06:26,498 INFO [train.py:591] (4/6) Epoch 497, validation: discriminator_loss=2.775, discriminator_real_loss=1.402, discriminator_fake_loss=1.373, generator_loss=26.94, generator_mel_loss=18.35, generator_kl_loss=1.209, generator_dur_loss=1.826, generator_adv_loss=1.895, generator_feat_match_loss=3.657, over 100.00 samples. +2024-03-14 03:06:26,499 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 03:07:42,599 INFO [train.py:919] (4/6) Start epoch 498 +2024-03-14 03:09:08,304 INFO [train.py:527] (4/6) Epoch 498, batch 22, global_batch_idx: 61650, batch size: 52, loss[discriminator_loss=2.68, discriminator_real_loss=1.326, discriminator_fake_loss=1.354, generator_loss=27.84, generator_mel_loss=17.76, generator_kl_loss=1.44, generator_dur_loss=1.721, generator_adv_loss=1.99, generator_feat_match_loss=4.928, over 52.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.382, discriminator_fake_loss=1.33, generator_loss=28.25, generator_mel_loss=18.08, generator_kl_loss=1.422, generator_dur_loss=1.732, generator_adv_loss=1.949, generator_feat_match_loss=5.062, over 1242.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:11:26,795 INFO [train.py:527] (4/6) Epoch 498, batch 72, global_batch_idx: 61700, batch size: 50, loss[discriminator_loss=2.706, discriminator_real_loss=1.447, discriminator_fake_loss=1.26, generator_loss=27.53, generator_mel_loss=17.71, generator_kl_loss=1.413, generator_dur_loss=1.7, generator_adv_loss=1.952, generator_feat_match_loss=4.753, over 50.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.385, discriminator_fake_loss=1.332, generator_loss=28.04, generator_mel_loss=17.96, generator_kl_loss=1.428, generator_dur_loss=1.743, generator_adv_loss=1.946, generator_feat_match_loss=4.964, over 3955.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:13:45,223 INFO [train.py:527] (4/6) Epoch 498, batch 122, global_batch_idx: 61750, batch size: 31, loss[discriminator_loss=2.612, discriminator_real_loss=1.247, discriminator_fake_loss=1.365, generator_loss=30.19, generator_mel_loss=18.67, generator_kl_loss=1.551, generator_dur_loss=1.636, generator_adv_loss=2.079, generator_feat_match_loss=6.261, over 31.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.379, discriminator_fake_loss=1.334, generator_loss=28.13, generator_mel_loss=17.99, generator_kl_loss=1.427, generator_dur_loss=1.749, generator_adv_loss=1.952, generator_feat_match_loss=5.008, over 6780.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:13:50,100 INFO [train.py:919] (4/6) Start epoch 499 +2024-03-14 03:16:23,903 INFO [train.py:527] (4/6) Epoch 499, batch 48, global_batch_idx: 61800, batch size: 64, loss[discriminator_loss=2.715, discriminator_real_loss=1.407, discriminator_fake_loss=1.308, generator_loss=28.17, generator_mel_loss=17.77, generator_kl_loss=1.528, generator_dur_loss=1.75, generator_adv_loss=1.923, generator_feat_match_loss=5.2, over 64.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=28.14, generator_mel_loss=17.96, generator_kl_loss=1.427, generator_dur_loss=1.771, generator_adv_loss=1.966, generator_feat_match_loss=5.017, over 2782.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:16:23,905 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 03:16:31,894 INFO [train.py:591] (4/6) Epoch 499, validation: discriminator_loss=2.737, discriminator_real_loss=1.354, discriminator_fake_loss=1.383, generator_loss=27.07, generator_mel_loss=17.98, generator_kl_loss=1.183, generator_dur_loss=1.836, generator_adv_loss=1.837, generator_feat_match_loss=4.243, over 100.00 samples. +2024-03-14 03:16:31,895 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 03:18:47,800 INFO [train.py:527] (4/6) Epoch 499, batch 98, global_batch_idx: 61850, batch size: 45, loss[discriminator_loss=2.665, discriminator_real_loss=1.355, discriminator_fake_loss=1.31, generator_loss=28.4, generator_mel_loss=18.11, generator_kl_loss=1.585, generator_dur_loss=1.665, generator_adv_loss=2.07, generator_feat_match_loss=4.976, over 45.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.377, discriminator_fake_loss=1.338, generator_loss=28.11, generator_mel_loss=17.93, generator_kl_loss=1.422, generator_dur_loss=1.773, generator_adv_loss=1.967, generator_feat_match_loss=5.016, over 5694.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:20:01,248 INFO [train.py:919] (4/6) Start epoch 500 +2024-03-14 03:21:33,369 INFO [train.py:527] (4/6) Epoch 500, batch 24, global_batch_idx: 61900, batch size: 13, loss[discriminator_loss=2.592, discriminator_real_loss=1.346, discriminator_fake_loss=1.246, generator_loss=30.92, generator_mel_loss=18.6, generator_kl_loss=2.033, generator_dur_loss=1.625, generator_adv_loss=2.078, generator_feat_match_loss=6.589, over 13.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.373, discriminator_fake_loss=1.337, generator_loss=28.22, generator_mel_loss=18.04, generator_kl_loss=1.411, generator_dur_loss=1.758, generator_adv_loss=1.933, generator_feat_match_loss=5.085, over 1428.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:23:53,850 INFO [train.py:527] (4/6) Epoch 500, batch 74, global_batch_idx: 61950, batch size: 47, loss[discriminator_loss=2.752, discriminator_real_loss=1.489, discriminator_fake_loss=1.263, generator_loss=27.19, generator_mel_loss=18.12, generator_kl_loss=1.42, generator_dur_loss=1.692, generator_adv_loss=1.865, generator_feat_match_loss=4.088, over 47.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.37, discriminator_fake_loss=1.339, generator_loss=28.24, generator_mel_loss=18.04, generator_kl_loss=1.412, generator_dur_loss=1.765, generator_adv_loss=1.952, generator_feat_match_loss=5.064, over 4397.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:26:08,039 INFO [train.py:919] (4/6) Start epoch 501 +2024-03-14 03:26:30,843 INFO [train.py:527] (4/6) Epoch 501, batch 0, global_batch_idx: 62000, batch size: 55, loss[discriminator_loss=2.746, discriminator_real_loss=1.388, discriminator_fake_loss=1.358, generator_loss=27.83, generator_mel_loss=18.07, generator_kl_loss=1.481, generator_dur_loss=1.702, generator_adv_loss=1.928, generator_feat_match_loss=4.649, over 55.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.388, discriminator_fake_loss=1.358, generator_loss=27.83, generator_mel_loss=18.07, generator_kl_loss=1.481, generator_dur_loss=1.702, generator_adv_loss=1.928, generator_feat_match_loss=4.649, over 55.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:26:30,845 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 03:26:38,670 INFO [train.py:591] (4/6) Epoch 501, validation: discriminator_loss=2.8, discriminator_real_loss=1.447, discriminator_fake_loss=1.353, generator_loss=27, generator_mel_loss=18.25, generator_kl_loss=1.239, generator_dur_loss=1.813, generator_adv_loss=1.859, generator_feat_match_loss=3.835, over 100.00 samples. +2024-03-14 03:26:38,672 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 03:29:01,840 INFO [train.py:527] (4/6) Epoch 501, batch 50, global_batch_idx: 62050, batch size: 64, loss[discriminator_loss=2.773, discriminator_real_loss=1.428, discriminator_fake_loss=1.344, generator_loss=27.27, generator_mel_loss=17.56, generator_kl_loss=1.412, generator_dur_loss=1.761, generator_adv_loss=1.964, generator_feat_match_loss=4.579, over 64.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.39, discriminator_fake_loss=1.341, generator_loss=27.98, generator_mel_loss=18, generator_kl_loss=1.376, generator_dur_loss=1.755, generator_adv_loss=1.937, generator_feat_match_loss=4.917, over 2910.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:31:20,013 INFO [train.py:527] (4/6) Epoch 501, batch 100, global_batch_idx: 62100, batch size: 45, loss[discriminator_loss=2.647, discriminator_real_loss=1.352, discriminator_fake_loss=1.295, generator_loss=27.79, generator_mel_loss=17.84, generator_kl_loss=1.48, generator_dur_loss=1.664, generator_adv_loss=1.844, generator_feat_match_loss=4.96, over 45.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.378, discriminator_fake_loss=1.34, generator_loss=28.04, generator_mel_loss=17.95, generator_kl_loss=1.393, generator_dur_loss=1.759, generator_adv_loss=1.941, generator_feat_match_loss=5.002, over 5768.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:32:19,741 INFO [train.py:919] (4/6) Start epoch 502 +2024-03-14 03:33:55,538 INFO [train.py:527] (4/6) Epoch 502, batch 26, global_batch_idx: 62150, batch size: 25, loss[discriminator_loss=2.69, discriminator_real_loss=1.294, discriminator_fake_loss=1.396, generator_loss=28.86, generator_mel_loss=18.29, generator_kl_loss=1.739, generator_dur_loss=1.586, generator_adv_loss=2.247, generator_feat_match_loss=4.995, over 25.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.373, discriminator_fake_loss=1.329, generator_loss=28.19, generator_mel_loss=17.99, generator_kl_loss=1.383, generator_dur_loss=1.747, generator_adv_loss=1.989, generator_feat_match_loss=5.077, over 1565.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:36:14,977 INFO [train.py:527] (4/6) Epoch 502, batch 76, global_batch_idx: 62200, batch size: 13, loss[discriminator_loss=2.688, discriminator_real_loss=1.386, discriminator_fake_loss=1.301, generator_loss=31.33, generator_mel_loss=20.07, generator_kl_loss=1.834, generator_dur_loss=1.54, generator_adv_loss=2.148, generator_feat_match_loss=5.743, over 13.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.368, discriminator_fake_loss=1.336, generator_loss=28.26, generator_mel_loss=18.01, generator_kl_loss=1.415, generator_dur_loss=1.749, generator_adv_loss=1.97, generator_feat_match_loss=5.113, over 4323.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:36:14,978 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 03:36:23,146 INFO [train.py:591] (4/6) Epoch 502, validation: discriminator_loss=2.775, discriminator_real_loss=1.546, discriminator_fake_loss=1.23, generator_loss=27.43, generator_mel_loss=17.95, generator_kl_loss=1.265, generator_dur_loss=1.815, generator_adv_loss=2.04, generator_feat_match_loss=4.354, over 100.00 samples. +2024-03-14 03:36:23,146 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 03:38:34,256 INFO [train.py:919] (4/6) Start epoch 503 +2024-03-14 03:39:02,988 INFO [train.py:527] (4/6) Epoch 503, batch 2, global_batch_idx: 62250, batch size: 25, loss[discriminator_loss=2.719, discriminator_real_loss=1.344, discriminator_fake_loss=1.375, generator_loss=30.22, generator_mel_loss=18.28, generator_kl_loss=1.997, generator_dur_loss=1.555, generator_adv_loss=2.029, generator_feat_match_loss=6.353, over 25.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.393, discriminator_fake_loss=1.336, generator_loss=28.1, generator_mel_loss=17.73, generator_kl_loss=1.457, generator_dur_loss=1.773, generator_adv_loss=2.002, generator_feat_match_loss=5.137, over 173.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:41:21,209 INFO [train.py:527] (4/6) Epoch 503, batch 52, global_batch_idx: 62300, batch size: 36, loss[discriminator_loss=2.781, discriminator_real_loss=1.372, discriminator_fake_loss=1.409, generator_loss=27.24, generator_mel_loss=17.6, generator_kl_loss=1.428, generator_dur_loss=1.744, generator_adv_loss=1.917, generator_feat_match_loss=4.546, over 36.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.365, discriminator_fake_loss=1.344, generator_loss=28.1, generator_mel_loss=17.96, generator_kl_loss=1.41, generator_dur_loss=1.772, generator_adv_loss=1.956, generator_feat_match_loss=5.003, over 3099.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:43:38,644 INFO [train.py:527] (4/6) Epoch 503, batch 102, global_batch_idx: 62350, batch size: 77, loss[discriminator_loss=2.716, discriminator_real_loss=1.438, discriminator_fake_loss=1.278, generator_loss=27.88, generator_mel_loss=17.66, generator_kl_loss=1.325, generator_dur_loss=1.808, generator_adv_loss=2.004, generator_feat_match_loss=5.08, over 77.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.369, discriminator_fake_loss=1.346, generator_loss=28.17, generator_mel_loss=17.99, generator_kl_loss=1.406, generator_dur_loss=1.768, generator_adv_loss=1.958, generator_feat_match_loss=5.047, over 6111.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:44:40,095 INFO [train.py:919] (4/6) Start epoch 504 +2024-03-14 03:46:22,741 INFO [train.py:527] (4/6) Epoch 504, batch 28, global_batch_idx: 62400, batch size: 25, loss[discriminator_loss=2.646, discriminator_real_loss=1.357, discriminator_fake_loss=1.289, generator_loss=29.41, generator_mel_loss=19.18, generator_kl_loss=1.638, generator_dur_loss=1.58, generator_adv_loss=2.085, generator_feat_match_loss=4.922, over 25.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.367, discriminator_fake_loss=1.323, generator_loss=28.3, generator_mel_loss=18, generator_kl_loss=1.418, generator_dur_loss=1.748, generator_adv_loss=1.97, generator_feat_match_loss=5.162, over 1627.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:46:22,742 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 03:46:30,661 INFO [train.py:591] (4/6) Epoch 504, validation: discriminator_loss=2.765, discriminator_real_loss=1.536, discriminator_fake_loss=1.229, generator_loss=27.01, generator_mel_loss=18.04, generator_kl_loss=1.208, generator_dur_loss=1.818, generator_adv_loss=2.003, generator_feat_match_loss=3.946, over 100.00 samples. +2024-03-14 03:46:30,662 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 03:48:48,870 INFO [train.py:527] (4/6) Epoch 504, batch 78, global_batch_idx: 62450, batch size: 66, loss[discriminator_loss=2.699, discriminator_real_loss=1.417, discriminator_fake_loss=1.283, generator_loss=28.14, generator_mel_loss=18.28, generator_kl_loss=1.328, generator_dur_loss=1.795, generator_adv_loss=1.835, generator_feat_match_loss=4.904, over 66.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.376, discriminator_fake_loss=1.333, generator_loss=28.22, generator_mel_loss=17.99, generator_kl_loss=1.417, generator_dur_loss=1.751, generator_adv_loss=1.959, generator_feat_match_loss=5.103, over 4398.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:50:54,033 INFO [train.py:919] (4/6) Start epoch 505 +2024-03-14 03:51:28,728 INFO [train.py:527] (4/6) Epoch 505, batch 4, global_batch_idx: 62500, batch size: 31, loss[discriminator_loss=2.737, discriminator_real_loss=1.329, discriminator_fake_loss=1.409, generator_loss=28.22, generator_mel_loss=17.82, generator_kl_loss=1.471, generator_dur_loss=1.612, generator_adv_loss=1.929, generator_feat_match_loss=5.387, over 31.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.357, discriminator_fake_loss=1.343, generator_loss=27.88, generator_mel_loss=17.95, generator_kl_loss=1.353, generator_dur_loss=1.763, generator_adv_loss=1.935, generator_feat_match_loss=4.88, over 270.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:53:49,002 INFO [train.py:527] (4/6) Epoch 505, batch 54, global_batch_idx: 62550, batch size: 39, loss[discriminator_loss=2.641, discriminator_real_loss=1.315, discriminator_fake_loss=1.327, generator_loss=29.12, generator_mel_loss=18.29, generator_kl_loss=1.45, generator_dur_loss=1.712, generator_adv_loss=1.968, generator_feat_match_loss=5.698, over 39.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.339, generator_loss=28.23, generator_mel_loss=18.02, generator_kl_loss=1.397, generator_dur_loss=1.773, generator_adv_loss=1.956, generator_feat_match_loss=5.084, over 3145.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:56:08,505 INFO [train.py:527] (4/6) Epoch 505, batch 104, global_batch_idx: 62600, batch size: 64, loss[discriminator_loss=2.786, discriminator_real_loss=1.372, discriminator_fake_loss=1.414, generator_loss=28.52, generator_mel_loss=18.39, generator_kl_loss=1.462, generator_dur_loss=1.767, generator_adv_loss=1.875, generator_feat_match_loss=5.022, over 64.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.22, generator_mel_loss=18.03, generator_kl_loss=1.407, generator_dur_loss=1.766, generator_adv_loss=1.957, generator_feat_match_loss=5.059, over 5966.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:56:08,507 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 03:56:17,700 INFO [train.py:591] (4/6) Epoch 505, validation: discriminator_loss=2.747, discriminator_real_loss=1.384, discriminator_fake_loss=1.363, generator_loss=26.82, generator_mel_loss=17.9, generator_kl_loss=1.215, generator_dur_loss=1.821, generator_adv_loss=1.816, generator_feat_match_loss=4.067, over 100.00 samples. +2024-03-14 03:56:17,700 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 03:57:09,669 INFO [train.py:919] (4/6) Start epoch 506 +2024-03-14 03:58:56,623 INFO [train.py:527] (4/6) Epoch 506, batch 30, global_batch_idx: 62650, batch size: 36, loss[discriminator_loss=2.661, discriminator_real_loss=1.344, discriminator_fake_loss=1.317, generator_loss=28.61, generator_mel_loss=18.03, generator_kl_loss=1.474, generator_dur_loss=1.731, generator_adv_loss=2.177, generator_feat_match_loss=5.203, over 36.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.43, generator_mel_loss=18.08, generator_kl_loss=1.413, generator_dur_loss=1.74, generator_adv_loss=1.975, generator_feat_match_loss=5.214, over 1700.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:01:18,713 INFO [train.py:527] (4/6) Epoch 506, batch 80, global_batch_idx: 62700, batch size: 72, loss[discriminator_loss=2.715, discriminator_real_loss=1.326, discriminator_fake_loss=1.389, generator_loss=27.41, generator_mel_loss=17.79, generator_kl_loss=1.34, generator_dur_loss=1.784, generator_adv_loss=1.903, generator_feat_match_loss=4.594, over 72.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=28.27, generator_mel_loss=18.01, generator_kl_loss=1.393, generator_dur_loss=1.763, generator_adv_loss=1.966, generator_feat_match_loss=5.141, over 4610.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:03:16,895 INFO [train.py:919] (4/6) Start epoch 507 +2024-03-14 04:03:55,926 INFO [train.py:527] (4/6) Epoch 507, batch 6, global_batch_idx: 62750, batch size: 56, loss[discriminator_loss=2.732, discriminator_real_loss=1.35, discriminator_fake_loss=1.382, generator_loss=28.13, generator_mel_loss=17.96, generator_kl_loss=1.51, generator_dur_loss=1.721, generator_adv_loss=1.945, generator_feat_match_loss=5.001, over 56.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.34, discriminator_fake_loss=1.349, generator_loss=28.05, generator_mel_loss=17.78, generator_kl_loss=1.525, generator_dur_loss=1.725, generator_adv_loss=1.958, generator_feat_match_loss=5.06, over 320.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:06:15,461 INFO [train.py:527] (4/6) Epoch 507, batch 56, global_batch_idx: 62800, batch size: 44, loss[discriminator_loss=2.708, discriminator_real_loss=1.309, discriminator_fake_loss=1.399, generator_loss=28.76, generator_mel_loss=17.92, generator_kl_loss=1.641, generator_dur_loss=1.679, generator_adv_loss=1.941, generator_feat_match_loss=5.579, over 44.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.37, discriminator_fake_loss=1.34, generator_loss=28.13, generator_mel_loss=17.91, generator_kl_loss=1.415, generator_dur_loss=1.763, generator_adv_loss=1.963, generator_feat_match_loss=5.073, over 3278.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:06:15,462 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 04:06:23,517 INFO [train.py:591] (4/6) Epoch 507, validation: discriminator_loss=2.725, discriminator_real_loss=1.389, discriminator_fake_loss=1.335, generator_loss=26.94, generator_mel_loss=18.08, generator_kl_loss=1.132, generator_dur_loss=1.809, generator_adv_loss=1.812, generator_feat_match_loss=4.108, over 100.00 samples. +2024-03-14 04:06:23,519 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 04:08:41,880 INFO [train.py:527] (4/6) Epoch 507, batch 106, global_batch_idx: 62850, batch size: 45, loss[discriminator_loss=2.737, discriminator_real_loss=1.415, discriminator_fake_loss=1.322, generator_loss=28.2, generator_mel_loss=18.16, generator_kl_loss=1.467, generator_dur_loss=1.71, generator_adv_loss=1.987, generator_feat_match_loss=4.874, over 45.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.368, discriminator_fake_loss=1.341, generator_loss=28.14, generator_mel_loss=17.97, generator_kl_loss=1.406, generator_dur_loss=1.76, generator_adv_loss=1.964, generator_feat_match_loss=5.037, over 6162.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:09:30,177 INFO [train.py:919] (4/6) Start epoch 508 +2024-03-14 04:11:20,161 INFO [train.py:527] (4/6) Epoch 508, batch 32, global_batch_idx: 62900, batch size: 74, loss[discriminator_loss=2.687, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=27.74, generator_mel_loss=17.77, generator_kl_loss=1.257, generator_dur_loss=1.813, generator_adv_loss=1.837, generator_feat_match_loss=5.056, over 74.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.344, generator_loss=28.27, generator_mel_loss=18.04, generator_kl_loss=1.414, generator_dur_loss=1.74, generator_adv_loss=1.98, generator_feat_match_loss=5.095, over 1780.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:13:41,934 INFO [train.py:527] (4/6) Epoch 508, batch 82, global_batch_idx: 62950, batch size: 50, loss[discriminator_loss=2.718, discriminator_real_loss=1.42, discriminator_fake_loss=1.299, generator_loss=28.3, generator_mel_loss=18.1, generator_kl_loss=1.536, generator_dur_loss=1.671, generator_adv_loss=1.917, generator_feat_match_loss=5.074, over 50.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=28.23, generator_mel_loss=17.99, generator_kl_loss=1.411, generator_dur_loss=1.752, generator_adv_loss=1.972, generator_feat_match_loss=5.102, over 4899.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:15:35,860 INFO [train.py:919] (4/6) Start epoch 509 +2024-03-14 04:16:22,831 INFO [train.py:527] (4/6) Epoch 509, batch 8, global_batch_idx: 63000, batch size: 59, loss[discriminator_loss=2.693, discriminator_real_loss=1.366, discriminator_fake_loss=1.327, generator_loss=27.85, generator_mel_loss=17.87, generator_kl_loss=1.499, generator_dur_loss=1.709, generator_adv_loss=1.836, generator_feat_match_loss=4.938, over 59.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.361, discriminator_fake_loss=1.326, generator_loss=28.36, generator_mel_loss=18.05, generator_kl_loss=1.44, generator_dur_loss=1.743, generator_adv_loss=1.962, generator_feat_match_loss=5.159, over 493.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:16:22,839 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 04:16:30,826 INFO [train.py:591] (4/6) Epoch 509, validation: discriminator_loss=2.748, discriminator_real_loss=1.351, discriminator_fake_loss=1.397, generator_loss=26.76, generator_mel_loss=17.9, generator_kl_loss=1.346, generator_dur_loss=1.796, generator_adv_loss=1.768, generator_feat_match_loss=3.948, over 100.00 samples. +2024-03-14 04:16:30,829 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 04:18:51,689 INFO [train.py:527] (4/6) Epoch 509, batch 58, global_batch_idx: 63050, batch size: 68, loss[discriminator_loss=2.717, discriminator_real_loss=1.288, discriminator_fake_loss=1.428, generator_loss=28.46, generator_mel_loss=17.86, generator_kl_loss=1.519, generator_dur_loss=1.765, generator_adv_loss=2.012, generator_feat_match_loss=5.301, over 68.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.34, generator_loss=28.21, generator_mel_loss=18.02, generator_kl_loss=1.435, generator_dur_loss=1.735, generator_adv_loss=1.954, generator_feat_match_loss=5.065, over 3176.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:21:11,251 INFO [train.py:527] (4/6) Epoch 509, batch 108, global_batch_idx: 63100, batch size: 56, loss[discriminator_loss=2.772, discriminator_real_loss=1.481, discriminator_fake_loss=1.29, generator_loss=28.61, generator_mel_loss=18.12, generator_kl_loss=1.397, generator_dur_loss=1.685, generator_adv_loss=1.867, generator_feat_match_loss=5.546, over 56.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.378, discriminator_fake_loss=1.338, generator_loss=28.23, generator_mel_loss=18.04, generator_kl_loss=1.434, generator_dur_loss=1.736, generator_adv_loss=1.959, generator_feat_match_loss=5.06, over 5897.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:21:55,450 INFO [train.py:919] (4/6) Start epoch 510 +2024-03-14 04:23:52,083 INFO [train.py:527] (4/6) Epoch 510, batch 34, global_batch_idx: 63150, batch size: 62, loss[discriminator_loss=2.801, discriminator_real_loss=1.297, discriminator_fake_loss=1.504, generator_loss=28.13, generator_mel_loss=18.2, generator_kl_loss=1.488, generator_dur_loss=1.765, generator_adv_loss=2.046, generator_feat_match_loss=4.623, over 62.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.366, discriminator_fake_loss=1.353, generator_loss=28.1, generator_mel_loss=18, generator_kl_loss=1.406, generator_dur_loss=1.736, generator_adv_loss=1.942, generator_feat_match_loss=5.018, over 2014.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:26:11,893 INFO [train.py:527] (4/6) Epoch 510, batch 84, global_batch_idx: 63200, batch size: 80, loss[discriminator_loss=2.725, discriminator_real_loss=1.322, discriminator_fake_loss=1.403, generator_loss=28.38, generator_mel_loss=18.2, generator_kl_loss=1.283, generator_dur_loss=1.833, generator_adv_loss=1.999, generator_feat_match_loss=5.065, over 80.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.365, discriminator_fake_loss=1.351, generator_loss=28.15, generator_mel_loss=17.99, generator_kl_loss=1.421, generator_dur_loss=1.746, generator_adv_loss=1.949, generator_feat_match_loss=5.042, over 4785.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:26:11,894 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 04:26:21,110 INFO [train.py:591] (4/6) Epoch 510, validation: discriminator_loss=2.738, discriminator_real_loss=1.447, discriminator_fake_loss=1.291, generator_loss=26.77, generator_mel_loss=17.99, generator_kl_loss=1.289, generator_dur_loss=1.815, generator_adv_loss=1.943, generator_feat_match_loss=3.734, over 100.00 samples. +2024-03-14 04:26:21,111 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 04:28:09,685 INFO [train.py:919] (4/6) Start epoch 511 +2024-03-14 04:29:03,938 INFO [train.py:527] (4/6) Epoch 511, batch 10, global_batch_idx: 63250, batch size: 31, loss[discriminator_loss=2.721, discriminator_real_loss=1.283, discriminator_fake_loss=1.439, generator_loss=28.5, generator_mel_loss=18.41, generator_kl_loss=1.323, generator_dur_loss=1.687, generator_adv_loss=2.048, generator_feat_match_loss=5.037, over 31.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.385, discriminator_fake_loss=1.303, generator_loss=28.39, generator_mel_loss=18, generator_kl_loss=1.369, generator_dur_loss=1.777, generator_adv_loss=1.988, generator_feat_match_loss=5.262, over 618.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:31:24,649 INFO [train.py:527] (4/6) Epoch 511, batch 60, global_batch_idx: 63300, batch size: 31, loss[discriminator_loss=2.775, discriminator_real_loss=1.412, discriminator_fake_loss=1.363, generator_loss=28.03, generator_mel_loss=17.89, generator_kl_loss=1.637, generator_dur_loss=1.59, generator_adv_loss=1.828, generator_feat_match_loss=5.086, over 31.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.374, discriminator_fake_loss=1.336, generator_loss=28.22, generator_mel_loss=17.99, generator_kl_loss=1.389, generator_dur_loss=1.761, generator_adv_loss=1.96, generator_feat_match_loss=5.123, over 3517.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:33:43,487 INFO [train.py:527] (4/6) Epoch 511, batch 110, global_batch_idx: 63350, batch size: 56, loss[discriminator_loss=2.747, discriminator_real_loss=1.352, discriminator_fake_loss=1.395, generator_loss=27.71, generator_mel_loss=17.87, generator_kl_loss=1.445, generator_dur_loss=1.773, generator_adv_loss=1.977, generator_feat_match_loss=4.65, over 56.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=28.23, generator_mel_loss=17.99, generator_kl_loss=1.403, generator_dur_loss=1.759, generator_adv_loss=1.96, generator_feat_match_loss=5.118, over 6319.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:34:19,081 INFO [train.py:919] (4/6) Start epoch 512 +2024-03-14 04:36:22,519 INFO [train.py:527] (4/6) Epoch 512, batch 36, global_batch_idx: 63400, batch size: 48, loss[discriminator_loss=2.7, discriminator_real_loss=1.371, discriminator_fake_loss=1.329, generator_loss=29.54, generator_mel_loss=18.89, generator_kl_loss=1.502, generator_dur_loss=1.708, generator_adv_loss=2.009, generator_feat_match_loss=5.425, over 48.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.374, discriminator_fake_loss=1.344, generator_loss=28.2, generator_mel_loss=18, generator_kl_loss=1.425, generator_dur_loss=1.777, generator_adv_loss=1.96, generator_feat_match_loss=5.045, over 2105.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:36:22,521 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 04:36:30,268 INFO [train.py:591] (4/6) Epoch 512, validation: discriminator_loss=2.746, discriminator_real_loss=1.444, discriminator_fake_loss=1.302, generator_loss=27.11, generator_mel_loss=18.15, generator_kl_loss=1.337, generator_dur_loss=1.83, generator_adv_loss=1.897, generator_feat_match_loss=3.894, over 100.00 samples. +2024-03-14 04:36:30,269 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 04:38:46,880 INFO [train.py:527] (4/6) Epoch 512, batch 86, global_batch_idx: 63450, batch size: 52, loss[discriminator_loss=2.721, discriminator_real_loss=1.452, discriminator_fake_loss=1.269, generator_loss=29.31, generator_mel_loss=18.63, generator_kl_loss=1.577, generator_dur_loss=1.692, generator_adv_loss=1.965, generator_feat_match_loss=5.443, over 52.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.373, discriminator_fake_loss=1.343, generator_loss=28.2, generator_mel_loss=18, generator_kl_loss=1.421, generator_dur_loss=1.779, generator_adv_loss=1.963, generator_feat_match_loss=5.044, over 5074.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:40:33,576 INFO [train.py:919] (4/6) Start epoch 513 +2024-03-14 04:41:33,128 INFO [train.py:527] (4/6) Epoch 513, batch 12, global_batch_idx: 63500, batch size: 36, loss[discriminator_loss=2.671, discriminator_real_loss=1.435, discriminator_fake_loss=1.236, generator_loss=28.04, generator_mel_loss=17.67, generator_kl_loss=1.637, generator_dur_loss=1.695, generator_adv_loss=1.871, generator_feat_match_loss=5.169, over 36.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.371, discriminator_fake_loss=1.335, generator_loss=28.08, generator_mel_loss=17.94, generator_kl_loss=1.421, generator_dur_loss=1.76, generator_adv_loss=1.938, generator_feat_match_loss=5.02, over 712.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:43:53,084 INFO [train.py:527] (4/6) Epoch 513, batch 62, global_batch_idx: 63550, batch size: 42, loss[discriminator_loss=2.7, discriminator_real_loss=1.436, discriminator_fake_loss=1.264, generator_loss=28.22, generator_mel_loss=17.91, generator_kl_loss=1.466, generator_dur_loss=1.717, generator_adv_loss=1.978, generator_feat_match_loss=5.14, over 42.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=28.2, generator_mel_loss=18.04, generator_kl_loss=1.381, generator_dur_loss=1.776, generator_adv_loss=1.95, generator_feat_match_loss=5.055, over 3628.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:46:10,243 INFO [train.py:527] (4/6) Epoch 513, batch 112, global_batch_idx: 63600, batch size: 48, loss[discriminator_loss=2.714, discriminator_real_loss=1.42, discriminator_fake_loss=1.294, generator_loss=29.18, generator_mel_loss=18.28, generator_kl_loss=1.65, generator_dur_loss=1.661, generator_adv_loss=2.006, generator_feat_match_loss=5.587, over 48.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=28.22, generator_mel_loss=18.01, generator_kl_loss=1.39, generator_dur_loss=1.769, generator_adv_loss=1.977, generator_feat_match_loss=5.071, over 6328.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:46:10,244 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 04:46:19,191 INFO [train.py:591] (4/6) Epoch 513, validation: discriminator_loss=2.719, discriminator_real_loss=1.431, discriminator_fake_loss=1.288, generator_loss=27.09, generator_mel_loss=18.16, generator_kl_loss=1.158, generator_dur_loss=1.847, generator_adv_loss=1.954, generator_feat_match_loss=3.979, over 100.00 samples. +2024-03-14 04:46:19,191 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 04:46:48,070 INFO [train.py:919] (4/6) Start epoch 514 +2024-03-14 04:48:59,895 INFO [train.py:527] (4/6) Epoch 514, batch 38, global_batch_idx: 63650, batch size: 80, loss[discriminator_loss=2.682, discriminator_real_loss=1.333, discriminator_fake_loss=1.349, generator_loss=26.92, generator_mel_loss=17.34, generator_kl_loss=1.314, generator_dur_loss=1.763, generator_adv_loss=1.978, generator_feat_match_loss=4.526, over 80.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.343, generator_loss=28.18, generator_mel_loss=18, generator_kl_loss=1.406, generator_dur_loss=1.758, generator_adv_loss=1.95, generator_feat_match_loss=5.066, over 2215.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:51:21,591 INFO [train.py:527] (4/6) Epoch 514, batch 88, global_batch_idx: 63700, batch size: 61, loss[discriminator_loss=2.677, discriminator_real_loss=1.349, discriminator_fake_loss=1.328, generator_loss=28.09, generator_mel_loss=18.04, generator_kl_loss=1.428, generator_dur_loss=1.735, generator_adv_loss=1.83, generator_feat_match_loss=5.053, over 61.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.37, discriminator_fake_loss=1.341, generator_loss=28.11, generator_mel_loss=17.97, generator_kl_loss=1.397, generator_dur_loss=1.766, generator_adv_loss=1.948, generator_feat_match_loss=5.029, over 5256.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:52:58,734 INFO [train.py:919] (4/6) Start epoch 515 +2024-03-14 04:54:00,983 INFO [train.py:527] (4/6) Epoch 515, batch 14, global_batch_idx: 63750, batch size: 74, loss[discriminator_loss=2.71, discriminator_real_loss=1.389, discriminator_fake_loss=1.321, generator_loss=28.69, generator_mel_loss=18.12, generator_kl_loss=1.374, generator_dur_loss=1.762, generator_adv_loss=1.982, generator_feat_match_loss=5.455, over 74.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.364, discriminator_fake_loss=1.357, generator_loss=28.36, generator_mel_loss=18.05, generator_kl_loss=1.411, generator_dur_loss=1.736, generator_adv_loss=1.979, generator_feat_match_loss=5.186, over 848.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:56:20,911 INFO [train.py:527] (4/6) Epoch 515, batch 64, global_batch_idx: 63800, batch size: 58, loss[discriminator_loss=2.722, discriminator_real_loss=1.437, discriminator_fake_loss=1.285, generator_loss=27.5, generator_mel_loss=18.01, generator_kl_loss=1.454, generator_dur_loss=1.778, generator_adv_loss=1.976, generator_feat_match_loss=4.28, over 58.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.37, discriminator_fake_loss=1.346, generator_loss=28.36, generator_mel_loss=18.06, generator_kl_loss=1.421, generator_dur_loss=1.752, generator_adv_loss=1.964, generator_feat_match_loss=5.172, over 3762.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:56:20,912 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 04:56:28,857 INFO [train.py:591] (4/6) Epoch 515, validation: discriminator_loss=2.737, discriminator_real_loss=1.473, discriminator_fake_loss=1.264, generator_loss=27.6, generator_mel_loss=18.64, generator_kl_loss=1.278, generator_dur_loss=1.822, generator_adv_loss=1.891, generator_feat_match_loss=3.967, over 100.00 samples. +2024-03-14 04:56:28,858 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 04:58:48,090 INFO [train.py:527] (4/6) Epoch 515, batch 114, global_batch_idx: 63850, batch size: 36, loss[discriminator_loss=2.761, discriminator_real_loss=1.447, discriminator_fake_loss=1.313, generator_loss=28.49, generator_mel_loss=18.7, generator_kl_loss=1.496, generator_dur_loss=1.685, generator_adv_loss=1.832, generator_feat_match_loss=4.775, over 36.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.344, generator_loss=28.29, generator_mel_loss=18.03, generator_kl_loss=1.422, generator_dur_loss=1.75, generator_adv_loss=1.957, generator_feat_match_loss=5.133, over 6492.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:59:15,600 INFO [train.py:919] (4/6) Start epoch 516 +2024-03-14 05:01:29,140 INFO [train.py:527] (4/6) Epoch 516, batch 40, global_batch_idx: 63900, batch size: 72, loss[discriminator_loss=2.748, discriminator_real_loss=1.492, discriminator_fake_loss=1.256, generator_loss=27.37, generator_mel_loss=17.63, generator_kl_loss=1.338, generator_dur_loss=1.773, generator_adv_loss=1.911, generator_feat_match_loss=4.723, over 72.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.375, discriminator_fake_loss=1.325, generator_loss=28.26, generator_mel_loss=18.05, generator_kl_loss=1.429, generator_dur_loss=1.744, generator_adv_loss=1.964, generator_feat_match_loss=5.072, over 2207.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:03:47,513 INFO [train.py:527] (4/6) Epoch 516, batch 90, global_batch_idx: 63950, batch size: 70, loss[discriminator_loss=2.693, discriminator_real_loss=1.376, discriminator_fake_loss=1.318, generator_loss=28.22, generator_mel_loss=17.67, generator_kl_loss=1.208, generator_dur_loss=1.834, generator_adv_loss=1.969, generator_feat_match_loss=5.529, over 70.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.375, discriminator_fake_loss=1.331, generator_loss=28.24, generator_mel_loss=18, generator_kl_loss=1.425, generator_dur_loss=1.746, generator_adv_loss=1.96, generator_feat_match_loss=5.102, over 5106.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:05:20,013 INFO [train.py:919] (4/6) Start epoch 517 +2024-03-14 05:06:28,767 INFO [train.py:527] (4/6) Epoch 517, batch 16, global_batch_idx: 64000, batch size: 20, loss[discriminator_loss=2.652, discriminator_real_loss=1.341, discriminator_fake_loss=1.311, generator_loss=30.35, generator_mel_loss=19.01, generator_kl_loss=1.879, generator_dur_loss=1.562, generator_adv_loss=1.984, generator_feat_match_loss=5.914, over 20.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.33, generator_loss=28.2, generator_mel_loss=17.91, generator_kl_loss=1.423, generator_dur_loss=1.754, generator_adv_loss=1.977, generator_feat_match_loss=5.13, over 925.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:06:28,768 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 05:06:36,606 INFO [train.py:591] (4/6) Epoch 517, validation: discriminator_loss=2.771, discriminator_real_loss=1.411, discriminator_fake_loss=1.36, generator_loss=26.76, generator_mel_loss=17.97, generator_kl_loss=1.252, generator_dur_loss=1.828, generator_adv_loss=1.814, generator_feat_match_loss=3.899, over 100.00 samples. +2024-03-14 05:06:36,607 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 05:08:59,591 INFO [train.py:527] (4/6) Epoch 517, batch 66, global_batch_idx: 64050, batch size: 61, loss[discriminator_loss=2.63, discriminator_real_loss=1.311, discriminator_fake_loss=1.319, generator_loss=28.63, generator_mel_loss=18.11, generator_kl_loss=1.333, generator_dur_loss=1.756, generator_adv_loss=2.077, generator_feat_match_loss=5.359, over 61.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.36, discriminator_fake_loss=1.34, generator_loss=28.17, generator_mel_loss=17.98, generator_kl_loss=1.377, generator_dur_loss=1.752, generator_adv_loss=1.96, generator_feat_match_loss=5.101, over 3878.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:11:15,854 INFO [train.py:527] (4/6) Epoch 517, batch 116, global_batch_idx: 64100, batch size: 48, loss[discriminator_loss=2.74, discriminator_real_loss=1.514, discriminator_fake_loss=1.227, generator_loss=27.74, generator_mel_loss=17.99, generator_kl_loss=1.34, generator_dur_loss=1.706, generator_adv_loss=2.008, generator_feat_match_loss=4.696, over 48.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.367, discriminator_fake_loss=1.341, generator_loss=28.19, generator_mel_loss=17.99, generator_kl_loss=1.411, generator_dur_loss=1.744, generator_adv_loss=1.955, generator_feat_match_loss=5.096, over 6467.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:11:37,271 INFO [train.py:919] (4/6) Start epoch 518 +2024-03-14 05:13:58,921 INFO [train.py:527] (4/6) Epoch 518, batch 42, global_batch_idx: 64150, batch size: 48, loss[discriminator_loss=2.719, discriminator_real_loss=1.448, discriminator_fake_loss=1.271, generator_loss=28.21, generator_mel_loss=17.86, generator_kl_loss=1.48, generator_dur_loss=1.723, generator_adv_loss=1.882, generator_feat_match_loss=5.266, over 48.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.383, discriminator_fake_loss=1.34, generator_loss=28.13, generator_mel_loss=17.93, generator_kl_loss=1.406, generator_dur_loss=1.763, generator_adv_loss=1.951, generator_feat_match_loss=5.088, over 2474.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:16:18,670 INFO [train.py:527] (4/6) Epoch 518, batch 92, global_batch_idx: 64200, batch size: 70, loss[discriminator_loss=2.702, discriminator_real_loss=1.418, discriminator_fake_loss=1.283, generator_loss=28.34, generator_mel_loss=18.47, generator_kl_loss=1.367, generator_dur_loss=1.791, generator_adv_loss=1.854, generator_feat_match_loss=4.863, over 70.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=28.23, generator_mel_loss=17.98, generator_kl_loss=1.414, generator_dur_loss=1.758, generator_adv_loss=1.952, generator_feat_match_loss=5.126, over 5191.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:16:18,672 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 05:16:27,424 INFO [train.py:591] (4/6) Epoch 518, validation: discriminator_loss=2.75, discriminator_real_loss=1.371, discriminator_fake_loss=1.379, generator_loss=27.32, generator_mel_loss=18.42, generator_kl_loss=1.186, generator_dur_loss=1.828, generator_adv_loss=1.755, generator_feat_match_loss=4.132, over 100.00 samples. +2024-03-14 05:16:27,425 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 05:17:52,789 INFO [train.py:919] (4/6) Start epoch 519 +2024-03-14 05:19:05,945 INFO [train.py:527] (4/6) Epoch 519, batch 18, global_batch_idx: 64250, batch size: 72, loss[discriminator_loss=2.711, discriminator_real_loss=1.366, discriminator_fake_loss=1.345, generator_loss=28.38, generator_mel_loss=17.87, generator_kl_loss=1.333, generator_dur_loss=1.78, generator_adv_loss=1.989, generator_feat_match_loss=5.409, over 72.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.353, discriminator_fake_loss=1.349, generator_loss=28.48, generator_mel_loss=18.14, generator_kl_loss=1.443, generator_dur_loss=1.756, generator_adv_loss=1.956, generator_feat_match_loss=5.185, over 1035.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:21:24,475 INFO [train.py:527] (4/6) Epoch 519, batch 68, global_batch_idx: 64300, batch size: 58, loss[discriminator_loss=2.779, discriminator_real_loss=1.416, discriminator_fake_loss=1.363, generator_loss=28.35, generator_mel_loss=18.04, generator_kl_loss=1.557, generator_dur_loss=1.706, generator_adv_loss=1.962, generator_feat_match_loss=5.088, over 58.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=28.39, generator_mel_loss=18.1, generator_kl_loss=1.437, generator_dur_loss=1.76, generator_adv_loss=1.962, generator_feat_match_loss=5.137, over 3902.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:23:42,042 INFO [train.py:527] (4/6) Epoch 519, batch 118, global_batch_idx: 64350, batch size: 16, loss[discriminator_loss=2.486, discriminator_real_loss=1.225, discriminator_fake_loss=1.261, generator_loss=31.44, generator_mel_loss=18.25, generator_kl_loss=1.82, generator_dur_loss=1.53, generator_adv_loss=2.353, generator_feat_match_loss=7.482, over 16.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.371, discriminator_fake_loss=1.344, generator_loss=28.38, generator_mel_loss=18.04, generator_kl_loss=1.439, generator_dur_loss=1.755, generator_adv_loss=1.982, generator_feat_match_loss=5.161, over 6638.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:23:58,083 INFO [train.py:919] (4/6) Start epoch 520 +2024-03-14 05:26:22,268 INFO [train.py:527] (4/6) Epoch 520, batch 44, global_batch_idx: 64400, batch size: 39, loss[discriminator_loss=2.678, discriminator_real_loss=1.338, discriminator_fake_loss=1.34, generator_loss=28.59, generator_mel_loss=17.94, generator_kl_loss=1.516, generator_dur_loss=1.705, generator_adv_loss=1.948, generator_feat_match_loss=5.483, over 39.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.389, discriminator_fake_loss=1.333, generator_loss=28.01, generator_mel_loss=17.9, generator_kl_loss=1.424, generator_dur_loss=1.752, generator_adv_loss=1.96, generator_feat_match_loss=4.978, over 2445.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:26:22,270 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 05:26:30,251 INFO [train.py:591] (4/6) Epoch 520, validation: discriminator_loss=2.739, discriminator_real_loss=1.432, discriminator_fake_loss=1.306, generator_loss=27.31, generator_mel_loss=18.27, generator_kl_loss=1.261, generator_dur_loss=1.832, generator_adv_loss=1.899, generator_feat_match_loss=4.044, over 100.00 samples. +2024-03-14 05:26:30,252 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 05:28:48,703 INFO [train.py:527] (4/6) Epoch 520, batch 94, global_batch_idx: 64450, batch size: 66, loss[discriminator_loss=2.734, discriminator_real_loss=1.381, discriminator_fake_loss=1.352, generator_loss=28.9, generator_mel_loss=18.41, generator_kl_loss=1.361, generator_dur_loss=1.78, generator_adv_loss=1.817, generator_feat_match_loss=5.531, over 66.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.385, discriminator_fake_loss=1.336, generator_loss=28.04, generator_mel_loss=17.93, generator_kl_loss=1.412, generator_dur_loss=1.758, generator_adv_loss=1.945, generator_feat_match_loss=4.999, over 5510.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:30:09,983 INFO [train.py:919] (4/6) Start epoch 521 +2024-03-14 05:31:30,894 INFO [train.py:527] (4/6) Epoch 521, batch 20, global_batch_idx: 64500, batch size: 77, loss[discriminator_loss=2.727, discriminator_real_loss=1.38, discriminator_fake_loss=1.347, generator_loss=27.78, generator_mel_loss=17.91, generator_kl_loss=1.491, generator_dur_loss=1.783, generator_adv_loss=2.028, generator_feat_match_loss=4.561, over 77.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.343, generator_loss=28.16, generator_mel_loss=17.98, generator_kl_loss=1.399, generator_dur_loss=1.776, generator_adv_loss=1.942, generator_feat_match_loss=5.063, over 1393.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:33:50,004 INFO [train.py:527] (4/6) Epoch 521, batch 70, global_batch_idx: 64550, batch size: 48, loss[discriminator_loss=2.732, discriminator_real_loss=1.316, discriminator_fake_loss=1.416, generator_loss=27.82, generator_mel_loss=17.85, generator_kl_loss=1.478, generator_dur_loss=1.709, generator_adv_loss=1.95, generator_feat_match_loss=4.831, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.369, discriminator_fake_loss=1.346, generator_loss=28.19, generator_mel_loss=18, generator_kl_loss=1.404, generator_dur_loss=1.763, generator_adv_loss=1.947, generator_feat_match_loss=5.069, over 4204.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:36:07,752 INFO [train.py:527] (4/6) Epoch 521, batch 120, global_batch_idx: 64600, batch size: 25, loss[discriminator_loss=2.656, discriminator_real_loss=1.361, discriminator_fake_loss=1.295, generator_loss=29.22, generator_mel_loss=17.97, generator_kl_loss=1.764, generator_dur_loss=1.582, generator_adv_loss=2.141, generator_feat_match_loss=5.76, over 25.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.37, discriminator_fake_loss=1.35, generator_loss=28.19, generator_mel_loss=17.99, generator_kl_loss=1.41, generator_dur_loss=1.762, generator_adv_loss=1.952, generator_feat_match_loss=5.072, over 7011.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:36:07,754 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 05:36:16,582 INFO [train.py:591] (4/6) Epoch 521, validation: discriminator_loss=2.667, discriminator_real_loss=1.456, discriminator_fake_loss=1.211, generator_loss=27.99, generator_mel_loss=18.52, generator_kl_loss=1.214, generator_dur_loss=1.819, generator_adv_loss=2.026, generator_feat_match_loss=4.408, over 100.00 samples. +2024-03-14 05:36:16,582 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 05:36:25,328 INFO [train.py:919] (4/6) Start epoch 522 +2024-03-14 05:38:57,183 INFO [train.py:527] (4/6) Epoch 522, batch 46, global_batch_idx: 64650, batch size: 68, loss[discriminator_loss=2.687, discriminator_real_loss=1.352, discriminator_fake_loss=1.335, generator_loss=28.28, generator_mel_loss=18.08, generator_kl_loss=1.279, generator_dur_loss=1.767, generator_adv_loss=1.905, generator_feat_match_loss=5.249, over 68.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.36, discriminator_fake_loss=1.344, generator_loss=28.31, generator_mel_loss=18.06, generator_kl_loss=1.416, generator_dur_loss=1.754, generator_adv_loss=1.948, generator_feat_match_loss=5.131, over 2661.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:41:15,698 INFO [train.py:527] (4/6) Epoch 522, batch 96, global_batch_idx: 64700, batch size: 45, loss[discriminator_loss=2.694, discriminator_real_loss=1.417, discriminator_fake_loss=1.278, generator_loss=28.67, generator_mel_loss=18.59, generator_kl_loss=1.34, generator_dur_loss=1.653, generator_adv_loss=1.852, generator_feat_match_loss=5.231, over 45.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.364, discriminator_fake_loss=1.343, generator_loss=28.37, generator_mel_loss=18.04, generator_kl_loss=1.435, generator_dur_loss=1.748, generator_adv_loss=1.957, generator_feat_match_loss=5.183, over 5366.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:42:30,465 INFO [train.py:919] (4/6) Start epoch 523 +2024-03-14 05:43:53,437 INFO [train.py:527] (4/6) Epoch 523, batch 22, global_batch_idx: 64750, batch size: 25, loss[discriminator_loss=2.734, discriminator_real_loss=1.426, discriminator_fake_loss=1.308, generator_loss=28.75, generator_mel_loss=18.34, generator_kl_loss=1.537, generator_dur_loss=1.554, generator_adv_loss=1.905, generator_feat_match_loss=5.416, over 25.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.366, discriminator_fake_loss=1.359, generator_loss=28.15, generator_mel_loss=18.07, generator_kl_loss=1.388, generator_dur_loss=1.731, generator_adv_loss=1.943, generator_feat_match_loss=5.021, over 1231.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:46:13,637 INFO [train.py:527] (4/6) Epoch 523, batch 72, global_batch_idx: 64800, batch size: 72, loss[discriminator_loss=2.733, discriminator_real_loss=1.421, discriminator_fake_loss=1.312, generator_loss=28.16, generator_mel_loss=17.95, generator_kl_loss=1.357, generator_dur_loss=1.784, generator_adv_loss=1.92, generator_feat_match_loss=5.15, over 72.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.374, discriminator_fake_loss=1.35, generator_loss=28.07, generator_mel_loss=17.97, generator_kl_loss=1.409, generator_dur_loss=1.75, generator_adv_loss=1.947, generator_feat_match_loss=4.995, over 4147.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:46:13,639 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 05:46:21,896 INFO [train.py:591] (4/6) Epoch 523, validation: discriminator_loss=2.77, discriminator_real_loss=1.417, discriminator_fake_loss=1.352, generator_loss=26.84, generator_mel_loss=18.17, generator_kl_loss=1.277, generator_dur_loss=1.817, generator_adv_loss=1.897, generator_feat_match_loss=3.677, over 100.00 samples. +2024-03-14 05:46:21,897 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 05:48:40,688 INFO [train.py:527] (4/6) Epoch 523, batch 122, global_batch_idx: 64850, batch size: 42, loss[discriminator_loss=2.622, discriminator_real_loss=1.283, discriminator_fake_loss=1.339, generator_loss=28.26, generator_mel_loss=17.84, generator_kl_loss=1.468, generator_dur_loss=1.658, generator_adv_loss=2.126, generator_feat_match_loss=5.177, over 42.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.369, discriminator_fake_loss=1.349, generator_loss=28.09, generator_mel_loss=17.96, generator_kl_loss=1.403, generator_dur_loss=1.757, generator_adv_loss=1.948, generator_feat_match_loss=5.024, over 7065.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:48:45,857 INFO [train.py:919] (4/6) Start epoch 524 +2024-03-14 05:51:24,178 INFO [train.py:527] (4/6) Epoch 524, batch 48, global_batch_idx: 64900, batch size: 80, loss[discriminator_loss=2.722, discriminator_real_loss=1.373, discriminator_fake_loss=1.349, generator_loss=28.36, generator_mel_loss=18.34, generator_kl_loss=1.243, generator_dur_loss=1.815, generator_adv_loss=1.989, generator_feat_match_loss=4.976, over 80.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.373, discriminator_fake_loss=1.332, generator_loss=28.25, generator_mel_loss=17.93, generator_kl_loss=1.417, generator_dur_loss=1.761, generator_adv_loss=1.995, generator_feat_match_loss=5.138, over 2869.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:53:42,490 INFO [train.py:527] (4/6) Epoch 524, batch 98, global_batch_idx: 64950, batch size: 66, loss[discriminator_loss=2.748, discriminator_real_loss=1.3, discriminator_fake_loss=1.448, generator_loss=28.38, generator_mel_loss=17.93, generator_kl_loss=1.448, generator_dur_loss=1.776, generator_adv_loss=2.034, generator_feat_match_loss=5.185, over 66.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.25, generator_mel_loss=17.97, generator_kl_loss=1.434, generator_dur_loss=1.746, generator_adv_loss=1.977, generator_feat_match_loss=5.131, over 5605.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:54:51,244 INFO [train.py:919] (4/6) Start epoch 525 +2024-03-14 05:56:19,701 INFO [train.py:527] (4/6) Epoch 525, batch 24, global_batch_idx: 65000, batch size: 59, loss[discriminator_loss=2.704, discriminator_real_loss=1.369, discriminator_fake_loss=1.335, generator_loss=27.53, generator_mel_loss=17.83, generator_kl_loss=1.405, generator_dur_loss=1.75, generator_adv_loss=1.943, generator_feat_match_loss=4.608, over 59.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.363, discriminator_fake_loss=1.345, generator_loss=28.33, generator_mel_loss=17.99, generator_kl_loss=1.434, generator_dur_loss=1.736, generator_adv_loss=1.959, generator_feat_match_loss=5.214, over 1299.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:56:19,702 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 05:56:27,761 INFO [train.py:591] (4/6) Epoch 525, validation: discriminator_loss=2.767, discriminator_real_loss=1.462, discriminator_fake_loss=1.305, generator_loss=27.39, generator_mel_loss=18.51, generator_kl_loss=1.305, generator_dur_loss=1.834, generator_adv_loss=1.915, generator_feat_match_loss=3.833, over 100.00 samples. +2024-03-14 05:56:27,762 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 05:58:47,339 INFO [train.py:527] (4/6) Epoch 525, batch 74, global_batch_idx: 65050, batch size: 39, loss[discriminator_loss=2.75, discriminator_real_loss=1.306, discriminator_fake_loss=1.445, generator_loss=27.94, generator_mel_loss=17.76, generator_kl_loss=1.418, generator_dur_loss=1.702, generator_adv_loss=2.031, generator_feat_match_loss=5.035, over 39.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.31, generator_mel_loss=17.98, generator_kl_loss=1.444, generator_dur_loss=1.736, generator_adv_loss=1.964, generator_feat_match_loss=5.182, over 4021.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:01:06,799 INFO [train.py:919] (4/6) Start epoch 526 +2024-03-14 06:01:28,251 INFO [train.py:527] (4/6) Epoch 526, batch 0, global_batch_idx: 65100, batch size: 77, loss[discriminator_loss=2.698, discriminator_real_loss=1.343, discriminator_fake_loss=1.355, generator_loss=28.49, generator_mel_loss=18.19, generator_kl_loss=1.337, generator_dur_loss=1.81, generator_adv_loss=1.952, generator_feat_match_loss=5.209, over 77.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.343, discriminator_fake_loss=1.355, generator_loss=28.49, generator_mel_loss=18.19, generator_kl_loss=1.337, generator_dur_loss=1.81, generator_adv_loss=1.952, generator_feat_match_loss=5.209, over 77.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:03:46,198 INFO [train.py:527] (4/6) Epoch 526, batch 50, global_batch_idx: 65150, batch size: 61, loss[discriminator_loss=2.666, discriminator_real_loss=1.32, discriminator_fake_loss=1.347, generator_loss=28.71, generator_mel_loss=18.06, generator_kl_loss=1.551, generator_dur_loss=1.696, generator_adv_loss=1.901, generator_feat_match_loss=5.505, over 61.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.371, discriminator_fake_loss=1.349, generator_loss=28.23, generator_mel_loss=18.01, generator_kl_loss=1.391, generator_dur_loss=1.771, generator_adv_loss=1.946, generator_feat_match_loss=5.117, over 3079.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:06:02,915 INFO [train.py:527] (4/6) Epoch 526, batch 100, global_batch_idx: 65200, batch size: 36, loss[discriminator_loss=2.708, discriminator_real_loss=1.366, discriminator_fake_loss=1.342, generator_loss=29.42, generator_mel_loss=18.63, generator_kl_loss=1.538, generator_dur_loss=1.735, generator_adv_loss=1.956, generator_feat_match_loss=5.558, over 36.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=28.25, generator_mel_loss=18.03, generator_kl_loss=1.4, generator_dur_loss=1.761, generator_adv_loss=1.953, generator_feat_match_loss=5.113, over 5861.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:06:02,916 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 06:06:11,751 INFO [train.py:591] (4/6) Epoch 526, validation: discriminator_loss=2.717, discriminator_real_loss=1.44, discriminator_fake_loss=1.277, generator_loss=26.98, generator_mel_loss=18.07, generator_kl_loss=1.209, generator_dur_loss=1.835, generator_adv_loss=1.877, generator_feat_match_loss=3.989, over 100.00 samples. +2024-03-14 06:06:11,752 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 06:07:16,738 INFO [train.py:919] (4/6) Start epoch 527 +2024-03-14 06:08:53,224 INFO [train.py:527] (4/6) Epoch 527, batch 26, global_batch_idx: 65250, batch size: 62, loss[discriminator_loss=2.707, discriminator_real_loss=1.384, discriminator_fake_loss=1.323, generator_loss=27.73, generator_mel_loss=18.19, generator_kl_loss=1.252, generator_dur_loss=1.754, generator_adv_loss=1.935, generator_feat_match_loss=4.594, over 62.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.364, discriminator_fake_loss=1.325, generator_loss=28.39, generator_mel_loss=17.96, generator_kl_loss=1.394, generator_dur_loss=1.782, generator_adv_loss=2.003, generator_feat_match_loss=5.249, over 1640.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:11:14,295 INFO [train.py:527] (4/6) Epoch 527, batch 76, global_batch_idx: 65300, batch size: 52, loss[discriminator_loss=2.689, discriminator_real_loss=1.309, discriminator_fake_loss=1.38, generator_loss=27.62, generator_mel_loss=17.67, generator_kl_loss=1.37, generator_dur_loss=1.646, generator_adv_loss=1.932, generator_feat_match_loss=5.007, over 52.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.367, discriminator_fake_loss=1.333, generator_loss=28.23, generator_mel_loss=17.92, generator_kl_loss=1.391, generator_dur_loss=1.788, generator_adv_loss=1.968, generator_feat_match_loss=5.157, over 4822.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:13:24,715 INFO [train.py:919] (4/6) Start epoch 528 +2024-03-14 06:13:54,212 INFO [train.py:527] (4/6) Epoch 528, batch 2, global_batch_idx: 65350, batch size: 77, loss[discriminator_loss=2.732, discriminator_real_loss=1.391, discriminator_fake_loss=1.341, generator_loss=27.77, generator_mel_loss=17.96, generator_kl_loss=1.276, generator_dur_loss=1.766, generator_adv_loss=1.856, generator_feat_match_loss=4.912, over 77.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.401, discriminator_fake_loss=1.32, generator_loss=27.79, generator_mel_loss=17.94, generator_kl_loss=1.374, generator_dur_loss=1.751, generator_adv_loss=1.952, generator_feat_match_loss=4.779, over 193.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:16:12,069 INFO [train.py:527] (4/6) Epoch 528, batch 52, global_batch_idx: 65400, batch size: 53, loss[discriminator_loss=2.663, discriminator_real_loss=1.311, discriminator_fake_loss=1.353, generator_loss=28.89, generator_mel_loss=18.16, generator_kl_loss=1.419, generator_dur_loss=1.711, generator_adv_loss=1.959, generator_feat_match_loss=5.634, over 53.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.366, discriminator_fake_loss=1.345, generator_loss=28.26, generator_mel_loss=17.96, generator_kl_loss=1.389, generator_dur_loss=1.758, generator_adv_loss=1.953, generator_feat_match_loss=5.195, over 2951.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:16:12,070 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 06:16:20,250 INFO [train.py:591] (4/6) Epoch 528, validation: discriminator_loss=2.737, discriminator_real_loss=1.382, discriminator_fake_loss=1.355, generator_loss=28.12, generator_mel_loss=18.68, generator_kl_loss=1.231, generator_dur_loss=1.843, generator_adv_loss=1.888, generator_feat_match_loss=4.475, over 100.00 samples. +2024-03-14 06:16:20,251 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 06:18:39,605 INFO [train.py:527] (4/6) Epoch 528, batch 102, global_batch_idx: 65450, batch size: 31, loss[discriminator_loss=2.732, discriminator_real_loss=1.359, discriminator_fake_loss=1.372, generator_loss=28.86, generator_mel_loss=18.08, generator_kl_loss=1.631, generator_dur_loss=1.621, generator_adv_loss=2.028, generator_feat_match_loss=5.496, over 31.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.361, discriminator_fake_loss=1.345, generator_loss=28.28, generator_mel_loss=17.97, generator_kl_loss=1.405, generator_dur_loss=1.759, generator_adv_loss=1.954, generator_feat_match_loss=5.195, over 5863.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:19:39,561 INFO [train.py:919] (4/6) Start epoch 529 +2024-03-14 06:21:21,293 INFO [train.py:527] (4/6) Epoch 529, batch 28, global_batch_idx: 65500, batch size: 80, loss[discriminator_loss=2.749, discriminator_real_loss=1.421, discriminator_fake_loss=1.327, generator_loss=28.06, generator_mel_loss=17.97, generator_kl_loss=1.364, generator_dur_loss=1.795, generator_adv_loss=2.065, generator_feat_match_loss=4.869, over 80.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.37, discriminator_fake_loss=1.331, generator_loss=28.43, generator_mel_loss=17.96, generator_kl_loss=1.404, generator_dur_loss=1.778, generator_adv_loss=1.975, generator_feat_match_loss=5.319, over 1737.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:23:39,360 INFO [train.py:527] (4/6) Epoch 529, batch 78, global_batch_idx: 65550, batch size: 62, loss[discriminator_loss=2.709, discriminator_real_loss=1.422, discriminator_fake_loss=1.287, generator_loss=28.17, generator_mel_loss=17.98, generator_kl_loss=1.435, generator_dur_loss=1.748, generator_adv_loss=1.842, generator_feat_match_loss=5.17, over 62.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.34, generator_loss=28.32, generator_mel_loss=17.96, generator_kl_loss=1.405, generator_dur_loss=1.774, generator_adv_loss=1.971, generator_feat_match_loss=5.205, over 4817.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:25:44,927 INFO [train.py:919] (4/6) Start epoch 530 +2024-03-14 06:26:19,635 INFO [train.py:527] (4/6) Epoch 530, batch 4, global_batch_idx: 65600, batch size: 44, loss[discriminator_loss=2.748, discriminator_real_loss=1.333, discriminator_fake_loss=1.415, generator_loss=28.48, generator_mel_loss=17.83, generator_kl_loss=1.667, generator_dur_loss=1.652, generator_adv_loss=1.914, generator_feat_match_loss=5.417, over 44.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.366, discriminator_fake_loss=1.358, generator_loss=28.07, generator_mel_loss=17.97, generator_kl_loss=1.484, generator_dur_loss=1.721, generator_adv_loss=1.923, generator_feat_match_loss=4.97, over 256.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:26:19,637 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 06:26:27,497 INFO [train.py:591] (4/6) Epoch 530, validation: discriminator_loss=2.788, discriminator_real_loss=1.417, discriminator_fake_loss=1.371, generator_loss=27.05, generator_mel_loss=18.14, generator_kl_loss=1.191, generator_dur_loss=1.821, generator_adv_loss=1.847, generator_feat_match_loss=4.059, over 100.00 samples. +2024-03-14 06:26:27,500 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 06:28:44,890 INFO [train.py:527] (4/6) Epoch 530, batch 54, global_batch_idx: 65650, batch size: 77, loss[discriminator_loss=2.677, discriminator_real_loss=1.382, discriminator_fake_loss=1.295, generator_loss=27.58, generator_mel_loss=17.8, generator_kl_loss=1.308, generator_dur_loss=1.847, generator_adv_loss=1.961, generator_feat_match_loss=4.66, over 77.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.371, discriminator_fake_loss=1.335, generator_loss=28.12, generator_mel_loss=17.91, generator_kl_loss=1.411, generator_dur_loss=1.749, generator_adv_loss=1.965, generator_feat_match_loss=5.082, over 3119.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:31:02,610 INFO [train.py:527] (4/6) Epoch 530, batch 104, global_batch_idx: 65700, batch size: 59, loss[discriminator_loss=2.767, discriminator_real_loss=1.336, discriminator_fake_loss=1.43, generator_loss=28.15, generator_mel_loss=17.97, generator_kl_loss=1.433, generator_dur_loss=1.788, generator_adv_loss=1.937, generator_feat_match_loss=5.017, over 59.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.37, discriminator_fake_loss=1.336, generator_loss=28.17, generator_mel_loss=17.94, generator_kl_loss=1.413, generator_dur_loss=1.752, generator_adv_loss=1.967, generator_feat_match_loss=5.094, over 5971.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:31:58,177 INFO [train.py:919] (4/6) Start epoch 531 +2024-03-14 06:33:44,008 INFO [train.py:527] (4/6) Epoch 531, batch 30, global_batch_idx: 65750, batch size: 64, loss[discriminator_loss=2.771, discriminator_real_loss=1.409, discriminator_fake_loss=1.362, generator_loss=28.09, generator_mel_loss=17.78, generator_kl_loss=1.47, generator_dur_loss=1.775, generator_adv_loss=1.794, generator_feat_match_loss=5.276, over 64.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.363, discriminator_fake_loss=1.357, generator_loss=28.23, generator_mel_loss=17.95, generator_kl_loss=1.429, generator_dur_loss=1.773, generator_adv_loss=1.946, generator_feat_match_loss=5.132, over 1878.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:36:02,401 INFO [train.py:527] (4/6) Epoch 531, batch 80, global_batch_idx: 65800, batch size: 74, loss[discriminator_loss=2.695, discriminator_real_loss=1.383, discriminator_fake_loss=1.312, generator_loss=27.24, generator_mel_loss=17.45, generator_kl_loss=1.233, generator_dur_loss=1.76, generator_adv_loss=2.024, generator_feat_match_loss=4.772, over 74.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.367, discriminator_fake_loss=1.344, generator_loss=28.25, generator_mel_loss=17.96, generator_kl_loss=1.415, generator_dur_loss=1.762, generator_adv_loss=1.963, generator_feat_match_loss=5.152, over 4724.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:36:02,403 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 06:36:10,371 INFO [train.py:591] (4/6) Epoch 531, validation: discriminator_loss=2.722, discriminator_real_loss=1.419, discriminator_fake_loss=1.303, generator_loss=27.24, generator_mel_loss=18.22, generator_kl_loss=1.31, generator_dur_loss=1.819, generator_adv_loss=1.926, generator_feat_match_loss=3.968, over 100.00 samples. +2024-03-14 06:36:10,372 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 06:38:11,180 INFO [train.py:919] (4/6) Start epoch 532 +2024-03-14 06:38:51,770 INFO [train.py:527] (4/6) Epoch 532, batch 6, global_batch_idx: 65850, batch size: 72, loss[discriminator_loss=2.741, discriminator_real_loss=1.502, discriminator_fake_loss=1.239, generator_loss=28.51, generator_mel_loss=18.02, generator_kl_loss=1.388, generator_dur_loss=1.745, generator_adv_loss=1.848, generator_feat_match_loss=5.503, over 72.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.388, discriminator_fake_loss=1.323, generator_loss=28.22, generator_mel_loss=17.93, generator_kl_loss=1.367, generator_dur_loss=1.769, generator_adv_loss=2.003, generator_feat_match_loss=5.153, over 450.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:41:13,541 INFO [train.py:527] (4/6) Epoch 532, batch 56, global_batch_idx: 65900, batch size: 31, loss[discriminator_loss=2.691, discriminator_real_loss=1.384, discriminator_fake_loss=1.307, generator_loss=28.87, generator_mel_loss=18.23, generator_kl_loss=1.698, generator_dur_loss=1.664, generator_adv_loss=1.938, generator_feat_match_loss=5.343, over 31.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.23, generator_mel_loss=17.97, generator_kl_loss=1.4, generator_dur_loss=1.745, generator_adv_loss=1.972, generator_feat_match_loss=5.14, over 3361.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:43:37,063 INFO [train.py:527] (4/6) Epoch 532, batch 106, global_batch_idx: 65950, batch size: 88, loss[discriminator_loss=2.752, discriminator_real_loss=1.434, discriminator_fake_loss=1.318, generator_loss=27.34, generator_mel_loss=17.89, generator_kl_loss=1.193, generator_dur_loss=1.877, generator_adv_loss=1.819, generator_feat_match_loss=4.56, over 88.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=28.22, generator_mel_loss=17.97, generator_kl_loss=1.408, generator_dur_loss=1.747, generator_adv_loss=1.964, generator_feat_match_loss=5.129, over 6181.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:44:26,213 INFO [train.py:919] (4/6) Start epoch 533 +2024-03-14 06:46:21,566 INFO [train.py:527] (4/6) Epoch 533, batch 32, global_batch_idx: 66000, batch size: 96, loss[discriminator_loss=2.676, discriminator_real_loss=1.301, discriminator_fake_loss=1.375, generator_loss=28.02, generator_mel_loss=17.75, generator_kl_loss=1.324, generator_dur_loss=1.859, generator_adv_loss=2.087, generator_feat_match_loss=5.002, over 96.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.338, generator_loss=28.47, generator_mel_loss=18.09, generator_kl_loss=1.45, generator_dur_loss=1.739, generator_adv_loss=1.986, generator_feat_match_loss=5.205, over 1782.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:46:21,567 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 06:46:29,365 INFO [train.py:591] (4/6) Epoch 533, validation: discriminator_loss=2.755, discriminator_real_loss=1.419, discriminator_fake_loss=1.335, generator_loss=27, generator_mel_loss=18.14, generator_kl_loss=1.185, generator_dur_loss=1.804, generator_adv_loss=1.902, generator_feat_match_loss=3.976, over 100.00 samples. +2024-03-14 06:46:29,366 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 06:48:50,856 INFO [train.py:527] (4/6) Epoch 533, batch 82, global_batch_idx: 66050, batch size: 47, loss[discriminator_loss=2.7, discriminator_real_loss=1.396, discriminator_fake_loss=1.303, generator_loss=28.68, generator_mel_loss=18.41, generator_kl_loss=1.5, generator_dur_loss=1.648, generator_adv_loss=1.948, generator_feat_match_loss=5.175, over 47.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.35, generator_mel_loss=18.05, generator_kl_loss=1.416, generator_dur_loss=1.742, generator_adv_loss=1.968, generator_feat_match_loss=5.174, over 4545.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:50:46,649 INFO [train.py:919] (4/6) Start epoch 534 +2024-03-14 06:51:32,909 INFO [train.py:527] (4/6) Epoch 534, batch 8, global_batch_idx: 66100, batch size: 31, loss[discriminator_loss=2.726, discriminator_real_loss=1.404, discriminator_fake_loss=1.322, generator_loss=27.31, generator_mel_loss=17.83, generator_kl_loss=1.476, generator_dur_loss=1.648, generator_adv_loss=2.014, generator_feat_match_loss=4.34, over 31.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.407, discriminator_fake_loss=1.302, generator_loss=28.66, generator_mel_loss=18.12, generator_kl_loss=1.439, generator_dur_loss=1.705, generator_adv_loss=2.033, generator_feat_match_loss=5.359, over 444.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:53:53,997 INFO [train.py:527] (4/6) Epoch 534, batch 58, global_batch_idx: 66150, batch size: 58, loss[discriminator_loss=2.703, discriminator_real_loss=1.465, discriminator_fake_loss=1.238, generator_loss=28.83, generator_mel_loss=18.27, generator_kl_loss=1.323, generator_dur_loss=1.742, generator_adv_loss=1.898, generator_feat_match_loss=5.597, over 58.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.377, discriminator_fake_loss=1.336, generator_loss=28.37, generator_mel_loss=18.01, generator_kl_loss=1.411, generator_dur_loss=1.755, generator_adv_loss=1.971, generator_feat_match_loss=5.222, over 3374.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:56:16,904 INFO [train.py:527] (4/6) Epoch 534, batch 108, global_batch_idx: 66200, batch size: 31, loss[discriminator_loss=2.679, discriminator_real_loss=1.318, discriminator_fake_loss=1.361, generator_loss=28.17, generator_mel_loss=17.75, generator_kl_loss=1.761, generator_dur_loss=1.573, generator_adv_loss=2.035, generator_feat_match_loss=5.053, over 31.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=28.28, generator_mel_loss=18.01, generator_kl_loss=1.41, generator_dur_loss=1.76, generator_adv_loss=1.959, generator_feat_match_loss=5.138, over 6449.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:56:16,906 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 06:56:25,597 INFO [train.py:591] (4/6) Epoch 534, validation: discriminator_loss=2.697, discriminator_real_loss=1.422, discriminator_fake_loss=1.275, generator_loss=27.49, generator_mel_loss=18.53, generator_kl_loss=1.262, generator_dur_loss=1.822, generator_adv_loss=1.92, generator_feat_match_loss=3.952, over 100.00 samples. +2024-03-14 06:56:25,598 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 06:57:08,110 INFO [train.py:919] (4/6) Start epoch 535 +2024-03-14 06:59:13,053 INFO [train.py:527] (4/6) Epoch 535, batch 34, global_batch_idx: 66250, batch size: 31, loss[discriminator_loss=2.722, discriminator_real_loss=1.307, discriminator_fake_loss=1.415, generator_loss=27.92, generator_mel_loss=18, generator_kl_loss=1.49, generator_dur_loss=1.581, generator_adv_loss=2.074, generator_feat_match_loss=4.778, over 31.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.382, discriminator_fake_loss=1.333, generator_loss=28.28, generator_mel_loss=17.93, generator_kl_loss=1.43, generator_dur_loss=1.736, generator_adv_loss=1.953, generator_feat_match_loss=5.23, over 1985.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:01:34,853 INFO [train.py:527] (4/6) Epoch 535, batch 84, global_batch_idx: 66300, batch size: 66, loss[discriminator_loss=2.656, discriminator_real_loss=1.284, discriminator_fake_loss=1.372, generator_loss=27.78, generator_mel_loss=17.93, generator_kl_loss=1.38, generator_dur_loss=1.81, generator_adv_loss=2.135, generator_feat_match_loss=4.526, over 66.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.37, generator_mel_loss=17.99, generator_kl_loss=1.448, generator_dur_loss=1.739, generator_adv_loss=1.966, generator_feat_match_loss=5.227, over 4845.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:03:22,225 INFO [train.py:919] (4/6) Start epoch 536 +2024-03-14 07:04:14,402 INFO [train.py:527] (4/6) Epoch 536, batch 10, global_batch_idx: 66350, batch size: 59, loss[discriminator_loss=2.786, discriminator_real_loss=1.499, discriminator_fake_loss=1.286, generator_loss=27.08, generator_mel_loss=17.76, generator_kl_loss=1.422, generator_dur_loss=1.752, generator_adv_loss=1.809, generator_feat_match_loss=4.338, over 59.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.423, discriminator_fake_loss=1.338, generator_loss=27.94, generator_mel_loss=17.91, generator_kl_loss=1.43, generator_dur_loss=1.727, generator_adv_loss=1.949, generator_feat_match_loss=4.919, over 586.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:06:35,730 INFO [train.py:527] (4/6) Epoch 536, batch 60, global_batch_idx: 66400, batch size: 66, loss[discriminator_loss=2.67, discriminator_real_loss=1.274, discriminator_fake_loss=1.396, generator_loss=28.78, generator_mel_loss=18.05, generator_kl_loss=1.565, generator_dur_loss=1.706, generator_adv_loss=1.994, generator_feat_match_loss=5.462, over 66.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=28.15, generator_mel_loss=17.98, generator_kl_loss=1.428, generator_dur_loss=1.729, generator_adv_loss=1.934, generator_feat_match_loss=5.089, over 3595.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:06:35,731 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 07:06:43,889 INFO [train.py:591] (4/6) Epoch 536, validation: discriminator_loss=2.741, discriminator_real_loss=1.502, discriminator_fake_loss=1.239, generator_loss=27.44, generator_mel_loss=18.3, generator_kl_loss=1.335, generator_dur_loss=1.789, generator_adv_loss=2.005, generator_feat_match_loss=4.017, over 100.00 samples. +2024-03-14 07:06:43,890 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 07:09:05,006 INFO [train.py:527] (4/6) Epoch 536, batch 110, global_batch_idx: 66450, batch size: 48, loss[discriminator_loss=2.704, discriminator_real_loss=1.325, discriminator_fake_loss=1.378, generator_loss=27.7, generator_mel_loss=17.89, generator_kl_loss=1.525, generator_dur_loss=1.708, generator_adv_loss=1.764, generator_feat_match_loss=4.81, over 48.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.18, generator_mel_loss=17.98, generator_kl_loss=1.427, generator_dur_loss=1.735, generator_adv_loss=1.942, generator_feat_match_loss=5.102, over 6403.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:09:43,146 INFO [train.py:919] (4/6) Start epoch 537 +2024-03-14 07:11:48,632 INFO [train.py:527] (4/6) Epoch 537, batch 36, global_batch_idx: 66500, batch size: 48, loss[discriminator_loss=2.719, discriminator_real_loss=1.447, discriminator_fake_loss=1.272, generator_loss=28.75, generator_mel_loss=18.13, generator_kl_loss=1.557, generator_dur_loss=1.625, generator_adv_loss=1.822, generator_feat_match_loss=5.615, over 48.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.372, discriminator_fake_loss=1.335, generator_loss=28.12, generator_mel_loss=17.86, generator_kl_loss=1.389, generator_dur_loss=1.761, generator_adv_loss=1.95, generator_feat_match_loss=5.155, over 2255.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:14:06,694 INFO [train.py:527] (4/6) Epoch 537, batch 86, global_batch_idx: 66550, batch size: 44, loss[discriminator_loss=2.772, discriminator_real_loss=1.29, discriminator_fake_loss=1.481, generator_loss=29.2, generator_mel_loss=17.85, generator_kl_loss=1.771, generator_dur_loss=1.691, generator_adv_loss=2.017, generator_feat_match_loss=5.863, over 44.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=28.2, generator_mel_loss=17.96, generator_kl_loss=1.415, generator_dur_loss=1.749, generator_adv_loss=1.953, generator_feat_match_loss=5.118, over 5008.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:15:54,076 INFO [train.py:919] (4/6) Start epoch 538 +2024-03-14 07:16:52,565 INFO [train.py:527] (4/6) Epoch 538, batch 12, global_batch_idx: 66600, batch size: 83, loss[discriminator_loss=2.674, discriminator_real_loss=1.361, discriminator_fake_loss=1.313, generator_loss=28.32, generator_mel_loss=18.43, generator_kl_loss=1.38, generator_dur_loss=1.814, generator_adv_loss=1.814, generator_feat_match_loss=4.889, over 83.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.372, discriminator_fake_loss=1.337, generator_loss=28.27, generator_mel_loss=18.07, generator_kl_loss=1.397, generator_dur_loss=1.737, generator_adv_loss=1.94, generator_feat_match_loss=5.126, over 806.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:16:52,568 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 07:17:00,243 INFO [train.py:591] (4/6) Epoch 538, validation: discriminator_loss=2.742, discriminator_real_loss=1.367, discriminator_fake_loss=1.375, generator_loss=27.95, generator_mel_loss=18.66, generator_kl_loss=1.243, generator_dur_loss=1.795, generator_adv_loss=1.757, generator_feat_match_loss=4.496, over 100.00 samples. +2024-03-14 07:17:00,244 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 07:19:21,648 INFO [train.py:527] (4/6) Epoch 538, batch 62, global_batch_idx: 66650, batch size: 39, loss[discriminator_loss=2.783, discriminator_real_loss=1.313, discriminator_fake_loss=1.47, generator_loss=27.85, generator_mel_loss=18.15, generator_kl_loss=1.439, generator_dur_loss=1.728, generator_adv_loss=1.892, generator_feat_match_loss=4.642, over 39.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.365, discriminator_fake_loss=1.349, generator_loss=28.28, generator_mel_loss=18.02, generator_kl_loss=1.415, generator_dur_loss=1.727, generator_adv_loss=1.954, generator_feat_match_loss=5.159, over 3881.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:21:42,607 INFO [train.py:527] (4/6) Epoch 538, batch 112, global_batch_idx: 66700, batch size: 45, loss[discriminator_loss=2.74, discriminator_real_loss=1.347, discriminator_fake_loss=1.393, generator_loss=27.89, generator_mel_loss=17.81, generator_kl_loss=1.62, generator_dur_loss=1.669, generator_adv_loss=1.992, generator_feat_match_loss=4.804, over 45.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.366, discriminator_fake_loss=1.347, generator_loss=28.29, generator_mel_loss=18.04, generator_kl_loss=1.423, generator_dur_loss=1.72, generator_adv_loss=1.953, generator_feat_match_loss=5.15, over 6501.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:22:15,603 INFO [train.py:919] (4/6) Start epoch 539 +2024-03-14 07:24:30,658 INFO [train.py:527] (4/6) Epoch 539, batch 38, global_batch_idx: 66750, batch size: 52, loss[discriminator_loss=2.648, discriminator_real_loss=1.382, discriminator_fake_loss=1.267, generator_loss=28.04, generator_mel_loss=17.91, generator_kl_loss=1.488, generator_dur_loss=1.692, generator_adv_loss=1.92, generator_feat_match_loss=5.028, over 52.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.368, discriminator_fake_loss=1.351, generator_loss=28.34, generator_mel_loss=18.07, generator_kl_loss=1.4, generator_dur_loss=1.763, generator_adv_loss=1.966, generator_feat_match_loss=5.136, over 2428.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:26:51,960 INFO [train.py:527] (4/6) Epoch 539, batch 88, global_batch_idx: 66800, batch size: 53, loss[discriminator_loss=2.707, discriminator_real_loss=1.422, discriminator_fake_loss=1.285, generator_loss=28.01, generator_mel_loss=17.72, generator_kl_loss=1.254, generator_dur_loss=1.692, generator_adv_loss=2.09, generator_feat_match_loss=5.251, over 53.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.339, generator_loss=28.3, generator_mel_loss=18, generator_kl_loss=1.389, generator_dur_loss=1.758, generator_adv_loss=1.967, generator_feat_match_loss=5.187, over 5412.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:26:51,962 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 07:27:00,805 INFO [train.py:591] (4/6) Epoch 539, validation: discriminator_loss=2.707, discriminator_real_loss=1.441, discriminator_fake_loss=1.266, generator_loss=27.6, generator_mel_loss=18.11, generator_kl_loss=1.223, generator_dur_loss=1.811, generator_adv_loss=1.981, generator_feat_match_loss=4.481, over 100.00 samples. +2024-03-14 07:27:00,806 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 07:28:39,483 INFO [train.py:919] (4/6) Start epoch 540 +2024-03-14 07:29:44,246 INFO [train.py:527] (4/6) Epoch 540, batch 14, global_batch_idx: 66850, batch size: 58, loss[discriminator_loss=2.718, discriminator_real_loss=1.444, discriminator_fake_loss=1.274, generator_loss=27.61, generator_mel_loss=17.62, generator_kl_loss=1.375, generator_dur_loss=1.714, generator_adv_loss=1.828, generator_feat_match_loss=5.077, over 58.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.378, discriminator_fake_loss=1.322, generator_loss=28.12, generator_mel_loss=17.91, generator_kl_loss=1.481, generator_dur_loss=1.712, generator_adv_loss=1.961, generator_feat_match_loss=5.059, over 745.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:32:07,911 INFO [train.py:527] (4/6) Epoch 540, batch 64, global_batch_idx: 66900, batch size: 39, loss[discriminator_loss=2.724, discriminator_real_loss=1.408, discriminator_fake_loss=1.316, generator_loss=28.9, generator_mel_loss=18.43, generator_kl_loss=1.44, generator_dur_loss=1.682, generator_adv_loss=2.051, generator_feat_match_loss=5.297, over 39.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.29, generator_mel_loss=17.99, generator_kl_loss=1.433, generator_dur_loss=1.746, generator_adv_loss=1.956, generator_feat_match_loss=5.17, over 3633.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:34:28,361 INFO [train.py:527] (4/6) Epoch 540, batch 114, global_batch_idx: 66950, batch size: 45, loss[discriminator_loss=2.669, discriminator_real_loss=1.336, discriminator_fake_loss=1.333, generator_loss=28, generator_mel_loss=17.67, generator_kl_loss=1.453, generator_dur_loss=1.645, generator_adv_loss=1.867, generator_feat_match_loss=5.363, over 45.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.363, discriminator_fake_loss=1.336, generator_loss=28.33, generator_mel_loss=18, generator_kl_loss=1.436, generator_dur_loss=1.75, generator_adv_loss=1.971, generator_feat_match_loss=5.18, over 6358.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:34:52,675 INFO [train.py:919] (4/6) Start epoch 541 +2024-03-14 07:37:09,347 INFO [train.py:527] (4/6) Epoch 541, batch 40, global_batch_idx: 67000, batch size: 68, loss[discriminator_loss=2.706, discriminator_real_loss=1.354, discriminator_fake_loss=1.352, generator_loss=27.87, generator_mel_loss=17.81, generator_kl_loss=1.383, generator_dur_loss=1.764, generator_adv_loss=1.948, generator_feat_match_loss=4.965, over 68.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.383, discriminator_fake_loss=1.333, generator_loss=28.25, generator_mel_loss=17.95, generator_kl_loss=1.408, generator_dur_loss=1.742, generator_adv_loss=1.963, generator_feat_match_loss=5.187, over 2339.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:37:09,349 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 07:37:17,460 INFO [train.py:591] (4/6) Epoch 541, validation: discriminator_loss=2.687, discriminator_real_loss=1.429, discriminator_fake_loss=1.258, generator_loss=26.9, generator_mel_loss=18, generator_kl_loss=1.238, generator_dur_loss=1.771, generator_adv_loss=1.944, generator_feat_match_loss=3.941, over 100.00 samples. +2024-03-14 07:37:17,461 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 07:39:39,326 INFO [train.py:527] (4/6) Epoch 541, batch 90, global_batch_idx: 67050, batch size: 52, loss[discriminator_loss=2.739, discriminator_real_loss=1.397, discriminator_fake_loss=1.342, generator_loss=28.22, generator_mel_loss=18.11, generator_kl_loss=1.452, generator_dur_loss=1.649, generator_adv_loss=1.894, generator_feat_match_loss=5.116, over 52.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.374, discriminator_fake_loss=1.338, generator_loss=28.25, generator_mel_loss=17.94, generator_kl_loss=1.412, generator_dur_loss=1.731, generator_adv_loss=1.957, generator_feat_match_loss=5.208, over 5117.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:41:13,444 INFO [train.py:919] (4/6) Start epoch 542 +2024-03-14 07:42:24,576 INFO [train.py:527] (4/6) Epoch 542, batch 16, global_batch_idx: 67100, batch size: 68, loss[discriminator_loss=2.711, discriminator_real_loss=1.307, discriminator_fake_loss=1.404, generator_loss=28.86, generator_mel_loss=18.31, generator_kl_loss=1.432, generator_dur_loss=1.712, generator_adv_loss=2.062, generator_feat_match_loss=5.345, over 68.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.375, discriminator_fake_loss=1.343, generator_loss=28.2, generator_mel_loss=18.05, generator_kl_loss=1.404, generator_dur_loss=1.722, generator_adv_loss=1.957, generator_feat_match_loss=5.065, over 991.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:44:46,027 INFO [train.py:527] (4/6) Epoch 542, batch 66, global_batch_idx: 67150, batch size: 39, loss[discriminator_loss=2.807, discriminator_real_loss=1.413, discriminator_fake_loss=1.395, generator_loss=26.34, generator_mel_loss=17.46, generator_kl_loss=1.44, generator_dur_loss=1.741, generator_adv_loss=1.921, generator_feat_match_loss=3.775, over 39.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.374, discriminator_fake_loss=1.35, generator_loss=28.19, generator_mel_loss=18, generator_kl_loss=1.43, generator_dur_loss=1.724, generator_adv_loss=1.951, generator_feat_match_loss=5.085, over 3871.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:47:07,924 INFO [train.py:527] (4/6) Epoch 542, batch 116, global_batch_idx: 67200, batch size: 62, loss[discriminator_loss=2.708, discriminator_real_loss=1.345, discriminator_fake_loss=1.363, generator_loss=26.94, generator_mel_loss=17.59, generator_kl_loss=1.429, generator_dur_loss=1.738, generator_adv_loss=1.963, generator_feat_match_loss=4.224, over 62.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=28.22, generator_mel_loss=18, generator_kl_loss=1.425, generator_dur_loss=1.73, generator_adv_loss=1.963, generator_feat_match_loss=5.102, over 6758.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:47:07,925 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 07:47:16,875 INFO [train.py:591] (4/6) Epoch 542, validation: discriminator_loss=2.756, discriminator_real_loss=1.447, discriminator_fake_loss=1.308, generator_loss=26.58, generator_mel_loss=17.74, generator_kl_loss=1.208, generator_dur_loss=1.803, generator_adv_loss=1.908, generator_feat_match_loss=3.927, over 100.00 samples. +2024-03-14 07:47:16,876 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 07:47:39,026 INFO [train.py:919] (4/6) Start epoch 543 +2024-03-14 07:50:02,772 INFO [train.py:527] (4/6) Epoch 543, batch 42, global_batch_idx: 67250, batch size: 45, loss[discriminator_loss=2.721, discriminator_real_loss=1.389, discriminator_fake_loss=1.332, generator_loss=28.12, generator_mel_loss=17.95, generator_kl_loss=1.538, generator_dur_loss=1.719, generator_adv_loss=1.975, generator_feat_match_loss=4.941, over 45.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.368, discriminator_fake_loss=1.348, generator_loss=28.21, generator_mel_loss=17.94, generator_kl_loss=1.434, generator_dur_loss=1.745, generator_adv_loss=1.952, generator_feat_match_loss=5.145, over 2474.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:52:24,892 INFO [train.py:527] (4/6) Epoch 543, batch 92, global_batch_idx: 67300, batch size: 50, loss[discriminator_loss=2.73, discriminator_real_loss=1.353, discriminator_fake_loss=1.377, generator_loss=27.78, generator_mel_loss=18.04, generator_kl_loss=1.419, generator_dur_loss=1.669, generator_adv_loss=1.921, generator_feat_match_loss=4.731, over 50.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.372, discriminator_fake_loss=1.346, generator_loss=28.24, generator_mel_loss=17.98, generator_kl_loss=1.433, generator_dur_loss=1.741, generator_adv_loss=1.952, generator_feat_match_loss=5.133, over 5213.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:53:50,990 INFO [train.py:919] (4/6) Start epoch 544 +2024-03-14 07:55:06,283 INFO [train.py:527] (4/6) Epoch 544, batch 18, global_batch_idx: 67350, batch size: 15, loss[discriminator_loss=2.675, discriminator_real_loss=1.302, discriminator_fake_loss=1.373, generator_loss=30.45, generator_mel_loss=19.24, generator_kl_loss=1.715, generator_dur_loss=1.65, generator_adv_loss=2.093, generator_feat_match_loss=5.749, over 15.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.383, discriminator_fake_loss=1.341, generator_loss=28.55, generator_mel_loss=18.11, generator_kl_loss=1.36, generator_dur_loss=1.758, generator_adv_loss=1.981, generator_feat_match_loss=5.34, over 1095.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:57:29,521 INFO [train.py:527] (4/6) Epoch 544, batch 68, global_batch_idx: 67400, batch size: 88, loss[discriminator_loss=2.678, discriminator_real_loss=1.266, discriminator_fake_loss=1.412, generator_loss=27.63, generator_mel_loss=17.7, generator_kl_loss=1.239, generator_dur_loss=1.894, generator_adv_loss=1.989, generator_feat_match_loss=4.81, over 88.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.367, discriminator_fake_loss=1.34, generator_loss=28.33, generator_mel_loss=18, generator_kl_loss=1.403, generator_dur_loss=1.757, generator_adv_loss=1.987, generator_feat_match_loss=5.191, over 3972.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:57:29,522 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 07:57:37,700 INFO [train.py:591] (4/6) Epoch 544, validation: discriminator_loss=2.705, discriminator_real_loss=1.46, discriminator_fake_loss=1.245, generator_loss=26.94, generator_mel_loss=18.07, generator_kl_loss=1.162, generator_dur_loss=1.826, generator_adv_loss=1.986, generator_feat_match_loss=3.896, over 100.00 samples. +2024-03-14 07:57:37,702 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 07:59:57,194 INFO [train.py:527] (4/6) Epoch 544, batch 118, global_batch_idx: 67450, batch size: 72, loss[discriminator_loss=2.647, discriminator_real_loss=1.341, discriminator_fake_loss=1.306, generator_loss=29.35, generator_mel_loss=18.05, generator_kl_loss=1.469, generator_dur_loss=1.777, generator_adv_loss=2.026, generator_feat_match_loss=6.022, over 72.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.368, discriminator_fake_loss=1.335, generator_loss=28.29, generator_mel_loss=17.96, generator_kl_loss=1.418, generator_dur_loss=1.759, generator_adv_loss=1.981, generator_feat_match_loss=5.173, over 6833.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:00:12,409 INFO [train.py:919] (4/6) Start epoch 545 +2024-03-14 08:02:42,087 INFO [train.py:527] (4/6) Epoch 545, batch 44, global_batch_idx: 67500, batch size: 42, loss[discriminator_loss=2.615, discriminator_real_loss=1.293, discriminator_fake_loss=1.322, generator_loss=28.54, generator_mel_loss=18.18, generator_kl_loss=1.618, generator_dur_loss=1.692, generator_adv_loss=1.856, generator_feat_match_loss=5.194, over 42.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.3, generator_mel_loss=18.03, generator_kl_loss=1.403, generator_dur_loss=1.755, generator_adv_loss=1.962, generator_feat_match_loss=5.157, over 2639.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:05:03,566 INFO [train.py:527] (4/6) Epoch 545, batch 94, global_batch_idx: 67550, batch size: 25, loss[discriminator_loss=2.65, discriminator_real_loss=1.373, discriminator_fake_loss=1.277, generator_loss=30.45, generator_mel_loss=18.62, generator_kl_loss=1.664, generator_dur_loss=1.529, generator_adv_loss=2.065, generator_feat_match_loss=6.573, over 25.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.34, generator_mel_loss=18.03, generator_kl_loss=1.408, generator_dur_loss=1.747, generator_adv_loss=1.961, generator_feat_match_loss=5.198, over 5550.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:06:25,803 INFO [train.py:919] (4/6) Start epoch 546 +2024-03-14 08:07:45,776 INFO [train.py:527] (4/6) Epoch 546, batch 20, global_batch_idx: 67600, batch size: 48, loss[discriminator_loss=2.761, discriminator_real_loss=1.356, discriminator_fake_loss=1.405, generator_loss=27.96, generator_mel_loss=17.8, generator_kl_loss=1.427, generator_dur_loss=1.677, generator_adv_loss=1.915, generator_feat_match_loss=5.14, over 48.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.386, discriminator_fake_loss=1.334, generator_loss=28.21, generator_mel_loss=17.96, generator_kl_loss=1.448, generator_dur_loss=1.751, generator_adv_loss=1.97, generator_feat_match_loss=5.078, over 1154.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:07:45,778 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 08:07:53,539 INFO [train.py:591] (4/6) Epoch 546, validation: discriminator_loss=2.74, discriminator_real_loss=1.402, discriminator_fake_loss=1.338, generator_loss=27.3, generator_mel_loss=18.33, generator_kl_loss=1.175, generator_dur_loss=1.816, generator_adv_loss=1.849, generator_feat_match_loss=4.129, over 100.00 samples. +2024-03-14 08:07:53,540 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 08:10:17,442 INFO [train.py:527] (4/6) Epoch 546, batch 70, global_batch_idx: 67650, batch size: 53, loss[discriminator_loss=2.606, discriminator_real_loss=1.239, discriminator_fake_loss=1.366, generator_loss=28.89, generator_mel_loss=18.3, generator_kl_loss=1.511, generator_dur_loss=1.725, generator_adv_loss=2.133, generator_feat_match_loss=5.221, over 53.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.344, generator_loss=28.24, generator_mel_loss=18, generator_kl_loss=1.422, generator_dur_loss=1.753, generator_adv_loss=1.955, generator_feat_match_loss=5.113, over 3993.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:12:33,726 INFO [train.py:527] (4/6) Epoch 546, batch 120, global_batch_idx: 67700, batch size: 77, loss[discriminator_loss=2.71, discriminator_real_loss=1.424, discriminator_fake_loss=1.286, generator_loss=27.73, generator_mel_loss=17.74, generator_kl_loss=1.249, generator_dur_loss=1.834, generator_adv_loss=1.924, generator_feat_match_loss=4.984, over 77.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.376, discriminator_fake_loss=1.342, generator_loss=28.3, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.75, generator_adv_loss=1.959, generator_feat_match_loss=5.163, over 6749.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:12:44,183 INFO [train.py:919] (4/6) Start epoch 547 +2024-03-14 08:15:20,992 INFO [train.py:527] (4/6) Epoch 547, batch 46, global_batch_idx: 67750, batch size: 88, loss[discriminator_loss=2.674, discriminator_real_loss=1.333, discriminator_fake_loss=1.341, generator_loss=28.72, generator_mel_loss=18.1, generator_kl_loss=1.338, generator_dur_loss=1.804, generator_adv_loss=2.043, generator_feat_match_loss=5.429, over 88.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.367, discriminator_fake_loss=1.338, generator_loss=28.48, generator_mel_loss=18.1, generator_kl_loss=1.427, generator_dur_loss=1.754, generator_adv_loss=1.976, generator_feat_match_loss=5.229, over 2811.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:17:44,059 INFO [train.py:527] (4/6) Epoch 547, batch 96, global_batch_idx: 67800, batch size: 77, loss[discriminator_loss=2.692, discriminator_real_loss=1.291, discriminator_fake_loss=1.401, generator_loss=28.39, generator_mel_loss=18, generator_kl_loss=1.493, generator_dur_loss=1.791, generator_adv_loss=1.942, generator_feat_match_loss=5.167, over 77.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=28.31, generator_mel_loss=18.01, generator_kl_loss=1.428, generator_dur_loss=1.744, generator_adv_loss=1.963, generator_feat_match_loss=5.164, over 5677.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:17:44,061 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 08:17:52,790 INFO [train.py:591] (4/6) Epoch 547, validation: discriminator_loss=2.694, discriminator_real_loss=1.453, discriminator_fake_loss=1.242, generator_loss=27.37, generator_mel_loss=18.18, generator_kl_loss=1.259, generator_dur_loss=1.805, generator_adv_loss=1.966, generator_feat_match_loss=4.164, over 100.00 samples. +2024-03-14 08:17:52,792 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 08:19:06,963 INFO [train.py:919] (4/6) Start epoch 548 +2024-03-14 08:20:34,432 INFO [train.py:527] (4/6) Epoch 548, batch 22, global_batch_idx: 67850, batch size: 53, loss[discriminator_loss=2.645, discriminator_real_loss=1.326, discriminator_fake_loss=1.318, generator_loss=29.58, generator_mel_loss=18.81, generator_kl_loss=1.42, generator_dur_loss=1.608, generator_adv_loss=2.06, generator_feat_match_loss=5.677, over 53.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.368, discriminator_fake_loss=1.33, generator_loss=28.4, generator_mel_loss=17.98, generator_kl_loss=1.427, generator_dur_loss=1.718, generator_adv_loss=1.975, generator_feat_match_loss=5.292, over 1294.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:22:55,352 INFO [train.py:527] (4/6) Epoch 548, batch 72, global_batch_idx: 67900, batch size: 47, loss[discriminator_loss=2.753, discriminator_real_loss=1.325, discriminator_fake_loss=1.427, generator_loss=28.85, generator_mel_loss=18.55, generator_kl_loss=1.502, generator_dur_loss=1.702, generator_adv_loss=1.97, generator_feat_match_loss=5.12, over 47.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.367, discriminator_fake_loss=1.34, generator_loss=28.3, generator_mel_loss=18, generator_kl_loss=1.425, generator_dur_loss=1.726, generator_adv_loss=1.965, generator_feat_match_loss=5.181, over 4079.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:25:18,942 INFO [train.py:527] (4/6) Epoch 548, batch 122, global_batch_idx: 67950, batch size: 53, loss[discriminator_loss=2.806, discriminator_real_loss=1.421, discriminator_fake_loss=1.385, generator_loss=28.26, generator_mel_loss=17.65, generator_kl_loss=1.556, generator_dur_loss=1.676, generator_adv_loss=1.905, generator_feat_match_loss=5.474, over 53.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.366, discriminator_fake_loss=1.343, generator_loss=28.3, generator_mel_loss=17.99, generator_kl_loss=1.432, generator_dur_loss=1.724, generator_adv_loss=1.959, generator_feat_match_loss=5.193, over 7055.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:25:23,501 INFO [train.py:919] (4/6) Start epoch 549 +2024-03-14 08:28:01,981 INFO [train.py:527] (4/6) Epoch 549, batch 48, global_batch_idx: 68000, batch size: 55, loss[discriminator_loss=2.698, discriminator_real_loss=1.415, discriminator_fake_loss=1.283, generator_loss=28.82, generator_mel_loss=18.28, generator_kl_loss=1.323, generator_dur_loss=1.763, generator_adv_loss=1.91, generator_feat_match_loss=5.545, over 55.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.39, generator_mel_loss=18.12, generator_kl_loss=1.393, generator_dur_loss=1.735, generator_adv_loss=1.955, generator_feat_match_loss=5.185, over 2746.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:28:01,982 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 08:28:10,815 INFO [train.py:591] (4/6) Epoch 549, validation: discriminator_loss=2.754, discriminator_real_loss=1.367, discriminator_fake_loss=1.386, generator_loss=27.6, generator_mel_loss=18.45, generator_kl_loss=1.279, generator_dur_loss=1.814, generator_adv_loss=1.831, generator_feat_match_loss=4.225, over 100.00 samples. +2024-03-14 08:28:10,817 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 08:30:31,619 INFO [train.py:527] (4/6) Epoch 549, batch 98, global_batch_idx: 68050, batch size: 39, loss[discriminator_loss=2.685, discriminator_real_loss=1.337, discriminator_fake_loss=1.348, generator_loss=28.63, generator_mel_loss=17.82, generator_kl_loss=1.436, generator_dur_loss=1.699, generator_adv_loss=2.19, generator_feat_match_loss=5.484, over 39.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.367, discriminator_fake_loss=1.34, generator_loss=28.48, generator_mel_loss=18.08, generator_kl_loss=1.415, generator_dur_loss=1.74, generator_adv_loss=1.979, generator_feat_match_loss=5.267, over 5478.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:31:43,436 INFO [train.py:919] (4/6) Start epoch 550 +2024-03-14 08:33:15,742 INFO [train.py:527] (4/6) Epoch 550, batch 24, global_batch_idx: 68100, batch size: 62, loss[discriminator_loss=2.697, discriminator_real_loss=1.347, discriminator_fake_loss=1.35, generator_loss=27.87, generator_mel_loss=18.03, generator_kl_loss=1.461, generator_dur_loss=1.73, generator_adv_loss=1.958, generator_feat_match_loss=4.689, over 62.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.379, discriminator_fake_loss=1.326, generator_loss=28.26, generator_mel_loss=18.05, generator_kl_loss=1.423, generator_dur_loss=1.744, generator_adv_loss=1.962, generator_feat_match_loss=5.082, over 1317.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:35:33,980 INFO [train.py:527] (4/6) Epoch 550, batch 74, global_batch_idx: 68150, batch size: 48, loss[discriminator_loss=2.656, discriminator_real_loss=1.363, discriminator_fake_loss=1.292, generator_loss=29.55, generator_mel_loss=18.42, generator_kl_loss=1.491, generator_dur_loss=1.686, generator_adv_loss=2.086, generator_feat_match_loss=5.869, over 48.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.378, discriminator_fake_loss=1.331, generator_loss=28.27, generator_mel_loss=18, generator_kl_loss=1.414, generator_dur_loss=1.749, generator_adv_loss=1.96, generator_feat_match_loss=5.153, over 4176.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:37:55,103 INFO [train.py:919] (4/6) Start epoch 551 +2024-03-14 08:38:18,889 INFO [train.py:527] (4/6) Epoch 551, batch 0, global_batch_idx: 68200, batch size: 39, loss[discriminator_loss=2.787, discriminator_real_loss=1.391, discriminator_fake_loss=1.396, generator_loss=29.35, generator_mel_loss=18.33, generator_kl_loss=1.601, generator_dur_loss=1.697, generator_adv_loss=1.906, generator_feat_match_loss=5.815, over 39.00 samples.], tot_loss[discriminator_loss=2.787, discriminator_real_loss=1.391, discriminator_fake_loss=1.396, generator_loss=29.35, generator_mel_loss=18.33, generator_kl_loss=1.601, generator_dur_loss=1.697, generator_adv_loss=1.906, generator_feat_match_loss=5.815, over 39.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:38:18,892 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 08:38:26,773 INFO [train.py:591] (4/6) Epoch 551, validation: discriminator_loss=2.758, discriminator_real_loss=1.493, discriminator_fake_loss=1.265, generator_loss=27.76, generator_mel_loss=18.48, generator_kl_loss=1.211, generator_dur_loss=1.817, generator_adv_loss=1.948, generator_feat_match_loss=4.306, over 100.00 samples. +2024-03-14 08:38:26,775 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 08:40:50,347 INFO [train.py:527] (4/6) Epoch 551, batch 50, global_batch_idx: 68250, batch size: 83, loss[discriminator_loss=2.691, discriminator_real_loss=1.354, discriminator_fake_loss=1.337, generator_loss=27.94, generator_mel_loss=17.64, generator_kl_loss=1.507, generator_dur_loss=1.808, generator_adv_loss=1.993, generator_feat_match_loss=4.991, over 83.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.36, discriminator_fake_loss=1.34, generator_loss=28.52, generator_mel_loss=18.03, generator_kl_loss=1.434, generator_dur_loss=1.764, generator_adv_loss=1.977, generator_feat_match_loss=5.318, over 2855.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:43:11,403 INFO [train.py:527] (4/6) Epoch 551, batch 100, global_batch_idx: 68300, batch size: 74, loss[discriminator_loss=2.666, discriminator_real_loss=1.405, discriminator_fake_loss=1.261, generator_loss=28.04, generator_mel_loss=17.68, generator_kl_loss=1.35, generator_dur_loss=1.808, generator_adv_loss=1.833, generator_feat_match_loss=5.37, over 74.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.366, discriminator_fake_loss=1.336, generator_loss=28.38, generator_mel_loss=17.99, generator_kl_loss=1.427, generator_dur_loss=1.752, generator_adv_loss=1.972, generator_feat_match_loss=5.246, over 5725.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:44:14,749 INFO [train.py:919] (4/6) Start epoch 552 +2024-03-14 08:45:53,543 INFO [train.py:527] (4/6) Epoch 552, batch 26, global_batch_idx: 68350, batch size: 47, loss[discriminator_loss=2.66, discriminator_real_loss=1.23, discriminator_fake_loss=1.431, generator_loss=29.1, generator_mel_loss=18.37, generator_kl_loss=1.364, generator_dur_loss=1.722, generator_adv_loss=1.913, generator_feat_match_loss=5.732, over 47.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.36, discriminator_fake_loss=1.338, generator_loss=28.28, generator_mel_loss=17.95, generator_kl_loss=1.413, generator_dur_loss=1.741, generator_adv_loss=1.965, generator_feat_match_loss=5.209, over 1560.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:48:15,143 INFO [train.py:527] (4/6) Epoch 552, batch 76, global_batch_idx: 68400, batch size: 55, loss[discriminator_loss=2.685, discriminator_real_loss=1.422, discriminator_fake_loss=1.263, generator_loss=28.75, generator_mel_loss=18.18, generator_kl_loss=1.553, generator_dur_loss=1.741, generator_adv_loss=1.986, generator_feat_match_loss=5.29, over 55.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.338, generator_loss=28.27, generator_mel_loss=17.97, generator_kl_loss=1.414, generator_dur_loss=1.741, generator_adv_loss=1.968, generator_feat_match_loss=5.174, over 4334.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:48:15,144 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 08:48:23,803 INFO [train.py:591] (4/6) Epoch 552, validation: discriminator_loss=2.71, discriminator_real_loss=1.453, discriminator_fake_loss=1.257, generator_loss=27.34, generator_mel_loss=18.36, generator_kl_loss=1.226, generator_dur_loss=1.809, generator_adv_loss=1.958, generator_feat_match_loss=3.99, over 100.00 samples. +2024-03-14 08:48:23,804 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 08:50:33,747 INFO [train.py:919] (4/6) Start epoch 553 +2024-03-14 08:51:04,257 INFO [train.py:527] (4/6) Epoch 553, batch 2, global_batch_idx: 68450, batch size: 55, loss[discriminator_loss=2.724, discriminator_real_loss=1.37, discriminator_fake_loss=1.354, generator_loss=28.48, generator_mel_loss=18.23, generator_kl_loss=1.561, generator_dur_loss=1.68, generator_adv_loss=1.953, generator_feat_match_loss=5.061, over 55.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.319, discriminator_fake_loss=1.436, generator_loss=28.11, generator_mel_loss=18.11, generator_kl_loss=1.426, generator_dur_loss=1.715, generator_adv_loss=1.958, generator_feat_match_loss=4.898, over 171.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:53:26,845 INFO [train.py:527] (4/6) Epoch 553, batch 52, global_batch_idx: 68500, batch size: 45, loss[discriminator_loss=2.783, discriminator_real_loss=1.454, discriminator_fake_loss=1.329, generator_loss=28.39, generator_mel_loss=18.01, generator_kl_loss=1.5, generator_dur_loss=1.649, generator_adv_loss=2.015, generator_feat_match_loss=5.215, over 45.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.363, discriminator_fake_loss=1.342, generator_loss=28.24, generator_mel_loss=17.94, generator_kl_loss=1.436, generator_dur_loss=1.731, generator_adv_loss=1.966, generator_feat_match_loss=5.165, over 2872.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:55:46,702 INFO [train.py:527] (4/6) Epoch 553, batch 102, global_batch_idx: 68550, batch size: 48, loss[discriminator_loss=2.703, discriminator_real_loss=1.374, discriminator_fake_loss=1.329, generator_loss=28.61, generator_mel_loss=18.25, generator_kl_loss=1.35, generator_dur_loss=1.706, generator_adv_loss=1.927, generator_feat_match_loss=5.377, over 48.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.28, generator_mel_loss=17.94, generator_kl_loss=1.439, generator_dur_loss=1.737, generator_adv_loss=1.958, generator_feat_match_loss=5.197, over 5626.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:56:48,009 INFO [train.py:919] (4/6) Start epoch 554 +2024-03-14 08:58:31,894 INFO [train.py:527] (4/6) Epoch 554, batch 28, global_batch_idx: 68600, batch size: 80, loss[discriminator_loss=2.744, discriminator_real_loss=1.429, discriminator_fake_loss=1.316, generator_loss=28.13, generator_mel_loss=18.24, generator_kl_loss=1.344, generator_dur_loss=1.83, generator_adv_loss=1.806, generator_feat_match_loss=4.903, over 80.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.367, discriminator_fake_loss=1.352, generator_loss=28.16, generator_mel_loss=17.88, generator_kl_loss=1.415, generator_dur_loss=1.748, generator_adv_loss=1.951, generator_feat_match_loss=5.164, over 1703.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:58:31,896 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 08:58:40,019 INFO [train.py:591] (4/6) Epoch 554, validation: discriminator_loss=2.799, discriminator_real_loss=1.363, discriminator_fake_loss=1.436, generator_loss=26.83, generator_mel_loss=18.15, generator_kl_loss=1.3, generator_dur_loss=1.813, generator_adv_loss=1.742, generator_feat_match_loss=3.828, over 100.00 samples. +2024-03-14 08:58:40,034 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 09:00:58,371 INFO [train.py:527] (4/6) Epoch 554, batch 78, global_batch_idx: 68650, batch size: 66, loss[discriminator_loss=2.708, discriminator_real_loss=1.393, discriminator_fake_loss=1.315, generator_loss=27.43, generator_mel_loss=17.9, generator_kl_loss=1.376, generator_dur_loss=1.733, generator_adv_loss=1.908, generator_feat_match_loss=4.505, over 66.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.366, discriminator_fake_loss=1.346, generator_loss=28.26, generator_mel_loss=17.96, generator_kl_loss=1.416, generator_dur_loss=1.752, generator_adv_loss=1.952, generator_feat_match_loss=5.184, over 4541.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:03:08,253 INFO [train.py:919] (4/6) Start epoch 555 +2024-03-14 09:03:43,893 INFO [train.py:527] (4/6) Epoch 555, batch 4, global_batch_idx: 68700, batch size: 45, loss[discriminator_loss=2.651, discriminator_real_loss=1.357, discriminator_fake_loss=1.294, generator_loss=29.62, generator_mel_loss=18.46, generator_kl_loss=1.594, generator_dur_loss=1.63, generator_adv_loss=2.131, generator_feat_match_loss=5.81, over 45.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.378, discriminator_fake_loss=1.312, generator_loss=28.44, generator_mel_loss=18.05, generator_kl_loss=1.355, generator_dur_loss=1.713, generator_adv_loss=1.995, generator_feat_match_loss=5.325, over 277.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:06:07,491 INFO [train.py:527] (4/6) Epoch 555, batch 54, global_batch_idx: 68750, batch size: 52, loss[discriminator_loss=2.755, discriminator_real_loss=1.417, discriminator_fake_loss=1.338, generator_loss=26.84, generator_mel_loss=17.32, generator_kl_loss=1.393, generator_dur_loss=1.695, generator_adv_loss=1.962, generator_feat_match_loss=4.467, over 52.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.381, discriminator_fake_loss=1.332, generator_loss=28.13, generator_mel_loss=17.92, generator_kl_loss=1.391, generator_dur_loss=1.733, generator_adv_loss=1.971, generator_feat_match_loss=5.114, over 3108.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:08:29,731 INFO [train.py:527] (4/6) Epoch 555, batch 104, global_batch_idx: 68800, batch size: 48, loss[discriminator_loss=2.74, discriminator_real_loss=1.402, discriminator_fake_loss=1.338, generator_loss=29.27, generator_mel_loss=18.22, generator_kl_loss=1.595, generator_dur_loss=1.663, generator_adv_loss=1.849, generator_feat_match_loss=5.939, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.376, discriminator_fake_loss=1.336, generator_loss=28.16, generator_mel_loss=17.94, generator_kl_loss=1.389, generator_dur_loss=1.743, generator_adv_loss=1.957, generator_feat_match_loss=5.131, over 5975.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:08:29,732 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 09:08:38,552 INFO [train.py:591] (4/6) Epoch 555, validation: discriminator_loss=2.751, discriminator_real_loss=1.348, discriminator_fake_loss=1.403, generator_loss=27.21, generator_mel_loss=18.31, generator_kl_loss=1.253, generator_dur_loss=1.809, generator_adv_loss=1.761, generator_feat_match_loss=4.076, over 100.00 samples. +2024-03-14 09:08:38,553 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 09:09:32,514 INFO [train.py:919] (4/6) Start epoch 556 +2024-03-14 09:11:22,868 INFO [train.py:527] (4/6) Epoch 556, batch 30, global_batch_idx: 68850, batch size: 80, loss[discriminator_loss=2.729, discriminator_real_loss=1.423, discriminator_fake_loss=1.306, generator_loss=27.81, generator_mel_loss=17.79, generator_kl_loss=1.314, generator_dur_loss=1.806, generator_adv_loss=1.99, generator_feat_match_loss=4.912, over 80.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.352, discriminator_fake_loss=1.33, generator_loss=28.52, generator_mel_loss=17.94, generator_kl_loss=1.438, generator_dur_loss=1.762, generator_adv_loss=2.023, generator_feat_match_loss=5.357, over 1758.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:13:42,360 INFO [train.py:527] (4/6) Epoch 556, batch 80, global_batch_idx: 68900, batch size: 62, loss[discriminator_loss=2.661, discriminator_real_loss=1.275, discriminator_fake_loss=1.385, generator_loss=28.51, generator_mel_loss=17.99, generator_kl_loss=1.349, generator_dur_loss=1.708, generator_adv_loss=1.978, generator_feat_match_loss=5.488, over 62.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.36, discriminator_fake_loss=1.343, generator_loss=28.43, generator_mel_loss=18.02, generator_kl_loss=1.42, generator_dur_loss=1.758, generator_adv_loss=1.986, generator_feat_match_loss=5.243, over 4555.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:15:44,194 INFO [train.py:919] (4/6) Start epoch 557 +2024-03-14 09:16:26,715 INFO [train.py:527] (4/6) Epoch 557, batch 6, global_batch_idx: 68950, batch size: 52, loss[discriminator_loss=2.789, discriminator_real_loss=1.5, discriminator_fake_loss=1.289, generator_loss=28.86, generator_mel_loss=18.25, generator_kl_loss=1.448, generator_dur_loss=1.622, generator_adv_loss=1.869, generator_feat_match_loss=5.664, over 52.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.38, discriminator_fake_loss=1.335, generator_loss=28.2, generator_mel_loss=17.86, generator_kl_loss=1.441, generator_dur_loss=1.709, generator_adv_loss=1.979, generator_feat_match_loss=5.213, over 366.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:18:48,185 INFO [train.py:527] (4/6) Epoch 557, batch 56, global_batch_idx: 69000, batch size: 42, loss[discriminator_loss=2.737, discriminator_real_loss=1.433, discriminator_fake_loss=1.304, generator_loss=27.85, generator_mel_loss=17.65, generator_kl_loss=1.482, generator_dur_loss=1.666, generator_adv_loss=1.956, generator_feat_match_loss=5.103, over 42.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.369, discriminator_fake_loss=1.348, generator_loss=28.26, generator_mel_loss=18.02, generator_kl_loss=1.411, generator_dur_loss=1.747, generator_adv_loss=1.955, generator_feat_match_loss=5.127, over 3224.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:18:48,186 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 09:18:56,044 INFO [train.py:591] (4/6) Epoch 557, validation: discriminator_loss=2.752, discriminator_real_loss=1.44, discriminator_fake_loss=1.312, generator_loss=27.3, generator_mel_loss=17.78, generator_kl_loss=1.173, generator_dur_loss=1.798, generator_adv_loss=1.926, generator_feat_match_loss=4.623, over 100.00 samples. +2024-03-14 09:18:56,045 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 09:21:15,849 INFO [train.py:527] (4/6) Epoch 557, batch 106, global_batch_idx: 69050, batch size: 74, loss[discriminator_loss=2.72, discriminator_real_loss=1.406, discriminator_fake_loss=1.314, generator_loss=27.58, generator_mel_loss=17.88, generator_kl_loss=1.413, generator_dur_loss=1.799, generator_adv_loss=1.869, generator_feat_match_loss=4.628, over 74.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.368, discriminator_fake_loss=1.35, generator_loss=28.37, generator_mel_loss=18.04, generator_kl_loss=1.412, generator_dur_loss=1.748, generator_adv_loss=1.961, generator_feat_match_loss=5.205, over 5999.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:22:06,379 INFO [train.py:919] (4/6) Start epoch 558 +2024-03-14 09:24:03,535 INFO [train.py:527] (4/6) Epoch 558, batch 32, global_batch_idx: 69100, batch size: 58, loss[discriminator_loss=2.84, discriminator_real_loss=1.534, discriminator_fake_loss=1.306, generator_loss=27.75, generator_mel_loss=17.49, generator_kl_loss=1.348, generator_dur_loss=1.704, generator_adv_loss=1.876, generator_feat_match_loss=5.332, over 58.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.37, discriminator_fake_loss=1.333, generator_loss=28.28, generator_mel_loss=17.9, generator_kl_loss=1.384, generator_dur_loss=1.759, generator_adv_loss=1.972, generator_feat_match_loss=5.262, over 2027.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:26:24,097 INFO [train.py:527] (4/6) Epoch 558, batch 82, global_batch_idx: 69150, batch size: 62, loss[discriminator_loss=2.694, discriminator_real_loss=1.371, discriminator_fake_loss=1.323, generator_loss=28.04, generator_mel_loss=17.67, generator_kl_loss=1.589, generator_dur_loss=1.734, generator_adv_loss=1.907, generator_feat_match_loss=5.138, over 62.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.28, generator_mel_loss=17.91, generator_kl_loss=1.408, generator_dur_loss=1.752, generator_adv_loss=1.966, generator_feat_match_loss=5.247, over 4934.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:28:20,942 INFO [train.py:919] (4/6) Start epoch 559 +2024-03-14 09:29:07,817 INFO [train.py:527] (4/6) Epoch 559, batch 8, global_batch_idx: 69200, batch size: 48, loss[discriminator_loss=2.704, discriminator_real_loss=1.357, discriminator_fake_loss=1.347, generator_loss=29.42, generator_mel_loss=18.44, generator_kl_loss=1.484, generator_dur_loss=1.607, generator_adv_loss=1.884, generator_feat_match_loss=6.009, over 48.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.404, discriminator_fake_loss=1.334, generator_loss=28.93, generator_mel_loss=18.24, generator_kl_loss=1.459, generator_dur_loss=1.698, generator_adv_loss=1.978, generator_feat_match_loss=5.557, over 474.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:29:07,820 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 09:29:15,986 INFO [train.py:591] (4/6) Epoch 559, validation: discriminator_loss=2.722, discriminator_real_loss=1.383, discriminator_fake_loss=1.339, generator_loss=27.66, generator_mel_loss=18.38, generator_kl_loss=1.27, generator_dur_loss=1.798, generator_adv_loss=1.825, generator_feat_match_loss=4.393, over 100.00 samples. +2024-03-14 09:29:15,989 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 09:31:36,857 INFO [train.py:527] (4/6) Epoch 559, batch 58, global_batch_idx: 69250, batch size: 59, loss[discriminator_loss=2.745, discriminator_real_loss=1.496, discriminator_fake_loss=1.25, generator_loss=28.59, generator_mel_loss=18.4, generator_kl_loss=1.322, generator_dur_loss=1.73, generator_adv_loss=1.907, generator_feat_match_loss=5.229, over 59.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.376, discriminator_fake_loss=1.332, generator_loss=28.38, generator_mel_loss=18.02, generator_kl_loss=1.433, generator_dur_loss=1.73, generator_adv_loss=1.967, generator_feat_match_loss=5.227, over 3374.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:33:58,965 INFO [train.py:527] (4/6) Epoch 559, batch 108, global_batch_idx: 69300, batch size: 77, loss[discriminator_loss=2.683, discriminator_real_loss=1.367, discriminator_fake_loss=1.316, generator_loss=28.87, generator_mel_loss=18.05, generator_kl_loss=1.436, generator_dur_loss=1.828, generator_adv_loss=1.891, generator_feat_match_loss=5.671, over 77.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.4, generator_mel_loss=18.04, generator_kl_loss=1.418, generator_dur_loss=1.739, generator_adv_loss=1.971, generator_feat_match_loss=5.236, over 6266.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:34:40,975 INFO [train.py:919] (4/6) Start epoch 560 +2024-03-14 09:36:42,399 INFO [train.py:527] (4/6) Epoch 560, batch 34, global_batch_idx: 69350, batch size: 25, loss[discriminator_loss=2.596, discriminator_real_loss=1.298, discriminator_fake_loss=1.298, generator_loss=29.68, generator_mel_loss=18.66, generator_kl_loss=1.805, generator_dur_loss=1.603, generator_adv_loss=1.863, generator_feat_match_loss=5.752, over 25.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.36, discriminator_fake_loss=1.329, generator_loss=28.5, generator_mel_loss=18.01, generator_kl_loss=1.429, generator_dur_loss=1.759, generator_adv_loss=1.968, generator_feat_match_loss=5.337, over 1907.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:39:03,986 INFO [train.py:527] (4/6) Epoch 560, batch 84, global_batch_idx: 69400, batch size: 44, loss[discriminator_loss=2.679, discriminator_real_loss=1.333, discriminator_fake_loss=1.346, generator_loss=29.71, generator_mel_loss=18.31, generator_kl_loss=1.677, generator_dur_loss=1.684, generator_adv_loss=1.929, generator_feat_match_loss=6.101, over 44.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.339, generator_loss=28.47, generator_mel_loss=18.01, generator_kl_loss=1.455, generator_dur_loss=1.751, generator_adv_loss=1.967, generator_feat_match_loss=5.282, over 4661.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:39:03,988 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 09:39:13,162 INFO [train.py:591] (4/6) Epoch 560, validation: discriminator_loss=2.76, discriminator_real_loss=1.461, discriminator_fake_loss=1.3, generator_loss=27.48, generator_mel_loss=18.44, generator_kl_loss=1.17, generator_dur_loss=1.817, generator_adv_loss=1.925, generator_feat_match_loss=4.129, over 100.00 samples. +2024-03-14 09:39:13,163 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 09:41:00,468 INFO [train.py:919] (4/6) Start epoch 561 +2024-03-14 09:41:52,103 INFO [train.py:527] (4/6) Epoch 561, batch 10, global_batch_idx: 69450, batch size: 88, loss[discriminator_loss=2.72, discriminator_real_loss=1.378, discriminator_fake_loss=1.342, generator_loss=27.75, generator_mel_loss=17.87, generator_kl_loss=1.269, generator_dur_loss=1.783, generator_adv_loss=1.853, generator_feat_match_loss=4.971, over 88.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.398, discriminator_fake_loss=1.332, generator_loss=28.02, generator_mel_loss=17.91, generator_kl_loss=1.359, generator_dur_loss=1.769, generator_adv_loss=1.937, generator_feat_match_loss=5.046, over 642.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:44:11,641 INFO [train.py:527] (4/6) Epoch 561, batch 60, global_batch_idx: 69500, batch size: 31, loss[discriminator_loss=2.681, discriminator_real_loss=1.309, discriminator_fake_loss=1.372, generator_loss=28.38, generator_mel_loss=18.02, generator_kl_loss=1.569, generator_dur_loss=1.59, generator_adv_loss=2.173, generator_feat_match_loss=5.028, over 31.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.378, discriminator_fake_loss=1.332, generator_loss=28.25, generator_mel_loss=17.96, generator_kl_loss=1.423, generator_dur_loss=1.73, generator_adv_loss=1.962, generator_feat_match_loss=5.177, over 3325.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:46:29,814 INFO [train.py:527] (4/6) Epoch 561, batch 110, global_batch_idx: 69550, batch size: 40, loss[discriminator_loss=2.646, discriminator_real_loss=1.332, discriminator_fake_loss=1.314, generator_loss=28.78, generator_mel_loss=17.92, generator_kl_loss=1.491, generator_dur_loss=1.652, generator_adv_loss=2.07, generator_feat_match_loss=5.645, over 40.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.378, discriminator_fake_loss=1.332, generator_loss=28.29, generator_mel_loss=17.96, generator_kl_loss=1.419, generator_dur_loss=1.735, generator_adv_loss=1.964, generator_feat_match_loss=5.212, over 6161.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:47:07,792 INFO [train.py:919] (4/6) Start epoch 562 +2024-03-14 09:49:14,026 INFO [train.py:527] (4/6) Epoch 562, batch 36, global_batch_idx: 69600, batch size: 62, loss[discriminator_loss=2.742, discriminator_real_loss=1.401, discriminator_fake_loss=1.341, generator_loss=27.35, generator_mel_loss=17.7, generator_kl_loss=1.441, generator_dur_loss=1.786, generator_adv_loss=2.026, generator_feat_match_loss=4.405, over 62.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.342, generator_loss=28.3, generator_mel_loss=17.97, generator_kl_loss=1.402, generator_dur_loss=1.773, generator_adv_loss=1.955, generator_feat_match_loss=5.206, over 2207.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:49:14,028 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 09:49:21,868 INFO [train.py:591] (4/6) Epoch 562, validation: discriminator_loss=2.774, discriminator_real_loss=1.497, discriminator_fake_loss=1.277, generator_loss=27.95, generator_mel_loss=18.5, generator_kl_loss=1.244, generator_dur_loss=1.815, generator_adv_loss=2.012, generator_feat_match_loss=4.385, over 100.00 samples. +2024-03-14 09:49:21,869 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 09:51:41,031 INFO [train.py:527] (4/6) Epoch 562, batch 86, global_batch_idx: 69650, batch size: 96, loss[discriminator_loss=2.717, discriminator_real_loss=1.323, discriminator_fake_loss=1.394, generator_loss=27.58, generator_mel_loss=17.76, generator_kl_loss=1.332, generator_dur_loss=1.867, generator_adv_loss=1.924, generator_feat_match_loss=4.699, over 96.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.368, discriminator_fake_loss=1.341, generator_loss=28.4, generator_mel_loss=18, generator_kl_loss=1.407, generator_dur_loss=1.768, generator_adv_loss=1.963, generator_feat_match_loss=5.26, over 5307.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:53:22,566 INFO [train.py:919] (4/6) Start epoch 563 +2024-03-14 09:54:19,203 INFO [train.py:527] (4/6) Epoch 563, batch 12, global_batch_idx: 69700, batch size: 62, loss[discriminator_loss=2.674, discriminator_real_loss=1.318, discriminator_fake_loss=1.356, generator_loss=29.47, generator_mel_loss=18.65, generator_kl_loss=1.359, generator_dur_loss=1.743, generator_adv_loss=2.007, generator_feat_match_loss=5.715, over 62.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.373, discriminator_fake_loss=1.324, generator_loss=28.67, generator_mel_loss=18, generator_kl_loss=1.427, generator_dur_loss=1.746, generator_adv_loss=1.979, generator_feat_match_loss=5.515, over 698.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:56:37,474 INFO [train.py:527] (4/6) Epoch 563, batch 62, global_batch_idx: 69750, batch size: 77, loss[discriminator_loss=2.719, discriminator_real_loss=1.497, discriminator_fake_loss=1.222, generator_loss=29.07, generator_mel_loss=18.06, generator_kl_loss=1.43, generator_dur_loss=1.858, generator_adv_loss=2.122, generator_feat_match_loss=5.605, over 77.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.367, discriminator_fake_loss=1.341, generator_loss=28.62, generator_mel_loss=18.04, generator_kl_loss=1.446, generator_dur_loss=1.75, generator_adv_loss=1.966, generator_feat_match_loss=5.421, over 3423.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:59:00,170 INFO [train.py:527] (4/6) Epoch 563, batch 112, global_batch_idx: 69800, batch size: 80, loss[discriminator_loss=2.68, discriminator_real_loss=1.371, discriminator_fake_loss=1.309, generator_loss=27.28, generator_mel_loss=17.61, generator_kl_loss=1.299, generator_dur_loss=1.783, generator_adv_loss=1.889, generator_feat_match_loss=4.697, over 80.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.368, discriminator_fake_loss=1.335, generator_loss=28.45, generator_mel_loss=17.98, generator_kl_loss=1.43, generator_dur_loss=1.755, generator_adv_loss=1.965, generator_feat_match_loss=5.323, over 6299.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:59:00,171 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 09:59:08,734 INFO [train.py:591] (4/6) Epoch 563, validation: discriminator_loss=2.728, discriminator_real_loss=1.386, discriminator_fake_loss=1.342, generator_loss=26.35, generator_mel_loss=17.75, generator_kl_loss=1.292, generator_dur_loss=1.808, generator_adv_loss=1.812, generator_feat_match_loss=3.694, over 100.00 samples. +2024-03-14 09:59:08,735 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 09:59:37,500 INFO [train.py:919] (4/6) Start epoch 564 +2024-03-14 10:01:47,490 INFO [train.py:527] (4/6) Epoch 564, batch 38, global_batch_idx: 69850, batch size: 83, loss[discriminator_loss=2.67, discriminator_real_loss=1.457, discriminator_fake_loss=1.213, generator_loss=28.33, generator_mel_loss=17.99, generator_kl_loss=1.369, generator_dur_loss=1.867, generator_adv_loss=1.853, generator_feat_match_loss=5.256, over 83.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.368, discriminator_fake_loss=1.33, generator_loss=28.5, generator_mel_loss=18.01, generator_kl_loss=1.426, generator_dur_loss=1.765, generator_adv_loss=1.971, generator_feat_match_loss=5.322, over 2340.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:04:04,311 INFO [train.py:527] (4/6) Epoch 564, batch 88, global_batch_idx: 69900, batch size: 58, loss[discriminator_loss=2.679, discriminator_real_loss=1.395, discriminator_fake_loss=1.284, generator_loss=28.69, generator_mel_loss=17.96, generator_kl_loss=1.536, generator_dur_loss=1.679, generator_adv_loss=1.934, generator_feat_match_loss=5.575, over 58.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.371, discriminator_fake_loss=1.332, generator_loss=28.44, generator_mel_loss=18, generator_kl_loss=1.446, generator_dur_loss=1.752, generator_adv_loss=1.972, generator_feat_match_loss=5.268, over 4999.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:05:43,465 INFO [train.py:919] (4/6) Start epoch 565 +2024-03-14 10:06:48,909 INFO [train.py:527] (4/6) Epoch 565, batch 14, global_batch_idx: 69950, batch size: 48, loss[discriminator_loss=2.718, discriminator_real_loss=1.296, discriminator_fake_loss=1.423, generator_loss=28.01, generator_mel_loss=17.94, generator_kl_loss=1.583, generator_dur_loss=1.676, generator_adv_loss=1.977, generator_feat_match_loss=4.832, over 48.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.352, discriminator_fake_loss=1.351, generator_loss=28.45, generator_mel_loss=17.99, generator_kl_loss=1.452, generator_dur_loss=1.751, generator_adv_loss=1.955, generator_feat_match_loss=5.307, over 818.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:09:07,116 INFO [train.py:527] (4/6) Epoch 565, batch 64, global_batch_idx: 70000, batch size: 52, loss[discriminator_loss=2.7, discriminator_real_loss=1.356, discriminator_fake_loss=1.345, generator_loss=28.84, generator_mel_loss=18.03, generator_kl_loss=1.491, generator_dur_loss=1.74, generator_adv_loss=1.95, generator_feat_match_loss=5.631, over 52.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.361, discriminator_fake_loss=1.34, generator_loss=28.35, generator_mel_loss=17.95, generator_kl_loss=1.421, generator_dur_loss=1.745, generator_adv_loss=1.962, generator_feat_match_loss=5.277, over 3592.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:09:07,117 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 10:09:15,109 INFO [train.py:591] (4/6) Epoch 565, validation: discriminator_loss=2.737, discriminator_real_loss=1.367, discriminator_fake_loss=1.37, generator_loss=27.8, generator_mel_loss=18.61, generator_kl_loss=1.183, generator_dur_loss=1.796, generator_adv_loss=1.851, generator_feat_match_loss=4.361, over 100.00 samples. +2024-03-14 10:09:15,110 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 10:11:33,316 INFO [train.py:527] (4/6) Epoch 565, batch 114, global_batch_idx: 70050, batch size: 72, loss[discriminator_loss=2.734, discriminator_real_loss=1.3, discriminator_fake_loss=1.435, generator_loss=28.62, generator_mel_loss=18.11, generator_kl_loss=1.401, generator_dur_loss=1.793, generator_adv_loss=2.036, generator_feat_match_loss=5.282, over 72.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.359, discriminator_fake_loss=1.339, generator_loss=28.42, generator_mel_loss=18, generator_kl_loss=1.413, generator_dur_loss=1.754, generator_adv_loss=1.968, generator_feat_match_loss=5.293, over 6533.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:11:58,834 INFO [train.py:919] (4/6) Start epoch 566 +2024-03-14 10:14:15,735 INFO [train.py:527] (4/6) Epoch 566, batch 40, global_batch_idx: 70100, batch size: 88, loss[discriminator_loss=2.715, discriminator_real_loss=1.332, discriminator_fake_loss=1.383, generator_loss=28.25, generator_mel_loss=17.62, generator_kl_loss=1.357, generator_dur_loss=1.838, generator_adv_loss=1.989, generator_feat_match_loss=5.443, over 88.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.25, generator_mel_loss=17.89, generator_kl_loss=1.429, generator_dur_loss=1.75, generator_adv_loss=1.961, generator_feat_match_loss=5.219, over 2370.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:16:36,056 INFO [train.py:527] (4/6) Epoch 566, batch 90, global_batch_idx: 70150, batch size: 61, loss[discriminator_loss=2.702, discriminator_real_loss=1.365, discriminator_fake_loss=1.337, generator_loss=27.66, generator_mel_loss=17.87, generator_kl_loss=1.358, generator_dur_loss=1.719, generator_adv_loss=1.981, generator_feat_match_loss=4.729, over 61.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.365, discriminator_fake_loss=1.338, generator_loss=28.37, generator_mel_loss=17.97, generator_kl_loss=1.425, generator_dur_loss=1.748, generator_adv_loss=1.967, generator_feat_match_loss=5.258, over 5184.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:18:08,509 INFO [train.py:919] (4/6) Start epoch 567 +2024-03-14 10:19:16,562 INFO [train.py:527] (4/6) Epoch 567, batch 16, global_batch_idx: 70200, batch size: 39, loss[discriminator_loss=2.684, discriminator_real_loss=1.368, discriminator_fake_loss=1.317, generator_loss=28.2, generator_mel_loss=17.93, generator_kl_loss=1.391, generator_dur_loss=1.729, generator_adv_loss=1.988, generator_feat_match_loss=5.163, over 39.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.344, generator_loss=28.36, generator_mel_loss=17.93, generator_kl_loss=1.391, generator_dur_loss=1.763, generator_adv_loss=1.963, generator_feat_match_loss=5.311, over 1026.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:19:16,563 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 10:19:24,539 INFO [train.py:591] (4/6) Epoch 567, validation: discriminator_loss=2.776, discriminator_real_loss=1.402, discriminator_fake_loss=1.374, generator_loss=27.35, generator_mel_loss=18.82, generator_kl_loss=1.179, generator_dur_loss=1.806, generator_adv_loss=1.829, generator_feat_match_loss=3.711, over 100.00 samples. +2024-03-14 10:19:24,540 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 10:21:43,337 INFO [train.py:527] (4/6) Epoch 567, batch 66, global_batch_idx: 70250, batch size: 83, loss[discriminator_loss=2.681, discriminator_real_loss=1.413, discriminator_fake_loss=1.268, generator_loss=28.62, generator_mel_loss=18, generator_kl_loss=1.41, generator_dur_loss=1.792, generator_adv_loss=1.895, generator_feat_match_loss=5.52, over 83.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=28.43, generator_mel_loss=17.99, generator_kl_loss=1.42, generator_dur_loss=1.738, generator_adv_loss=1.974, generator_feat_match_loss=5.307, over 3757.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:24:02,161 INFO [train.py:527] (4/6) Epoch 567, batch 116, global_batch_idx: 70300, batch size: 62, loss[discriminator_loss=2.741, discriminator_real_loss=1.37, discriminator_fake_loss=1.371, generator_loss=27.33, generator_mel_loss=17.64, generator_kl_loss=1.341, generator_dur_loss=1.756, generator_adv_loss=2.007, generator_feat_match_loss=4.583, over 62.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=28.37, generator_mel_loss=17.99, generator_kl_loss=1.424, generator_dur_loss=1.734, generator_adv_loss=1.971, generator_feat_match_loss=5.248, over 6581.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:24:22,995 INFO [train.py:919] (4/6) Start epoch 568 +2024-03-14 10:26:47,087 INFO [train.py:527] (4/6) Epoch 568, batch 42, global_batch_idx: 70350, batch size: 39, loss[discriminator_loss=2.757, discriminator_real_loss=1.343, discriminator_fake_loss=1.414, generator_loss=27.95, generator_mel_loss=17.94, generator_kl_loss=1.507, generator_dur_loss=1.685, generator_adv_loss=2.046, generator_feat_match_loss=4.773, over 39.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.364, discriminator_fake_loss=1.345, generator_loss=28.32, generator_mel_loss=18.02, generator_kl_loss=1.451, generator_dur_loss=1.73, generator_adv_loss=1.954, generator_feat_match_loss=5.163, over 2432.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:29:06,513 INFO [train.py:527] (4/6) Epoch 568, batch 92, global_batch_idx: 70400, batch size: 88, loss[discriminator_loss=2.663, discriminator_real_loss=1.307, discriminator_fake_loss=1.356, generator_loss=28.13, generator_mel_loss=17.64, generator_kl_loss=1.282, generator_dur_loss=1.826, generator_adv_loss=1.922, generator_feat_match_loss=5.469, over 88.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.364, discriminator_fake_loss=1.343, generator_loss=28.4, generator_mel_loss=18, generator_kl_loss=1.427, generator_dur_loss=1.742, generator_adv_loss=1.959, generator_feat_match_loss=5.274, over 5379.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:29:06,515 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 10:29:15,278 INFO [train.py:591] (4/6) Epoch 568, validation: discriminator_loss=2.809, discriminator_real_loss=1.305, discriminator_fake_loss=1.504, generator_loss=27.65, generator_mel_loss=18.58, generator_kl_loss=1.196, generator_dur_loss=1.813, generator_adv_loss=1.674, generator_feat_match_loss=4.381, over 100.00 samples. +2024-03-14 10:29:15,279 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 10:30:42,214 INFO [train.py:919] (4/6) Start epoch 569 +2024-03-14 10:31:56,619 INFO [train.py:527] (4/6) Epoch 569, batch 18, global_batch_idx: 70450, batch size: 48, loss[discriminator_loss=2.677, discriminator_real_loss=1.351, discriminator_fake_loss=1.326, generator_loss=28.08, generator_mel_loss=17.93, generator_kl_loss=1.508, generator_dur_loss=1.736, generator_adv_loss=1.864, generator_feat_match_loss=5.042, over 48.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.357, discriminator_fake_loss=1.351, generator_loss=28.65, generator_mel_loss=18.07, generator_kl_loss=1.425, generator_dur_loss=1.718, generator_adv_loss=1.966, generator_feat_match_loss=5.477, over 1043.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:34:16,899 INFO [train.py:527] (4/6) Epoch 569, batch 68, global_batch_idx: 70500, batch size: 55, loss[discriminator_loss=2.739, discriminator_real_loss=1.272, discriminator_fake_loss=1.467, generator_loss=28.88, generator_mel_loss=18.12, generator_kl_loss=1.444, generator_dur_loss=1.714, generator_adv_loss=2.099, generator_feat_match_loss=5.502, over 55.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.365, discriminator_fake_loss=1.344, generator_loss=28.42, generator_mel_loss=17.97, generator_kl_loss=1.402, generator_dur_loss=1.756, generator_adv_loss=1.96, generator_feat_match_loss=5.326, over 4154.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:36:35,855 INFO [train.py:527] (4/6) Epoch 569, batch 118, global_batch_idx: 70550, batch size: 36, loss[discriminator_loss=2.785, discriminator_real_loss=1.264, discriminator_fake_loss=1.52, generator_loss=27.46, generator_mel_loss=18.02, generator_kl_loss=1.794, generator_dur_loss=1.647, generator_adv_loss=1.902, generator_feat_match_loss=4.096, over 36.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.367, discriminator_fake_loss=1.338, generator_loss=28.44, generator_mel_loss=17.99, generator_kl_loss=1.412, generator_dur_loss=1.752, generator_adv_loss=1.974, generator_feat_match_loss=5.316, over 6897.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:36:51,814 INFO [train.py:919] (4/6) Start epoch 570 +2024-03-14 10:39:15,972 INFO [train.py:527] (4/6) Epoch 570, batch 44, global_batch_idx: 70600, batch size: 36, loss[discriminator_loss=2.653, discriminator_real_loss=1.348, discriminator_fake_loss=1.305, generator_loss=29.47, generator_mel_loss=18.5, generator_kl_loss=1.481, generator_dur_loss=1.676, generator_adv_loss=1.848, generator_feat_match_loss=5.966, over 36.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.361, discriminator_fake_loss=1.336, generator_loss=28.47, generator_mel_loss=18.04, generator_kl_loss=1.409, generator_dur_loss=1.76, generator_adv_loss=1.96, generator_feat_match_loss=5.302, over 2781.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:39:15,973 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 10:39:24,036 INFO [train.py:591] (4/6) Epoch 570, validation: discriminator_loss=2.732, discriminator_real_loss=1.37, discriminator_fake_loss=1.362, generator_loss=27.4, generator_mel_loss=18.38, generator_kl_loss=1.202, generator_dur_loss=1.795, generator_adv_loss=1.795, generator_feat_match_loss=4.225, over 100.00 samples. +2024-03-14 10:39:24,038 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 10:41:42,164 INFO [train.py:527] (4/6) Epoch 570, batch 94, global_batch_idx: 70650, batch size: 31, loss[discriminator_loss=2.777, discriminator_real_loss=1.409, discriminator_fake_loss=1.368, generator_loss=29.23, generator_mel_loss=18.31, generator_kl_loss=1.655, generator_dur_loss=1.62, generator_adv_loss=1.967, generator_feat_match_loss=5.679, over 31.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.363, discriminator_fake_loss=1.336, generator_loss=28.42, generator_mel_loss=18.04, generator_kl_loss=1.412, generator_dur_loss=1.743, generator_adv_loss=1.963, generator_feat_match_loss=5.269, over 5462.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:43:04,648 INFO [train.py:919] (4/6) Start epoch 571 +2024-03-14 10:44:25,904 INFO [train.py:527] (4/6) Epoch 571, batch 20, global_batch_idx: 70700, batch size: 45, loss[discriminator_loss=2.787, discriminator_real_loss=1.486, discriminator_fake_loss=1.301, generator_loss=28.58, generator_mel_loss=17.87, generator_kl_loss=1.444, generator_dur_loss=1.663, generator_adv_loss=1.91, generator_feat_match_loss=5.693, over 45.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.361, discriminator_fake_loss=1.335, generator_loss=28.62, generator_mel_loss=18.02, generator_kl_loss=1.433, generator_dur_loss=1.751, generator_adv_loss=1.965, generator_feat_match_loss=5.448, over 1153.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:46:43,889 INFO [train.py:527] (4/6) Epoch 571, batch 70, global_batch_idx: 70750, batch size: 96, loss[discriminator_loss=2.622, discriminator_real_loss=1.356, discriminator_fake_loss=1.266, generator_loss=28.72, generator_mel_loss=18.12, generator_kl_loss=1.245, generator_dur_loss=1.845, generator_adv_loss=1.941, generator_feat_match_loss=5.569, over 96.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.367, discriminator_fake_loss=1.338, generator_loss=28.41, generator_mel_loss=17.99, generator_kl_loss=1.414, generator_dur_loss=1.748, generator_adv_loss=1.957, generator_feat_match_loss=5.293, over 4030.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:49:02,181 INFO [train.py:527] (4/6) Epoch 571, batch 120, global_batch_idx: 70800, batch size: 62, loss[discriminator_loss=2.705, discriminator_real_loss=1.387, discriminator_fake_loss=1.318, generator_loss=28.79, generator_mel_loss=18.57, generator_kl_loss=1.481, generator_dur_loss=1.736, generator_adv_loss=1.822, generator_feat_match_loss=5.178, over 62.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.364, discriminator_fake_loss=1.339, generator_loss=28.43, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.746, generator_adv_loss=1.963, generator_feat_match_loss=5.29, over 6858.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:49:02,183 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 10:49:10,966 INFO [train.py:591] (4/6) Epoch 571, validation: discriminator_loss=2.778, discriminator_real_loss=1.319, discriminator_fake_loss=1.459, generator_loss=27.05, generator_mel_loss=18.21, generator_kl_loss=1.249, generator_dur_loss=1.805, generator_adv_loss=1.701, generator_feat_match_loss=4.082, over 100.00 samples. +2024-03-14 10:49:10,966 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 10:49:20,623 INFO [train.py:919] (4/6) Start epoch 572 +2024-03-14 10:51:52,177 INFO [train.py:527] (4/6) Epoch 572, batch 46, global_batch_idx: 70850, batch size: 56, loss[discriminator_loss=2.668, discriminator_real_loss=1.382, discriminator_fake_loss=1.286, generator_loss=28.38, generator_mel_loss=18, generator_kl_loss=1.382, generator_dur_loss=1.747, generator_adv_loss=1.95, generator_feat_match_loss=5.309, over 56.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.364, discriminator_fake_loss=1.341, generator_loss=28.57, generator_mel_loss=18.06, generator_kl_loss=1.423, generator_dur_loss=1.743, generator_adv_loss=1.966, generator_feat_match_loss=5.38, over 2665.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:54:11,160 INFO [train.py:527] (4/6) Epoch 572, batch 96, global_batch_idx: 70900, batch size: 66, loss[discriminator_loss=2.714, discriminator_real_loss=1.396, discriminator_fake_loss=1.318, generator_loss=28.93, generator_mel_loss=18.16, generator_kl_loss=1.41, generator_dur_loss=1.79, generator_adv_loss=1.949, generator_feat_match_loss=5.629, over 66.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.366, discriminator_fake_loss=1.34, generator_loss=28.49, generator_mel_loss=18.01, generator_kl_loss=1.419, generator_dur_loss=1.74, generator_adv_loss=1.964, generator_feat_match_loss=5.358, over 5556.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:55:27,791 INFO [train.py:919] (4/6) Start epoch 573 +2024-03-14 10:56:53,831 INFO [train.py:527] (4/6) Epoch 573, batch 22, global_batch_idx: 70950, batch size: 56, loss[discriminator_loss=2.638, discriminator_real_loss=1.282, discriminator_fake_loss=1.357, generator_loss=29, generator_mel_loss=18.51, generator_kl_loss=1.378, generator_dur_loss=1.719, generator_adv_loss=1.99, generator_feat_match_loss=5.395, over 56.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.365, discriminator_fake_loss=1.334, generator_loss=28.34, generator_mel_loss=18.08, generator_kl_loss=1.384, generator_dur_loss=1.748, generator_adv_loss=1.957, generator_feat_match_loss=5.17, over 1303.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:59:14,979 INFO [train.py:527] (4/6) Epoch 573, batch 72, global_batch_idx: 71000, batch size: 45, loss[discriminator_loss=2.723, discriminator_real_loss=1.408, discriminator_fake_loss=1.315, generator_loss=28.71, generator_mel_loss=18.45, generator_kl_loss=1.433, generator_dur_loss=1.631, generator_adv_loss=1.971, generator_feat_match_loss=5.227, over 45.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.372, discriminator_fake_loss=1.336, generator_loss=28.43, generator_mel_loss=18.06, generator_kl_loss=1.403, generator_dur_loss=1.759, generator_adv_loss=1.961, generator_feat_match_loss=5.252, over 4315.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:59:14,980 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 10:59:23,685 INFO [train.py:591] (4/6) Epoch 573, validation: discriminator_loss=2.787, discriminator_real_loss=1.403, discriminator_fake_loss=1.384, generator_loss=27.98, generator_mel_loss=18.39, generator_kl_loss=1.265, generator_dur_loss=1.82, generator_adv_loss=1.848, generator_feat_match_loss=4.654, over 100.00 samples. +2024-03-14 10:59:23,686 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 11:01:39,165 INFO [train.py:527] (4/6) Epoch 573, batch 122, global_batch_idx: 71050, batch size: 31, loss[discriminator_loss=2.727, discriminator_real_loss=1.325, discriminator_fake_loss=1.402, generator_loss=29.39, generator_mel_loss=18.21, generator_kl_loss=1.471, generator_dur_loss=1.669, generator_adv_loss=1.901, generator_feat_match_loss=6.138, over 31.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.371, discriminator_fake_loss=1.335, generator_loss=28.41, generator_mel_loss=18.02, generator_kl_loss=1.409, generator_dur_loss=1.753, generator_adv_loss=1.967, generator_feat_match_loss=5.256, over 6922.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:01:44,076 INFO [train.py:919] (4/6) Start epoch 574 +2024-03-14 11:04:18,827 INFO [train.py:527] (4/6) Epoch 574, batch 48, global_batch_idx: 71100, batch size: 52, loss[discriminator_loss=2.709, discriminator_real_loss=1.462, discriminator_fake_loss=1.248, generator_loss=28.34, generator_mel_loss=18.19, generator_kl_loss=1.499, generator_dur_loss=1.734, generator_adv_loss=1.98, generator_feat_match_loss=4.934, over 52.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.37, discriminator_fake_loss=1.333, generator_loss=28.36, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.753, generator_adv_loss=1.962, generator_feat_match_loss=5.214, over 2806.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:06:39,732 INFO [train.py:527] (4/6) Epoch 574, batch 98, global_batch_idx: 71150, batch size: 36, loss[discriminator_loss=2.645, discriminator_real_loss=1.399, discriminator_fake_loss=1.246, generator_loss=28.72, generator_mel_loss=18.17, generator_kl_loss=1.364, generator_dur_loss=1.645, generator_adv_loss=2.018, generator_feat_match_loss=5.526, over 36.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.366, discriminator_fake_loss=1.335, generator_loss=28.47, generator_mel_loss=18.06, generator_kl_loss=1.419, generator_dur_loss=1.75, generator_adv_loss=1.97, generator_feat_match_loss=5.275, over 5586.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:07:51,469 INFO [train.py:919] (4/6) Start epoch 575 +2024-03-14 11:09:22,375 INFO [train.py:527] (4/6) Epoch 575, batch 24, global_batch_idx: 71200, batch size: 61, loss[discriminator_loss=2.726, discriminator_real_loss=1.329, discriminator_fake_loss=1.397, generator_loss=28.72, generator_mel_loss=17.72, generator_kl_loss=1.401, generator_dur_loss=1.752, generator_adv_loss=2.066, generator_feat_match_loss=5.778, over 61.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.361, discriminator_fake_loss=1.338, generator_loss=28.38, generator_mel_loss=18.02, generator_kl_loss=1.428, generator_dur_loss=1.746, generator_adv_loss=1.954, generator_feat_match_loss=5.23, over 1385.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:09:22,377 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 11:09:30,378 INFO [train.py:591] (4/6) Epoch 575, validation: discriminator_loss=2.755, discriminator_real_loss=1.447, discriminator_fake_loss=1.308, generator_loss=27.86, generator_mel_loss=18.44, generator_kl_loss=1.293, generator_dur_loss=1.801, generator_adv_loss=1.956, generator_feat_match_loss=4.369, over 100.00 samples. +2024-03-14 11:09:30,379 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 11:11:48,592 INFO [train.py:527] (4/6) Epoch 575, batch 74, global_batch_idx: 71250, batch size: 42, loss[discriminator_loss=2.651, discriminator_real_loss=1.288, discriminator_fake_loss=1.363, generator_loss=29.18, generator_mel_loss=18.05, generator_kl_loss=1.361, generator_dur_loss=1.706, generator_adv_loss=2.032, generator_feat_match_loss=6.032, over 42.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.37, discriminator_fake_loss=1.336, generator_loss=28.38, generator_mel_loss=18.01, generator_kl_loss=1.399, generator_dur_loss=1.749, generator_adv_loss=1.97, generator_feat_match_loss=5.253, over 4472.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:14:03,777 INFO [train.py:919] (4/6) Start epoch 576 +2024-03-14 11:14:27,753 INFO [train.py:527] (4/6) Epoch 576, batch 0, global_batch_idx: 71300, batch size: 42, loss[discriminator_loss=2.683, discriminator_real_loss=1.301, discriminator_fake_loss=1.382, generator_loss=28.25, generator_mel_loss=18.16, generator_kl_loss=1.477, generator_dur_loss=1.683, generator_adv_loss=2.074, generator_feat_match_loss=4.848, over 42.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.301, discriminator_fake_loss=1.382, generator_loss=28.25, generator_mel_loss=18.16, generator_kl_loss=1.477, generator_dur_loss=1.683, generator_adv_loss=2.074, generator_feat_match_loss=4.848, over 42.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:16:46,110 INFO [train.py:527] (4/6) Epoch 576, batch 50, global_batch_idx: 71350, batch size: 96, loss[discriminator_loss=2.696, discriminator_real_loss=1.449, discriminator_fake_loss=1.247, generator_loss=28.33, generator_mel_loss=17.71, generator_kl_loss=1.278, generator_dur_loss=1.886, generator_adv_loss=1.842, generator_feat_match_loss=5.611, over 96.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.362, discriminator_fake_loss=1.347, generator_loss=28.38, generator_mel_loss=18.04, generator_kl_loss=1.409, generator_dur_loss=1.742, generator_adv_loss=1.963, generator_feat_match_loss=5.228, over 2901.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:19:06,144 INFO [train.py:527] (4/6) Epoch 576, batch 100, global_batch_idx: 71400, batch size: 96, loss[discriminator_loss=2.728, discriminator_real_loss=1.317, discriminator_fake_loss=1.411, generator_loss=28.55, generator_mel_loss=17.81, generator_kl_loss=1.353, generator_dur_loss=1.919, generator_adv_loss=2.058, generator_feat_match_loss=5.415, over 96.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.363, discriminator_fake_loss=1.343, generator_loss=28.38, generator_mel_loss=18, generator_kl_loss=1.409, generator_dur_loss=1.747, generator_adv_loss=1.963, generator_feat_match_loss=5.26, over 5808.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:19:06,145 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 11:19:15,118 INFO [train.py:591] (4/6) Epoch 576, validation: discriminator_loss=2.765, discriminator_real_loss=1.525, discriminator_fake_loss=1.24, generator_loss=27.47, generator_mel_loss=17.97, generator_kl_loss=1.262, generator_dur_loss=1.826, generator_adv_loss=2.129, generator_feat_match_loss=4.284, over 100.00 samples. +2024-03-14 11:19:15,119 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 11:20:16,522 INFO [train.py:919] (4/6) Start epoch 577 +2024-03-14 11:21:54,981 INFO [train.py:527] (4/6) Epoch 577, batch 26, global_batch_idx: 71450, batch size: 44, loss[discriminator_loss=2.712, discriminator_real_loss=1.379, discriminator_fake_loss=1.333, generator_loss=28.22, generator_mel_loss=18.12, generator_kl_loss=1.445, generator_dur_loss=1.727, generator_adv_loss=1.832, generator_feat_match_loss=5.094, over 44.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.369, discriminator_fake_loss=1.339, generator_loss=28.4, generator_mel_loss=18.03, generator_kl_loss=1.405, generator_dur_loss=1.774, generator_adv_loss=1.96, generator_feat_match_loss=5.235, over 1687.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:24:14,038 INFO [train.py:527] (4/6) Epoch 577, batch 76, global_batch_idx: 71500, batch size: 52, loss[discriminator_loss=2.73, discriminator_real_loss=1.435, discriminator_fake_loss=1.295, generator_loss=28.09, generator_mel_loss=17.92, generator_kl_loss=1.467, generator_dur_loss=1.683, generator_adv_loss=1.872, generator_feat_match_loss=5.146, over 52.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.369, discriminator_fake_loss=1.337, generator_loss=28.33, generator_mel_loss=17.97, generator_kl_loss=1.39, generator_dur_loss=1.777, generator_adv_loss=1.962, generator_feat_match_loss=5.226, over 4804.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:26:20,982 INFO [train.py:919] (4/6) Start epoch 578 +2024-03-14 11:26:50,739 INFO [train.py:527] (4/6) Epoch 578, batch 2, global_batch_idx: 71550, batch size: 80, loss[discriminator_loss=2.713, discriminator_real_loss=1.41, discriminator_fake_loss=1.303, generator_loss=28.42, generator_mel_loss=18.17, generator_kl_loss=1.404, generator_dur_loss=1.812, generator_adv_loss=1.999, generator_feat_match_loss=5.042, over 80.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.396, discriminator_fake_loss=1.35, generator_loss=27.77, generator_mel_loss=17.81, generator_kl_loss=1.4, generator_dur_loss=1.832, generator_adv_loss=1.898, generator_feat_match_loss=4.823, over 207.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:29:09,122 INFO [train.py:527] (4/6) Epoch 578, batch 52, global_batch_idx: 71600, batch size: 80, loss[discriminator_loss=2.72, discriminator_real_loss=1.363, discriminator_fake_loss=1.356, generator_loss=28.51, generator_mel_loss=17.67, generator_kl_loss=1.313, generator_dur_loss=1.81, generator_adv_loss=2.025, generator_feat_match_loss=5.691, over 80.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.371, discriminator_fake_loss=1.337, generator_loss=28.28, generator_mel_loss=17.95, generator_kl_loss=1.399, generator_dur_loss=1.742, generator_adv_loss=1.959, generator_feat_match_loss=5.238, over 2945.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:29:09,123 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 11:29:17,345 INFO [train.py:591] (4/6) Epoch 578, validation: discriminator_loss=2.695, discriminator_real_loss=1.409, discriminator_fake_loss=1.287, generator_loss=26.72, generator_mel_loss=17.84, generator_kl_loss=1.224, generator_dur_loss=1.803, generator_adv_loss=1.928, generator_feat_match_loss=3.927, over 100.00 samples. +2024-03-14 11:29:17,346 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 11:31:36,485 INFO [train.py:527] (4/6) Epoch 578, batch 102, global_batch_idx: 71650, batch size: 45, loss[discriminator_loss=2.698, discriminator_real_loss=1.208, discriminator_fake_loss=1.49, generator_loss=29.9, generator_mel_loss=18.12, generator_kl_loss=1.617, generator_dur_loss=1.637, generator_adv_loss=2.004, generator_feat_match_loss=6.526, over 45.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.31, generator_mel_loss=17.94, generator_kl_loss=1.401, generator_dur_loss=1.743, generator_adv_loss=1.965, generator_feat_match_loss=5.267, over 5812.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:32:34,927 INFO [train.py:919] (4/6) Start epoch 579 +2024-03-14 11:34:17,662 INFO [train.py:527] (4/6) Epoch 579, batch 28, global_batch_idx: 71700, batch size: 44, loss[discriminator_loss=2.695, discriminator_real_loss=1.319, discriminator_fake_loss=1.376, generator_loss=28.18, generator_mel_loss=17.79, generator_kl_loss=1.248, generator_dur_loss=1.682, generator_adv_loss=1.862, generator_feat_match_loss=5.599, over 44.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.366, discriminator_fake_loss=1.348, generator_loss=28.45, generator_mel_loss=18.03, generator_kl_loss=1.415, generator_dur_loss=1.757, generator_adv_loss=1.953, generator_feat_match_loss=5.294, over 1757.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:36:37,132 INFO [train.py:527] (4/6) Epoch 579, batch 78, global_batch_idx: 71750, batch size: 14, loss[discriminator_loss=2.582, discriminator_real_loss=1.298, discriminator_fake_loss=1.285, generator_loss=30.49, generator_mel_loss=19.09, generator_kl_loss=1.625, generator_dur_loss=1.638, generator_adv_loss=2.206, generator_feat_match_loss=5.925, over 14.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.362, discriminator_fake_loss=1.343, generator_loss=28.46, generator_mel_loss=17.97, generator_kl_loss=1.388, generator_dur_loss=1.775, generator_adv_loss=1.987, generator_feat_match_loss=5.338, over 4976.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:38:43,437 INFO [train.py:919] (4/6) Start epoch 580 +2024-03-14 11:39:18,842 INFO [train.py:527] (4/6) Epoch 580, batch 4, global_batch_idx: 71800, batch size: 47, loss[discriminator_loss=2.74, discriminator_real_loss=1.268, discriminator_fake_loss=1.473, generator_loss=29.3, generator_mel_loss=18.44, generator_kl_loss=1.53, generator_dur_loss=1.72, generator_adv_loss=2.1, generator_feat_match_loss=5.504, over 47.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.368, discriminator_fake_loss=1.348, generator_loss=28.22, generator_mel_loss=17.91, generator_kl_loss=1.416, generator_dur_loss=1.744, generator_adv_loss=1.984, generator_feat_match_loss=5.166, over 272.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:39:18,844 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 11:39:26,757 INFO [train.py:591] (4/6) Epoch 580, validation: discriminator_loss=2.809, discriminator_real_loss=1.511, discriminator_fake_loss=1.299, generator_loss=27.2, generator_mel_loss=18.01, generator_kl_loss=1.221, generator_dur_loss=1.822, generator_adv_loss=1.993, generator_feat_match_loss=4.153, over 100.00 samples. +2024-03-14 11:39:26,759 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 11:41:44,317 INFO [train.py:527] (4/6) Epoch 580, batch 54, global_batch_idx: 71850, batch size: 77, loss[discriminator_loss=2.736, discriminator_real_loss=1.471, discriminator_fake_loss=1.265, generator_loss=28.57, generator_mel_loss=17.8, generator_kl_loss=1.31, generator_dur_loss=1.771, generator_adv_loss=1.851, generator_feat_match_loss=5.838, over 77.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.379, discriminator_fake_loss=1.329, generator_loss=28.49, generator_mel_loss=18.06, generator_kl_loss=1.403, generator_dur_loss=1.766, generator_adv_loss=1.963, generator_feat_match_loss=5.297, over 3136.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:44:03,553 INFO [train.py:527] (4/6) Epoch 580, batch 104, global_batch_idx: 71900, batch size: 48, loss[discriminator_loss=2.687, discriminator_real_loss=1.455, discriminator_fake_loss=1.232, generator_loss=28.6, generator_mel_loss=18.19, generator_kl_loss=1.5, generator_dur_loss=1.672, generator_adv_loss=1.942, generator_feat_match_loss=5.296, over 48.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.376, discriminator_fake_loss=1.334, generator_loss=28.39, generator_mel_loss=17.99, generator_kl_loss=1.4, generator_dur_loss=1.766, generator_adv_loss=1.964, generator_feat_match_loss=5.266, over 6150.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:44:56,811 INFO [train.py:919] (4/6) Start epoch 581 +2024-03-14 11:46:43,671 INFO [train.py:527] (4/6) Epoch 581, batch 30, global_batch_idx: 71950, batch size: 61, loss[discriminator_loss=2.683, discriminator_real_loss=1.328, discriminator_fake_loss=1.355, generator_loss=28.24, generator_mel_loss=17.67, generator_kl_loss=1.391, generator_dur_loss=1.736, generator_adv_loss=2.028, generator_feat_match_loss=5.412, over 61.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.37, discriminator_fake_loss=1.328, generator_loss=28.46, generator_mel_loss=17.99, generator_kl_loss=1.437, generator_dur_loss=1.753, generator_adv_loss=1.975, generator_feat_match_loss=5.307, over 1779.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:49:02,157 INFO [train.py:527] (4/6) Epoch 581, batch 80, global_batch_idx: 72000, batch size: 83, loss[discriminator_loss=2.686, discriminator_real_loss=1.436, discriminator_fake_loss=1.249, generator_loss=28.06, generator_mel_loss=17.81, generator_kl_loss=1.282, generator_dur_loss=1.846, generator_adv_loss=1.943, generator_feat_match_loss=5.178, over 83.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.37, discriminator_fake_loss=1.333, generator_loss=28.46, generator_mel_loss=18.01, generator_kl_loss=1.427, generator_dur_loss=1.75, generator_adv_loss=1.968, generator_feat_match_loss=5.308, over 4444.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:49:02,159 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 11:49:11,002 INFO [train.py:591] (4/6) Epoch 581, validation: discriminator_loss=2.754, discriminator_real_loss=1.407, discriminator_fake_loss=1.347, generator_loss=27.71, generator_mel_loss=18.3, generator_kl_loss=1.216, generator_dur_loss=1.806, generator_adv_loss=1.855, generator_feat_match_loss=4.537, over 100.00 samples. +2024-03-14 11:49:11,003 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 11:51:11,292 INFO [train.py:919] (4/6) Start epoch 582 +2024-03-14 11:51:50,764 INFO [train.py:527] (4/6) Epoch 582, batch 6, global_batch_idx: 72050, batch size: 72, loss[discriminator_loss=2.716, discriminator_real_loss=1.337, discriminator_fake_loss=1.379, generator_loss=27.81, generator_mel_loss=17.56, generator_kl_loss=1.37, generator_dur_loss=1.826, generator_adv_loss=1.935, generator_feat_match_loss=5.12, over 72.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.344, discriminator_fake_loss=1.335, generator_loss=28.47, generator_mel_loss=17.84, generator_kl_loss=1.494, generator_dur_loss=1.733, generator_adv_loss=1.966, generator_feat_match_loss=5.446, over 319.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:54:09,121 INFO [train.py:527] (4/6) Epoch 582, batch 56, global_batch_idx: 72100, batch size: 42, loss[discriminator_loss=2.753, discriminator_real_loss=1.441, discriminator_fake_loss=1.312, generator_loss=28.83, generator_mel_loss=17.97, generator_kl_loss=1.73, generator_dur_loss=1.698, generator_adv_loss=1.813, generator_feat_match_loss=5.623, over 42.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.362, discriminator_fake_loss=1.332, generator_loss=28.57, generator_mel_loss=18.07, generator_kl_loss=1.422, generator_dur_loss=1.757, generator_adv_loss=1.965, generator_feat_match_loss=5.355, over 3242.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:56:29,808 INFO [train.py:527] (4/6) Epoch 582, batch 106, global_batch_idx: 72150, batch size: 42, loss[discriminator_loss=2.718, discriminator_real_loss=1.416, discriminator_fake_loss=1.302, generator_loss=28.92, generator_mel_loss=18.25, generator_kl_loss=1.507, generator_dur_loss=1.65, generator_adv_loss=1.721, generator_feat_match_loss=5.793, over 42.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.363, discriminator_fake_loss=1.334, generator_loss=28.59, generator_mel_loss=18.09, generator_kl_loss=1.42, generator_dur_loss=1.746, generator_adv_loss=1.968, generator_feat_match_loss=5.365, over 6090.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:57:17,716 INFO [train.py:919] (4/6) Start epoch 583 +2024-03-14 11:59:10,447 INFO [train.py:527] (4/6) Epoch 583, batch 32, global_batch_idx: 72200, batch size: 66, loss[discriminator_loss=2.721, discriminator_real_loss=1.297, discriminator_fake_loss=1.423, generator_loss=27.89, generator_mel_loss=17.64, generator_kl_loss=1.461, generator_dur_loss=1.778, generator_adv_loss=1.937, generator_feat_match_loss=5.079, over 66.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.368, discriminator_fake_loss=1.33, generator_loss=28.44, generator_mel_loss=17.99, generator_kl_loss=1.38, generator_dur_loss=1.771, generator_adv_loss=1.986, generator_feat_match_loss=5.31, over 2107.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:59:10,448 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 11:59:18,463 INFO [train.py:591] (4/6) Epoch 583, validation: discriminator_loss=2.773, discriminator_real_loss=1.405, discriminator_fake_loss=1.368, generator_loss=27.33, generator_mel_loss=18.31, generator_kl_loss=1.174, generator_dur_loss=1.809, generator_adv_loss=1.85, generator_feat_match_loss=4.194, over 100.00 samples. +2024-03-14 11:59:18,464 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 12:01:36,791 INFO [train.py:527] (4/6) Epoch 583, batch 82, global_batch_idx: 72250, batch size: 59, loss[discriminator_loss=2.765, discriminator_real_loss=1.389, discriminator_fake_loss=1.376, generator_loss=29.05, generator_mel_loss=18.34, generator_kl_loss=1.521, generator_dur_loss=1.763, generator_adv_loss=2.046, generator_feat_match_loss=5.38, over 59.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.375, discriminator_fake_loss=1.333, generator_loss=28.41, generator_mel_loss=18.01, generator_kl_loss=1.401, generator_dur_loss=1.759, generator_adv_loss=1.975, generator_feat_match_loss=5.271, over 4899.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:03:31,377 INFO [train.py:919] (4/6) Start epoch 584 +2024-03-14 12:04:18,534 INFO [train.py:527] (4/6) Epoch 584, batch 8, global_batch_idx: 72300, batch size: 68, loss[discriminator_loss=2.694, discriminator_real_loss=1.341, discriminator_fake_loss=1.353, generator_loss=28.78, generator_mel_loss=17.91, generator_kl_loss=1.345, generator_dur_loss=1.822, generator_adv_loss=2.112, generator_feat_match_loss=5.588, over 68.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.337, discriminator_fake_loss=1.328, generator_loss=28.69, generator_mel_loss=18.09, generator_kl_loss=1.408, generator_dur_loss=1.772, generator_adv_loss=2.006, generator_feat_match_loss=5.417, over 550.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:06:33,501 INFO [train.py:527] (4/6) Epoch 584, batch 58, global_batch_idx: 72350, batch size: 68, loss[discriminator_loss=2.764, discriminator_real_loss=1.382, discriminator_fake_loss=1.382, generator_loss=27.97, generator_mel_loss=17.88, generator_kl_loss=1.387, generator_dur_loss=1.778, generator_adv_loss=1.875, generator_feat_match_loss=5.044, over 68.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.339, generator_loss=28.48, generator_mel_loss=18.02, generator_kl_loss=1.425, generator_dur_loss=1.733, generator_adv_loss=1.973, generator_feat_match_loss=5.326, over 3291.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:08:53,107 INFO [train.py:527] (4/6) Epoch 584, batch 108, global_batch_idx: 72400, batch size: 66, loss[discriminator_loss=2.679, discriminator_real_loss=1.337, discriminator_fake_loss=1.341, generator_loss=28.09, generator_mel_loss=18.21, generator_kl_loss=1.338, generator_dur_loss=1.763, generator_adv_loss=2.007, generator_feat_match_loss=4.779, over 66.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.367, discriminator_fake_loss=1.343, generator_loss=28.44, generator_mel_loss=18.03, generator_kl_loss=1.407, generator_dur_loss=1.738, generator_adv_loss=1.975, generator_feat_match_loss=5.291, over 6196.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:08:53,109 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 12:09:02,013 INFO [train.py:591] (4/6) Epoch 584, validation: discriminator_loss=2.738, discriminator_real_loss=1.487, discriminator_fake_loss=1.251, generator_loss=27.72, generator_mel_loss=18.38, generator_kl_loss=1.309, generator_dur_loss=1.808, generator_adv_loss=1.99, generator_feat_match_loss=4.232, over 100.00 samples. +2024-03-14 12:09:02,014 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 12:09:45,700 INFO [train.py:919] (4/6) Start epoch 585 +2024-03-14 12:11:44,640 INFO [train.py:527] (4/6) Epoch 585, batch 34, global_batch_idx: 72450, batch size: 52, loss[discriminator_loss=2.714, discriminator_real_loss=1.325, discriminator_fake_loss=1.389, generator_loss=28.85, generator_mel_loss=18.16, generator_kl_loss=1.452, generator_dur_loss=1.65, generator_adv_loss=2.013, generator_feat_match_loss=5.577, over 52.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=28.64, generator_mel_loss=18.18, generator_kl_loss=1.402, generator_dur_loss=1.739, generator_adv_loss=1.98, generator_feat_match_loss=5.339, over 1939.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:14:02,781 INFO [train.py:527] (4/6) Epoch 585, batch 84, global_batch_idx: 72500, batch size: 96, loss[discriminator_loss=2.648, discriminator_real_loss=1.368, discriminator_fake_loss=1.28, generator_loss=27.71, generator_mel_loss=17.46, generator_kl_loss=1.294, generator_dur_loss=1.914, generator_adv_loss=1.973, generator_feat_match_loss=5.061, over 96.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.54, generator_mel_loss=18.06, generator_kl_loss=1.406, generator_dur_loss=1.748, generator_adv_loss=1.984, generator_feat_match_loss=5.341, over 4739.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:15:54,149 INFO [train.py:919] (4/6) Start epoch 586 +2024-03-14 12:16:44,933 INFO [train.py:527] (4/6) Epoch 586, batch 10, global_batch_idx: 72550, batch size: 25, loss[discriminator_loss=2.608, discriminator_real_loss=1.287, discriminator_fake_loss=1.322, generator_loss=30.86, generator_mel_loss=18.84, generator_kl_loss=1.705, generator_dur_loss=1.525, generator_adv_loss=2.2, generator_feat_match_loss=6.598, over 25.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.368, discriminator_fake_loss=1.343, generator_loss=28.42, generator_mel_loss=17.99, generator_kl_loss=1.421, generator_dur_loss=1.719, generator_adv_loss=1.957, generator_feat_match_loss=5.339, over 616.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:19:05,881 INFO [train.py:527] (4/6) Epoch 586, batch 60, global_batch_idx: 72600, batch size: 70, loss[discriminator_loss=2.706, discriminator_real_loss=1.421, discriminator_fake_loss=1.285, generator_loss=28.96, generator_mel_loss=17.6, generator_kl_loss=1.425, generator_dur_loss=1.798, generator_adv_loss=2.025, generator_feat_match_loss=6.113, over 70.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=28.36, generator_mel_loss=17.98, generator_kl_loss=1.416, generator_dur_loss=1.744, generator_adv_loss=1.963, generator_feat_match_loss=5.257, over 3453.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:19:05,882 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 12:19:13,986 INFO [train.py:591] (4/6) Epoch 586, validation: discriminator_loss=2.753, discriminator_real_loss=1.447, discriminator_fake_loss=1.306, generator_loss=27.23, generator_mel_loss=18.08, generator_kl_loss=1.24, generator_dur_loss=1.796, generator_adv_loss=1.931, generator_feat_match_loss=4.183, over 100.00 samples. +2024-03-14 12:19:13,986 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 12:21:36,537 INFO [train.py:527] (4/6) Epoch 586, batch 110, global_batch_idx: 72650, batch size: 53, loss[discriminator_loss=2.71, discriminator_real_loss=1.391, discriminator_fake_loss=1.318, generator_loss=27.8, generator_mel_loss=17.8, generator_kl_loss=1.407, generator_dur_loss=1.744, generator_adv_loss=1.967, generator_feat_match_loss=4.883, over 53.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.37, discriminator_fake_loss=1.337, generator_loss=28.4, generator_mel_loss=18, generator_kl_loss=1.412, generator_dur_loss=1.744, generator_adv_loss=1.96, generator_feat_match_loss=5.281, over 6122.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:22:13,319 INFO [train.py:919] (4/6) Start epoch 587 +2024-03-14 12:24:15,687 INFO [train.py:527] (4/6) Epoch 587, batch 36, global_batch_idx: 72700, batch size: 12, loss[discriminator_loss=2.703, discriminator_real_loss=1.421, discriminator_fake_loss=1.282, generator_loss=29.47, generator_mel_loss=17.74, generator_kl_loss=1.849, generator_dur_loss=1.574, generator_adv_loss=2.016, generator_feat_match_loss=6.292, over 12.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.36, discriminator_fake_loss=1.337, generator_loss=28.4, generator_mel_loss=18.03, generator_kl_loss=1.385, generator_dur_loss=1.749, generator_adv_loss=1.955, generator_feat_match_loss=5.28, over 2147.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:26:32,063 INFO [train.py:527] (4/6) Epoch 587, batch 86, global_batch_idx: 72750, batch size: 66, loss[discriminator_loss=2.675, discriminator_real_loss=1.409, discriminator_fake_loss=1.267, generator_loss=28.25, generator_mel_loss=17.9, generator_kl_loss=1.383, generator_dur_loss=1.733, generator_adv_loss=1.91, generator_feat_match_loss=5.324, over 66.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.363, discriminator_fake_loss=1.331, generator_loss=28.58, generator_mel_loss=18.06, generator_kl_loss=1.414, generator_dur_loss=1.737, generator_adv_loss=1.972, generator_feat_match_loss=5.394, over 4719.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:28:18,386 INFO [train.py:919] (4/6) Start epoch 588 +2024-03-14 12:29:14,988 INFO [train.py:527] (4/6) Epoch 588, batch 12, global_batch_idx: 72800, batch size: 74, loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.34, generator_loss=27.96, generator_mel_loss=17.87, generator_kl_loss=1.31, generator_dur_loss=1.813, generator_adv_loss=1.911, generator_feat_match_loss=5.062, over 74.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.345, discriminator_fake_loss=1.325, generator_loss=28.48, generator_mel_loss=17.96, generator_kl_loss=1.4, generator_dur_loss=1.746, generator_adv_loss=1.969, generator_feat_match_loss=5.41, over 775.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:29:14,991 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 12:29:22,920 INFO [train.py:591] (4/6) Epoch 588, validation: discriminator_loss=2.725, discriminator_real_loss=1.376, discriminator_fake_loss=1.349, generator_loss=27.57, generator_mel_loss=18.14, generator_kl_loss=1.264, generator_dur_loss=1.813, generator_adv_loss=1.845, generator_feat_match_loss=4.51, over 100.00 samples. +2024-03-14 12:29:22,921 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 12:31:42,099 INFO [train.py:527] (4/6) Epoch 588, batch 62, global_batch_idx: 72850, batch size: 64, loss[discriminator_loss=2.715, discriminator_real_loss=1.396, discriminator_fake_loss=1.319, generator_loss=27.77, generator_mel_loss=17.77, generator_kl_loss=1.449, generator_dur_loss=1.73, generator_adv_loss=1.884, generator_feat_match_loss=4.935, over 64.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.365, discriminator_fake_loss=1.331, generator_loss=28.32, generator_mel_loss=17.91, generator_kl_loss=1.408, generator_dur_loss=1.745, generator_adv_loss=1.971, generator_feat_match_loss=5.285, over 3601.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:34:01,613 INFO [train.py:527] (4/6) Epoch 588, batch 112, global_batch_idx: 72900, batch size: 77, loss[discriminator_loss=2.682, discriminator_real_loss=1.321, discriminator_fake_loss=1.361, generator_loss=28.78, generator_mel_loss=18.25, generator_kl_loss=1.309, generator_dur_loss=1.755, generator_adv_loss=2.044, generator_feat_match_loss=5.418, over 77.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.363, discriminator_fake_loss=1.335, generator_loss=28.36, generator_mel_loss=17.93, generator_kl_loss=1.4, generator_dur_loss=1.752, generator_adv_loss=1.977, generator_feat_match_loss=5.296, over 6783.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:34:33,231 INFO [train.py:919] (4/6) Start epoch 589 +2024-03-14 12:36:39,770 INFO [train.py:527] (4/6) Epoch 589, batch 38, global_batch_idx: 72950, batch size: 88, loss[discriminator_loss=2.722, discriminator_real_loss=1.322, discriminator_fake_loss=1.4, generator_loss=28.05, generator_mel_loss=17.66, generator_kl_loss=1.352, generator_dur_loss=1.847, generator_adv_loss=1.923, generator_feat_match_loss=5.269, over 88.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.339, generator_loss=28.65, generator_mel_loss=18.11, generator_kl_loss=1.433, generator_dur_loss=1.743, generator_adv_loss=1.975, generator_feat_match_loss=5.388, over 2206.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:38:59,569 INFO [train.py:527] (4/6) Epoch 589, batch 88, global_batch_idx: 73000, batch size: 59, loss[discriminator_loss=2.707, discriminator_real_loss=1.384, discriminator_fake_loss=1.323, generator_loss=27.8, generator_mel_loss=18.01, generator_kl_loss=1.457, generator_dur_loss=1.764, generator_adv_loss=1.909, generator_feat_match_loss=4.658, over 59.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.366, discriminator_fake_loss=1.341, generator_loss=28.55, generator_mel_loss=18.05, generator_kl_loss=1.422, generator_dur_loss=1.752, generator_adv_loss=1.977, generator_feat_match_loss=5.351, over 5022.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:38:59,570 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 12:39:08,252 INFO [train.py:591] (4/6) Epoch 589, validation: discriminator_loss=2.756, discriminator_real_loss=1.378, discriminator_fake_loss=1.378, generator_loss=27.21, generator_mel_loss=18.36, generator_kl_loss=1.156, generator_dur_loss=1.813, generator_adv_loss=1.789, generator_feat_match_loss=4.092, over 100.00 samples. +2024-03-14 12:39:08,253 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 12:40:45,278 INFO [train.py:919] (4/6) Start epoch 590 +2024-03-14 12:41:47,465 INFO [train.py:527] (4/6) Epoch 590, batch 14, global_batch_idx: 73050, batch size: 55, loss[discriminator_loss=2.737, discriminator_real_loss=1.358, discriminator_fake_loss=1.379, generator_loss=28.38, generator_mel_loss=18.4, generator_kl_loss=1.474, generator_dur_loss=1.717, generator_adv_loss=1.949, generator_feat_match_loss=4.839, over 55.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.392, discriminator_fake_loss=1.347, generator_loss=28.56, generator_mel_loss=18.16, generator_kl_loss=1.502, generator_dur_loss=1.702, generator_adv_loss=1.946, generator_feat_match_loss=5.249, over 685.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:44:08,835 INFO [train.py:527] (4/6) Epoch 590, batch 64, global_batch_idx: 73100, batch size: 70, loss[discriminator_loss=2.721, discriminator_real_loss=1.277, discriminator_fake_loss=1.443, generator_loss=28.33, generator_mel_loss=17.78, generator_kl_loss=1.481, generator_dur_loss=1.805, generator_adv_loss=2.048, generator_feat_match_loss=5.22, over 70.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.368, discriminator_fake_loss=1.341, generator_loss=28.54, generator_mel_loss=18.1, generator_kl_loss=1.412, generator_dur_loss=1.752, generator_adv_loss=1.953, generator_feat_match_loss=5.323, over 3609.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:46:26,559 INFO [train.py:527] (4/6) Epoch 590, batch 114, global_batch_idx: 73150, batch size: 52, loss[discriminator_loss=2.701, discriminator_real_loss=1.455, discriminator_fake_loss=1.246, generator_loss=28.44, generator_mel_loss=17.83, generator_kl_loss=1.322, generator_dur_loss=1.71, generator_adv_loss=2.119, generator_feat_match_loss=5.457, over 52.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.37, discriminator_fake_loss=1.342, generator_loss=28.59, generator_mel_loss=18.09, generator_kl_loss=1.427, generator_dur_loss=1.749, generator_adv_loss=1.971, generator_feat_match_loss=5.353, over 6239.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:46:53,338 INFO [train.py:919] (4/6) Start epoch 591 +2024-03-14 12:49:10,693 INFO [train.py:527] (4/6) Epoch 591, batch 40, global_batch_idx: 73200, batch size: 55, loss[discriminator_loss=2.676, discriminator_real_loss=1.442, discriminator_fake_loss=1.234, generator_loss=28.65, generator_mel_loss=18.4, generator_kl_loss=1.378, generator_dur_loss=1.68, generator_adv_loss=2, generator_feat_match_loss=5.195, over 55.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.366, discriminator_fake_loss=1.331, generator_loss=28.6, generator_mel_loss=18.1, generator_kl_loss=1.415, generator_dur_loss=1.745, generator_adv_loss=1.975, generator_feat_match_loss=5.367, over 2201.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:49:10,695 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 12:49:18,754 INFO [train.py:591] (4/6) Epoch 591, validation: discriminator_loss=2.735, discriminator_real_loss=1.399, discriminator_fake_loss=1.336, generator_loss=27.3, generator_mel_loss=18.09, generator_kl_loss=1.265, generator_dur_loss=1.809, generator_adv_loss=1.869, generator_feat_match_loss=4.263, over 100.00 samples. +2024-03-14 12:49:18,755 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 12:51:37,312 INFO [train.py:527] (4/6) Epoch 591, batch 90, global_batch_idx: 73250, batch size: 58, loss[discriminator_loss=2.715, discriminator_real_loss=1.24, discriminator_fake_loss=1.475, generator_loss=28.51, generator_mel_loss=18.14, generator_kl_loss=1.574, generator_dur_loss=1.717, generator_adv_loss=1.913, generator_feat_match_loss=5.158, over 58.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.36, discriminator_fake_loss=1.342, generator_loss=28.64, generator_mel_loss=18.12, generator_kl_loss=1.427, generator_dur_loss=1.748, generator_adv_loss=1.967, generator_feat_match_loss=5.376, over 4914.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:53:08,845 INFO [train.py:919] (4/6) Start epoch 592 +2024-03-14 12:54:18,444 INFO [train.py:527] (4/6) Epoch 592, batch 16, global_batch_idx: 73300, batch size: 45, loss[discriminator_loss=2.709, discriminator_real_loss=1.379, discriminator_fake_loss=1.33, generator_loss=28.37, generator_mel_loss=17.57, generator_kl_loss=1.534, generator_dur_loss=1.702, generator_adv_loss=2.109, generator_feat_match_loss=5.455, over 45.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.54, generator_mel_loss=17.96, generator_kl_loss=1.406, generator_dur_loss=1.764, generator_adv_loss=1.976, generator_feat_match_loss=5.437, over 1025.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:56:37,586 INFO [train.py:527] (4/6) Epoch 592, batch 66, global_batch_idx: 73350, batch size: 58, loss[discriminator_loss=2.722, discriminator_real_loss=1.346, discriminator_fake_loss=1.376, generator_loss=28.14, generator_mel_loss=17.73, generator_kl_loss=1.344, generator_dur_loss=1.775, generator_adv_loss=1.904, generator_feat_match_loss=5.38, over 58.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.364, discriminator_fake_loss=1.337, generator_loss=28.43, generator_mel_loss=17.99, generator_kl_loss=1.421, generator_dur_loss=1.759, generator_adv_loss=1.954, generator_feat_match_loss=5.3, over 3774.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:58:58,799 INFO [train.py:527] (4/6) Epoch 592, batch 116, global_batch_idx: 73400, batch size: 55, loss[discriminator_loss=2.719, discriminator_real_loss=1.234, discriminator_fake_loss=1.485, generator_loss=28.45, generator_mel_loss=18.19, generator_kl_loss=1.536, generator_dur_loss=1.73, generator_adv_loss=2.022, generator_feat_match_loss=4.966, over 55.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.338, generator_loss=28.52, generator_mel_loss=18.03, generator_kl_loss=1.423, generator_dur_loss=1.765, generator_adv_loss=1.959, generator_feat_match_loss=5.349, over 6632.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:58:58,800 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 12:59:07,930 INFO [train.py:591] (4/6) Epoch 592, validation: discriminator_loss=2.751, discriminator_real_loss=1.511, discriminator_fake_loss=1.239, generator_loss=27.84, generator_mel_loss=18.23, generator_kl_loss=1.2, generator_dur_loss=1.824, generator_adv_loss=2.03, generator_feat_match_loss=4.553, over 100.00 samples. +2024-03-14 12:59:07,931 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 12:59:29,726 INFO [train.py:919] (4/6) Start epoch 593 +2024-03-14 13:01:48,745 INFO [train.py:527] (4/6) Epoch 593, batch 42, global_batch_idx: 73450, batch size: 66, loss[discriminator_loss=2.685, discriminator_real_loss=1.341, discriminator_fake_loss=1.344, generator_loss=29.66, generator_mel_loss=18.81, generator_kl_loss=1.595, generator_dur_loss=1.769, generator_adv_loss=1.928, generator_feat_match_loss=5.557, over 66.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.358, discriminator_fake_loss=1.339, generator_loss=28.56, generator_mel_loss=18.02, generator_kl_loss=1.421, generator_dur_loss=1.775, generator_adv_loss=1.974, generator_feat_match_loss=5.374, over 2545.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:04:07,610 INFO [train.py:527] (4/6) Epoch 593, batch 92, global_batch_idx: 73500, batch size: 61, loss[discriminator_loss=2.616, discriminator_real_loss=1.314, discriminator_fake_loss=1.302, generator_loss=29.37, generator_mel_loss=18.36, generator_kl_loss=1.404, generator_dur_loss=1.721, generator_adv_loss=1.947, generator_feat_match_loss=5.938, over 61.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.357, discriminator_fake_loss=1.34, generator_loss=28.54, generator_mel_loss=18.03, generator_kl_loss=1.435, generator_dur_loss=1.758, generator_adv_loss=1.971, generator_feat_match_loss=5.346, over 5350.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:05:33,905 INFO [train.py:919] (4/6) Start epoch 594 +2024-03-14 13:06:50,847 INFO [train.py:527] (4/6) Epoch 594, batch 18, global_batch_idx: 73550, batch size: 77, loss[discriminator_loss=2.742, discriminator_real_loss=1.36, discriminator_fake_loss=1.382, generator_loss=27.6, generator_mel_loss=17.85, generator_kl_loss=1.376, generator_dur_loss=1.871, generator_adv_loss=1.998, generator_feat_match_loss=4.503, over 77.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.363, discriminator_fake_loss=1.338, generator_loss=28.22, generator_mel_loss=17.94, generator_kl_loss=1.37, generator_dur_loss=1.773, generator_adv_loss=1.977, generator_feat_match_loss=5.167, over 1275.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:09:08,316 INFO [train.py:527] (4/6) Epoch 594, batch 68, global_batch_idx: 73600, batch size: 64, loss[discriminator_loss=2.711, discriminator_real_loss=1.338, discriminator_fake_loss=1.373, generator_loss=28.54, generator_mel_loss=17.75, generator_kl_loss=1.311, generator_dur_loss=1.755, generator_adv_loss=1.911, generator_feat_match_loss=5.813, over 64.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.36, discriminator_fake_loss=1.338, generator_loss=28.34, generator_mel_loss=17.97, generator_kl_loss=1.378, generator_dur_loss=1.777, generator_adv_loss=1.965, generator_feat_match_loss=5.259, over 4443.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:09:08,317 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 13:09:17,139 INFO [train.py:591] (4/6) Epoch 594, validation: discriminator_loss=2.726, discriminator_real_loss=1.396, discriminator_fake_loss=1.33, generator_loss=27.5, generator_mel_loss=18.34, generator_kl_loss=1.188, generator_dur_loss=1.814, generator_adv_loss=1.887, generator_feat_match_loss=4.266, over 100.00 samples. +2024-03-14 13:09:17,139 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 13:11:34,981 INFO [train.py:527] (4/6) Epoch 594, batch 118, global_batch_idx: 73650, batch size: 68, loss[discriminator_loss=2.71, discriminator_real_loss=1.375, discriminator_fake_loss=1.335, generator_loss=28.73, generator_mel_loss=18.14, generator_kl_loss=1.365, generator_dur_loss=1.782, generator_adv_loss=2.004, generator_feat_match_loss=5.434, over 68.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.36, discriminator_fake_loss=1.335, generator_loss=28.42, generator_mel_loss=17.99, generator_kl_loss=1.389, generator_dur_loss=1.766, generator_adv_loss=1.972, generator_feat_match_loss=5.303, over 7166.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:11:50,407 INFO [train.py:919] (4/6) Start epoch 595 +2024-03-14 13:14:15,306 INFO [train.py:527] (4/6) Epoch 595, batch 44, global_batch_idx: 73700, batch size: 70, loss[discriminator_loss=2.674, discriminator_real_loss=1.396, discriminator_fake_loss=1.278, generator_loss=28.64, generator_mel_loss=18.23, generator_kl_loss=1.254, generator_dur_loss=1.762, generator_adv_loss=1.864, generator_feat_match_loss=5.523, over 70.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.354, discriminator_fake_loss=1.338, generator_loss=28.53, generator_mel_loss=18, generator_kl_loss=1.404, generator_dur_loss=1.749, generator_adv_loss=1.972, generator_feat_match_loss=5.403, over 2578.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:16:36,126 INFO [train.py:527] (4/6) Epoch 595, batch 94, global_batch_idx: 73750, batch size: 66, loss[discriminator_loss=2.744, discriminator_real_loss=1.357, discriminator_fake_loss=1.387, generator_loss=28.56, generator_mel_loss=18.09, generator_kl_loss=1.255, generator_dur_loss=1.81, generator_adv_loss=1.952, generator_feat_match_loss=5.459, over 66.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.362, discriminator_fake_loss=1.339, generator_loss=28.54, generator_mel_loss=18.04, generator_kl_loss=1.41, generator_dur_loss=1.75, generator_adv_loss=1.977, generator_feat_match_loss=5.367, over 5372.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:17:55,629 INFO [train.py:919] (4/6) Start epoch 596 +2024-03-14 13:19:12,849 INFO [train.py:527] (4/6) Epoch 596, batch 20, global_batch_idx: 73800, batch size: 50, loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.347, generator_loss=28.34, generator_mel_loss=17.85, generator_kl_loss=1.516, generator_dur_loss=1.668, generator_adv_loss=1.845, generator_feat_match_loss=5.468, over 50.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.362, discriminator_fake_loss=1.346, generator_loss=28.76, generator_mel_loss=18.15, generator_kl_loss=1.43, generator_dur_loss=1.752, generator_adv_loss=1.973, generator_feat_match_loss=5.454, over 1243.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:19:12,851 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 13:19:20,980 INFO [train.py:591] (4/6) Epoch 596, validation: discriminator_loss=2.747, discriminator_real_loss=1.402, discriminator_fake_loss=1.344, generator_loss=27.21, generator_mel_loss=18.33, generator_kl_loss=1.287, generator_dur_loss=1.792, generator_adv_loss=1.801, generator_feat_match_loss=4.006, over 100.00 samples. +2024-03-14 13:19:20,981 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 13:21:39,519 INFO [train.py:527] (4/6) Epoch 596, batch 70, global_batch_idx: 73850, batch size: 53, loss[discriminator_loss=2.631, discriminator_real_loss=1.311, discriminator_fake_loss=1.32, generator_loss=28.91, generator_mel_loss=18.02, generator_kl_loss=1.444, generator_dur_loss=1.707, generator_adv_loss=2.016, generator_feat_match_loss=5.731, over 53.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.37, discriminator_fake_loss=1.335, generator_loss=28.55, generator_mel_loss=18.08, generator_kl_loss=1.428, generator_dur_loss=1.737, generator_adv_loss=1.977, generator_feat_match_loss=5.333, over 3930.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:23:57,246 INFO [train.py:527] (4/6) Epoch 596, batch 120, global_batch_idx: 73900, batch size: 59, loss[discriminator_loss=2.746, discriminator_real_loss=1.38, discriminator_fake_loss=1.367, generator_loss=27.51, generator_mel_loss=17.84, generator_kl_loss=1.444, generator_dur_loss=1.749, generator_adv_loss=1.917, generator_feat_match_loss=4.566, over 59.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.37, discriminator_fake_loss=1.335, generator_loss=28.5, generator_mel_loss=18.05, generator_kl_loss=1.422, generator_dur_loss=1.737, generator_adv_loss=1.979, generator_feat_match_loss=5.312, over 6744.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:24:08,633 INFO [train.py:919] (4/6) Start epoch 597 +2024-03-14 13:26:40,741 INFO [train.py:527] (4/6) Epoch 597, batch 46, global_batch_idx: 73950, batch size: 36, loss[discriminator_loss=2.647, discriminator_real_loss=1.31, discriminator_fake_loss=1.337, generator_loss=29.92, generator_mel_loss=18.25, generator_kl_loss=1.485, generator_dur_loss=1.662, generator_adv_loss=2, generator_feat_match_loss=6.517, over 36.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=28.7, generator_mel_loss=18.07, generator_kl_loss=1.405, generator_dur_loss=1.762, generator_adv_loss=1.984, generator_feat_match_loss=5.471, over 2678.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:29:01,380 INFO [train.py:527] (4/6) Epoch 597, batch 96, global_batch_idx: 74000, batch size: 25, loss[discriminator_loss=2.695, discriminator_real_loss=1.26, discriminator_fake_loss=1.435, generator_loss=29.68, generator_mel_loss=18.31, generator_kl_loss=1.793, generator_dur_loss=1.564, generator_adv_loss=2.025, generator_feat_match_loss=5.991, over 25.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.363, discriminator_fake_loss=1.336, generator_loss=28.57, generator_mel_loss=18.08, generator_kl_loss=1.409, generator_dur_loss=1.761, generator_adv_loss=1.968, generator_feat_match_loss=5.352, over 5484.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:29:01,382 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 13:29:10,116 INFO [train.py:591] (4/6) Epoch 597, validation: discriminator_loss=2.738, discriminator_real_loss=1.466, discriminator_fake_loss=1.271, generator_loss=27, generator_mel_loss=18.13, generator_kl_loss=1.24, generator_dur_loss=1.825, generator_adv_loss=1.864, generator_feat_match_loss=3.936, over 100.00 samples. +2024-03-14 13:29:10,117 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 13:30:22,197 INFO [train.py:919] (4/6) Start epoch 598 +2024-03-14 13:31:45,913 INFO [train.py:527] (4/6) Epoch 598, batch 22, global_batch_idx: 74050, batch size: 47, loss[discriminator_loss=2.629, discriminator_real_loss=1.269, discriminator_fake_loss=1.361, generator_loss=30.05, generator_mel_loss=18.51, generator_kl_loss=1.56, generator_dur_loss=1.681, generator_adv_loss=1.941, generator_feat_match_loss=6.361, over 47.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.366, discriminator_fake_loss=1.341, generator_loss=28.62, generator_mel_loss=18.08, generator_kl_loss=1.464, generator_dur_loss=1.737, generator_adv_loss=1.945, generator_feat_match_loss=5.4, over 1323.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:34:05,448 INFO [train.py:527] (4/6) Epoch 598, batch 72, global_batch_idx: 74100, batch size: 44, loss[discriminator_loss=2.648, discriminator_real_loss=1.379, discriminator_fake_loss=1.269, generator_loss=29.94, generator_mel_loss=18.25, generator_kl_loss=1.59, generator_dur_loss=1.694, generator_adv_loss=1.993, generator_feat_match_loss=6.416, over 44.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.365, discriminator_fake_loss=1.333, generator_loss=28.56, generator_mel_loss=18.02, generator_kl_loss=1.436, generator_dur_loss=1.747, generator_adv_loss=1.968, generator_feat_match_loss=5.389, over 4105.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:36:21,489 INFO [train.py:527] (4/6) Epoch 598, batch 122, global_batch_idx: 74150, batch size: 42, loss[discriminator_loss=2.716, discriminator_real_loss=1.341, discriminator_fake_loss=1.375, generator_loss=29.21, generator_mel_loss=18.6, generator_kl_loss=1.482, generator_dur_loss=1.698, generator_adv_loss=1.945, generator_feat_match_loss=5.487, over 42.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.367, discriminator_fake_loss=1.334, generator_loss=28.55, generator_mel_loss=18.03, generator_kl_loss=1.426, generator_dur_loss=1.745, generator_adv_loss=1.968, generator_feat_match_loss=5.384, over 6829.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:36:26,688 INFO [train.py:919] (4/6) Start epoch 599 +2024-03-14 13:39:04,746 INFO [train.py:527] (4/6) Epoch 599, batch 48, global_batch_idx: 74200, batch size: 66, loss[discriminator_loss=2.735, discriminator_real_loss=1.3, discriminator_fake_loss=1.435, generator_loss=28.67, generator_mel_loss=18.3, generator_kl_loss=1.293, generator_dur_loss=1.743, generator_adv_loss=2.053, generator_feat_match_loss=5.282, over 66.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.381, discriminator_fake_loss=1.339, generator_loss=28.42, generator_mel_loss=17.98, generator_kl_loss=1.399, generator_dur_loss=1.75, generator_adv_loss=1.965, generator_feat_match_loss=5.326, over 2799.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:39:04,748 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 13:39:12,920 INFO [train.py:591] (4/6) Epoch 599, validation: discriminator_loss=2.758, discriminator_real_loss=1.444, discriminator_fake_loss=1.314, generator_loss=26.97, generator_mel_loss=17.84, generator_kl_loss=1.218, generator_dur_loss=1.802, generator_adv_loss=1.894, generator_feat_match_loss=4.218, over 100.00 samples. +2024-03-14 13:39:12,921 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 13:41:35,629 INFO [train.py:527] (4/6) Epoch 599, batch 98, global_batch_idx: 74250, batch size: 39, loss[discriminator_loss=2.66, discriminator_real_loss=1.377, discriminator_fake_loss=1.283, generator_loss=28.24, generator_mel_loss=17.82, generator_kl_loss=1.503, generator_dur_loss=1.639, generator_adv_loss=1.96, generator_feat_match_loss=5.319, over 39.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.372, discriminator_fake_loss=1.336, generator_loss=28.49, generator_mel_loss=17.98, generator_kl_loss=1.408, generator_dur_loss=1.751, generator_adv_loss=1.971, generator_feat_match_loss=5.375, over 5751.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:42:42,979 INFO [train.py:919] (4/6) Start epoch 600 +2024-03-14 13:44:13,772 INFO [train.py:527] (4/6) Epoch 600, batch 24, global_batch_idx: 74300, batch size: 36, loss[discriminator_loss=2.653, discriminator_real_loss=1.362, discriminator_fake_loss=1.29, generator_loss=29.67, generator_mel_loss=18.57, generator_kl_loss=1.538, generator_dur_loss=1.74, generator_adv_loss=2.005, generator_feat_match_loss=5.819, over 36.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.361, discriminator_fake_loss=1.341, generator_loss=28.66, generator_mel_loss=18.11, generator_kl_loss=1.379, generator_dur_loss=1.759, generator_adv_loss=1.96, generator_feat_match_loss=5.448, over 1444.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:46:29,164 INFO [train.py:527] (4/6) Epoch 600, batch 74, global_batch_idx: 74350, batch size: 59, loss[discriminator_loss=2.699, discriminator_real_loss=1.326, discriminator_fake_loss=1.374, generator_loss=30.06, generator_mel_loss=18.39, generator_kl_loss=1.428, generator_dur_loss=1.74, generator_adv_loss=2.017, generator_feat_match_loss=6.489, over 59.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.358, discriminator_fake_loss=1.332, generator_loss=28.62, generator_mel_loss=18.07, generator_kl_loss=1.391, generator_dur_loss=1.741, generator_adv_loss=1.992, generator_feat_match_loss=5.429, over 4104.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:48:48,208 INFO [train.py:919] (4/6) Start epoch 601 +2024-03-14 13:49:13,043 INFO [train.py:527] (4/6) Epoch 601, batch 0, global_batch_idx: 74400, batch size: 77, loss[discriminator_loss=2.685, discriminator_real_loss=1.398, discriminator_fake_loss=1.287, generator_loss=28.57, generator_mel_loss=17.85, generator_kl_loss=1.402, generator_dur_loss=1.821, generator_adv_loss=1.948, generator_feat_match_loss=5.544, over 77.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.398, discriminator_fake_loss=1.287, generator_loss=28.57, generator_mel_loss=17.85, generator_kl_loss=1.402, generator_dur_loss=1.821, generator_adv_loss=1.948, generator_feat_match_loss=5.544, over 77.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:49:13,046 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 13:49:21,056 INFO [train.py:591] (4/6) Epoch 601, validation: discriminator_loss=2.735, discriminator_real_loss=1.363, discriminator_fake_loss=1.373, generator_loss=26.98, generator_mel_loss=18.09, generator_kl_loss=1.216, generator_dur_loss=1.794, generator_adv_loss=1.801, generator_feat_match_loss=4.075, over 100.00 samples. +2024-03-14 13:49:21,058 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 13:51:41,503 INFO [train.py:527] (4/6) Epoch 601, batch 50, global_batch_idx: 74450, batch size: 83, loss[discriminator_loss=2.697, discriminator_real_loss=1.328, discriminator_fake_loss=1.369, generator_loss=27.99, generator_mel_loss=17.81, generator_kl_loss=1.344, generator_dur_loss=1.829, generator_adv_loss=2.047, generator_feat_match_loss=4.968, over 83.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.363, discriminator_fake_loss=1.34, generator_loss=28.55, generator_mel_loss=18.05, generator_kl_loss=1.427, generator_dur_loss=1.749, generator_adv_loss=1.96, generator_feat_match_loss=5.363, over 2913.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:53:59,506 INFO [train.py:527] (4/6) Epoch 601, batch 100, global_batch_idx: 74500, batch size: 59, loss[discriminator_loss=2.657, discriminator_real_loss=1.241, discriminator_fake_loss=1.417, generator_loss=29.31, generator_mel_loss=18.29, generator_kl_loss=1.559, generator_dur_loss=1.697, generator_adv_loss=2.091, generator_feat_match_loss=5.668, over 59.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=28.56, generator_mel_loss=18.07, generator_kl_loss=1.426, generator_dur_loss=1.749, generator_adv_loss=1.964, generator_feat_match_loss=5.35, over 5725.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:55:06,981 INFO [train.py:919] (4/6) Start epoch 602 +2024-03-14 13:56:40,405 INFO [train.py:527] (4/6) Epoch 602, batch 26, global_batch_idx: 74550, batch size: 31, loss[discriminator_loss=2.69, discriminator_real_loss=1.374, discriminator_fake_loss=1.316, generator_loss=29.49, generator_mel_loss=18.49, generator_kl_loss=1.675, generator_dur_loss=1.607, generator_adv_loss=2.102, generator_feat_match_loss=5.613, over 31.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.373, discriminator_fake_loss=1.333, generator_loss=28.65, generator_mel_loss=18.11, generator_kl_loss=1.439, generator_dur_loss=1.727, generator_adv_loss=1.978, generator_feat_match_loss=5.393, over 1430.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:58:59,318 INFO [train.py:527] (4/6) Epoch 602, batch 76, global_batch_idx: 74600, batch size: 62, loss[discriminator_loss=2.719, discriminator_real_loss=1.482, discriminator_fake_loss=1.237, generator_loss=28.96, generator_mel_loss=18.35, generator_kl_loss=1.44, generator_dur_loss=1.724, generator_adv_loss=1.797, generator_feat_match_loss=5.65, over 62.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.57, generator_mel_loss=18.08, generator_kl_loss=1.443, generator_dur_loss=1.743, generator_adv_loss=1.959, generator_feat_match_loss=5.34, over 4265.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:58:59,320 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 13:59:08,242 INFO [train.py:591] (4/6) Epoch 602, validation: discriminator_loss=2.754, discriminator_real_loss=1.314, discriminator_fake_loss=1.44, generator_loss=27.16, generator_mel_loss=18.23, generator_kl_loss=1.299, generator_dur_loss=1.809, generator_adv_loss=1.708, generator_feat_match_loss=4.116, over 100.00 samples. +2024-03-14 13:59:08,242 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 14:01:20,568 INFO [train.py:919] (4/6) Start epoch 603 +2024-03-14 14:01:46,510 INFO [train.py:527] (4/6) Epoch 603, batch 2, global_batch_idx: 74650, batch size: 45, loss[discriminator_loss=2.652, discriminator_real_loss=1.389, discriminator_fake_loss=1.263, generator_loss=28.5, generator_mel_loss=17.81, generator_kl_loss=1.55, generator_dur_loss=1.68, generator_adv_loss=1.993, generator_feat_match_loss=5.473, over 45.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.348, discriminator_fake_loss=1.322, generator_loss=28.88, generator_mel_loss=18.21, generator_kl_loss=1.448, generator_dur_loss=1.71, generator_adv_loss=1.984, generator_feat_match_loss=5.532, over 161.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 14:04:04,831 INFO [train.py:527] (4/6) Epoch 603, batch 52, global_batch_idx: 74700, batch size: 56, loss[discriminator_loss=2.747, discriminator_real_loss=1.427, discriminator_fake_loss=1.321, generator_loss=27.84, generator_mel_loss=18.15, generator_kl_loss=1.308, generator_dur_loss=1.721, generator_adv_loss=2.001, generator_feat_match_loss=4.66, over 56.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.361, discriminator_fake_loss=1.339, generator_loss=28.5, generator_mel_loss=18.03, generator_kl_loss=1.422, generator_dur_loss=1.729, generator_adv_loss=1.969, generator_feat_match_loss=5.349, over 2809.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 14:06:22,681 INFO [train.py:527] (4/6) Epoch 603, batch 102, global_batch_idx: 74750, batch size: 59, loss[discriminator_loss=2.733, discriminator_real_loss=1.39, discriminator_fake_loss=1.343, generator_loss=30.11, generator_mel_loss=18.26, generator_kl_loss=1.414, generator_dur_loss=1.733, generator_adv_loss=1.943, generator_feat_match_loss=6.767, over 59.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.368, discriminator_fake_loss=1.336, generator_loss=28.55, generator_mel_loss=18.02, generator_kl_loss=1.425, generator_dur_loss=1.732, generator_adv_loss=1.972, generator_feat_match_loss=5.403, over 5697.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 14:07:25,559 INFO [train.py:919] (4/6) Start epoch 604 +2024-03-14 14:09:09,325 INFO [train.py:527] (4/6) Epoch 604, batch 28, global_batch_idx: 74800, batch size: 58, loss[discriminator_loss=2.742, discriminator_real_loss=1.498, discriminator_fake_loss=1.244, generator_loss=27.7, generator_mel_loss=17.8, generator_kl_loss=1.247, generator_dur_loss=1.673, generator_adv_loss=1.928, generator_feat_match_loss=5.055, over 58.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.356, discriminator_fake_loss=1.342, generator_loss=28.28, generator_mel_loss=17.95, generator_kl_loss=1.36, generator_dur_loss=1.748, generator_adv_loss=1.964, generator_feat_match_loss=5.257, over 1772.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:09:09,327 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 14:09:17,229 INFO [train.py:591] (4/6) Epoch 604, validation: discriminator_loss=2.716, discriminator_real_loss=1.339, discriminator_fake_loss=1.377, generator_loss=27.63, generator_mel_loss=18.5, generator_kl_loss=1.246, generator_dur_loss=1.771, generator_adv_loss=1.832, generator_feat_match_loss=4.284, over 100.00 samples. +2024-03-14 14:09:17,230 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 14:11:36,615 INFO [train.py:527] (4/6) Epoch 604, batch 78, global_batch_idx: 74850, batch size: 31, loss[discriminator_loss=2.725, discriminator_real_loss=1.43, discriminator_fake_loss=1.294, generator_loss=29.16, generator_mel_loss=18.08, generator_kl_loss=1.669, generator_dur_loss=1.602, generator_adv_loss=1.93, generator_feat_match_loss=5.877, over 31.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.371, discriminator_fake_loss=1.344, generator_loss=28.39, generator_mel_loss=17.99, generator_kl_loss=1.415, generator_dur_loss=1.742, generator_adv_loss=1.96, generator_feat_match_loss=5.288, over 4606.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:13:40,002 INFO [train.py:919] (4/6) Start epoch 605 +2024-03-14 14:14:16,482 INFO [train.py:527] (4/6) Epoch 605, batch 4, global_batch_idx: 74900, batch size: 42, loss[discriminator_loss=2.759, discriminator_real_loss=1.4, discriminator_fake_loss=1.359, generator_loss=28.22, generator_mel_loss=18.1, generator_kl_loss=1.575, generator_dur_loss=1.676, generator_adv_loss=1.854, generator_feat_match_loss=5.02, over 42.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.38, discriminator_fake_loss=1.357, generator_loss=28.73, generator_mel_loss=18.34, generator_kl_loss=1.486, generator_dur_loss=1.718, generator_adv_loss=1.951, generator_feat_match_loss=5.234, over 269.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:16:33,107 INFO [train.py:527] (4/6) Epoch 605, batch 54, global_batch_idx: 74950, batch size: 36, loss[discriminator_loss=2.807, discriminator_real_loss=1.324, discriminator_fake_loss=1.483, generator_loss=28.69, generator_mel_loss=18.03, generator_kl_loss=1.624, generator_dur_loss=1.643, generator_adv_loss=1.971, generator_feat_match_loss=5.423, over 36.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.374, discriminator_fake_loss=1.33, generator_loss=28.28, generator_mel_loss=18.03, generator_kl_loss=1.419, generator_dur_loss=1.73, generator_adv_loss=1.951, generator_feat_match_loss=5.155, over 3090.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:18:54,460 INFO [train.py:527] (4/6) Epoch 605, batch 104, global_batch_idx: 75000, batch size: 20, loss[discriminator_loss=2.622, discriminator_real_loss=1.454, discriminator_fake_loss=1.168, generator_loss=27.5, generator_mel_loss=17.52, generator_kl_loss=1.794, generator_dur_loss=1.588, generator_adv_loss=1.949, generator_feat_match_loss=4.657, over 20.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.376, discriminator_fake_loss=1.334, generator_loss=28.41, generator_mel_loss=18.06, generator_kl_loss=1.424, generator_dur_loss=1.734, generator_adv_loss=1.958, generator_feat_match_loss=5.236, over 5652.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:18:54,462 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 14:19:03,049 INFO [train.py:591] (4/6) Epoch 605, validation: discriminator_loss=2.722, discriminator_real_loss=1.388, discriminator_fake_loss=1.334, generator_loss=27.13, generator_mel_loss=18.15, generator_kl_loss=1.287, generator_dur_loss=1.809, generator_adv_loss=1.841, generator_feat_match_loss=4.041, over 100.00 samples. +2024-03-14 14:19:03,050 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 14:19:55,090 INFO [train.py:919] (4/6) Start epoch 606 +2024-03-14 14:21:42,307 INFO [train.py:527] (4/6) Epoch 606, batch 30, global_batch_idx: 75050, batch size: 31, loss[discriminator_loss=2.654, discriminator_real_loss=1.305, discriminator_fake_loss=1.35, generator_loss=29.59, generator_mel_loss=18.87, generator_kl_loss=1.495, generator_dur_loss=1.641, generator_adv_loss=1.989, generator_feat_match_loss=5.592, over 31.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.358, discriminator_fake_loss=1.335, generator_loss=28.57, generator_mel_loss=18.05, generator_kl_loss=1.414, generator_dur_loss=1.749, generator_adv_loss=1.978, generator_feat_match_loss=5.387, over 1743.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:24:04,829 INFO [train.py:527] (4/6) Epoch 606, batch 80, global_batch_idx: 75100, batch size: 62, loss[discriminator_loss=2.682, discriminator_real_loss=1.301, discriminator_fake_loss=1.381, generator_loss=28.19, generator_mel_loss=18.13, generator_kl_loss=1.411, generator_dur_loss=1.716, generator_adv_loss=1.904, generator_feat_match_loss=5.029, over 62.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.367, discriminator_fake_loss=1.335, generator_loss=28.51, generator_mel_loss=18.02, generator_kl_loss=1.417, generator_dur_loss=1.752, generator_adv_loss=1.964, generator_feat_match_loss=5.359, over 4617.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:26:05,387 INFO [train.py:919] (4/6) Start epoch 607 +2024-03-14 14:26:45,410 INFO [train.py:527] (4/6) Epoch 607, batch 6, global_batch_idx: 75150, batch size: 39, loss[discriminator_loss=2.668, discriminator_real_loss=1.283, discriminator_fake_loss=1.385, generator_loss=28.76, generator_mel_loss=18.1, generator_kl_loss=1.436, generator_dur_loss=1.69, generator_adv_loss=1.949, generator_feat_match_loss=5.59, over 39.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.387, discriminator_fake_loss=1.327, generator_loss=28.57, generator_mel_loss=18.05, generator_kl_loss=1.449, generator_dur_loss=1.733, generator_adv_loss=1.969, generator_feat_match_loss=5.37, over 379.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:29:00,952 INFO [train.py:527] (4/6) Epoch 607, batch 56, global_batch_idx: 75200, batch size: 68, loss[discriminator_loss=2.706, discriminator_real_loss=1.377, discriminator_fake_loss=1.329, generator_loss=28.96, generator_mel_loss=18.22, generator_kl_loss=1.325, generator_dur_loss=1.802, generator_adv_loss=1.985, generator_feat_match_loss=5.623, over 68.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.366, discriminator_fake_loss=1.337, generator_loss=28.71, generator_mel_loss=18.1, generator_kl_loss=1.44, generator_dur_loss=1.733, generator_adv_loss=1.976, generator_feat_match_loss=5.462, over 3046.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:29:00,953 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 14:29:09,186 INFO [train.py:591] (4/6) Epoch 607, validation: discriminator_loss=2.745, discriminator_real_loss=1.433, discriminator_fake_loss=1.312, generator_loss=27.1, generator_mel_loss=18.15, generator_kl_loss=1.205, generator_dur_loss=1.797, generator_adv_loss=1.883, generator_feat_match_loss=4.067, over 100.00 samples. +2024-03-14 14:29:09,187 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 14:31:28,903 INFO [train.py:527] (4/6) Epoch 607, batch 106, global_batch_idx: 75250, batch size: 83, loss[discriminator_loss=2.809, discriminator_real_loss=1.224, discriminator_fake_loss=1.584, generator_loss=29.45, generator_mel_loss=18.14, generator_kl_loss=1.356, generator_dur_loss=1.82, generator_adv_loss=2.314, generator_feat_match_loss=5.824, over 83.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=28.62, generator_mel_loss=18.04, generator_kl_loss=1.436, generator_dur_loss=1.737, generator_adv_loss=1.971, generator_feat_match_loss=5.432, over 5850.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:32:17,938 INFO [train.py:919] (4/6) Start epoch 608 +2024-03-14 14:34:09,976 INFO [train.py:527] (4/6) Epoch 608, batch 32, global_batch_idx: 75300, batch size: 52, loss[discriminator_loss=2.703, discriminator_real_loss=1.411, discriminator_fake_loss=1.292, generator_loss=28.63, generator_mel_loss=18.41, generator_kl_loss=1.436, generator_dur_loss=1.682, generator_adv_loss=2.038, generator_feat_match_loss=5.072, over 52.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.37, discriminator_fake_loss=1.312, generator_loss=28.65, generator_mel_loss=18.02, generator_kl_loss=1.45, generator_dur_loss=1.72, generator_adv_loss=2.004, generator_feat_match_loss=5.459, over 1876.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:36:29,334 INFO [train.py:527] (4/6) Epoch 608, batch 82, global_batch_idx: 75350, batch size: 68, loss[discriminator_loss=2.666, discriminator_real_loss=1.283, discriminator_fake_loss=1.383, generator_loss=28.94, generator_mel_loss=17.77, generator_kl_loss=1.447, generator_dur_loss=1.745, generator_adv_loss=2.11, generator_feat_match_loss=5.869, over 68.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.356, discriminator_fake_loss=1.33, generator_loss=28.64, generator_mel_loss=18.01, generator_kl_loss=1.434, generator_dur_loss=1.737, generator_adv_loss=1.978, generator_feat_match_loss=5.488, over 4885.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:38:20,880 INFO [train.py:919] (4/6) Start epoch 609 +2024-03-14 14:39:08,789 INFO [train.py:527] (4/6) Epoch 609, batch 8, global_batch_idx: 75400, batch size: 66, loss[discriminator_loss=2.706, discriminator_real_loss=1.375, discriminator_fake_loss=1.33, generator_loss=28.53, generator_mel_loss=17.99, generator_kl_loss=1.389, generator_dur_loss=1.764, generator_adv_loss=2.082, generator_feat_match_loss=5.309, over 66.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=28.36, generator_mel_loss=17.84, generator_kl_loss=1.399, generator_dur_loss=1.782, generator_adv_loss=1.947, generator_feat_match_loss=5.383, over 596.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:39:08,792 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 14:39:16,785 INFO [train.py:591] (4/6) Epoch 609, validation: discriminator_loss=2.72, discriminator_real_loss=1.486, discriminator_fake_loss=1.235, generator_loss=27.72, generator_mel_loss=18.25, generator_kl_loss=1.291, generator_dur_loss=1.806, generator_adv_loss=2.001, generator_feat_match_loss=4.371, over 100.00 samples. +2024-03-14 14:39:16,787 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 14:41:33,502 INFO [train.py:527] (4/6) Epoch 609, batch 58, global_batch_idx: 75450, batch size: 44, loss[discriminator_loss=2.774, discriminator_real_loss=1.397, discriminator_fake_loss=1.377, generator_loss=28.41, generator_mel_loss=17.88, generator_kl_loss=1.568, generator_dur_loss=1.667, generator_adv_loss=1.943, generator_feat_match_loss=5.357, over 44.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.339, generator_loss=28.52, generator_mel_loss=18.04, generator_kl_loss=1.416, generator_dur_loss=1.741, generator_adv_loss=1.954, generator_feat_match_loss=5.364, over 3332.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:43:52,363 INFO [train.py:527] (4/6) Epoch 609, batch 108, global_batch_idx: 75500, batch size: 39, loss[discriminator_loss=2.67, discriminator_real_loss=1.31, discriminator_fake_loss=1.36, generator_loss=28.35, generator_mel_loss=17.8, generator_kl_loss=1.573, generator_dur_loss=1.695, generator_adv_loss=2.008, generator_feat_match_loss=5.275, over 39.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.361, discriminator_fake_loss=1.341, generator_loss=28.59, generator_mel_loss=18.06, generator_kl_loss=1.423, generator_dur_loss=1.742, generator_adv_loss=1.96, generator_feat_match_loss=5.402, over 6221.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:44:36,088 INFO [train.py:919] (4/6) Start epoch 610 +2024-03-14 14:46:32,230 INFO [train.py:527] (4/6) Epoch 610, batch 34, global_batch_idx: 75550, batch size: 55, loss[discriminator_loss=2.659, discriminator_real_loss=1.37, discriminator_fake_loss=1.289, generator_loss=29.32, generator_mel_loss=18.07, generator_kl_loss=1.576, generator_dur_loss=1.669, generator_adv_loss=2.153, generator_feat_match_loss=5.846, over 55.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.378, discriminator_fake_loss=1.327, generator_loss=28.55, generator_mel_loss=18.01, generator_kl_loss=1.432, generator_dur_loss=1.736, generator_adv_loss=1.997, generator_feat_match_loss=5.378, over 1891.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:48:50,513 INFO [train.py:527] (4/6) Epoch 610, batch 84, global_batch_idx: 75600, batch size: 39, loss[discriminator_loss=2.751, discriminator_real_loss=1.455, discriminator_fake_loss=1.297, generator_loss=28.31, generator_mel_loss=18.18, generator_kl_loss=1.473, generator_dur_loss=1.686, generator_adv_loss=1.887, generator_feat_match_loss=5.088, over 39.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.37, discriminator_fake_loss=1.334, generator_loss=28.58, generator_mel_loss=18.01, generator_kl_loss=1.427, generator_dur_loss=1.742, generator_adv_loss=1.976, generator_feat_match_loss=5.434, over 4620.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:48:50,514 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 14:48:59,261 INFO [train.py:591] (4/6) Epoch 610, validation: discriminator_loss=2.757, discriminator_real_loss=1.375, discriminator_fake_loss=1.382, generator_loss=27.32, generator_mel_loss=18.16, generator_kl_loss=1.119, generator_dur_loss=1.829, generator_adv_loss=1.845, generator_feat_match_loss=4.371, over 100.00 samples. +2024-03-14 14:48:59,262 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 14:50:49,945 INFO [train.py:919] (4/6) Start epoch 611 +2024-03-14 14:51:41,448 INFO [train.py:527] (4/6) Epoch 611, batch 10, global_batch_idx: 75650, batch size: 56, loss[discriminator_loss=2.733, discriminator_real_loss=1.395, discriminator_fake_loss=1.338, generator_loss=28.68, generator_mel_loss=17.77, generator_kl_loss=1.564, generator_dur_loss=1.722, generator_adv_loss=2.046, generator_feat_match_loss=5.576, over 56.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.365, discriminator_fake_loss=1.362, generator_loss=28.51, generator_mel_loss=17.99, generator_kl_loss=1.379, generator_dur_loss=1.776, generator_adv_loss=1.964, generator_feat_match_loss=5.401, over 691.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:53:58,047 INFO [train.py:527] (4/6) Epoch 611, batch 60, global_batch_idx: 75700, batch size: 53, loss[discriminator_loss=2.741, discriminator_real_loss=1.382, discriminator_fake_loss=1.359, generator_loss=27.64, generator_mel_loss=17.92, generator_kl_loss=1.261, generator_dur_loss=1.704, generator_adv_loss=1.963, generator_feat_match_loss=4.788, over 53.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.372, discriminator_fake_loss=1.339, generator_loss=28.59, generator_mel_loss=18.08, generator_kl_loss=1.423, generator_dur_loss=1.745, generator_adv_loss=1.981, generator_feat_match_loss=5.368, over 3238.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:56:18,208 INFO [train.py:527] (4/6) Epoch 611, batch 110, global_batch_idx: 75750, batch size: 45, loss[discriminator_loss=2.658, discriminator_real_loss=1.302, discriminator_fake_loss=1.355, generator_loss=28.71, generator_mel_loss=18.27, generator_kl_loss=1.505, generator_dur_loss=1.628, generator_adv_loss=1.958, generator_feat_match_loss=5.349, over 45.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.55, generator_mel_loss=18.01, generator_kl_loss=1.427, generator_dur_loss=1.745, generator_adv_loss=1.974, generator_feat_match_loss=5.387, over 6013.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:56:56,468 INFO [train.py:919] (4/6) Start epoch 612 +2024-03-14 14:59:01,023 INFO [train.py:527] (4/6) Epoch 612, batch 36, global_batch_idx: 75800, batch size: 58, loss[discriminator_loss=2.645, discriminator_real_loss=1.311, discriminator_fake_loss=1.333, generator_loss=28.7, generator_mel_loss=18.11, generator_kl_loss=1.412, generator_dur_loss=1.778, generator_adv_loss=2.061, generator_feat_match_loss=5.343, over 58.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.354, discriminator_fake_loss=1.338, generator_loss=28.47, generator_mel_loss=17.87, generator_kl_loss=1.436, generator_dur_loss=1.753, generator_adv_loss=1.97, generator_feat_match_loss=5.435, over 2045.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:59:01,024 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 14:59:08,881 INFO [train.py:591] (4/6) Epoch 612, validation: discriminator_loss=2.739, discriminator_real_loss=1.439, discriminator_fake_loss=1.3, generator_loss=28.23, generator_mel_loss=18.53, generator_kl_loss=1.212, generator_dur_loss=1.82, generator_adv_loss=1.946, generator_feat_match_loss=4.718, over 100.00 samples. +2024-03-14 14:59:08,882 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 15:01:27,410 INFO [train.py:527] (4/6) Epoch 612, batch 86, global_batch_idx: 75850, batch size: 14, loss[discriminator_loss=2.702, discriminator_real_loss=1.336, discriminator_fake_loss=1.365, generator_loss=30.31, generator_mel_loss=18.51, generator_kl_loss=2.024, generator_dur_loss=1.591, generator_adv_loss=2.133, generator_feat_match_loss=6.052, over 14.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.362, discriminator_fake_loss=1.336, generator_loss=28.54, generator_mel_loss=17.95, generator_kl_loss=1.414, generator_dur_loss=1.772, generator_adv_loss=1.972, generator_feat_match_loss=5.428, over 5119.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:03:12,529 INFO [train.py:919] (4/6) Start epoch 613 +2024-03-14 15:04:09,375 INFO [train.py:527] (4/6) Epoch 613, batch 12, global_batch_idx: 75900, batch size: 77, loss[discriminator_loss=2.689, discriminator_real_loss=1.344, discriminator_fake_loss=1.346, generator_loss=28.43, generator_mel_loss=17.97, generator_kl_loss=1.215, generator_dur_loss=1.841, generator_adv_loss=1.838, generator_feat_match_loss=5.569, over 77.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.35, discriminator_fake_loss=1.336, generator_loss=28.41, generator_mel_loss=17.96, generator_kl_loss=1.373, generator_dur_loss=1.743, generator_adv_loss=1.967, generator_feat_match_loss=5.366, over 731.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:06:27,939 INFO [train.py:527] (4/6) Epoch 613, batch 62, global_batch_idx: 75950, batch size: 56, loss[discriminator_loss=2.696, discriminator_real_loss=1.427, discriminator_fake_loss=1.269, generator_loss=28.63, generator_mel_loss=17.7, generator_kl_loss=1.344, generator_dur_loss=1.747, generator_adv_loss=2.058, generator_feat_match_loss=5.778, over 56.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.338, generator_loss=28.46, generator_mel_loss=17.94, generator_kl_loss=1.409, generator_dur_loss=1.756, generator_adv_loss=1.974, generator_feat_match_loss=5.388, over 3749.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:08:46,718 INFO [train.py:527] (4/6) Epoch 613, batch 112, global_batch_idx: 76000, batch size: 25, loss[discriminator_loss=2.584, discriminator_real_loss=1.318, discriminator_fake_loss=1.266, generator_loss=31.19, generator_mel_loss=18.91, generator_kl_loss=1.939, generator_dur_loss=1.567, generator_adv_loss=1.955, generator_feat_match_loss=6.827, over 25.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.367, discriminator_fake_loss=1.336, generator_loss=28.51, generator_mel_loss=17.98, generator_kl_loss=1.41, generator_dur_loss=1.755, generator_adv_loss=1.973, generator_feat_match_loss=5.398, over 6531.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:08:46,720 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 15:08:55,767 INFO [train.py:591] (4/6) Epoch 613, validation: discriminator_loss=2.732, discriminator_real_loss=1.369, discriminator_fake_loss=1.363, generator_loss=26.41, generator_mel_loss=17.51, generator_kl_loss=1.192, generator_dur_loss=1.815, generator_adv_loss=1.822, generator_feat_match_loss=4.074, over 100.00 samples. +2024-03-14 15:08:55,768 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 15:09:29,237 INFO [train.py:919] (4/6) Start epoch 614 +2024-03-14 15:11:40,162 INFO [train.py:527] (4/6) Epoch 614, batch 38, global_batch_idx: 76050, batch size: 50, loss[discriminator_loss=2.724, discriminator_real_loss=1.321, discriminator_fake_loss=1.403, generator_loss=28.78, generator_mel_loss=17.94, generator_kl_loss=1.411, generator_dur_loss=1.678, generator_adv_loss=1.999, generator_feat_match_loss=5.753, over 50.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.363, discriminator_fake_loss=1.337, generator_loss=28.49, generator_mel_loss=17.96, generator_kl_loss=1.418, generator_dur_loss=1.751, generator_adv_loss=1.976, generator_feat_match_loss=5.384, over 2165.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:14:00,557 INFO [train.py:527] (4/6) Epoch 614, batch 88, global_batch_idx: 76100, batch size: 56, loss[discriminator_loss=2.714, discriminator_real_loss=1.315, discriminator_fake_loss=1.399, generator_loss=28.58, generator_mel_loss=18.39, generator_kl_loss=1.408, generator_dur_loss=1.736, generator_adv_loss=1.947, generator_feat_match_loss=5.106, over 56.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.339, generator_loss=28.59, generator_mel_loss=17.97, generator_kl_loss=1.428, generator_dur_loss=1.758, generator_adv_loss=1.974, generator_feat_match_loss=5.46, over 5079.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:15:36,885 INFO [train.py:919] (4/6) Start epoch 615 +2024-03-14 15:16:41,717 INFO [train.py:527] (4/6) Epoch 615, batch 14, global_batch_idx: 76150, batch size: 72, loss[discriminator_loss=2.715, discriminator_real_loss=1.317, discriminator_fake_loss=1.398, generator_loss=28.72, generator_mel_loss=17.8, generator_kl_loss=1.399, generator_dur_loss=1.79, generator_adv_loss=2.003, generator_feat_match_loss=5.721, over 72.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.358, discriminator_fake_loss=1.349, generator_loss=28.44, generator_mel_loss=18, generator_kl_loss=1.43, generator_dur_loss=1.739, generator_adv_loss=1.965, generator_feat_match_loss=5.306, over 845.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:18:59,492 INFO [train.py:527] (4/6) Epoch 615, batch 64, global_batch_idx: 76200, batch size: 64, loss[discriminator_loss=2.733, discriminator_real_loss=1.445, discriminator_fake_loss=1.288, generator_loss=27.65, generator_mel_loss=17.62, generator_kl_loss=1.421, generator_dur_loss=1.755, generator_adv_loss=1.851, generator_feat_match_loss=5.009, over 64.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.367, discriminator_fake_loss=1.335, generator_loss=28.55, generator_mel_loss=18.01, generator_kl_loss=1.431, generator_dur_loss=1.744, generator_adv_loss=1.978, generator_feat_match_loss=5.388, over 3671.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:18:59,493 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 15:19:08,428 INFO [train.py:591] (4/6) Epoch 615, validation: discriminator_loss=2.76, discriminator_real_loss=1.37, discriminator_fake_loss=1.391, generator_loss=27.78, generator_mel_loss=18.31, generator_kl_loss=1.348, generator_dur_loss=1.799, generator_adv_loss=1.79, generator_feat_match_loss=4.526, over 100.00 samples. +2024-03-14 15:19:08,429 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 15:21:28,235 INFO [train.py:527] (4/6) Epoch 615, batch 114, global_batch_idx: 76250, batch size: 72, loss[discriminator_loss=2.708, discriminator_real_loss=1.346, discriminator_fake_loss=1.362, generator_loss=28.55, generator_mel_loss=18.04, generator_kl_loss=1.271, generator_dur_loss=1.766, generator_adv_loss=2.01, generator_feat_match_loss=5.464, over 72.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.367, discriminator_fake_loss=1.334, generator_loss=28.53, generator_mel_loss=18.02, generator_kl_loss=1.423, generator_dur_loss=1.745, generator_adv_loss=1.974, generator_feat_match_loss=5.365, over 6591.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:21:54,301 INFO [train.py:919] (4/6) Start epoch 616 +2024-03-14 15:24:09,280 INFO [train.py:527] (4/6) Epoch 616, batch 40, global_batch_idx: 76300, batch size: 88, loss[discriminator_loss=2.702, discriminator_real_loss=1.285, discriminator_fake_loss=1.416, generator_loss=28.6, generator_mel_loss=17.64, generator_kl_loss=1.308, generator_dur_loss=1.849, generator_adv_loss=2.05, generator_feat_match_loss=5.751, over 88.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.362, discriminator_fake_loss=1.336, generator_loss=28.51, generator_mel_loss=17.97, generator_kl_loss=1.44, generator_dur_loss=1.752, generator_adv_loss=1.973, generator_feat_match_loss=5.378, over 2410.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:26:26,328 INFO [train.py:527] (4/6) Epoch 616, batch 90, global_batch_idx: 76350, batch size: 44, loss[discriminator_loss=2.782, discriminator_real_loss=1.513, discriminator_fake_loss=1.269, generator_loss=27.22, generator_mel_loss=17.29, generator_kl_loss=1.42, generator_dur_loss=1.675, generator_adv_loss=1.971, generator_feat_match_loss=4.866, over 44.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.37, discriminator_fake_loss=1.333, generator_loss=28.46, generator_mel_loss=17.97, generator_kl_loss=1.42, generator_dur_loss=1.75, generator_adv_loss=1.975, generator_feat_match_loss=5.347, over 5379.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:28:00,598 INFO [train.py:919] (4/6) Start epoch 617 +2024-03-14 15:29:07,972 INFO [train.py:527] (4/6) Epoch 617, batch 16, global_batch_idx: 76400, batch size: 77, loss[discriminator_loss=2.705, discriminator_real_loss=1.335, discriminator_fake_loss=1.37, generator_loss=28.21, generator_mel_loss=17.8, generator_kl_loss=1.274, generator_dur_loss=1.833, generator_adv_loss=1.829, generator_feat_match_loss=5.471, over 77.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.347, discriminator_fake_loss=1.338, generator_loss=28.39, generator_mel_loss=17.9, generator_kl_loss=1.411, generator_dur_loss=1.771, generator_adv_loss=1.953, generator_feat_match_loss=5.356, over 1078.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:29:07,975 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 15:29:16,008 INFO [train.py:591] (4/6) Epoch 617, validation: discriminator_loss=2.792, discriminator_real_loss=1.402, discriminator_fake_loss=1.39, generator_loss=26.97, generator_mel_loss=18.09, generator_kl_loss=1.223, generator_dur_loss=1.79, generator_adv_loss=1.766, generator_feat_match_loss=4.101, over 100.00 samples. +2024-03-14 15:29:16,009 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 15:31:36,494 INFO [train.py:527] (4/6) Epoch 617, batch 66, global_batch_idx: 76450, batch size: 70, loss[discriminator_loss=2.702, discriminator_real_loss=1.346, discriminator_fake_loss=1.356, generator_loss=28.25, generator_mel_loss=17.85, generator_kl_loss=1.38, generator_dur_loss=1.801, generator_adv_loss=1.96, generator_feat_match_loss=5.256, over 70.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.357, discriminator_fake_loss=1.339, generator_loss=28.51, generator_mel_loss=17.98, generator_kl_loss=1.435, generator_dur_loss=1.745, generator_adv_loss=1.957, generator_feat_match_loss=5.39, over 3851.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:33:53,714 INFO [train.py:527] (4/6) Epoch 617, batch 116, global_batch_idx: 76500, batch size: 31, loss[discriminator_loss=2.714, discriminator_real_loss=1.34, discriminator_fake_loss=1.374, generator_loss=30.09, generator_mel_loss=18.72, generator_kl_loss=1.726, generator_dur_loss=1.612, generator_adv_loss=2.107, generator_feat_match_loss=5.922, over 31.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.359, discriminator_fake_loss=1.336, generator_loss=28.55, generator_mel_loss=18, generator_kl_loss=1.428, generator_dur_loss=1.749, generator_adv_loss=1.964, generator_feat_match_loss=5.413, over 6579.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:34:14,030 INFO [train.py:919] (4/6) Start epoch 618 +2024-03-14 15:36:33,314 INFO [train.py:527] (4/6) Epoch 618, batch 42, global_batch_idx: 76550, batch size: 64, loss[discriminator_loss=2.682, discriminator_real_loss=1.42, discriminator_fake_loss=1.262, generator_loss=28.53, generator_mel_loss=18.14, generator_kl_loss=1.508, generator_dur_loss=1.73, generator_adv_loss=1.95, generator_feat_match_loss=5.202, over 64.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.365, discriminator_fake_loss=1.341, generator_loss=28.51, generator_mel_loss=17.98, generator_kl_loss=1.414, generator_dur_loss=1.759, generator_adv_loss=1.954, generator_feat_match_loss=5.398, over 2579.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:38:53,466 INFO [train.py:527] (4/6) Epoch 618, batch 92, global_batch_idx: 76600, batch size: 15, loss[discriminator_loss=2.695, discriminator_real_loss=1.328, discriminator_fake_loss=1.367, generator_loss=29.89, generator_mel_loss=18.16, generator_kl_loss=1.839, generator_dur_loss=1.486, generator_adv_loss=1.988, generator_feat_match_loss=6.419, over 15.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=28.45, generator_mel_loss=18, generator_kl_loss=1.41, generator_dur_loss=1.747, generator_adv_loss=1.959, generator_feat_match_loss=5.343, over 5423.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:38:53,467 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 15:39:02,305 INFO [train.py:591] (4/6) Epoch 618, validation: discriminator_loss=2.737, discriminator_real_loss=1.457, discriminator_fake_loss=1.28, generator_loss=28.13, generator_mel_loss=18.57, generator_kl_loss=1.192, generator_dur_loss=1.785, generator_adv_loss=2.007, generator_feat_match_loss=4.574, over 100.00 samples. +2024-03-14 15:39:02,306 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 15:40:29,376 INFO [train.py:919] (4/6) Start epoch 619 +2024-03-14 15:41:44,088 INFO [train.py:527] (4/6) Epoch 619, batch 18, global_batch_idx: 76650, batch size: 50, loss[discriminator_loss=2.632, discriminator_real_loss=1.225, discriminator_fake_loss=1.407, generator_loss=30.43, generator_mel_loss=18.22, generator_kl_loss=1.62, generator_dur_loss=1.625, generator_adv_loss=2.069, generator_feat_match_loss=6.892, over 50.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.363, discriminator_fake_loss=1.327, generator_loss=28.6, generator_mel_loss=17.94, generator_kl_loss=1.434, generator_dur_loss=1.701, generator_adv_loss=1.994, generator_feat_match_loss=5.538, over 1005.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:44:04,011 INFO [train.py:527] (4/6) Epoch 619, batch 68, global_batch_idx: 76700, batch size: 59, loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.1, generator_mel_loss=18.41, generator_kl_loss=1.471, generator_dur_loss=1.682, generator_adv_loss=1.949, generator_feat_match_loss=5.585, over 59.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.355, discriminator_fake_loss=1.344, generator_loss=28.61, generator_mel_loss=18, generator_kl_loss=1.444, generator_dur_loss=1.717, generator_adv_loss=1.964, generator_feat_match_loss=5.49, over 3827.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:46:21,626 INFO [train.py:527] (4/6) Epoch 619, batch 118, global_batch_idx: 76750, batch size: 64, loss[discriminator_loss=2.731, discriminator_real_loss=1.312, discriminator_fake_loss=1.419, generator_loss=28.57, generator_mel_loss=18, generator_kl_loss=1.387, generator_dur_loss=1.701, generator_adv_loss=2.099, generator_feat_match_loss=5.382, over 64.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.355, discriminator_fake_loss=1.343, generator_loss=28.65, generator_mel_loss=18.02, generator_kl_loss=1.444, generator_dur_loss=1.719, generator_adv_loss=1.973, generator_feat_match_loss=5.493, over 6584.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:46:36,991 INFO [train.py:919] (4/6) Start epoch 620 +2024-03-14 15:49:01,581 INFO [train.py:527] (4/6) Epoch 620, batch 44, global_batch_idx: 76800, batch size: 74, loss[discriminator_loss=2.748, discriminator_real_loss=1.5, discriminator_fake_loss=1.248, generator_loss=28.17, generator_mel_loss=18.06, generator_kl_loss=1.41, generator_dur_loss=1.771, generator_adv_loss=1.818, generator_feat_match_loss=5.114, over 74.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.369, discriminator_fake_loss=1.328, generator_loss=28.63, generator_mel_loss=18.05, generator_kl_loss=1.429, generator_dur_loss=1.717, generator_adv_loss=1.979, generator_feat_match_loss=5.453, over 2517.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:49:01,582 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 15:49:09,771 INFO [train.py:591] (4/6) Epoch 620, validation: discriminator_loss=2.76, discriminator_real_loss=1.379, discriminator_fake_loss=1.381, generator_loss=27.65, generator_mel_loss=18.3, generator_kl_loss=1.32, generator_dur_loss=1.786, generator_adv_loss=1.781, generator_feat_match_loss=4.468, over 100.00 samples. +2024-03-14 15:49:09,772 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 15:51:27,793 INFO [train.py:527] (4/6) Epoch 620, batch 94, global_batch_idx: 76850, batch size: 36, loss[discriminator_loss=2.695, discriminator_real_loss=1.351, discriminator_fake_loss=1.344, generator_loss=27.68, generator_mel_loss=17.63, generator_kl_loss=1.316, generator_dur_loss=1.7, generator_adv_loss=2.007, generator_feat_match_loss=5.025, over 36.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.366, discriminator_fake_loss=1.331, generator_loss=28.59, generator_mel_loss=18.02, generator_kl_loss=1.437, generator_dur_loss=1.722, generator_adv_loss=1.978, generator_feat_match_loss=5.43, over 5245.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:52:52,164 INFO [train.py:919] (4/6) Start epoch 621 +2024-03-14 15:54:12,775 INFO [train.py:527] (4/6) Epoch 621, batch 20, global_batch_idx: 76900, batch size: 68, loss[discriminator_loss=2.633, discriminator_real_loss=1.253, discriminator_fake_loss=1.38, generator_loss=29.33, generator_mel_loss=18.11, generator_kl_loss=1.491, generator_dur_loss=1.767, generator_adv_loss=2.074, generator_feat_match_loss=5.89, over 68.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.77, generator_mel_loss=18.02, generator_kl_loss=1.432, generator_dur_loss=1.714, generator_adv_loss=2.007, generator_feat_match_loss=5.6, over 1156.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:56:29,080 INFO [train.py:527] (4/6) Epoch 621, batch 70, global_batch_idx: 76950, batch size: 68, loss[discriminator_loss=2.687, discriminator_real_loss=1.474, discriminator_fake_loss=1.214, generator_loss=27.53, generator_mel_loss=18.07, generator_kl_loss=1.336, generator_dur_loss=1.728, generator_adv_loss=1.923, generator_feat_match_loss=4.47, over 68.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.366, discriminator_fake_loss=1.33, generator_loss=28.66, generator_mel_loss=18.04, generator_kl_loss=1.428, generator_dur_loss=1.73, generator_adv_loss=1.99, generator_feat_match_loss=5.468, over 4037.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:58:46,178 INFO [train.py:527] (4/6) Epoch 621, batch 120, global_batch_idx: 77000, batch size: 15, loss[discriminator_loss=2.677, discriminator_real_loss=1.475, discriminator_fake_loss=1.202, generator_loss=29.74, generator_mel_loss=18.36, generator_kl_loss=1.601, generator_dur_loss=1.534, generator_adv_loss=1.95, generator_feat_match_loss=6.297, over 15.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.339, generator_loss=28.67, generator_mel_loss=18.01, generator_kl_loss=1.419, generator_dur_loss=1.747, generator_adv_loss=2.01, generator_feat_match_loss=5.489, over 6926.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:58:46,179 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 15:58:54,769 INFO [train.py:591] (4/6) Epoch 621, validation: discriminator_loss=2.75, discriminator_real_loss=1.291, discriminator_fake_loss=1.459, generator_loss=27.94, generator_mel_loss=18.84, generator_kl_loss=1.224, generator_dur_loss=1.817, generator_adv_loss=1.724, generator_feat_match_loss=4.334, over 100.00 samples. +2024-03-14 15:58:54,769 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 15:59:04,349 INFO [train.py:919] (4/6) Start epoch 622 +2024-03-14 16:01:37,784 INFO [train.py:527] (4/6) Epoch 622, batch 46, global_batch_idx: 77050, batch size: 61, loss[discriminator_loss=2.723, discriminator_real_loss=1.366, discriminator_fake_loss=1.357, generator_loss=28.57, generator_mel_loss=18.13, generator_kl_loss=1.524, generator_dur_loss=1.718, generator_adv_loss=2.036, generator_feat_match_loss=5.167, over 61.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.369, discriminator_fake_loss=1.328, generator_loss=28.49, generator_mel_loss=17.98, generator_kl_loss=1.384, generator_dur_loss=1.757, generator_adv_loss=1.984, generator_feat_match_loss=5.388, over 2672.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:03:59,788 INFO [train.py:527] (4/6) Epoch 622, batch 96, global_batch_idx: 77100, batch size: 59, loss[discriminator_loss=2.697, discriminator_real_loss=1.422, discriminator_fake_loss=1.275, generator_loss=27.99, generator_mel_loss=18.03, generator_kl_loss=1.335, generator_dur_loss=1.708, generator_adv_loss=1.934, generator_feat_match_loss=4.991, over 59.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.371, discriminator_fake_loss=1.331, generator_loss=28.48, generator_mel_loss=17.97, generator_kl_loss=1.395, generator_dur_loss=1.75, generator_adv_loss=1.977, generator_feat_match_loss=5.392, over 5517.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:05:15,177 INFO [train.py:919] (4/6) Start epoch 623 +2024-03-14 16:06:39,186 INFO [train.py:527] (4/6) Epoch 623, batch 22, global_batch_idx: 77150, batch size: 52, loss[discriminator_loss=2.687, discriminator_real_loss=1.386, discriminator_fake_loss=1.3, generator_loss=28.6, generator_mel_loss=17.81, generator_kl_loss=1.612, generator_dur_loss=1.668, generator_adv_loss=1.849, generator_feat_match_loss=5.664, over 52.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.379, discriminator_fake_loss=1.335, generator_loss=28.42, generator_mel_loss=18.01, generator_kl_loss=1.456, generator_dur_loss=1.722, generator_adv_loss=1.978, generator_feat_match_loss=5.257, over 1234.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:08:58,387 INFO [train.py:527] (4/6) Epoch 623, batch 72, global_batch_idx: 77200, batch size: 62, loss[discriminator_loss=2.687, discriminator_real_loss=1.374, discriminator_fake_loss=1.313, generator_loss=27.35, generator_mel_loss=17.64, generator_kl_loss=1.342, generator_dur_loss=1.715, generator_adv_loss=1.941, generator_feat_match_loss=4.708, over 62.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.365, discriminator_fake_loss=1.333, generator_loss=28.37, generator_mel_loss=17.96, generator_kl_loss=1.417, generator_dur_loss=1.722, generator_adv_loss=1.976, generator_feat_match_loss=5.287, over 4065.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:08:58,389 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 16:09:07,223 INFO [train.py:591] (4/6) Epoch 623, validation: discriminator_loss=2.696, discriminator_real_loss=1.386, discriminator_fake_loss=1.31, generator_loss=26.81, generator_mel_loss=18.35, generator_kl_loss=1.231, generator_dur_loss=1.774, generator_adv_loss=1.872, generator_feat_match_loss=3.584, over 100.00 samples. +2024-03-14 16:09:07,223 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 16:11:25,824 INFO [train.py:527] (4/6) Epoch 623, batch 122, global_batch_idx: 77250, batch size: 31, loss[discriminator_loss=2.757, discriminator_real_loss=1.348, discriminator_fake_loss=1.41, generator_loss=28.92, generator_mel_loss=18.35, generator_kl_loss=1.512, generator_dur_loss=1.618, generator_adv_loss=2.128, generator_feat_match_loss=5.316, over 31.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.48, generator_mel_loss=17.97, generator_kl_loss=1.42, generator_dur_loss=1.73, generator_adv_loss=1.98, generator_feat_match_loss=5.376, over 7077.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:11:30,474 INFO [train.py:919] (4/6) Start epoch 624 +2024-03-14 16:14:06,379 INFO [train.py:527] (4/6) Epoch 624, batch 48, global_batch_idx: 77300, batch size: 55, loss[discriminator_loss=2.618, discriminator_real_loss=1.235, discriminator_fake_loss=1.383, generator_loss=29.89, generator_mel_loss=18.44, generator_kl_loss=1.504, generator_dur_loss=1.708, generator_adv_loss=2.062, generator_feat_match_loss=6.182, over 55.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.53, generator_mel_loss=17.91, generator_kl_loss=1.395, generator_dur_loss=1.736, generator_adv_loss=1.98, generator_feat_match_loss=5.507, over 2963.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:16:24,545 INFO [train.py:527] (4/6) Epoch 624, batch 98, global_batch_idx: 77350, batch size: 66, loss[discriminator_loss=2.733, discriminator_real_loss=1.421, discriminator_fake_loss=1.313, generator_loss=29.05, generator_mel_loss=17.89, generator_kl_loss=1.498, generator_dur_loss=1.751, generator_adv_loss=1.827, generator_feat_match_loss=6.077, over 66.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.366, discriminator_fake_loss=1.332, generator_loss=28.56, generator_mel_loss=17.94, generator_kl_loss=1.419, generator_dur_loss=1.733, generator_adv_loss=1.981, generator_feat_match_loss=5.487, over 5779.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:17:35,300 INFO [train.py:919] (4/6) Start epoch 625 +2024-03-14 16:19:06,949 INFO [train.py:527] (4/6) Epoch 625, batch 24, global_batch_idx: 77400, batch size: 36, loss[discriminator_loss=2.792, discriminator_real_loss=1.438, discriminator_fake_loss=1.354, generator_loss=27.46, generator_mel_loss=18.22, generator_kl_loss=1.471, generator_dur_loss=1.631, generator_adv_loss=1.885, generator_feat_match_loss=4.258, over 36.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.363, discriminator_fake_loss=1.342, generator_loss=28.69, generator_mel_loss=18.03, generator_kl_loss=1.433, generator_dur_loss=1.735, generator_adv_loss=2.001, generator_feat_match_loss=5.49, over 1382.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:19:06,950 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 16:19:15,007 INFO [train.py:591] (4/6) Epoch 625, validation: discriminator_loss=2.787, discriminator_real_loss=1.394, discriminator_fake_loss=1.393, generator_loss=27.75, generator_mel_loss=18.58, generator_kl_loss=1.218, generator_dur_loss=1.788, generator_adv_loss=1.8, generator_feat_match_loss=4.369, over 100.00 samples. +2024-03-14 16:19:15,008 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 16:21:33,744 INFO [train.py:527] (4/6) Epoch 625, batch 74, global_batch_idx: 77450, batch size: 77, loss[discriminator_loss=2.757, discriminator_real_loss=1.354, discriminator_fake_loss=1.402, generator_loss=28.59, generator_mel_loss=18.01, generator_kl_loss=1.373, generator_dur_loss=1.828, generator_adv_loss=2.009, generator_feat_match_loss=5.373, over 77.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.366, discriminator_fake_loss=1.34, generator_loss=28.64, generator_mel_loss=18.01, generator_kl_loss=1.43, generator_dur_loss=1.744, generator_adv_loss=1.988, generator_feat_match_loss=5.468, over 4341.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:23:50,684 INFO [train.py:919] (4/6) Start epoch 626 +2024-03-14 16:24:13,162 INFO [train.py:527] (4/6) Epoch 626, batch 0, global_batch_idx: 77500, batch size: 83, loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.4, generator_mel_loss=18.49, generator_kl_loss=1.335, generator_dur_loss=1.842, generator_adv_loss=2.088, generator_feat_match_loss=5.645, over 83.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.4, generator_mel_loss=18.49, generator_kl_loss=1.335, generator_dur_loss=1.842, generator_adv_loss=2.088, generator_feat_match_loss=5.645, over 83.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:26:30,663 INFO [train.py:527] (4/6) Epoch 626, batch 50, global_batch_idx: 77550, batch size: 56, loss[discriminator_loss=2.732, discriminator_real_loss=1.368, discriminator_fake_loss=1.364, generator_loss=28.97, generator_mel_loss=18.17, generator_kl_loss=1.423, generator_dur_loss=1.777, generator_adv_loss=2.034, generator_feat_match_loss=5.56, over 56.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.338, generator_loss=28.49, generator_mel_loss=17.94, generator_kl_loss=1.434, generator_dur_loss=1.76, generator_adv_loss=1.963, generator_feat_match_loss=5.389, over 2972.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:28:48,501 INFO [train.py:527] (4/6) Epoch 626, batch 100, global_batch_idx: 77600, batch size: 96, loss[discriminator_loss=2.708, discriminator_real_loss=1.403, discriminator_fake_loss=1.305, generator_loss=28.06, generator_mel_loss=17.63, generator_kl_loss=1.42, generator_dur_loss=1.86, generator_adv_loss=1.986, generator_feat_match_loss=5.171, over 96.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.52, generator_mel_loss=17.96, generator_kl_loss=1.437, generator_dur_loss=1.753, generator_adv_loss=1.967, generator_feat_match_loss=5.413, over 5765.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:28:48,503 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 16:28:57,308 INFO [train.py:591] (4/6) Epoch 626, validation: discriminator_loss=2.77, discriminator_real_loss=1.421, discriminator_fake_loss=1.349, generator_loss=28.55, generator_mel_loss=18.8, generator_kl_loss=1.313, generator_dur_loss=1.817, generator_adv_loss=1.934, generator_feat_match_loss=4.69, over 100.00 samples. +2024-03-14 16:28:57,308 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 16:30:02,631 INFO [train.py:919] (4/6) Start epoch 627 +2024-03-14 16:31:38,235 INFO [train.py:527] (4/6) Epoch 627, batch 26, global_batch_idx: 77650, batch size: 74, loss[discriminator_loss=2.722, discriminator_real_loss=1.39, discriminator_fake_loss=1.332, generator_loss=27.44, generator_mel_loss=17.76, generator_kl_loss=1.279, generator_dur_loss=1.77, generator_adv_loss=1.823, generator_feat_match_loss=4.813, over 74.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.364, discriminator_fake_loss=1.341, generator_loss=28.49, generator_mel_loss=17.93, generator_kl_loss=1.428, generator_dur_loss=1.74, generator_adv_loss=1.961, generator_feat_match_loss=5.428, over 1573.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:33:58,475 INFO [train.py:527] (4/6) Epoch 627, batch 76, global_batch_idx: 77700, batch size: 39, loss[discriminator_loss=2.631, discriminator_real_loss=1.333, discriminator_fake_loss=1.298, generator_loss=28.9, generator_mel_loss=18.01, generator_kl_loss=1.446, generator_dur_loss=1.694, generator_adv_loss=2.058, generator_feat_match_loss=5.693, over 39.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.356, discriminator_fake_loss=1.336, generator_loss=28.55, generator_mel_loss=17.96, generator_kl_loss=1.425, generator_dur_loss=1.748, generator_adv_loss=1.974, generator_feat_match_loss=5.44, over 4493.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:36:09,715 INFO [train.py:919] (4/6) Start epoch 628 +2024-03-14 16:36:40,019 INFO [train.py:527] (4/6) Epoch 628, batch 2, global_batch_idx: 77750, batch size: 31, loss[discriminator_loss=2.624, discriminator_real_loss=1.309, discriminator_fake_loss=1.314, generator_loss=29.93, generator_mel_loss=18.75, generator_kl_loss=1.577, generator_dur_loss=1.585, generator_adv_loss=2.162, generator_feat_match_loss=5.861, over 31.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.367, discriminator_fake_loss=1.333, generator_loss=28.52, generator_mel_loss=18.08, generator_kl_loss=1.318, generator_dur_loss=1.756, generator_adv_loss=1.946, generator_feat_match_loss=5.42, over 167.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:39:00,310 INFO [train.py:527] (4/6) Epoch 628, batch 52, global_batch_idx: 77800, batch size: 39, loss[discriminator_loss=2.71, discriminator_real_loss=1.424, discriminator_fake_loss=1.286, generator_loss=28.89, generator_mel_loss=17.98, generator_kl_loss=1.565, generator_dur_loss=1.671, generator_adv_loss=1.924, generator_feat_match_loss=5.75, over 39.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.378, discriminator_fake_loss=1.336, generator_loss=28.48, generator_mel_loss=17.97, generator_kl_loss=1.399, generator_dur_loss=1.761, generator_adv_loss=1.964, generator_feat_match_loss=5.386, over 3165.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:39:00,311 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 16:39:08,520 INFO [train.py:591] (4/6) Epoch 628, validation: discriminator_loss=2.75, discriminator_real_loss=1.409, discriminator_fake_loss=1.341, generator_loss=26.74, generator_mel_loss=17.86, generator_kl_loss=1.223, generator_dur_loss=1.809, generator_adv_loss=1.878, generator_feat_match_loss=3.963, over 100.00 samples. +2024-03-14 16:39:08,520 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 16:41:26,674 INFO [train.py:527] (4/6) Epoch 628, batch 102, global_batch_idx: 77850, batch size: 42, loss[discriminator_loss=2.728, discriminator_real_loss=1.389, discriminator_fake_loss=1.339, generator_loss=28.71, generator_mel_loss=17.6, generator_kl_loss=1.46, generator_dur_loss=1.715, generator_adv_loss=2.045, generator_feat_match_loss=5.883, over 42.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.369, discriminator_fake_loss=1.336, generator_loss=28.51, generator_mel_loss=17.96, generator_kl_loss=1.411, generator_dur_loss=1.756, generator_adv_loss=1.962, generator_feat_match_loss=5.422, over 6002.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:42:25,089 INFO [train.py:919] (4/6) Start epoch 629 +2024-03-14 16:44:06,953 INFO [train.py:527] (4/6) Epoch 629, batch 28, global_batch_idx: 77900, batch size: 70, loss[discriminator_loss=2.692, discriminator_real_loss=1.42, discriminator_fake_loss=1.272, generator_loss=28.68, generator_mel_loss=17.87, generator_kl_loss=1.507, generator_dur_loss=1.826, generator_adv_loss=2.045, generator_feat_match_loss=5.432, over 70.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.364, discriminator_fake_loss=1.344, generator_loss=28.56, generator_mel_loss=18, generator_kl_loss=1.416, generator_dur_loss=1.778, generator_adv_loss=1.964, generator_feat_match_loss=5.406, over 1841.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:46:27,940 INFO [train.py:527] (4/6) Epoch 629, batch 78, global_batch_idx: 77950, batch size: 80, loss[discriminator_loss=2.665, discriminator_real_loss=1.408, discriminator_fake_loss=1.257, generator_loss=28.13, generator_mel_loss=17.68, generator_kl_loss=1.235, generator_dur_loss=1.801, generator_adv_loss=2.011, generator_feat_match_loss=5.404, over 80.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.363, discriminator_fake_loss=1.333, generator_loss=28.6, generator_mel_loss=17.97, generator_kl_loss=1.414, generator_dur_loss=1.772, generator_adv_loss=1.972, generator_feat_match_loss=5.473, over 4907.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:48:28,509 INFO [train.py:919] (4/6) Start epoch 630 +2024-03-14 16:49:03,348 INFO [train.py:527] (4/6) Epoch 630, batch 4, global_batch_idx: 78000, batch size: 31, loss[discriminator_loss=2.764, discriminator_real_loss=1.452, discriminator_fake_loss=1.312, generator_loss=27.39, generator_mel_loss=17.78, generator_kl_loss=1.622, generator_dur_loss=1.615, generator_adv_loss=1.956, generator_feat_match_loss=4.417, over 31.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.382, discriminator_fake_loss=1.355, generator_loss=28.54, generator_mel_loss=18.12, generator_kl_loss=1.494, generator_dur_loss=1.745, generator_adv_loss=1.902, generator_feat_match_loss=5.277, over 229.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:49:03,350 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 16:49:11,212 INFO [train.py:591] (4/6) Epoch 630, validation: discriminator_loss=2.726, discriminator_real_loss=1.44, discriminator_fake_loss=1.286, generator_loss=27.32, generator_mel_loss=18.55, generator_kl_loss=1.224, generator_dur_loss=1.794, generator_adv_loss=1.879, generator_feat_match_loss=3.873, over 100.00 samples. +2024-03-14 16:49:11,214 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 16:51:32,558 INFO [train.py:527] (4/6) Epoch 630, batch 54, global_batch_idx: 78050, batch size: 56, loss[discriminator_loss=2.73, discriminator_real_loss=1.378, discriminator_fake_loss=1.353, generator_loss=29.52, generator_mel_loss=18.49, generator_kl_loss=1.609, generator_dur_loss=1.662, generator_adv_loss=1.864, generator_feat_match_loss=5.895, over 56.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.372, discriminator_fake_loss=1.34, generator_loss=28.74, generator_mel_loss=18.05, generator_kl_loss=1.433, generator_dur_loss=1.726, generator_adv_loss=1.97, generator_feat_match_loss=5.557, over 3102.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:53:47,788 INFO [train.py:527] (4/6) Epoch 630, batch 104, global_batch_idx: 78100, batch size: 55, loss[discriminator_loss=2.705, discriminator_real_loss=1.367, discriminator_fake_loss=1.338, generator_loss=29.48, generator_mel_loss=18.8, generator_kl_loss=1.456, generator_dur_loss=1.69, generator_adv_loss=2.006, generator_feat_match_loss=5.527, over 55.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.338, generator_loss=28.64, generator_mel_loss=18.04, generator_kl_loss=1.432, generator_dur_loss=1.726, generator_adv_loss=1.962, generator_feat_match_loss=5.483, over 5871.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:54:41,248 INFO [train.py:919] (4/6) Start epoch 631 +2024-03-14 16:56:27,400 INFO [train.py:527] (4/6) Epoch 631, batch 30, global_batch_idx: 78150, batch size: 45, loss[discriminator_loss=2.66, discriminator_real_loss=1.357, discriminator_fake_loss=1.304, generator_loss=29.23, generator_mel_loss=18.23, generator_kl_loss=1.497, generator_dur_loss=1.644, generator_adv_loss=2.123, generator_feat_match_loss=5.739, over 45.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.376, discriminator_fake_loss=1.342, generator_loss=28.64, generator_mel_loss=17.97, generator_kl_loss=1.482, generator_dur_loss=1.712, generator_adv_loss=1.971, generator_feat_match_loss=5.498, over 1542.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:58:46,208 INFO [train.py:527] (4/6) Epoch 631, batch 80, global_batch_idx: 78200, batch size: 44, loss[discriminator_loss=2.708, discriminator_real_loss=1.458, discriminator_fake_loss=1.25, generator_loss=27.4, generator_mel_loss=17.17, generator_kl_loss=1.647, generator_dur_loss=1.69, generator_adv_loss=1.966, generator_feat_match_loss=4.926, over 44.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.37, discriminator_fake_loss=1.335, generator_loss=28.62, generator_mel_loss=17.97, generator_kl_loss=1.448, generator_dur_loss=1.738, generator_adv_loss=1.983, generator_feat_match_loss=5.484, over 4484.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:58:46,209 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 16:58:55,114 INFO [train.py:591] (4/6) Epoch 631, validation: discriminator_loss=2.755, discriminator_real_loss=1.418, discriminator_fake_loss=1.337, generator_loss=28.09, generator_mel_loss=18.49, generator_kl_loss=1.212, generator_dur_loss=1.811, generator_adv_loss=1.905, generator_feat_match_loss=4.679, over 100.00 samples. +2024-03-14 16:58:55,115 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 17:00:56,730 INFO [train.py:919] (4/6) Start epoch 632 +2024-03-14 17:01:38,896 INFO [train.py:527] (4/6) Epoch 632, batch 6, global_batch_idx: 78250, batch size: 47, loss[discriminator_loss=2.642, discriminator_real_loss=1.372, discriminator_fake_loss=1.27, generator_loss=29.29, generator_mel_loss=18.12, generator_kl_loss=1.611, generator_dur_loss=1.672, generator_adv_loss=2.021, generator_feat_match_loss=5.865, over 47.00 samples.], tot_loss[discriminator_loss=2.651, discriminator_real_loss=1.347, discriminator_fake_loss=1.304, generator_loss=28.9, generator_mel_loss=17.88, generator_kl_loss=1.45, generator_dur_loss=1.755, generator_adv_loss=1.989, generator_feat_match_loss=5.819, over 372.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:03:55,685 INFO [train.py:527] (4/6) Epoch 632, batch 56, global_batch_idx: 78300, batch size: 77, loss[discriminator_loss=2.709, discriminator_real_loss=1.383, discriminator_fake_loss=1.326, generator_loss=27.31, generator_mel_loss=17.44, generator_kl_loss=1.468, generator_dur_loss=1.833, generator_adv_loss=1.946, generator_feat_match_loss=4.623, over 77.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.32, generator_loss=28.54, generator_mel_loss=17.87, generator_kl_loss=1.427, generator_dur_loss=1.744, generator_adv_loss=1.986, generator_feat_match_loss=5.508, over 3014.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:06:17,436 INFO [train.py:527] (4/6) Epoch 632, batch 106, global_batch_idx: 78350, batch size: 15, loss[discriminator_loss=2.73, discriminator_real_loss=1.453, discriminator_fake_loss=1.277, generator_loss=27.79, generator_mel_loss=17.71, generator_kl_loss=1.699, generator_dur_loss=1.562, generator_adv_loss=1.843, generator_feat_match_loss=4.977, over 15.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.366, discriminator_fake_loss=1.328, generator_loss=28.55, generator_mel_loss=17.92, generator_kl_loss=1.416, generator_dur_loss=1.749, generator_adv_loss=1.975, generator_feat_match_loss=5.487, over 5955.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:07:03,569 INFO [train.py:919] (4/6) Start epoch 633 +2024-03-14 17:08:58,850 INFO [train.py:527] (4/6) Epoch 633, batch 32, global_batch_idx: 78400, batch size: 80, loss[discriminator_loss=2.705, discriminator_real_loss=1.323, discriminator_fake_loss=1.382, generator_loss=28.78, generator_mel_loss=18.07, generator_kl_loss=1.408, generator_dur_loss=1.783, generator_adv_loss=2.082, generator_feat_match_loss=5.446, over 80.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.364, discriminator_fake_loss=1.35, generator_loss=28.76, generator_mel_loss=18.04, generator_kl_loss=1.423, generator_dur_loss=1.725, generator_adv_loss=1.977, generator_feat_match_loss=5.59, over 1788.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:08:58,851 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 17:09:07,180 INFO [train.py:591] (4/6) Epoch 633, validation: discriminator_loss=2.741, discriminator_real_loss=1.458, discriminator_fake_loss=1.283, generator_loss=28.12, generator_mel_loss=18.28, generator_kl_loss=1.256, generator_dur_loss=1.797, generator_adv_loss=2.012, generator_feat_match_loss=4.773, over 100.00 samples. +2024-03-14 17:09:07,182 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 17:11:28,215 INFO [train.py:527] (4/6) Epoch 633, batch 82, global_batch_idx: 78450, batch size: 74, loss[discriminator_loss=2.706, discriminator_real_loss=1.394, discriminator_fake_loss=1.313, generator_loss=28.51, generator_mel_loss=17.95, generator_kl_loss=1.379, generator_dur_loss=1.762, generator_adv_loss=2.056, generator_feat_match_loss=5.354, over 74.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.356, discriminator_fake_loss=1.342, generator_loss=28.69, generator_mel_loss=18.02, generator_kl_loss=1.415, generator_dur_loss=1.737, generator_adv_loss=1.969, generator_feat_match_loss=5.557, over 4831.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:13:21,181 INFO [train.py:919] (4/6) Start epoch 634 +2024-03-14 17:14:04,058 INFO [train.py:527] (4/6) Epoch 634, batch 8, global_batch_idx: 78500, batch size: 53, loss[discriminator_loss=2.701, discriminator_real_loss=1.338, discriminator_fake_loss=1.363, generator_loss=27.58, generator_mel_loss=17.63, generator_kl_loss=1.32, generator_dur_loss=1.726, generator_adv_loss=1.948, generator_feat_match_loss=4.95, over 53.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.366, discriminator_fake_loss=1.355, generator_loss=28.69, generator_mel_loss=18.11, generator_kl_loss=1.475, generator_dur_loss=1.665, generator_adv_loss=1.973, generator_feat_match_loss=5.466, over 381.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:16:22,880 INFO [train.py:527] (4/6) Epoch 634, batch 58, global_batch_idx: 78550, batch size: 59, loss[discriminator_loss=2.753, discriminator_real_loss=1.389, discriminator_fake_loss=1.364, generator_loss=28.35, generator_mel_loss=17.64, generator_kl_loss=1.27, generator_dur_loss=1.779, generator_adv_loss=2.016, generator_feat_match_loss=5.638, over 59.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.5, generator_mel_loss=17.97, generator_kl_loss=1.429, generator_dur_loss=1.723, generator_adv_loss=1.968, generator_feat_match_loss=5.412, over 3229.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:18:42,618 INFO [train.py:527] (4/6) Epoch 634, batch 108, global_batch_idx: 78600, batch size: 72, loss[discriminator_loss=2.737, discriminator_real_loss=1.386, discriminator_fake_loss=1.352, generator_loss=27.33, generator_mel_loss=17.9, generator_kl_loss=1.328, generator_dur_loss=1.763, generator_adv_loss=1.866, generator_feat_match_loss=4.47, over 72.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.49, generator_mel_loss=17.97, generator_kl_loss=1.406, generator_dur_loss=1.741, generator_adv_loss=1.973, generator_feat_match_loss=5.408, over 6380.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:18:42,619 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 17:18:51,362 INFO [train.py:591] (4/6) Epoch 634, validation: discriminator_loss=2.718, discriminator_real_loss=1.369, discriminator_fake_loss=1.348, generator_loss=26.9, generator_mel_loss=18.02, generator_kl_loss=1.312, generator_dur_loss=1.802, generator_adv_loss=1.768, generator_feat_match_loss=3.992, over 100.00 samples. +2024-03-14 17:18:51,363 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 17:19:33,552 INFO [train.py:919] (4/6) Start epoch 635 +2024-03-14 17:21:33,245 INFO [train.py:527] (4/6) Epoch 635, batch 34, global_batch_idx: 78650, batch size: 16, loss[discriminator_loss=2.651, discriminator_real_loss=1.31, discriminator_fake_loss=1.341, generator_loss=30.01, generator_mel_loss=18.58, generator_kl_loss=2.017, generator_dur_loss=1.555, generator_adv_loss=1.878, generator_feat_match_loss=5.973, over 16.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=28.6, generator_mel_loss=17.94, generator_kl_loss=1.46, generator_dur_loss=1.744, generator_adv_loss=1.978, generator_feat_match_loss=5.477, over 2099.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:23:53,316 INFO [train.py:527] (4/6) Epoch 635, batch 84, global_batch_idx: 78700, batch size: 42, loss[discriminator_loss=2.678, discriminator_real_loss=1.369, discriminator_fake_loss=1.309, generator_loss=28.84, generator_mel_loss=18.3, generator_kl_loss=1.468, generator_dur_loss=1.698, generator_adv_loss=1.973, generator_feat_match_loss=5.404, over 42.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.334, generator_loss=28.54, generator_mel_loss=17.97, generator_kl_loss=1.422, generator_dur_loss=1.743, generator_adv_loss=1.973, generator_feat_match_loss=5.432, over 4895.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:25:40,369 INFO [train.py:919] (4/6) Start epoch 636 +2024-03-14 17:26:32,561 INFO [train.py:527] (4/6) Epoch 636, batch 10, global_batch_idx: 78750, batch size: 66, loss[discriminator_loss=2.653, discriminator_real_loss=1.376, discriminator_fake_loss=1.277, generator_loss=28.94, generator_mel_loss=17.8, generator_kl_loss=1.31, generator_dur_loss=1.735, generator_adv_loss=2.036, generator_feat_match_loss=6.06, over 66.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=28.57, generator_mel_loss=17.99, generator_kl_loss=1.385, generator_dur_loss=1.77, generator_adv_loss=1.983, generator_feat_match_loss=5.446, over 690.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:28:51,982 INFO [train.py:527] (4/6) Epoch 636, batch 60, global_batch_idx: 78800, batch size: 68, loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=28.31, generator_mel_loss=17.96, generator_kl_loss=1.214, generator_dur_loss=1.766, generator_adv_loss=1.914, generator_feat_match_loss=5.459, over 68.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.362, discriminator_fake_loss=1.34, generator_loss=28.67, generator_mel_loss=18.03, generator_kl_loss=1.401, generator_dur_loss=1.743, generator_adv_loss=1.98, generator_feat_match_loss=5.522, over 3405.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:28:51,984 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 17:29:00,091 INFO [train.py:591] (4/6) Epoch 636, validation: discriminator_loss=2.778, discriminator_real_loss=1.396, discriminator_fake_loss=1.382, generator_loss=27.24, generator_mel_loss=18.22, generator_kl_loss=1.223, generator_dur_loss=1.803, generator_adv_loss=1.794, generator_feat_match_loss=4.2, over 100.00 samples. +2024-03-14 17:29:00,092 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 17:31:19,350 INFO [train.py:527] (4/6) Epoch 636, batch 110, global_batch_idx: 78850, batch size: 83, loss[discriminator_loss=2.711, discriminator_real_loss=1.272, discriminator_fake_loss=1.439, generator_loss=28.89, generator_mel_loss=17.94, generator_kl_loss=1.372, generator_dur_loss=1.796, generator_adv_loss=1.937, generator_feat_match_loss=5.841, over 83.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.369, discriminator_fake_loss=1.336, generator_loss=28.66, generator_mel_loss=17.98, generator_kl_loss=1.396, generator_dur_loss=1.743, generator_adv_loss=1.981, generator_feat_match_loss=5.553, over 6175.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:31:55,347 INFO [train.py:919] (4/6) Start epoch 637 +2024-03-14 17:34:01,432 INFO [train.py:527] (4/6) Epoch 637, batch 36, global_batch_idx: 78900, batch size: 96, loss[discriminator_loss=2.661, discriminator_real_loss=1.279, discriminator_fake_loss=1.382, generator_loss=28.33, generator_mel_loss=17.75, generator_kl_loss=1.337, generator_dur_loss=1.819, generator_adv_loss=1.998, generator_feat_match_loss=5.428, over 96.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.365, discriminator_fake_loss=1.32, generator_loss=28.68, generator_mel_loss=17.97, generator_kl_loss=1.419, generator_dur_loss=1.733, generator_adv_loss=2.009, generator_feat_match_loss=5.549, over 2065.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:36:21,643 INFO [train.py:527] (4/6) Epoch 637, batch 86, global_batch_idx: 78950, batch size: 68, loss[discriminator_loss=2.663, discriminator_real_loss=1.397, discriminator_fake_loss=1.266, generator_loss=30.31, generator_mel_loss=18.46, generator_kl_loss=1.614, generator_dur_loss=1.787, generator_adv_loss=1.975, generator_feat_match_loss=6.476, over 68.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.364, discriminator_fake_loss=1.329, generator_loss=28.52, generator_mel_loss=17.95, generator_kl_loss=1.411, generator_dur_loss=1.737, generator_adv_loss=1.981, generator_feat_match_loss=5.448, over 4914.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:38:05,360 INFO [train.py:919] (4/6) Start epoch 638 +2024-03-14 17:39:02,845 INFO [train.py:527] (4/6) Epoch 638, batch 12, global_batch_idx: 79000, batch size: 80, loss[discriminator_loss=2.681, discriminator_real_loss=1.302, discriminator_fake_loss=1.379, generator_loss=28.65, generator_mel_loss=17.73, generator_kl_loss=1.305, generator_dur_loss=1.787, generator_adv_loss=2.132, generator_feat_match_loss=5.693, over 80.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.34, discriminator_fake_loss=1.323, generator_loss=28.7, generator_mel_loss=17.9, generator_kl_loss=1.415, generator_dur_loss=1.727, generator_adv_loss=2.011, generator_feat_match_loss=5.645, over 757.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:39:02,847 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 17:39:10,863 INFO [train.py:591] (4/6) Epoch 638, validation: discriminator_loss=2.766, discriminator_real_loss=1.487, discriminator_fake_loss=1.279, generator_loss=27.09, generator_mel_loss=17.94, generator_kl_loss=1.158, generator_dur_loss=1.771, generator_adv_loss=2.034, generator_feat_match_loss=4.187, over 100.00 samples. +2024-03-14 17:39:10,864 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 17:41:29,721 INFO [train.py:527] (4/6) Epoch 638, batch 62, global_batch_idx: 79050, batch size: 59, loss[discriminator_loss=2.762, discriminator_real_loss=1.497, discriminator_fake_loss=1.265, generator_loss=27.35, generator_mel_loss=17.55, generator_kl_loss=1.321, generator_dur_loss=1.719, generator_adv_loss=2.032, generator_feat_match_loss=4.731, over 59.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.353, discriminator_fake_loss=1.337, generator_loss=28.61, generator_mel_loss=17.96, generator_kl_loss=1.418, generator_dur_loss=1.723, generator_adv_loss=1.979, generator_feat_match_loss=5.527, over 3498.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:43:48,993 INFO [train.py:527] (4/6) Epoch 638, batch 112, global_batch_idx: 79100, batch size: 56, loss[discriminator_loss=2.65, discriminator_real_loss=1.448, discriminator_fake_loss=1.202, generator_loss=28.95, generator_mel_loss=18.6, generator_kl_loss=1.313, generator_dur_loss=1.697, generator_adv_loss=1.89, generator_feat_match_loss=5.449, over 56.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.359, discriminator_fake_loss=1.338, generator_loss=28.6, generator_mel_loss=18, generator_kl_loss=1.408, generator_dur_loss=1.734, generator_adv_loss=1.977, generator_feat_match_loss=5.488, over 6310.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:44:19,698 INFO [train.py:919] (4/6) Start epoch 639 +2024-03-14 17:46:30,011 INFO [train.py:527] (4/6) Epoch 639, batch 38, global_batch_idx: 79150, batch size: 80, loss[discriminator_loss=2.742, discriminator_real_loss=1.375, discriminator_fake_loss=1.368, generator_loss=28.17, generator_mel_loss=17.72, generator_kl_loss=1.389, generator_dur_loss=1.808, generator_adv_loss=1.982, generator_feat_match_loss=5.266, over 80.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.356, discriminator_fake_loss=1.338, generator_loss=28.74, generator_mel_loss=18.02, generator_kl_loss=1.431, generator_dur_loss=1.756, generator_adv_loss=1.975, generator_feat_match_loss=5.554, over 2282.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:48:47,226 INFO [train.py:527] (4/6) Epoch 639, batch 88, global_batch_idx: 79200, batch size: 58, loss[discriminator_loss=2.718, discriminator_real_loss=1.383, discriminator_fake_loss=1.335, generator_loss=27.63, generator_mel_loss=17.79, generator_kl_loss=1.479, generator_dur_loss=1.733, generator_adv_loss=2.015, generator_feat_match_loss=4.617, over 58.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.362, discriminator_fake_loss=1.331, generator_loss=28.62, generator_mel_loss=17.95, generator_kl_loss=1.42, generator_dur_loss=1.753, generator_adv_loss=2.004, generator_feat_match_loss=5.492, over 5100.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:48:47,227 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 17:48:56,150 INFO [train.py:591] (4/6) Epoch 639, validation: discriminator_loss=2.739, discriminator_real_loss=1.471, discriminator_fake_loss=1.268, generator_loss=27.94, generator_mel_loss=18.03, generator_kl_loss=1.41, generator_dur_loss=1.795, generator_adv_loss=2.035, generator_feat_match_loss=4.675, over 100.00 samples. +2024-03-14 17:48:56,151 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 17:50:32,488 INFO [train.py:919] (4/6) Start epoch 640 +2024-03-14 17:51:37,843 INFO [train.py:527] (4/6) Epoch 640, batch 14, global_batch_idx: 79250, batch size: 31, loss[discriminator_loss=2.648, discriminator_real_loss=1.381, discriminator_fake_loss=1.267, generator_loss=28.87, generator_mel_loss=18.19, generator_kl_loss=1.526, generator_dur_loss=1.657, generator_adv_loss=1.956, generator_feat_match_loss=5.543, over 31.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.386, discriminator_fake_loss=1.32, generator_loss=28.53, generator_mel_loss=17.9, generator_kl_loss=1.438, generator_dur_loss=1.72, generator_adv_loss=1.976, generator_feat_match_loss=5.499, over 762.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:53:54,761 INFO [train.py:527] (4/6) Epoch 640, batch 64, global_batch_idx: 79300, batch size: 58, loss[discriminator_loss=2.741, discriminator_real_loss=1.442, discriminator_fake_loss=1.299, generator_loss=28.05, generator_mel_loss=17.69, generator_kl_loss=1.313, generator_dur_loss=1.735, generator_adv_loss=2.027, generator_feat_match_loss=5.282, over 58.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.372, discriminator_fake_loss=1.324, generator_loss=28.47, generator_mel_loss=17.9, generator_kl_loss=1.414, generator_dur_loss=1.739, generator_adv_loss=1.99, generator_feat_match_loss=5.429, over 3660.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:56:14,335 INFO [train.py:527] (4/6) Epoch 640, batch 114, global_batch_idx: 79350, batch size: 42, loss[discriminator_loss=2.688, discriminator_real_loss=1.282, discriminator_fake_loss=1.406, generator_loss=29.53, generator_mel_loss=18.18, generator_kl_loss=1.568, generator_dur_loss=1.673, generator_adv_loss=1.872, generator_feat_match_loss=6.239, over 42.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.372, discriminator_fake_loss=1.329, generator_loss=28.47, generator_mel_loss=17.91, generator_kl_loss=1.416, generator_dur_loss=1.748, generator_adv_loss=1.977, generator_feat_match_loss=5.419, over 6608.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:56:40,693 INFO [train.py:919] (4/6) Start epoch 641 +2024-03-14 17:58:56,538 INFO [train.py:527] (4/6) Epoch 641, batch 40, global_batch_idx: 79400, batch size: 59, loss[discriminator_loss=2.709, discriminator_real_loss=1.348, discriminator_fake_loss=1.361, generator_loss=29.8, generator_mel_loss=18.13, generator_kl_loss=1.397, generator_dur_loss=1.739, generator_adv_loss=1.887, generator_feat_match_loss=6.645, over 59.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.362, discriminator_fake_loss=1.34, generator_loss=28.58, generator_mel_loss=17.91, generator_kl_loss=1.415, generator_dur_loss=1.766, generator_adv_loss=1.963, generator_feat_match_loss=5.529, over 2471.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:58:56,540 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 17:59:04,409 INFO [train.py:591] (4/6) Epoch 641, validation: discriminator_loss=2.781, discriminator_real_loss=1.342, discriminator_fake_loss=1.439, generator_loss=27.18, generator_mel_loss=18.07, generator_kl_loss=1.282, generator_dur_loss=1.84, generator_adv_loss=1.758, generator_feat_match_loss=4.235, over 100.00 samples. +2024-03-14 17:59:04,409 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 18:01:24,863 INFO [train.py:527] (4/6) Epoch 641, batch 90, global_batch_idx: 79450, batch size: 36, loss[discriminator_loss=2.724, discriminator_real_loss=1.332, discriminator_fake_loss=1.392, generator_loss=28.75, generator_mel_loss=18.1, generator_kl_loss=1.492, generator_dur_loss=1.695, generator_adv_loss=1.885, generator_feat_match_loss=5.575, over 36.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.56, generator_mel_loss=17.91, generator_kl_loss=1.422, generator_dur_loss=1.761, generator_adv_loss=1.971, generator_feat_match_loss=5.497, over 5242.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:02:55,759 INFO [train.py:919] (4/6) Start epoch 642 +2024-03-14 18:04:05,215 INFO [train.py:527] (4/6) Epoch 642, batch 16, global_batch_idx: 79500, batch size: 59, loss[discriminator_loss=2.734, discriminator_real_loss=1.503, discriminator_fake_loss=1.231, generator_loss=28.67, generator_mel_loss=18.04, generator_kl_loss=1.646, generator_dur_loss=1.721, generator_adv_loss=1.888, generator_feat_match_loss=5.379, over 59.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.63, generator_mel_loss=17.98, generator_kl_loss=1.402, generator_dur_loss=1.763, generator_adv_loss=1.965, generator_feat_match_loss=5.524, over 1022.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:06:24,281 INFO [train.py:527] (4/6) Epoch 642, batch 66, global_batch_idx: 79550, batch size: 52, loss[discriminator_loss=2.731, discriminator_real_loss=1.28, discriminator_fake_loss=1.451, generator_loss=30.09, generator_mel_loss=18.54, generator_kl_loss=1.658, generator_dur_loss=1.659, generator_adv_loss=2.148, generator_feat_match_loss=6.087, over 52.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.338, generator_loss=28.62, generator_mel_loss=18, generator_kl_loss=1.43, generator_dur_loss=1.756, generator_adv_loss=1.97, generator_feat_match_loss=5.461, over 3808.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:08:44,237 INFO [train.py:527] (4/6) Epoch 642, batch 116, global_batch_idx: 79600, batch size: 59, loss[discriminator_loss=2.662, discriminator_real_loss=1.384, discriminator_fake_loss=1.278, generator_loss=28.9, generator_mel_loss=17.89, generator_kl_loss=1.432, generator_dur_loss=1.721, generator_adv_loss=1.969, generator_feat_match_loss=5.89, over 59.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.66, generator_mel_loss=18.01, generator_kl_loss=1.424, generator_dur_loss=1.757, generator_adv_loss=1.989, generator_feat_match_loss=5.482, over 6711.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:08:44,239 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 18:08:52,919 INFO [train.py:591] (4/6) Epoch 642, validation: discriminator_loss=2.71, discriminator_real_loss=1.316, discriminator_fake_loss=1.395, generator_loss=27.72, generator_mel_loss=18.36, generator_kl_loss=1.407, generator_dur_loss=1.821, generator_adv_loss=1.883, generator_feat_match_loss=4.249, over 100.00 samples. +2024-03-14 18:08:52,920 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 18:09:14,910 INFO [train.py:919] (4/6) Start epoch 643 +2024-03-14 18:11:35,655 INFO [train.py:527] (4/6) Epoch 643, batch 42, global_batch_idx: 79650, batch size: 25, loss[discriminator_loss=2.608, discriminator_real_loss=1.335, discriminator_fake_loss=1.273, generator_loss=28.88, generator_mel_loss=18.34, generator_kl_loss=1.616, generator_dur_loss=1.606, generator_adv_loss=1.987, generator_feat_match_loss=5.331, over 25.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.375, discriminator_fake_loss=1.325, generator_loss=28.68, generator_mel_loss=17.95, generator_kl_loss=1.463, generator_dur_loss=1.721, generator_adv_loss=1.992, generator_feat_match_loss=5.552, over 2133.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:13:54,503 INFO [train.py:527] (4/6) Epoch 643, batch 92, global_batch_idx: 79700, batch size: 74, loss[discriminator_loss=2.727, discriminator_real_loss=1.457, discriminator_fake_loss=1.27, generator_loss=28.24, generator_mel_loss=17.92, generator_kl_loss=1.468, generator_dur_loss=1.797, generator_adv_loss=1.964, generator_feat_match_loss=5.096, over 74.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.371, discriminator_fake_loss=1.326, generator_loss=28.55, generator_mel_loss=17.93, generator_kl_loss=1.434, generator_dur_loss=1.741, generator_adv_loss=1.982, generator_feat_match_loss=5.457, over 5025.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:15:22,449 INFO [train.py:919] (4/6) Start epoch 644 +2024-03-14 18:16:39,342 INFO [train.py:527] (4/6) Epoch 644, batch 18, global_batch_idx: 79750, batch size: 14, loss[discriminator_loss=2.649, discriminator_real_loss=1.393, discriminator_fake_loss=1.256, generator_loss=30.39, generator_mel_loss=18.35, generator_kl_loss=2.132, generator_dur_loss=1.585, generator_adv_loss=2.113, generator_feat_match_loss=6.21, over 14.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.356, discriminator_fake_loss=1.339, generator_loss=28.57, generator_mel_loss=17.91, generator_kl_loss=1.473, generator_dur_loss=1.714, generator_adv_loss=1.981, generator_feat_match_loss=5.486, over 895.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:18:58,842 INFO [train.py:527] (4/6) Epoch 644, batch 68, global_batch_idx: 79800, batch size: 70, loss[discriminator_loss=2.632, discriminator_real_loss=1.355, discriminator_fake_loss=1.277, generator_loss=29.55, generator_mel_loss=17.95, generator_kl_loss=1.44, generator_dur_loss=1.784, generator_adv_loss=1.974, generator_feat_match_loss=6.4, over 70.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.358, discriminator_fake_loss=1.335, generator_loss=28.63, generator_mel_loss=17.95, generator_kl_loss=1.443, generator_dur_loss=1.748, generator_adv_loss=1.969, generator_feat_match_loss=5.522, over 3644.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:18:58,844 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 18:19:07,779 INFO [train.py:591] (4/6) Epoch 644, validation: discriminator_loss=2.725, discriminator_real_loss=1.409, discriminator_fake_loss=1.316, generator_loss=27.35, generator_mel_loss=18.23, generator_kl_loss=1.156, generator_dur_loss=1.814, generator_adv_loss=1.872, generator_feat_match_loss=4.274, over 100.00 samples. +2024-03-14 18:19:07,780 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 18:21:27,746 INFO [train.py:527] (4/6) Epoch 644, batch 118, global_batch_idx: 79850, batch size: 55, loss[discriminator_loss=2.679, discriminator_real_loss=1.325, discriminator_fake_loss=1.354, generator_loss=28.88, generator_mel_loss=17.97, generator_kl_loss=1.566, generator_dur_loss=1.736, generator_adv_loss=2, generator_feat_match_loss=5.607, over 55.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.6, generator_mel_loss=17.97, generator_kl_loss=1.439, generator_dur_loss=1.753, generator_adv_loss=1.965, generator_feat_match_loss=5.478, over 6537.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:21:43,560 INFO [train.py:919] (4/6) Start epoch 645 +2024-03-14 18:24:13,484 INFO [train.py:527] (4/6) Epoch 645, batch 44, global_batch_idx: 79900, batch size: 68, loss[discriminator_loss=2.725, discriminator_real_loss=1.411, discriminator_fake_loss=1.314, generator_loss=28.37, generator_mel_loss=17.86, generator_kl_loss=1.271, generator_dur_loss=1.751, generator_adv_loss=1.929, generator_feat_match_loss=5.554, over 68.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.361, discriminator_fake_loss=1.331, generator_loss=28.59, generator_mel_loss=17.99, generator_kl_loss=1.392, generator_dur_loss=1.759, generator_adv_loss=1.979, generator_feat_match_loss=5.47, over 2565.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:26:34,833 INFO [train.py:527] (4/6) Epoch 645, batch 94, global_batch_idx: 79950, batch size: 59, loss[discriminator_loss=2.742, discriminator_real_loss=1.317, discriminator_fake_loss=1.425, generator_loss=28.57, generator_mel_loss=18.17, generator_kl_loss=1.432, generator_dur_loss=1.741, generator_adv_loss=2.11, generator_feat_match_loss=5.11, over 59.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.365, discriminator_fake_loss=1.33, generator_loss=28.56, generator_mel_loss=17.98, generator_kl_loss=1.399, generator_dur_loss=1.756, generator_adv_loss=1.982, generator_feat_match_loss=5.445, over 5452.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:27:54,774 INFO [train.py:919] (4/6) Start epoch 646 +2024-03-14 18:29:16,806 INFO [train.py:527] (4/6) Epoch 646, batch 20, global_batch_idx: 80000, batch size: 64, loss[discriminator_loss=2.673, discriminator_real_loss=1.435, discriminator_fake_loss=1.238, generator_loss=28.75, generator_mel_loss=18.09, generator_kl_loss=1.348, generator_dur_loss=1.77, generator_adv_loss=1.935, generator_feat_match_loss=5.608, over 64.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.365, discriminator_fake_loss=1.346, generator_loss=28.44, generator_mel_loss=17.86, generator_kl_loss=1.389, generator_dur_loss=1.773, generator_adv_loss=1.995, generator_feat_match_loss=5.421, over 1196.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:29:16,807 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 18:29:24,878 INFO [train.py:591] (4/6) Epoch 646, validation: discriminator_loss=2.734, discriminator_real_loss=1.391, discriminator_fake_loss=1.343, generator_loss=27.28, generator_mel_loss=18.04, generator_kl_loss=1.169, generator_dur_loss=1.815, generator_adv_loss=1.777, generator_feat_match_loss=4.481, over 100.00 samples. +2024-03-14 18:29:24,879 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 18:31:44,470 INFO [train.py:527] (4/6) Epoch 646, batch 70, global_batch_idx: 80050, batch size: 68, loss[discriminator_loss=2.69, discriminator_real_loss=1.371, discriminator_fake_loss=1.319, generator_loss=28.18, generator_mel_loss=17.75, generator_kl_loss=1.216, generator_dur_loss=1.771, generator_adv_loss=2.001, generator_feat_match_loss=5.443, over 68.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.368, discriminator_fake_loss=1.335, generator_loss=28.54, generator_mel_loss=17.95, generator_kl_loss=1.4, generator_dur_loss=1.757, generator_adv_loss=1.978, generator_feat_match_loss=5.458, over 3960.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:34:03,605 INFO [train.py:527] (4/6) Epoch 646, batch 120, global_batch_idx: 80100, batch size: 56, loss[discriminator_loss=2.791, discriminator_real_loss=1.326, discriminator_fake_loss=1.465, generator_loss=28.7, generator_mel_loss=18.7, generator_kl_loss=1.389, generator_dur_loss=1.718, generator_adv_loss=2.116, generator_feat_match_loss=4.78, over 56.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.366, discriminator_fake_loss=1.334, generator_loss=28.57, generator_mel_loss=17.97, generator_kl_loss=1.41, generator_dur_loss=1.761, generator_adv_loss=1.979, generator_feat_match_loss=5.456, over 6779.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:34:12,619 INFO [train.py:919] (4/6) Start epoch 647 +2024-03-14 18:36:49,347 INFO [train.py:527] (4/6) Epoch 647, batch 46, global_batch_idx: 80150, batch size: 66, loss[discriminator_loss=2.686, discriminator_real_loss=1.378, discriminator_fake_loss=1.308, generator_loss=30.03, generator_mel_loss=18.42, generator_kl_loss=1.376, generator_dur_loss=1.719, generator_adv_loss=2.113, generator_feat_match_loss=6.408, over 66.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.371, discriminator_fake_loss=1.335, generator_loss=28.77, generator_mel_loss=18.05, generator_kl_loss=1.434, generator_dur_loss=1.737, generator_adv_loss=1.988, generator_feat_match_loss=5.563, over 2367.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:39:08,841 INFO [train.py:527] (4/6) Epoch 647, batch 96, global_batch_idx: 80200, batch size: 39, loss[discriminator_loss=2.653, discriminator_real_loss=1.332, discriminator_fake_loss=1.321, generator_loss=27.84, generator_mel_loss=17.6, generator_kl_loss=1.452, generator_dur_loss=1.716, generator_adv_loss=2.088, generator_feat_match_loss=4.989, over 39.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.365, discriminator_fake_loss=1.339, generator_loss=28.64, generator_mel_loss=18, generator_kl_loss=1.407, generator_dur_loss=1.755, generator_adv_loss=1.974, generator_feat_match_loss=5.512, over 5377.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:39:08,842 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 18:39:17,850 INFO [train.py:591] (4/6) Epoch 647, validation: discriminator_loss=2.737, discriminator_real_loss=1.457, discriminator_fake_loss=1.28, generator_loss=27.18, generator_mel_loss=17.86, generator_kl_loss=1.308, generator_dur_loss=1.817, generator_adv_loss=1.983, generator_feat_match_loss=4.211, over 100.00 samples. +2024-03-14 18:39:17,851 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 18:40:35,692 INFO [train.py:919] (4/6) Start epoch 648 +2024-03-14 18:42:03,937 INFO [train.py:527] (4/6) Epoch 648, batch 22, global_batch_idx: 80250, batch size: 80, loss[discriminator_loss=2.646, discriminator_real_loss=1.3, discriminator_fake_loss=1.347, generator_loss=29.25, generator_mel_loss=18.34, generator_kl_loss=1.388, generator_dur_loss=1.823, generator_adv_loss=2.023, generator_feat_match_loss=5.674, over 80.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.366, discriminator_fake_loss=1.343, generator_loss=28.59, generator_mel_loss=18.01, generator_kl_loss=1.405, generator_dur_loss=1.764, generator_adv_loss=1.961, generator_feat_match_loss=5.448, over 1451.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:44:22,200 INFO [train.py:527] (4/6) Epoch 648, batch 72, global_batch_idx: 80300, batch size: 39, loss[discriminator_loss=2.675, discriminator_real_loss=1.376, discriminator_fake_loss=1.299, generator_loss=28.86, generator_mel_loss=18.32, generator_kl_loss=1.626, generator_dur_loss=1.688, generator_adv_loss=1.95, generator_feat_match_loss=5.282, over 39.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.339, generator_loss=28.56, generator_mel_loss=18.03, generator_kl_loss=1.41, generator_dur_loss=1.748, generator_adv_loss=1.973, generator_feat_match_loss=5.401, over 4158.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:46:42,997 INFO [train.py:527] (4/6) Epoch 648, batch 122, global_batch_idx: 80350, batch size: 58, loss[discriminator_loss=2.769, discriminator_real_loss=1.465, discriminator_fake_loss=1.304, generator_loss=28.16, generator_mel_loss=17.96, generator_kl_loss=1.51, generator_dur_loss=1.739, generator_adv_loss=1.888, generator_feat_match_loss=5.064, over 58.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.371, discriminator_fake_loss=1.337, generator_loss=28.54, generator_mel_loss=17.99, generator_kl_loss=1.414, generator_dur_loss=1.751, generator_adv_loss=1.98, generator_feat_match_loss=5.403, over 7055.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:46:48,153 INFO [train.py:919] (4/6) Start epoch 649 +2024-03-14 18:49:23,137 INFO [train.py:527] (4/6) Epoch 649, batch 48, global_batch_idx: 80400, batch size: 61, loss[discriminator_loss=2.639, discriminator_real_loss=1.347, discriminator_fake_loss=1.292, generator_loss=28.94, generator_mel_loss=18.13, generator_kl_loss=1.415, generator_dur_loss=1.739, generator_adv_loss=2.077, generator_feat_match_loss=5.581, over 61.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.353, discriminator_fake_loss=1.338, generator_loss=28.62, generator_mel_loss=17.93, generator_kl_loss=1.428, generator_dur_loss=1.764, generator_adv_loss=1.974, generator_feat_match_loss=5.524, over 2895.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:49:23,138 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 18:49:31,230 INFO [train.py:591] (4/6) Epoch 649, validation: discriminator_loss=2.692, discriminator_real_loss=1.378, discriminator_fake_loss=1.313, generator_loss=27.1, generator_mel_loss=17.88, generator_kl_loss=1.266, generator_dur_loss=1.811, generator_adv_loss=1.928, generator_feat_match_loss=4.224, over 100.00 samples. +2024-03-14 18:49:31,231 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 18:51:51,865 INFO [train.py:527] (4/6) Epoch 649, batch 98, global_batch_idx: 80450, batch size: 15, loss[discriminator_loss=2.662, discriminator_real_loss=1.35, discriminator_fake_loss=1.312, generator_loss=29.56, generator_mel_loss=18.65, generator_kl_loss=1.628, generator_dur_loss=1.622, generator_adv_loss=1.938, generator_feat_match_loss=5.718, over 15.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.355, discriminator_fake_loss=1.329, generator_loss=28.65, generator_mel_loss=17.92, generator_kl_loss=1.4, generator_dur_loss=1.771, generator_adv_loss=1.999, generator_feat_match_loss=5.564, over 6057.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:53:00,422 INFO [train.py:919] (4/6) Start epoch 650 +2024-03-14 18:54:29,805 INFO [train.py:527] (4/6) Epoch 650, batch 24, global_batch_idx: 80500, batch size: 36, loss[discriminator_loss=2.7, discriminator_real_loss=1.329, discriminator_fake_loss=1.371, generator_loss=28.39, generator_mel_loss=17.84, generator_kl_loss=1.669, generator_dur_loss=1.643, generator_adv_loss=1.958, generator_feat_match_loss=5.283, over 36.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.365, discriminator_fake_loss=1.329, generator_loss=28.8, generator_mel_loss=18, generator_kl_loss=1.398, generator_dur_loss=1.765, generator_adv_loss=1.964, generator_feat_match_loss=5.671, over 1471.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:56:49,018 INFO [train.py:527] (4/6) Epoch 650, batch 74, global_batch_idx: 80550, batch size: 58, loss[discriminator_loss=2.784, discriminator_real_loss=1.492, discriminator_fake_loss=1.291, generator_loss=28.91, generator_mel_loss=17.95, generator_kl_loss=1.55, generator_dur_loss=1.717, generator_adv_loss=1.905, generator_feat_match_loss=5.785, over 58.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.362, discriminator_fake_loss=1.327, generator_loss=28.8, generator_mel_loss=18.05, generator_kl_loss=1.404, generator_dur_loss=1.754, generator_adv_loss=1.971, generator_feat_match_loss=5.62, over 4294.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:59:05,379 INFO [train.py:919] (4/6) Start epoch 651 +2024-03-14 18:59:29,150 INFO [train.py:527] (4/6) Epoch 651, batch 0, global_batch_idx: 80600, batch size: 58, loss[discriminator_loss=2.644, discriminator_real_loss=1.435, discriminator_fake_loss=1.209, generator_loss=29.68, generator_mel_loss=18.78, generator_kl_loss=1.478, generator_dur_loss=1.687, generator_adv_loss=2.03, generator_feat_match_loss=5.711, over 58.00 samples.], tot_loss[discriminator_loss=2.644, discriminator_real_loss=1.435, discriminator_fake_loss=1.209, generator_loss=29.68, generator_mel_loss=18.78, generator_kl_loss=1.478, generator_dur_loss=1.687, generator_adv_loss=2.03, generator_feat_match_loss=5.711, over 58.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:59:29,152 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 18:59:36,909 INFO [train.py:591] (4/6) Epoch 651, validation: discriminator_loss=2.733, discriminator_real_loss=1.396, discriminator_fake_loss=1.337, generator_loss=27.56, generator_mel_loss=18.3, generator_kl_loss=1.281, generator_dur_loss=1.793, generator_adv_loss=1.882, generator_feat_match_loss=4.305, over 100.00 samples. +2024-03-14 18:59:36,911 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 19:01:55,866 INFO [train.py:527] (4/6) Epoch 651, batch 50, global_batch_idx: 80650, batch size: 80, loss[discriminator_loss=2.696, discriminator_real_loss=1.361, discriminator_fake_loss=1.336, generator_loss=28.44, generator_mel_loss=17.91, generator_kl_loss=1.373, generator_dur_loss=1.807, generator_adv_loss=1.968, generator_feat_match_loss=5.381, over 80.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.367, discriminator_fake_loss=1.327, generator_loss=28.62, generator_mel_loss=18, generator_kl_loss=1.41, generator_dur_loss=1.754, generator_adv_loss=1.988, generator_feat_match_loss=5.477, over 3011.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:04:15,053 INFO [train.py:527] (4/6) Epoch 651, batch 100, global_batch_idx: 80700, batch size: 64, loss[discriminator_loss=2.75, discriminator_real_loss=1.39, discriminator_fake_loss=1.359, generator_loss=27.4, generator_mel_loss=17.6, generator_kl_loss=1.323, generator_dur_loss=1.762, generator_adv_loss=1.89, generator_feat_match_loss=4.829, over 64.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.365, discriminator_fake_loss=1.326, generator_loss=28.74, generator_mel_loss=18, generator_kl_loss=1.431, generator_dur_loss=1.743, generator_adv_loss=1.989, generator_feat_match_loss=5.575, over 5587.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:05:19,616 INFO [train.py:919] (4/6) Start epoch 652 +2024-03-14 19:06:57,767 INFO [train.py:527] (4/6) Epoch 652, batch 26, global_batch_idx: 80750, batch size: 83, loss[discriminator_loss=2.682, discriminator_real_loss=1.338, discriminator_fake_loss=1.343, generator_loss=28.78, generator_mel_loss=17.89, generator_kl_loss=1.284, generator_dur_loss=1.851, generator_adv_loss=1.988, generator_feat_match_loss=5.758, over 83.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.356, discriminator_fake_loss=1.336, generator_loss=28.64, generator_mel_loss=17.99, generator_kl_loss=1.42, generator_dur_loss=1.765, generator_adv_loss=1.983, generator_feat_match_loss=5.484, over 1570.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:09:15,690 INFO [train.py:527] (4/6) Epoch 652, batch 76, global_batch_idx: 80800, batch size: 95, loss[discriminator_loss=2.709, discriminator_real_loss=1.41, discriminator_fake_loss=1.298, generator_loss=28.58, generator_mel_loss=18.01, generator_kl_loss=1.424, generator_dur_loss=1.847, generator_adv_loss=1.868, generator_feat_match_loss=5.43, over 95.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.33, generator_loss=28.62, generator_mel_loss=17.96, generator_kl_loss=1.423, generator_dur_loss=1.755, generator_adv_loss=1.982, generator_feat_match_loss=5.498, over 4333.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:09:15,692 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 19:09:24,373 INFO [train.py:591] (4/6) Epoch 652, validation: discriminator_loss=2.745, discriminator_real_loss=1.351, discriminator_fake_loss=1.393, generator_loss=27.25, generator_mel_loss=17.96, generator_kl_loss=1.27, generator_dur_loss=1.824, generator_adv_loss=1.842, generator_feat_match_loss=4.36, over 100.00 samples. +2024-03-14 19:09:24,374 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 19:11:35,321 INFO [train.py:919] (4/6) Start epoch 653 +2024-03-14 19:12:04,987 INFO [train.py:527] (4/6) Epoch 653, batch 2, global_batch_idx: 80850, batch size: 48, loss[discriminator_loss=2.709, discriminator_real_loss=1.347, discriminator_fake_loss=1.362, generator_loss=29.61, generator_mel_loss=18.03, generator_kl_loss=1.46, generator_dur_loss=1.7, generator_adv_loss=2.071, generator_feat_match_loss=6.354, over 48.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.34, discriminator_fake_loss=1.36, generator_loss=28.95, generator_mel_loss=17.86, generator_kl_loss=1.452, generator_dur_loss=1.745, generator_adv_loss=1.977, generator_feat_match_loss=5.911, over 164.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:14:23,230 INFO [train.py:527] (4/6) Epoch 653, batch 52, global_batch_idx: 80900, batch size: 61, loss[discriminator_loss=2.614, discriminator_real_loss=1.262, discriminator_fake_loss=1.353, generator_loss=29.72, generator_mel_loss=18.06, generator_kl_loss=1.52, generator_dur_loss=1.736, generator_adv_loss=1.947, generator_feat_match_loss=6.453, over 61.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.364, discriminator_fake_loss=1.335, generator_loss=28.67, generator_mel_loss=17.96, generator_kl_loss=1.44, generator_dur_loss=1.751, generator_adv_loss=1.982, generator_feat_match_loss=5.543, over 2923.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:16:43,662 INFO [train.py:527] (4/6) Epoch 653, batch 102, global_batch_idx: 80950, batch size: 88, loss[discriminator_loss=2.648, discriminator_real_loss=1.274, discriminator_fake_loss=1.374, generator_loss=29.61, generator_mel_loss=18.26, generator_kl_loss=1.343, generator_dur_loss=1.852, generator_adv_loss=2.12, generator_feat_match_loss=6.036, over 88.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.358, discriminator_fake_loss=1.332, generator_loss=28.72, generator_mel_loss=17.97, generator_kl_loss=1.434, generator_dur_loss=1.753, generator_adv_loss=1.989, generator_feat_match_loss=5.578, over 5843.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:17:43,474 INFO [train.py:919] (4/6) Start epoch 654 +2024-03-14 19:19:27,174 INFO [train.py:527] (4/6) Epoch 654, batch 28, global_batch_idx: 81000, batch size: 37, loss[discriminator_loss=2.695, discriminator_real_loss=1.346, discriminator_fake_loss=1.35, generator_loss=28.83, generator_mel_loss=18.18, generator_kl_loss=1.662, generator_dur_loss=1.611, generator_adv_loss=2.005, generator_feat_match_loss=5.368, over 37.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.367, discriminator_fake_loss=1.317, generator_loss=28.83, generator_mel_loss=18.02, generator_kl_loss=1.425, generator_dur_loss=1.743, generator_adv_loss=1.989, generator_feat_match_loss=5.653, over 1592.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:19:27,175 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 19:19:35,257 INFO [train.py:591] (4/6) Epoch 654, validation: discriminator_loss=2.727, discriminator_real_loss=1.422, discriminator_fake_loss=1.305, generator_loss=28.25, generator_mel_loss=18.52, generator_kl_loss=1.227, generator_dur_loss=1.821, generator_adv_loss=1.954, generator_feat_match_loss=4.725, over 100.00 samples. +2024-03-14 19:19:35,258 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 19:21:51,323 INFO [train.py:527] (4/6) Epoch 654, batch 78, global_batch_idx: 81050, batch size: 42, loss[discriminator_loss=2.716, discriminator_real_loss=1.37, discriminator_fake_loss=1.346, generator_loss=28.66, generator_mel_loss=18.15, generator_kl_loss=1.548, generator_dur_loss=1.689, generator_adv_loss=1.852, generator_feat_match_loss=5.421, over 42.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=28.63, generator_mel_loss=17.96, generator_kl_loss=1.421, generator_dur_loss=1.752, generator_adv_loss=1.978, generator_feat_match_loss=5.524, over 4569.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:24:00,257 INFO [train.py:919] (4/6) Start epoch 655 +2024-03-14 19:24:35,432 INFO [train.py:527] (4/6) Epoch 655, batch 4, global_batch_idx: 81100, batch size: 66, loss[discriminator_loss=2.757, discriminator_real_loss=1.289, discriminator_fake_loss=1.468, generator_loss=28.75, generator_mel_loss=18.26, generator_kl_loss=1.235, generator_dur_loss=1.766, generator_adv_loss=2.086, generator_feat_match_loss=5.402, over 66.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.345, discriminator_fake_loss=1.348, generator_loss=28.8, generator_mel_loss=18.06, generator_kl_loss=1.376, generator_dur_loss=1.774, generator_adv_loss=2.065, generator_feat_match_loss=5.525, over 343.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:26:53,567 INFO [train.py:527] (4/6) Epoch 655, batch 54, global_batch_idx: 81150, batch size: 52, loss[discriminator_loss=2.694, discriminator_real_loss=1.37, discriminator_fake_loss=1.324, generator_loss=28.25, generator_mel_loss=17.98, generator_kl_loss=1.431, generator_dur_loss=1.697, generator_adv_loss=1.993, generator_feat_match_loss=5.15, over 52.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=28.76, generator_mel_loss=17.98, generator_kl_loss=1.417, generator_dur_loss=1.753, generator_adv_loss=2.037, generator_feat_match_loss=5.581, over 3196.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:29:13,894 INFO [train.py:527] (4/6) Epoch 655, batch 104, global_batch_idx: 81200, batch size: 77, loss[discriminator_loss=2.719, discriminator_real_loss=1.365, discriminator_fake_loss=1.355, generator_loss=28.29, generator_mel_loss=17.63, generator_kl_loss=1.388, generator_dur_loss=1.767, generator_adv_loss=2.027, generator_feat_match_loss=5.478, over 77.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=28.74, generator_mel_loss=17.97, generator_kl_loss=1.41, generator_dur_loss=1.753, generator_adv_loss=2.006, generator_feat_match_loss=5.596, over 6343.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:29:13,895 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 19:29:22,650 INFO [train.py:591] (4/6) Epoch 655, validation: discriminator_loss=2.713, discriminator_real_loss=1.431, discriminator_fake_loss=1.283, generator_loss=26.94, generator_mel_loss=18.27, generator_kl_loss=1.221, generator_dur_loss=1.801, generator_adv_loss=1.897, generator_feat_match_loss=3.747, over 100.00 samples. +2024-03-14 19:29:22,651 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 19:30:14,412 INFO [train.py:919] (4/6) Start epoch 656 +2024-03-14 19:32:03,254 INFO [train.py:527] (4/6) Epoch 656, batch 30, global_batch_idx: 81250, batch size: 31, loss[discriminator_loss=2.664, discriminator_real_loss=1.382, discriminator_fake_loss=1.281, generator_loss=29.87, generator_mel_loss=18.23, generator_kl_loss=1.641, generator_dur_loss=1.58, generator_adv_loss=2.011, generator_feat_match_loss=6.416, over 31.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.372, discriminator_fake_loss=1.33, generator_loss=28.68, generator_mel_loss=17.95, generator_kl_loss=1.431, generator_dur_loss=1.762, generator_adv_loss=1.959, generator_feat_match_loss=5.572, over 1844.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:34:21,018 INFO [train.py:527] (4/6) Epoch 656, batch 80, global_batch_idx: 81300, batch size: 44, loss[discriminator_loss=2.733, discriminator_real_loss=1.323, discriminator_fake_loss=1.41, generator_loss=27.61, generator_mel_loss=17.96, generator_kl_loss=1.212, generator_dur_loss=1.72, generator_adv_loss=2.004, generator_feat_match_loss=4.716, over 44.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.36, discriminator_fake_loss=1.332, generator_loss=28.69, generator_mel_loss=17.9, generator_kl_loss=1.408, generator_dur_loss=1.758, generator_adv_loss=1.985, generator_feat_match_loss=5.638, over 4743.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:36:16,638 INFO [train.py:919] (4/6) Start epoch 657 +2024-03-14 19:36:56,979 INFO [train.py:527] (4/6) Epoch 657, batch 6, global_batch_idx: 81350, batch size: 58, loss[discriminator_loss=2.673, discriminator_real_loss=1.323, discriminator_fake_loss=1.35, generator_loss=29.09, generator_mel_loss=18.08, generator_kl_loss=1.423, generator_dur_loss=1.719, generator_adv_loss=1.925, generator_feat_match_loss=5.943, over 58.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.329, discriminator_fake_loss=1.366, generator_loss=28.69, generator_mel_loss=18.03, generator_kl_loss=1.422, generator_dur_loss=1.762, generator_adv_loss=1.968, generator_feat_match_loss=5.502, over 410.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:39:13,457 INFO [train.py:527] (4/6) Epoch 657, batch 56, global_batch_idx: 81400, batch size: 61, loss[discriminator_loss=2.663, discriminator_real_loss=1.257, discriminator_fake_loss=1.406, generator_loss=28.34, generator_mel_loss=17.66, generator_kl_loss=1.521, generator_dur_loss=1.736, generator_adv_loss=2.074, generator_feat_match_loss=5.354, over 61.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.341, discriminator_fake_loss=1.333, generator_loss=28.81, generator_mel_loss=18.01, generator_kl_loss=1.423, generator_dur_loss=1.751, generator_adv_loss=1.982, generator_feat_match_loss=5.642, over 3252.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:39:13,458 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 19:39:21,549 INFO [train.py:591] (4/6) Epoch 657, validation: discriminator_loss=2.725, discriminator_real_loss=1.434, discriminator_fake_loss=1.291, generator_loss=28.32, generator_mel_loss=18.37, generator_kl_loss=1.234, generator_dur_loss=1.811, generator_adv_loss=1.975, generator_feat_match_loss=4.928, over 100.00 samples. +2024-03-14 19:39:21,550 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 19:41:38,973 INFO [train.py:527] (4/6) Epoch 657, batch 106, global_batch_idx: 81450, batch size: 50, loss[discriminator_loss=2.656, discriminator_real_loss=1.351, discriminator_fake_loss=1.305, generator_loss=27.85, generator_mel_loss=17.71, generator_kl_loss=1.406, generator_dur_loss=1.664, generator_adv_loss=1.974, generator_feat_match_loss=5.093, over 50.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=28.76, generator_mel_loss=17.97, generator_kl_loss=1.426, generator_dur_loss=1.75, generator_adv_loss=1.987, generator_feat_match_loss=5.625, over 6014.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:42:30,120 INFO [train.py:919] (4/6) Start epoch 658 +2024-03-14 19:44:21,192 INFO [train.py:527] (4/6) Epoch 658, batch 32, global_batch_idx: 81500, batch size: 47, loss[discriminator_loss=2.69, discriminator_real_loss=1.349, discriminator_fake_loss=1.342, generator_loss=28.8, generator_mel_loss=17.81, generator_kl_loss=1.512, generator_dur_loss=1.67, generator_adv_loss=1.938, generator_feat_match_loss=5.866, over 47.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.329, generator_loss=28.57, generator_mel_loss=17.99, generator_kl_loss=1.393, generator_dur_loss=1.741, generator_adv_loss=1.977, generator_feat_match_loss=5.465, over 1878.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:46:41,868 INFO [train.py:527] (4/6) Epoch 658, batch 82, global_batch_idx: 81550, batch size: 31, loss[discriminator_loss=2.658, discriminator_real_loss=1.391, discriminator_fake_loss=1.267, generator_loss=29.58, generator_mel_loss=18.48, generator_kl_loss=1.559, generator_dur_loss=1.571, generator_adv_loss=2.16, generator_feat_match_loss=5.806, over 31.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.357, discriminator_fake_loss=1.336, generator_loss=28.77, generator_mel_loss=18.01, generator_kl_loss=1.396, generator_dur_loss=1.751, generator_adv_loss=1.996, generator_feat_match_loss=5.619, over 4835.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:48:37,639 INFO [train.py:919] (4/6) Start epoch 659 +2024-03-14 19:49:23,443 INFO [train.py:527] (4/6) Epoch 659, batch 8, global_batch_idx: 81600, batch size: 52, loss[discriminator_loss=2.743, discriminator_real_loss=1.341, discriminator_fake_loss=1.402, generator_loss=28.24, generator_mel_loss=17.54, generator_kl_loss=1.433, generator_dur_loss=1.706, generator_adv_loss=2.019, generator_feat_match_loss=5.542, over 52.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.336, discriminator_fake_loss=1.34, generator_loss=28.68, generator_mel_loss=17.91, generator_kl_loss=1.448, generator_dur_loss=1.772, generator_adv_loss=1.986, generator_feat_match_loss=5.565, over 532.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:49:23,446 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 19:49:31,215 INFO [train.py:591] (4/6) Epoch 659, validation: discriminator_loss=2.73, discriminator_real_loss=1.438, discriminator_fake_loss=1.292, generator_loss=27.54, generator_mel_loss=17.99, generator_kl_loss=1.298, generator_dur_loss=1.818, generator_adv_loss=1.966, generator_feat_match_loss=4.469, over 100.00 samples. +2024-03-14 19:49:31,217 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 19:51:49,916 INFO [train.py:527] (4/6) Epoch 659, batch 58, global_batch_idx: 81650, batch size: 59, loss[discriminator_loss=2.631, discriminator_real_loss=1.403, discriminator_fake_loss=1.227, generator_loss=29.3, generator_mel_loss=17.93, generator_kl_loss=1.539, generator_dur_loss=1.675, generator_adv_loss=1.886, generator_feat_match_loss=6.269, over 59.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.363, discriminator_fake_loss=1.33, generator_loss=28.58, generator_mel_loss=17.94, generator_kl_loss=1.409, generator_dur_loss=1.746, generator_adv_loss=1.978, generator_feat_match_loss=5.512, over 3315.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:54:08,729 INFO [train.py:527] (4/6) Epoch 659, batch 108, global_batch_idx: 81700, batch size: 58, loss[discriminator_loss=2.621, discriminator_real_loss=1.323, discriminator_fake_loss=1.299, generator_loss=28.45, generator_mel_loss=17.85, generator_kl_loss=1.267, generator_dur_loss=1.713, generator_adv_loss=2.065, generator_feat_match_loss=5.56, over 58.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.66, generator_mel_loss=17.95, generator_kl_loss=1.412, generator_dur_loss=1.743, generator_adv_loss=1.982, generator_feat_match_loss=5.568, over 6106.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:54:51,411 INFO [train.py:919] (4/6) Start epoch 660 +2024-03-14 19:56:52,268 INFO [train.py:527] (4/6) Epoch 660, batch 34, global_batch_idx: 81750, batch size: 88, loss[discriminator_loss=2.682, discriminator_real_loss=1.383, discriminator_fake_loss=1.3, generator_loss=27.69, generator_mel_loss=17.82, generator_kl_loss=1.299, generator_dur_loss=1.835, generator_adv_loss=1.941, generator_feat_match_loss=4.791, over 88.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.338, generator_loss=28.42, generator_mel_loss=17.9, generator_kl_loss=1.396, generator_dur_loss=1.758, generator_adv_loss=1.964, generator_feat_match_loss=5.401, over 2038.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:59:10,382 INFO [train.py:527] (4/6) Epoch 660, batch 84, global_batch_idx: 81800, batch size: 36, loss[discriminator_loss=2.742, discriminator_real_loss=1.384, discriminator_fake_loss=1.358, generator_loss=29.03, generator_mel_loss=18.36, generator_kl_loss=1.528, generator_dur_loss=1.651, generator_adv_loss=1.972, generator_feat_match_loss=5.525, over 36.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.367, discriminator_fake_loss=1.344, generator_loss=28.51, generator_mel_loss=17.93, generator_kl_loss=1.392, generator_dur_loss=1.753, generator_adv_loss=1.963, generator_feat_match_loss=5.475, over 5028.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:59:10,384 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 19:59:19,066 INFO [train.py:591] (4/6) Epoch 660, validation: discriminator_loss=2.743, discriminator_real_loss=1.505, discriminator_fake_loss=1.239, generator_loss=27.23, generator_mel_loss=17.83, generator_kl_loss=1.269, generator_dur_loss=1.817, generator_adv_loss=1.957, generator_feat_match_loss=4.361, over 100.00 samples. +2024-03-14 19:59:19,067 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 20:01:07,806 INFO [train.py:919] (4/6) Start epoch 661 +2024-03-14 20:02:02,458 INFO [train.py:527] (4/6) Epoch 661, batch 10, global_batch_idx: 81850, batch size: 31, loss[discriminator_loss=2.748, discriminator_real_loss=1.367, discriminator_fake_loss=1.381, generator_loss=29.2, generator_mel_loss=17.94, generator_kl_loss=1.56, generator_dur_loss=1.697, generator_adv_loss=2.014, generator_feat_match_loss=5.995, over 31.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.366, discriminator_fake_loss=1.347, generator_loss=28.97, generator_mel_loss=18.11, generator_kl_loss=1.439, generator_dur_loss=1.749, generator_adv_loss=1.965, generator_feat_match_loss=5.707, over 535.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:04:22,924 INFO [train.py:527] (4/6) Epoch 661, batch 60, global_batch_idx: 81900, batch size: 50, loss[discriminator_loss=2.757, discriminator_real_loss=1.326, discriminator_fake_loss=1.431, generator_loss=28.15, generator_mel_loss=17.68, generator_kl_loss=1.391, generator_dur_loss=1.684, generator_adv_loss=1.986, generator_feat_match_loss=5.412, over 50.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.363, discriminator_fake_loss=1.345, generator_loss=28.55, generator_mel_loss=17.91, generator_kl_loss=1.396, generator_dur_loss=1.756, generator_adv_loss=1.968, generator_feat_match_loss=5.519, over 3601.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:06:41,389 INFO [train.py:527] (4/6) Epoch 661, batch 110, global_batch_idx: 81950, batch size: 39, loss[discriminator_loss=2.658, discriminator_real_loss=1.3, discriminator_fake_loss=1.358, generator_loss=28.25, generator_mel_loss=17.87, generator_kl_loss=1.532, generator_dur_loss=1.672, generator_adv_loss=2.001, generator_feat_match_loss=5.181, over 39.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.337, generator_loss=28.58, generator_mel_loss=17.93, generator_kl_loss=1.402, generator_dur_loss=1.746, generator_adv_loss=1.973, generator_feat_match_loss=5.529, over 6474.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:07:18,031 INFO [train.py:919] (4/6) Start epoch 662 +2024-03-14 20:09:23,456 INFO [train.py:527] (4/6) Epoch 662, batch 36, global_batch_idx: 82000, batch size: 74, loss[discriminator_loss=2.688, discriminator_real_loss=1.383, discriminator_fake_loss=1.304, generator_loss=28.49, generator_mel_loss=17.66, generator_kl_loss=1.421, generator_dur_loss=1.77, generator_adv_loss=1.918, generator_feat_match_loss=5.729, over 74.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=28.65, generator_mel_loss=17.89, generator_kl_loss=1.424, generator_dur_loss=1.739, generator_adv_loss=2.006, generator_feat_match_loss=5.591, over 2159.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:09:23,457 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 20:09:31,284 INFO [train.py:591] (4/6) Epoch 662, validation: discriminator_loss=2.753, discriminator_real_loss=1.382, discriminator_fake_loss=1.371, generator_loss=27.58, generator_mel_loss=18.17, generator_kl_loss=1.241, generator_dur_loss=1.811, generator_adv_loss=1.83, generator_feat_match_loss=4.526, over 100.00 samples. +2024-03-14 20:09:31,285 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 20:11:49,936 INFO [train.py:527] (4/6) Epoch 662, batch 86, global_batch_idx: 82050, batch size: 52, loss[discriminator_loss=2.721, discriminator_real_loss=1.331, discriminator_fake_loss=1.389, generator_loss=29.13, generator_mel_loss=17.8, generator_kl_loss=1.718, generator_dur_loss=1.653, generator_adv_loss=1.982, generator_feat_match_loss=5.979, over 52.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.359, discriminator_fake_loss=1.34, generator_loss=28.66, generator_mel_loss=17.91, generator_kl_loss=1.425, generator_dur_loss=1.747, generator_adv_loss=1.996, generator_feat_match_loss=5.584, over 5155.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:13:30,407 INFO [train.py:919] (4/6) Start epoch 663 +2024-03-14 20:14:30,114 INFO [train.py:527] (4/6) Epoch 663, batch 12, global_batch_idx: 82100, batch size: 74, loss[discriminator_loss=2.72, discriminator_real_loss=1.341, discriminator_fake_loss=1.379, generator_loss=28.91, generator_mel_loss=18.3, generator_kl_loss=1.254, generator_dur_loss=1.79, generator_adv_loss=2.01, generator_feat_match_loss=5.555, over 74.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.359, discriminator_fake_loss=1.339, generator_loss=28.81, generator_mel_loss=18.11, generator_kl_loss=1.431, generator_dur_loss=1.752, generator_adv_loss=1.976, generator_feat_match_loss=5.542, over 768.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:16:47,568 INFO [train.py:527] (4/6) Epoch 663, batch 62, global_batch_idx: 82150, batch size: 48, loss[discriminator_loss=2.73, discriminator_real_loss=1.367, discriminator_fake_loss=1.363, generator_loss=27.7, generator_mel_loss=17.92, generator_kl_loss=1.369, generator_dur_loss=1.676, generator_adv_loss=1.946, generator_feat_match_loss=4.781, over 48.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.369, discriminator_fake_loss=1.333, generator_loss=28.74, generator_mel_loss=18, generator_kl_loss=1.42, generator_dur_loss=1.749, generator_adv_loss=1.974, generator_feat_match_loss=5.59, over 3732.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:19:06,826 INFO [train.py:527] (4/6) Epoch 663, batch 112, global_batch_idx: 82200, batch size: 56, loss[discriminator_loss=2.667, discriminator_real_loss=1.387, discriminator_fake_loss=1.28, generator_loss=28.76, generator_mel_loss=17.94, generator_kl_loss=1.52, generator_dur_loss=1.697, generator_adv_loss=1.938, generator_feat_match_loss=5.664, over 56.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.71, generator_mel_loss=17.97, generator_kl_loss=1.421, generator_dur_loss=1.749, generator_adv_loss=1.98, generator_feat_match_loss=5.584, over 6571.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:19:06,827 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 20:19:15,746 INFO [train.py:591] (4/6) Epoch 663, validation: discriminator_loss=2.75, discriminator_real_loss=1.405, discriminator_fake_loss=1.344, generator_loss=27.35, generator_mel_loss=18.29, generator_kl_loss=1.192, generator_dur_loss=1.804, generator_adv_loss=1.894, generator_feat_match_loss=4.17, over 100.00 samples. +2024-03-14 20:19:15,747 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 20:19:44,621 INFO [train.py:919] (4/6) Start epoch 664 +2024-03-14 20:21:53,672 INFO [train.py:527] (4/6) Epoch 664, batch 38, global_batch_idx: 82250, batch size: 55, loss[discriminator_loss=2.718, discriminator_real_loss=1.398, discriminator_fake_loss=1.32, generator_loss=28.49, generator_mel_loss=17.59, generator_kl_loss=1.565, generator_dur_loss=1.711, generator_adv_loss=2.042, generator_feat_match_loss=5.586, over 55.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.367, discriminator_fake_loss=1.333, generator_loss=28.56, generator_mel_loss=17.97, generator_kl_loss=1.434, generator_dur_loss=1.746, generator_adv_loss=1.97, generator_feat_match_loss=5.438, over 2255.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:24:12,492 INFO [train.py:527] (4/6) Epoch 664, batch 88, global_batch_idx: 82300, batch size: 74, loss[discriminator_loss=2.742, discriminator_real_loss=1.408, discriminator_fake_loss=1.334, generator_loss=27.16, generator_mel_loss=17.49, generator_kl_loss=1.323, generator_dur_loss=1.773, generator_adv_loss=1.836, generator_feat_match_loss=4.736, over 74.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.365, discriminator_fake_loss=1.329, generator_loss=28.58, generator_mel_loss=17.94, generator_kl_loss=1.435, generator_dur_loss=1.746, generator_adv_loss=1.973, generator_feat_match_loss=5.48, over 5109.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:25:51,598 INFO [train.py:919] (4/6) Start epoch 665 +2024-03-14 20:26:54,133 INFO [train.py:527] (4/6) Epoch 665, batch 14, global_batch_idx: 82350, batch size: 72, loss[discriminator_loss=2.693, discriminator_real_loss=1.312, discriminator_fake_loss=1.382, generator_loss=28.94, generator_mel_loss=17.72, generator_kl_loss=1.497, generator_dur_loss=1.795, generator_adv_loss=2.279, generator_feat_match_loss=5.652, over 72.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.347, discriminator_fake_loss=1.333, generator_loss=28.59, generator_mel_loss=17.86, generator_kl_loss=1.435, generator_dur_loss=1.755, generator_adv_loss=2.003, generator_feat_match_loss=5.534, over 920.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:29:13,270 INFO [train.py:527] (4/6) Epoch 665, batch 64, global_batch_idx: 82400, batch size: 61, loss[discriminator_loss=2.685, discriminator_real_loss=1.416, discriminator_fake_loss=1.269, generator_loss=27.97, generator_mel_loss=17.49, generator_kl_loss=1.326, generator_dur_loss=1.747, generator_adv_loss=1.874, generator_feat_match_loss=5.53, over 61.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.368, discriminator_fake_loss=1.326, generator_loss=28.4, generator_mel_loss=17.8, generator_kl_loss=1.396, generator_dur_loss=1.767, generator_adv_loss=1.985, generator_feat_match_loss=5.449, over 4006.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:29:13,272 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 20:29:21,240 INFO [train.py:591] (4/6) Epoch 665, validation: discriminator_loss=2.712, discriminator_real_loss=1.331, discriminator_fake_loss=1.381, generator_loss=27.3, generator_mel_loss=18.25, generator_kl_loss=1.151, generator_dur_loss=1.805, generator_adv_loss=1.796, generator_feat_match_loss=4.296, over 100.00 samples. +2024-03-14 20:29:21,241 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 20:31:40,104 INFO [train.py:527] (4/6) Epoch 665, batch 114, global_batch_idx: 82450, batch size: 14, loss[discriminator_loss=2.759, discriminator_real_loss=1.479, discriminator_fake_loss=1.28, generator_loss=29.12, generator_mel_loss=18.3, generator_kl_loss=1.704, generator_dur_loss=1.592, generator_adv_loss=2.033, generator_feat_match_loss=5.485, over 14.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.36, discriminator_fake_loss=1.332, generator_loss=28.5, generator_mel_loss=17.86, generator_kl_loss=1.406, generator_dur_loss=1.762, generator_adv_loss=1.982, generator_feat_match_loss=5.496, over 6913.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:32:03,003 INFO [train.py:919] (4/6) Start epoch 666 +2024-03-14 20:34:16,497 INFO [train.py:527] (4/6) Epoch 666, batch 40, global_batch_idx: 82500, batch size: 44, loss[discriminator_loss=2.678, discriminator_real_loss=1.399, discriminator_fake_loss=1.279, generator_loss=29.02, generator_mel_loss=17.74, generator_kl_loss=1.672, generator_dur_loss=1.684, generator_adv_loss=1.974, generator_feat_match_loss=5.945, over 44.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.365, discriminator_fake_loss=1.331, generator_loss=28.48, generator_mel_loss=17.87, generator_kl_loss=1.43, generator_dur_loss=1.734, generator_adv_loss=1.981, generator_feat_match_loss=5.456, over 2311.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:36:37,914 INFO [train.py:527] (4/6) Epoch 666, batch 90, global_batch_idx: 82550, batch size: 68, loss[discriminator_loss=2.717, discriminator_real_loss=1.39, discriminator_fake_loss=1.327, generator_loss=28.3, generator_mel_loss=18.21, generator_kl_loss=1.397, generator_dur_loss=1.795, generator_adv_loss=1.938, generator_feat_match_loss=4.964, over 68.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.364, discriminator_fake_loss=1.329, generator_loss=28.62, generator_mel_loss=17.93, generator_kl_loss=1.431, generator_dur_loss=1.745, generator_adv_loss=1.984, generator_feat_match_loss=5.539, over 5239.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:38:13,454 INFO [train.py:919] (4/6) Start epoch 667 +2024-03-14 20:39:20,967 INFO [train.py:527] (4/6) Epoch 667, batch 16, global_batch_idx: 82600, batch size: 47, loss[discriminator_loss=2.748, discriminator_real_loss=1.32, discriminator_fake_loss=1.428, generator_loss=27.88, generator_mel_loss=17.89, generator_kl_loss=1.481, generator_dur_loss=1.712, generator_adv_loss=1.946, generator_feat_match_loss=4.851, over 47.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.344, discriminator_fake_loss=1.361, generator_loss=28.68, generator_mel_loss=18, generator_kl_loss=1.4, generator_dur_loss=1.741, generator_adv_loss=1.958, generator_feat_match_loss=5.584, over 963.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:39:20,968 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 20:39:28,900 INFO [train.py:591] (4/6) Epoch 667, validation: discriminator_loss=2.756, discriminator_real_loss=1.445, discriminator_fake_loss=1.311, generator_loss=27.3, generator_mel_loss=18.35, generator_kl_loss=1.207, generator_dur_loss=1.823, generator_adv_loss=1.978, generator_feat_match_loss=3.941, over 100.00 samples. +2024-03-14 20:39:28,901 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 20:41:48,961 INFO [train.py:527] (4/6) Epoch 667, batch 66, global_batch_idx: 82650, batch size: 61, loss[discriminator_loss=2.622, discriminator_real_loss=1.442, discriminator_fake_loss=1.18, generator_loss=29.11, generator_mel_loss=18.09, generator_kl_loss=1.387, generator_dur_loss=1.752, generator_adv_loss=2.071, generator_feat_match_loss=5.807, over 61.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=28.75, generator_mel_loss=17.97, generator_kl_loss=1.413, generator_dur_loss=1.744, generator_adv_loss=1.979, generator_feat_match_loss=5.641, over 3687.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:44:05,847 INFO [train.py:527] (4/6) Epoch 667, batch 116, global_batch_idx: 82700, batch size: 70, loss[discriminator_loss=2.747, discriminator_real_loss=1.377, discriminator_fake_loss=1.37, generator_loss=28.45, generator_mel_loss=17.74, generator_kl_loss=1.223, generator_dur_loss=1.771, generator_adv_loss=2.191, generator_feat_match_loss=5.527, over 70.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.356, discriminator_fake_loss=1.335, generator_loss=28.78, generator_mel_loss=17.98, generator_kl_loss=1.425, generator_dur_loss=1.744, generator_adv_loss=2.003, generator_feat_match_loss=5.627, over 6500.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:44:27,474 INFO [train.py:919] (4/6) Start epoch 668 +2024-03-14 20:46:49,157 INFO [train.py:527] (4/6) Epoch 668, batch 42, global_batch_idx: 82750, batch size: 39, loss[discriminator_loss=2.632, discriminator_real_loss=1.309, discriminator_fake_loss=1.322, generator_loss=28.95, generator_mel_loss=18.04, generator_kl_loss=1.477, generator_dur_loss=1.706, generator_adv_loss=2.044, generator_feat_match_loss=5.677, over 39.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.363, discriminator_fake_loss=1.316, generator_loss=28.48, generator_mel_loss=17.84, generator_kl_loss=1.387, generator_dur_loss=1.744, generator_adv_loss=1.989, generator_feat_match_loss=5.523, over 2491.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:49:06,885 INFO [train.py:527] (4/6) Epoch 668, batch 92, global_batch_idx: 82800, batch size: 55, loss[discriminator_loss=2.615, discriminator_real_loss=1.334, discriminator_fake_loss=1.282, generator_loss=29.39, generator_mel_loss=18.11, generator_kl_loss=1.489, generator_dur_loss=1.683, generator_adv_loss=1.939, generator_feat_match_loss=6.175, over 55.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.362, discriminator_fake_loss=1.324, generator_loss=28.62, generator_mel_loss=17.88, generator_kl_loss=1.404, generator_dur_loss=1.748, generator_adv_loss=1.982, generator_feat_match_loss=5.597, over 5370.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:49:06,887 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 20:49:15,916 INFO [train.py:591] (4/6) Epoch 668, validation: discriminator_loss=2.728, discriminator_real_loss=1.387, discriminator_fake_loss=1.341, generator_loss=27.17, generator_mel_loss=17.94, generator_kl_loss=1.286, generator_dur_loss=1.82, generator_adv_loss=1.855, generator_feat_match_loss=4.277, over 100.00 samples. +2024-03-14 20:49:15,917 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 20:50:40,279 INFO [train.py:919] (4/6) Start epoch 669 +2024-03-14 20:51:52,997 INFO [train.py:527] (4/6) Epoch 669, batch 18, global_batch_idx: 82850, batch size: 56, loss[discriminator_loss=2.698, discriminator_real_loss=1.28, discriminator_fake_loss=1.419, generator_loss=28.58, generator_mel_loss=17.9, generator_kl_loss=1.238, generator_dur_loss=1.763, generator_adv_loss=2.109, generator_feat_match_loss=5.569, over 56.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.354, discriminator_fake_loss=1.332, generator_loss=28.83, generator_mel_loss=18.04, generator_kl_loss=1.398, generator_dur_loss=1.754, generator_adv_loss=1.981, generator_feat_match_loss=5.656, over 1102.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:54:14,664 INFO [train.py:527] (4/6) Epoch 669, batch 68, global_batch_idx: 82900, batch size: 58, loss[discriminator_loss=2.62, discriminator_real_loss=1.366, discriminator_fake_loss=1.254, generator_loss=27.92, generator_mel_loss=18.22, generator_kl_loss=1.304, generator_dur_loss=1.757, generator_adv_loss=1.862, generator_feat_match_loss=4.775, over 58.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.359, discriminator_fake_loss=1.333, generator_loss=28.63, generator_mel_loss=17.94, generator_kl_loss=1.393, generator_dur_loss=1.759, generator_adv_loss=1.967, generator_feat_match_loss=5.565, over 4105.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:56:30,681 INFO [train.py:527] (4/6) Epoch 669, batch 118, global_batch_idx: 82950, batch size: 52, loss[discriminator_loss=2.692, discriminator_real_loss=1.37, discriminator_fake_loss=1.322, generator_loss=29.74, generator_mel_loss=18.27, generator_kl_loss=1.511, generator_dur_loss=1.685, generator_adv_loss=1.989, generator_feat_match_loss=6.284, over 52.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.365, discriminator_fake_loss=1.333, generator_loss=28.67, generator_mel_loss=17.96, generator_kl_loss=1.403, generator_dur_loss=1.755, generator_adv_loss=1.969, generator_feat_match_loss=5.579, over 6856.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:56:44,731 INFO [train.py:919] (4/6) Start epoch 670 +2024-03-14 20:59:08,306 INFO [train.py:527] (4/6) Epoch 670, batch 44, global_batch_idx: 83000, batch size: 48, loss[discriminator_loss=2.635, discriminator_real_loss=1.275, discriminator_fake_loss=1.361, generator_loss=29.47, generator_mel_loss=18.1, generator_kl_loss=1.465, generator_dur_loss=1.699, generator_adv_loss=1.964, generator_feat_match_loss=6.24, over 48.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.361, discriminator_fake_loss=1.328, generator_loss=28.69, generator_mel_loss=17.96, generator_kl_loss=1.396, generator_dur_loss=1.756, generator_adv_loss=1.986, generator_feat_match_loss=5.589, over 2710.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:59:08,307 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 20:59:16,319 INFO [train.py:591] (4/6) Epoch 670, validation: discriminator_loss=2.723, discriminator_real_loss=1.346, discriminator_fake_loss=1.377, generator_loss=27.62, generator_mel_loss=18.49, generator_kl_loss=1.238, generator_dur_loss=1.816, generator_adv_loss=1.838, generator_feat_match_loss=4.239, over 100.00 samples. +2024-03-14 20:59:16,321 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 21:01:37,726 INFO [train.py:527] (4/6) Epoch 670, batch 94, global_batch_idx: 83050, batch size: 62, loss[discriminator_loss=2.625, discriminator_real_loss=1.299, discriminator_fake_loss=1.327, generator_loss=29.45, generator_mel_loss=18.33, generator_kl_loss=1.366, generator_dur_loss=1.737, generator_adv_loss=2.065, generator_feat_match_loss=5.953, over 62.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=28.64, generator_mel_loss=17.92, generator_kl_loss=1.398, generator_dur_loss=1.753, generator_adv_loss=1.991, generator_feat_match_loss=5.584, over 5591.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:02:57,020 INFO [train.py:919] (4/6) Start epoch 671 +2024-03-14 21:04:15,667 INFO [train.py:527] (4/6) Epoch 671, batch 20, global_batch_idx: 83100, batch size: 52, loss[discriminator_loss=2.787, discriminator_real_loss=1.526, discriminator_fake_loss=1.261, generator_loss=27.83, generator_mel_loss=17.85, generator_kl_loss=1.365, generator_dur_loss=1.699, generator_adv_loss=1.839, generator_feat_match_loss=5.072, over 52.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.367, discriminator_fake_loss=1.33, generator_loss=28.72, generator_mel_loss=18.01, generator_kl_loss=1.376, generator_dur_loss=1.76, generator_adv_loss=1.968, generator_feat_match_loss=5.608, over 1245.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:06:31,273 INFO [train.py:527] (4/6) Epoch 671, batch 70, global_batch_idx: 83150, batch size: 31, loss[discriminator_loss=2.655, discriminator_real_loss=1.305, discriminator_fake_loss=1.35, generator_loss=28.57, generator_mel_loss=18.17, generator_kl_loss=1.508, generator_dur_loss=1.606, generator_adv_loss=2.085, generator_feat_match_loss=5.198, over 31.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.367, discriminator_fake_loss=1.329, generator_loss=28.61, generator_mel_loss=17.94, generator_kl_loss=1.377, generator_dur_loss=1.752, generator_adv_loss=1.977, generator_feat_match_loss=5.564, over 4127.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:08:49,689 INFO [train.py:527] (4/6) Epoch 671, batch 120, global_batch_idx: 83200, batch size: 88, loss[discriminator_loss=2.712, discriminator_real_loss=1.292, discriminator_fake_loss=1.421, generator_loss=28.59, generator_mel_loss=18.13, generator_kl_loss=1.317, generator_dur_loss=1.843, generator_adv_loss=2.019, generator_feat_match_loss=5.289, over 88.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.365, discriminator_fake_loss=1.334, generator_loss=28.57, generator_mel_loss=17.92, generator_kl_loss=1.392, generator_dur_loss=1.757, generator_adv_loss=1.975, generator_feat_match_loss=5.529, over 7181.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:08:49,691 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 21:08:58,667 INFO [train.py:591] (4/6) Epoch 671, validation: discriminator_loss=2.733, discriminator_real_loss=1.469, discriminator_fake_loss=1.264, generator_loss=28.24, generator_mel_loss=18.47, generator_kl_loss=1.303, generator_dur_loss=1.81, generator_adv_loss=1.97, generator_feat_match_loss=4.685, over 100.00 samples. +2024-03-14 21:08:58,669 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 21:09:09,417 INFO [train.py:919] (4/6) Start epoch 672 +2024-03-14 21:11:38,112 INFO [train.py:527] (4/6) Epoch 672, batch 46, global_batch_idx: 83250, batch size: 64, loss[discriminator_loss=2.731, discriminator_real_loss=1.369, discriminator_fake_loss=1.362, generator_loss=27.3, generator_mel_loss=17.62, generator_kl_loss=1.233, generator_dur_loss=1.799, generator_adv_loss=1.952, generator_feat_match_loss=4.694, over 64.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.373, discriminator_fake_loss=1.331, generator_loss=28.81, generator_mel_loss=18.06, generator_kl_loss=1.421, generator_dur_loss=1.727, generator_adv_loss=1.985, generator_feat_match_loss=5.616, over 2532.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:13:55,424 INFO [train.py:527] (4/6) Epoch 672, batch 96, global_batch_idx: 83300, batch size: 96, loss[discriminator_loss=2.693, discriminator_real_loss=1.327, discriminator_fake_loss=1.366, generator_loss=27.71, generator_mel_loss=17.59, generator_kl_loss=1.358, generator_dur_loss=1.895, generator_adv_loss=1.88, generator_feat_match_loss=4.99, over 96.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.372, discriminator_fake_loss=1.329, generator_loss=28.78, generator_mel_loss=18.02, generator_kl_loss=1.426, generator_dur_loss=1.735, generator_adv_loss=1.987, generator_feat_match_loss=5.609, over 5349.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:15:13,959 INFO [train.py:919] (4/6) Start epoch 673 +2024-03-14 21:16:38,366 INFO [train.py:527] (4/6) Epoch 673, batch 22, global_batch_idx: 83350, batch size: 61, loss[discriminator_loss=2.685, discriminator_real_loss=1.374, discriminator_fake_loss=1.311, generator_loss=28.21, generator_mel_loss=17.69, generator_kl_loss=1.501, generator_dur_loss=1.74, generator_adv_loss=2.032, generator_feat_match_loss=5.245, over 61.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.35, discriminator_fake_loss=1.331, generator_loss=28.78, generator_mel_loss=17.97, generator_kl_loss=1.415, generator_dur_loss=1.757, generator_adv_loss=1.999, generator_feat_match_loss=5.643, over 1386.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:18:57,298 INFO [train.py:527] (4/6) Epoch 673, batch 72, global_batch_idx: 83400, batch size: 55, loss[discriminator_loss=2.703, discriminator_real_loss=1.309, discriminator_fake_loss=1.394, generator_loss=28.72, generator_mel_loss=17.53, generator_kl_loss=1.454, generator_dur_loss=1.757, generator_adv_loss=2.145, generator_feat_match_loss=5.835, over 55.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.355, discriminator_fake_loss=1.327, generator_loss=28.77, generator_mel_loss=17.92, generator_kl_loss=1.435, generator_dur_loss=1.742, generator_adv_loss=1.993, generator_feat_match_loss=5.679, over 4102.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:18:57,300 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 21:19:06,072 INFO [train.py:591] (4/6) Epoch 673, validation: discriminator_loss=2.768, discriminator_real_loss=1.496, discriminator_fake_loss=1.272, generator_loss=27.4, generator_mel_loss=18.23, generator_kl_loss=1.259, generator_dur_loss=1.813, generator_adv_loss=1.969, generator_feat_match_loss=4.125, over 100.00 samples. +2024-03-14 21:19:06,073 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 21:21:22,894 INFO [train.py:527] (4/6) Epoch 673, batch 122, global_batch_idx: 83450, batch size: 62, loss[discriminator_loss=2.625, discriminator_real_loss=1.367, discriminator_fake_loss=1.258, generator_loss=29.53, generator_mel_loss=18.79, generator_kl_loss=1.271, generator_dur_loss=1.739, generator_adv_loss=1.977, generator_feat_match_loss=5.751, over 62.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.362, discriminator_fake_loss=1.327, generator_loss=28.74, generator_mel_loss=17.95, generator_kl_loss=1.435, generator_dur_loss=1.748, generator_adv_loss=1.986, generator_feat_match_loss=5.618, over 7007.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:21:28,132 INFO [train.py:919] (4/6) Start epoch 674 +2024-03-14 21:24:02,509 INFO [train.py:527] (4/6) Epoch 674, batch 48, global_batch_idx: 83500, batch size: 50, loss[discriminator_loss=2.676, discriminator_real_loss=1.341, discriminator_fake_loss=1.335, generator_loss=28.13, generator_mel_loss=17.69, generator_kl_loss=1.472, generator_dur_loss=1.71, generator_adv_loss=1.926, generator_feat_match_loss=5.332, over 50.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.365, discriminator_fake_loss=1.333, generator_loss=28.54, generator_mel_loss=17.91, generator_kl_loss=1.444, generator_dur_loss=1.743, generator_adv_loss=1.968, generator_feat_match_loss=5.474, over 2809.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:26:21,367 INFO [train.py:527] (4/6) Epoch 674, batch 98, global_batch_idx: 83550, batch size: 66, loss[discriminator_loss=2.725, discriminator_real_loss=1.324, discriminator_fake_loss=1.401, generator_loss=28.95, generator_mel_loss=18.29, generator_kl_loss=1.317, generator_dur_loss=1.764, generator_adv_loss=2.041, generator_feat_match_loss=5.54, over 66.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.363, discriminator_fake_loss=1.331, generator_loss=28.63, generator_mel_loss=17.93, generator_kl_loss=1.42, generator_dur_loss=1.746, generator_adv_loss=1.976, generator_feat_match_loss=5.56, over 5796.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:27:33,946 INFO [train.py:919] (4/6) Start epoch 675 +2024-03-14 21:29:05,991 INFO [train.py:527] (4/6) Epoch 675, batch 24, global_batch_idx: 83600, batch size: 80, loss[discriminator_loss=2.716, discriminator_real_loss=1.433, discriminator_fake_loss=1.283, generator_loss=28.05, generator_mel_loss=18.02, generator_kl_loss=1.336, generator_dur_loss=1.846, generator_adv_loss=1.891, generator_feat_match_loss=4.962, over 80.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=28.78, generator_mel_loss=17.97, generator_kl_loss=1.414, generator_dur_loss=1.736, generator_adv_loss=1.983, generator_feat_match_loss=5.676, over 1503.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:29:05,993 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 21:29:14,047 INFO [train.py:591] (4/6) Epoch 675, validation: discriminator_loss=2.754, discriminator_real_loss=1.38, discriminator_fake_loss=1.374, generator_loss=26.77, generator_mel_loss=17.64, generator_kl_loss=1.234, generator_dur_loss=1.797, generator_adv_loss=1.825, generator_feat_match_loss=4.268, over 100.00 samples. +2024-03-14 21:29:14,048 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 21:31:34,572 INFO [train.py:527] (4/6) Epoch 675, batch 74, global_batch_idx: 83650, batch size: 44, loss[discriminator_loss=2.68, discriminator_real_loss=1.314, discriminator_fake_loss=1.365, generator_loss=29.24, generator_mel_loss=18.21, generator_kl_loss=1.571, generator_dur_loss=1.651, generator_adv_loss=2.043, generator_feat_match_loss=5.756, over 44.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=28.8, generator_mel_loss=17.98, generator_kl_loss=1.41, generator_dur_loss=1.74, generator_adv_loss=1.985, generator_feat_match_loss=5.689, over 4401.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:33:51,119 INFO [train.py:919] (4/6) Start epoch 676 +2024-03-14 21:34:15,348 INFO [train.py:527] (4/6) Epoch 676, batch 0, global_batch_idx: 83700, batch size: 47, loss[discriminator_loss=2.729, discriminator_real_loss=1.412, discriminator_fake_loss=1.317, generator_loss=28.21, generator_mel_loss=17.49, generator_kl_loss=1.551, generator_dur_loss=1.6, generator_adv_loss=1.93, generator_feat_match_loss=5.631, over 47.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.412, discriminator_fake_loss=1.317, generator_loss=28.21, generator_mel_loss=17.49, generator_kl_loss=1.551, generator_dur_loss=1.6, generator_adv_loss=1.93, generator_feat_match_loss=5.631, over 47.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:36:34,361 INFO [train.py:527] (4/6) Epoch 676, batch 50, global_batch_idx: 83750, batch size: 59, loss[discriminator_loss=2.666, discriminator_real_loss=1.344, discriminator_fake_loss=1.322, generator_loss=28.77, generator_mel_loss=18.16, generator_kl_loss=1.334, generator_dur_loss=1.743, generator_adv_loss=2.039, generator_feat_match_loss=5.493, over 59.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.35, discriminator_fake_loss=1.331, generator_loss=28.94, generator_mel_loss=17.94, generator_kl_loss=1.419, generator_dur_loss=1.75, generator_adv_loss=2.045, generator_feat_match_loss=5.785, over 2835.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:38:52,519 INFO [train.py:527] (4/6) Epoch 676, batch 100, global_batch_idx: 83800, batch size: 31, loss[discriminator_loss=2.685, discriminator_real_loss=1.352, discriminator_fake_loss=1.333, generator_loss=28.82, generator_mel_loss=18.16, generator_kl_loss=1.602, generator_dur_loss=1.629, generator_adv_loss=1.991, generator_feat_match_loss=5.433, over 31.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.326, generator_loss=28.75, generator_mel_loss=17.91, generator_kl_loss=1.41, generator_dur_loss=1.747, generator_adv_loss=2.022, generator_feat_match_loss=5.664, over 5589.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:38:52,520 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 21:39:01,205 INFO [train.py:591] (4/6) Epoch 676, validation: discriminator_loss=2.722, discriminator_real_loss=1.392, discriminator_fake_loss=1.33, generator_loss=28, generator_mel_loss=18.27, generator_kl_loss=1.278, generator_dur_loss=1.804, generator_adv_loss=1.943, generator_feat_match_loss=4.706, over 100.00 samples. +2024-03-14 21:39:01,206 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 21:40:06,060 INFO [train.py:919] (4/6) Start epoch 677 +2024-03-14 21:41:40,152 INFO [train.py:527] (4/6) Epoch 677, batch 26, global_batch_idx: 83850, batch size: 53, loss[discriminator_loss=2.626, discriminator_real_loss=1.314, discriminator_fake_loss=1.311, generator_loss=28.96, generator_mel_loss=18.03, generator_kl_loss=1.329, generator_dur_loss=1.671, generator_adv_loss=1.946, generator_feat_match_loss=5.983, over 53.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.363, discriminator_fake_loss=1.328, generator_loss=28.73, generator_mel_loss=18, generator_kl_loss=1.44, generator_dur_loss=1.73, generator_adv_loss=1.966, generator_feat_match_loss=5.595, over 1455.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:43:57,741 INFO [train.py:527] (4/6) Epoch 677, batch 76, global_batch_idx: 83900, batch size: 64, loss[discriminator_loss=2.684, discriminator_real_loss=1.433, discriminator_fake_loss=1.252, generator_loss=29.41, generator_mel_loss=18.58, generator_kl_loss=1.435, generator_dur_loss=1.786, generator_adv_loss=2.035, generator_feat_match_loss=5.567, over 64.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.364, discriminator_fake_loss=1.323, generator_loss=28.71, generator_mel_loss=17.99, generator_kl_loss=1.432, generator_dur_loss=1.744, generator_adv_loss=1.982, generator_feat_match_loss=5.561, over 4323.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:46:08,914 INFO [train.py:919] (4/6) Start epoch 678 +2024-03-14 21:46:39,040 INFO [train.py:527] (4/6) Epoch 678, batch 2, global_batch_idx: 83950, batch size: 17, loss[discriminator_loss=2.656, discriminator_real_loss=1.24, discriminator_fake_loss=1.416, generator_loss=29.3, generator_mel_loss=18.41, generator_kl_loss=1.906, generator_dur_loss=1.589, generator_adv_loss=1.958, generator_feat_match_loss=5.435, over 17.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.391, discriminator_fake_loss=1.323, generator_loss=29.34, generator_mel_loss=18.16, generator_kl_loss=1.598, generator_dur_loss=1.717, generator_adv_loss=1.978, generator_feat_match_loss=5.891, over 118.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:48:57,992 INFO [train.py:527] (4/6) Epoch 678, batch 52, global_batch_idx: 84000, batch size: 31, loss[discriminator_loss=2.694, discriminator_real_loss=1.299, discriminator_fake_loss=1.395, generator_loss=28.53, generator_mel_loss=17.87, generator_kl_loss=1.576, generator_dur_loss=1.639, generator_adv_loss=2.168, generator_feat_match_loss=5.272, over 31.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.365, discriminator_fake_loss=1.332, generator_loss=28.56, generator_mel_loss=17.91, generator_kl_loss=1.404, generator_dur_loss=1.758, generator_adv_loss=1.973, generator_feat_match_loss=5.52, over 3037.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:48:57,993 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 21:49:05,962 INFO [train.py:591] (4/6) Epoch 678, validation: discriminator_loss=2.726, discriminator_real_loss=1.487, discriminator_fake_loss=1.239, generator_loss=27.38, generator_mel_loss=18.14, generator_kl_loss=1.248, generator_dur_loss=1.812, generator_adv_loss=2.012, generator_feat_match_loss=4.169, over 100.00 samples. +2024-03-14 21:49:05,963 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 21:51:21,517 INFO [train.py:527] (4/6) Epoch 678, batch 102, global_batch_idx: 84050, batch size: 59, loss[discriminator_loss=2.733, discriminator_real_loss=1.353, discriminator_fake_loss=1.38, generator_loss=28.94, generator_mel_loss=18.07, generator_kl_loss=1.543, generator_dur_loss=1.719, generator_adv_loss=1.966, generator_feat_match_loss=5.646, over 59.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.56, generator_mel_loss=17.89, generator_kl_loss=1.408, generator_dur_loss=1.757, generator_adv_loss=1.969, generator_feat_match_loss=5.531, over 5896.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:52:21,651 INFO [train.py:919] (4/6) Start epoch 679 +2024-03-14 21:54:00,107 INFO [train.py:527] (4/6) Epoch 679, batch 28, global_batch_idx: 84100, batch size: 36, loss[discriminator_loss=2.726, discriminator_real_loss=1.472, discriminator_fake_loss=1.254, generator_loss=27.59, generator_mel_loss=17.41, generator_kl_loss=1.513, generator_dur_loss=1.666, generator_adv_loss=1.97, generator_feat_match_loss=5.028, over 36.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.37, discriminator_fake_loss=1.325, generator_loss=28.93, generator_mel_loss=18.08, generator_kl_loss=1.404, generator_dur_loss=1.746, generator_adv_loss=1.993, generator_feat_match_loss=5.704, over 1657.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:56:16,805 INFO [train.py:527] (4/6) Epoch 679, batch 78, global_batch_idx: 84150, batch size: 80, loss[discriminator_loss=2.687, discriminator_real_loss=1.382, discriminator_fake_loss=1.305, generator_loss=29.41, generator_mel_loss=18.01, generator_kl_loss=1.334, generator_dur_loss=1.859, generator_adv_loss=2.084, generator_feat_match_loss=6.123, over 80.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.81, generator_mel_loss=17.99, generator_kl_loss=1.407, generator_dur_loss=1.749, generator_adv_loss=1.996, generator_feat_match_loss=5.667, over 4459.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:58:24,115 INFO [train.py:919] (4/6) Start epoch 680 +2024-03-14 21:58:59,335 INFO [train.py:527] (4/6) Epoch 680, batch 4, global_batch_idx: 84200, batch size: 47, loss[discriminator_loss=2.713, discriminator_real_loss=1.429, discriminator_fake_loss=1.284, generator_loss=28.12, generator_mel_loss=17.68, generator_kl_loss=1.565, generator_dur_loss=1.671, generator_adv_loss=2.023, generator_feat_match_loss=5.184, over 47.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.378, discriminator_fake_loss=1.333, generator_loss=29.1, generator_mel_loss=18.11, generator_kl_loss=1.445, generator_dur_loss=1.751, generator_adv_loss=1.968, generator_feat_match_loss=5.829, over 294.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:58:59,338 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 21:59:07,139 INFO [train.py:591] (4/6) Epoch 680, validation: discriminator_loss=2.709, discriminator_real_loss=1.417, discriminator_fake_loss=1.293, generator_loss=28.22, generator_mel_loss=18.34, generator_kl_loss=1.261, generator_dur_loss=1.817, generator_adv_loss=1.975, generator_feat_match_loss=4.836, over 100.00 samples. +2024-03-14 21:59:07,141 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 22:01:24,570 INFO [train.py:527] (4/6) Epoch 680, batch 54, global_batch_idx: 84250, batch size: 55, loss[discriminator_loss=2.686, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=28.82, generator_mel_loss=18.31, generator_kl_loss=1.54, generator_dur_loss=1.733, generator_adv_loss=1.947, generator_feat_match_loss=5.286, over 55.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.67, generator_mel_loss=17.92, generator_kl_loss=1.426, generator_dur_loss=1.757, generator_adv_loss=1.987, generator_feat_match_loss=5.578, over 3121.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:03:44,659 INFO [train.py:527] (4/6) Epoch 680, batch 104, global_batch_idx: 84300, batch size: 14, loss[discriminator_loss=2.782, discriminator_real_loss=1.416, discriminator_fake_loss=1.366, generator_loss=28.05, generator_mel_loss=18.1, generator_kl_loss=1.643, generator_dur_loss=1.664, generator_adv_loss=1.869, generator_feat_match_loss=4.777, over 14.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.359, discriminator_fake_loss=1.335, generator_loss=28.69, generator_mel_loss=17.9, generator_kl_loss=1.406, generator_dur_loss=1.763, generator_adv_loss=1.999, generator_feat_match_loss=5.623, over 6041.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:04:39,730 INFO [train.py:919] (4/6) Start epoch 681 +2024-03-14 22:06:29,068 INFO [train.py:527] (4/6) Epoch 681, batch 30, global_batch_idx: 84350, batch size: 16, loss[discriminator_loss=2.722, discriminator_real_loss=1.424, discriminator_fake_loss=1.298, generator_loss=30.19, generator_mel_loss=18.2, generator_kl_loss=1.843, generator_dur_loss=1.523, generator_adv_loss=1.981, generator_feat_match_loss=6.643, over 16.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.359, discriminator_fake_loss=1.331, generator_loss=28.75, generator_mel_loss=17.91, generator_kl_loss=1.407, generator_dur_loss=1.75, generator_adv_loss=1.98, generator_feat_match_loss=5.706, over 1898.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:08:49,535 INFO [train.py:527] (4/6) Epoch 681, batch 80, global_batch_idx: 84400, batch size: 83, loss[discriminator_loss=2.696, discriminator_real_loss=1.35, discriminator_fake_loss=1.347, generator_loss=27.68, generator_mel_loss=17.66, generator_kl_loss=1.196, generator_dur_loss=1.823, generator_adv_loss=1.929, generator_feat_match_loss=5.069, over 83.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.63, generator_mel_loss=17.89, generator_kl_loss=1.415, generator_dur_loss=1.74, generator_adv_loss=1.977, generator_feat_match_loss=5.609, over 4658.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:08:49,536 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 22:08:58,438 INFO [train.py:591] (4/6) Epoch 681, validation: discriminator_loss=2.728, discriminator_real_loss=1.42, discriminator_fake_loss=1.308, generator_loss=27.78, generator_mel_loss=18.41, generator_kl_loss=1.212, generator_dur_loss=1.813, generator_adv_loss=1.913, generator_feat_match_loss=4.433, over 100.00 samples. +2024-03-14 22:08:58,438 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 22:10:54,481 INFO [train.py:919] (4/6) Start epoch 682 +2024-03-14 22:11:36,737 INFO [train.py:527] (4/6) Epoch 682, batch 6, global_batch_idx: 84450, batch size: 88, loss[discriminator_loss=2.649, discriminator_real_loss=1.311, discriminator_fake_loss=1.338, generator_loss=29.57, generator_mel_loss=17.93, generator_kl_loss=1.454, generator_dur_loss=1.876, generator_adv_loss=1.903, generator_feat_match_loss=6.413, over 88.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.359, discriminator_fake_loss=1.318, generator_loss=28.97, generator_mel_loss=17.93, generator_kl_loss=1.426, generator_dur_loss=1.792, generator_adv_loss=1.965, generator_feat_match_loss=5.859, over 453.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:13:52,687 INFO [train.py:527] (4/6) Epoch 682, batch 56, global_batch_idx: 84500, batch size: 47, loss[discriminator_loss=2.689, discriminator_real_loss=1.404, discriminator_fake_loss=1.285, generator_loss=28.57, generator_mel_loss=18.04, generator_kl_loss=1.429, generator_dur_loss=1.682, generator_adv_loss=1.841, generator_feat_match_loss=5.582, over 47.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=28.71, generator_mel_loss=17.95, generator_kl_loss=1.416, generator_dur_loss=1.752, generator_adv_loss=1.981, generator_feat_match_loss=5.615, over 3367.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:16:13,609 INFO [train.py:527] (4/6) Epoch 682, batch 106, global_batch_idx: 84550, batch size: 56, loss[discriminator_loss=2.692, discriminator_real_loss=1.384, discriminator_fake_loss=1.308, generator_loss=29.3, generator_mel_loss=18.62, generator_kl_loss=1.377, generator_dur_loss=1.764, generator_adv_loss=1.869, generator_feat_match_loss=5.663, over 56.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=28.73, generator_mel_loss=17.98, generator_kl_loss=1.406, generator_dur_loss=1.749, generator_adv_loss=1.984, generator_feat_match_loss=5.604, over 6352.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:17:04,762 INFO [train.py:919] (4/6) Start epoch 683 +2024-03-14 22:18:57,672 INFO [train.py:527] (4/6) Epoch 683, batch 32, global_batch_idx: 84600, batch size: 50, loss[discriminator_loss=2.729, discriminator_real_loss=1.358, discriminator_fake_loss=1.372, generator_loss=28.79, generator_mel_loss=18.31, generator_kl_loss=1.378, generator_dur_loss=1.682, generator_adv_loss=2.068, generator_feat_match_loss=5.354, over 50.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.368, discriminator_fake_loss=1.344, generator_loss=28.76, generator_mel_loss=17.98, generator_kl_loss=1.411, generator_dur_loss=1.743, generator_adv_loss=1.976, generator_feat_match_loss=5.652, over 1859.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:18:57,674 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 22:19:05,696 INFO [train.py:591] (4/6) Epoch 683, validation: discriminator_loss=2.729, discriminator_real_loss=1.501, discriminator_fake_loss=1.228, generator_loss=27.2, generator_mel_loss=17.99, generator_kl_loss=1.323, generator_dur_loss=1.799, generator_adv_loss=1.946, generator_feat_match_loss=4.144, over 100.00 samples. +2024-03-14 22:19:05,697 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 22:21:28,121 INFO [train.py:527] (4/6) Epoch 683, batch 82, global_batch_idx: 84650, batch size: 31, loss[discriminator_loss=2.716, discriminator_real_loss=1.356, discriminator_fake_loss=1.36, generator_loss=27.2, generator_mel_loss=17.68, generator_kl_loss=1.41, generator_dur_loss=1.646, generator_adv_loss=1.861, generator_feat_match_loss=4.605, over 31.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.364, discriminator_fake_loss=1.338, generator_loss=28.75, generator_mel_loss=17.97, generator_kl_loss=1.402, generator_dur_loss=1.755, generator_adv_loss=1.973, generator_feat_match_loss=5.653, over 4906.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:23:20,473 INFO [train.py:919] (4/6) Start epoch 684 +2024-03-14 22:24:06,481 INFO [train.py:527] (4/6) Epoch 684, batch 8, global_batch_idx: 84700, batch size: 31, loss[discriminator_loss=2.757, discriminator_real_loss=1.4, discriminator_fake_loss=1.357, generator_loss=30.44, generator_mel_loss=19.41, generator_kl_loss=1.618, generator_dur_loss=1.614, generator_adv_loss=1.955, generator_feat_match_loss=5.839, over 31.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.337, discriminator_fake_loss=1.349, generator_loss=28.8, generator_mel_loss=18.04, generator_kl_loss=1.44, generator_dur_loss=1.769, generator_adv_loss=1.994, generator_feat_match_loss=5.556, over 547.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:26:26,010 INFO [train.py:527] (4/6) Epoch 684, batch 58, global_batch_idx: 84750, batch size: 25, loss[discriminator_loss=2.679, discriminator_real_loss=1.401, discriminator_fake_loss=1.278, generator_loss=30.03, generator_mel_loss=18.32, generator_kl_loss=1.796, generator_dur_loss=1.533, generator_adv_loss=2.071, generator_feat_match_loss=6.305, over 25.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.351, discriminator_fake_loss=1.331, generator_loss=28.82, generator_mel_loss=17.96, generator_kl_loss=1.423, generator_dur_loss=1.741, generator_adv_loss=1.991, generator_feat_match_loss=5.714, over 3352.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:28:47,647 INFO [train.py:527] (4/6) Epoch 684, batch 108, global_batch_idx: 84800, batch size: 44, loss[discriminator_loss=2.63, discriminator_real_loss=1.288, discriminator_fake_loss=1.342, generator_loss=29.4, generator_mel_loss=18.55, generator_kl_loss=1.459, generator_dur_loss=1.705, generator_adv_loss=2.121, generator_feat_match_loss=5.573, over 44.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.354, discriminator_fake_loss=1.33, generator_loss=28.76, generator_mel_loss=17.92, generator_kl_loss=1.422, generator_dur_loss=1.75, generator_adv_loss=1.989, generator_feat_match_loss=5.681, over 6305.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:28:47,649 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 22:28:56,611 INFO [train.py:591] (4/6) Epoch 684, validation: discriminator_loss=2.73, discriminator_real_loss=1.43, discriminator_fake_loss=1.3, generator_loss=28.12, generator_mel_loss=18.59, generator_kl_loss=1.203, generator_dur_loss=1.813, generator_adv_loss=1.939, generator_feat_match_loss=4.572, over 100.00 samples. +2024-03-14 22:28:56,611 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 22:29:38,831 INFO [train.py:919] (4/6) Start epoch 685 +2024-03-14 22:31:37,853 INFO [train.py:527] (4/6) Epoch 685, batch 34, global_batch_idx: 84850, batch size: 74, loss[discriminator_loss=2.674, discriminator_real_loss=1.341, discriminator_fake_loss=1.333, generator_loss=28.6, generator_mel_loss=17.92, generator_kl_loss=1.238, generator_dur_loss=1.804, generator_adv_loss=1.996, generator_feat_match_loss=5.641, over 74.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.37, discriminator_fake_loss=1.322, generator_loss=28.73, generator_mel_loss=17.93, generator_kl_loss=1.39, generator_dur_loss=1.75, generator_adv_loss=1.991, generator_feat_match_loss=5.668, over 2093.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:33:56,805 INFO [train.py:527] (4/6) Epoch 685, batch 84, global_batch_idx: 84900, batch size: 45, loss[discriminator_loss=2.692, discriminator_real_loss=1.322, discriminator_fake_loss=1.37, generator_loss=28.54, generator_mel_loss=17.56, generator_kl_loss=1.486, generator_dur_loss=1.65, generator_adv_loss=2.053, generator_feat_match_loss=5.787, over 45.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.368, discriminator_fake_loss=1.328, generator_loss=28.69, generator_mel_loss=17.92, generator_kl_loss=1.396, generator_dur_loss=1.748, generator_adv_loss=1.986, generator_feat_match_loss=5.639, over 4874.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:35:46,267 INFO [train.py:919] (4/6) Start epoch 686 +2024-03-14 22:36:40,456 INFO [train.py:527] (4/6) Epoch 686, batch 10, global_batch_idx: 84950, batch size: 72, loss[discriminator_loss=2.703, discriminator_real_loss=1.346, discriminator_fake_loss=1.357, generator_loss=29.19, generator_mel_loss=17.98, generator_kl_loss=1.388, generator_dur_loss=1.796, generator_adv_loss=1.904, generator_feat_match_loss=6.117, over 72.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.355, discriminator_fake_loss=1.351, generator_loss=28.9, generator_mel_loss=17.91, generator_kl_loss=1.393, generator_dur_loss=1.761, generator_adv_loss=1.977, generator_feat_match_loss=5.862, over 722.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:38:59,290 INFO [train.py:527] (4/6) Epoch 686, batch 60, global_batch_idx: 85000, batch size: 31, loss[discriminator_loss=2.682, discriminator_real_loss=1.321, discriminator_fake_loss=1.362, generator_loss=28.88, generator_mel_loss=18.47, generator_kl_loss=1.494, generator_dur_loss=1.657, generator_adv_loss=2.028, generator_feat_match_loss=5.238, over 31.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.359, discriminator_fake_loss=1.341, generator_loss=28.69, generator_mel_loss=17.94, generator_kl_loss=1.406, generator_dur_loss=1.75, generator_adv_loss=1.969, generator_feat_match_loss=5.625, over 3788.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:38:59,291 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 22:39:07,401 INFO [train.py:591] (4/6) Epoch 686, validation: discriminator_loss=2.731, discriminator_real_loss=1.513, discriminator_fake_loss=1.218, generator_loss=27.11, generator_mel_loss=17.73, generator_kl_loss=1.243, generator_dur_loss=1.814, generator_adv_loss=2.099, generator_feat_match_loss=4.224, over 100.00 samples. +2024-03-14 22:39:07,402 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 22:41:26,958 INFO [train.py:527] (4/6) Epoch 686, batch 110, global_batch_idx: 85050, batch size: 47, loss[discriminator_loss=2.737, discriminator_real_loss=1.42, discriminator_fake_loss=1.318, generator_loss=29.6, generator_mel_loss=18.1, generator_kl_loss=1.671, generator_dur_loss=1.687, generator_adv_loss=1.96, generator_feat_match_loss=6.18, over 47.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.361, discriminator_fake_loss=1.335, generator_loss=28.69, generator_mel_loss=17.95, generator_kl_loss=1.407, generator_dur_loss=1.749, generator_adv_loss=1.97, generator_feat_match_loss=5.614, over 6580.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:42:04,528 INFO [train.py:919] (4/6) Start epoch 687 +2024-03-14 22:44:09,379 INFO [train.py:527] (4/6) Epoch 687, batch 36, global_batch_idx: 85100, batch size: 64, loss[discriminator_loss=2.725, discriminator_real_loss=1.487, discriminator_fake_loss=1.238, generator_loss=27, generator_mel_loss=17.33, generator_kl_loss=1.185, generator_dur_loss=1.802, generator_adv_loss=1.941, generator_feat_match_loss=4.745, over 64.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.367, discriminator_fake_loss=1.327, generator_loss=28.59, generator_mel_loss=17.9, generator_kl_loss=1.406, generator_dur_loss=1.761, generator_adv_loss=1.988, generator_feat_match_loss=5.537, over 2130.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:46:28,376 INFO [train.py:527] (4/6) Epoch 687, batch 86, global_batch_idx: 85150, batch size: 39, loss[discriminator_loss=2.711, discriminator_real_loss=1.523, discriminator_fake_loss=1.189, generator_loss=26.61, generator_mel_loss=16.79, generator_kl_loss=1.646, generator_dur_loss=1.671, generator_adv_loss=2.021, generator_feat_match_loss=4.489, over 39.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.362, discriminator_fake_loss=1.335, generator_loss=28.81, generator_mel_loss=17.91, generator_kl_loss=1.408, generator_dur_loss=1.748, generator_adv_loss=2.027, generator_feat_match_loss=5.714, over 5060.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:48:12,353 INFO [train.py:919] (4/6) Start epoch 688 +2024-03-14 22:49:10,872 INFO [train.py:527] (4/6) Epoch 688, batch 12, global_batch_idx: 85200, batch size: 62, loss[discriminator_loss=2.645, discriminator_real_loss=1.314, discriminator_fake_loss=1.332, generator_loss=29.83, generator_mel_loss=18.16, generator_kl_loss=1.491, generator_dur_loss=1.774, generator_adv_loss=1.922, generator_feat_match_loss=6.477, over 62.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.351, discriminator_fake_loss=1.309, generator_loss=28.79, generator_mel_loss=17.96, generator_kl_loss=1.422, generator_dur_loss=1.763, generator_adv_loss=1.993, generator_feat_match_loss=5.652, over 841.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:49:10,875 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 22:49:18,930 INFO [train.py:591] (4/6) Epoch 688, validation: discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=27.38, generator_mel_loss=18, generator_kl_loss=1.274, generator_dur_loss=1.805, generator_adv_loss=1.824, generator_feat_match_loss=4.473, over 100.00 samples. +2024-03-14 22:49:18,931 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 22:51:34,918 INFO [train.py:527] (4/6) Epoch 688, batch 62, global_batch_idx: 85250, batch size: 36, loss[discriminator_loss=2.746, discriminator_real_loss=1.36, discriminator_fake_loss=1.386, generator_loss=27.5, generator_mel_loss=17.49, generator_kl_loss=1.504, generator_dur_loss=1.615, generator_adv_loss=1.836, generator_feat_match_loss=5.057, over 36.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.356, discriminator_fake_loss=1.33, generator_loss=28.69, generator_mel_loss=17.94, generator_kl_loss=1.434, generator_dur_loss=1.748, generator_adv_loss=1.983, generator_feat_match_loss=5.586, over 3658.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:53:54,573 INFO [train.py:527] (4/6) Epoch 688, batch 112, global_batch_idx: 85300, batch size: 45, loss[discriminator_loss=2.708, discriminator_real_loss=1.343, discriminator_fake_loss=1.365, generator_loss=29.38, generator_mel_loss=18.58, generator_kl_loss=1.419, generator_dur_loss=1.709, generator_adv_loss=1.963, generator_feat_match_loss=5.706, over 45.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.361, discriminator_fake_loss=1.334, generator_loss=28.69, generator_mel_loss=17.95, generator_kl_loss=1.431, generator_dur_loss=1.75, generator_adv_loss=1.979, generator_feat_match_loss=5.583, over 6594.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:54:26,687 INFO [train.py:919] (4/6) Start epoch 689 +2024-03-14 22:56:34,993 INFO [train.py:527] (4/6) Epoch 689, batch 38, global_batch_idx: 85350, batch size: 31, loss[discriminator_loss=2.702, discriminator_real_loss=1.341, discriminator_fake_loss=1.361, generator_loss=28.49, generator_mel_loss=17.94, generator_kl_loss=1.748, generator_dur_loss=1.723, generator_adv_loss=2.132, generator_feat_match_loss=4.944, over 31.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.364, discriminator_fake_loss=1.335, generator_loss=28.51, generator_mel_loss=17.92, generator_kl_loss=1.401, generator_dur_loss=1.757, generator_adv_loss=1.974, generator_feat_match_loss=5.458, over 2292.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:58:51,902 INFO [train.py:527] (4/6) Epoch 689, batch 88, global_batch_idx: 85400, batch size: 77, loss[discriminator_loss=2.661, discriminator_real_loss=1.303, discriminator_fake_loss=1.358, generator_loss=29.42, generator_mel_loss=17.85, generator_kl_loss=1.328, generator_dur_loss=1.775, generator_adv_loss=2.025, generator_feat_match_loss=6.445, over 77.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.362, discriminator_fake_loss=1.334, generator_loss=28.56, generator_mel_loss=17.9, generator_kl_loss=1.41, generator_dur_loss=1.747, generator_adv_loss=1.974, generator_feat_match_loss=5.532, over 5108.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:58:51,904 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 22:59:00,798 INFO [train.py:591] (4/6) Epoch 689, validation: discriminator_loss=2.737, discriminator_real_loss=1.46, discriminator_fake_loss=1.277, generator_loss=27.34, generator_mel_loss=18.04, generator_kl_loss=1.309, generator_dur_loss=1.806, generator_adv_loss=1.958, generator_feat_match_loss=4.23, over 100.00 samples. +2024-03-14 22:59:00,799 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 23:00:41,832 INFO [train.py:919] (4/6) Start epoch 690 +2024-03-14 23:01:46,911 INFO [train.py:527] (4/6) Epoch 690, batch 14, global_batch_idx: 85450, batch size: 25, loss[discriminator_loss=2.584, discriminator_real_loss=1.272, discriminator_fake_loss=1.312, generator_loss=32.38, generator_mel_loss=19.04, generator_kl_loss=1.7, generator_dur_loss=1.584, generator_adv_loss=2.46, generator_feat_match_loss=7.593, over 25.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.365, discriminator_fake_loss=1.348, generator_loss=28.91, generator_mel_loss=17.95, generator_kl_loss=1.475, generator_dur_loss=1.745, generator_adv_loss=2.023, generator_feat_match_loss=5.726, over 843.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:04:07,217 INFO [train.py:527] (4/6) Epoch 690, batch 64, global_batch_idx: 85500, batch size: 68, loss[discriminator_loss=2.64, discriminator_real_loss=1.373, discriminator_fake_loss=1.267, generator_loss=29.41, generator_mel_loss=18.05, generator_kl_loss=1.336, generator_dur_loss=1.772, generator_adv_loss=2.097, generator_feat_match_loss=6.152, over 68.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=28.78, generator_mel_loss=17.94, generator_kl_loss=1.422, generator_dur_loss=1.75, generator_adv_loss=2.008, generator_feat_match_loss=5.665, over 3747.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:06:22,283 INFO [train.py:527] (4/6) Epoch 690, batch 114, global_batch_idx: 85550, batch size: 48, loss[discriminator_loss=2.687, discriminator_real_loss=1.385, discriminator_fake_loss=1.302, generator_loss=29.63, generator_mel_loss=18.21, generator_kl_loss=1.64, generator_dur_loss=1.677, generator_adv_loss=1.94, generator_feat_match_loss=6.164, over 48.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.365, discriminator_fake_loss=1.329, generator_loss=28.68, generator_mel_loss=17.9, generator_kl_loss=1.422, generator_dur_loss=1.747, generator_adv_loss=1.993, generator_feat_match_loss=5.619, over 6632.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:06:47,856 INFO [train.py:919] (4/6) Start epoch 691 +2024-03-14 23:09:01,330 INFO [train.py:527] (4/6) Epoch 691, batch 40, global_batch_idx: 85600, batch size: 44, loss[discriminator_loss=2.659, discriminator_real_loss=1.363, discriminator_fake_loss=1.296, generator_loss=29.37, generator_mel_loss=17.89, generator_kl_loss=1.632, generator_dur_loss=1.663, generator_adv_loss=2.066, generator_feat_match_loss=6.123, over 44.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.367, discriminator_fake_loss=1.334, generator_loss=28.72, generator_mel_loss=17.94, generator_kl_loss=1.434, generator_dur_loss=1.736, generator_adv_loss=1.99, generator_feat_match_loss=5.619, over 2310.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:09:01,331 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 23:09:09,449 INFO [train.py:591] (4/6) Epoch 691, validation: discriminator_loss=2.715, discriminator_real_loss=1.397, discriminator_fake_loss=1.319, generator_loss=27.18, generator_mel_loss=17.87, generator_kl_loss=1.276, generator_dur_loss=1.788, generator_adv_loss=1.895, generator_feat_match_loss=4.349, over 100.00 samples. +2024-03-14 23:09:09,450 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 23:11:27,700 INFO [train.py:527] (4/6) Epoch 691, batch 90, global_batch_idx: 85650, batch size: 48, loss[discriminator_loss=2.671, discriminator_real_loss=1.401, discriminator_fake_loss=1.27, generator_loss=29.15, generator_mel_loss=18.27, generator_kl_loss=1.317, generator_dur_loss=1.695, generator_adv_loss=2.021, generator_feat_match_loss=5.843, over 48.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.362, discriminator_fake_loss=1.334, generator_loss=28.71, generator_mel_loss=17.92, generator_kl_loss=1.422, generator_dur_loss=1.732, generator_adv_loss=1.979, generator_feat_match_loss=5.66, over 5271.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:13:01,177 INFO [train.py:919] (4/6) Start epoch 692 +2024-03-14 23:14:07,494 INFO [train.py:527] (4/6) Epoch 692, batch 16, global_batch_idx: 85700, batch size: 44, loss[discriminator_loss=2.687, discriminator_real_loss=1.248, discriminator_fake_loss=1.439, generator_loss=30.19, generator_mel_loss=17.99, generator_kl_loss=1.471, generator_dur_loss=1.647, generator_adv_loss=2.136, generator_feat_match_loss=6.951, over 44.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.377, discriminator_fake_loss=1.315, generator_loss=28.76, generator_mel_loss=17.94, generator_kl_loss=1.427, generator_dur_loss=1.743, generator_adv_loss=2.018, generator_feat_match_loss=5.623, over 958.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:16:26,988 INFO [train.py:527] (4/6) Epoch 692, batch 66, global_batch_idx: 85750, batch size: 74, loss[discriminator_loss=2.673, discriminator_real_loss=1.378, discriminator_fake_loss=1.295, generator_loss=29.02, generator_mel_loss=17.71, generator_kl_loss=1.442, generator_dur_loss=1.808, generator_adv_loss=2.044, generator_feat_match_loss=6.013, over 74.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.371, discriminator_fake_loss=1.327, generator_loss=28.56, generator_mel_loss=17.87, generator_kl_loss=1.406, generator_dur_loss=1.745, generator_adv_loss=1.987, generator_feat_match_loss=5.553, over 3908.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:18:45,476 INFO [train.py:527] (4/6) Epoch 692, batch 116, global_batch_idx: 85800, batch size: 59, loss[discriminator_loss=2.725, discriminator_real_loss=1.286, discriminator_fake_loss=1.439, generator_loss=28.95, generator_mel_loss=18.07, generator_kl_loss=1.311, generator_dur_loss=1.76, generator_adv_loss=2.108, generator_feat_match_loss=5.701, over 59.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.365, discriminator_fake_loss=1.328, generator_loss=28.65, generator_mel_loss=17.92, generator_kl_loss=1.413, generator_dur_loss=1.742, generator_adv_loss=1.987, generator_feat_match_loss=5.583, over 6715.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:18:45,478 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 23:18:54,408 INFO [train.py:591] (4/6) Epoch 692, validation: discriminator_loss=2.73, discriminator_real_loss=1.472, discriminator_fake_loss=1.258, generator_loss=27.34, generator_mel_loss=18.13, generator_kl_loss=1.279, generator_dur_loss=1.808, generator_adv_loss=2.034, generator_feat_match_loss=4.085, over 100.00 samples. +2024-03-14 23:18:54,409 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 23:19:15,281 INFO [train.py:919] (4/6) Start epoch 693 +2024-03-14 23:21:35,672 INFO [train.py:527] (4/6) Epoch 693, batch 42, global_batch_idx: 85850, batch size: 42, loss[discriminator_loss=2.757, discriminator_real_loss=1.409, discriminator_fake_loss=1.348, generator_loss=28.76, generator_mel_loss=18.22, generator_kl_loss=1.513, generator_dur_loss=1.663, generator_adv_loss=1.954, generator_feat_match_loss=5.413, over 42.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.372, discriminator_fake_loss=1.329, generator_loss=28.7, generator_mel_loss=17.97, generator_kl_loss=1.432, generator_dur_loss=1.732, generator_adv_loss=1.989, generator_feat_match_loss=5.584, over 2229.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:23:55,488 INFO [train.py:527] (4/6) Epoch 693, batch 92, global_batch_idx: 85900, batch size: 96, loss[discriminator_loss=2.739, discriminator_real_loss=1.384, discriminator_fake_loss=1.355, generator_loss=29.2, generator_mel_loss=18.12, generator_kl_loss=1.42, generator_dur_loss=1.869, generator_adv_loss=1.976, generator_feat_match_loss=5.816, over 96.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.366, discriminator_fake_loss=1.331, generator_loss=28.67, generator_mel_loss=17.94, generator_kl_loss=1.422, generator_dur_loss=1.74, generator_adv_loss=1.982, generator_feat_match_loss=5.584, over 5081.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:25:20,000 INFO [train.py:919] (4/6) Start epoch 694 +2024-03-14 23:26:34,840 INFO [train.py:527] (4/6) Epoch 694, batch 18, global_batch_idx: 85950, batch size: 83, loss[discriminator_loss=2.727, discriminator_real_loss=1.332, discriminator_fake_loss=1.395, generator_loss=27.92, generator_mel_loss=17.95, generator_kl_loss=1.356, generator_dur_loss=1.821, generator_adv_loss=1.905, generator_feat_match_loss=4.89, over 83.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.372, discriminator_fake_loss=1.336, generator_loss=28.43, generator_mel_loss=17.78, generator_kl_loss=1.351, generator_dur_loss=1.781, generator_adv_loss=1.967, generator_feat_match_loss=5.556, over 1245.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:28:56,362 INFO [train.py:527] (4/6) Epoch 694, batch 68, global_batch_idx: 86000, batch size: 61, loss[discriminator_loss=2.599, discriminator_real_loss=1.341, discriminator_fake_loss=1.259, generator_loss=29.76, generator_mel_loss=18.24, generator_kl_loss=1.537, generator_dur_loss=1.741, generator_adv_loss=2.142, generator_feat_match_loss=6.094, over 61.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.56, generator_mel_loss=17.87, generator_kl_loss=1.386, generator_dur_loss=1.754, generator_adv_loss=1.977, generator_feat_match_loss=5.567, over 4114.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:28:56,363 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 23:29:05,263 INFO [train.py:591] (4/6) Epoch 694, validation: discriminator_loss=2.703, discriminator_real_loss=1.38, discriminator_fake_loss=1.323, generator_loss=26.96, generator_mel_loss=17.87, generator_kl_loss=1.186, generator_dur_loss=1.786, generator_adv_loss=1.937, generator_feat_match_loss=4.183, over 100.00 samples. +2024-03-14 23:29:05,264 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 23:31:20,797 INFO [train.py:527] (4/6) Epoch 694, batch 118, global_batch_idx: 86050, batch size: 68, loss[discriminator_loss=2.665, discriminator_real_loss=1.347, discriminator_fake_loss=1.318, generator_loss=28.05, generator_mel_loss=17.5, generator_kl_loss=1.322, generator_dur_loss=1.766, generator_adv_loss=1.893, generator_feat_match_loss=5.57, over 68.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.364, discriminator_fake_loss=1.328, generator_loss=28.65, generator_mel_loss=17.9, generator_kl_loss=1.392, generator_dur_loss=1.748, generator_adv_loss=1.979, generator_feat_match_loss=5.632, over 6935.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:31:36,593 INFO [train.py:919] (4/6) Start epoch 695 +2024-03-14 23:34:03,457 INFO [train.py:527] (4/6) Epoch 695, batch 44, global_batch_idx: 86100, batch size: 74, loss[discriminator_loss=2.676, discriminator_real_loss=1.415, discriminator_fake_loss=1.261, generator_loss=29.27, generator_mel_loss=18.07, generator_kl_loss=1.353, generator_dur_loss=1.757, generator_adv_loss=2.087, generator_feat_match_loss=6.003, over 74.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.363, discriminator_fake_loss=1.339, generator_loss=28.73, generator_mel_loss=17.99, generator_kl_loss=1.41, generator_dur_loss=1.734, generator_adv_loss=1.972, generator_feat_match_loss=5.631, over 2492.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:36:21,463 INFO [train.py:527] (4/6) Epoch 695, batch 94, global_batch_idx: 86150, batch size: 50, loss[discriminator_loss=2.699, discriminator_real_loss=1.341, discriminator_fake_loss=1.359, generator_loss=28.83, generator_mel_loss=18.03, generator_kl_loss=1.354, generator_dur_loss=1.707, generator_adv_loss=2.025, generator_feat_match_loss=5.71, over 50.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.362, discriminator_fake_loss=1.335, generator_loss=28.67, generator_mel_loss=17.96, generator_kl_loss=1.408, generator_dur_loss=1.729, generator_adv_loss=1.984, generator_feat_match_loss=5.593, over 5242.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:37:40,744 INFO [train.py:919] (4/6) Start epoch 696 +2024-03-14 23:39:00,752 INFO [train.py:527] (4/6) Epoch 696, batch 20, global_batch_idx: 86200, batch size: 59, loss[discriminator_loss=2.698, discriminator_real_loss=1.351, discriminator_fake_loss=1.347, generator_loss=28.83, generator_mel_loss=18.36, generator_kl_loss=1.426, generator_dur_loss=1.726, generator_adv_loss=2.009, generator_feat_match_loss=5.311, over 59.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.362, discriminator_fake_loss=1.332, generator_loss=28.79, generator_mel_loss=17.96, generator_kl_loss=1.421, generator_dur_loss=1.727, generator_adv_loss=1.981, generator_feat_match_loss=5.692, over 1183.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:39:00,753 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 23:39:09,014 INFO [train.py:591] (4/6) Epoch 696, validation: discriminator_loss=2.771, discriminator_real_loss=1.451, discriminator_fake_loss=1.32, generator_loss=27.59, generator_mel_loss=18.14, generator_kl_loss=1.222, generator_dur_loss=1.806, generator_adv_loss=1.954, generator_feat_match_loss=4.465, over 100.00 samples. +2024-03-14 23:39:09,015 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 23:41:26,967 INFO [train.py:527] (4/6) Epoch 696, batch 70, global_batch_idx: 86250, batch size: 80, loss[discriminator_loss=2.727, discriminator_real_loss=1.398, discriminator_fake_loss=1.329, generator_loss=28.57, generator_mel_loss=18.1, generator_kl_loss=1.275, generator_dur_loss=1.805, generator_adv_loss=2.017, generator_feat_match_loss=5.376, over 80.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.363, discriminator_fake_loss=1.34, generator_loss=28.75, generator_mel_loss=18, generator_kl_loss=1.408, generator_dur_loss=1.735, generator_adv_loss=1.981, generator_feat_match_loss=5.625, over 3959.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:43:47,172 INFO [train.py:527] (4/6) Epoch 696, batch 120, global_batch_idx: 86300, batch size: 52, loss[discriminator_loss=2.7, discriminator_real_loss=1.378, discriminator_fake_loss=1.323, generator_loss=27.86, generator_mel_loss=17.86, generator_kl_loss=1.407, generator_dur_loss=1.685, generator_adv_loss=1.974, generator_feat_match_loss=4.932, over 52.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.365, discriminator_fake_loss=1.333, generator_loss=28.71, generator_mel_loss=17.97, generator_kl_loss=1.414, generator_dur_loss=1.738, generator_adv_loss=1.981, generator_feat_match_loss=5.61, over 6580.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:43:56,279 INFO [train.py:919] (4/6) Start epoch 697 +2024-03-14 23:46:28,272 INFO [train.py:527] (4/6) Epoch 697, batch 46, global_batch_idx: 86350, batch size: 58, loss[discriminator_loss=2.683, discriminator_real_loss=1.389, discriminator_fake_loss=1.293, generator_loss=29.41, generator_mel_loss=18.18, generator_kl_loss=1.536, generator_dur_loss=1.699, generator_adv_loss=2.123, generator_feat_match_loss=5.866, over 58.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.371, discriminator_fake_loss=1.335, generator_loss=28.88, generator_mel_loss=18.09, generator_kl_loss=1.407, generator_dur_loss=1.741, generator_adv_loss=2.002, generator_feat_match_loss=5.64, over 2766.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:48:45,596 INFO [train.py:527] (4/6) Epoch 697, batch 96, global_batch_idx: 86400, batch size: 48, loss[discriminator_loss=2.608, discriminator_real_loss=1.246, discriminator_fake_loss=1.361, generator_loss=30, generator_mel_loss=18.46, generator_kl_loss=1.57, generator_dur_loss=1.658, generator_adv_loss=2.062, generator_feat_match_loss=6.25, over 48.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.367, discriminator_fake_loss=1.332, generator_loss=28.79, generator_mel_loss=18.01, generator_kl_loss=1.421, generator_dur_loss=1.733, generator_adv_loss=1.992, generator_feat_match_loss=5.638, over 5500.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:48:45,597 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 23:48:54,260 INFO [train.py:591] (4/6) Epoch 697, validation: discriminator_loss=2.725, discriminator_real_loss=1.412, discriminator_fake_loss=1.313, generator_loss=28.36, generator_mel_loss=18.65, generator_kl_loss=1.19, generator_dur_loss=1.793, generator_adv_loss=1.97, generator_feat_match_loss=4.759, over 100.00 samples. +2024-03-14 23:48:54,260 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-14 23:50:11,008 INFO [train.py:919] (4/6) Start epoch 698 +2024-03-14 23:51:37,758 INFO [train.py:527] (4/6) Epoch 698, batch 22, global_batch_idx: 86450, batch size: 77, loss[discriminator_loss=2.724, discriminator_real_loss=1.464, discriminator_fake_loss=1.26, generator_loss=28.11, generator_mel_loss=17.38, generator_kl_loss=1.239, generator_dur_loss=1.814, generator_adv_loss=1.926, generator_feat_match_loss=5.753, over 77.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.58, generator_mel_loss=17.88, generator_kl_loss=1.404, generator_dur_loss=1.737, generator_adv_loss=1.964, generator_feat_match_loss=5.592, over 1354.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:53:59,530 INFO [train.py:527] (4/6) Epoch 698, batch 72, global_batch_idx: 86500, batch size: 45, loss[discriminator_loss=2.685, discriminator_real_loss=1.336, discriminator_fake_loss=1.348, generator_loss=29.4, generator_mel_loss=18.01, generator_kl_loss=1.556, generator_dur_loss=1.608, generator_adv_loss=2.135, generator_feat_match_loss=6.092, over 45.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.56, generator_mel_loss=17.86, generator_kl_loss=1.405, generator_dur_loss=1.736, generator_adv_loss=1.969, generator_feat_match_loss=5.585, over 4293.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:56:14,835 INFO [train.py:527] (4/6) Epoch 698, batch 122, global_batch_idx: 86550, batch size: 13, loss[discriminator_loss=2.645, discriminator_real_loss=1.337, discriminator_fake_loss=1.308, generator_loss=31.91, generator_mel_loss=18.92, generator_kl_loss=2.01, generator_dur_loss=1.615, generator_adv_loss=1.956, generator_feat_match_loss=7.403, over 13.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.359, discriminator_fake_loss=1.335, generator_loss=28.7, generator_mel_loss=17.92, generator_kl_loss=1.416, generator_dur_loss=1.73, generator_adv_loss=1.976, generator_feat_match_loss=5.654, over 6938.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:56:19,279 INFO [train.py:919] (4/6) Start epoch 699 +2024-03-14 23:58:57,582 INFO [train.py:527] (4/6) Epoch 699, batch 48, global_batch_idx: 86600, batch size: 59, loss[discriminator_loss=2.688, discriminator_real_loss=1.446, discriminator_fake_loss=1.241, generator_loss=28.09, generator_mel_loss=17.59, generator_kl_loss=1.305, generator_dur_loss=1.774, generator_adv_loss=2.137, generator_feat_match_loss=5.281, over 59.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.362, discriminator_fake_loss=1.322, generator_loss=28.77, generator_mel_loss=17.91, generator_kl_loss=1.387, generator_dur_loss=1.747, generator_adv_loss=2.005, generator_feat_match_loss=5.719, over 2937.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:58:57,583 INFO [train.py:581] (4/6) Computing validation loss +2024-03-14 23:59:05,346 INFO [train.py:591] (4/6) Epoch 699, validation: discriminator_loss=2.727, discriminator_real_loss=1.469, discriminator_fake_loss=1.258, generator_loss=28.32, generator_mel_loss=18.63, generator_kl_loss=1.148, generator_dur_loss=1.815, generator_adv_loss=2.007, generator_feat_match_loss=4.716, over 100.00 samples. +2024-03-14 23:59:05,347 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 00:01:23,423 INFO [train.py:527] (4/6) Epoch 699, batch 98, global_batch_idx: 86650, batch size: 48, loss[discriminator_loss=2.684, discriminator_real_loss=1.394, discriminator_fake_loss=1.29, generator_loss=30.21, generator_mel_loss=18.2, generator_kl_loss=1.575, generator_dur_loss=1.648, generator_adv_loss=1.975, generator_feat_match_loss=6.807, over 48.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.363, discriminator_fake_loss=1.321, generator_loss=28.71, generator_mel_loss=17.9, generator_kl_loss=1.385, generator_dur_loss=1.746, generator_adv_loss=2, generator_feat_match_loss=5.679, over 5907.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:02:32,019 INFO [train.py:919] (4/6) Start epoch 700 +2024-03-15 00:04:01,446 INFO [train.py:527] (4/6) Epoch 700, batch 24, global_batch_idx: 86700, batch size: 53, loss[discriminator_loss=2.659, discriminator_real_loss=1.308, discriminator_fake_loss=1.351, generator_loss=29, generator_mel_loss=18.05, generator_kl_loss=1.448, generator_dur_loss=1.672, generator_adv_loss=2.042, generator_feat_match_loss=5.783, over 53.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.338, discriminator_fake_loss=1.332, generator_loss=29.02, generator_mel_loss=18.05, generator_kl_loss=1.446, generator_dur_loss=1.732, generator_adv_loss=1.982, generator_feat_match_loss=5.813, over 1365.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:06:21,324 INFO [train.py:527] (4/6) Epoch 700, batch 74, global_batch_idx: 86750, batch size: 55, loss[discriminator_loss=2.654, discriminator_real_loss=1.405, discriminator_fake_loss=1.249, generator_loss=28.06, generator_mel_loss=17.92, generator_kl_loss=1.413, generator_dur_loss=1.731, generator_adv_loss=2.017, generator_feat_match_loss=4.987, over 55.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.354, discriminator_fake_loss=1.333, generator_loss=28.81, generator_mel_loss=17.98, generator_kl_loss=1.419, generator_dur_loss=1.732, generator_adv_loss=1.985, generator_feat_match_loss=5.685, over 4045.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:08:36,959 INFO [train.py:919] (4/6) Start epoch 701 +2024-03-15 00:09:01,141 INFO [train.py:527] (4/6) Epoch 701, batch 0, global_batch_idx: 86800, batch size: 58, loss[discriminator_loss=2.673, discriminator_real_loss=1.46, discriminator_fake_loss=1.213, generator_loss=28.34, generator_mel_loss=17.73, generator_kl_loss=1.46, generator_dur_loss=1.729, generator_adv_loss=1.958, generator_feat_match_loss=5.464, over 58.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.46, discriminator_fake_loss=1.213, generator_loss=28.34, generator_mel_loss=17.73, generator_kl_loss=1.46, generator_dur_loss=1.729, generator_adv_loss=1.958, generator_feat_match_loss=5.464, over 58.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:09:01,143 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 00:09:08,771 INFO [train.py:591] (4/6) Epoch 701, validation: discriminator_loss=2.773, discriminator_real_loss=1.408, discriminator_fake_loss=1.366, generator_loss=26.99, generator_mel_loss=17.93, generator_kl_loss=1.158, generator_dur_loss=1.813, generator_adv_loss=1.858, generator_feat_match_loss=4.228, over 100.00 samples. +2024-03-15 00:09:08,773 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 00:11:26,791 INFO [train.py:527] (4/6) Epoch 701, batch 50, global_batch_idx: 86850, batch size: 45, loss[discriminator_loss=2.715, discriminator_real_loss=1.399, discriminator_fake_loss=1.315, generator_loss=27.02, generator_mel_loss=17.15, generator_kl_loss=1.674, generator_dur_loss=1.668, generator_adv_loss=1.878, generator_feat_match_loss=4.642, over 45.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.366, discriminator_fake_loss=1.329, generator_loss=28.66, generator_mel_loss=17.87, generator_kl_loss=1.448, generator_dur_loss=1.731, generator_adv_loss=2.013, generator_feat_match_loss=5.598, over 2807.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:13:42,999 INFO [train.py:527] (4/6) Epoch 701, batch 100, global_batch_idx: 86900, batch size: 83, loss[discriminator_loss=2.684, discriminator_real_loss=1.354, discriminator_fake_loss=1.33, generator_loss=27.8, generator_mel_loss=17.76, generator_kl_loss=1.325, generator_dur_loss=1.746, generator_adv_loss=1.822, generator_feat_match_loss=5.148, over 83.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.366, discriminator_fake_loss=1.332, generator_loss=28.63, generator_mel_loss=17.88, generator_kl_loss=1.435, generator_dur_loss=1.729, generator_adv_loss=1.988, generator_feat_match_loss=5.59, over 5738.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:14:51,537 INFO [train.py:919] (4/6) Start epoch 702 +2024-03-15 00:16:27,451 INFO [train.py:527] (4/6) Epoch 702, batch 26, global_batch_idx: 86950, batch size: 68, loss[discriminator_loss=2.691, discriminator_real_loss=1.357, discriminator_fake_loss=1.334, generator_loss=28.66, generator_mel_loss=18.12, generator_kl_loss=1.454, generator_dur_loss=1.796, generator_adv_loss=1.911, generator_feat_match_loss=5.377, over 68.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.369, discriminator_fake_loss=1.328, generator_loss=28.82, generator_mel_loss=17.98, generator_kl_loss=1.444, generator_dur_loss=1.729, generator_adv_loss=1.965, generator_feat_match_loss=5.703, over 1546.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:18:46,850 INFO [train.py:527] (4/6) Epoch 702, batch 76, global_batch_idx: 87000, batch size: 61, loss[discriminator_loss=2.655, discriminator_real_loss=1.375, discriminator_fake_loss=1.28, generator_loss=29.14, generator_mel_loss=17.76, generator_kl_loss=1.415, generator_dur_loss=1.765, generator_adv_loss=2.116, generator_feat_match_loss=6.086, over 61.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.366, discriminator_fake_loss=1.329, generator_loss=28.77, generator_mel_loss=17.92, generator_kl_loss=1.427, generator_dur_loss=1.736, generator_adv_loss=1.973, generator_feat_match_loss=5.712, over 4401.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:18:46,852 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 00:18:55,634 INFO [train.py:591] (4/6) Epoch 702, validation: discriminator_loss=2.731, discriminator_real_loss=1.453, discriminator_fake_loss=1.278, generator_loss=26.92, generator_mel_loss=17.75, generator_kl_loss=1.205, generator_dur_loss=1.808, generator_adv_loss=1.981, generator_feat_match_loss=4.181, over 100.00 samples. +2024-03-15 00:18:55,635 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 00:21:07,744 INFO [train.py:919] (4/6) Start epoch 703 +2024-03-15 00:21:37,881 INFO [train.py:527] (4/6) Epoch 703, batch 2, global_batch_idx: 87050, batch size: 39, loss[discriminator_loss=2.714, discriminator_real_loss=1.339, discriminator_fake_loss=1.375, generator_loss=28.62, generator_mel_loss=18.21, generator_kl_loss=1.405, generator_dur_loss=1.678, generator_adv_loss=2.098, generator_feat_match_loss=5.233, over 39.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.37, discriminator_fake_loss=1.327, generator_loss=29.36, generator_mel_loss=18.17, generator_kl_loss=1.468, generator_dur_loss=1.692, generator_adv_loss=2.024, generator_feat_match_loss=6.005, over 139.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:23:57,370 INFO [train.py:527] (4/6) Epoch 703, batch 52, global_batch_idx: 87100, batch size: 50, loss[discriminator_loss=2.669, discriminator_real_loss=1.383, discriminator_fake_loss=1.287, generator_loss=28.46, generator_mel_loss=17.73, generator_kl_loss=1.511, generator_dur_loss=1.645, generator_adv_loss=1.977, generator_feat_match_loss=5.597, over 50.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=28.82, generator_mel_loss=18.02, generator_kl_loss=1.459, generator_dur_loss=1.714, generator_adv_loss=1.98, generator_feat_match_loss=5.64, over 2755.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:26:16,248 INFO [train.py:527] (4/6) Epoch 703, batch 102, global_batch_idx: 87150, batch size: 45, loss[discriminator_loss=2.657, discriminator_real_loss=1.235, discriminator_fake_loss=1.422, generator_loss=30.18, generator_mel_loss=18.44, generator_kl_loss=1.442, generator_dur_loss=1.688, generator_adv_loss=1.912, generator_feat_match_loss=6.7, over 45.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.83, generator_mel_loss=17.98, generator_kl_loss=1.445, generator_dur_loss=1.732, generator_adv_loss=1.985, generator_feat_match_loss=5.685, over 5726.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:27:15,766 INFO [train.py:919] (4/6) Start epoch 704 +2024-03-15 00:28:55,056 INFO [train.py:527] (4/6) Epoch 704, batch 28, global_batch_idx: 87200, batch size: 74, loss[discriminator_loss=2.689, discriminator_real_loss=1.412, discriminator_fake_loss=1.277, generator_loss=28.34, generator_mel_loss=17.96, generator_kl_loss=1.342, generator_dur_loss=1.786, generator_adv_loss=1.882, generator_feat_match_loss=5.374, over 74.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.373, discriminator_fake_loss=1.316, generator_loss=28.62, generator_mel_loss=17.92, generator_kl_loss=1.442, generator_dur_loss=1.741, generator_adv_loss=1.98, generator_feat_match_loss=5.537, over 1630.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:28:55,058 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 00:29:02,880 INFO [train.py:591] (4/6) Epoch 704, validation: discriminator_loss=2.716, discriminator_real_loss=1.348, discriminator_fake_loss=1.368, generator_loss=27.35, generator_mel_loss=17.63, generator_kl_loss=1.321, generator_dur_loss=1.806, generator_adv_loss=1.855, generator_feat_match_loss=4.73, over 100.00 samples. +2024-03-15 00:29:02,881 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 00:31:24,696 INFO [train.py:527] (4/6) Epoch 704, batch 78, global_batch_idx: 87250, batch size: 62, loss[discriminator_loss=2.681, discriminator_real_loss=1.248, discriminator_fake_loss=1.434, generator_loss=29.03, generator_mel_loss=17.7, generator_kl_loss=1.382, generator_dur_loss=1.752, generator_adv_loss=2.165, generator_feat_match_loss=6.034, over 62.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.65, generator_mel_loss=17.89, generator_kl_loss=1.441, generator_dur_loss=1.734, generator_adv_loss=1.985, generator_feat_match_loss=5.6, over 4459.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:33:32,993 INFO [train.py:919] (4/6) Start epoch 705 +2024-03-15 00:34:08,443 INFO [train.py:527] (4/6) Epoch 705, batch 4, global_batch_idx: 87300, batch size: 36, loss[discriminator_loss=2.657, discriminator_real_loss=1.289, discriminator_fake_loss=1.368, generator_loss=28.81, generator_mel_loss=18.21, generator_kl_loss=1.511, generator_dur_loss=1.678, generator_adv_loss=1.996, generator_feat_match_loss=5.412, over 36.00 samples.], tot_loss[discriminator_loss=2.652, discriminator_real_loss=1.333, discriminator_fake_loss=1.319, generator_loss=28.6, generator_mel_loss=18.04, generator_kl_loss=1.423, generator_dur_loss=1.718, generator_adv_loss=1.962, generator_feat_match_loss=5.456, over 286.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:36:28,318 INFO [train.py:527] (4/6) Epoch 705, batch 54, global_batch_idx: 87350, batch size: 83, loss[discriminator_loss=2.723, discriminator_real_loss=1.469, discriminator_fake_loss=1.255, generator_loss=28.21, generator_mel_loss=17.96, generator_kl_loss=1.148, generator_dur_loss=1.794, generator_adv_loss=1.901, generator_feat_match_loss=5.411, over 83.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.37, discriminator_fake_loss=1.328, generator_loss=28.64, generator_mel_loss=17.93, generator_kl_loss=1.403, generator_dur_loss=1.73, generator_adv_loss=1.99, generator_feat_match_loss=5.589, over 3337.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:38:47,704 INFO [train.py:527] (4/6) Epoch 705, batch 104, global_batch_idx: 87400, batch size: 77, loss[discriminator_loss=2.72, discriminator_real_loss=1.412, discriminator_fake_loss=1.308, generator_loss=27.97, generator_mel_loss=17.5, generator_kl_loss=1.249, generator_dur_loss=1.737, generator_adv_loss=1.929, generator_feat_match_loss=5.553, over 77.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.364, discriminator_fake_loss=1.33, generator_loss=28.71, generator_mel_loss=17.93, generator_kl_loss=1.402, generator_dur_loss=1.723, generator_adv_loss=1.988, generator_feat_match_loss=5.661, over 6427.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:38:47,705 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 00:38:56,480 INFO [train.py:591] (4/6) Epoch 705, validation: discriminator_loss=2.711, discriminator_real_loss=1.31, discriminator_fake_loss=1.4, generator_loss=27, generator_mel_loss=18, generator_kl_loss=1.117, generator_dur_loss=1.766, generator_adv_loss=1.803, generator_feat_match_loss=4.321, over 100.00 samples. +2024-03-15 00:38:56,481 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 00:39:48,906 INFO [train.py:919] (4/6) Start epoch 706 +2024-03-15 00:41:36,838 INFO [train.py:527] (4/6) Epoch 706, batch 30, global_batch_idx: 87450, batch size: 47, loss[discriminator_loss=2.742, discriminator_real_loss=1.421, discriminator_fake_loss=1.322, generator_loss=28.35, generator_mel_loss=17.97, generator_kl_loss=1.512, generator_dur_loss=1.64, generator_adv_loss=1.89, generator_feat_match_loss=5.336, over 47.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.353, discriminator_fake_loss=1.332, generator_loss=28.75, generator_mel_loss=17.95, generator_kl_loss=1.44, generator_dur_loss=1.711, generator_adv_loss=1.977, generator_feat_match_loss=5.677, over 1759.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:43:57,087 INFO [train.py:527] (4/6) Epoch 706, batch 80, global_batch_idx: 87500, batch size: 53, loss[discriminator_loss=2.693, discriminator_real_loss=1.291, discriminator_fake_loss=1.402, generator_loss=29.5, generator_mel_loss=18.29, generator_kl_loss=1.509, generator_dur_loss=1.713, generator_adv_loss=2.142, generator_feat_match_loss=5.842, over 53.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.328, generator_loss=28.77, generator_mel_loss=17.97, generator_kl_loss=1.43, generator_dur_loss=1.712, generator_adv_loss=1.989, generator_feat_match_loss=5.666, over 4529.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:45:56,406 INFO [train.py:919] (4/6) Start epoch 707 +2024-03-15 00:46:35,731 INFO [train.py:527] (4/6) Epoch 707, batch 6, global_batch_idx: 87550, batch size: 58, loss[discriminator_loss=2.688, discriminator_real_loss=1.364, discriminator_fake_loss=1.324, generator_loss=27.92, generator_mel_loss=17.94, generator_kl_loss=1.433, generator_dur_loss=1.691, generator_adv_loss=1.833, generator_feat_match_loss=5.024, over 58.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.314, discriminator_fake_loss=1.342, generator_loss=28.8, generator_mel_loss=17.87, generator_kl_loss=1.397, generator_dur_loss=1.702, generator_adv_loss=1.996, generator_feat_match_loss=5.829, over 407.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:48:55,428 INFO [train.py:527] (4/6) Epoch 707, batch 56, global_batch_idx: 87600, batch size: 68, loss[discriminator_loss=2.649, discriminator_real_loss=1.325, discriminator_fake_loss=1.324, generator_loss=28.9, generator_mel_loss=17.81, generator_kl_loss=1.428, generator_dur_loss=1.772, generator_adv_loss=1.962, generator_feat_match_loss=5.922, over 68.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.354, discriminator_fake_loss=1.326, generator_loss=28.72, generator_mel_loss=17.86, generator_kl_loss=1.417, generator_dur_loss=1.734, generator_adv_loss=1.991, generator_feat_match_loss=5.713, over 3306.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:48:55,430 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 00:49:03,327 INFO [train.py:591] (4/6) Epoch 707, validation: discriminator_loss=2.718, discriminator_real_loss=1.365, discriminator_fake_loss=1.353, generator_loss=28.44, generator_mel_loss=18.22, generator_kl_loss=1.361, generator_dur_loss=1.792, generator_adv_loss=1.916, generator_feat_match_loss=5.142, over 100.00 samples. +2024-03-15 00:49:03,328 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 00:51:23,015 INFO [train.py:527] (4/6) Epoch 707, batch 106, global_batch_idx: 87650, batch size: 42, loss[discriminator_loss=2.709, discriminator_real_loss=1.43, discriminator_fake_loss=1.279, generator_loss=28.39, generator_mel_loss=17.64, generator_kl_loss=1.523, generator_dur_loss=1.7, generator_adv_loss=1.871, generator_feat_match_loss=5.657, over 42.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=28.7, generator_mel_loss=17.88, generator_kl_loss=1.431, generator_dur_loss=1.731, generator_adv_loss=1.99, generator_feat_match_loss=5.668, over 5996.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:52:08,590 INFO [train.py:919] (4/6) Start epoch 708 +2024-03-15 00:54:01,576 INFO [train.py:527] (4/6) Epoch 708, batch 32, global_batch_idx: 87700, batch size: 80, loss[discriminator_loss=2.677, discriminator_real_loss=1.39, discriminator_fake_loss=1.286, generator_loss=28.61, generator_mel_loss=18.02, generator_kl_loss=1.375, generator_dur_loss=1.778, generator_adv_loss=1.958, generator_feat_match_loss=5.473, over 80.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.361, discriminator_fake_loss=1.328, generator_loss=28.9, generator_mel_loss=17.95, generator_kl_loss=1.468, generator_dur_loss=1.723, generator_adv_loss=1.982, generator_feat_match_loss=5.771, over 1865.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:56:16,822 INFO [train.py:527] (4/6) Epoch 708, batch 82, global_batch_idx: 87750, batch size: 68, loss[discriminator_loss=2.729, discriminator_real_loss=1.417, discriminator_fake_loss=1.311, generator_loss=28.46, generator_mel_loss=17.59, generator_kl_loss=1.539, generator_dur_loss=1.717, generator_adv_loss=1.975, generator_feat_match_loss=5.643, over 68.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.8, generator_mel_loss=17.91, generator_kl_loss=1.455, generator_dur_loss=1.727, generator_adv_loss=1.988, generator_feat_match_loss=5.721, over 4576.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:58:15,832 INFO [train.py:919] (4/6) Start epoch 709 +2024-03-15 00:59:02,268 INFO [train.py:527] (4/6) Epoch 709, batch 8, global_batch_idx: 87800, batch size: 64, loss[discriminator_loss=2.677, discriminator_real_loss=1.424, discriminator_fake_loss=1.253, generator_loss=29, generator_mel_loss=17.83, generator_kl_loss=1.389, generator_dur_loss=1.742, generator_adv_loss=1.982, generator_feat_match_loss=6.055, over 64.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.369, discriminator_fake_loss=1.33, generator_loss=29.04, generator_mel_loss=17.98, generator_kl_loss=1.464, generator_dur_loss=1.716, generator_adv_loss=1.987, generator_feat_match_loss=5.893, over 491.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:59:02,271 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 00:59:10,125 INFO [train.py:591] (4/6) Epoch 709, validation: discriminator_loss=2.722, discriminator_real_loss=1.349, discriminator_fake_loss=1.373, generator_loss=27.44, generator_mel_loss=18.06, generator_kl_loss=1.264, generator_dur_loss=1.792, generator_adv_loss=1.873, generator_feat_match_loss=4.445, over 100.00 samples. +2024-03-15 00:59:10,126 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 01:01:30,074 INFO [train.py:527] (4/6) Epoch 709, batch 58, global_batch_idx: 87850, batch size: 59, loss[discriminator_loss=2.638, discriminator_real_loss=1.352, discriminator_fake_loss=1.286, generator_loss=28.92, generator_mel_loss=18.13, generator_kl_loss=1.441, generator_dur_loss=1.715, generator_adv_loss=2.02, generator_feat_match_loss=5.613, over 59.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.362, discriminator_fake_loss=1.335, generator_loss=28.77, generator_mel_loss=17.88, generator_kl_loss=1.435, generator_dur_loss=1.73, generator_adv_loss=1.978, generator_feat_match_loss=5.747, over 3572.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:03:45,911 INFO [train.py:527] (4/6) Epoch 709, batch 108, global_batch_idx: 87900, batch size: 45, loss[discriminator_loss=2.593, discriminator_real_loss=1.346, discriminator_fake_loss=1.247, generator_loss=29.76, generator_mel_loss=18.3, generator_kl_loss=1.613, generator_dur_loss=1.685, generator_adv_loss=2.009, generator_feat_match_loss=6.155, over 45.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.363, discriminator_fake_loss=1.331, generator_loss=28.77, generator_mel_loss=17.9, generator_kl_loss=1.435, generator_dur_loss=1.725, generator_adv_loss=1.985, generator_feat_match_loss=5.724, over 6390.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:04:30,320 INFO [train.py:919] (4/6) Start epoch 710 +2024-03-15 01:06:28,027 INFO [train.py:527] (4/6) Epoch 710, batch 34, global_batch_idx: 87950, batch size: 70, loss[discriminator_loss=2.652, discriminator_real_loss=1.359, discriminator_fake_loss=1.293, generator_loss=28.98, generator_mel_loss=18.05, generator_kl_loss=1.457, generator_dur_loss=1.763, generator_adv_loss=1.975, generator_feat_match_loss=5.733, over 70.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.331, generator_loss=28.64, generator_mel_loss=17.84, generator_kl_loss=1.437, generator_dur_loss=1.733, generator_adv_loss=1.988, generator_feat_match_loss=5.644, over 2015.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:08:49,111 INFO [train.py:527] (4/6) Epoch 710, batch 84, global_batch_idx: 88000, batch size: 31, loss[discriminator_loss=2.685, discriminator_real_loss=1.37, discriminator_fake_loss=1.315, generator_loss=28.31, generator_mel_loss=18.17, generator_kl_loss=1.556, generator_dur_loss=1.634, generator_adv_loss=1.985, generator_feat_match_loss=4.96, over 31.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.36, discriminator_fake_loss=1.333, generator_loss=28.7, generator_mel_loss=17.89, generator_kl_loss=1.429, generator_dur_loss=1.73, generator_adv_loss=1.99, generator_feat_match_loss=5.664, over 4874.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:08:49,112 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 01:08:57,851 INFO [train.py:591] (4/6) Epoch 710, validation: discriminator_loss=2.703, discriminator_real_loss=1.4, discriminator_fake_loss=1.303, generator_loss=28.46, generator_mel_loss=18.24, generator_kl_loss=1.254, generator_dur_loss=1.804, generator_adv_loss=1.891, generator_feat_match_loss=5.269, over 100.00 samples. +2024-03-15 01:08:57,852 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 01:10:45,298 INFO [train.py:919] (4/6) Start epoch 711 +2024-03-15 01:11:36,982 INFO [train.py:527] (4/6) Epoch 711, batch 10, global_batch_idx: 88050, batch size: 25, loss[discriminator_loss=2.786, discriminator_real_loss=1.284, discriminator_fake_loss=1.502, generator_loss=29.87, generator_mel_loss=18.6, generator_kl_loss=1.894, generator_dur_loss=1.596, generator_adv_loss=2.051, generator_feat_match_loss=5.727, over 25.00 samples.], tot_loss[discriminator_loss=2.642, discriminator_real_loss=1.321, discriminator_fake_loss=1.321, generator_loss=29.41, generator_mel_loss=17.89, generator_kl_loss=1.391, generator_dur_loss=1.759, generator_adv_loss=2.166, generator_feat_match_loss=6.211, over 655.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:13:57,358 INFO [train.py:527] (4/6) Epoch 711, batch 60, global_batch_idx: 88100, batch size: 58, loss[discriminator_loss=2.601, discriminator_real_loss=1.248, discriminator_fake_loss=1.353, generator_loss=28.91, generator_mel_loss=18.23, generator_kl_loss=1.45, generator_dur_loss=1.683, generator_adv_loss=2.014, generator_feat_match_loss=5.533, over 58.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.363, discriminator_fake_loss=1.322, generator_loss=28.9, generator_mel_loss=17.9, generator_kl_loss=1.413, generator_dur_loss=1.739, generator_adv_loss=2.049, generator_feat_match_loss=5.8, over 3498.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:16:14,974 INFO [train.py:527] (4/6) Epoch 711, batch 110, global_batch_idx: 88150, batch size: 16, loss[discriminator_loss=2.698, discriminator_real_loss=1.373, discriminator_fake_loss=1.325, generator_loss=31.17, generator_mel_loss=18.91, generator_kl_loss=1.89, generator_dur_loss=1.622, generator_adv_loss=2.204, generator_feat_match_loss=6.545, over 16.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.321, generator_loss=28.89, generator_mel_loss=17.92, generator_kl_loss=1.419, generator_dur_loss=1.738, generator_adv_loss=2.025, generator_feat_match_loss=5.791, over 6331.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:16:51,180 INFO [train.py:919] (4/6) Start epoch 712 +2024-03-15 01:18:56,786 INFO [train.py:527] (4/6) Epoch 712, batch 36, global_batch_idx: 88200, batch size: 59, loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=28.25, generator_mel_loss=17.83, generator_kl_loss=1.421, generator_dur_loss=1.696, generator_adv_loss=1.949, generator_feat_match_loss=5.349, over 59.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.364, discriminator_fake_loss=1.327, generator_loss=28.77, generator_mel_loss=17.89, generator_kl_loss=1.441, generator_dur_loss=1.734, generator_adv_loss=2, generator_feat_match_loss=5.703, over 2088.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:18:56,787 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 01:19:04,760 INFO [train.py:591] (4/6) Epoch 712, validation: discriminator_loss=2.741, discriminator_real_loss=1.424, discriminator_fake_loss=1.318, generator_loss=27.55, generator_mel_loss=18.3, generator_kl_loss=1.286, generator_dur_loss=1.807, generator_adv_loss=1.945, generator_feat_match_loss=4.211, over 100.00 samples. +2024-03-15 01:19:04,761 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 01:21:21,723 INFO [train.py:527] (4/6) Epoch 712, batch 86, global_batch_idx: 88250, batch size: 13, loss[discriminator_loss=2.708, discriminator_real_loss=1.291, discriminator_fake_loss=1.417, generator_loss=29.1, generator_mel_loss=18.15, generator_kl_loss=1.774, generator_dur_loss=1.537, generator_adv_loss=2.112, generator_feat_match_loss=5.53, over 13.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.75, generator_mel_loss=17.9, generator_kl_loss=1.434, generator_dur_loss=1.75, generator_adv_loss=1.994, generator_feat_match_loss=5.673, over 5262.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:23:08,200 INFO [train.py:919] (4/6) Start epoch 713 +2024-03-15 01:24:05,840 INFO [train.py:527] (4/6) Epoch 713, batch 12, global_batch_idx: 88300, batch size: 55, loss[discriminator_loss=2.771, discriminator_real_loss=1.411, discriminator_fake_loss=1.36, generator_loss=27.61, generator_mel_loss=17.45, generator_kl_loss=1.473, generator_dur_loss=1.723, generator_adv_loss=1.986, generator_feat_match_loss=4.984, over 55.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.386, discriminator_fake_loss=1.32, generator_loss=28.59, generator_mel_loss=17.82, generator_kl_loss=1.425, generator_dur_loss=1.765, generator_adv_loss=1.999, generator_feat_match_loss=5.584, over 794.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:26:24,718 INFO [train.py:527] (4/6) Epoch 713, batch 62, global_batch_idx: 88350, batch size: 80, loss[discriminator_loss=2.682, discriminator_real_loss=1.331, discriminator_fake_loss=1.351, generator_loss=28.48, generator_mel_loss=17.6, generator_kl_loss=1.418, generator_dur_loss=1.805, generator_adv_loss=2.145, generator_feat_match_loss=5.521, over 80.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.374, discriminator_fake_loss=1.329, generator_loss=28.73, generator_mel_loss=17.89, generator_kl_loss=1.397, generator_dur_loss=1.759, generator_adv_loss=1.993, generator_feat_match_loss=5.686, over 3853.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:28:42,443 INFO [train.py:527] (4/6) Epoch 713, batch 112, global_batch_idx: 88400, batch size: 53, loss[discriminator_loss=2.714, discriminator_real_loss=1.395, discriminator_fake_loss=1.319, generator_loss=28.65, generator_mel_loss=17.74, generator_kl_loss=1.511, generator_dur_loss=1.684, generator_adv_loss=1.971, generator_feat_match_loss=5.744, over 53.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.373, discriminator_fake_loss=1.332, generator_loss=28.76, generator_mel_loss=17.92, generator_kl_loss=1.423, generator_dur_loss=1.756, generator_adv_loss=1.992, generator_feat_match_loss=5.672, over 6653.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:28:42,444 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 01:28:51,050 INFO [train.py:591] (4/6) Epoch 713, validation: discriminator_loss=2.737, discriminator_real_loss=1.362, discriminator_fake_loss=1.375, generator_loss=27.91, generator_mel_loss=18.27, generator_kl_loss=1.279, generator_dur_loss=1.808, generator_adv_loss=1.828, generator_feat_match_loss=4.725, over 100.00 samples. +2024-03-15 01:28:51,051 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 01:29:23,012 INFO [train.py:919] (4/6) Start epoch 714 +2024-03-15 01:31:29,663 INFO [train.py:527] (4/6) Epoch 714, batch 38, global_batch_idx: 88450, batch size: 48, loss[discriminator_loss=2.717, discriminator_real_loss=1.394, discriminator_fake_loss=1.323, generator_loss=28.64, generator_mel_loss=18.14, generator_kl_loss=1.628, generator_dur_loss=1.712, generator_adv_loss=1.946, generator_feat_match_loss=5.214, over 48.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.378, discriminator_fake_loss=1.328, generator_loss=28.75, generator_mel_loss=17.91, generator_kl_loss=1.451, generator_dur_loss=1.734, generator_adv_loss=1.979, generator_feat_match_loss=5.673, over 2154.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:33:51,465 INFO [train.py:527] (4/6) Epoch 714, batch 88, global_batch_idx: 88500, batch size: 70, loss[discriminator_loss=2.635, discriminator_real_loss=1.229, discriminator_fake_loss=1.406, generator_loss=29.31, generator_mel_loss=17.9, generator_kl_loss=1.361, generator_dur_loss=1.781, generator_adv_loss=1.881, generator_feat_match_loss=6.387, over 70.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=28.92, generator_mel_loss=17.87, generator_kl_loss=1.418, generator_dur_loss=1.748, generator_adv_loss=2.03, generator_feat_match_loss=5.847, over 5129.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:35:29,525 INFO [train.py:919] (4/6) Start epoch 715 +2024-03-15 01:36:33,265 INFO [train.py:527] (4/6) Epoch 715, batch 14, global_batch_idx: 88550, batch size: 53, loss[discriminator_loss=2.66, discriminator_real_loss=1.299, discriminator_fake_loss=1.361, generator_loss=29.89, generator_mel_loss=18, generator_kl_loss=1.657, generator_dur_loss=1.612, generator_adv_loss=2.004, generator_feat_match_loss=6.616, over 53.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.363, discriminator_fake_loss=1.331, generator_loss=28.94, generator_mel_loss=18.07, generator_kl_loss=1.496, generator_dur_loss=1.726, generator_adv_loss=1.963, generator_feat_match_loss=5.692, over 814.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:38:51,285 INFO [train.py:527] (4/6) Epoch 715, batch 64, global_batch_idx: 88600, batch size: 72, loss[discriminator_loss=2.629, discriminator_real_loss=1.306, discriminator_fake_loss=1.323, generator_loss=28.62, generator_mel_loss=17.65, generator_kl_loss=1.471, generator_dur_loss=1.772, generator_adv_loss=1.942, generator_feat_match_loss=5.786, over 72.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=28.71, generator_mel_loss=17.9, generator_kl_loss=1.421, generator_dur_loss=1.736, generator_adv_loss=1.971, generator_feat_match_loss=5.674, over 3584.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:38:51,287 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 01:38:59,984 INFO [train.py:591] (4/6) Epoch 715, validation: discriminator_loss=2.713, discriminator_real_loss=1.368, discriminator_fake_loss=1.345, generator_loss=27.33, generator_mel_loss=18.22, generator_kl_loss=1.306, generator_dur_loss=1.798, generator_adv_loss=1.841, generator_feat_match_loss=4.168, over 100.00 samples. +2024-03-15 01:38:59,985 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 01:41:17,117 INFO [train.py:527] (4/6) Epoch 715, batch 114, global_batch_idx: 88650, batch size: 80, loss[discriminator_loss=2.697, discriminator_real_loss=1.27, discriminator_fake_loss=1.427, generator_loss=28.56, generator_mel_loss=17.85, generator_kl_loss=1.468, generator_dur_loss=1.837, generator_adv_loss=2.004, generator_feat_match_loss=5.409, over 80.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.362, discriminator_fake_loss=1.325, generator_loss=28.62, generator_mel_loss=17.87, generator_kl_loss=1.416, generator_dur_loss=1.746, generator_adv_loss=1.971, generator_feat_match_loss=5.624, over 6439.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:41:44,635 INFO [train.py:919] (4/6) Start epoch 716 +2024-03-15 01:44:01,699 INFO [train.py:527] (4/6) Epoch 716, batch 40, global_batch_idx: 88700, batch size: 88, loss[discriminator_loss=2.621, discriminator_real_loss=1.384, discriminator_fake_loss=1.238, generator_loss=28.67, generator_mel_loss=17.44, generator_kl_loss=1.408, generator_dur_loss=1.837, generator_adv_loss=1.989, generator_feat_match_loss=5.995, over 88.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=28.83, generator_mel_loss=17.87, generator_kl_loss=1.443, generator_dur_loss=1.742, generator_adv_loss=1.987, generator_feat_match_loss=5.795, over 2266.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:46:19,761 INFO [train.py:527] (4/6) Epoch 716, batch 90, global_batch_idx: 88750, batch size: 66, loss[discriminator_loss=2.686, discriminator_real_loss=1.373, discriminator_fake_loss=1.313, generator_loss=28.65, generator_mel_loss=17.89, generator_kl_loss=1.278, generator_dur_loss=1.738, generator_adv_loss=2.06, generator_feat_match_loss=5.688, over 66.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.364, discriminator_fake_loss=1.324, generator_loss=28.68, generator_mel_loss=17.85, generator_kl_loss=1.407, generator_dur_loss=1.753, generator_adv_loss=1.987, generator_feat_match_loss=5.677, over 5176.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:47:50,726 INFO [train.py:919] (4/6) Start epoch 717 +2024-03-15 01:48:57,891 INFO [train.py:527] (4/6) Epoch 717, batch 16, global_batch_idx: 88800, batch size: 58, loss[discriminator_loss=2.664, discriminator_real_loss=1.3, discriminator_fake_loss=1.364, generator_loss=28.72, generator_mel_loss=17.97, generator_kl_loss=1.391, generator_dur_loss=1.762, generator_adv_loss=2.129, generator_feat_match_loss=5.471, over 58.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.365, discriminator_fake_loss=1.338, generator_loss=28.64, generator_mel_loss=17.82, generator_kl_loss=1.418, generator_dur_loss=1.744, generator_adv_loss=1.982, generator_feat_match_loss=5.677, over 970.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:48:57,893 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 01:49:05,800 INFO [train.py:591] (4/6) Epoch 717, validation: discriminator_loss=2.746, discriminator_real_loss=1.555, discriminator_fake_loss=1.192, generator_loss=28.01, generator_mel_loss=18.04, generator_kl_loss=1.166, generator_dur_loss=1.807, generator_adv_loss=2.09, generator_feat_match_loss=4.911, over 100.00 samples. +2024-03-15 01:49:05,801 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 01:51:22,291 INFO [train.py:527] (4/6) Epoch 717, batch 66, global_batch_idx: 88850, batch size: 80, loss[discriminator_loss=2.72, discriminator_real_loss=1.297, discriminator_fake_loss=1.423, generator_loss=27.78, generator_mel_loss=18.12, generator_kl_loss=1.446, generator_dur_loss=1.822, generator_adv_loss=1.953, generator_feat_match_loss=4.436, over 80.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.36, discriminator_fake_loss=1.334, generator_loss=28.63, generator_mel_loss=17.86, generator_kl_loss=1.432, generator_dur_loss=1.739, generator_adv_loss=1.977, generator_feat_match_loss=5.622, over 3616.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:53:43,572 INFO [train.py:527] (4/6) Epoch 717, batch 116, global_batch_idx: 88900, batch size: 56, loss[discriminator_loss=2.688, discriminator_real_loss=1.393, discriminator_fake_loss=1.295, generator_loss=28.76, generator_mel_loss=18.41, generator_kl_loss=1.407, generator_dur_loss=1.726, generator_adv_loss=1.809, generator_feat_match_loss=5.403, over 56.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.361, discriminator_fake_loss=1.333, generator_loss=28.67, generator_mel_loss=17.87, generator_kl_loss=1.414, generator_dur_loss=1.75, generator_adv_loss=1.975, generator_feat_match_loss=5.667, over 6512.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:54:04,099 INFO [train.py:919] (4/6) Start epoch 718 +2024-03-15 01:56:26,073 INFO [train.py:527] (4/6) Epoch 718, batch 42, global_batch_idx: 88950, batch size: 39, loss[discriminator_loss=2.714, discriminator_real_loss=1.451, discriminator_fake_loss=1.263, generator_loss=28.97, generator_mel_loss=17.88, generator_kl_loss=1.429, generator_dur_loss=1.669, generator_adv_loss=2.268, generator_feat_match_loss=5.724, over 39.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=28.85, generator_mel_loss=17.86, generator_kl_loss=1.406, generator_dur_loss=1.749, generator_adv_loss=2.044, generator_feat_match_loss=5.797, over 2488.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:58:45,624 INFO [train.py:527] (4/6) Epoch 718, batch 92, global_batch_idx: 89000, batch size: 42, loss[discriminator_loss=2.654, discriminator_real_loss=1.399, discriminator_fake_loss=1.255, generator_loss=28.22, generator_mel_loss=17.77, generator_kl_loss=1.572, generator_dur_loss=1.652, generator_adv_loss=1.914, generator_feat_match_loss=5.311, over 42.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=28.75, generator_mel_loss=17.88, generator_kl_loss=1.406, generator_dur_loss=1.747, generator_adv_loss=2.01, generator_feat_match_loss=5.705, over 5308.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:58:45,626 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 01:58:54,589 INFO [train.py:591] (4/6) Epoch 718, validation: discriminator_loss=2.751, discriminator_real_loss=1.377, discriminator_fake_loss=1.374, generator_loss=27.9, generator_mel_loss=18.22, generator_kl_loss=1.23, generator_dur_loss=1.799, generator_adv_loss=1.831, generator_feat_match_loss=4.812, over 100.00 samples. +2024-03-15 01:58:54,589 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 02:00:21,398 INFO [train.py:919] (4/6) Start epoch 719 +2024-03-15 02:01:33,230 INFO [train.py:527] (4/6) Epoch 719, batch 18, global_batch_idx: 89050, batch size: 39, loss[discriminator_loss=2.735, discriminator_real_loss=1.361, discriminator_fake_loss=1.374, generator_loss=27.75, generator_mel_loss=18.13, generator_kl_loss=1.657, generator_dur_loss=1.703, generator_adv_loss=1.804, generator_feat_match_loss=4.461, over 39.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=29.06, generator_mel_loss=18.12, generator_kl_loss=1.406, generator_dur_loss=1.758, generator_adv_loss=1.964, generator_feat_match_loss=5.813, over 1199.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:03:52,490 INFO [train.py:527] (4/6) Epoch 719, batch 68, global_batch_idx: 89100, batch size: 36, loss[discriminator_loss=2.731, discriminator_real_loss=1.42, discriminator_fake_loss=1.311, generator_loss=28.12, generator_mel_loss=17.76, generator_kl_loss=1.514, generator_dur_loss=1.689, generator_adv_loss=1.911, generator_feat_match_loss=5.247, over 36.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.81, generator_mel_loss=17.97, generator_kl_loss=1.407, generator_dur_loss=1.754, generator_adv_loss=1.978, generator_feat_match_loss=5.695, over 4143.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:06:13,116 INFO [train.py:527] (4/6) Epoch 719, batch 118, global_batch_idx: 89150, batch size: 25, loss[discriminator_loss=2.664, discriminator_real_loss=1.449, discriminator_fake_loss=1.215, generator_loss=29.45, generator_mel_loss=18.54, generator_kl_loss=1.864, generator_dur_loss=1.551, generator_adv_loss=1.923, generator_feat_match_loss=5.573, over 25.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.36, discriminator_fake_loss=1.332, generator_loss=28.77, generator_mel_loss=17.94, generator_kl_loss=1.417, generator_dur_loss=1.748, generator_adv_loss=1.978, generator_feat_match_loss=5.685, over 6744.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:06:30,673 INFO [train.py:919] (4/6) Start epoch 720 +2024-03-15 02:09:00,251 INFO [train.py:527] (4/6) Epoch 720, batch 44, global_batch_idx: 89200, batch size: 48, loss[discriminator_loss=2.695, discriminator_real_loss=1.361, discriminator_fake_loss=1.334, generator_loss=29.42, generator_mel_loss=18.45, generator_kl_loss=1.527, generator_dur_loss=1.67, generator_adv_loss=1.968, generator_feat_match_loss=5.81, over 48.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.354, discriminator_fake_loss=1.328, generator_loss=28.75, generator_mel_loss=17.91, generator_kl_loss=1.433, generator_dur_loss=1.719, generator_adv_loss=1.995, generator_feat_match_loss=5.697, over 2490.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:09:00,253 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 02:09:08,034 INFO [train.py:591] (4/6) Epoch 720, validation: discriminator_loss=2.705, discriminator_real_loss=1.422, discriminator_fake_loss=1.283, generator_loss=27.27, generator_mel_loss=18.08, generator_kl_loss=1.23, generator_dur_loss=1.789, generator_adv_loss=1.945, generator_feat_match_loss=4.221, over 100.00 samples. +2024-03-15 02:09:08,035 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 02:11:30,283 INFO [train.py:527] (4/6) Epoch 720, batch 94, global_batch_idx: 89250, batch size: 45, loss[discriminator_loss=2.503, discriminator_real_loss=1.301, discriminator_fake_loss=1.202, generator_loss=30.05, generator_mel_loss=17.75, generator_kl_loss=1.711, generator_dur_loss=1.685, generator_adv_loss=2.291, generator_feat_match_loss=6.609, over 45.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.355, discriminator_fake_loss=1.331, generator_loss=28.79, generator_mel_loss=17.9, generator_kl_loss=1.413, generator_dur_loss=1.737, generator_adv_loss=2.003, generator_feat_match_loss=5.737, over 5575.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:12:55,208 INFO [train.py:919] (4/6) Start epoch 721 +2024-03-15 02:14:20,753 INFO [train.py:527] (4/6) Epoch 721, batch 20, global_batch_idx: 89300, batch size: 50, loss[discriminator_loss=2.682, discriminator_real_loss=1.394, discriminator_fake_loss=1.288, generator_loss=28.32, generator_mel_loss=17.74, generator_kl_loss=1.593, generator_dur_loss=1.738, generator_adv_loss=1.972, generator_feat_match_loss=5.271, over 50.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.366, discriminator_fake_loss=1.332, generator_loss=28.79, generator_mel_loss=17.93, generator_kl_loss=1.397, generator_dur_loss=1.746, generator_adv_loss=1.974, generator_feat_match_loss=5.743, over 1145.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:16:43,035 INFO [train.py:527] (4/6) Epoch 721, batch 70, global_batch_idx: 89350, batch size: 70, loss[discriminator_loss=2.684, discriminator_real_loss=1.282, discriminator_fake_loss=1.402, generator_loss=28.88, generator_mel_loss=18.01, generator_kl_loss=1.297, generator_dur_loss=1.761, generator_adv_loss=2.062, generator_feat_match_loss=5.755, over 70.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.364, discriminator_fake_loss=1.328, generator_loss=28.69, generator_mel_loss=17.87, generator_kl_loss=1.377, generator_dur_loss=1.75, generator_adv_loss=1.985, generator_feat_match_loss=5.708, over 4294.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:18:59,435 INFO [train.py:527] (4/6) Epoch 721, batch 120, global_batch_idx: 89400, batch size: 68, loss[discriminator_loss=2.692, discriminator_real_loss=1.355, discriminator_fake_loss=1.337, generator_loss=29.88, generator_mel_loss=18.25, generator_kl_loss=1.529, generator_dur_loss=1.741, generator_adv_loss=1.918, generator_feat_match_loss=6.442, over 68.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.366, discriminator_fake_loss=1.325, generator_loss=28.74, generator_mel_loss=17.89, generator_kl_loss=1.396, generator_dur_loss=1.742, generator_adv_loss=1.985, generator_feat_match_loss=5.727, over 7088.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:18:59,437 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 02:19:08,219 INFO [train.py:591] (4/6) Epoch 721, validation: discriminator_loss=2.729, discriminator_real_loss=1.382, discriminator_fake_loss=1.347, generator_loss=26.98, generator_mel_loss=17.75, generator_kl_loss=1.304, generator_dur_loss=1.82, generator_adv_loss=1.826, generator_feat_match_loss=4.277, over 100.00 samples. +2024-03-15 02:19:08,220 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 02:19:17,420 INFO [train.py:919] (4/6) Start epoch 722 +2024-03-15 02:21:54,432 INFO [train.py:527] (4/6) Epoch 722, batch 46, global_batch_idx: 89450, batch size: 96, loss[discriminator_loss=2.637, discriminator_real_loss=1.286, discriminator_fake_loss=1.351, generator_loss=28.51, generator_mel_loss=17.79, generator_kl_loss=1.285, generator_dur_loss=1.87, generator_adv_loss=2.023, generator_feat_match_loss=5.545, over 96.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.367, discriminator_fake_loss=1.332, generator_loss=28.71, generator_mel_loss=17.92, generator_kl_loss=1.411, generator_dur_loss=1.762, generator_adv_loss=1.98, generator_feat_match_loss=5.633, over 2778.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:24:16,961 INFO [train.py:527] (4/6) Epoch 722, batch 96, global_batch_idx: 89500, batch size: 68, loss[discriminator_loss=2.705, discriminator_real_loss=1.374, discriminator_fake_loss=1.331, generator_loss=27.48, generator_mel_loss=17.39, generator_kl_loss=1.333, generator_dur_loss=1.779, generator_adv_loss=1.991, generator_feat_match_loss=4.992, over 68.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=28.74, generator_mel_loss=17.92, generator_kl_loss=1.4, generator_dur_loss=1.761, generator_adv_loss=1.989, generator_feat_match_loss=5.669, over 5797.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:25:32,860 INFO [train.py:919] (4/6) Start epoch 723 +2024-03-15 02:26:58,611 INFO [train.py:527] (4/6) Epoch 723, batch 22, global_batch_idx: 89550, batch size: 80, loss[discriminator_loss=2.702, discriminator_real_loss=1.299, discriminator_fake_loss=1.402, generator_loss=29.21, generator_mel_loss=17.99, generator_kl_loss=1.253, generator_dur_loss=1.803, generator_adv_loss=2.054, generator_feat_match_loss=6.111, over 80.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.359, discriminator_fake_loss=1.337, generator_loss=28.73, generator_mel_loss=17.87, generator_kl_loss=1.409, generator_dur_loss=1.736, generator_adv_loss=2, generator_feat_match_loss=5.715, over 1283.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:29:24,580 INFO [train.py:527] (4/6) Epoch 723, batch 72, global_batch_idx: 89600, batch size: 74, loss[discriminator_loss=2.672, discriminator_real_loss=1.324, discriminator_fake_loss=1.348, generator_loss=28.03, generator_mel_loss=17.85, generator_kl_loss=1.201, generator_dur_loss=1.78, generator_adv_loss=2.011, generator_feat_match_loss=5.194, over 74.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.353, discriminator_fake_loss=1.336, generator_loss=28.64, generator_mel_loss=17.84, generator_kl_loss=1.412, generator_dur_loss=1.743, generator_adv_loss=1.986, generator_feat_match_loss=5.664, over 4269.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:29:24,582 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 02:29:32,617 INFO [train.py:591] (4/6) Epoch 723, validation: discriminator_loss=2.731, discriminator_real_loss=1.438, discriminator_fake_loss=1.293, generator_loss=27.29, generator_mel_loss=18.05, generator_kl_loss=1.203, generator_dur_loss=1.799, generator_adv_loss=1.916, generator_feat_match_loss=4.325, over 100.00 samples. +2024-03-15 02:29:32,618 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 02:31:49,232 INFO [train.py:527] (4/6) Epoch 723, batch 122, global_batch_idx: 89650, batch size: 36, loss[discriminator_loss=2.719, discriminator_real_loss=1.413, discriminator_fake_loss=1.306, generator_loss=28.17, generator_mel_loss=17.93, generator_kl_loss=1.469, generator_dur_loss=1.774, generator_adv_loss=1.898, generator_feat_match_loss=5.098, over 36.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.358, discriminator_fake_loss=1.337, generator_loss=28.7, generator_mel_loss=17.87, generator_kl_loss=1.427, generator_dur_loss=1.735, generator_adv_loss=1.979, generator_feat_match_loss=5.682, over 6930.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:31:53,854 INFO [train.py:919] (4/6) Start epoch 724 +2024-03-15 02:34:30,926 INFO [train.py:527] (4/6) Epoch 724, batch 48, global_batch_idx: 89700, batch size: 64, loss[discriminator_loss=2.687, discriminator_real_loss=1.272, discriminator_fake_loss=1.415, generator_loss=28.27, generator_mel_loss=17.76, generator_kl_loss=1.412, generator_dur_loss=1.807, generator_adv_loss=2.049, generator_feat_match_loss=5.246, over 64.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.359, discriminator_fake_loss=1.336, generator_loss=28.94, generator_mel_loss=18.02, generator_kl_loss=1.427, generator_dur_loss=1.736, generator_adv_loss=1.974, generator_feat_match_loss=5.777, over 2672.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:36:53,238 INFO [train.py:527] (4/6) Epoch 724, batch 98, global_batch_idx: 89750, batch size: 53, loss[discriminator_loss=2.634, discriminator_real_loss=1.332, discriminator_fake_loss=1.302, generator_loss=27.51, generator_mel_loss=17.36, generator_kl_loss=1.321, generator_dur_loss=1.697, generator_adv_loss=2.047, generator_feat_match_loss=5.089, over 53.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=28.9, generator_mel_loss=17.95, generator_kl_loss=1.424, generator_dur_loss=1.742, generator_adv_loss=1.995, generator_feat_match_loss=5.785, over 5733.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:38:04,265 INFO [train.py:919] (4/6) Start epoch 725 +2024-03-15 02:39:31,913 INFO [train.py:527] (4/6) Epoch 725, batch 24, global_batch_idx: 89800, batch size: 59, loss[discriminator_loss=2.703, discriminator_real_loss=1.353, discriminator_fake_loss=1.351, generator_loss=28.22, generator_mel_loss=17.64, generator_kl_loss=1.339, generator_dur_loss=1.757, generator_adv_loss=1.9, generator_feat_match_loss=5.587, over 59.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.362, discriminator_fake_loss=1.34, generator_loss=28.64, generator_mel_loss=17.98, generator_kl_loss=1.441, generator_dur_loss=1.739, generator_adv_loss=1.972, generator_feat_match_loss=5.507, over 1341.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:39:31,915 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 02:39:39,701 INFO [train.py:591] (4/6) Epoch 725, validation: discriminator_loss=2.751, discriminator_real_loss=1.342, discriminator_fake_loss=1.409, generator_loss=27.8, generator_mel_loss=18.4, generator_kl_loss=1.14, generator_dur_loss=1.812, generator_adv_loss=1.788, generator_feat_match_loss=4.655, over 100.00 samples. +2024-03-15 02:39:39,702 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 02:41:56,803 INFO [train.py:527] (4/6) Epoch 725, batch 74, global_batch_idx: 89850, batch size: 77, loss[discriminator_loss=2.648, discriminator_real_loss=1.361, discriminator_fake_loss=1.287, generator_loss=28.12, generator_mel_loss=17.79, generator_kl_loss=1.386, generator_dur_loss=1.805, generator_adv_loss=1.903, generator_feat_match_loss=5.238, over 77.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.364, discriminator_fake_loss=1.329, generator_loss=28.77, generator_mel_loss=17.92, generator_kl_loss=1.435, generator_dur_loss=1.745, generator_adv_loss=1.984, generator_feat_match_loss=5.686, over 4240.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:44:14,626 INFO [train.py:919] (4/6) Start epoch 726 +2024-03-15 02:44:38,500 INFO [train.py:527] (4/6) Epoch 726, batch 0, global_batch_idx: 89900, batch size: 52, loss[discriminator_loss=2.698, discriminator_real_loss=1.436, discriminator_fake_loss=1.262, generator_loss=29.12, generator_mel_loss=18.09, generator_kl_loss=1.558, generator_dur_loss=1.729, generator_adv_loss=1.942, generator_feat_match_loss=5.801, over 52.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.436, discriminator_fake_loss=1.262, generator_loss=29.12, generator_mel_loss=18.09, generator_kl_loss=1.558, generator_dur_loss=1.729, generator_adv_loss=1.942, generator_feat_match_loss=5.801, over 52.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:46:55,355 INFO [train.py:527] (4/6) Epoch 726, batch 50, global_batch_idx: 89950, batch size: 62, loss[discriminator_loss=2.672, discriminator_real_loss=1.364, discriminator_fake_loss=1.308, generator_loss=29.29, generator_mel_loss=18.15, generator_kl_loss=1.341, generator_dur_loss=1.712, generator_adv_loss=2.067, generator_feat_match_loss=6.019, over 62.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.349, discriminator_fake_loss=1.331, generator_loss=29.03, generator_mel_loss=17.98, generator_kl_loss=1.447, generator_dur_loss=1.718, generator_adv_loss=2.009, generator_feat_match_loss=5.876, over 2657.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:49:12,524 INFO [train.py:527] (4/6) Epoch 726, batch 100, global_batch_idx: 90000, batch size: 72, loss[discriminator_loss=2.712, discriminator_real_loss=1.352, discriminator_fake_loss=1.361, generator_loss=29.05, generator_mel_loss=17.74, generator_kl_loss=1.33, generator_dur_loss=1.807, generator_adv_loss=1.927, generator_feat_match_loss=6.242, over 72.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.355, discriminator_fake_loss=1.326, generator_loss=28.89, generator_mel_loss=17.91, generator_kl_loss=1.434, generator_dur_loss=1.73, generator_adv_loss=2.003, generator_feat_match_loss=5.814, over 5432.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:49:12,526 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 02:49:21,317 INFO [train.py:591] (4/6) Epoch 726, validation: discriminator_loss=2.744, discriminator_real_loss=1.41, discriminator_fake_loss=1.334, generator_loss=27.19, generator_mel_loss=17.97, generator_kl_loss=1.278, generator_dur_loss=1.804, generator_adv_loss=1.87, generator_feat_match_loss=4.264, over 100.00 samples. +2024-03-15 02:49:21,318 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 02:50:26,416 INFO [train.py:919] (4/6) Start epoch 727 +2024-03-15 02:52:03,334 INFO [train.py:527] (4/6) Epoch 727, batch 26, global_batch_idx: 90050, batch size: 58, loss[discriminator_loss=2.704, discriminator_real_loss=1.392, discriminator_fake_loss=1.311, generator_loss=27.92, generator_mel_loss=17.46, generator_kl_loss=1.387, generator_dur_loss=1.72, generator_adv_loss=1.915, generator_feat_match_loss=5.435, over 58.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=28.9, generator_mel_loss=17.86, generator_kl_loss=1.428, generator_dur_loss=1.726, generator_adv_loss=2.004, generator_feat_match_loss=5.885, over 1425.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:54:22,646 INFO [train.py:527] (4/6) Epoch 727, batch 76, global_batch_idx: 90100, batch size: 56, loss[discriminator_loss=2.713, discriminator_real_loss=1.387, discriminator_fake_loss=1.325, generator_loss=28.47, generator_mel_loss=17.64, generator_kl_loss=1.369, generator_dur_loss=1.72, generator_adv_loss=2.021, generator_feat_match_loss=5.73, over 56.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=28.86, generator_mel_loss=17.9, generator_kl_loss=1.404, generator_dur_loss=1.735, generator_adv_loss=2.004, generator_feat_match_loss=5.819, over 4425.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:56:31,085 INFO [train.py:919] (4/6) Start epoch 728 +2024-03-15 02:56:59,591 INFO [train.py:527] (4/6) Epoch 728, batch 2, global_batch_idx: 90150, batch size: 58, loss[discriminator_loss=2.693, discriminator_real_loss=1.437, discriminator_fake_loss=1.256, generator_loss=28.28, generator_mel_loss=17.71, generator_kl_loss=1.423, generator_dur_loss=1.745, generator_adv_loss=2.039, generator_feat_match_loss=5.366, over 58.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.376, discriminator_fake_loss=1.309, generator_loss=29.04, generator_mel_loss=18.03, generator_kl_loss=1.517, generator_dur_loss=1.725, generator_adv_loss=2.024, generator_feat_match_loss=5.749, over 174.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:59:17,577 INFO [train.py:527] (4/6) Epoch 728, batch 52, global_batch_idx: 90200, batch size: 74, loss[discriminator_loss=2.766, discriminator_real_loss=1.389, discriminator_fake_loss=1.377, generator_loss=28.31, generator_mel_loss=17.72, generator_kl_loss=1.233, generator_dur_loss=1.8, generator_adv_loss=2.051, generator_feat_match_loss=5.5, over 74.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.8, generator_mel_loss=17.94, generator_kl_loss=1.429, generator_dur_loss=1.731, generator_adv_loss=1.985, generator_feat_match_loss=5.722, over 2823.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:59:17,579 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 02:59:25,700 INFO [train.py:591] (4/6) Epoch 728, validation: discriminator_loss=2.75, discriminator_real_loss=1.473, discriminator_fake_loss=1.277, generator_loss=28.74, generator_mel_loss=18.58, generator_kl_loss=1.261, generator_dur_loss=1.818, generator_adv_loss=1.979, generator_feat_match_loss=5.101, over 100.00 samples. +2024-03-15 02:59:25,701 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 03:01:42,904 INFO [train.py:527] (4/6) Epoch 728, batch 102, global_batch_idx: 90250, batch size: 68, loss[discriminator_loss=2.668, discriminator_real_loss=1.346, discriminator_fake_loss=1.323, generator_loss=28.6, generator_mel_loss=17.51, generator_kl_loss=1.338, generator_dur_loss=1.759, generator_adv_loss=2.047, generator_feat_match_loss=5.95, over 68.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.362, discriminator_fake_loss=1.331, generator_loss=28.87, generator_mel_loss=17.93, generator_kl_loss=1.431, generator_dur_loss=1.739, generator_adv_loss=1.992, generator_feat_match_loss=5.779, over 5673.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:02:43,988 INFO [train.py:919] (4/6) Start epoch 729 +2024-03-15 03:04:24,196 INFO [train.py:527] (4/6) Epoch 729, batch 28, global_batch_idx: 90300, batch size: 47, loss[discriminator_loss=2.67, discriminator_real_loss=1.317, discriminator_fake_loss=1.353, generator_loss=29.17, generator_mel_loss=17.81, generator_kl_loss=1.495, generator_dur_loss=1.685, generator_adv_loss=2.048, generator_feat_match_loss=6.135, over 47.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.358, discriminator_fake_loss=1.329, generator_loss=28.79, generator_mel_loss=17.85, generator_kl_loss=1.45, generator_dur_loss=1.752, generator_adv_loss=2.001, generator_feat_match_loss=5.73, over 1687.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:06:43,514 INFO [train.py:527] (4/6) Epoch 729, batch 78, global_batch_idx: 90350, batch size: 48, loss[discriminator_loss=2.716, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=29.03, generator_mel_loss=17.9, generator_kl_loss=1.433, generator_dur_loss=1.706, generator_adv_loss=1.97, generator_feat_match_loss=6.021, over 48.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.362, discriminator_fake_loss=1.333, generator_loss=28.84, generator_mel_loss=17.89, generator_kl_loss=1.43, generator_dur_loss=1.74, generator_adv_loss=1.998, generator_feat_match_loss=5.775, over 4466.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:08:48,256 INFO [train.py:919] (4/6) Start epoch 730 +2024-03-15 03:09:24,007 INFO [train.py:527] (4/6) Epoch 730, batch 4, global_batch_idx: 90400, batch size: 62, loss[discriminator_loss=2.634, discriminator_real_loss=1.301, discriminator_fake_loss=1.334, generator_loss=30.61, generator_mel_loss=18.24, generator_kl_loss=1.434, generator_dur_loss=1.731, generator_adv_loss=2.031, generator_feat_match_loss=7.166, over 62.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.347, discriminator_fake_loss=1.335, generator_loss=29.16, generator_mel_loss=17.92, generator_kl_loss=1.461, generator_dur_loss=1.737, generator_adv_loss=1.969, generator_feat_match_loss=6.072, over 306.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:09:24,009 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 03:09:31,910 INFO [train.py:591] (4/6) Epoch 730, validation: discriminator_loss=2.701, discriminator_real_loss=1.391, discriminator_fake_loss=1.31, generator_loss=27.51, generator_mel_loss=18.27, generator_kl_loss=1.258, generator_dur_loss=1.811, generator_adv_loss=1.869, generator_feat_match_loss=4.308, over 100.00 samples. +2024-03-15 03:09:31,935 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 03:11:51,523 INFO [train.py:527] (4/6) Epoch 730, batch 54, global_batch_idx: 90450, batch size: 39, loss[discriminator_loss=2.741, discriminator_real_loss=1.389, discriminator_fake_loss=1.352, generator_loss=28.23, generator_mel_loss=18.49, generator_kl_loss=1.421, generator_dur_loss=1.666, generator_adv_loss=1.934, generator_feat_match_loss=4.716, over 39.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.351, discriminator_fake_loss=1.329, generator_loss=28.81, generator_mel_loss=17.88, generator_kl_loss=1.396, generator_dur_loss=1.755, generator_adv_loss=1.996, generator_feat_match_loss=5.786, over 3317.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:14:08,333 INFO [train.py:527] (4/6) Epoch 730, batch 104, global_batch_idx: 90500, batch size: 17, loss[discriminator_loss=2.824, discriminator_real_loss=1.455, discriminator_fake_loss=1.369, generator_loss=30.81, generator_mel_loss=19.35, generator_kl_loss=1.935, generator_dur_loss=1.525, generator_adv_loss=1.83, generator_feat_match_loss=6.168, over 17.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=28.82, generator_mel_loss=17.93, generator_kl_loss=1.41, generator_dur_loss=1.745, generator_adv_loss=1.988, generator_feat_match_loss=5.742, over 6107.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:14:59,050 INFO [train.py:919] (4/6) Start epoch 731 +2024-03-15 03:16:42,929 INFO [train.py:527] (4/6) Epoch 731, batch 30, global_batch_idx: 90550, batch size: 59, loss[discriminator_loss=2.661, discriminator_real_loss=1.358, discriminator_fake_loss=1.303, generator_loss=29.21, generator_mel_loss=18.06, generator_kl_loss=1.484, generator_dur_loss=1.745, generator_adv_loss=2.209, generator_feat_match_loss=5.713, over 59.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.366, discriminator_fake_loss=1.328, generator_loss=28.79, generator_mel_loss=17.87, generator_kl_loss=1.429, generator_dur_loss=1.742, generator_adv_loss=1.998, generator_feat_match_loss=5.75, over 1904.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:18:59,991 INFO [train.py:527] (4/6) Epoch 731, batch 80, global_batch_idx: 90600, batch size: 64, loss[discriminator_loss=2.789, discriminator_real_loss=1.559, discriminator_fake_loss=1.23, generator_loss=28.54, generator_mel_loss=18.07, generator_kl_loss=1.378, generator_dur_loss=1.759, generator_adv_loss=1.781, generator_feat_match_loss=5.547, over 64.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.363, discriminator_fake_loss=1.326, generator_loss=28.8, generator_mel_loss=17.87, generator_kl_loss=1.421, generator_dur_loss=1.748, generator_adv_loss=1.991, generator_feat_match_loss=5.764, over 4852.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:18:59,992 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 03:19:08,866 INFO [train.py:591] (4/6) Epoch 731, validation: discriminator_loss=2.754, discriminator_real_loss=1.286, discriminator_fake_loss=1.468, generator_loss=27.33, generator_mel_loss=17.98, generator_kl_loss=1.2, generator_dur_loss=1.825, generator_adv_loss=1.702, generator_feat_match_loss=4.624, over 100.00 samples. +2024-03-15 03:19:08,867 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 03:21:10,382 INFO [train.py:919] (4/6) Start epoch 732 +2024-03-15 03:21:47,912 INFO [train.py:527] (4/6) Epoch 732, batch 6, global_batch_idx: 90650, batch size: 36, loss[discriminator_loss=2.703, discriminator_real_loss=1.394, discriminator_fake_loss=1.309, generator_loss=28.56, generator_mel_loss=17.78, generator_kl_loss=1.549, generator_dur_loss=1.677, generator_adv_loss=1.955, generator_feat_match_loss=5.604, over 36.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.35, discriminator_fake_loss=1.318, generator_loss=28.52, generator_mel_loss=17.75, generator_kl_loss=1.396, generator_dur_loss=1.751, generator_adv_loss=2.001, generator_feat_match_loss=5.63, over 403.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:24:06,772 INFO [train.py:527] (4/6) Epoch 732, batch 56, global_batch_idx: 90700, batch size: 58, loss[discriminator_loss=2.671, discriminator_real_loss=1.301, discriminator_fake_loss=1.37, generator_loss=29.54, generator_mel_loss=18.16, generator_kl_loss=1.497, generator_dur_loss=1.686, generator_adv_loss=1.918, generator_feat_match_loss=6.279, over 58.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.356, discriminator_fake_loss=1.33, generator_loss=28.83, generator_mel_loss=17.9, generator_kl_loss=1.41, generator_dur_loss=1.741, generator_adv_loss=1.992, generator_feat_match_loss=5.786, over 3305.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:26:26,685 INFO [train.py:527] (4/6) Epoch 732, batch 106, global_batch_idx: 90750, batch size: 62, loss[discriminator_loss=2.67, discriminator_real_loss=1.392, discriminator_fake_loss=1.277, generator_loss=29.08, generator_mel_loss=18.18, generator_kl_loss=1.38, generator_dur_loss=1.76, generator_adv_loss=2.048, generator_feat_match_loss=5.715, over 62.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.358, discriminator_fake_loss=1.331, generator_loss=28.8, generator_mel_loss=17.89, generator_kl_loss=1.412, generator_dur_loss=1.748, generator_adv_loss=1.995, generator_feat_match_loss=5.756, over 6240.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:27:13,758 INFO [train.py:919] (4/6) Start epoch 733 +2024-03-15 03:29:06,623 INFO [train.py:527] (4/6) Epoch 733, batch 32, global_batch_idx: 90800, batch size: 74, loss[discriminator_loss=2.701, discriminator_real_loss=1.377, discriminator_fake_loss=1.325, generator_loss=27.73, generator_mel_loss=17.58, generator_kl_loss=1.227, generator_dur_loss=1.809, generator_adv_loss=1.889, generator_feat_match_loss=5.232, over 74.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.363, discriminator_fake_loss=1.325, generator_loss=28.84, generator_mel_loss=17.87, generator_kl_loss=1.438, generator_dur_loss=1.757, generator_adv_loss=1.998, generator_feat_match_loss=5.771, over 1851.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:29:06,625 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 03:29:14,668 INFO [train.py:591] (4/6) Epoch 733, validation: discriminator_loss=2.702, discriminator_real_loss=1.341, discriminator_fake_loss=1.361, generator_loss=27.22, generator_mel_loss=17.85, generator_kl_loss=1.268, generator_dur_loss=1.817, generator_adv_loss=1.856, generator_feat_match_loss=4.433, over 100.00 samples. +2024-03-15 03:29:14,669 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 03:31:35,955 INFO [train.py:527] (4/6) Epoch 733, batch 82, global_batch_idx: 90850, batch size: 77, loss[discriminator_loss=2.63, discriminator_real_loss=1.362, discriminator_fake_loss=1.268, generator_loss=29.15, generator_mel_loss=17.84, generator_kl_loss=1.499, generator_dur_loss=1.812, generator_adv_loss=1.912, generator_feat_match_loss=6.093, over 77.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.86, generator_mel_loss=17.89, generator_kl_loss=1.431, generator_dur_loss=1.752, generator_adv_loss=1.995, generator_feat_match_loss=5.791, over 4761.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:33:27,930 INFO [train.py:919] (4/6) Start epoch 734 +2024-03-15 03:34:14,611 INFO [train.py:527] (4/6) Epoch 734, batch 8, global_batch_idx: 90900, batch size: 83, loss[discriminator_loss=2.654, discriminator_real_loss=1.372, discriminator_fake_loss=1.282, generator_loss=28.8, generator_mel_loss=18.15, generator_kl_loss=1.49, generator_dur_loss=1.795, generator_adv_loss=1.957, generator_feat_match_loss=5.411, over 83.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=28.65, generator_mel_loss=17.94, generator_kl_loss=1.457, generator_dur_loss=1.708, generator_adv_loss=1.983, generator_feat_match_loss=5.561, over 451.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:36:32,441 INFO [train.py:527] (4/6) Epoch 734, batch 58, global_batch_idx: 90950, batch size: 53, loss[discriminator_loss=2.725, discriminator_real_loss=1.297, discriminator_fake_loss=1.428, generator_loss=29.28, generator_mel_loss=17.83, generator_kl_loss=1.525, generator_dur_loss=1.634, generator_adv_loss=2.062, generator_feat_match_loss=6.232, over 53.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.35, discriminator_fake_loss=1.329, generator_loss=28.86, generator_mel_loss=17.9, generator_kl_loss=1.414, generator_dur_loss=1.736, generator_adv_loss=2.014, generator_feat_match_loss=5.793, over 3350.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:38:52,239 INFO [train.py:527] (4/6) Epoch 734, batch 108, global_batch_idx: 91000, batch size: 59, loss[discriminator_loss=2.677, discriminator_real_loss=1.401, discriminator_fake_loss=1.276, generator_loss=28.77, generator_mel_loss=17.63, generator_kl_loss=1.429, generator_dur_loss=1.758, generator_adv_loss=2.026, generator_feat_match_loss=5.928, over 59.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=28.86, generator_mel_loss=17.88, generator_kl_loss=1.412, generator_dur_loss=1.746, generator_adv_loss=2.01, generator_feat_match_loss=5.806, over 6270.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:38:52,240 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 03:39:01,387 INFO [train.py:591] (4/6) Epoch 734, validation: discriminator_loss=2.736, discriminator_real_loss=1.413, discriminator_fake_loss=1.323, generator_loss=27.26, generator_mel_loss=18.04, generator_kl_loss=1.267, generator_dur_loss=1.818, generator_adv_loss=1.838, generator_feat_match_loss=4.298, over 100.00 samples. +2024-03-15 03:39:01,388 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 03:39:44,489 INFO [train.py:919] (4/6) Start epoch 735 +2024-03-15 03:41:43,584 INFO [train.py:527] (4/6) Epoch 735, batch 34, global_batch_idx: 91050, batch size: 64, loss[discriminator_loss=2.74, discriminator_real_loss=1.337, discriminator_fake_loss=1.403, generator_loss=27.73, generator_mel_loss=17.59, generator_kl_loss=1.324, generator_dur_loss=1.733, generator_adv_loss=2.116, generator_feat_match_loss=4.973, over 64.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.369, discriminator_fake_loss=1.334, generator_loss=28.9, generator_mel_loss=17.97, generator_kl_loss=1.424, generator_dur_loss=1.754, generator_adv_loss=1.983, generator_feat_match_loss=5.77, over 2013.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:44:01,033 INFO [train.py:527] (4/6) Epoch 735, batch 84, global_batch_idx: 91100, batch size: 15, loss[discriminator_loss=2.773, discriminator_real_loss=1.445, discriminator_fake_loss=1.328, generator_loss=29.17, generator_mel_loss=18.63, generator_kl_loss=1.698, generator_dur_loss=1.575, generator_adv_loss=2.129, generator_feat_match_loss=5.133, over 15.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.366, discriminator_fake_loss=1.325, generator_loss=28.95, generator_mel_loss=17.96, generator_kl_loss=1.427, generator_dur_loss=1.745, generator_adv_loss=1.997, generator_feat_match_loss=5.818, over 4780.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:45:50,991 INFO [train.py:919] (4/6) Start epoch 736 +2024-03-15 03:46:42,796 INFO [train.py:527] (4/6) Epoch 736, batch 10, global_batch_idx: 91150, batch size: 62, loss[discriminator_loss=2.669, discriminator_real_loss=1.411, discriminator_fake_loss=1.259, generator_loss=29.48, generator_mel_loss=17.9, generator_kl_loss=1.42, generator_dur_loss=1.771, generator_adv_loss=2.048, generator_feat_match_loss=6.347, over 62.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.373, discriminator_fake_loss=1.327, generator_loss=28.7, generator_mel_loss=17.74, generator_kl_loss=1.396, generator_dur_loss=1.756, generator_adv_loss=1.981, generator_feat_match_loss=5.82, over 667.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:49:01,763 INFO [train.py:527] (4/6) Epoch 736, batch 60, global_batch_idx: 91200, batch size: 88, loss[discriminator_loss=2.712, discriminator_real_loss=1.34, discriminator_fake_loss=1.371, generator_loss=28.94, generator_mel_loss=17.68, generator_kl_loss=1.377, generator_dur_loss=1.857, generator_adv_loss=2.079, generator_feat_match_loss=5.943, over 88.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.357, discriminator_fake_loss=1.323, generator_loss=28.94, generator_mel_loss=17.88, generator_kl_loss=1.413, generator_dur_loss=1.755, generator_adv_loss=2.015, generator_feat_match_loss=5.881, over 3722.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:49:01,765 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 03:49:09,684 INFO [train.py:591] (4/6) Epoch 736, validation: discriminator_loss=2.739, discriminator_real_loss=1.47, discriminator_fake_loss=1.269, generator_loss=27.55, generator_mel_loss=17.91, generator_kl_loss=1.142, generator_dur_loss=1.814, generator_adv_loss=2.003, generator_feat_match_loss=4.68, over 100.00 samples. +2024-03-15 03:49:09,685 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 03:51:27,962 INFO [train.py:527] (4/6) Epoch 736, batch 110, global_batch_idx: 91250, batch size: 36, loss[discriminator_loss=2.729, discriminator_real_loss=1.333, discriminator_fake_loss=1.396, generator_loss=29.15, generator_mel_loss=18.62, generator_kl_loss=1.566, generator_dur_loss=1.643, generator_adv_loss=2.041, generator_feat_match_loss=5.281, over 36.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.9, generator_mel_loss=17.89, generator_kl_loss=1.431, generator_dur_loss=1.745, generator_adv_loss=2.003, generator_feat_match_loss=5.831, over 6400.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:52:04,261 INFO [train.py:919] (4/6) Start epoch 737 +2024-03-15 03:54:10,830 INFO [train.py:527] (4/6) Epoch 737, batch 36, global_batch_idx: 91300, batch size: 83, loss[discriminator_loss=2.685, discriminator_real_loss=1.422, discriminator_fake_loss=1.263, generator_loss=28.78, generator_mel_loss=18.13, generator_kl_loss=1.272, generator_dur_loss=1.842, generator_adv_loss=2.05, generator_feat_match_loss=5.479, over 83.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.35, discriminator_fake_loss=1.325, generator_loss=28.76, generator_mel_loss=17.86, generator_kl_loss=1.399, generator_dur_loss=1.765, generator_adv_loss=1.993, generator_feat_match_loss=5.744, over 2332.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:56:27,607 INFO [train.py:527] (4/6) Epoch 737, batch 86, global_batch_idx: 91350, batch size: 83, loss[discriminator_loss=2.73, discriminator_real_loss=1.484, discriminator_fake_loss=1.246, generator_loss=28.53, generator_mel_loss=17.87, generator_kl_loss=1.086, generator_dur_loss=1.866, generator_adv_loss=1.816, generator_feat_match_loss=5.897, over 83.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.355, discriminator_fake_loss=1.327, generator_loss=28.91, generator_mel_loss=17.93, generator_kl_loss=1.416, generator_dur_loss=1.754, generator_adv_loss=1.992, generator_feat_match_loss=5.818, over 5066.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:58:11,730 INFO [train.py:919] (4/6) Start epoch 738 +2024-03-15 03:59:07,718 INFO [train.py:527] (4/6) Epoch 738, batch 12, global_batch_idx: 91400, batch size: 58, loss[discriminator_loss=2.712, discriminator_real_loss=1.474, discriminator_fake_loss=1.238, generator_loss=28.89, generator_mel_loss=18.04, generator_kl_loss=1.328, generator_dur_loss=1.773, generator_adv_loss=1.812, generator_feat_match_loss=5.933, over 58.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.369, discriminator_fake_loss=1.315, generator_loss=28.5, generator_mel_loss=17.72, generator_kl_loss=1.364, generator_dur_loss=1.771, generator_adv_loss=1.986, generator_feat_match_loss=5.655, over 807.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:59:07,721 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 03:59:15,743 INFO [train.py:591] (4/6) Epoch 738, validation: discriminator_loss=2.79, discriminator_real_loss=1.33, discriminator_fake_loss=1.461, generator_loss=27.54, generator_mel_loss=18.02, generator_kl_loss=1.271, generator_dur_loss=1.801, generator_adv_loss=1.711, generator_feat_match_loss=4.737, over 100.00 samples. +2024-03-15 03:59:15,744 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 04:01:31,894 INFO [train.py:527] (4/6) Epoch 738, batch 62, global_batch_idx: 91450, batch size: 80, loss[discriminator_loss=2.679, discriminator_real_loss=1.361, discriminator_fake_loss=1.318, generator_loss=28.94, generator_mel_loss=17.79, generator_kl_loss=1.257, generator_dur_loss=1.817, generator_adv_loss=1.955, generator_feat_match_loss=6.119, over 80.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.363, discriminator_fake_loss=1.333, generator_loss=28.7, generator_mel_loss=17.87, generator_kl_loss=1.407, generator_dur_loss=1.753, generator_adv_loss=1.987, generator_feat_match_loss=5.684, over 3720.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:03:50,136 INFO [train.py:527] (4/6) Epoch 738, batch 112, global_batch_idx: 91500, batch size: 66, loss[discriminator_loss=2.723, discriminator_real_loss=1.367, discriminator_fake_loss=1.356, generator_loss=27.93, generator_mel_loss=17.93, generator_kl_loss=1.413, generator_dur_loss=1.721, generator_adv_loss=1.877, generator_feat_match_loss=4.985, over 66.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.358, discriminator_fake_loss=1.335, generator_loss=28.83, generator_mel_loss=17.94, generator_kl_loss=1.404, generator_dur_loss=1.746, generator_adv_loss=1.988, generator_feat_match_loss=5.755, over 6584.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:04:25,309 INFO [train.py:919] (4/6) Start epoch 739 +2024-03-15 04:06:35,962 INFO [train.py:527] (4/6) Epoch 739, batch 38, global_batch_idx: 91550, batch size: 88, loss[discriminator_loss=2.658, discriminator_real_loss=1.346, discriminator_fake_loss=1.312, generator_loss=28.05, generator_mel_loss=17.45, generator_kl_loss=1.288, generator_dur_loss=1.813, generator_adv_loss=2, generator_feat_match_loss=5.497, over 88.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.361, discriminator_fake_loss=1.339, generator_loss=28.62, generator_mel_loss=17.88, generator_kl_loss=1.412, generator_dur_loss=1.752, generator_adv_loss=1.964, generator_feat_match_loss=5.616, over 2454.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:08:56,302 INFO [train.py:527] (4/6) Epoch 739, batch 88, global_batch_idx: 91600, batch size: 77, loss[discriminator_loss=2.686, discriminator_real_loss=1.191, discriminator_fake_loss=1.495, generator_loss=29.17, generator_mel_loss=17.81, generator_kl_loss=1.402, generator_dur_loss=1.762, generator_adv_loss=2.018, generator_feat_match_loss=6.182, over 77.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.359, discriminator_fake_loss=1.332, generator_loss=28.74, generator_mel_loss=17.89, generator_kl_loss=1.416, generator_dur_loss=1.747, generator_adv_loss=1.978, generator_feat_match_loss=5.708, over 5427.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:08:56,303 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 04:09:05,236 INFO [train.py:591] (4/6) Epoch 739, validation: discriminator_loss=2.733, discriminator_real_loss=1.412, discriminator_fake_loss=1.321, generator_loss=28.2, generator_mel_loss=17.99, generator_kl_loss=1.303, generator_dur_loss=1.802, generator_adv_loss=1.993, generator_feat_match_loss=5.106, over 100.00 samples. +2024-03-15 04:09:05,237 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 04:10:43,641 INFO [train.py:919] (4/6) Start epoch 740 +2024-03-15 04:11:45,677 INFO [train.py:527] (4/6) Epoch 740, batch 14, global_batch_idx: 91650, batch size: 59, loss[discriminator_loss=2.712, discriminator_real_loss=1.399, discriminator_fake_loss=1.313, generator_loss=28.36, generator_mel_loss=17.53, generator_kl_loss=1.328, generator_dur_loss=1.74, generator_adv_loss=1.998, generator_feat_match_loss=5.762, over 59.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.35, discriminator_fake_loss=1.348, generator_loss=28.92, generator_mel_loss=17.95, generator_kl_loss=1.436, generator_dur_loss=1.736, generator_adv_loss=2.016, generator_feat_match_loss=5.783, over 790.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:14:05,767 INFO [train.py:527] (4/6) Epoch 740, batch 64, global_batch_idx: 91700, batch size: 77, loss[discriminator_loss=2.734, discriminator_real_loss=1.361, discriminator_fake_loss=1.373, generator_loss=28.12, generator_mel_loss=17.9, generator_kl_loss=1.304, generator_dur_loss=1.8, generator_adv_loss=1.894, generator_feat_match_loss=5.22, over 77.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.358, discriminator_fake_loss=1.332, generator_loss=28.82, generator_mel_loss=17.87, generator_kl_loss=1.42, generator_dur_loss=1.74, generator_adv_loss=2.01, generator_feat_match_loss=5.78, over 3664.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:16:23,868 INFO [train.py:527] (4/6) Epoch 740, batch 114, global_batch_idx: 91750, batch size: 56, loss[discriminator_loss=2.737, discriminator_real_loss=1.375, discriminator_fake_loss=1.361, generator_loss=27.15, generator_mel_loss=17.47, generator_kl_loss=1.297, generator_dur_loss=1.744, generator_adv_loss=1.916, generator_feat_match_loss=4.723, over 56.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=28.83, generator_mel_loss=17.87, generator_kl_loss=1.424, generator_dur_loss=1.743, generator_adv_loss=2.007, generator_feat_match_loss=5.787, over 6419.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:16:51,447 INFO [train.py:919] (4/6) Start epoch 741 +2024-03-15 04:19:12,514 INFO [train.py:527] (4/6) Epoch 741, batch 40, global_batch_idx: 91800, batch size: 44, loss[discriminator_loss=2.761, discriminator_real_loss=1.317, discriminator_fake_loss=1.444, generator_loss=28.06, generator_mel_loss=17.58, generator_kl_loss=1.409, generator_dur_loss=1.657, generator_adv_loss=2.107, generator_feat_match_loss=5.313, over 44.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.329, generator_loss=28.79, generator_mel_loss=17.94, generator_kl_loss=1.421, generator_dur_loss=1.74, generator_adv_loss=1.987, generator_feat_match_loss=5.705, over 2290.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:19:12,515 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 04:19:20,740 INFO [train.py:591] (4/6) Epoch 741, validation: discriminator_loss=2.737, discriminator_real_loss=1.545, discriminator_fake_loss=1.191, generator_loss=28.37, generator_mel_loss=18.29, generator_kl_loss=1.231, generator_dur_loss=1.814, generator_adv_loss=2.147, generator_feat_match_loss=4.895, over 100.00 samples. +2024-03-15 04:19:20,741 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 04:21:40,812 INFO [train.py:527] (4/6) Epoch 741, batch 90, global_batch_idx: 91850, batch size: 50, loss[discriminator_loss=2.655, discriminator_real_loss=1.382, discriminator_fake_loss=1.273, generator_loss=28.49, generator_mel_loss=17.53, generator_kl_loss=1.445, generator_dur_loss=1.671, generator_adv_loss=1.938, generator_feat_match_loss=5.908, over 50.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.73, generator_mel_loss=17.87, generator_kl_loss=1.419, generator_dur_loss=1.741, generator_adv_loss=1.985, generator_feat_match_loss=5.711, over 5056.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:23:13,562 INFO [train.py:919] (4/6) Start epoch 742 +2024-03-15 04:24:23,312 INFO [train.py:527] (4/6) Epoch 742, batch 16, global_batch_idx: 91900, batch size: 25, loss[discriminator_loss=2.697, discriminator_real_loss=1.392, discriminator_fake_loss=1.305, generator_loss=28.2, generator_mel_loss=18.14, generator_kl_loss=1.632, generator_dur_loss=1.62, generator_adv_loss=2.05, generator_feat_match_loss=4.767, over 25.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.362, discriminator_fake_loss=1.306, generator_loss=28.81, generator_mel_loss=17.91, generator_kl_loss=1.382, generator_dur_loss=1.735, generator_adv_loss=2.027, generator_feat_match_loss=5.754, over 980.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:26:43,260 INFO [train.py:527] (4/6) Epoch 742, batch 66, global_batch_idx: 91950, batch size: 74, loss[discriminator_loss=2.708, discriminator_real_loss=1.408, discriminator_fake_loss=1.3, generator_loss=27.95, generator_mel_loss=17.72, generator_kl_loss=1.315, generator_dur_loss=1.798, generator_adv_loss=1.929, generator_feat_match_loss=5.196, over 74.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.37, discriminator_fake_loss=1.319, generator_loss=28.73, generator_mel_loss=17.86, generator_kl_loss=1.408, generator_dur_loss=1.747, generator_adv_loss=1.994, generator_feat_match_loss=5.712, over 3911.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:29:01,505 INFO [train.py:527] (4/6) Epoch 742, batch 116, global_batch_idx: 92000, batch size: 31, loss[discriminator_loss=2.682, discriminator_real_loss=1.384, discriminator_fake_loss=1.298, generator_loss=28.06, generator_mel_loss=17.63, generator_kl_loss=1.486, generator_dur_loss=1.704, generator_adv_loss=1.954, generator_feat_match_loss=5.287, over 31.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.369, discriminator_fake_loss=1.323, generator_loss=28.7, generator_mel_loss=17.88, generator_kl_loss=1.401, generator_dur_loss=1.749, generator_adv_loss=1.991, generator_feat_match_loss=5.68, over 6710.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:29:01,506 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 04:29:10,224 INFO [train.py:591] (4/6) Epoch 742, validation: discriminator_loss=2.768, discriminator_real_loss=1.436, discriminator_fake_loss=1.332, generator_loss=26.91, generator_mel_loss=18.07, generator_kl_loss=1.166, generator_dur_loss=1.816, generator_adv_loss=1.841, generator_feat_match_loss=4.017, over 100.00 samples. +2024-03-15 04:29:10,225 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 04:29:30,666 INFO [train.py:919] (4/6) Start epoch 743 +2024-03-15 04:31:51,690 INFO [train.py:527] (4/6) Epoch 743, batch 42, global_batch_idx: 92050, batch size: 44, loss[discriminator_loss=2.67, discriminator_real_loss=1.361, discriminator_fake_loss=1.308, generator_loss=28.02, generator_mel_loss=17.67, generator_kl_loss=1.448, generator_dur_loss=1.646, generator_adv_loss=2.004, generator_feat_match_loss=5.249, over 44.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=28.91, generator_mel_loss=17.91, generator_kl_loss=1.428, generator_dur_loss=1.741, generator_adv_loss=1.984, generator_feat_match_loss=5.851, over 2400.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:34:13,570 INFO [train.py:527] (4/6) Epoch 743, batch 92, global_batch_idx: 92100, batch size: 50, loss[discriminator_loss=2.749, discriminator_real_loss=1.372, discriminator_fake_loss=1.377, generator_loss=29.25, generator_mel_loss=18.14, generator_kl_loss=1.489, generator_dur_loss=1.642, generator_adv_loss=1.959, generator_feat_match_loss=6.019, over 50.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.357, discriminator_fake_loss=1.332, generator_loss=28.88, generator_mel_loss=17.92, generator_kl_loss=1.42, generator_dur_loss=1.737, generator_adv_loss=1.989, generator_feat_match_loss=5.821, over 5333.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:35:40,209 INFO [train.py:919] (4/6) Start epoch 744 +2024-03-15 04:36:54,219 INFO [train.py:527] (4/6) Epoch 744, batch 18, global_batch_idx: 92150, batch size: 25, loss[discriminator_loss=2.682, discriminator_real_loss=1.267, discriminator_fake_loss=1.415, generator_loss=30.87, generator_mel_loss=18.38, generator_kl_loss=1.801, generator_dur_loss=1.544, generator_adv_loss=2.033, generator_feat_match_loss=7.109, over 25.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.348, discriminator_fake_loss=1.321, generator_loss=28.85, generator_mel_loss=17.86, generator_kl_loss=1.411, generator_dur_loss=1.741, generator_adv_loss=1.997, generator_feat_match_loss=5.847, over 1056.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:39:13,577 INFO [train.py:527] (4/6) Epoch 744, batch 68, global_batch_idx: 92200, batch size: 31, loss[discriminator_loss=2.683, discriminator_real_loss=1.417, discriminator_fake_loss=1.266, generator_loss=27.84, generator_mel_loss=17.83, generator_kl_loss=1.674, generator_dur_loss=1.637, generator_adv_loss=1.957, generator_feat_match_loss=4.743, over 31.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.35, discriminator_fake_loss=1.331, generator_loss=28.82, generator_mel_loss=17.88, generator_kl_loss=1.412, generator_dur_loss=1.75, generator_adv_loss=1.987, generator_feat_match_loss=5.795, over 4030.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:39:13,578 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 04:39:22,314 INFO [train.py:591] (4/6) Epoch 744, validation: discriminator_loss=2.756, discriminator_real_loss=1.355, discriminator_fake_loss=1.401, generator_loss=28.36, generator_mel_loss=18.23, generator_kl_loss=1.309, generator_dur_loss=1.822, generator_adv_loss=1.904, generator_feat_match_loss=5.097, over 100.00 samples. +2024-03-15 04:39:22,315 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 04:41:40,510 INFO [train.py:527] (4/6) Epoch 744, batch 118, global_batch_idx: 92250, batch size: 56, loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=29.12, generator_mel_loss=18.1, generator_kl_loss=1.492, generator_dur_loss=1.689, generator_adv_loss=1.951, generator_feat_match_loss=5.885, over 56.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.86, generator_mel_loss=17.89, generator_kl_loss=1.423, generator_dur_loss=1.748, generator_adv_loss=1.989, generator_feat_match_loss=5.811, over 6661.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:41:54,651 INFO [train.py:919] (4/6) Start epoch 745 +2024-03-15 04:44:22,405 INFO [train.py:527] (4/6) Epoch 745, batch 44, global_batch_idx: 92300, batch size: 77, loss[discriminator_loss=2.704, discriminator_real_loss=1.371, discriminator_fake_loss=1.334, generator_loss=27.56, generator_mel_loss=17.09, generator_kl_loss=1.296, generator_dur_loss=1.83, generator_adv_loss=2.027, generator_feat_match_loss=5.318, over 77.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.365, discriminator_fake_loss=1.329, generator_loss=28.89, generator_mel_loss=17.92, generator_kl_loss=1.415, generator_dur_loss=1.756, generator_adv_loss=1.987, generator_feat_match_loss=5.807, over 2716.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:46:40,586 INFO [train.py:527] (4/6) Epoch 745, batch 94, global_batch_idx: 92350, batch size: 55, loss[discriminator_loss=2.711, discriminator_real_loss=1.512, discriminator_fake_loss=1.199, generator_loss=29.01, generator_mel_loss=17.91, generator_kl_loss=1.512, generator_dur_loss=1.666, generator_adv_loss=1.835, generator_feat_match_loss=6.086, over 55.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.87, generator_mel_loss=17.9, generator_kl_loss=1.428, generator_dur_loss=1.744, generator_adv_loss=1.987, generator_feat_match_loss=5.816, over 5461.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:48:01,760 INFO [train.py:919] (4/6) Start epoch 746 +2024-03-15 04:49:24,198 INFO [train.py:527] (4/6) Epoch 746, batch 20, global_batch_idx: 92400, batch size: 64, loss[discriminator_loss=2.591, discriminator_real_loss=1.276, discriminator_fake_loss=1.315, generator_loss=29.58, generator_mel_loss=18.01, generator_kl_loss=1.442, generator_dur_loss=1.768, generator_adv_loss=2.086, generator_feat_match_loss=6.27, over 64.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.347, discriminator_fake_loss=1.334, generator_loss=28.82, generator_mel_loss=17.87, generator_kl_loss=1.409, generator_dur_loss=1.728, generator_adv_loss=1.995, generator_feat_match_loss=5.814, over 1154.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:49:24,199 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 04:49:32,189 INFO [train.py:591] (4/6) Epoch 746, validation: discriminator_loss=2.636, discriminator_real_loss=1.282, discriminator_fake_loss=1.355, generator_loss=28.28, generator_mel_loss=18.31, generator_kl_loss=1.258, generator_dur_loss=1.822, generator_adv_loss=1.904, generator_feat_match_loss=4.993, over 100.00 samples. +2024-03-15 04:49:32,190 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 04:51:51,688 INFO [train.py:527] (4/6) Epoch 746, batch 70, global_batch_idx: 92450, batch size: 83, loss[discriminator_loss=2.698, discriminator_real_loss=1.329, discriminator_fake_loss=1.37, generator_loss=28.04, generator_mel_loss=17.71, generator_kl_loss=1.329, generator_dur_loss=1.831, generator_adv_loss=2.005, generator_feat_match_loss=5.172, over 83.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=28.86, generator_mel_loss=17.85, generator_kl_loss=1.405, generator_dur_loss=1.74, generator_adv_loss=2.02, generator_feat_match_loss=5.841, over 4042.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:54:05,630 INFO [train.py:527] (4/6) Epoch 746, batch 120, global_batch_idx: 92500, batch size: 16, loss[discriminator_loss=2.6, discriminator_real_loss=1.27, discriminator_fake_loss=1.329, generator_loss=30.42, generator_mel_loss=18.12, generator_kl_loss=1.766, generator_dur_loss=1.531, generator_adv_loss=2.23, generator_feat_match_loss=6.773, over 16.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.327, generator_loss=28.83, generator_mel_loss=17.87, generator_kl_loss=1.416, generator_dur_loss=1.736, generator_adv_loss=2.008, generator_feat_match_loss=5.796, over 6918.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:54:16,345 INFO [train.py:919] (4/6) Start epoch 747 +2024-03-15 04:56:48,734 INFO [train.py:527] (4/6) Epoch 747, batch 46, global_batch_idx: 92550, batch size: 74, loss[discriminator_loss=2.686, discriminator_real_loss=1.366, discriminator_fake_loss=1.321, generator_loss=28.68, generator_mel_loss=17.86, generator_kl_loss=1.256, generator_dur_loss=1.808, generator_adv_loss=2.018, generator_feat_match_loss=5.74, over 74.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.362, discriminator_fake_loss=1.334, generator_loss=28.85, generator_mel_loss=17.89, generator_kl_loss=1.411, generator_dur_loss=1.765, generator_adv_loss=1.987, generator_feat_match_loss=5.799, over 2855.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:59:03,904 INFO [train.py:527] (4/6) Epoch 747, batch 96, global_batch_idx: 92600, batch size: 36, loss[discriminator_loss=2.657, discriminator_real_loss=1.213, discriminator_fake_loss=1.444, generator_loss=30.52, generator_mel_loss=18.82, generator_kl_loss=1.5, generator_dur_loss=1.712, generator_adv_loss=2.088, generator_feat_match_loss=6.394, over 36.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.358, discriminator_fake_loss=1.332, generator_loss=28.76, generator_mel_loss=17.86, generator_kl_loss=1.416, generator_dur_loss=1.75, generator_adv_loss=1.99, generator_feat_match_loss=5.746, over 5585.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:59:03,906 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 04:59:13,109 INFO [train.py:591] (4/6) Epoch 747, validation: discriminator_loss=2.757, discriminator_real_loss=1.545, discriminator_fake_loss=1.213, generator_loss=27.3, generator_mel_loss=18.04, generator_kl_loss=1.274, generator_dur_loss=1.836, generator_adv_loss=2.056, generator_feat_match_loss=4.094, over 100.00 samples. +2024-03-15 04:59:13,110 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 05:00:27,408 INFO [train.py:919] (4/6) Start epoch 748 +2024-03-15 05:01:51,832 INFO [train.py:527] (4/6) Epoch 748, batch 22, global_batch_idx: 92650, batch size: 15, loss[discriminator_loss=2.749, discriminator_real_loss=1.497, discriminator_fake_loss=1.251, generator_loss=28.63, generator_mel_loss=18.3, generator_kl_loss=1.775, generator_dur_loss=1.609, generator_adv_loss=2.22, generator_feat_match_loss=4.725, over 15.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.352, discriminator_fake_loss=1.331, generator_loss=28.77, generator_mel_loss=17.9, generator_kl_loss=1.41, generator_dur_loss=1.739, generator_adv_loss=1.987, generator_feat_match_loss=5.738, over 1272.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:04:13,330 INFO [train.py:527] (4/6) Epoch 748, batch 72, global_batch_idx: 92700, batch size: 83, loss[discriminator_loss=2.651, discriminator_real_loss=1.304, discriminator_fake_loss=1.346, generator_loss=28.79, generator_mel_loss=18.06, generator_kl_loss=1.342, generator_dur_loss=1.835, generator_adv_loss=1.995, generator_feat_match_loss=5.565, over 83.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=28.73, generator_mel_loss=17.88, generator_kl_loss=1.391, generator_dur_loss=1.756, generator_adv_loss=1.982, generator_feat_match_loss=5.717, over 4342.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:06:30,459 INFO [train.py:527] (4/6) Epoch 748, batch 122, global_batch_idx: 92750, batch size: 62, loss[discriminator_loss=2.628, discriminator_real_loss=1.358, discriminator_fake_loss=1.27, generator_loss=29.66, generator_mel_loss=18.41, generator_kl_loss=1.536, generator_dur_loss=1.769, generator_adv_loss=2.01, generator_feat_match_loss=5.929, over 62.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.8, generator_mel_loss=17.89, generator_kl_loss=1.399, generator_dur_loss=1.753, generator_adv_loss=1.992, generator_feat_match_loss=5.762, over 7190.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:06:35,734 INFO [train.py:919] (4/6) Start epoch 749 +2024-03-15 05:09:10,685 INFO [train.py:527] (4/6) Epoch 749, batch 48, global_batch_idx: 92800, batch size: 39, loss[discriminator_loss=2.679, discriminator_real_loss=1.286, discriminator_fake_loss=1.393, generator_loss=29, generator_mel_loss=18.06, generator_kl_loss=1.468, generator_dur_loss=1.651, generator_adv_loss=1.948, generator_feat_match_loss=5.875, over 39.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.354, discriminator_fake_loss=1.336, generator_loss=28.73, generator_mel_loss=17.83, generator_kl_loss=1.419, generator_dur_loss=1.746, generator_adv_loss=1.995, generator_feat_match_loss=5.744, over 2703.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:09:10,686 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 05:09:18,731 INFO [train.py:591] (4/6) Epoch 749, validation: discriminator_loss=2.763, discriminator_real_loss=1.358, discriminator_fake_loss=1.406, generator_loss=27.93, generator_mel_loss=18.16, generator_kl_loss=1.235, generator_dur_loss=1.818, generator_adv_loss=1.757, generator_feat_match_loss=4.961, over 100.00 samples. +2024-03-15 05:09:18,731 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 05:11:40,226 INFO [train.py:527] (4/6) Epoch 749, batch 98, global_batch_idx: 92850, batch size: 52, loss[discriminator_loss=2.713, discriminator_real_loss=1.358, discriminator_fake_loss=1.355, generator_loss=29.49, generator_mel_loss=17.86, generator_kl_loss=1.402, generator_dur_loss=1.725, generator_adv_loss=1.975, generator_feat_match_loss=6.523, over 52.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.359, discriminator_fake_loss=1.331, generator_loss=28.71, generator_mel_loss=17.86, generator_kl_loss=1.411, generator_dur_loss=1.745, generator_adv_loss=1.995, generator_feat_match_loss=5.698, over 5468.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:12:52,709 INFO [train.py:919] (4/6) Start epoch 750 +2024-03-15 05:14:21,265 INFO [train.py:527] (4/6) Epoch 750, batch 24, global_batch_idx: 92900, batch size: 36, loss[discriminator_loss=2.649, discriminator_real_loss=1.379, discriminator_fake_loss=1.27, generator_loss=28.46, generator_mel_loss=18.07, generator_kl_loss=1.643, generator_dur_loss=1.722, generator_adv_loss=2.132, generator_feat_match_loss=4.895, over 36.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.346, discriminator_fake_loss=1.325, generator_loss=28.92, generator_mel_loss=18, generator_kl_loss=1.367, generator_dur_loss=1.769, generator_adv_loss=2.003, generator_feat_match_loss=5.775, over 1547.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:16:41,118 INFO [train.py:527] (4/6) Epoch 750, batch 74, global_batch_idx: 92950, batch size: 72, loss[discriminator_loss=2.661, discriminator_real_loss=1.328, discriminator_fake_loss=1.333, generator_loss=29.9, generator_mel_loss=18.14, generator_kl_loss=1.339, generator_dur_loss=1.711, generator_adv_loss=1.951, generator_feat_match_loss=6.76, over 72.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.35, discriminator_fake_loss=1.326, generator_loss=28.88, generator_mel_loss=17.9, generator_kl_loss=1.416, generator_dur_loss=1.745, generator_adv_loss=1.994, generator_feat_match_loss=5.82, over 4257.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:18:58,471 INFO [train.py:919] (4/6) Start epoch 751 +2024-03-15 05:19:23,016 INFO [train.py:527] (4/6) Epoch 751, batch 0, global_batch_idx: 93000, batch size: 64, loss[discriminator_loss=2.739, discriminator_real_loss=1.366, discriminator_fake_loss=1.373, generator_loss=28.24, generator_mel_loss=17.93, generator_kl_loss=1.389, generator_dur_loss=1.803, generator_adv_loss=1.862, generator_feat_match_loss=5.257, over 64.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.366, discriminator_fake_loss=1.373, generator_loss=28.24, generator_mel_loss=17.93, generator_kl_loss=1.389, generator_dur_loss=1.803, generator_adv_loss=1.862, generator_feat_match_loss=5.257, over 64.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:19:23,019 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 05:19:30,713 INFO [train.py:591] (4/6) Epoch 751, validation: discriminator_loss=2.758, discriminator_real_loss=1.427, discriminator_fake_loss=1.331, generator_loss=27.35, generator_mel_loss=18.12, generator_kl_loss=1.288, generator_dur_loss=1.803, generator_adv_loss=1.841, generator_feat_match_loss=4.294, over 100.00 samples. +2024-03-15 05:19:30,731 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 05:21:47,053 INFO [train.py:527] (4/6) Epoch 751, batch 50, global_batch_idx: 93050, batch size: 39, loss[discriminator_loss=2.658, discriminator_real_loss=1.439, discriminator_fake_loss=1.219, generator_loss=28.53, generator_mel_loss=17.53, generator_kl_loss=1.426, generator_dur_loss=1.682, generator_adv_loss=1.85, generator_feat_match_loss=6.04, over 39.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=28.81, generator_mel_loss=17.87, generator_kl_loss=1.411, generator_dur_loss=1.754, generator_adv_loss=2.001, generator_feat_match_loss=5.768, over 3046.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:24:07,023 INFO [train.py:527] (4/6) Epoch 751, batch 100, global_batch_idx: 93100, batch size: 55, loss[discriminator_loss=2.629, discriminator_real_loss=1.306, discriminator_fake_loss=1.324, generator_loss=28.96, generator_mel_loss=17.74, generator_kl_loss=1.469, generator_dur_loss=1.706, generator_adv_loss=1.978, generator_feat_match_loss=6.073, over 55.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.357, discriminator_fake_loss=1.329, generator_loss=28.83, generator_mel_loss=17.89, generator_kl_loss=1.41, generator_dur_loss=1.748, generator_adv_loss=1.997, generator_feat_match_loss=5.79, over 5762.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:25:11,041 INFO [train.py:919] (4/6) Start epoch 752 +2024-03-15 05:26:49,702 INFO [train.py:527] (4/6) Epoch 752, batch 26, global_batch_idx: 93150, batch size: 56, loss[discriminator_loss=2.67, discriminator_real_loss=1.331, discriminator_fake_loss=1.339, generator_loss=29.59, generator_mel_loss=18.28, generator_kl_loss=1.466, generator_dur_loss=1.713, generator_adv_loss=2.124, generator_feat_match_loss=6.001, over 56.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.362, discriminator_fake_loss=1.322, generator_loss=28.91, generator_mel_loss=17.88, generator_kl_loss=1.41, generator_dur_loss=1.74, generator_adv_loss=1.991, generator_feat_match_loss=5.883, over 1539.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:29:10,376 INFO [train.py:527] (4/6) Epoch 752, batch 76, global_batch_idx: 93200, batch size: 72, loss[discriminator_loss=2.712, discriminator_real_loss=1.344, discriminator_fake_loss=1.369, generator_loss=27.74, generator_mel_loss=17.48, generator_kl_loss=1.359, generator_dur_loss=1.721, generator_adv_loss=1.951, generator_feat_match_loss=5.229, over 72.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=28.85, generator_mel_loss=17.86, generator_kl_loss=1.407, generator_dur_loss=1.736, generator_adv_loss=1.988, generator_feat_match_loss=5.858, over 4461.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:29:10,377 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 05:29:19,323 INFO [train.py:591] (4/6) Epoch 752, validation: discriminator_loss=2.762, discriminator_real_loss=1.426, discriminator_fake_loss=1.336, generator_loss=28.03, generator_mel_loss=18.4, generator_kl_loss=1.133, generator_dur_loss=1.79, generator_adv_loss=1.924, generator_feat_match_loss=4.788, over 100.00 samples. +2024-03-15 05:29:19,324 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 05:31:28,171 INFO [train.py:919] (4/6) Start epoch 753 +2024-03-15 05:31:58,218 INFO [train.py:527] (4/6) Epoch 753, batch 2, global_batch_idx: 93250, batch size: 55, loss[discriminator_loss=2.775, discriminator_real_loss=1.345, discriminator_fake_loss=1.43, generator_loss=29.11, generator_mel_loss=18.26, generator_kl_loss=1.482, generator_dur_loss=1.632, generator_adv_loss=2.007, generator_feat_match_loss=5.725, over 55.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.347, discriminator_fake_loss=1.406, generator_loss=29.22, generator_mel_loss=18.37, generator_kl_loss=1.455, generator_dur_loss=1.667, generator_adv_loss=1.935, generator_feat_match_loss=5.797, over 162.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:34:17,871 INFO [train.py:527] (4/6) Epoch 753, batch 52, global_batch_idx: 93300, batch size: 77, loss[discriminator_loss=2.75, discriminator_real_loss=1.396, discriminator_fake_loss=1.354, generator_loss=28.59, generator_mel_loss=17.65, generator_kl_loss=1.311, generator_dur_loss=1.785, generator_adv_loss=1.836, generator_feat_match_loss=6.012, over 77.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=28.85, generator_mel_loss=17.91, generator_kl_loss=1.434, generator_dur_loss=1.725, generator_adv_loss=1.985, generator_feat_match_loss=5.803, over 3171.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:36:40,896 INFO [train.py:527] (4/6) Epoch 753, batch 102, global_batch_idx: 93350, batch size: 66, loss[discriminator_loss=2.655, discriminator_real_loss=1.32, discriminator_fake_loss=1.335, generator_loss=28.83, generator_mel_loss=17.87, generator_kl_loss=1.341, generator_dur_loss=1.76, generator_adv_loss=1.994, generator_feat_match_loss=5.87, over 66.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.361, discriminator_fake_loss=1.322, generator_loss=28.85, generator_mel_loss=17.88, generator_kl_loss=1.432, generator_dur_loss=1.729, generator_adv_loss=1.993, generator_feat_match_loss=5.817, over 6058.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:37:38,790 INFO [train.py:919] (4/6) Start epoch 754 +2024-03-15 05:39:17,176 INFO [train.py:527] (4/6) Epoch 754, batch 28, global_batch_idx: 93400, batch size: 74, loss[discriminator_loss=2.735, discriminator_real_loss=1.452, discriminator_fake_loss=1.283, generator_loss=28.31, generator_mel_loss=17.85, generator_kl_loss=1.284, generator_dur_loss=1.775, generator_adv_loss=1.932, generator_feat_match_loss=5.478, over 74.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.371, discriminator_fake_loss=1.315, generator_loss=28.84, generator_mel_loss=17.91, generator_kl_loss=1.43, generator_dur_loss=1.739, generator_adv_loss=1.995, generator_feat_match_loss=5.76, over 1634.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:39:17,178 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 05:39:25,178 INFO [train.py:591] (4/6) Epoch 754, validation: discriminator_loss=2.741, discriminator_real_loss=1.343, discriminator_fake_loss=1.398, generator_loss=27.34, generator_mel_loss=18.17, generator_kl_loss=1.323, generator_dur_loss=1.803, generator_adv_loss=1.826, generator_feat_match_loss=4.214, over 100.00 samples. +2024-03-15 05:39:25,179 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 05:41:46,173 INFO [train.py:527] (4/6) Epoch 754, batch 78, global_batch_idx: 93450, batch size: 52, loss[discriminator_loss=2.715, discriminator_real_loss=1.397, discriminator_fake_loss=1.317, generator_loss=27.92, generator_mel_loss=17.9, generator_kl_loss=1.348, generator_dur_loss=1.681, generator_adv_loss=1.946, generator_feat_match_loss=5.04, over 52.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.363, discriminator_fake_loss=1.321, generator_loss=28.98, generator_mel_loss=17.92, generator_kl_loss=1.432, generator_dur_loss=1.736, generator_adv_loss=2.027, generator_feat_match_loss=5.863, over 4531.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:43:50,249 INFO [train.py:919] (4/6) Start epoch 755 +2024-03-15 05:44:26,471 INFO [train.py:527] (4/6) Epoch 755, batch 4, global_batch_idx: 93500, batch size: 39, loss[discriminator_loss=2.59, discriminator_real_loss=1.288, discriminator_fake_loss=1.302, generator_loss=30.06, generator_mel_loss=18.16, generator_kl_loss=1.409, generator_dur_loss=1.653, generator_adv_loss=1.896, generator_feat_match_loss=6.936, over 39.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.341, discriminator_fake_loss=1.34, generator_loss=29.37, generator_mel_loss=17.95, generator_kl_loss=1.45, generator_dur_loss=1.683, generator_adv_loss=2.026, generator_feat_match_loss=6.256, over 250.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:46:47,407 INFO [train.py:527] (4/6) Epoch 755, batch 54, global_batch_idx: 93550, batch size: 66, loss[discriminator_loss=2.682, discriminator_real_loss=1.363, discriminator_fake_loss=1.318, generator_loss=28.53, generator_mel_loss=17.88, generator_kl_loss=1.33, generator_dur_loss=1.726, generator_adv_loss=1.984, generator_feat_match_loss=5.611, over 66.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.363, discriminator_fake_loss=1.318, generator_loss=28.93, generator_mel_loss=17.88, generator_kl_loss=1.465, generator_dur_loss=1.698, generator_adv_loss=2.005, generator_feat_match_loss=5.883, over 2931.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:49:04,323 INFO [train.py:527] (4/6) Epoch 755, batch 104, global_batch_idx: 93600, batch size: 83, loss[discriminator_loss=2.656, discriminator_real_loss=1.366, discriminator_fake_loss=1.291, generator_loss=28.42, generator_mel_loss=17.73, generator_kl_loss=1.305, generator_dur_loss=1.769, generator_adv_loss=2, generator_feat_match_loss=5.622, over 83.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.361, discriminator_fake_loss=1.32, generator_loss=28.86, generator_mel_loss=17.88, generator_kl_loss=1.453, generator_dur_loss=1.71, generator_adv_loss=1.996, generator_feat_match_loss=5.815, over 5598.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:49:04,325 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 05:49:13,245 INFO [train.py:591] (4/6) Epoch 755, validation: discriminator_loss=2.697, discriminator_real_loss=1.382, discriminator_fake_loss=1.315, generator_loss=28.98, generator_mel_loss=18.98, generator_kl_loss=1.299, generator_dur_loss=1.78, generator_adv_loss=1.937, generator_feat_match_loss=4.986, over 100.00 samples. +2024-03-15 05:49:13,246 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 05:50:07,828 INFO [train.py:919] (4/6) Start epoch 756 +2024-03-15 05:51:56,052 INFO [train.py:527] (4/6) Epoch 756, batch 30, global_batch_idx: 93650, batch size: 61, loss[discriminator_loss=2.589, discriminator_real_loss=1.256, discriminator_fake_loss=1.333, generator_loss=30.15, generator_mel_loss=18.04, generator_kl_loss=1.507, generator_dur_loss=1.653, generator_adv_loss=2.042, generator_feat_match_loss=6.91, over 61.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.346, discriminator_fake_loss=1.318, generator_loss=29.14, generator_mel_loss=17.96, generator_kl_loss=1.466, generator_dur_loss=1.722, generator_adv_loss=2.008, generator_feat_match_loss=5.985, over 1756.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:54:15,503 INFO [train.py:527] (4/6) Epoch 756, batch 80, global_batch_idx: 93700, batch size: 80, loss[discriminator_loss=2.722, discriminator_real_loss=1.475, discriminator_fake_loss=1.247, generator_loss=28.03, generator_mel_loss=17.67, generator_kl_loss=1.363, generator_dur_loss=1.805, generator_adv_loss=1.977, generator_feat_match_loss=5.218, over 80.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=28.86, generator_mel_loss=17.87, generator_kl_loss=1.434, generator_dur_loss=1.731, generator_adv_loss=1.997, generator_feat_match_loss=5.832, over 4628.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:56:12,938 INFO [train.py:919] (4/6) Start epoch 757 +2024-03-15 05:56:54,371 INFO [train.py:527] (4/6) Epoch 757, batch 6, global_batch_idx: 93750, batch size: 66, loss[discriminator_loss=2.652, discriminator_real_loss=1.379, discriminator_fake_loss=1.273, generator_loss=28.97, generator_mel_loss=18.22, generator_kl_loss=1.399, generator_dur_loss=1.781, generator_adv_loss=2.006, generator_feat_match_loss=5.559, over 66.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.367, discriminator_fake_loss=1.302, generator_loss=29.13, generator_mel_loss=18.02, generator_kl_loss=1.451, generator_dur_loss=1.714, generator_adv_loss=2.023, generator_feat_match_loss=5.921, over 347.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:59:17,031 INFO [train.py:527] (4/6) Epoch 757, batch 56, global_batch_idx: 93800, batch size: 58, loss[discriminator_loss=2.751, discriminator_real_loss=1.459, discriminator_fake_loss=1.291, generator_loss=28.26, generator_mel_loss=17.91, generator_kl_loss=1.46, generator_dur_loss=1.727, generator_adv_loss=1.954, generator_feat_match_loss=5.21, over 58.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.363, discriminator_fake_loss=1.326, generator_loss=28.88, generator_mel_loss=17.94, generator_kl_loss=1.421, generator_dur_loss=1.741, generator_adv_loss=1.992, generator_feat_match_loss=5.785, over 3310.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:59:17,032 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 05:59:25,148 INFO [train.py:591] (4/6) Epoch 757, validation: discriminator_loss=2.754, discriminator_real_loss=1.402, discriminator_fake_loss=1.353, generator_loss=28.61, generator_mel_loss=18.39, generator_kl_loss=1.35, generator_dur_loss=1.799, generator_adv_loss=1.918, generator_feat_match_loss=5.149, over 100.00 samples. +2024-03-15 05:59:25,149 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 06:01:45,085 INFO [train.py:527] (4/6) Epoch 757, batch 106, global_batch_idx: 93850, batch size: 26, loss[discriminator_loss=2.662, discriminator_real_loss=1.339, discriminator_fake_loss=1.323, generator_loss=31.2, generator_mel_loss=18.98, generator_kl_loss=1.85, generator_dur_loss=1.532, generator_adv_loss=2.108, generator_feat_match_loss=6.73, over 26.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.357, discriminator_fake_loss=1.33, generator_loss=28.87, generator_mel_loss=17.91, generator_kl_loss=1.415, generator_dur_loss=1.74, generator_adv_loss=2.003, generator_feat_match_loss=5.802, over 6165.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:02:29,021 INFO [train.py:919] (4/6) Start epoch 758 +2024-03-15 06:04:21,218 INFO [train.py:527] (4/6) Epoch 758, batch 32, global_batch_idx: 93900, batch size: 59, loss[discriminator_loss=2.667, discriminator_real_loss=1.362, discriminator_fake_loss=1.305, generator_loss=29.09, generator_mel_loss=17.77, generator_kl_loss=1.406, generator_dur_loss=1.711, generator_adv_loss=1.994, generator_feat_match_loss=6.205, over 59.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.361, discriminator_fake_loss=1.315, generator_loss=29.02, generator_mel_loss=17.93, generator_kl_loss=1.447, generator_dur_loss=1.712, generator_adv_loss=2.013, generator_feat_match_loss=5.91, over 1819.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:06:40,721 INFO [train.py:527] (4/6) Epoch 758, batch 82, global_batch_idx: 93950, batch size: 31, loss[discriminator_loss=2.71, discriminator_real_loss=1.491, discriminator_fake_loss=1.22, generator_loss=28.63, generator_mel_loss=18.6, generator_kl_loss=1.407, generator_dur_loss=1.594, generator_adv_loss=2.011, generator_feat_match_loss=5.019, over 31.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.91, generator_mel_loss=17.92, generator_kl_loss=1.424, generator_dur_loss=1.725, generator_adv_loss=2, generator_feat_match_loss=5.835, over 4781.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:08:35,723 INFO [train.py:919] (4/6) Start epoch 759 +2024-03-15 06:09:22,065 INFO [train.py:527] (4/6) Epoch 759, batch 8, global_batch_idx: 94000, batch size: 74, loss[discriminator_loss=2.701, discriminator_real_loss=1.35, discriminator_fake_loss=1.351, generator_loss=28.62, generator_mel_loss=17.61, generator_kl_loss=1.323, generator_dur_loss=1.775, generator_adv_loss=2.08, generator_feat_match_loss=5.834, over 74.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.34, discriminator_fake_loss=1.338, generator_loss=29.04, generator_mel_loss=17.77, generator_kl_loss=1.518, generator_dur_loss=1.676, generator_adv_loss=2.028, generator_feat_match_loss=6.044, over 429.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:09:22,069 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 06:09:29,728 INFO [train.py:591] (4/6) Epoch 759, validation: discriminator_loss=2.756, discriminator_real_loss=1.459, discriminator_fake_loss=1.296, generator_loss=28.04, generator_mel_loss=18.45, generator_kl_loss=1.287, generator_dur_loss=1.769, generator_adv_loss=1.976, generator_feat_match_loss=4.554, over 100.00 samples. +2024-03-15 06:09:29,729 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 06:11:48,991 INFO [train.py:527] (4/6) Epoch 759, batch 58, global_batch_idx: 94050, batch size: 39, loss[discriminator_loss=2.64, discriminator_real_loss=1.309, discriminator_fake_loss=1.332, generator_loss=30.41, generator_mel_loss=18.46, generator_kl_loss=1.558, generator_dur_loss=1.606, generator_adv_loss=2.091, generator_feat_match_loss=6.69, over 39.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=28.93, generator_mel_loss=17.84, generator_kl_loss=1.406, generator_dur_loss=1.736, generator_adv_loss=2.011, generator_feat_match_loss=5.937, over 3626.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:14:05,467 INFO [train.py:527] (4/6) Epoch 759, batch 108, global_batch_idx: 94100, batch size: 45, loss[discriminator_loss=2.738, discriminator_real_loss=1.399, discriminator_fake_loss=1.339, generator_loss=29, generator_mel_loss=18.15, generator_kl_loss=1.422, generator_dur_loss=1.7, generator_adv_loss=1.81, generator_feat_match_loss=5.919, over 45.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=28.92, generator_mel_loss=17.83, generator_kl_loss=1.416, generator_dur_loss=1.728, generator_adv_loss=2.006, generator_feat_match_loss=5.933, over 6432.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:14:46,915 INFO [train.py:919] (4/6) Start epoch 760 +2024-03-15 06:16:44,658 INFO [train.py:527] (4/6) Epoch 760, batch 34, global_batch_idx: 94150, batch size: 53, loss[discriminator_loss=2.683, discriminator_real_loss=1.327, discriminator_fake_loss=1.356, generator_loss=29.81, generator_mel_loss=18.76, generator_kl_loss=1.448, generator_dur_loss=1.646, generator_adv_loss=2.097, generator_feat_match_loss=5.867, over 53.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.371, discriminator_fake_loss=1.311, generator_loss=28.87, generator_mel_loss=17.93, generator_kl_loss=1.459, generator_dur_loss=1.693, generator_adv_loss=2.005, generator_feat_match_loss=5.784, over 1708.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:19:03,106 INFO [train.py:527] (4/6) Epoch 760, batch 84, global_batch_idx: 94200, batch size: 52, loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.321, generator_loss=28.9, generator_mel_loss=17.71, generator_kl_loss=1.652, generator_dur_loss=1.65, generator_adv_loss=2.029, generator_feat_match_loss=5.86, over 52.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.363, discriminator_fake_loss=1.32, generator_loss=28.85, generator_mel_loss=17.91, generator_kl_loss=1.45, generator_dur_loss=1.707, generator_adv_loss=1.995, generator_feat_match_loss=5.788, over 4383.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:19:03,107 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 06:19:12,124 INFO [train.py:591] (4/6) Epoch 760, validation: discriminator_loss=2.739, discriminator_real_loss=1.356, discriminator_fake_loss=1.383, generator_loss=27.28, generator_mel_loss=17.93, generator_kl_loss=1.275, generator_dur_loss=1.789, generator_adv_loss=1.87, generator_feat_match_loss=4.417, over 100.00 samples. +2024-03-15 06:19:12,125 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 06:21:03,630 INFO [train.py:919] (4/6) Start epoch 761 +2024-03-15 06:22:00,572 INFO [train.py:527] (4/6) Epoch 761, batch 10, global_batch_idx: 94250, batch size: 64, loss[discriminator_loss=2.709, discriminator_real_loss=1.413, discriminator_fake_loss=1.296, generator_loss=28.23, generator_mel_loss=17.6, generator_kl_loss=1.402, generator_dur_loss=1.771, generator_adv_loss=1.953, generator_feat_match_loss=5.504, over 64.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.376, discriminator_fake_loss=1.327, generator_loss=28.65, generator_mel_loss=17.83, generator_kl_loss=1.388, generator_dur_loss=1.75, generator_adv_loss=1.997, generator_feat_match_loss=5.688, over 675.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:24:17,488 INFO [train.py:527] (4/6) Epoch 761, batch 60, global_batch_idx: 94300, batch size: 52, loss[discriminator_loss=2.771, discriminator_real_loss=1.457, discriminator_fake_loss=1.314, generator_loss=26.64, generator_mel_loss=17.2, generator_kl_loss=1.209, generator_dur_loss=1.722, generator_adv_loss=1.939, generator_feat_match_loss=4.566, over 52.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.365, discriminator_fake_loss=1.315, generator_loss=28.73, generator_mel_loss=17.82, generator_kl_loss=1.418, generator_dur_loss=1.743, generator_adv_loss=2.009, generator_feat_match_loss=5.738, over 3417.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:26:33,148 INFO [train.py:527] (4/6) Epoch 761, batch 110, global_batch_idx: 94350, batch size: 31, loss[discriminator_loss=2.647, discriminator_real_loss=1.327, discriminator_fake_loss=1.32, generator_loss=30.56, generator_mel_loss=17.86, generator_kl_loss=1.657, generator_dur_loss=1.544, generator_adv_loss=2.064, generator_feat_match_loss=7.439, over 31.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.367, discriminator_fake_loss=1.318, generator_loss=28.81, generator_mel_loss=17.86, generator_kl_loss=1.424, generator_dur_loss=1.742, generator_adv_loss=2.007, generator_feat_match_loss=5.779, over 6083.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:27:12,162 INFO [train.py:919] (4/6) Start epoch 762 +2024-03-15 06:29:16,002 INFO [train.py:527] (4/6) Epoch 762, batch 36, global_batch_idx: 94400, batch size: 61, loss[discriminator_loss=2.665, discriminator_real_loss=1.321, discriminator_fake_loss=1.345, generator_loss=29.18, generator_mel_loss=17.46, generator_kl_loss=1.543, generator_dur_loss=1.768, generator_adv_loss=2.053, generator_feat_match_loss=6.356, over 61.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=28.91, generator_mel_loss=17.89, generator_kl_loss=1.409, generator_dur_loss=1.736, generator_adv_loss=1.994, generator_feat_match_loss=5.882, over 2050.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:29:16,004 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 06:29:24,052 INFO [train.py:591] (4/6) Epoch 762, validation: discriminator_loss=2.744, discriminator_real_loss=1.394, discriminator_fake_loss=1.35, generator_loss=27.27, generator_mel_loss=17.94, generator_kl_loss=1.246, generator_dur_loss=1.804, generator_adv_loss=1.849, generator_feat_match_loss=4.43, over 100.00 samples. +2024-03-15 06:29:24,053 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 06:31:41,075 INFO [train.py:527] (4/6) Epoch 762, batch 86, global_batch_idx: 94450, batch size: 62, loss[discriminator_loss=2.728, discriminator_real_loss=1.415, discriminator_fake_loss=1.313, generator_loss=28.82, generator_mel_loss=18.03, generator_kl_loss=1.393, generator_dur_loss=1.768, generator_adv_loss=1.811, generator_feat_match_loss=5.818, over 62.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.81, generator_mel_loss=17.9, generator_kl_loss=1.417, generator_dur_loss=1.729, generator_adv_loss=1.997, generator_feat_match_loss=5.765, over 4694.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:33:29,510 INFO [train.py:919] (4/6) Start epoch 763 +2024-03-15 06:34:26,582 INFO [train.py:527] (4/6) Epoch 763, batch 12, global_batch_idx: 94500, batch size: 31, loss[discriminator_loss=2.71, discriminator_real_loss=1.346, discriminator_fake_loss=1.364, generator_loss=28.45, generator_mel_loss=17.88, generator_kl_loss=1.511, generator_dur_loss=1.621, generator_adv_loss=2.014, generator_feat_match_loss=5.43, over 31.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.365, discriminator_fake_loss=1.329, generator_loss=28.56, generator_mel_loss=17.8, generator_kl_loss=1.419, generator_dur_loss=1.731, generator_adv_loss=1.978, generator_feat_match_loss=5.628, over 743.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:36:48,792 INFO [train.py:527] (4/6) Epoch 763, batch 62, global_batch_idx: 94550, batch size: 66, loss[discriminator_loss=2.679, discriminator_real_loss=1.389, discriminator_fake_loss=1.29, generator_loss=28.84, generator_mel_loss=18.02, generator_kl_loss=1.53, generator_dur_loss=1.809, generator_adv_loss=1.864, generator_feat_match_loss=5.617, over 66.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.84, generator_mel_loss=17.87, generator_kl_loss=1.411, generator_dur_loss=1.747, generator_adv_loss=1.991, generator_feat_match_loss=5.816, over 3645.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:39:08,289 INFO [train.py:527] (4/6) Epoch 763, batch 112, global_batch_idx: 94600, batch size: 52, loss[discriminator_loss=2.633, discriminator_real_loss=1.277, discriminator_fake_loss=1.356, generator_loss=29.8, generator_mel_loss=17.83, generator_kl_loss=1.54, generator_dur_loss=1.722, generator_adv_loss=1.922, generator_feat_match_loss=6.789, over 52.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.81, generator_mel_loss=17.88, generator_kl_loss=1.421, generator_dur_loss=1.745, generator_adv_loss=1.987, generator_feat_match_loss=5.777, over 6401.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:39:08,290 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 06:39:17,117 INFO [train.py:591] (4/6) Epoch 763, validation: discriminator_loss=2.725, discriminator_real_loss=1.361, discriminator_fake_loss=1.364, generator_loss=28.29, generator_mel_loss=18.46, generator_kl_loss=1.198, generator_dur_loss=1.825, generator_adv_loss=1.876, generator_feat_match_loss=4.932, over 100.00 samples. +2024-03-15 06:39:17,118 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 06:39:50,421 INFO [train.py:919] (4/6) Start epoch 764 +2024-03-15 06:42:01,015 INFO [train.py:527] (4/6) Epoch 764, batch 38, global_batch_idx: 94650, batch size: 59, loss[discriminator_loss=2.709, discriminator_real_loss=1.419, discriminator_fake_loss=1.29, generator_loss=29.03, generator_mel_loss=18.2, generator_kl_loss=1.411, generator_dur_loss=1.756, generator_adv_loss=2.017, generator_feat_match_loss=5.639, over 59.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.364, discriminator_fake_loss=1.325, generator_loss=28.76, generator_mel_loss=17.86, generator_kl_loss=1.403, generator_dur_loss=1.767, generator_adv_loss=1.985, generator_feat_match_loss=5.739, over 2236.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:44:20,454 INFO [train.py:527] (4/6) Epoch 764, batch 88, global_batch_idx: 94700, batch size: 66, loss[discriminator_loss=2.651, discriminator_real_loss=1.311, discriminator_fake_loss=1.34, generator_loss=29.01, generator_mel_loss=17.88, generator_kl_loss=1.342, generator_dur_loss=1.777, generator_adv_loss=1.936, generator_feat_match_loss=6.075, over 66.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.74, generator_mel_loss=17.88, generator_kl_loss=1.376, generator_dur_loss=1.774, generator_adv_loss=1.986, generator_feat_match_loss=5.72, over 5356.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:45:59,250 INFO [train.py:919] (4/6) Start epoch 765 +2024-03-15 06:47:02,917 INFO [train.py:527] (4/6) Epoch 765, batch 14, global_batch_idx: 94750, batch size: 61, loss[discriminator_loss=2.649, discriminator_real_loss=1.318, discriminator_fake_loss=1.33, generator_loss=29.17, generator_mel_loss=18.24, generator_kl_loss=1.454, generator_dur_loss=1.77, generator_adv_loss=2.026, generator_feat_match_loss=5.676, over 61.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.338, discriminator_fake_loss=1.326, generator_loss=29.09, generator_mel_loss=18.1, generator_kl_loss=1.472, generator_dur_loss=1.727, generator_adv_loss=2.003, generator_feat_match_loss=5.782, over 763.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:49:20,869 INFO [train.py:527] (4/6) Epoch 765, batch 64, global_batch_idx: 94800, batch size: 48, loss[discriminator_loss=2.765, discriminator_real_loss=1.404, discriminator_fake_loss=1.362, generator_loss=27.46, generator_mel_loss=17.48, generator_kl_loss=1.508, generator_dur_loss=1.685, generator_adv_loss=1.932, generator_feat_match_loss=4.86, over 48.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.324, generator_loss=28.96, generator_mel_loss=17.92, generator_kl_loss=1.453, generator_dur_loss=1.732, generator_adv_loss=1.998, generator_feat_match_loss=5.859, over 3513.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:49:20,870 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 06:49:28,784 INFO [train.py:591] (4/6) Epoch 765, validation: discriminator_loss=2.703, discriminator_real_loss=1.356, discriminator_fake_loss=1.347, generator_loss=27.22, generator_mel_loss=18.13, generator_kl_loss=1.24, generator_dur_loss=1.814, generator_adv_loss=1.827, generator_feat_match_loss=4.21, over 100.00 samples. +2024-03-15 06:49:28,785 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 06:51:46,888 INFO [train.py:527] (4/6) Epoch 765, batch 114, global_batch_idx: 94850, batch size: 72, loss[discriminator_loss=2.767, discriminator_real_loss=1.52, discriminator_fake_loss=1.247, generator_loss=28.33, generator_mel_loss=17.78, generator_kl_loss=1.299, generator_dur_loss=1.827, generator_adv_loss=1.863, generator_feat_match_loss=5.566, over 72.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.323, generator_loss=28.93, generator_mel_loss=17.9, generator_kl_loss=1.436, generator_dur_loss=1.74, generator_adv_loss=2.005, generator_feat_match_loss=5.848, over 6276.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:52:15,631 INFO [train.py:919] (4/6) Start epoch 766 +2024-03-15 06:54:37,646 INFO [train.py:527] (4/6) Epoch 766, batch 40, global_batch_idx: 94900, batch size: 70, loss[discriminator_loss=2.643, discriminator_real_loss=1.346, discriminator_fake_loss=1.297, generator_loss=28.63, generator_mel_loss=17.71, generator_kl_loss=1.399, generator_dur_loss=1.775, generator_adv_loss=2.042, generator_feat_match_loss=5.709, over 70.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.346, discriminator_fake_loss=1.328, generator_loss=29.03, generator_mel_loss=17.89, generator_kl_loss=1.388, generator_dur_loss=1.766, generator_adv_loss=1.994, generator_feat_match_loss=5.989, over 2528.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:56:57,340 INFO [train.py:527] (4/6) Epoch 766, batch 90, global_batch_idx: 94950, batch size: 52, loss[discriminator_loss=2.694, discriminator_real_loss=1.369, discriminator_fake_loss=1.325, generator_loss=29.18, generator_mel_loss=17.84, generator_kl_loss=1.518, generator_dur_loss=1.688, generator_adv_loss=1.948, generator_feat_match_loss=6.194, over 52.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.359, discriminator_fake_loss=1.321, generator_loss=28.96, generator_mel_loss=17.88, generator_kl_loss=1.39, generator_dur_loss=1.763, generator_adv_loss=2.001, generator_feat_match_loss=5.922, over 5478.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:58:29,127 INFO [train.py:919] (4/6) Start epoch 767 +2024-03-15 06:59:38,292 INFO [train.py:527] (4/6) Epoch 767, batch 16, global_batch_idx: 95000, batch size: 62, loss[discriminator_loss=2.672, discriminator_real_loss=1.338, discriminator_fake_loss=1.334, generator_loss=28.77, generator_mel_loss=17.54, generator_kl_loss=1.407, generator_dur_loss=1.775, generator_adv_loss=1.964, generator_feat_match_loss=6.091, over 62.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.33, generator_loss=29.06, generator_mel_loss=17.96, generator_kl_loss=1.437, generator_dur_loss=1.728, generator_adv_loss=1.981, generator_feat_match_loss=5.949, over 931.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:59:38,294 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 06:59:46,427 INFO [train.py:591] (4/6) Epoch 767, validation: discriminator_loss=2.728, discriminator_real_loss=1.395, discriminator_fake_loss=1.333, generator_loss=28.28, generator_mel_loss=18.79, generator_kl_loss=1.264, generator_dur_loss=1.824, generator_adv_loss=1.876, generator_feat_match_loss=4.527, over 100.00 samples. +2024-03-15 06:59:46,428 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 07:02:10,557 INFO [train.py:527] (4/6) Epoch 767, batch 66, global_batch_idx: 95050, batch size: 31, loss[discriminator_loss=2.669, discriminator_real_loss=1.319, discriminator_fake_loss=1.351, generator_loss=28.44, generator_mel_loss=17.54, generator_kl_loss=1.604, generator_dur_loss=1.624, generator_adv_loss=2.095, generator_feat_match_loss=5.574, over 31.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=28.9, generator_mel_loss=17.88, generator_kl_loss=1.406, generator_dur_loss=1.747, generator_adv_loss=1.994, generator_feat_match_loss=5.878, over 3855.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:04:29,917 INFO [train.py:527] (4/6) Epoch 767, batch 116, global_batch_idx: 95100, batch size: 25, loss[discriminator_loss=2.741, discriminator_real_loss=1.297, discriminator_fake_loss=1.444, generator_loss=28.5, generator_mel_loss=18.11, generator_kl_loss=1.512, generator_dur_loss=1.565, generator_adv_loss=2.066, generator_feat_match_loss=5.248, over 25.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.357, discriminator_fake_loss=1.323, generator_loss=28.87, generator_mel_loss=17.86, generator_kl_loss=1.403, generator_dur_loss=1.751, generator_adv_loss=1.994, generator_feat_match_loss=5.865, over 6822.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:04:49,892 INFO [train.py:919] (4/6) Start epoch 768 +2024-03-15 07:07:12,334 INFO [train.py:527] (4/6) Epoch 768, batch 42, global_batch_idx: 95150, batch size: 45, loss[discriminator_loss=2.592, discriminator_real_loss=1.244, discriminator_fake_loss=1.348, generator_loss=30.58, generator_mel_loss=18.25, generator_kl_loss=1.647, generator_dur_loss=1.656, generator_adv_loss=2.133, generator_feat_match_loss=6.894, over 45.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=28.89, generator_mel_loss=17.85, generator_kl_loss=1.455, generator_dur_loss=1.736, generator_adv_loss=2.005, generator_feat_match_loss=5.841, over 2409.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:09:33,684 INFO [train.py:527] (4/6) Epoch 768, batch 92, global_batch_idx: 95200, batch size: 66, loss[discriminator_loss=2.692, discriminator_real_loss=1.39, discriminator_fake_loss=1.302, generator_loss=28.81, generator_mel_loss=18.06, generator_kl_loss=1.507, generator_dur_loss=1.786, generator_adv_loss=2.109, generator_feat_match_loss=5.349, over 66.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=29, generator_mel_loss=17.85, generator_kl_loss=1.427, generator_dur_loss=1.747, generator_adv_loss=2.028, generator_feat_match_loss=5.949, over 5396.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:09:33,685 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 07:09:42,415 INFO [train.py:591] (4/6) Epoch 768, validation: discriminator_loss=2.752, discriminator_real_loss=1.483, discriminator_fake_loss=1.269, generator_loss=27.97, generator_mel_loss=18.22, generator_kl_loss=1.299, generator_dur_loss=1.825, generator_adv_loss=2.032, generator_feat_match_loss=4.6, over 100.00 samples. +2024-03-15 07:09:42,416 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 07:11:12,404 INFO [train.py:919] (4/6) Start epoch 769 +2024-03-15 07:12:30,244 INFO [train.py:527] (4/6) Epoch 769, batch 18, global_batch_idx: 95250, batch size: 52, loss[discriminator_loss=2.736, discriminator_real_loss=1.296, discriminator_fake_loss=1.44, generator_loss=29, generator_mel_loss=17.93, generator_kl_loss=1.495, generator_dur_loss=1.703, generator_adv_loss=2.057, generator_feat_match_loss=5.816, over 52.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.371, discriminator_fake_loss=1.325, generator_loss=28.83, generator_mel_loss=17.88, generator_kl_loss=1.395, generator_dur_loss=1.735, generator_adv_loss=1.993, generator_feat_match_loss=5.827, over 1039.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:14:48,428 INFO [train.py:527] (4/6) Epoch 769, batch 68, global_batch_idx: 95300, batch size: 44, loss[discriminator_loss=2.659, discriminator_real_loss=1.372, discriminator_fake_loss=1.288, generator_loss=30.53, generator_mel_loss=18.28, generator_kl_loss=1.627, generator_dur_loss=1.632, generator_adv_loss=1.929, generator_feat_match_loss=7.064, over 44.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.363, discriminator_fake_loss=1.33, generator_loss=28.89, generator_mel_loss=17.93, generator_kl_loss=1.417, generator_dur_loss=1.741, generator_adv_loss=1.991, generator_feat_match_loss=5.811, over 3820.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:17:09,069 INFO [train.py:527] (4/6) Epoch 769, batch 118, global_batch_idx: 95350, batch size: 72, loss[discriminator_loss=2.736, discriminator_real_loss=1.296, discriminator_fake_loss=1.44, generator_loss=28.62, generator_mel_loss=17.75, generator_kl_loss=1.388, generator_dur_loss=1.824, generator_adv_loss=2.235, generator_feat_match_loss=5.419, over 72.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.91, generator_mel_loss=17.9, generator_kl_loss=1.413, generator_dur_loss=1.753, generator_adv_loss=1.997, generator_feat_match_loss=5.838, over 6746.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:17:24,709 INFO [train.py:919] (4/6) Start epoch 770 +2024-03-15 07:19:54,030 INFO [train.py:527] (4/6) Epoch 770, batch 44, global_batch_idx: 95400, batch size: 83, loss[discriminator_loss=2.696, discriminator_real_loss=1.414, discriminator_fake_loss=1.281, generator_loss=28.96, generator_mel_loss=18.13, generator_kl_loss=1.382, generator_dur_loss=1.814, generator_adv_loss=1.816, generator_feat_match_loss=5.82, over 83.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.365, discriminator_fake_loss=1.33, generator_loss=28.9, generator_mel_loss=17.93, generator_kl_loss=1.414, generator_dur_loss=1.759, generator_adv_loss=1.977, generator_feat_match_loss=5.815, over 2724.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:19:54,031 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 07:20:02,102 INFO [train.py:591] (4/6) Epoch 770, validation: discriminator_loss=2.748, discriminator_real_loss=1.255, discriminator_fake_loss=1.493, generator_loss=27.08, generator_mel_loss=18.05, generator_kl_loss=1.151, generator_dur_loss=1.836, generator_adv_loss=1.717, generator_feat_match_loss=4.324, over 100.00 samples. +2024-03-15 07:20:02,103 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 07:22:21,259 INFO [train.py:527] (4/6) Epoch 770, batch 94, global_batch_idx: 95450, batch size: 88, loss[discriminator_loss=2.684, discriminator_real_loss=1.335, discriminator_fake_loss=1.349, generator_loss=28.96, generator_mel_loss=17.82, generator_kl_loss=1.404, generator_dur_loss=1.831, generator_adv_loss=2.013, generator_feat_match_loss=5.898, over 88.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.326, generator_loss=28.85, generator_mel_loss=17.9, generator_kl_loss=1.414, generator_dur_loss=1.763, generator_adv_loss=1.984, generator_feat_match_loss=5.79, over 5703.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:23:43,977 INFO [train.py:919] (4/6) Start epoch 771 +2024-03-15 07:25:07,404 INFO [train.py:527] (4/6) Epoch 771, batch 20, global_batch_idx: 95500, batch size: 83, loss[discriminator_loss=2.638, discriminator_real_loss=1.322, discriminator_fake_loss=1.317, generator_loss=29.19, generator_mel_loss=18.07, generator_kl_loss=1.394, generator_dur_loss=1.82, generator_adv_loss=1.909, generator_feat_match_loss=5.994, over 83.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.356, discriminator_fake_loss=1.314, generator_loss=28.8, generator_mel_loss=17.77, generator_kl_loss=1.39, generator_dur_loss=1.778, generator_adv_loss=1.997, generator_feat_match_loss=5.87, over 1344.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:27:27,453 INFO [train.py:527] (4/6) Epoch 771, batch 70, global_batch_idx: 95550, batch size: 31, loss[discriminator_loss=2.684, discriminator_real_loss=1.37, discriminator_fake_loss=1.314, generator_loss=28.98, generator_mel_loss=18.04, generator_kl_loss=1.503, generator_dur_loss=1.68, generator_adv_loss=1.955, generator_feat_match_loss=5.804, over 31.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.362, discriminator_fake_loss=1.316, generator_loss=28.98, generator_mel_loss=17.91, generator_kl_loss=1.41, generator_dur_loss=1.756, generator_adv_loss=2.001, generator_feat_match_loss=5.907, over 4068.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:29:46,217 INFO [train.py:527] (4/6) Epoch 771, batch 120, global_batch_idx: 95600, batch size: 42, loss[discriminator_loss=2.648, discriminator_real_loss=1.345, discriminator_fake_loss=1.302, generator_loss=28.84, generator_mel_loss=17.62, generator_kl_loss=1.505, generator_dur_loss=1.658, generator_adv_loss=1.97, generator_feat_match_loss=6.086, over 42.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.358, discriminator_fake_loss=1.321, generator_loss=28.98, generator_mel_loss=17.92, generator_kl_loss=1.416, generator_dur_loss=1.752, generator_adv_loss=1.995, generator_feat_match_loss=5.898, over 6918.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:29:46,218 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 07:29:54,766 INFO [train.py:591] (4/6) Epoch 771, validation: discriminator_loss=2.72, discriminator_real_loss=1.362, discriminator_fake_loss=1.358, generator_loss=27.5, generator_mel_loss=18, generator_kl_loss=1.266, generator_dur_loss=1.818, generator_adv_loss=1.857, generator_feat_match_loss=4.555, over 100.00 samples. +2024-03-15 07:29:54,767 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 07:30:05,309 INFO [train.py:919] (4/6) Start epoch 772 +2024-03-15 07:32:36,063 INFO [train.py:527] (4/6) Epoch 772, batch 46, global_batch_idx: 95650, batch size: 88, loss[discriminator_loss=2.587, discriminator_real_loss=1.277, discriminator_fake_loss=1.31, generator_loss=29.19, generator_mel_loss=17.83, generator_kl_loss=1.309, generator_dur_loss=1.827, generator_adv_loss=1.955, generator_feat_match_loss=6.271, over 88.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.357, discriminator_fake_loss=1.315, generator_loss=28.87, generator_mel_loss=17.8, generator_kl_loss=1.396, generator_dur_loss=1.755, generator_adv_loss=2.028, generator_feat_match_loss=5.882, over 2836.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:34:53,251 INFO [train.py:527] (4/6) Epoch 772, batch 96, global_batch_idx: 95700, batch size: 42, loss[discriminator_loss=2.7, discriminator_real_loss=1.387, discriminator_fake_loss=1.313, generator_loss=28.67, generator_mel_loss=17.64, generator_kl_loss=1.466, generator_dur_loss=1.628, generator_adv_loss=2.073, generator_feat_match_loss=5.856, over 42.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.357, discriminator_fake_loss=1.318, generator_loss=28.91, generator_mel_loss=17.85, generator_kl_loss=1.412, generator_dur_loss=1.748, generator_adv_loss=2.016, generator_feat_match_loss=5.887, over 5740.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:36:04,906 INFO [train.py:919] (4/6) Start epoch 773 +2024-03-15 07:37:29,978 INFO [train.py:527] (4/6) Epoch 773, batch 22, global_batch_idx: 95750, batch size: 48, loss[discriminator_loss=2.624, discriminator_real_loss=1.31, discriminator_fake_loss=1.314, generator_loss=29.92, generator_mel_loss=18.12, generator_kl_loss=1.503, generator_dur_loss=1.654, generator_adv_loss=2.128, generator_feat_match_loss=6.513, over 48.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.358, discriminator_fake_loss=1.334, generator_loss=28.81, generator_mel_loss=17.87, generator_kl_loss=1.421, generator_dur_loss=1.731, generator_adv_loss=1.99, generator_feat_match_loss=5.797, over 1299.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:39:47,974 INFO [train.py:527] (4/6) Epoch 773, batch 72, global_batch_idx: 95800, batch size: 55, loss[discriminator_loss=2.604, discriminator_real_loss=1.377, discriminator_fake_loss=1.227, generator_loss=29.38, generator_mel_loss=17.88, generator_kl_loss=1.349, generator_dur_loss=1.726, generator_adv_loss=2.104, generator_feat_match_loss=6.319, over 55.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.352, discriminator_fake_loss=1.331, generator_loss=28.98, generator_mel_loss=17.93, generator_kl_loss=1.44, generator_dur_loss=1.723, generator_adv_loss=1.991, generator_feat_match_loss=5.897, over 3882.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:39:47,975 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 07:39:56,232 INFO [train.py:591] (4/6) Epoch 773, validation: discriminator_loss=2.719, discriminator_real_loss=1.421, discriminator_fake_loss=1.298, generator_loss=27.94, generator_mel_loss=18.55, generator_kl_loss=1.249, generator_dur_loss=1.8, generator_adv_loss=1.939, generator_feat_match_loss=4.4, over 100.00 samples. +2024-03-15 07:39:56,233 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 07:42:19,719 INFO [train.py:527] (4/6) Epoch 773, batch 122, global_batch_idx: 95850, batch size: 47, loss[discriminator_loss=2.742, discriminator_real_loss=1.479, discriminator_fake_loss=1.263, generator_loss=27.74, generator_mel_loss=17.51, generator_kl_loss=1.485, generator_dur_loss=1.678, generator_adv_loss=1.968, generator_feat_match_loss=5.1, over 47.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.354, discriminator_fake_loss=1.33, generator_loss=28.92, generator_mel_loss=17.89, generator_kl_loss=1.434, generator_dur_loss=1.737, generator_adv_loss=1.995, generator_feat_match_loss=5.867, over 6677.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:42:24,477 INFO [train.py:919] (4/6) Start epoch 774 +2024-03-15 07:45:02,318 INFO [train.py:527] (4/6) Epoch 774, batch 48, global_batch_idx: 95900, batch size: 80, loss[discriminator_loss=2.625, discriminator_real_loss=1.369, discriminator_fake_loss=1.256, generator_loss=29.78, generator_mel_loss=18.21, generator_kl_loss=1.367, generator_dur_loss=1.797, generator_adv_loss=2.092, generator_feat_match_loss=6.315, over 80.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=28.75, generator_mel_loss=17.86, generator_kl_loss=1.381, generator_dur_loss=1.76, generator_adv_loss=1.984, generator_feat_match_loss=5.769, over 2871.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:47:22,477 INFO [train.py:527] (4/6) Epoch 774, batch 98, global_batch_idx: 95950, batch size: 58, loss[discriminator_loss=2.735, discriminator_real_loss=1.249, discriminator_fake_loss=1.486, generator_loss=28.12, generator_mel_loss=17.57, generator_kl_loss=1.355, generator_dur_loss=1.7, generator_adv_loss=2.238, generator_feat_match_loss=5.265, over 58.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=28.84, generator_mel_loss=17.85, generator_kl_loss=1.391, generator_dur_loss=1.754, generator_adv_loss=1.996, generator_feat_match_loss=5.848, over 5628.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:48:32,653 INFO [train.py:919] (4/6) Start epoch 775 +2024-03-15 07:50:03,361 INFO [train.py:527] (4/6) Epoch 775, batch 24, global_batch_idx: 96000, batch size: 52, loss[discriminator_loss=2.69, discriminator_real_loss=1.337, discriminator_fake_loss=1.354, generator_loss=28.76, generator_mel_loss=17.88, generator_kl_loss=1.42, generator_dur_loss=1.726, generator_adv_loss=2.024, generator_feat_match_loss=5.713, over 52.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.362, discriminator_fake_loss=1.332, generator_loss=28.82, generator_mel_loss=17.87, generator_kl_loss=1.463, generator_dur_loss=1.724, generator_adv_loss=1.979, generator_feat_match_loss=5.789, over 1456.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:50:03,362 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 07:50:11,376 INFO [train.py:591] (4/6) Epoch 775, validation: discriminator_loss=2.773, discriminator_real_loss=1.462, discriminator_fake_loss=1.31, generator_loss=28.08, generator_mel_loss=18.36, generator_kl_loss=1.192, generator_dur_loss=1.785, generator_adv_loss=1.999, generator_feat_match_loss=4.746, over 100.00 samples. +2024-03-15 07:50:11,377 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 07:52:32,673 INFO [train.py:527] (4/6) Epoch 775, batch 74, global_batch_idx: 96050, batch size: 25, loss[discriminator_loss=2.644, discriminator_real_loss=1.403, discriminator_fake_loss=1.241, generator_loss=29.76, generator_mel_loss=18.22, generator_kl_loss=1.746, generator_dur_loss=1.49, generator_adv_loss=1.976, generator_feat_match_loss=6.324, over 25.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.321, generator_loss=28.81, generator_mel_loss=17.83, generator_kl_loss=1.43, generator_dur_loss=1.733, generator_adv_loss=1.995, generator_feat_match_loss=5.826, over 4385.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:54:47,020 INFO [train.py:919] (4/6) Start epoch 776 +2024-03-15 07:55:09,615 INFO [train.py:527] (4/6) Epoch 776, batch 0, global_batch_idx: 96100, batch size: 58, loss[discriminator_loss=2.789, discriminator_real_loss=1.491, discriminator_fake_loss=1.298, generator_loss=29.5, generator_mel_loss=18.44, generator_kl_loss=1.584, generator_dur_loss=1.669, generator_adv_loss=1.939, generator_feat_match_loss=5.867, over 58.00 samples.], tot_loss[discriminator_loss=2.789, discriminator_real_loss=1.491, discriminator_fake_loss=1.298, generator_loss=29.5, generator_mel_loss=18.44, generator_kl_loss=1.584, generator_dur_loss=1.669, generator_adv_loss=1.939, generator_feat_match_loss=5.867, over 58.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:57:27,816 INFO [train.py:527] (4/6) Epoch 776, batch 50, global_batch_idx: 96150, batch size: 61, loss[discriminator_loss=2.73, discriminator_real_loss=1.418, discriminator_fake_loss=1.311, generator_loss=28.72, generator_mel_loss=17.99, generator_kl_loss=1.522, generator_dur_loss=1.64, generator_adv_loss=2.015, generator_feat_match_loss=5.55, over 61.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.369, discriminator_fake_loss=1.333, generator_loss=28.91, generator_mel_loss=17.88, generator_kl_loss=1.45, generator_dur_loss=1.726, generator_adv_loss=1.999, generator_feat_match_loss=5.852, over 2937.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:59:46,358 INFO [train.py:527] (4/6) Epoch 776, batch 100, global_batch_idx: 96200, batch size: 70, loss[discriminator_loss=2.655, discriminator_real_loss=1.245, discriminator_fake_loss=1.41, generator_loss=28.18, generator_mel_loss=17.63, generator_kl_loss=1.337, generator_dur_loss=1.798, generator_adv_loss=2.085, generator_feat_match_loss=5.322, over 70.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.364, discriminator_fake_loss=1.33, generator_loss=28.86, generator_mel_loss=17.87, generator_kl_loss=1.443, generator_dur_loss=1.725, generator_adv_loss=1.994, generator_feat_match_loss=5.821, over 5711.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:59:46,360 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 07:59:55,169 INFO [train.py:591] (4/6) Epoch 776, validation: discriminator_loss=2.694, discriminator_real_loss=1.449, discriminator_fake_loss=1.245, generator_loss=27.8, generator_mel_loss=17.95, generator_kl_loss=1.386, generator_dur_loss=1.784, generator_adv_loss=2.037, generator_feat_match_loss=4.642, over 100.00 samples. +2024-03-15 07:59:55,170 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 08:01:00,602 INFO [train.py:919] (4/6) Start epoch 777 +2024-03-15 08:02:38,113 INFO [train.py:527] (4/6) Epoch 777, batch 26, global_batch_idx: 96250, batch size: 56, loss[discriminator_loss=2.728, discriminator_real_loss=1.41, discriminator_fake_loss=1.318, generator_loss=28.09, generator_mel_loss=17.85, generator_kl_loss=1.385, generator_dur_loss=1.7, generator_adv_loss=2.051, generator_feat_match_loss=5.105, over 56.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.353, discriminator_fake_loss=1.326, generator_loss=28.81, generator_mel_loss=17.8, generator_kl_loss=1.463, generator_dur_loss=1.733, generator_adv_loss=2.005, generator_feat_match_loss=5.812, over 1624.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 08:04:57,619 INFO [train.py:527] (4/6) Epoch 777, batch 76, global_batch_idx: 96300, batch size: 58, loss[discriminator_loss=2.652, discriminator_real_loss=1.355, discriminator_fake_loss=1.297, generator_loss=28.61, generator_mel_loss=17.91, generator_kl_loss=1.36, generator_dur_loss=1.716, generator_adv_loss=1.919, generator_feat_match_loss=5.705, over 58.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.83, generator_mel_loss=17.8, generator_kl_loss=1.445, generator_dur_loss=1.724, generator_adv_loss=2.009, generator_feat_match_loss=5.852, over 4496.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 08:07:07,820 INFO [train.py:919] (4/6) Start epoch 778 +2024-03-15 08:07:37,267 INFO [train.py:527] (4/6) Epoch 778, batch 2, global_batch_idx: 96350, batch size: 80, loss[discriminator_loss=2.756, discriminator_real_loss=1.369, discriminator_fake_loss=1.388, generator_loss=28.66, generator_mel_loss=17.91, generator_kl_loss=1.465, generator_dur_loss=1.818, generator_adv_loss=2.008, generator_feat_match_loss=5.455, over 80.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.358, discriminator_fake_loss=1.354, generator_loss=29.19, generator_mel_loss=18.06, generator_kl_loss=1.417, generator_dur_loss=1.824, generator_adv_loss=2.01, generator_feat_match_loss=5.879, over 207.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:09:57,651 INFO [train.py:527] (4/6) Epoch 778, batch 52, global_batch_idx: 96400, batch size: 50, loss[discriminator_loss=2.791, discriminator_real_loss=1.459, discriminator_fake_loss=1.332, generator_loss=29.44, generator_mel_loss=18.26, generator_kl_loss=1.442, generator_dur_loss=1.711, generator_adv_loss=1.858, generator_feat_match_loss=6.174, over 50.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.331, generator_loss=28.94, generator_mel_loss=17.98, generator_kl_loss=1.396, generator_dur_loss=1.731, generator_adv_loss=1.998, generator_feat_match_loss=5.841, over 3031.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:09:57,653 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 08:10:05,729 INFO [train.py:591] (4/6) Epoch 778, validation: discriminator_loss=2.767, discriminator_real_loss=1.43, discriminator_fake_loss=1.337, generator_loss=26.99, generator_mel_loss=17.76, generator_kl_loss=1.265, generator_dur_loss=1.784, generator_adv_loss=1.821, generator_feat_match_loss=4.354, over 100.00 samples. +2024-03-15 08:10:05,730 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 08:12:23,783 INFO [train.py:527] (4/6) Epoch 778, batch 102, global_batch_idx: 96450, batch size: 45, loss[discriminator_loss=2.711, discriminator_real_loss=1.393, discriminator_fake_loss=1.318, generator_loss=28.18, generator_mel_loss=18.57, generator_kl_loss=1.553, generator_dur_loss=1.655, generator_adv_loss=1.945, generator_feat_match_loss=4.465, over 45.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.365, discriminator_fake_loss=1.333, generator_loss=28.86, generator_mel_loss=17.92, generator_kl_loss=1.411, generator_dur_loss=1.735, generator_adv_loss=1.997, generator_feat_match_loss=5.791, over 5945.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:13:21,131 INFO [train.py:919] (4/6) Start epoch 779 +2024-03-15 08:15:01,045 INFO [train.py:527] (4/6) Epoch 779, batch 28, global_batch_idx: 96500, batch size: 53, loss[discriminator_loss=2.725, discriminator_real_loss=1.462, discriminator_fake_loss=1.262, generator_loss=28.56, generator_mel_loss=18.33, generator_kl_loss=1.345, generator_dur_loss=1.634, generator_adv_loss=1.913, generator_feat_match_loss=5.331, over 53.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.368, discriminator_fake_loss=1.331, generator_loss=29.02, generator_mel_loss=18.05, generator_kl_loss=1.422, generator_dur_loss=1.711, generator_adv_loss=1.986, generator_feat_match_loss=5.854, over 1602.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:17:20,554 INFO [train.py:527] (4/6) Epoch 779, batch 78, global_batch_idx: 96550, batch size: 45, loss[discriminator_loss=2.67, discriminator_real_loss=1.409, discriminator_fake_loss=1.261, generator_loss=29.04, generator_mel_loss=17.95, generator_kl_loss=1.499, generator_dur_loss=1.646, generator_adv_loss=2.055, generator_feat_match_loss=5.895, over 45.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.323, generator_loss=28.91, generator_mel_loss=17.9, generator_kl_loss=1.421, generator_dur_loss=1.722, generator_adv_loss=2.002, generator_feat_match_loss=5.864, over 4529.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:19:30,987 INFO [train.py:919] (4/6) Start epoch 780 +2024-03-15 08:20:06,439 INFO [train.py:527] (4/6) Epoch 780, batch 4, global_batch_idx: 96600, batch size: 36, loss[discriminator_loss=2.677, discriminator_real_loss=1.413, discriminator_fake_loss=1.265, generator_loss=28.98, generator_mel_loss=17.59, generator_kl_loss=1.396, generator_dur_loss=1.675, generator_adv_loss=2.067, generator_feat_match_loss=6.261, over 36.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.382, discriminator_fake_loss=1.31, generator_loss=28.56, generator_mel_loss=17.77, generator_kl_loss=1.45, generator_dur_loss=1.711, generator_adv_loss=1.975, generator_feat_match_loss=5.653, over 240.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:20:06,442 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 08:20:14,315 INFO [train.py:591] (4/6) Epoch 780, validation: discriminator_loss=2.714, discriminator_real_loss=1.425, discriminator_fake_loss=1.289, generator_loss=27.68, generator_mel_loss=18.14, generator_kl_loss=1.341, generator_dur_loss=1.807, generator_adv_loss=1.923, generator_feat_match_loss=4.46, over 100.00 samples. +2024-03-15 08:20:14,318 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 08:22:32,337 INFO [train.py:527] (4/6) Epoch 780, batch 54, global_batch_idx: 96650, batch size: 70, loss[discriminator_loss=2.678, discriminator_real_loss=1.381, discriminator_fake_loss=1.297, generator_loss=28.58, generator_mel_loss=17.61, generator_kl_loss=1.333, generator_dur_loss=1.782, generator_adv_loss=1.968, generator_feat_match_loss=5.888, over 70.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.352, discriminator_fake_loss=1.327, generator_loss=28.92, generator_mel_loss=17.85, generator_kl_loss=1.427, generator_dur_loss=1.737, generator_adv_loss=1.979, generator_feat_match_loss=5.932, over 3034.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:24:52,715 INFO [train.py:527] (4/6) Epoch 780, batch 104, global_batch_idx: 96700, batch size: 39, loss[discriminator_loss=2.703, discriminator_real_loss=1.348, discriminator_fake_loss=1.354, generator_loss=28.97, generator_mel_loss=17.89, generator_kl_loss=1.391, generator_dur_loss=1.654, generator_adv_loss=1.907, generator_feat_match_loss=6.137, over 39.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.348, discriminator_fake_loss=1.325, generator_loss=29.04, generator_mel_loss=17.91, generator_kl_loss=1.423, generator_dur_loss=1.739, generator_adv_loss=1.99, generator_feat_match_loss=5.976, over 5951.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:25:47,368 INFO [train.py:919] (4/6) Start epoch 781 +2024-03-15 08:27:38,890 INFO [train.py:527] (4/6) Epoch 781, batch 30, global_batch_idx: 96750, batch size: 31, loss[discriminator_loss=2.7, discriminator_real_loss=1.344, discriminator_fake_loss=1.356, generator_loss=29.49, generator_mel_loss=18.01, generator_kl_loss=1.67, generator_dur_loss=1.597, generator_adv_loss=2.059, generator_feat_match_loss=6.155, over 31.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=28.83, generator_mel_loss=17.84, generator_kl_loss=1.423, generator_dur_loss=1.742, generator_adv_loss=1.984, generator_feat_match_loss=5.842, over 1906.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:29:55,244 INFO [train.py:527] (4/6) Epoch 781, batch 80, global_batch_idx: 96800, batch size: 68, loss[discriminator_loss=2.66, discriminator_real_loss=1.284, discriminator_fake_loss=1.377, generator_loss=28.26, generator_mel_loss=17.42, generator_kl_loss=1.379, generator_dur_loss=1.742, generator_adv_loss=2.074, generator_feat_match_loss=5.644, over 68.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=28.74, generator_mel_loss=17.81, generator_kl_loss=1.422, generator_dur_loss=1.74, generator_adv_loss=1.984, generator_feat_match_loss=5.785, over 4845.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:29:55,245 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 08:30:03,854 INFO [train.py:591] (4/6) Epoch 781, validation: discriminator_loss=2.686, discriminator_real_loss=1.39, discriminator_fake_loss=1.296, generator_loss=28.29, generator_mel_loss=18.24, generator_kl_loss=1.335, generator_dur_loss=1.794, generator_adv_loss=1.944, generator_feat_match_loss=4.978, over 100.00 samples. +2024-03-15 08:30:03,855 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 08:32:07,101 INFO [train.py:919] (4/6) Start epoch 782 +2024-03-15 08:32:49,275 INFO [train.py:527] (4/6) Epoch 782, batch 6, global_batch_idx: 96850, batch size: 48, loss[discriminator_loss=2.619, discriminator_real_loss=1.267, discriminator_fake_loss=1.352, generator_loss=31.71, generator_mel_loss=18.7, generator_kl_loss=1.645, generator_dur_loss=1.642, generator_adv_loss=2.178, generator_feat_match_loss=7.546, over 48.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.16, generator_mel_loss=17.83, generator_kl_loss=1.465, generator_dur_loss=1.746, generator_adv_loss=2.016, generator_feat_match_loss=6.099, over 414.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:35:10,459 INFO [train.py:527] (4/6) Epoch 782, batch 56, global_batch_idx: 96900, batch size: 50, loss[discriminator_loss=2.801, discriminator_real_loss=1.463, discriminator_fake_loss=1.338, generator_loss=28.47, generator_mel_loss=17.62, generator_kl_loss=1.453, generator_dur_loss=1.663, generator_adv_loss=1.888, generator_feat_match_loss=5.842, over 50.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=29.04, generator_mel_loss=17.86, generator_kl_loss=1.425, generator_dur_loss=1.742, generator_adv_loss=2.004, generator_feat_match_loss=6.015, over 3329.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:37:28,831 INFO [train.py:527] (4/6) Epoch 782, batch 106, global_batch_idx: 96950, batch size: 50, loss[discriminator_loss=2.706, discriminator_real_loss=1.298, discriminator_fake_loss=1.408, generator_loss=28.87, generator_mel_loss=17.72, generator_kl_loss=1.438, generator_dur_loss=1.657, generator_adv_loss=1.964, generator_feat_match_loss=6.093, over 50.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.365, discriminator_fake_loss=1.326, generator_loss=28.84, generator_mel_loss=17.81, generator_kl_loss=1.422, generator_dur_loss=1.74, generator_adv_loss=1.994, generator_feat_match_loss=5.877, over 6123.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:38:15,094 INFO [train.py:919] (4/6) Start epoch 783 +2024-03-15 08:40:07,162 INFO [train.py:527] (4/6) Epoch 783, batch 32, global_batch_idx: 97000, batch size: 15, loss[discriminator_loss=2.657, discriminator_real_loss=1.336, discriminator_fake_loss=1.321, generator_loss=30.69, generator_mel_loss=18.98, generator_kl_loss=1.79, generator_dur_loss=1.563, generator_adv_loss=1.979, generator_feat_match_loss=6.373, over 15.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.367, discriminator_fake_loss=1.32, generator_loss=29.05, generator_mel_loss=17.93, generator_kl_loss=1.498, generator_dur_loss=1.698, generator_adv_loss=1.984, generator_feat_match_loss=5.939, over 1680.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:40:07,164 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 08:40:15,060 INFO [train.py:591] (4/6) Epoch 783, validation: discriminator_loss=2.71, discriminator_real_loss=1.344, discriminator_fake_loss=1.366, generator_loss=27.44, generator_mel_loss=17.68, generator_kl_loss=1.241, generator_dur_loss=1.797, generator_adv_loss=1.822, generator_feat_match_loss=4.905, over 100.00 samples. +2024-03-15 08:40:15,061 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 08:42:36,394 INFO [train.py:527] (4/6) Epoch 783, batch 82, global_batch_idx: 97050, batch size: 52, loss[discriminator_loss=2.623, discriminator_real_loss=1.343, discriminator_fake_loss=1.28, generator_loss=29.26, generator_mel_loss=18.14, generator_kl_loss=1.43, generator_dur_loss=1.67, generator_adv_loss=1.924, generator_feat_match_loss=6.099, over 52.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.359, discriminator_fake_loss=1.322, generator_loss=28.92, generator_mel_loss=17.86, generator_kl_loss=1.449, generator_dur_loss=1.718, generator_adv_loss=1.996, generator_feat_match_loss=5.892, over 4754.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:44:28,164 INFO [train.py:919] (4/6) Start epoch 784 +2024-03-15 08:45:12,174 INFO [train.py:527] (4/6) Epoch 784, batch 8, global_batch_idx: 97100, batch size: 96, loss[discriminator_loss=2.649, discriminator_real_loss=1.315, discriminator_fake_loss=1.334, generator_loss=28.61, generator_mel_loss=17.57, generator_kl_loss=1.317, generator_dur_loss=1.809, generator_adv_loss=2.01, generator_feat_match_loss=5.905, over 96.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.358, discriminator_fake_loss=1.306, generator_loss=28.83, generator_mel_loss=17.81, generator_kl_loss=1.435, generator_dur_loss=1.751, generator_adv_loss=2.03, generator_feat_match_loss=5.81, over 606.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:47:31,161 INFO [train.py:527] (4/6) Epoch 784, batch 58, global_batch_idx: 97150, batch size: 44, loss[discriminator_loss=2.617, discriminator_real_loss=1.306, discriminator_fake_loss=1.312, generator_loss=29.93, generator_mel_loss=18.45, generator_kl_loss=1.737, generator_dur_loss=1.672, generator_adv_loss=1.929, generator_feat_match_loss=6.148, over 44.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.359, discriminator_fake_loss=1.321, generator_loss=28.84, generator_mel_loss=17.82, generator_kl_loss=1.421, generator_dur_loss=1.746, generator_adv_loss=2.002, generator_feat_match_loss=5.847, over 3581.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:49:49,907 INFO [train.py:527] (4/6) Epoch 784, batch 108, global_batch_idx: 97200, batch size: 66, loss[discriminator_loss=2.683, discriminator_real_loss=1.361, discriminator_fake_loss=1.322, generator_loss=29, generator_mel_loss=17.73, generator_kl_loss=1.436, generator_dur_loss=1.738, generator_adv_loss=1.951, generator_feat_match_loss=6.146, over 66.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.357, discriminator_fake_loss=1.326, generator_loss=28.86, generator_mel_loss=17.84, generator_kl_loss=1.422, generator_dur_loss=1.745, generator_adv_loss=1.996, generator_feat_match_loss=5.86, over 6379.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:49:49,908 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 08:49:58,658 INFO [train.py:591] (4/6) Epoch 784, validation: discriminator_loss=2.705, discriminator_real_loss=1.417, discriminator_fake_loss=1.288, generator_loss=28.02, generator_mel_loss=18.22, generator_kl_loss=1.303, generator_dur_loss=1.803, generator_adv_loss=1.934, generator_feat_match_loss=4.753, over 100.00 samples. +2024-03-15 08:49:58,659 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 08:50:43,192 INFO [train.py:919] (4/6) Start epoch 785 +2024-03-15 08:52:39,188 INFO [train.py:527] (4/6) Epoch 785, batch 34, global_batch_idx: 97250, batch size: 31, loss[discriminator_loss=2.587, discriminator_real_loss=1.295, discriminator_fake_loss=1.292, generator_loss=30.85, generator_mel_loss=18.76, generator_kl_loss=1.625, generator_dur_loss=1.557, generator_adv_loss=1.928, generator_feat_match_loss=6.978, over 31.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.344, discriminator_fake_loss=1.332, generator_loss=29.07, generator_mel_loss=17.93, generator_kl_loss=1.443, generator_dur_loss=1.714, generator_adv_loss=2.024, generator_feat_match_loss=5.958, over 1760.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:54:55,351 INFO [train.py:527] (4/6) Epoch 785, batch 84, global_batch_idx: 97300, batch size: 74, loss[discriminator_loss=2.689, discriminator_real_loss=1.341, discriminator_fake_loss=1.348, generator_loss=28.65, generator_mel_loss=17.96, generator_kl_loss=1.323, generator_dur_loss=1.781, generator_adv_loss=1.952, generator_feat_match_loss=5.641, over 74.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.352, discriminator_fake_loss=1.326, generator_loss=29, generator_mel_loss=17.92, generator_kl_loss=1.424, generator_dur_loss=1.731, generator_adv_loss=2.01, generator_feat_match_loss=5.911, over 4675.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:56:45,776 INFO [train.py:919] (4/6) Start epoch 786 +2024-03-15 08:57:37,853 INFO [train.py:527] (4/6) Epoch 786, batch 10, global_batch_idx: 97350, batch size: 62, loss[discriminator_loss=2.664, discriminator_real_loss=1.319, discriminator_fake_loss=1.346, generator_loss=29.47, generator_mel_loss=18.21, generator_kl_loss=1.491, generator_dur_loss=1.754, generator_adv_loss=1.985, generator_feat_match_loss=6.025, over 62.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.352, discriminator_fake_loss=1.351, generator_loss=28.71, generator_mel_loss=17.97, generator_kl_loss=1.407, generator_dur_loss=1.723, generator_adv_loss=1.96, generator_feat_match_loss=5.645, over 604.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:59:57,162 INFO [train.py:527] (4/6) Epoch 786, batch 60, global_batch_idx: 97400, batch size: 58, loss[discriminator_loss=2.693, discriminator_real_loss=1.3, discriminator_fake_loss=1.392, generator_loss=29.11, generator_mel_loss=17.99, generator_kl_loss=1.459, generator_dur_loss=1.723, generator_adv_loss=2.047, generator_feat_match_loss=5.887, over 58.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.365, discriminator_fake_loss=1.324, generator_loss=28.84, generator_mel_loss=17.89, generator_kl_loss=1.424, generator_dur_loss=1.734, generator_adv_loss=1.991, generator_feat_match_loss=5.804, over 3427.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:59:57,163 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 09:00:06,018 INFO [train.py:591] (4/6) Epoch 786, validation: discriminator_loss=2.691, discriminator_real_loss=1.437, discriminator_fake_loss=1.254, generator_loss=28.03, generator_mel_loss=18.48, generator_kl_loss=1.179, generator_dur_loss=1.793, generator_adv_loss=1.998, generator_feat_match_loss=4.582, over 100.00 samples. +2024-03-15 09:00:06,019 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 09:02:26,493 INFO [train.py:527] (4/6) Epoch 786, batch 110, global_batch_idx: 97450, batch size: 15, loss[discriminator_loss=2.567, discriminator_real_loss=1.361, discriminator_fake_loss=1.206, generator_loss=29.43, generator_mel_loss=18.24, generator_kl_loss=1.713, generator_dur_loss=1.515, generator_adv_loss=1.998, generator_feat_match_loss=5.957, over 15.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.83, generator_mel_loss=17.87, generator_kl_loss=1.426, generator_dur_loss=1.735, generator_adv_loss=1.994, generator_feat_match_loss=5.803, over 6228.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:03:03,708 INFO [train.py:919] (4/6) Start epoch 787 +2024-03-15 09:05:06,747 INFO [train.py:527] (4/6) Epoch 787, batch 36, global_batch_idx: 97500, batch size: 13, loss[discriminator_loss=2.753, discriminator_real_loss=1.358, discriminator_fake_loss=1.396, generator_loss=30.62, generator_mel_loss=18.54, generator_kl_loss=1.798, generator_dur_loss=1.534, generator_adv_loss=2.013, generator_feat_match_loss=6.729, over 13.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=28.93, generator_mel_loss=18, generator_kl_loss=1.424, generator_dur_loss=1.741, generator_adv_loss=1.988, generator_feat_match_loss=5.776, over 2087.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:07:26,547 INFO [train.py:527] (4/6) Epoch 787, batch 86, global_batch_idx: 97550, batch size: 25, loss[discriminator_loss=2.774, discriminator_real_loss=1.411, discriminator_fake_loss=1.363, generator_loss=29.27, generator_mel_loss=18.42, generator_kl_loss=1.803, generator_dur_loss=1.528, generator_adv_loss=1.91, generator_feat_match_loss=5.61, over 25.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.352, discriminator_fake_loss=1.329, generator_loss=29.04, generator_mel_loss=17.95, generator_kl_loss=1.416, generator_dur_loss=1.743, generator_adv_loss=2.004, generator_feat_match_loss=5.927, over 4925.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:09:15,497 INFO [train.py:919] (4/6) Start epoch 788 +2024-03-15 09:10:14,328 INFO [train.py:527] (4/6) Epoch 788, batch 12, global_batch_idx: 97600, batch size: 62, loss[discriminator_loss=2.669, discriminator_real_loss=1.37, discriminator_fake_loss=1.299, generator_loss=29.4, generator_mel_loss=18.28, generator_kl_loss=1.385, generator_dur_loss=1.751, generator_adv_loss=1.958, generator_feat_match_loss=6.021, over 62.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.348, discriminator_fake_loss=1.321, generator_loss=28.86, generator_mel_loss=17.86, generator_kl_loss=1.4, generator_dur_loss=1.765, generator_adv_loss=1.987, generator_feat_match_loss=5.844, over 857.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:10:14,330 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 09:10:21,986 INFO [train.py:591] (4/6) Epoch 788, validation: discriminator_loss=2.772, discriminator_real_loss=1.407, discriminator_fake_loss=1.365, generator_loss=27.53, generator_mel_loss=17.92, generator_kl_loss=1.166, generator_dur_loss=1.81, generator_adv_loss=1.816, generator_feat_match_loss=4.82, over 100.00 samples. +2024-03-15 09:10:21,987 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 09:12:41,226 INFO [train.py:527] (4/6) Epoch 788, batch 62, global_batch_idx: 97650, batch size: 58, loss[discriminator_loss=2.685, discriminator_real_loss=1.354, discriminator_fake_loss=1.331, generator_loss=28.2, generator_mel_loss=17.66, generator_kl_loss=1.595, generator_dur_loss=1.723, generator_adv_loss=1.891, generator_feat_match_loss=5.33, over 58.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.321, generator_loss=28.85, generator_mel_loss=17.86, generator_kl_loss=1.411, generator_dur_loss=1.743, generator_adv_loss=1.994, generator_feat_match_loss=5.845, over 3639.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:14:58,392 INFO [train.py:527] (4/6) Epoch 788, batch 112, global_batch_idx: 97700, batch size: 58, loss[discriminator_loss=2.659, discriminator_real_loss=1.247, discriminator_fake_loss=1.412, generator_loss=30.21, generator_mel_loss=17.95, generator_kl_loss=1.409, generator_dur_loss=1.77, generator_adv_loss=2.137, generator_feat_match_loss=6.944, over 58.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.364, discriminator_fake_loss=1.324, generator_loss=28.81, generator_mel_loss=17.83, generator_kl_loss=1.418, generator_dur_loss=1.745, generator_adv_loss=1.991, generator_feat_match_loss=5.819, over 6569.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:15:32,291 INFO [train.py:919] (4/6) Start epoch 789 +2024-03-15 09:17:42,787 INFO [train.py:527] (4/6) Epoch 789, batch 38, global_batch_idx: 97750, batch size: 45, loss[discriminator_loss=2.706, discriminator_real_loss=1.299, discriminator_fake_loss=1.407, generator_loss=29.59, generator_mel_loss=18.11, generator_kl_loss=1.599, generator_dur_loss=1.684, generator_adv_loss=2.029, generator_feat_match_loss=6.174, over 45.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.348, discriminator_fake_loss=1.333, generator_loss=29.22, generator_mel_loss=17.91, generator_kl_loss=1.438, generator_dur_loss=1.738, generator_adv_loss=2.035, generator_feat_match_loss=6.106, over 2181.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:19:58,620 INFO [train.py:527] (4/6) Epoch 789, batch 88, global_batch_idx: 97800, batch size: 44, loss[discriminator_loss=2.632, discriminator_real_loss=1.273, discriminator_fake_loss=1.359, generator_loss=28.56, generator_mel_loss=17.64, generator_kl_loss=1.458, generator_dur_loss=1.664, generator_adv_loss=1.849, generator_feat_match_loss=5.951, over 44.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.35, discriminator_fake_loss=1.324, generator_loss=28.97, generator_mel_loss=17.84, generator_kl_loss=1.42, generator_dur_loss=1.742, generator_adv_loss=2.012, generator_feat_match_loss=5.956, over 5155.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:19:58,621 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 09:20:07,303 INFO [train.py:591] (4/6) Epoch 789, validation: discriminator_loss=2.722, discriminator_real_loss=1.317, discriminator_fake_loss=1.405, generator_loss=27.95, generator_mel_loss=18.1, generator_kl_loss=1.351, generator_dur_loss=1.794, generator_adv_loss=1.854, generator_feat_match_loss=4.859, over 100.00 samples. +2024-03-15 09:20:07,304 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 09:21:50,950 INFO [train.py:919] (4/6) Start epoch 790 +2024-03-15 09:22:54,333 INFO [train.py:527] (4/6) Epoch 790, batch 14, global_batch_idx: 97850, batch size: 31, loss[discriminator_loss=2.712, discriminator_real_loss=1.386, discriminator_fake_loss=1.326, generator_loss=29.04, generator_mel_loss=18.12, generator_kl_loss=1.478, generator_dur_loss=1.597, generator_adv_loss=1.999, generator_feat_match_loss=5.852, over 31.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.359, discriminator_fake_loss=1.313, generator_loss=28.88, generator_mel_loss=17.73, generator_kl_loss=1.407, generator_dur_loss=1.757, generator_adv_loss=2.007, generator_feat_match_loss=5.985, over 928.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:25:11,760 INFO [train.py:527] (4/6) Epoch 790, batch 64, global_batch_idx: 97900, batch size: 39, loss[discriminator_loss=2.654, discriminator_real_loss=1.262, discriminator_fake_loss=1.392, generator_loss=30.44, generator_mel_loss=18.76, generator_kl_loss=1.474, generator_dur_loss=1.663, generator_adv_loss=2.007, generator_feat_match_loss=6.533, over 39.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.96, generator_mel_loss=17.86, generator_kl_loss=1.426, generator_dur_loss=1.738, generator_adv_loss=1.994, generator_feat_match_loss=5.941, over 3680.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:27:31,654 INFO [train.py:527] (4/6) Epoch 790, batch 114, global_batch_idx: 97950, batch size: 72, loss[discriminator_loss=2.658, discriminator_real_loss=1.278, discriminator_fake_loss=1.38, generator_loss=29.01, generator_mel_loss=18.36, generator_kl_loss=1.389, generator_dur_loss=1.807, generator_adv_loss=1.877, generator_feat_match_loss=5.568, over 72.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.361, discriminator_fake_loss=1.325, generator_loss=28.91, generator_mel_loss=17.87, generator_kl_loss=1.415, generator_dur_loss=1.746, generator_adv_loss=1.991, generator_feat_match_loss=5.886, over 6738.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:27:56,992 INFO [train.py:919] (4/6) Start epoch 791 +2024-03-15 09:30:13,432 INFO [train.py:527] (4/6) Epoch 791, batch 40, global_batch_idx: 98000, batch size: 50, loss[discriminator_loss=2.615, discriminator_real_loss=1.255, discriminator_fake_loss=1.36, generator_loss=29.49, generator_mel_loss=18.15, generator_kl_loss=1.463, generator_dur_loss=1.726, generator_adv_loss=2, generator_feat_match_loss=6.156, over 50.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=28.97, generator_mel_loss=17.89, generator_kl_loss=1.417, generator_dur_loss=1.729, generator_adv_loss=2.005, generator_feat_match_loss=5.926, over 2238.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:30:13,433 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 09:30:21,549 INFO [train.py:591] (4/6) Epoch 791, validation: discriminator_loss=2.693, discriminator_real_loss=1.39, discriminator_fake_loss=1.304, generator_loss=27.72, generator_mel_loss=18.37, generator_kl_loss=1.228, generator_dur_loss=1.794, generator_adv_loss=1.917, generator_feat_match_loss=4.414, over 100.00 samples. +2024-03-15 09:30:21,550 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 09:32:39,907 INFO [train.py:527] (4/6) Epoch 791, batch 90, global_batch_idx: 98050, batch size: 31, loss[discriminator_loss=2.727, discriminator_real_loss=1.429, discriminator_fake_loss=1.298, generator_loss=27.32, generator_mel_loss=17.25, generator_kl_loss=1.396, generator_dur_loss=1.673, generator_adv_loss=1.96, generator_feat_match_loss=5.036, over 31.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=28.88, generator_mel_loss=17.83, generator_kl_loss=1.435, generator_dur_loss=1.725, generator_adv_loss=2.007, generator_feat_match_loss=5.89, over 4979.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:34:13,834 INFO [train.py:919] (4/6) Start epoch 792 +2024-03-15 09:35:21,531 INFO [train.py:527] (4/6) Epoch 792, batch 16, global_batch_idx: 98100, batch size: 61, loss[discriminator_loss=2.652, discriminator_real_loss=1.274, discriminator_fake_loss=1.377, generator_loss=29.73, generator_mel_loss=17.9, generator_kl_loss=1.376, generator_dur_loss=1.747, generator_adv_loss=1.994, generator_feat_match_loss=6.712, over 61.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.347, discriminator_fake_loss=1.338, generator_loss=29.08, generator_mel_loss=17.89, generator_kl_loss=1.406, generator_dur_loss=1.735, generator_adv_loss=1.993, generator_feat_match_loss=6.057, over 1067.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:37:41,101 INFO [train.py:527] (4/6) Epoch 792, batch 66, global_batch_idx: 98150, batch size: 44, loss[discriminator_loss=2.658, discriminator_real_loss=1.355, discriminator_fake_loss=1.302, generator_loss=29.32, generator_mel_loss=18.22, generator_kl_loss=1.547, generator_dur_loss=1.651, generator_adv_loss=2.036, generator_feat_match_loss=5.868, over 44.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.354, discriminator_fake_loss=1.33, generator_loss=28.98, generator_mel_loss=17.85, generator_kl_loss=1.422, generator_dur_loss=1.743, generator_adv_loss=2.003, generator_feat_match_loss=5.965, over 3996.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:39:58,426 INFO [train.py:527] (4/6) Epoch 792, batch 116, global_batch_idx: 98200, batch size: 25, loss[discriminator_loss=2.632, discriminator_real_loss=1.399, discriminator_fake_loss=1.233, generator_loss=31.72, generator_mel_loss=18.53, generator_kl_loss=1.742, generator_dur_loss=1.542, generator_adv_loss=2.111, generator_feat_match_loss=7.803, over 25.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.358, discriminator_fake_loss=1.331, generator_loss=28.99, generator_mel_loss=17.85, generator_kl_loss=1.431, generator_dur_loss=1.742, generator_adv_loss=2.005, generator_feat_match_loss=5.962, over 6666.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:39:58,427 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 09:40:07,210 INFO [train.py:591] (4/6) Epoch 792, validation: discriminator_loss=2.773, discriminator_real_loss=1.417, discriminator_fake_loss=1.356, generator_loss=28.46, generator_mel_loss=18.35, generator_kl_loss=1.298, generator_dur_loss=1.806, generator_adv_loss=1.904, generator_feat_match_loss=5.105, over 100.00 samples. +2024-03-15 09:40:07,211 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 09:40:29,040 INFO [train.py:919] (4/6) Start epoch 793 +2024-03-15 09:42:50,129 INFO [train.py:527] (4/6) Epoch 793, batch 42, global_batch_idx: 98250, batch size: 68, loss[discriminator_loss=2.642, discriminator_real_loss=1.265, discriminator_fake_loss=1.377, generator_loss=28.88, generator_mel_loss=17.7, generator_kl_loss=1.491, generator_dur_loss=1.742, generator_adv_loss=2.023, generator_feat_match_loss=5.926, over 68.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.371, discriminator_fake_loss=1.329, generator_loss=28.75, generator_mel_loss=17.84, generator_kl_loss=1.418, generator_dur_loss=1.742, generator_adv_loss=1.993, generator_feat_match_loss=5.751, over 2456.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:45:08,676 INFO [train.py:527] (4/6) Epoch 793, batch 92, global_batch_idx: 98300, batch size: 59, loss[discriminator_loss=2.761, discriminator_real_loss=1.369, discriminator_fake_loss=1.392, generator_loss=28.73, generator_mel_loss=17.6, generator_kl_loss=1.389, generator_dur_loss=1.771, generator_adv_loss=1.953, generator_feat_match_loss=6.017, over 59.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.368, discriminator_fake_loss=1.332, generator_loss=28.82, generator_mel_loss=17.86, generator_kl_loss=1.412, generator_dur_loss=1.748, generator_adv_loss=1.994, generator_feat_match_loss=5.803, over 5331.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:46:37,726 INFO [train.py:919] (4/6) Start epoch 794 +2024-03-15 09:47:52,217 INFO [train.py:527] (4/6) Epoch 794, batch 18, global_batch_idx: 98350, batch size: 88, loss[discriminator_loss=2.68, discriminator_real_loss=1.337, discriminator_fake_loss=1.343, generator_loss=29.53, generator_mel_loss=18.13, generator_kl_loss=1.442, generator_dur_loss=1.896, generator_adv_loss=1.957, generator_feat_match_loss=6.1, over 88.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.328, generator_loss=29.11, generator_mel_loss=17.94, generator_kl_loss=1.445, generator_dur_loss=1.759, generator_adv_loss=1.993, generator_feat_match_loss=5.969, over 1165.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:50:10,960 INFO [train.py:527] (4/6) Epoch 794, batch 68, global_batch_idx: 98400, batch size: 88, loss[discriminator_loss=2.649, discriminator_real_loss=1.366, discriminator_fake_loss=1.283, generator_loss=29.57, generator_mel_loss=18.16, generator_kl_loss=1.429, generator_dur_loss=1.801, generator_adv_loss=2.145, generator_feat_match_loss=6.032, over 88.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.358, discriminator_fake_loss=1.335, generator_loss=28.91, generator_mel_loss=17.86, generator_kl_loss=1.429, generator_dur_loss=1.746, generator_adv_loss=2.005, generator_feat_match_loss=5.87, over 4122.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:50:10,961 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 09:50:19,874 INFO [train.py:591] (4/6) Epoch 794, validation: discriminator_loss=2.619, discriminator_real_loss=1.385, discriminator_fake_loss=1.234, generator_loss=28.83, generator_mel_loss=18.56, generator_kl_loss=1.309, generator_dur_loss=1.806, generator_adv_loss=2.049, generator_feat_match_loss=5.114, over 100.00 samples. +2024-03-15 09:50:19,875 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 09:52:39,509 INFO [train.py:527] (4/6) Epoch 794, batch 118, global_batch_idx: 98450, batch size: 72, loss[discriminator_loss=2.691, discriminator_real_loss=1.405, discriminator_fake_loss=1.285, generator_loss=29.29, generator_mel_loss=18.1, generator_kl_loss=1.328, generator_dur_loss=1.788, generator_adv_loss=2.023, generator_feat_match_loss=6.046, over 72.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=28.89, generator_mel_loss=17.83, generator_kl_loss=1.426, generator_dur_loss=1.742, generator_adv_loss=2.009, generator_feat_match_loss=5.88, over 6984.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:52:53,276 INFO [train.py:919] (4/6) Start epoch 795 +2024-03-15 09:55:22,436 INFO [train.py:527] (4/6) Epoch 795, batch 44, global_batch_idx: 98500, batch size: 88, loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=27.98, generator_mel_loss=17.41, generator_kl_loss=1.301, generator_dur_loss=1.812, generator_adv_loss=1.91, generator_feat_match_loss=5.541, over 88.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.361, discriminator_fake_loss=1.321, generator_loss=28.88, generator_mel_loss=17.79, generator_kl_loss=1.434, generator_dur_loss=1.734, generator_adv_loss=2.001, generator_feat_match_loss=5.916, over 2539.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:57:44,595 INFO [train.py:527] (4/6) Epoch 795, batch 94, global_batch_idx: 98550, batch size: 52, loss[discriminator_loss=2.666, discriminator_real_loss=1.364, discriminator_fake_loss=1.302, generator_loss=28.77, generator_mel_loss=17.81, generator_kl_loss=1.641, generator_dur_loss=1.697, generator_adv_loss=1.941, generator_feat_match_loss=5.684, over 52.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.361, discriminator_fake_loss=1.318, generator_loss=28.9, generator_mel_loss=17.83, generator_kl_loss=1.44, generator_dur_loss=1.733, generator_adv_loss=2.005, generator_feat_match_loss=5.891, over 5321.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:59:04,562 INFO [train.py:919] (4/6) Start epoch 796 +2024-03-15 10:00:25,460 INFO [train.py:527] (4/6) Epoch 796, batch 20, global_batch_idx: 98600, batch size: 68, loss[discriminator_loss=2.728, discriminator_real_loss=1.382, discriminator_fake_loss=1.347, generator_loss=28.61, generator_mel_loss=17.63, generator_kl_loss=1.466, generator_dur_loss=1.792, generator_adv_loss=1.961, generator_feat_match_loss=5.764, over 68.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.334, discriminator_fake_loss=1.34, generator_loss=28.94, generator_mel_loss=17.86, generator_kl_loss=1.442, generator_dur_loss=1.745, generator_adv_loss=1.985, generator_feat_match_loss=5.905, over 1092.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:00:25,462 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 10:00:33,304 INFO [train.py:591] (4/6) Epoch 796, validation: discriminator_loss=2.735, discriminator_real_loss=1.401, discriminator_fake_loss=1.334, generator_loss=28.03, generator_mel_loss=18.18, generator_kl_loss=1.285, generator_dur_loss=1.801, generator_adv_loss=1.94, generator_feat_match_loss=4.824, over 100.00 samples. +2024-03-15 10:00:33,305 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 10:02:53,469 INFO [train.py:527] (4/6) Epoch 796, batch 70, global_batch_idx: 98650, batch size: 45, loss[discriminator_loss=2.728, discriminator_real_loss=1.307, discriminator_fake_loss=1.421, generator_loss=28.8, generator_mel_loss=17.58, generator_kl_loss=1.494, generator_dur_loss=1.68, generator_adv_loss=2.168, generator_feat_match_loss=5.88, over 45.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.349, discriminator_fake_loss=1.331, generator_loss=29.03, generator_mel_loss=17.87, generator_kl_loss=1.43, generator_dur_loss=1.75, generator_adv_loss=2.012, generator_feat_match_loss=5.966, over 4046.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:05:10,165 INFO [train.py:527] (4/6) Epoch 796, batch 120, global_batch_idx: 98700, batch size: 56, loss[discriminator_loss=2.656, discriminator_real_loss=1.375, discriminator_fake_loss=1.281, generator_loss=29.35, generator_mel_loss=17.89, generator_kl_loss=1.371, generator_dur_loss=1.72, generator_adv_loss=2.033, generator_feat_match_loss=6.337, over 56.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=28.94, generator_mel_loss=17.85, generator_kl_loss=1.412, generator_dur_loss=1.754, generator_adv_loss=2.007, generator_feat_match_loss=5.917, over 7018.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:05:18,593 INFO [train.py:919] (4/6) Start epoch 797 +2024-03-15 10:07:52,399 INFO [train.py:527] (4/6) Epoch 797, batch 46, global_batch_idx: 98750, batch size: 61, loss[discriminator_loss=2.607, discriminator_real_loss=1.309, discriminator_fake_loss=1.298, generator_loss=30.14, generator_mel_loss=17.99, generator_kl_loss=1.451, generator_dur_loss=1.724, generator_adv_loss=2.092, generator_feat_match_loss=6.879, over 61.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.353, discriminator_fake_loss=1.33, generator_loss=28.89, generator_mel_loss=17.8, generator_kl_loss=1.431, generator_dur_loss=1.741, generator_adv_loss=1.99, generator_feat_match_loss=5.92, over 2665.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:10:12,927 INFO [train.py:527] (4/6) Epoch 797, batch 96, global_batch_idx: 98800, batch size: 25, loss[discriminator_loss=2.603, discriminator_real_loss=1.365, discriminator_fake_loss=1.238, generator_loss=30.15, generator_mel_loss=18.47, generator_kl_loss=1.577, generator_dur_loss=1.576, generator_adv_loss=2.066, generator_feat_match_loss=6.458, over 25.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=28.92, generator_mel_loss=17.87, generator_kl_loss=1.43, generator_dur_loss=1.738, generator_adv_loss=1.99, generator_feat_match_loss=5.901, over 5339.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:10:12,930 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 10:10:21,698 INFO [train.py:591] (4/6) Epoch 797, validation: discriminator_loss=2.68, discriminator_real_loss=1.382, discriminator_fake_loss=1.298, generator_loss=26.95, generator_mel_loss=18.03, generator_kl_loss=1.322, generator_dur_loss=1.8, generator_adv_loss=1.85, generator_feat_match_loss=3.944, over 100.00 samples. +2024-03-15 10:10:21,699 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 10:11:35,907 INFO [train.py:919] (4/6) Start epoch 798 +2024-03-15 10:12:59,896 INFO [train.py:527] (4/6) Epoch 798, batch 22, global_batch_idx: 98850, batch size: 45, loss[discriminator_loss=2.756, discriminator_real_loss=1.441, discriminator_fake_loss=1.316, generator_loss=27.7, generator_mel_loss=17.82, generator_kl_loss=1.46, generator_dur_loss=1.693, generator_adv_loss=1.964, generator_feat_match_loss=4.762, over 45.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.352, discriminator_fake_loss=1.319, generator_loss=29.04, generator_mel_loss=17.87, generator_kl_loss=1.409, generator_dur_loss=1.75, generator_adv_loss=2.008, generator_feat_match_loss=6.005, over 1393.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:15:18,124 INFO [train.py:527] (4/6) Epoch 798, batch 72, global_batch_idx: 98900, batch size: 66, loss[discriminator_loss=2.702, discriminator_real_loss=1.378, discriminator_fake_loss=1.325, generator_loss=28.85, generator_mel_loss=17.36, generator_kl_loss=1.396, generator_dur_loss=1.733, generator_adv_loss=2.076, generator_feat_match_loss=6.282, over 66.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.357, discriminator_fake_loss=1.322, generator_loss=28.86, generator_mel_loss=17.8, generator_kl_loss=1.406, generator_dur_loss=1.762, generator_adv_loss=2, generator_feat_match_loss=5.894, over 4479.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:17:35,953 INFO [train.py:527] (4/6) Epoch 798, batch 122, global_batch_idx: 98950, batch size: 31, loss[discriminator_loss=2.64, discriminator_real_loss=1.381, discriminator_fake_loss=1.259, generator_loss=28.35, generator_mel_loss=17.7, generator_kl_loss=1.534, generator_dur_loss=1.642, generator_adv_loss=2.077, generator_feat_match_loss=5.391, over 31.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=28.88, generator_mel_loss=17.84, generator_kl_loss=1.41, generator_dur_loss=1.756, generator_adv_loss=2.001, generator_feat_match_loss=5.873, over 7337.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:17:40,882 INFO [train.py:919] (4/6) Start epoch 799 +2024-03-15 10:20:18,110 INFO [train.py:527] (4/6) Epoch 799, batch 48, global_batch_idx: 99000, batch size: 44, loss[discriminator_loss=2.793, discriminator_real_loss=1.322, discriminator_fake_loss=1.471, generator_loss=28.56, generator_mel_loss=17.68, generator_kl_loss=1.459, generator_dur_loss=1.749, generator_adv_loss=2.092, generator_feat_match_loss=5.58, over 44.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=29.02, generator_mel_loss=17.93, generator_kl_loss=1.405, generator_dur_loss=1.736, generator_adv_loss=2.007, generator_feat_match_loss=5.937, over 2851.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:20:18,111 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 10:20:26,124 INFO [train.py:591] (4/6) Epoch 799, validation: discriminator_loss=2.793, discriminator_real_loss=1.59, discriminator_fake_loss=1.203, generator_loss=28.54, generator_mel_loss=18.53, generator_kl_loss=1.235, generator_dur_loss=1.793, generator_adv_loss=2.161, generator_feat_match_loss=4.818, over 100.00 samples. +2024-03-15 10:20:26,125 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 10:22:43,931 INFO [train.py:527] (4/6) Epoch 799, batch 98, global_batch_idx: 99050, batch size: 42, loss[discriminator_loss=2.718, discriminator_real_loss=1.312, discriminator_fake_loss=1.406, generator_loss=28.17, generator_mel_loss=18.18, generator_kl_loss=1.601, generator_dur_loss=1.664, generator_adv_loss=2.041, generator_feat_match_loss=4.689, over 42.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=29.01, generator_mel_loss=17.89, generator_kl_loss=1.421, generator_dur_loss=1.736, generator_adv_loss=2.004, generator_feat_match_loss=5.959, over 5735.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:23:55,088 INFO [train.py:919] (4/6) Start epoch 800 +2024-03-15 10:25:24,077 INFO [train.py:527] (4/6) Epoch 800, batch 24, global_batch_idx: 99100, batch size: 13, loss[discriminator_loss=2.462, discriminator_real_loss=1.267, discriminator_fake_loss=1.195, generator_loss=29.97, generator_mel_loss=18.39, generator_kl_loss=1.86, generator_dur_loss=1.555, generator_adv_loss=2.101, generator_feat_match_loss=6.07, over 13.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.324, generator_loss=29.07, generator_mel_loss=17.89, generator_kl_loss=1.441, generator_dur_loss=1.723, generator_adv_loss=1.986, generator_feat_match_loss=6.034, over 1208.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:27:42,611 INFO [train.py:527] (4/6) Epoch 800, batch 74, global_batch_idx: 99150, batch size: 44, loss[discriminator_loss=2.701, discriminator_real_loss=1.396, discriminator_fake_loss=1.305, generator_loss=28.41, generator_mel_loss=17.23, generator_kl_loss=1.562, generator_dur_loss=1.657, generator_adv_loss=2.151, generator_feat_match_loss=5.815, over 44.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.357, discriminator_fake_loss=1.328, generator_loss=28.86, generator_mel_loss=17.8, generator_kl_loss=1.436, generator_dur_loss=1.725, generator_adv_loss=1.997, generator_feat_match_loss=5.9, over 3876.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:30:01,386 INFO [train.py:919] (4/6) Start epoch 801 +2024-03-15 10:30:25,658 INFO [train.py:527] (4/6) Epoch 801, batch 0, global_batch_idx: 99200, batch size: 56, loss[discriminator_loss=2.64, discriminator_real_loss=1.314, discriminator_fake_loss=1.326, generator_loss=29.6, generator_mel_loss=17.93, generator_kl_loss=1.452, generator_dur_loss=1.714, generator_adv_loss=2.082, generator_feat_match_loss=6.418, over 56.00 samples.], tot_loss[discriminator_loss=2.64, discriminator_real_loss=1.314, discriminator_fake_loss=1.326, generator_loss=29.6, generator_mel_loss=17.93, generator_kl_loss=1.452, generator_dur_loss=1.714, generator_adv_loss=2.082, generator_feat_match_loss=6.418, over 56.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:30:25,661 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 10:30:33,648 INFO [train.py:591] (4/6) Epoch 801, validation: discriminator_loss=2.725, discriminator_real_loss=1.353, discriminator_fake_loss=1.371, generator_loss=27.21, generator_mel_loss=17.97, generator_kl_loss=1.262, generator_dur_loss=1.796, generator_adv_loss=1.878, generator_feat_match_loss=4.304, over 100.00 samples. +2024-03-15 10:30:33,650 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 10:32:53,855 INFO [train.py:527] (4/6) Epoch 801, batch 50, global_batch_idx: 99250, batch size: 42, loss[discriminator_loss=2.627, discriminator_real_loss=1.293, discriminator_fake_loss=1.334, generator_loss=29.68, generator_mel_loss=18.55, generator_kl_loss=1.476, generator_dur_loss=1.693, generator_adv_loss=1.915, generator_feat_match_loss=6.037, over 42.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.351, discriminator_fake_loss=1.323, generator_loss=28.93, generator_mel_loss=17.84, generator_kl_loss=1.431, generator_dur_loss=1.737, generator_adv_loss=2.003, generator_feat_match_loss=5.919, over 2772.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:35:13,709 INFO [train.py:527] (4/6) Epoch 801, batch 100, global_batch_idx: 99300, batch size: 48, loss[discriminator_loss=2.66, discriminator_real_loss=1.329, discriminator_fake_loss=1.33, generator_loss=28.92, generator_mel_loss=17.9, generator_kl_loss=1.405, generator_dur_loss=1.695, generator_adv_loss=2.084, generator_feat_match_loss=5.832, over 48.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.324, generator_loss=28.99, generator_mel_loss=17.86, generator_kl_loss=1.433, generator_dur_loss=1.743, generator_adv_loss=2.023, generator_feat_match_loss=5.934, over 5642.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:36:20,478 INFO [train.py:919] (4/6) Start epoch 802 +2024-03-15 10:37:54,651 INFO [train.py:527] (4/6) Epoch 802, batch 26, global_batch_idx: 99350, batch size: 31, loss[discriminator_loss=2.62, discriminator_real_loss=1.255, discriminator_fake_loss=1.364, generator_loss=30.03, generator_mel_loss=18.02, generator_kl_loss=1.649, generator_dur_loss=1.599, generator_adv_loss=2.186, generator_feat_match_loss=6.581, over 31.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.351, discriminator_fake_loss=1.323, generator_loss=29.1, generator_mel_loss=17.9, generator_kl_loss=1.456, generator_dur_loss=1.746, generator_adv_loss=2.009, generator_feat_match_loss=5.983, over 1494.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:40:14,171 INFO [train.py:527] (4/6) Epoch 802, batch 76, global_batch_idx: 99400, batch size: 64, loss[discriminator_loss=2.669, discriminator_real_loss=1.367, discriminator_fake_loss=1.302, generator_loss=28.49, generator_mel_loss=17.38, generator_kl_loss=1.42, generator_dur_loss=1.77, generator_adv_loss=1.873, generator_feat_match_loss=6.042, over 64.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=28.9, generator_mel_loss=17.87, generator_kl_loss=1.443, generator_dur_loss=1.751, generator_adv_loss=1.991, generator_feat_match_loss=5.855, over 4287.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:40:14,173 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 10:40:23,114 INFO [train.py:591] (4/6) Epoch 802, validation: discriminator_loss=2.677, discriminator_real_loss=1.347, discriminator_fake_loss=1.33, generator_loss=27.8, generator_mel_loss=18.01, generator_kl_loss=1.349, generator_dur_loss=1.825, generator_adv_loss=1.83, generator_feat_match_loss=4.787, over 100.00 samples. +2024-03-15 10:40:23,115 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 10:42:36,502 INFO [train.py:919] (4/6) Start epoch 803 +2024-03-15 10:43:06,996 INFO [train.py:527] (4/6) Epoch 803, batch 2, global_batch_idx: 99450, batch size: 44, loss[discriminator_loss=2.744, discriminator_real_loss=1.367, discriminator_fake_loss=1.377, generator_loss=29.63, generator_mel_loss=17.69, generator_kl_loss=1.599, generator_dur_loss=1.691, generator_adv_loss=1.898, generator_feat_match_loss=6.76, over 44.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.369, discriminator_fake_loss=1.347, generator_loss=28.74, generator_mel_loss=17.8, generator_kl_loss=1.513, generator_dur_loss=1.691, generator_adv_loss=1.953, generator_feat_match_loss=5.786, over 139.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:45:25,735 INFO [train.py:527] (4/6) Epoch 803, batch 52, global_batch_idx: 99500, batch size: 44, loss[discriminator_loss=2.736, discriminator_real_loss=1.415, discriminator_fake_loss=1.321, generator_loss=28.88, generator_mel_loss=17.69, generator_kl_loss=1.495, generator_dur_loss=1.689, generator_adv_loss=1.962, generator_feat_match_loss=6.045, over 44.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.362, discriminator_fake_loss=1.317, generator_loss=28.9, generator_mel_loss=17.82, generator_kl_loss=1.458, generator_dur_loss=1.73, generator_adv_loss=2.006, generator_feat_match_loss=5.881, over 2914.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:47:46,028 INFO [train.py:527] (4/6) Epoch 803, batch 102, global_batch_idx: 99550, batch size: 64, loss[discriminator_loss=2.69, discriminator_real_loss=1.374, discriminator_fake_loss=1.317, generator_loss=29.06, generator_mel_loss=17.71, generator_kl_loss=1.398, generator_dur_loss=1.798, generator_adv_loss=1.939, generator_feat_match_loss=6.208, over 64.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.361, discriminator_fake_loss=1.319, generator_loss=28.86, generator_mel_loss=17.82, generator_kl_loss=1.423, generator_dur_loss=1.735, generator_adv_loss=2.01, generator_feat_match_loss=5.875, over 5729.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:48:45,252 INFO [train.py:919] (4/6) Start epoch 804 +2024-03-15 10:50:28,517 INFO [train.py:527] (4/6) Epoch 804, batch 28, global_batch_idx: 99600, batch size: 80, loss[discriminator_loss=2.663, discriminator_real_loss=1.375, discriminator_fake_loss=1.288, generator_loss=29.72, generator_mel_loss=18.15, generator_kl_loss=1.348, generator_dur_loss=1.761, generator_adv_loss=1.986, generator_feat_match_loss=6.479, over 80.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.352, discriminator_fake_loss=1.318, generator_loss=29.08, generator_mel_loss=17.92, generator_kl_loss=1.44, generator_dur_loss=1.728, generator_adv_loss=2.014, generator_feat_match_loss=5.98, over 1662.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:50:28,519 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 10:50:36,563 INFO [train.py:591] (4/6) Epoch 804, validation: discriminator_loss=2.731, discriminator_real_loss=1.378, discriminator_fake_loss=1.353, generator_loss=27.59, generator_mel_loss=17.92, generator_kl_loss=1.318, generator_dur_loss=1.816, generator_adv_loss=1.884, generator_feat_match_loss=4.657, over 100.00 samples. +2024-03-15 10:50:36,564 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 10:52:54,897 INFO [train.py:527] (4/6) Epoch 804, batch 78, global_batch_idx: 99650, batch size: 36, loss[discriminator_loss=2.627, discriminator_real_loss=1.423, discriminator_fake_loss=1.204, generator_loss=28.9, generator_mel_loss=17.62, generator_kl_loss=1.37, generator_dur_loss=1.649, generator_adv_loss=2.062, generator_feat_match_loss=6.204, over 36.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.362, discriminator_fake_loss=1.322, generator_loss=29.04, generator_mel_loss=17.92, generator_kl_loss=1.434, generator_dur_loss=1.744, generator_adv_loss=2, generator_feat_match_loss=5.947, over 4499.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:54:58,300 INFO [train.py:919] (4/6) Start epoch 805 +2024-03-15 10:55:33,726 INFO [train.py:527] (4/6) Epoch 805, batch 4, global_batch_idx: 99700, batch size: 52, loss[discriminator_loss=2.755, discriminator_real_loss=1.34, discriminator_fake_loss=1.415, generator_loss=30.45, generator_mel_loss=18.19, generator_kl_loss=1.495, generator_dur_loss=1.659, generator_adv_loss=1.992, generator_feat_match_loss=7.11, over 52.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.359, discriminator_fake_loss=1.371, generator_loss=29.06, generator_mel_loss=17.88, generator_kl_loss=1.525, generator_dur_loss=1.699, generator_adv_loss=1.953, generator_feat_match_loss=6.007, over 276.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:57:51,564 INFO [train.py:527] (4/6) Epoch 805, batch 54, global_batch_idx: 99750, batch size: 55, loss[discriminator_loss=2.684, discriminator_real_loss=1.459, discriminator_fake_loss=1.224, generator_loss=29.6, generator_mel_loss=17.66, generator_kl_loss=1.333, generator_dur_loss=1.712, generator_adv_loss=2.106, generator_feat_match_loss=6.798, over 55.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=29.01, generator_mel_loss=17.88, generator_kl_loss=1.429, generator_dur_loss=1.739, generator_adv_loss=2.032, generator_feat_match_loss=5.931, over 3177.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:00:12,919 INFO [train.py:527] (4/6) Epoch 805, batch 104, global_batch_idx: 99800, batch size: 50, loss[discriminator_loss=2.708, discriminator_real_loss=1.406, discriminator_fake_loss=1.302, generator_loss=28.12, generator_mel_loss=17.72, generator_kl_loss=1.43, generator_dur_loss=1.673, generator_adv_loss=1.761, generator_feat_match_loss=5.534, over 50.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.358, discriminator_fake_loss=1.33, generator_loss=28.96, generator_mel_loss=17.86, generator_kl_loss=1.437, generator_dur_loss=1.741, generator_adv_loss=2.017, generator_feat_match_loss=5.904, over 6091.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:00:12,921 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 11:00:21,934 INFO [train.py:591] (4/6) Epoch 805, validation: discriminator_loss=2.746, discriminator_real_loss=1.292, discriminator_fake_loss=1.454, generator_loss=27.25, generator_mel_loss=17.79, generator_kl_loss=1.295, generator_dur_loss=1.805, generator_adv_loss=1.74, generator_feat_match_loss=4.622, over 100.00 samples. +2024-03-15 11:00:21,935 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 11:01:14,759 INFO [train.py:919] (4/6) Start epoch 806 +2024-03-15 11:03:00,287 INFO [train.py:527] (4/6) Epoch 806, batch 30, global_batch_idx: 99850, batch size: 58, loss[discriminator_loss=2.734, discriminator_real_loss=1.319, discriminator_fake_loss=1.416, generator_loss=29.32, generator_mel_loss=18.24, generator_kl_loss=1.419, generator_dur_loss=1.729, generator_adv_loss=2.003, generator_feat_match_loss=5.935, over 58.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.355, discriminator_fake_loss=1.33, generator_loss=28.94, generator_mel_loss=17.8, generator_kl_loss=1.429, generator_dur_loss=1.74, generator_adv_loss=1.986, generator_feat_match_loss=5.986, over 1746.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:05:18,910 INFO [train.py:527] (4/6) Epoch 806, batch 80, global_batch_idx: 99900, batch size: 58, loss[discriminator_loss=2.616, discriminator_real_loss=1.448, discriminator_fake_loss=1.168, generator_loss=29.12, generator_mel_loss=17.8, generator_kl_loss=1.446, generator_dur_loss=1.696, generator_adv_loss=1.903, generator_feat_match_loss=6.276, over 58.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=28.91, generator_mel_loss=17.83, generator_kl_loss=1.444, generator_dur_loss=1.726, generator_adv_loss=1.991, generator_feat_match_loss=5.913, over 4293.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:07:20,344 INFO [train.py:919] (4/6) Start epoch 807 +2024-03-15 11:08:01,159 INFO [train.py:527] (4/6) Epoch 807, batch 6, global_batch_idx: 99950, batch size: 55, loss[discriminator_loss=2.682, discriminator_real_loss=1.372, discriminator_fake_loss=1.31, generator_loss=28.7, generator_mel_loss=17.76, generator_kl_loss=1.503, generator_dur_loss=1.663, generator_adv_loss=2.031, generator_feat_match_loss=5.743, over 55.00 samples.], tot_loss[discriminator_loss=2.658, discriminator_real_loss=1.332, discriminator_fake_loss=1.326, generator_loss=29.11, generator_mel_loss=17.93, generator_kl_loss=1.317, generator_dur_loss=1.755, generator_adv_loss=2.03, generator_feat_match_loss=6.073, over 445.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:10:20,844 INFO [train.py:527] (4/6) Epoch 807, batch 56, global_batch_idx: 100000, batch size: 58, loss[discriminator_loss=2.673, discriminator_real_loss=1.382, discriminator_fake_loss=1.291, generator_loss=28.77, generator_mel_loss=17.7, generator_kl_loss=1.504, generator_dur_loss=1.74, generator_adv_loss=2.006, generator_feat_match_loss=5.816, over 58.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.358, discriminator_fake_loss=1.329, generator_loss=28.95, generator_mel_loss=17.92, generator_kl_loss=1.433, generator_dur_loss=1.738, generator_adv_loss=1.998, generator_feat_match_loss=5.862, over 3302.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:10:20,845 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 11:10:28,930 INFO [train.py:591] (4/6) Epoch 807, validation: discriminator_loss=2.774, discriminator_real_loss=1.429, discriminator_fake_loss=1.345, generator_loss=26.98, generator_mel_loss=18.01, generator_kl_loss=1.191, generator_dur_loss=1.766, generator_adv_loss=1.844, generator_feat_match_loss=4.171, over 100.00 samples. +2024-03-15 11:10:28,931 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 11:12:46,908 INFO [train.py:527] (4/6) Epoch 807, batch 106, global_batch_idx: 100050, batch size: 53, loss[discriminator_loss=2.786, discriminator_real_loss=1.521, discriminator_fake_loss=1.265, generator_loss=29.01, generator_mel_loss=18.3, generator_kl_loss=1.527, generator_dur_loss=1.673, generator_adv_loss=1.861, generator_feat_match_loss=5.655, over 53.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.357, discriminator_fake_loss=1.329, generator_loss=28.98, generator_mel_loss=17.88, generator_kl_loss=1.441, generator_dur_loss=1.726, generator_adv_loss=2, generator_feat_match_loss=5.932, over 6034.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:13:36,238 INFO [train.py:919] (4/6) Start epoch 808 +2024-03-15 11:15:28,513 INFO [train.py:527] (4/6) Epoch 808, batch 32, global_batch_idx: 100100, batch size: 44, loss[discriminator_loss=2.682, discriminator_real_loss=1.381, discriminator_fake_loss=1.301, generator_loss=28.51, generator_mel_loss=17.52, generator_kl_loss=1.742, generator_dur_loss=1.64, generator_adv_loss=2.126, generator_feat_match_loss=5.477, over 44.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.351, discriminator_fake_loss=1.33, generator_loss=29.05, generator_mel_loss=17.9, generator_kl_loss=1.444, generator_dur_loss=1.736, generator_adv_loss=1.986, generator_feat_match_loss=5.987, over 1978.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:17:49,499 INFO [train.py:527] (4/6) Epoch 808, batch 82, global_batch_idx: 100150, batch size: 47, loss[discriminator_loss=2.702, discriminator_real_loss=1.449, discriminator_fake_loss=1.253, generator_loss=28.04, generator_mel_loss=17.76, generator_kl_loss=1.467, generator_dur_loss=1.683, generator_adv_loss=1.995, generator_feat_match_loss=5.132, over 47.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.351, discriminator_fake_loss=1.332, generator_loss=28.95, generator_mel_loss=17.85, generator_kl_loss=1.435, generator_dur_loss=1.732, generator_adv_loss=1.991, generator_feat_match_loss=5.944, over 4801.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:19:46,973 INFO [train.py:919] (4/6) Start epoch 809 +2024-03-15 11:20:33,712 INFO [train.py:527] (4/6) Epoch 809, batch 8, global_batch_idx: 100200, batch size: 44, loss[discriminator_loss=2.719, discriminator_real_loss=1.341, discriminator_fake_loss=1.378, generator_loss=27.98, generator_mel_loss=17.54, generator_kl_loss=1.369, generator_dur_loss=1.693, generator_adv_loss=2.005, generator_feat_match_loss=5.379, over 44.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.343, discriminator_fake_loss=1.335, generator_loss=28.93, generator_mel_loss=17.75, generator_kl_loss=1.509, generator_dur_loss=1.721, generator_adv_loss=2.007, generator_feat_match_loss=5.936, over 470.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:20:33,715 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 11:20:41,723 INFO [train.py:591] (4/6) Epoch 809, validation: discriminator_loss=2.69, discriminator_real_loss=1.45, discriminator_fake_loss=1.241, generator_loss=27.66, generator_mel_loss=18.09, generator_kl_loss=1.268, generator_dur_loss=1.804, generator_adv_loss=2.02, generator_feat_match_loss=4.477, over 100.00 samples. +2024-03-15 11:20:41,726 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 11:22:59,991 INFO [train.py:527] (4/6) Epoch 809, batch 58, global_batch_idx: 100250, batch size: 64, loss[discriminator_loss=2.636, discriminator_real_loss=1.327, discriminator_fake_loss=1.309, generator_loss=30.07, generator_mel_loss=18.2, generator_kl_loss=1.473, generator_dur_loss=1.795, generator_adv_loss=2.028, generator_feat_match_loss=6.576, over 64.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.357, discriminator_fake_loss=1.322, generator_loss=28.94, generator_mel_loss=17.82, generator_kl_loss=1.438, generator_dur_loss=1.743, generator_adv_loss=2.012, generator_feat_match_loss=5.929, over 3317.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:25:18,769 INFO [train.py:527] (4/6) Epoch 809, batch 108, global_batch_idx: 100300, batch size: 52, loss[discriminator_loss=2.734, discriminator_real_loss=1.351, discriminator_fake_loss=1.383, generator_loss=27.9, generator_mel_loss=17.54, generator_kl_loss=1.361, generator_dur_loss=1.723, generator_adv_loss=1.984, generator_feat_match_loss=5.287, over 52.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.324, generator_loss=28.97, generator_mel_loss=17.86, generator_kl_loss=1.425, generator_dur_loss=1.741, generator_adv_loss=2.006, generator_feat_match_loss=5.941, over 6251.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:26:00,624 INFO [train.py:919] (4/6) Start epoch 810 +2024-03-15 11:27:58,484 INFO [train.py:527] (4/6) Epoch 810, batch 34, global_batch_idx: 100350, batch size: 74, loss[discriminator_loss=2.676, discriminator_real_loss=1.38, discriminator_fake_loss=1.296, generator_loss=29.62, generator_mel_loss=18.23, generator_kl_loss=1.331, generator_dur_loss=1.784, generator_adv_loss=1.886, generator_feat_match_loss=6.383, over 74.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.369, discriminator_fake_loss=1.315, generator_loss=28.91, generator_mel_loss=17.81, generator_kl_loss=1.405, generator_dur_loss=1.742, generator_adv_loss=1.999, generator_feat_match_loss=5.947, over 2041.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:30:15,709 INFO [train.py:527] (4/6) Epoch 810, batch 84, global_batch_idx: 100400, batch size: 52, loss[discriminator_loss=2.69, discriminator_real_loss=1.38, discriminator_fake_loss=1.31, generator_loss=29.28, generator_mel_loss=18.12, generator_kl_loss=1.561, generator_dur_loss=1.668, generator_adv_loss=1.998, generator_feat_match_loss=5.936, over 52.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.359, discriminator_fake_loss=1.322, generator_loss=28.99, generator_mel_loss=17.8, generator_kl_loss=1.429, generator_dur_loss=1.74, generator_adv_loss=2.001, generator_feat_match_loss=6.018, over 4761.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:30:15,711 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 11:30:24,305 INFO [train.py:591] (4/6) Epoch 810, validation: discriminator_loss=2.77, discriminator_real_loss=1.386, discriminator_fake_loss=1.384, generator_loss=27.57, generator_mel_loss=18.1, generator_kl_loss=1.194, generator_dur_loss=1.808, generator_adv_loss=1.82, generator_feat_match_loss=4.648, over 100.00 samples. +2024-03-15 11:30:24,306 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 11:32:15,591 INFO [train.py:919] (4/6) Start epoch 811 +2024-03-15 11:33:08,633 INFO [train.py:527] (4/6) Epoch 811, batch 10, global_batch_idx: 100450, batch size: 44, loss[discriminator_loss=2.71, discriminator_real_loss=1.328, discriminator_fake_loss=1.382, generator_loss=28.8, generator_mel_loss=18.05, generator_kl_loss=1.566, generator_dur_loss=1.695, generator_adv_loss=1.858, generator_feat_match_loss=5.624, over 44.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.362, discriminator_fake_loss=1.32, generator_loss=28.99, generator_mel_loss=17.8, generator_kl_loss=1.388, generator_dur_loss=1.761, generator_adv_loss=2.023, generator_feat_match_loss=6.016, over 627.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:35:26,320 INFO [train.py:527] (4/6) Epoch 811, batch 60, global_batch_idx: 100500, batch size: 74, loss[discriminator_loss=2.636, discriminator_real_loss=1.334, discriminator_fake_loss=1.303, generator_loss=29.7, generator_mel_loss=18.26, generator_kl_loss=1.184, generator_dur_loss=1.787, generator_adv_loss=2.048, generator_feat_match_loss=6.418, over 74.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.354, discriminator_fake_loss=1.319, generator_loss=29, generator_mel_loss=17.83, generator_kl_loss=1.4, generator_dur_loss=1.754, generator_adv_loss=2.01, generator_feat_match_loss=6.01, over 3523.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:37:43,262 INFO [train.py:527] (4/6) Epoch 811, batch 110, global_batch_idx: 100550, batch size: 64, loss[discriminator_loss=2.682, discriminator_real_loss=1.399, discriminator_fake_loss=1.282, generator_loss=28.43, generator_mel_loss=17.53, generator_kl_loss=1.449, generator_dur_loss=1.757, generator_adv_loss=1.959, generator_feat_match_loss=5.733, over 64.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.318, generator_loss=28.93, generator_mel_loss=17.79, generator_kl_loss=1.395, generator_dur_loss=1.752, generator_adv_loss=2.007, generator_feat_match_loss=5.982, over 6347.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:38:20,721 INFO [train.py:919] (4/6) Start epoch 812 +2024-03-15 11:40:25,003 INFO [train.py:527] (4/6) Epoch 812, batch 36, global_batch_idx: 100600, batch size: 39, loss[discriminator_loss=2.738, discriminator_real_loss=1.32, discriminator_fake_loss=1.418, generator_loss=29.86, generator_mel_loss=17.89, generator_kl_loss=1.642, generator_dur_loss=1.641, generator_adv_loss=1.968, generator_feat_match_loss=6.724, over 39.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.365, discriminator_fake_loss=1.338, generator_loss=28.9, generator_mel_loss=17.8, generator_kl_loss=1.418, generator_dur_loss=1.755, generator_adv_loss=1.981, generator_feat_match_loss=5.939, over 2216.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:40:25,004 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 11:40:33,077 INFO [train.py:591] (4/6) Epoch 812, validation: discriminator_loss=2.719, discriminator_real_loss=1.419, discriminator_fake_loss=1.3, generator_loss=28.17, generator_mel_loss=18.41, generator_kl_loss=1.234, generator_dur_loss=1.801, generator_adv_loss=1.905, generator_feat_match_loss=4.82, over 100.00 samples. +2024-03-15 11:40:33,079 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 11:42:53,706 INFO [train.py:527] (4/6) Epoch 812, batch 86, global_batch_idx: 100650, batch size: 80, loss[discriminator_loss=2.691, discriminator_real_loss=1.427, discriminator_fake_loss=1.264, generator_loss=29.63, generator_mel_loss=18.28, generator_kl_loss=1.435, generator_dur_loss=1.758, generator_adv_loss=2.015, generator_feat_match_loss=6.143, over 80.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=29.02, generator_mel_loss=17.86, generator_kl_loss=1.427, generator_dur_loss=1.746, generator_adv_loss=2.004, generator_feat_match_loss=5.986, over 5185.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:44:33,003 INFO [train.py:919] (4/6) Start epoch 813 +2024-03-15 11:45:30,546 INFO [train.py:527] (4/6) Epoch 813, batch 12, global_batch_idx: 100700, batch size: 58, loss[discriminator_loss=2.662, discriminator_real_loss=1.314, discriminator_fake_loss=1.348, generator_loss=29.41, generator_mel_loss=17.86, generator_kl_loss=1.499, generator_dur_loss=1.742, generator_adv_loss=1.872, generator_feat_match_loss=6.43, over 58.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.344, discriminator_fake_loss=1.33, generator_loss=29.21, generator_mel_loss=17.91, generator_kl_loss=1.518, generator_dur_loss=1.702, generator_adv_loss=2.005, generator_feat_match_loss=6.071, over 670.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:47:51,366 INFO [train.py:527] (4/6) Epoch 813, batch 62, global_batch_idx: 100750, batch size: 52, loss[discriminator_loss=2.69, discriminator_real_loss=1.399, discriminator_fake_loss=1.292, generator_loss=28.38, generator_mel_loss=17.54, generator_kl_loss=1.441, generator_dur_loss=1.654, generator_adv_loss=1.948, generator_feat_match_loss=5.802, over 52.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=28.91, generator_mel_loss=17.87, generator_kl_loss=1.448, generator_dur_loss=1.73, generator_adv_loss=1.999, generator_feat_match_loss=5.862, over 3480.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:50:11,299 INFO [train.py:527] (4/6) Epoch 813, batch 112, global_batch_idx: 100800, batch size: 47, loss[discriminator_loss=2.7, discriminator_real_loss=1.387, discriminator_fake_loss=1.313, generator_loss=29.2, generator_mel_loss=18.09, generator_kl_loss=1.43, generator_dur_loss=1.661, generator_adv_loss=2.099, generator_feat_match_loss=5.916, over 47.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=28.93, generator_mel_loss=17.82, generator_kl_loss=1.455, generator_dur_loss=1.736, generator_adv_loss=1.998, generator_feat_match_loss=5.915, over 6148.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:50:11,300 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 11:50:19,987 INFO [train.py:591] (4/6) Epoch 813, validation: discriminator_loss=2.742, discriminator_real_loss=1.424, discriminator_fake_loss=1.318, generator_loss=28.59, generator_mel_loss=18.42, generator_kl_loss=1.395, generator_dur_loss=1.819, generator_adv_loss=2.009, generator_feat_match_loss=4.94, over 100.00 samples. +2024-03-15 11:50:19,988 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 11:50:51,427 INFO [train.py:919] (4/6) Start epoch 814 +2024-03-15 11:52:57,889 INFO [train.py:527] (4/6) Epoch 814, batch 38, global_batch_idx: 100850, batch size: 70, loss[discriminator_loss=2.632, discriminator_real_loss=1.295, discriminator_fake_loss=1.338, generator_loss=29.36, generator_mel_loss=17.99, generator_kl_loss=1.444, generator_dur_loss=1.768, generator_adv_loss=2.087, generator_feat_match_loss=6.064, over 70.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=29.17, generator_mel_loss=17.88, generator_kl_loss=1.448, generator_dur_loss=1.732, generator_adv_loss=2.018, generator_feat_match_loss=6.1, over 2242.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:55:16,895 INFO [train.py:527] (4/6) Epoch 814, batch 88, global_batch_idx: 100900, batch size: 50, loss[discriminator_loss=2.719, discriminator_real_loss=1.365, discriminator_fake_loss=1.354, generator_loss=29.66, generator_mel_loss=17.7, generator_kl_loss=1.637, generator_dur_loss=1.651, generator_adv_loss=2.17, generator_feat_match_loss=6.497, over 50.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=29.09, generator_mel_loss=17.88, generator_kl_loss=1.436, generator_dur_loss=1.733, generator_adv_loss=2.019, generator_feat_match_loss=6.022, over 5148.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:56:58,319 INFO [train.py:919] (4/6) Start epoch 815 +2024-03-15 11:58:01,530 INFO [train.py:527] (4/6) Epoch 815, batch 14, global_batch_idx: 100950, batch size: 88, loss[discriminator_loss=2.712, discriminator_real_loss=1.355, discriminator_fake_loss=1.357, generator_loss=29.48, generator_mel_loss=18.37, generator_kl_loss=1.28, generator_dur_loss=1.827, generator_adv_loss=1.887, generator_feat_match_loss=6.109, over 88.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.364, discriminator_fake_loss=1.335, generator_loss=28.92, generator_mel_loss=17.84, generator_kl_loss=1.467, generator_dur_loss=1.748, generator_adv_loss=1.964, generator_feat_match_loss=5.901, over 833.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:00:23,989 INFO [train.py:527] (4/6) Epoch 815, batch 64, global_batch_idx: 101000, batch size: 62, loss[discriminator_loss=2.682, discriminator_real_loss=1.38, discriminator_fake_loss=1.302, generator_loss=28.34, generator_mel_loss=17.6, generator_kl_loss=1.271, generator_dur_loss=1.732, generator_adv_loss=1.908, generator_feat_match_loss=5.829, over 62.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.36, discriminator_fake_loss=1.323, generator_loss=28.93, generator_mel_loss=17.84, generator_kl_loss=1.44, generator_dur_loss=1.745, generator_adv_loss=1.991, generator_feat_match_loss=5.914, over 3582.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:00:23,991 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 12:00:32,151 INFO [train.py:591] (4/6) Epoch 815, validation: discriminator_loss=2.77, discriminator_real_loss=1.414, discriminator_fake_loss=1.356, generator_loss=27.53, generator_mel_loss=18, generator_kl_loss=1.165, generator_dur_loss=1.81, generator_adv_loss=1.9, generator_feat_match_loss=4.658, over 100.00 samples. +2024-03-15 12:00:32,152 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 12:02:51,516 INFO [train.py:527] (4/6) Epoch 815, batch 114, global_batch_idx: 101050, batch size: 48, loss[discriminator_loss=2.694, discriminator_real_loss=1.449, discriminator_fake_loss=1.245, generator_loss=28.3, generator_mel_loss=17.35, generator_kl_loss=1.516, generator_dur_loss=1.692, generator_adv_loss=2.03, generator_feat_match_loss=5.716, over 48.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.36, discriminator_fake_loss=1.329, generator_loss=28.88, generator_mel_loss=17.81, generator_kl_loss=1.434, generator_dur_loss=1.743, generator_adv_loss=1.989, generator_feat_match_loss=5.905, over 6462.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:03:20,220 INFO [train.py:919] (4/6) Start epoch 816 +2024-03-15 12:05:40,849 INFO [train.py:527] (4/6) Epoch 816, batch 40, global_batch_idx: 101100, batch size: 56, loss[discriminator_loss=2.702, discriminator_real_loss=1.381, discriminator_fake_loss=1.321, generator_loss=29.08, generator_mel_loss=17.97, generator_kl_loss=1.374, generator_dur_loss=1.679, generator_adv_loss=2.019, generator_feat_match_loss=6.04, over 56.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=29.07, generator_mel_loss=17.85, generator_kl_loss=1.407, generator_dur_loss=1.767, generator_adv_loss=2.002, generator_feat_match_loss=6.041, over 2463.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:07:55,598 INFO [train.py:527] (4/6) Epoch 816, batch 90, global_batch_idx: 101150, batch size: 74, loss[discriminator_loss=2.696, discriminator_real_loss=1.342, discriminator_fake_loss=1.354, generator_loss=27.96, generator_mel_loss=17.68, generator_kl_loss=1.248, generator_dur_loss=1.817, generator_adv_loss=2.026, generator_feat_match_loss=5.197, over 74.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.323, generator_loss=28.91, generator_mel_loss=17.82, generator_kl_loss=1.425, generator_dur_loss=1.751, generator_adv_loss=1.996, generator_feat_match_loss=5.916, over 5285.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:09:27,522 INFO [train.py:919] (4/6) Start epoch 817 +2024-03-15 12:10:35,966 INFO [train.py:527] (4/6) Epoch 817, batch 16, global_batch_idx: 101200, batch size: 80, loss[discriminator_loss=2.709, discriminator_real_loss=1.293, discriminator_fake_loss=1.416, generator_loss=28.14, generator_mel_loss=17.61, generator_kl_loss=1.133, generator_dur_loss=1.833, generator_adv_loss=2.185, generator_feat_match_loss=5.382, over 80.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.345, discriminator_fake_loss=1.33, generator_loss=29.28, generator_mel_loss=17.95, generator_kl_loss=1.379, generator_dur_loss=1.774, generator_adv_loss=2.034, generator_feat_match_loss=6.14, over 1037.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:10:35,967 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 12:10:44,064 INFO [train.py:591] (4/6) Epoch 817, validation: discriminator_loss=2.779, discriminator_real_loss=1.585, discriminator_fake_loss=1.194, generator_loss=27.21, generator_mel_loss=17.83, generator_kl_loss=1.27, generator_dur_loss=1.815, generator_adv_loss=2.099, generator_feat_match_loss=4.199, over 100.00 samples. +2024-03-15 12:10:44,065 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 12:13:03,851 INFO [train.py:527] (4/6) Epoch 817, batch 66, global_batch_idx: 101250, batch size: 42, loss[discriminator_loss=2.707, discriminator_real_loss=1.453, discriminator_fake_loss=1.254, generator_loss=30.23, generator_mel_loss=18.02, generator_kl_loss=1.587, generator_dur_loss=1.663, generator_adv_loss=1.899, generator_feat_match_loss=7.067, over 42.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.08, generator_mel_loss=17.84, generator_kl_loss=1.409, generator_dur_loss=1.761, generator_adv_loss=2.017, generator_feat_match_loss=6.046, over 4083.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:15:22,546 INFO [train.py:527] (4/6) Epoch 817, batch 116, global_batch_idx: 101300, batch size: 64, loss[discriminator_loss=2.608, discriminator_real_loss=1.314, discriminator_fake_loss=1.293, generator_loss=29.97, generator_mel_loss=17.82, generator_kl_loss=1.412, generator_dur_loss=1.772, generator_adv_loss=2.075, generator_feat_match_loss=6.889, over 64.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.353, discriminator_fake_loss=1.323, generator_loss=29.06, generator_mel_loss=17.86, generator_kl_loss=1.409, generator_dur_loss=1.762, generator_adv_loss=2.011, generator_feat_match_loss=6.018, over 6894.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:15:44,074 INFO [train.py:919] (4/6) Start epoch 818 +2024-03-15 12:18:05,842 INFO [train.py:527] (4/6) Epoch 818, batch 42, global_batch_idx: 101350, batch size: 70, loss[discriminator_loss=2.636, discriminator_real_loss=1.255, discriminator_fake_loss=1.381, generator_loss=28.91, generator_mel_loss=17.86, generator_kl_loss=1.399, generator_dur_loss=1.757, generator_adv_loss=2.223, generator_feat_match_loss=5.668, over 70.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=29.01, generator_mel_loss=17.89, generator_kl_loss=1.41, generator_dur_loss=1.761, generator_adv_loss=2.007, generator_feat_match_loss=5.95, over 2619.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:20:26,207 INFO [train.py:527] (4/6) Epoch 818, batch 92, global_batch_idx: 101400, batch size: 14, loss[discriminator_loss=2.658, discriminator_real_loss=1.328, discriminator_fake_loss=1.33, generator_loss=30.34, generator_mel_loss=18.23, generator_kl_loss=1.689, generator_dur_loss=1.558, generator_adv_loss=2.022, generator_feat_match_loss=6.842, over 14.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.356, discriminator_fake_loss=1.323, generator_loss=28.96, generator_mel_loss=17.83, generator_kl_loss=1.416, generator_dur_loss=1.755, generator_adv_loss=2.006, generator_feat_match_loss=5.96, over 5461.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:20:26,209 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 12:20:34,993 INFO [train.py:591] (4/6) Epoch 818, validation: discriminator_loss=2.716, discriminator_real_loss=1.288, discriminator_fake_loss=1.428, generator_loss=27.67, generator_mel_loss=18.01, generator_kl_loss=1.323, generator_dur_loss=1.802, generator_adv_loss=1.839, generator_feat_match_loss=4.698, over 100.00 samples. +2024-03-15 12:20:34,994 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 12:22:01,245 INFO [train.py:919] (4/6) Start epoch 819 +2024-03-15 12:23:14,773 INFO [train.py:527] (4/6) Epoch 819, batch 18, global_batch_idx: 101450, batch size: 59, loss[discriminator_loss=2.702, discriminator_real_loss=1.475, discriminator_fake_loss=1.227, generator_loss=28.59, generator_mel_loss=17.57, generator_kl_loss=1.46, generator_dur_loss=1.73, generator_adv_loss=1.978, generator_feat_match_loss=5.858, over 59.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=29.04, generator_mel_loss=17.72, generator_kl_loss=1.417, generator_dur_loss=1.754, generator_adv_loss=2.008, generator_feat_match_loss=6.146, over 1215.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:25:33,154 INFO [train.py:527] (4/6) Epoch 819, batch 68, global_batch_idx: 101500, batch size: 59, loss[discriminator_loss=2.736, discriminator_real_loss=1.423, discriminator_fake_loss=1.313, generator_loss=27.59, generator_mel_loss=17.59, generator_kl_loss=1.54, generator_dur_loss=1.687, generator_adv_loss=2.056, generator_feat_match_loss=4.71, over 59.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.357, discriminator_fake_loss=1.329, generator_loss=28.94, generator_mel_loss=17.74, generator_kl_loss=1.422, generator_dur_loss=1.751, generator_adv_loss=2.012, generator_feat_match_loss=6.021, over 4093.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:27:51,582 INFO [train.py:527] (4/6) Epoch 819, batch 118, global_batch_idx: 101550, batch size: 25, loss[discriminator_loss=2.626, discriminator_real_loss=1.292, discriminator_fake_loss=1.334, generator_loss=31.65, generator_mel_loss=18.22, generator_kl_loss=1.8, generator_dur_loss=1.522, generator_adv_loss=2.157, generator_feat_match_loss=7.947, over 25.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.322, generator_loss=28.89, generator_mel_loss=17.76, generator_kl_loss=1.409, generator_dur_loss=1.748, generator_adv_loss=2.007, generator_feat_match_loss=5.968, over 6972.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:28:08,598 INFO [train.py:919] (4/6) Start epoch 820 +2024-03-15 12:30:35,352 INFO [train.py:527] (4/6) Epoch 820, batch 44, global_batch_idx: 101600, batch size: 70, loss[discriminator_loss=2.681, discriminator_real_loss=1.341, discriminator_fake_loss=1.339, generator_loss=29.12, generator_mel_loss=17.84, generator_kl_loss=1.379, generator_dur_loss=1.845, generator_adv_loss=2.087, generator_feat_match_loss=5.96, over 70.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.352, discriminator_fake_loss=1.324, generator_loss=28.93, generator_mel_loss=17.82, generator_kl_loss=1.368, generator_dur_loss=1.765, generator_adv_loss=2.019, generator_feat_match_loss=5.958, over 2827.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:30:35,353 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 12:30:43,545 INFO [train.py:591] (4/6) Epoch 820, validation: discriminator_loss=2.753, discriminator_real_loss=1.358, discriminator_fake_loss=1.395, generator_loss=28.18, generator_mel_loss=18.39, generator_kl_loss=1.237, generator_dur_loss=1.813, generator_adv_loss=1.896, generator_feat_match_loss=4.836, over 100.00 samples. +2024-03-15 12:30:43,546 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 12:33:03,588 INFO [train.py:527] (4/6) Epoch 820, batch 94, global_batch_idx: 101650, batch size: 25, loss[discriminator_loss=2.747, discriminator_real_loss=1.443, discriminator_fake_loss=1.304, generator_loss=28.63, generator_mel_loss=18.04, generator_kl_loss=1.665, generator_dur_loss=1.601, generator_adv_loss=1.893, generator_feat_match_loss=5.43, over 25.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.05, generator_mel_loss=17.84, generator_kl_loss=1.404, generator_dur_loss=1.754, generator_adv_loss=2.012, generator_feat_match_loss=6.046, over 5690.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:34:23,311 INFO [train.py:919] (4/6) Start epoch 821 +2024-03-15 12:35:45,137 INFO [train.py:527] (4/6) Epoch 821, batch 20, global_batch_idx: 101700, batch size: 15, loss[discriminator_loss=2.587, discriminator_real_loss=1.276, discriminator_fake_loss=1.31, generator_loss=29.14, generator_mel_loss=17.39, generator_kl_loss=1.606, generator_dur_loss=1.59, generator_adv_loss=2.149, generator_feat_match_loss=6.406, over 15.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.354, discriminator_fake_loss=1.319, generator_loss=28.97, generator_mel_loss=17.8, generator_kl_loss=1.387, generator_dur_loss=1.76, generator_adv_loss=1.999, generator_feat_match_loss=6.024, over 1203.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:38:06,252 INFO [train.py:527] (4/6) Epoch 821, batch 70, global_batch_idx: 101750, batch size: 70, loss[discriminator_loss=2.672, discriminator_real_loss=1.344, discriminator_fake_loss=1.328, generator_loss=29.35, generator_mel_loss=17.83, generator_kl_loss=1.365, generator_dur_loss=1.79, generator_adv_loss=1.868, generator_feat_match_loss=6.498, over 70.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.363, discriminator_fake_loss=1.323, generator_loss=29.08, generator_mel_loss=17.91, generator_kl_loss=1.412, generator_dur_loss=1.753, generator_adv_loss=2.004, generator_feat_match_loss=5.995, over 3952.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:40:20,003 INFO [train.py:527] (4/6) Epoch 821, batch 120, global_batch_idx: 101800, batch size: 31, loss[discriminator_loss=2.643, discriminator_real_loss=1.418, discriminator_fake_loss=1.225, generator_loss=31.29, generator_mel_loss=18.76, generator_kl_loss=1.605, generator_dur_loss=1.627, generator_adv_loss=2.143, generator_feat_match_loss=7.151, over 31.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.363, discriminator_fake_loss=1.326, generator_loss=29.06, generator_mel_loss=17.93, generator_kl_loss=1.417, generator_dur_loss=1.748, generator_adv_loss=2.001, generator_feat_match_loss=5.971, over 6704.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:40:20,004 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 12:40:28,989 INFO [train.py:591] (4/6) Epoch 821, validation: discriminator_loss=2.727, discriminator_real_loss=1.443, discriminator_fake_loss=1.284, generator_loss=28.11, generator_mel_loss=18.16, generator_kl_loss=1.341, generator_dur_loss=1.825, generator_adv_loss=1.962, generator_feat_match_loss=4.823, over 100.00 samples. +2024-03-15 12:40:28,990 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 12:40:38,235 INFO [train.py:919] (4/6) Start epoch 822 +2024-03-15 12:43:10,197 INFO [train.py:527] (4/6) Epoch 822, batch 46, global_batch_idx: 101850, batch size: 44, loss[discriminator_loss=2.618, discriminator_real_loss=1.354, discriminator_fake_loss=1.264, generator_loss=30.4, generator_mel_loss=18.17, generator_kl_loss=1.644, generator_dur_loss=1.64, generator_adv_loss=2.03, generator_feat_match_loss=6.913, over 44.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.321, generator_loss=28.96, generator_mel_loss=17.79, generator_kl_loss=1.417, generator_dur_loss=1.743, generator_adv_loss=2.01, generator_feat_match_loss=5.995, over 2626.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:45:28,348 INFO [train.py:527] (4/6) Epoch 822, batch 96, global_batch_idx: 101900, batch size: 80, loss[discriminator_loss=2.692, discriminator_real_loss=1.278, discriminator_fake_loss=1.413, generator_loss=28.12, generator_mel_loss=17.59, generator_kl_loss=1.258, generator_dur_loss=1.826, generator_adv_loss=1.915, generator_feat_match_loss=5.528, over 80.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.355, discriminator_fake_loss=1.327, generator_loss=28.95, generator_mel_loss=17.83, generator_kl_loss=1.417, generator_dur_loss=1.744, generator_adv_loss=2, generator_feat_match_loss=5.958, over 5414.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:46:44,971 INFO [train.py:919] (4/6) Start epoch 823 +2024-03-15 12:48:11,378 INFO [train.py:527] (4/6) Epoch 823, batch 22, global_batch_idx: 101950, batch size: 88, loss[discriminator_loss=2.701, discriminator_real_loss=1.313, discriminator_fake_loss=1.388, generator_loss=29, generator_mel_loss=18.03, generator_kl_loss=1.411, generator_dur_loss=1.888, generator_adv_loss=1.952, generator_feat_match_loss=5.722, over 88.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.364, discriminator_fake_loss=1.344, generator_loss=28.9, generator_mel_loss=17.88, generator_kl_loss=1.453, generator_dur_loss=1.752, generator_adv_loss=1.971, generator_feat_match_loss=5.84, over 1233.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:50:31,145 INFO [train.py:527] (4/6) Epoch 823, batch 72, global_batch_idx: 102000, batch size: 72, loss[discriminator_loss=2.753, discriminator_real_loss=1.32, discriminator_fake_loss=1.434, generator_loss=28.81, generator_mel_loss=17.67, generator_kl_loss=1.428, generator_dur_loss=1.813, generator_adv_loss=1.97, generator_feat_match_loss=5.929, over 72.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.354, discriminator_fake_loss=1.333, generator_loss=28.93, generator_mel_loss=17.83, generator_kl_loss=1.423, generator_dur_loss=1.759, generator_adv_loss=2.003, generator_feat_match_loss=5.916, over 4136.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:50:31,147 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 12:50:39,871 INFO [train.py:591] (4/6) Epoch 823, validation: discriminator_loss=2.758, discriminator_real_loss=1.426, discriminator_fake_loss=1.331, generator_loss=27.91, generator_mel_loss=18.15, generator_kl_loss=1.312, generator_dur_loss=1.814, generator_adv_loss=1.929, generator_feat_match_loss=4.703, over 100.00 samples. +2024-03-15 12:50:39,872 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 12:52:57,137 INFO [train.py:527] (4/6) Epoch 823, batch 122, global_batch_idx: 102050, batch size: 77, loss[discriminator_loss=2.706, discriminator_real_loss=1.416, discriminator_fake_loss=1.29, generator_loss=28.57, generator_mel_loss=17.6, generator_kl_loss=1.319, generator_dur_loss=1.798, generator_adv_loss=2.018, generator_feat_match_loss=5.834, over 77.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=28.94, generator_mel_loss=17.81, generator_kl_loss=1.426, generator_dur_loss=1.752, generator_adv_loss=2.005, generator_feat_match_loss=5.946, over 6914.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:53:02,008 INFO [train.py:919] (4/6) Start epoch 824 +2024-03-15 12:55:40,408 INFO [train.py:527] (4/6) Epoch 824, batch 48, global_batch_idx: 102100, batch size: 83, loss[discriminator_loss=2.687, discriminator_real_loss=1.381, discriminator_fake_loss=1.306, generator_loss=28.84, generator_mel_loss=17.5, generator_kl_loss=1.405, generator_dur_loss=1.841, generator_adv_loss=1.876, generator_feat_match_loss=6.218, over 83.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.353, discriminator_fake_loss=1.322, generator_loss=28.99, generator_mel_loss=17.77, generator_kl_loss=1.413, generator_dur_loss=1.748, generator_adv_loss=2, generator_feat_match_loss=6.061, over 2904.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:57:58,899 INFO [train.py:527] (4/6) Epoch 824, batch 98, global_batch_idx: 102150, batch size: 16, loss[discriminator_loss=2.686, discriminator_real_loss=1.277, discriminator_fake_loss=1.409, generator_loss=27.99, generator_mel_loss=17.6, generator_kl_loss=1.582, generator_dur_loss=1.609, generator_adv_loss=2.085, generator_feat_match_loss=5.108, over 16.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.351, discriminator_fake_loss=1.323, generator_loss=28.97, generator_mel_loss=17.79, generator_kl_loss=1.394, generator_dur_loss=1.751, generator_adv_loss=2.009, generator_feat_match_loss=6.023, over 5866.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:59:04,955 INFO [train.py:919] (4/6) Start epoch 825 +2024-03-15 13:00:36,474 INFO [train.py:527] (4/6) Epoch 825, batch 24, global_batch_idx: 102200, batch size: 74, loss[discriminator_loss=2.676, discriminator_real_loss=1.376, discriminator_fake_loss=1.3, generator_loss=29.3, generator_mel_loss=17.96, generator_kl_loss=1.452, generator_dur_loss=1.789, generator_adv_loss=1.886, generator_feat_match_loss=6.214, over 74.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.349, discriminator_fake_loss=1.32, generator_loss=29.18, generator_mel_loss=17.87, generator_kl_loss=1.421, generator_dur_loss=1.773, generator_adv_loss=2.005, generator_feat_match_loss=6.107, over 1572.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:00:36,476 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 13:00:44,503 INFO [train.py:591] (4/6) Epoch 825, validation: discriminator_loss=2.737, discriminator_real_loss=1.38, discriminator_fake_loss=1.358, generator_loss=28.58, generator_mel_loss=18.35, generator_kl_loss=1.403, generator_dur_loss=1.801, generator_adv_loss=1.848, generator_feat_match_loss=5.17, over 100.00 samples. +2024-03-15 13:00:44,504 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 13:03:03,000 INFO [train.py:527] (4/6) Epoch 825, batch 74, global_batch_idx: 102250, batch size: 64, loss[discriminator_loss=2.736, discriminator_real_loss=1.407, discriminator_fake_loss=1.329, generator_loss=28.67, generator_mel_loss=17.84, generator_kl_loss=1.401, generator_dur_loss=1.745, generator_adv_loss=1.872, generator_feat_match_loss=5.807, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=29.08, generator_mel_loss=17.85, generator_kl_loss=1.43, generator_dur_loss=1.754, generator_adv_loss=2.004, generator_feat_match_loss=6.035, over 4521.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:05:19,991 INFO [train.py:919] (4/6) Start epoch 826 +2024-03-15 13:05:44,622 INFO [train.py:527] (4/6) Epoch 826, batch 0, global_batch_idx: 102300, batch size: 88, loss[discriminator_loss=2.692, discriminator_real_loss=1.361, discriminator_fake_loss=1.331, generator_loss=28.84, generator_mel_loss=17.88, generator_kl_loss=1.499, generator_dur_loss=1.849, generator_adv_loss=1.853, generator_feat_match_loss=5.753, over 88.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.361, discriminator_fake_loss=1.331, generator_loss=28.84, generator_mel_loss=17.88, generator_kl_loss=1.499, generator_dur_loss=1.849, generator_adv_loss=1.853, generator_feat_match_loss=5.753, over 88.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:08:00,690 INFO [train.py:527] (4/6) Epoch 826, batch 50, global_batch_idx: 102350, batch size: 70, loss[discriminator_loss=2.641, discriminator_real_loss=1.302, discriminator_fake_loss=1.339, generator_loss=29.33, generator_mel_loss=17.7, generator_kl_loss=1.269, generator_dur_loss=1.812, generator_adv_loss=2.171, generator_feat_match_loss=6.38, over 70.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.349, discriminator_fake_loss=1.328, generator_loss=29.06, generator_mel_loss=17.85, generator_kl_loss=1.443, generator_dur_loss=1.742, generator_adv_loss=1.995, generator_feat_match_loss=6.027, over 2845.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:10:20,752 INFO [train.py:527] (4/6) Epoch 826, batch 100, global_batch_idx: 102400, batch size: 77, loss[discriminator_loss=2.812, discriminator_real_loss=1.304, discriminator_fake_loss=1.508, generator_loss=28.02, generator_mel_loss=17.56, generator_kl_loss=1.353, generator_dur_loss=1.808, generator_adv_loss=1.881, generator_feat_match_loss=5.41, over 77.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.35, discriminator_fake_loss=1.333, generator_loss=29.07, generator_mel_loss=17.84, generator_kl_loss=1.438, generator_dur_loss=1.745, generator_adv_loss=1.995, generator_feat_match_loss=6.044, over 5656.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:10:20,754 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 13:10:29,554 INFO [train.py:591] (4/6) Epoch 826, validation: discriminator_loss=2.782, discriminator_real_loss=1.428, discriminator_fake_loss=1.355, generator_loss=27.5, generator_mel_loss=18.04, generator_kl_loss=1.325, generator_dur_loss=1.814, generator_adv_loss=1.874, generator_feat_match_loss=4.452, over 100.00 samples. +2024-03-15 13:10:29,555 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 13:11:33,669 INFO [train.py:919] (4/6) Start epoch 827 +2024-03-15 13:13:08,349 INFO [train.py:527] (4/6) Epoch 827, batch 26, global_batch_idx: 102450, batch size: 53, loss[discriminator_loss=2.656, discriminator_real_loss=1.315, discriminator_fake_loss=1.341, generator_loss=28.89, generator_mel_loss=17.95, generator_kl_loss=1.557, generator_dur_loss=1.699, generator_adv_loss=1.974, generator_feat_match_loss=5.703, over 53.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.339, discriminator_fake_loss=1.331, generator_loss=28.93, generator_mel_loss=17.91, generator_kl_loss=1.412, generator_dur_loss=1.747, generator_adv_loss=2.006, generator_feat_match_loss=5.85, over 1508.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:15:26,040 INFO [train.py:527] (4/6) Epoch 827, batch 76, global_batch_idx: 102500, batch size: 48, loss[discriminator_loss=2.641, discriminator_real_loss=1.31, discriminator_fake_loss=1.331, generator_loss=29.99, generator_mel_loss=18.01, generator_kl_loss=1.44, generator_dur_loss=1.69, generator_adv_loss=2.087, generator_feat_match_loss=6.76, over 48.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.35, discriminator_fake_loss=1.322, generator_loss=28.84, generator_mel_loss=17.81, generator_kl_loss=1.403, generator_dur_loss=1.749, generator_adv_loss=2.005, generator_feat_match_loss=5.869, over 4569.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:17:40,392 INFO [train.py:919] (4/6) Start epoch 828 +2024-03-15 13:18:09,388 INFO [train.py:527] (4/6) Epoch 828, batch 2, global_batch_idx: 102550, batch size: 44, loss[discriminator_loss=2.671, discriminator_real_loss=1.304, discriminator_fake_loss=1.367, generator_loss=29.39, generator_mel_loss=17.71, generator_kl_loss=1.496, generator_dur_loss=1.704, generator_adv_loss=2.112, generator_feat_match_loss=6.37, over 44.00 samples.], tot_loss[discriminator_loss=2.658, discriminator_real_loss=1.332, discriminator_fake_loss=1.326, generator_loss=28.93, generator_mel_loss=17.75, generator_kl_loss=1.454, generator_dur_loss=1.716, generator_adv_loss=2.044, generator_feat_match_loss=5.964, over 151.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:20:27,722 INFO [train.py:527] (4/6) Epoch 828, batch 52, global_batch_idx: 102600, batch size: 55, loss[discriminator_loss=2.644, discriminator_real_loss=1.428, discriminator_fake_loss=1.216, generator_loss=27.94, generator_mel_loss=17.69, generator_kl_loss=1.312, generator_dur_loss=1.724, generator_adv_loss=1.865, generator_feat_match_loss=5.349, over 55.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=28.83, generator_mel_loss=17.78, generator_kl_loss=1.442, generator_dur_loss=1.735, generator_adv_loss=1.994, generator_feat_match_loss=5.884, over 2951.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:20:27,723 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 13:20:35,763 INFO [train.py:591] (4/6) Epoch 828, validation: discriminator_loss=2.727, discriminator_real_loss=1.372, discriminator_fake_loss=1.355, generator_loss=27.59, generator_mel_loss=17.59, generator_kl_loss=1.329, generator_dur_loss=1.814, generator_adv_loss=1.834, generator_feat_match_loss=5.023, over 100.00 samples. +2024-03-15 13:20:35,764 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 13:22:54,501 INFO [train.py:527] (4/6) Epoch 828, batch 102, global_batch_idx: 102650, batch size: 50, loss[discriminator_loss=2.726, discriminator_real_loss=1.403, discriminator_fake_loss=1.323, generator_loss=28.22, generator_mel_loss=17.93, generator_kl_loss=1.337, generator_dur_loss=1.734, generator_adv_loss=1.902, generator_feat_match_loss=5.318, over 50.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=28.95, generator_mel_loss=17.81, generator_kl_loss=1.433, generator_dur_loss=1.741, generator_adv_loss=1.998, generator_feat_match_loss=5.964, over 5701.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:23:55,250 INFO [train.py:919] (4/6) Start epoch 829 +2024-03-15 13:25:38,719 INFO [train.py:527] (4/6) Epoch 829, batch 28, global_batch_idx: 102700, batch size: 15, loss[discriminator_loss=2.604, discriminator_real_loss=1.25, discriminator_fake_loss=1.354, generator_loss=30.52, generator_mel_loss=17.67, generator_kl_loss=1.965, generator_dur_loss=1.637, generator_adv_loss=2.147, generator_feat_match_loss=7.106, over 15.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.353, discriminator_fake_loss=1.329, generator_loss=29.02, generator_mel_loss=17.77, generator_kl_loss=1.448, generator_dur_loss=1.727, generator_adv_loss=1.987, generator_feat_match_loss=6.09, over 1536.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:27:57,164 INFO [train.py:527] (4/6) Epoch 829, batch 78, global_batch_idx: 102750, batch size: 62, loss[discriminator_loss=2.736, discriminator_real_loss=1.39, discriminator_fake_loss=1.347, generator_loss=28.49, generator_mel_loss=17.75, generator_kl_loss=1.491, generator_dur_loss=1.727, generator_adv_loss=1.936, generator_feat_match_loss=5.581, over 62.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=28.95, generator_mel_loss=17.8, generator_kl_loss=1.443, generator_dur_loss=1.727, generator_adv_loss=1.99, generator_feat_match_loss=5.999, over 4374.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:30:03,473 INFO [train.py:919] (4/6) Start epoch 830 +2024-03-15 13:30:37,949 INFO [train.py:527] (4/6) Epoch 830, batch 4, global_batch_idx: 102800, batch size: 96, loss[discriminator_loss=2.744, discriminator_real_loss=1.44, discriminator_fake_loss=1.304, generator_loss=28.81, generator_mel_loss=18.02, generator_kl_loss=1.37, generator_dur_loss=1.852, generator_adv_loss=1.956, generator_feat_match_loss=5.609, over 96.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.403, discriminator_fake_loss=1.298, generator_loss=29.01, generator_mel_loss=17.82, generator_kl_loss=1.429, generator_dur_loss=1.754, generator_adv_loss=2.128, generator_feat_match_loss=5.887, over 325.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:30:37,952 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 13:30:45,667 INFO [train.py:591] (4/6) Epoch 830, validation: discriminator_loss=2.748, discriminator_real_loss=1.426, discriminator_fake_loss=1.322, generator_loss=27.94, generator_mel_loss=17.7, generator_kl_loss=1.346, generator_dur_loss=1.804, generator_adv_loss=2.109, generator_feat_match_loss=4.984, over 100.00 samples. +2024-03-15 13:30:45,669 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 13:33:07,718 INFO [train.py:527] (4/6) Epoch 830, batch 54, global_batch_idx: 102850, batch size: 88, loss[discriminator_loss=2.647, discriminator_real_loss=1.303, discriminator_fake_loss=1.344, generator_loss=28.66, generator_mel_loss=17.66, generator_kl_loss=1.288, generator_dur_loss=1.846, generator_adv_loss=2.035, generator_feat_match_loss=5.83, over 88.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.37, discriminator_fake_loss=1.32, generator_loss=28.9, generator_mel_loss=17.77, generator_kl_loss=1.436, generator_dur_loss=1.738, generator_adv_loss=2.024, generator_feat_match_loss=5.933, over 3135.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:35:25,322 INFO [train.py:527] (4/6) Epoch 830, batch 104, global_batch_idx: 102900, batch size: 83, loss[discriminator_loss=2.637, discriminator_real_loss=1.374, discriminator_fake_loss=1.263, generator_loss=28.76, generator_mel_loss=17.74, generator_kl_loss=1.395, generator_dur_loss=1.796, generator_adv_loss=1.89, generator_feat_match_loss=5.935, over 83.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.369, discriminator_fake_loss=1.321, generator_loss=28.99, generator_mel_loss=17.8, generator_kl_loss=1.433, generator_dur_loss=1.737, generator_adv_loss=2.015, generator_feat_match_loss=6.007, over 6088.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:36:16,843 INFO [train.py:919] (4/6) Start epoch 831 +2024-03-15 13:38:02,971 INFO [train.py:527] (4/6) Epoch 831, batch 30, global_batch_idx: 102950, batch size: 50, loss[discriminator_loss=2.65, discriminator_real_loss=1.321, discriminator_fake_loss=1.329, generator_loss=28.98, generator_mel_loss=18.06, generator_kl_loss=1.502, generator_dur_loss=1.691, generator_adv_loss=2.041, generator_feat_match_loss=5.692, over 50.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.36, discriminator_fake_loss=1.341, generator_loss=29.03, generator_mel_loss=17.93, generator_kl_loss=1.403, generator_dur_loss=1.754, generator_adv_loss=1.986, generator_feat_match_loss=5.96, over 1861.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:40:21,272 INFO [train.py:527] (4/6) Epoch 831, batch 80, global_batch_idx: 103000, batch size: 77, loss[discriminator_loss=2.725, discriminator_real_loss=1.392, discriminator_fake_loss=1.334, generator_loss=28.64, generator_mel_loss=17.63, generator_kl_loss=1.435, generator_dur_loss=1.868, generator_adv_loss=2.006, generator_feat_match_loss=5.7, over 77.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=29.01, generator_mel_loss=17.84, generator_kl_loss=1.429, generator_dur_loss=1.744, generator_adv_loss=1.999, generator_feat_match_loss=5.996, over 4549.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:40:21,274 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 13:40:30,034 INFO [train.py:591] (4/6) Epoch 831, validation: discriminator_loss=2.713, discriminator_real_loss=1.441, discriminator_fake_loss=1.271, generator_loss=27.33, generator_mel_loss=17.78, generator_kl_loss=1.316, generator_dur_loss=1.814, generator_adv_loss=1.989, generator_feat_match_loss=4.432, over 100.00 samples. +2024-03-15 13:40:30,035 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 13:42:32,295 INFO [train.py:919] (4/6) Start epoch 832 +2024-03-15 13:43:12,288 INFO [train.py:527] (4/6) Epoch 832, batch 6, global_batch_idx: 103050, batch size: 52, loss[discriminator_loss=2.721, discriminator_real_loss=1.385, discriminator_fake_loss=1.336, generator_loss=27.82, generator_mel_loss=17.48, generator_kl_loss=1.411, generator_dur_loss=1.69, generator_adv_loss=1.828, generator_feat_match_loss=5.409, over 52.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.358, discriminator_fake_loss=1.318, generator_loss=28.84, generator_mel_loss=17.82, generator_kl_loss=1.489, generator_dur_loss=1.673, generator_adv_loss=1.987, generator_feat_match_loss=5.863, over 347.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:45:31,513 INFO [train.py:527] (4/6) Epoch 832, batch 56, global_batch_idx: 103100, batch size: 42, loss[discriminator_loss=2.689, discriminator_real_loss=1.427, discriminator_fake_loss=1.263, generator_loss=30.1, generator_mel_loss=18.22, generator_kl_loss=1.657, generator_dur_loss=1.622, generator_adv_loss=2.053, generator_feat_match_loss=6.555, over 42.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=28.92, generator_mel_loss=17.8, generator_kl_loss=1.442, generator_dur_loss=1.738, generator_adv_loss=2.001, generator_feat_match_loss=5.944, over 3243.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:47:51,704 INFO [train.py:527] (4/6) Epoch 832, batch 106, global_batch_idx: 103150, batch size: 36, loss[discriminator_loss=2.681, discriminator_real_loss=1.399, discriminator_fake_loss=1.282, generator_loss=28.37, generator_mel_loss=17.9, generator_kl_loss=1.574, generator_dur_loss=1.667, generator_adv_loss=1.915, generator_feat_match_loss=5.318, over 36.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.351, discriminator_fake_loss=1.326, generator_loss=29.07, generator_mel_loss=17.86, generator_kl_loss=1.445, generator_dur_loss=1.738, generator_adv_loss=2.006, generator_feat_match_loss=6.016, over 6106.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:48:36,887 INFO [train.py:919] (4/6) Start epoch 833 +2024-03-15 13:50:31,944 INFO [train.py:527] (4/6) Epoch 833, batch 32, global_batch_idx: 103200, batch size: 48, loss[discriminator_loss=2.639, discriminator_real_loss=1.329, discriminator_fake_loss=1.31, generator_loss=29.58, generator_mel_loss=17.95, generator_kl_loss=1.429, generator_dur_loss=1.7, generator_adv_loss=2.073, generator_feat_match_loss=6.421, over 48.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.363, discriminator_fake_loss=1.318, generator_loss=28.82, generator_mel_loss=17.77, generator_kl_loss=1.422, generator_dur_loss=1.713, generator_adv_loss=2.01, generator_feat_match_loss=5.909, over 1702.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:50:31,945 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 13:50:39,871 INFO [train.py:591] (4/6) Epoch 833, validation: discriminator_loss=2.712, discriminator_real_loss=1.41, discriminator_fake_loss=1.302, generator_loss=28.64, generator_mel_loss=18.45, generator_kl_loss=1.419, generator_dur_loss=1.795, generator_adv_loss=1.966, generator_feat_match_loss=5.01, over 100.00 samples. +2024-03-15 13:50:39,872 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 13:52:59,998 INFO [train.py:527] (4/6) Epoch 833, batch 82, global_batch_idx: 103250, batch size: 25, loss[discriminator_loss=2.711, discriminator_real_loss=1.487, discriminator_fake_loss=1.224, generator_loss=28.9, generator_mel_loss=18.12, generator_kl_loss=1.671, generator_dur_loss=1.586, generator_adv_loss=2.074, generator_feat_match_loss=5.448, over 25.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.362, discriminator_fake_loss=1.32, generator_loss=28.91, generator_mel_loss=17.77, generator_kl_loss=1.398, generator_dur_loss=1.743, generator_adv_loss=2.005, generator_feat_match_loss=5.988, over 4789.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:54:51,682 INFO [train.py:919] (4/6) Start epoch 834 +2024-03-15 13:55:35,797 INFO [train.py:527] (4/6) Epoch 834, batch 8, global_batch_idx: 103300, batch size: 62, loss[discriminator_loss=2.665, discriminator_real_loss=1.305, discriminator_fake_loss=1.36, generator_loss=28.68, generator_mel_loss=17.66, generator_kl_loss=1.546, generator_dur_loss=1.712, generator_adv_loss=2.187, generator_feat_match_loss=5.57, over 62.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.348, discriminator_fake_loss=1.323, generator_loss=29.05, generator_mel_loss=17.88, generator_kl_loss=1.421, generator_dur_loss=1.705, generator_adv_loss=2.006, generator_feat_match_loss=6.036, over 484.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:57:53,715 INFO [train.py:527] (4/6) Epoch 834, batch 58, global_batch_idx: 103350, batch size: 25, loss[discriminator_loss=2.565, discriminator_real_loss=1.278, discriminator_fake_loss=1.287, generator_loss=30.85, generator_mel_loss=18.47, generator_kl_loss=1.862, generator_dur_loss=1.54, generator_adv_loss=2.023, generator_feat_match_loss=6.957, over 25.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=29.11, generator_mel_loss=17.88, generator_kl_loss=1.443, generator_dur_loss=1.732, generator_adv_loss=1.992, generator_feat_match_loss=6.058, over 3261.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:00:12,106 INFO [train.py:527] (4/6) Epoch 834, batch 108, global_batch_idx: 103400, batch size: 15, loss[discriminator_loss=2.737, discriminator_real_loss=1.308, discriminator_fake_loss=1.429, generator_loss=29.77, generator_mel_loss=17.98, generator_kl_loss=1.863, generator_dur_loss=1.56, generator_adv_loss=1.916, generator_feat_match_loss=6.451, over 15.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=29.05, generator_mel_loss=17.84, generator_kl_loss=1.438, generator_dur_loss=1.729, generator_adv_loss=2.005, generator_feat_match_loss=6.039, over 5924.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:00:12,108 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 14:00:20,873 INFO [train.py:591] (4/6) Epoch 834, validation: discriminator_loss=2.701, discriminator_real_loss=1.406, discriminator_fake_loss=1.295, generator_loss=28.53, generator_mel_loss=18.59, generator_kl_loss=1.318, generator_dur_loss=1.791, generator_adv_loss=1.891, generator_feat_match_loss=4.939, over 100.00 samples. +2024-03-15 14:00:20,873 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 14:01:05,170 INFO [train.py:919] (4/6) Start epoch 835 +2024-03-15 14:03:05,128 INFO [train.py:527] (4/6) Epoch 835, batch 34, global_batch_idx: 103450, batch size: 83, loss[discriminator_loss=2.666, discriminator_real_loss=1.294, discriminator_fake_loss=1.372, generator_loss=28.7, generator_mel_loss=17.5, generator_kl_loss=1.62, generator_dur_loss=1.855, generator_adv_loss=2.043, generator_feat_match_loss=5.68, over 83.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.36, discriminator_fake_loss=1.319, generator_loss=28.89, generator_mel_loss=17.67, generator_kl_loss=1.43, generator_dur_loss=1.756, generator_adv_loss=2.002, generator_feat_match_loss=6.035, over 2149.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:05:22,171 INFO [train.py:527] (4/6) Epoch 835, batch 84, global_batch_idx: 103500, batch size: 53, loss[discriminator_loss=2.637, discriminator_real_loss=1.226, discriminator_fake_loss=1.411, generator_loss=29.21, generator_mel_loss=18.07, generator_kl_loss=1.496, generator_dur_loss=1.702, generator_adv_loss=2.07, generator_feat_match_loss=5.872, over 53.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=28.88, generator_mel_loss=17.74, generator_kl_loss=1.434, generator_dur_loss=1.748, generator_adv_loss=1.996, generator_feat_match_loss=5.964, over 4968.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:07:11,021 INFO [train.py:919] (4/6) Start epoch 836 +2024-03-15 14:08:02,022 INFO [train.py:527] (4/6) Epoch 836, batch 10, global_batch_idx: 103550, batch size: 47, loss[discriminator_loss=2.623, discriminator_real_loss=1.3, discriminator_fake_loss=1.323, generator_loss=29.12, generator_mel_loss=17.54, generator_kl_loss=1.454, generator_dur_loss=1.656, generator_adv_loss=2.119, generator_feat_match_loss=6.348, over 47.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.337, discriminator_fake_loss=1.331, generator_loss=29.05, generator_mel_loss=17.86, generator_kl_loss=1.46, generator_dur_loss=1.718, generator_adv_loss=1.991, generator_feat_match_loss=6.029, over 579.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:10:26,495 INFO [train.py:527] (4/6) Epoch 836, batch 60, global_batch_idx: 103600, batch size: 66, loss[discriminator_loss=2.666, discriminator_real_loss=1.308, discriminator_fake_loss=1.358, generator_loss=28.76, generator_mel_loss=17.68, generator_kl_loss=1.443, generator_dur_loss=1.758, generator_adv_loss=1.984, generator_feat_match_loss=5.894, over 66.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.358, discriminator_fake_loss=1.318, generator_loss=28.91, generator_mel_loss=17.75, generator_kl_loss=1.431, generator_dur_loss=1.739, generator_adv_loss=2.004, generator_feat_match_loss=5.982, over 3521.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:10:26,496 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 14:10:34,773 INFO [train.py:591] (4/6) Epoch 836, validation: discriminator_loss=2.731, discriminator_real_loss=1.39, discriminator_fake_loss=1.341, generator_loss=28.17, generator_mel_loss=18.22, generator_kl_loss=1.235, generator_dur_loss=1.796, generator_adv_loss=1.845, generator_feat_match_loss=5.078, over 100.00 samples. +2024-03-15 14:10:34,774 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 14:12:51,398 INFO [train.py:527] (4/6) Epoch 836, batch 110, global_batch_idx: 103650, batch size: 62, loss[discriminator_loss=2.685, discriminator_real_loss=1.392, discriminator_fake_loss=1.292, generator_loss=28.77, generator_mel_loss=17.93, generator_kl_loss=1.289, generator_dur_loss=1.715, generator_adv_loss=2.004, generator_feat_match_loss=5.828, over 62.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.357, discriminator_fake_loss=1.318, generator_loss=28.99, generator_mel_loss=17.79, generator_kl_loss=1.43, generator_dur_loss=1.731, generator_adv_loss=2.011, generator_feat_match_loss=6.03, over 6119.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:13:25,428 INFO [train.py:919] (4/6) Start epoch 837 +2024-03-15 14:15:31,694 INFO [train.py:527] (4/6) Epoch 837, batch 36, global_batch_idx: 103700, batch size: 58, loss[discriminator_loss=2.64, discriminator_real_loss=1.348, discriminator_fake_loss=1.292, generator_loss=29.22, generator_mel_loss=18.24, generator_kl_loss=1.4, generator_dur_loss=1.695, generator_adv_loss=2.073, generator_feat_match_loss=5.815, over 58.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.35, discriminator_fake_loss=1.326, generator_loss=29.01, generator_mel_loss=17.81, generator_kl_loss=1.402, generator_dur_loss=1.745, generator_adv_loss=2.028, generator_feat_match_loss=6.026, over 2260.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:17:48,983 INFO [train.py:527] (4/6) Epoch 837, batch 86, global_batch_idx: 103750, batch size: 64, loss[discriminator_loss=2.681, discriminator_real_loss=1.326, discriminator_fake_loss=1.355, generator_loss=28.82, generator_mel_loss=17.87, generator_kl_loss=1.326, generator_dur_loss=1.736, generator_adv_loss=1.929, generator_feat_match_loss=5.956, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=28.96, generator_mel_loss=17.79, generator_kl_loss=1.401, generator_dur_loss=1.746, generator_adv_loss=2.007, generator_feat_match_loss=6.017, over 5238.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:19:30,346 INFO [train.py:919] (4/6) Start epoch 838 +2024-03-15 14:20:25,545 INFO [train.py:527] (4/6) Epoch 838, batch 12, global_batch_idx: 103800, batch size: 55, loss[discriminator_loss=2.646, discriminator_real_loss=1.281, discriminator_fake_loss=1.365, generator_loss=29.77, generator_mel_loss=17.91, generator_kl_loss=1.319, generator_dur_loss=1.731, generator_adv_loss=1.994, generator_feat_match_loss=6.815, over 55.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.345, discriminator_fake_loss=1.332, generator_loss=28.87, generator_mel_loss=17.84, generator_kl_loss=1.365, generator_dur_loss=1.759, generator_adv_loss=1.986, generator_feat_match_loss=5.912, over 824.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:20:25,547 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 14:20:33,320 INFO [train.py:591] (4/6) Epoch 838, validation: discriminator_loss=2.724, discriminator_real_loss=1.304, discriminator_fake_loss=1.42, generator_loss=27.89, generator_mel_loss=18.08, generator_kl_loss=1.151, generator_dur_loss=1.8, generator_adv_loss=1.83, generator_feat_match_loss=5.03, over 100.00 samples. +2024-03-15 14:20:33,320 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 14:22:51,883 INFO [train.py:527] (4/6) Epoch 838, batch 62, global_batch_idx: 103850, batch size: 47, loss[discriminator_loss=2.716, discriminator_real_loss=1.431, discriminator_fake_loss=1.285, generator_loss=28.7, generator_mel_loss=18.02, generator_kl_loss=1.48, generator_dur_loss=1.697, generator_adv_loss=2.006, generator_feat_match_loss=5.499, over 47.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.353, discriminator_fake_loss=1.322, generator_loss=28.89, generator_mel_loss=17.82, generator_kl_loss=1.423, generator_dur_loss=1.735, generator_adv_loss=1.995, generator_feat_match_loss=5.921, over 3546.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:25:14,094 INFO [train.py:527] (4/6) Epoch 838, batch 112, global_batch_idx: 103900, batch size: 40, loss[discriminator_loss=2.64, discriminator_real_loss=1.363, discriminator_fake_loss=1.277, generator_loss=29.74, generator_mel_loss=18.54, generator_kl_loss=1.473, generator_dur_loss=1.665, generator_adv_loss=2.078, generator_feat_match_loss=5.978, over 40.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=28.99, generator_mel_loss=17.8, generator_kl_loss=1.423, generator_dur_loss=1.735, generator_adv_loss=2.004, generator_feat_match_loss=6.024, over 6165.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:25:44,406 INFO [train.py:919] (4/6) Start epoch 839 +2024-03-15 14:27:55,788 INFO [train.py:527] (4/6) Epoch 839, batch 38, global_batch_idx: 103950, batch size: 48, loss[discriminator_loss=2.684, discriminator_real_loss=1.424, discriminator_fake_loss=1.261, generator_loss=28.63, generator_mel_loss=17.84, generator_kl_loss=1.392, generator_dur_loss=1.695, generator_adv_loss=1.869, generator_feat_match_loss=5.832, over 48.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.357, discriminator_fake_loss=1.328, generator_loss=29.1, generator_mel_loss=17.88, generator_kl_loss=1.439, generator_dur_loss=1.757, generator_adv_loss=1.992, generator_feat_match_loss=6.033, over 2263.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:30:14,726 INFO [train.py:527] (4/6) Epoch 839, batch 88, global_batch_idx: 104000, batch size: 83, loss[discriminator_loss=2.689, discriminator_real_loss=1.312, discriminator_fake_loss=1.377, generator_loss=29.46, generator_mel_loss=17.94, generator_kl_loss=1.324, generator_dur_loss=1.811, generator_adv_loss=1.952, generator_feat_match_loss=6.431, over 83.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=29.03, generator_mel_loss=17.85, generator_kl_loss=1.421, generator_dur_loss=1.752, generator_adv_loss=1.998, generator_feat_match_loss=6.004, over 5019.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:30:14,728 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 14:30:23,579 INFO [train.py:591] (4/6) Epoch 839, validation: discriminator_loss=2.717, discriminator_real_loss=1.366, discriminator_fake_loss=1.351, generator_loss=28.05, generator_mel_loss=18.16, generator_kl_loss=1.27, generator_dur_loss=1.817, generator_adv_loss=1.861, generator_feat_match_loss=4.934, over 100.00 samples. +2024-03-15 14:30:23,580 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 14:31:59,478 INFO [train.py:919] (4/6) Start epoch 840 +2024-03-15 14:33:02,569 INFO [train.py:527] (4/6) Epoch 840, batch 14, global_batch_idx: 104050, batch size: 58, loss[discriminator_loss=2.695, discriminator_real_loss=1.33, discriminator_fake_loss=1.365, generator_loss=29.75, generator_mel_loss=17.89, generator_kl_loss=1.432, generator_dur_loss=1.767, generator_adv_loss=2.001, generator_feat_match_loss=6.657, over 58.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.354, discriminator_fake_loss=1.329, generator_loss=28.97, generator_mel_loss=17.79, generator_kl_loss=1.432, generator_dur_loss=1.767, generator_adv_loss=2.021, generator_feat_match_loss=5.962, over 947.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:35:22,492 INFO [train.py:527] (4/6) Epoch 840, batch 64, global_batch_idx: 104100, batch size: 53, loss[discriminator_loss=2.635, discriminator_real_loss=1.351, discriminator_fake_loss=1.283, generator_loss=28.58, generator_mel_loss=17.61, generator_kl_loss=1.511, generator_dur_loss=1.639, generator_adv_loss=2.01, generator_feat_match_loss=5.804, over 53.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.362, discriminator_fake_loss=1.336, generator_loss=28.9, generator_mel_loss=17.8, generator_kl_loss=1.407, generator_dur_loss=1.768, generator_adv_loss=2, generator_feat_match_loss=5.925, over 4139.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:37:43,141 INFO [train.py:527] (4/6) Epoch 840, batch 114, global_batch_idx: 104150, batch size: 59, loss[discriminator_loss=2.771, discriminator_real_loss=1.377, discriminator_fake_loss=1.394, generator_loss=28.01, generator_mel_loss=17.63, generator_kl_loss=1.344, generator_dur_loss=1.723, generator_adv_loss=1.948, generator_feat_match_loss=5.362, over 59.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.97, generator_mel_loss=17.82, generator_kl_loss=1.426, generator_dur_loss=1.755, generator_adv_loss=2.002, generator_feat_match_loss=5.963, over 6719.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:38:10,188 INFO [train.py:919] (4/6) Start epoch 841 +2024-03-15 14:40:29,574 INFO [train.py:527] (4/6) Epoch 841, batch 40, global_batch_idx: 104200, batch size: 55, loss[discriminator_loss=2.697, discriminator_real_loss=1.426, discriminator_fake_loss=1.27, generator_loss=28.42, generator_mel_loss=17.84, generator_kl_loss=1.341, generator_dur_loss=1.698, generator_adv_loss=1.843, generator_feat_match_loss=5.703, over 55.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.363, discriminator_fake_loss=1.324, generator_loss=29.15, generator_mel_loss=17.89, generator_kl_loss=1.446, generator_dur_loss=1.751, generator_adv_loss=2.013, generator_feat_match_loss=6.053, over 2294.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:40:29,575 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 14:40:37,611 INFO [train.py:591] (4/6) Epoch 841, validation: discriminator_loss=2.768, discriminator_real_loss=1.355, discriminator_fake_loss=1.413, generator_loss=28.2, generator_mel_loss=18.32, generator_kl_loss=1.233, generator_dur_loss=1.813, generator_adv_loss=1.759, generator_feat_match_loss=5.072, over 100.00 samples. +2024-03-15 14:40:37,612 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 14:42:54,628 INFO [train.py:527] (4/6) Epoch 841, batch 90, global_batch_idx: 104250, batch size: 72, loss[discriminator_loss=2.701, discriminator_real_loss=1.4, discriminator_fake_loss=1.301, generator_loss=27.86, generator_mel_loss=17.17, generator_kl_loss=1.447, generator_dur_loss=1.807, generator_adv_loss=1.948, generator_feat_match_loss=5.485, over 72.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.364, discriminator_fake_loss=1.318, generator_loss=28.93, generator_mel_loss=17.77, generator_kl_loss=1.427, generator_dur_loss=1.759, generator_adv_loss=2.007, generator_feat_match_loss=5.972, over 5178.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:44:26,699 INFO [train.py:919] (4/6) Start epoch 842 +2024-03-15 14:45:35,148 INFO [train.py:527] (4/6) Epoch 842, batch 16, global_batch_idx: 104300, batch size: 55, loss[discriminator_loss=2.625, discriminator_real_loss=1.314, discriminator_fake_loss=1.311, generator_loss=29.09, generator_mel_loss=17.77, generator_kl_loss=1.435, generator_dur_loss=1.687, generator_adv_loss=2.127, generator_feat_match_loss=6.072, over 55.00 samples.], tot_loss[discriminator_loss=2.655, discriminator_real_loss=1.351, discriminator_fake_loss=1.305, generator_loss=29.27, generator_mel_loss=17.89, generator_kl_loss=1.508, generator_dur_loss=1.724, generator_adv_loss=2.015, generator_feat_match_loss=6.136, over 862.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:47:53,210 INFO [train.py:527] (4/6) Epoch 842, batch 66, global_batch_idx: 104350, batch size: 64, loss[discriminator_loss=2.692, discriminator_real_loss=1.284, discriminator_fake_loss=1.408, generator_loss=28.82, generator_mel_loss=17.66, generator_kl_loss=1.375, generator_dur_loss=1.763, generator_adv_loss=2.063, generator_feat_match_loss=5.958, over 64.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=29.05, generator_mel_loss=17.81, generator_kl_loss=1.445, generator_dur_loss=1.745, generator_adv_loss=2.001, generator_feat_match_loss=6.049, over 3618.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:50:14,385 INFO [train.py:527] (4/6) Epoch 842, batch 116, global_batch_idx: 104400, batch size: 74, loss[discriminator_loss=2.681, discriminator_real_loss=1.318, discriminator_fake_loss=1.363, generator_loss=29.22, generator_mel_loss=18.19, generator_kl_loss=1.376, generator_dur_loss=1.839, generator_adv_loss=2.038, generator_feat_match_loss=5.774, over 74.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.357, discriminator_fake_loss=1.319, generator_loss=29, generator_mel_loss=17.82, generator_kl_loss=1.447, generator_dur_loss=1.743, generator_adv_loss=2.004, generator_feat_match_loss=5.98, over 6243.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:50:14,387 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 14:50:23,187 INFO [train.py:591] (4/6) Epoch 842, validation: discriminator_loss=2.706, discriminator_real_loss=1.497, discriminator_fake_loss=1.209, generator_loss=28.94, generator_mel_loss=18.29, generator_kl_loss=1.225, generator_dur_loss=1.809, generator_adv_loss=2.086, generator_feat_match_loss=5.528, over 100.00 samples. +2024-03-15 14:50:23,188 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 14:50:43,962 INFO [train.py:919] (4/6) Start epoch 843 +2024-03-15 14:53:01,719 INFO [train.py:527] (4/6) Epoch 843, batch 42, global_batch_idx: 104450, batch size: 31, loss[discriminator_loss=2.738, discriminator_real_loss=1.428, discriminator_fake_loss=1.31, generator_loss=28.63, generator_mel_loss=18.55, generator_kl_loss=1.537, generator_dur_loss=1.649, generator_adv_loss=1.925, generator_feat_match_loss=4.965, over 31.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.373, discriminator_fake_loss=1.322, generator_loss=28.86, generator_mel_loss=17.77, generator_kl_loss=1.439, generator_dur_loss=1.738, generator_adv_loss=1.999, generator_feat_match_loss=5.913, over 2357.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:55:21,072 INFO [train.py:527] (4/6) Epoch 843, batch 92, global_batch_idx: 104500, batch size: 58, loss[discriminator_loss=2.624, discriminator_real_loss=1.373, discriminator_fake_loss=1.251, generator_loss=29.17, generator_mel_loss=17.56, generator_kl_loss=1.462, generator_dur_loss=1.753, generator_adv_loss=1.947, generator_feat_match_loss=6.449, over 58.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.365, discriminator_fake_loss=1.325, generator_loss=28.95, generator_mel_loss=17.79, generator_kl_loss=1.436, generator_dur_loss=1.747, generator_adv_loss=2, generator_feat_match_loss=5.972, over 5329.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:56:46,530 INFO [train.py:919] (4/6) Start epoch 844 +2024-03-15 14:58:00,521 INFO [train.py:527] (4/6) Epoch 844, batch 18, global_batch_idx: 104550, batch size: 56, loss[discriminator_loss=2.651, discriminator_real_loss=1.318, discriminator_fake_loss=1.333, generator_loss=28.7, generator_mel_loss=17.56, generator_kl_loss=1.38, generator_dur_loss=1.709, generator_adv_loss=1.888, generator_feat_match_loss=6.164, over 56.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.348, discriminator_fake_loss=1.335, generator_loss=29.01, generator_mel_loss=17.82, generator_kl_loss=1.42, generator_dur_loss=1.727, generator_adv_loss=2.047, generator_feat_match_loss=5.996, over 1084.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:00:22,331 INFO [train.py:527] (4/6) Epoch 844, batch 68, global_batch_idx: 104600, batch size: 77, loss[discriminator_loss=2.707, discriminator_real_loss=1.388, discriminator_fake_loss=1.32, generator_loss=28.7, generator_mel_loss=17.83, generator_kl_loss=1.476, generator_dur_loss=1.79, generator_adv_loss=2.065, generator_feat_match_loss=5.533, over 77.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.353, discriminator_fake_loss=1.332, generator_loss=29.01, generator_mel_loss=17.82, generator_kl_loss=1.426, generator_dur_loss=1.746, generator_adv_loss=2.028, generator_feat_match_loss=5.983, over 4130.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:00:22,333 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 15:00:31,248 INFO [train.py:591] (4/6) Epoch 844, validation: discriminator_loss=2.775, discriminator_real_loss=1.483, discriminator_fake_loss=1.292, generator_loss=27.8, generator_mel_loss=18, generator_kl_loss=1.25, generator_dur_loss=1.795, generator_adv_loss=2.016, generator_feat_match_loss=4.74, over 100.00 samples. +2024-03-15 15:00:31,249 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 15:02:49,509 INFO [train.py:527] (4/6) Epoch 844, batch 118, global_batch_idx: 104650, batch size: 15, loss[discriminator_loss=2.67, discriminator_real_loss=1.381, discriminator_fake_loss=1.289, generator_loss=30.36, generator_mel_loss=18.42, generator_kl_loss=1.896, generator_dur_loss=1.601, generator_adv_loss=2.043, generator_feat_match_loss=6.404, over 15.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=28.99, generator_mel_loss=17.81, generator_kl_loss=1.427, generator_dur_loss=1.749, generator_adv_loss=2.011, generator_feat_match_loss=6, over 7157.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:03:04,351 INFO [train.py:919] (4/6) Start epoch 845 +2024-03-15 15:05:32,903 INFO [train.py:527] (4/6) Epoch 845, batch 44, global_batch_idx: 104700, batch size: 64, loss[discriminator_loss=2.689, discriminator_real_loss=1.384, discriminator_fake_loss=1.305, generator_loss=29.27, generator_mel_loss=17.65, generator_kl_loss=1.442, generator_dur_loss=1.772, generator_adv_loss=2.013, generator_feat_match_loss=6.395, over 64.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.364, discriminator_fake_loss=1.321, generator_loss=29.05, generator_mel_loss=17.92, generator_kl_loss=1.407, generator_dur_loss=1.742, generator_adv_loss=1.992, generator_feat_match_loss=5.992, over 2507.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:07:50,476 INFO [train.py:527] (4/6) Epoch 845, batch 94, global_batch_idx: 104750, batch size: 83, loss[discriminator_loss=2.735, discriminator_real_loss=1.35, discriminator_fake_loss=1.385, generator_loss=28.76, generator_mel_loss=17.78, generator_kl_loss=1.461, generator_dur_loss=1.831, generator_adv_loss=1.95, generator_feat_match_loss=5.741, over 83.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.323, generator_loss=29.06, generator_mel_loss=17.89, generator_kl_loss=1.421, generator_dur_loss=1.737, generator_adv_loss=1.986, generator_feat_match_loss=6.024, over 5269.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:09:11,477 INFO [train.py:919] (4/6) Start epoch 846 +2024-03-15 15:10:31,960 INFO [train.py:527] (4/6) Epoch 846, batch 20, global_batch_idx: 104800, batch size: 72, loss[discriminator_loss=2.708, discriminator_real_loss=1.324, discriminator_fake_loss=1.383, generator_loss=27.92, generator_mel_loss=17.55, generator_kl_loss=1.16, generator_dur_loss=1.86, generator_adv_loss=1.878, generator_feat_match_loss=5.473, over 72.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.91, generator_mel_loss=17.73, generator_kl_loss=1.425, generator_dur_loss=1.748, generator_adv_loss=1.994, generator_feat_match_loss=6.012, over 1188.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:10:31,962 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 15:10:40,200 INFO [train.py:591] (4/6) Epoch 846, validation: discriminator_loss=2.711, discriminator_real_loss=1.363, discriminator_fake_loss=1.349, generator_loss=27.95, generator_mel_loss=18.45, generator_kl_loss=1.275, generator_dur_loss=1.793, generator_adv_loss=1.845, generator_feat_match_loss=4.584, over 100.00 samples. +2024-03-15 15:10:40,201 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 15:12:56,270 INFO [train.py:527] (4/6) Epoch 846, batch 70, global_batch_idx: 104850, batch size: 70, loss[discriminator_loss=2.672, discriminator_real_loss=1.262, discriminator_fake_loss=1.41, generator_loss=28.69, generator_mel_loss=17.76, generator_kl_loss=1.391, generator_dur_loss=1.783, generator_adv_loss=2.134, generator_feat_match_loss=5.62, over 70.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=28.95, generator_mel_loss=17.79, generator_kl_loss=1.428, generator_dur_loss=1.74, generator_adv_loss=2, generator_feat_match_loss=5.996, over 4106.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:15:15,437 INFO [train.py:527] (4/6) Epoch 846, batch 120, global_batch_idx: 104900, batch size: 31, loss[discriminator_loss=2.67, discriminator_real_loss=1.406, discriminator_fake_loss=1.263, generator_loss=29.39, generator_mel_loss=18.45, generator_kl_loss=1.453, generator_dur_loss=1.643, generator_adv_loss=1.985, generator_feat_match_loss=5.865, over 31.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.354, discriminator_fake_loss=1.326, generator_loss=29.02, generator_mel_loss=17.81, generator_kl_loss=1.434, generator_dur_loss=1.741, generator_adv_loss=2.004, generator_feat_match_loss=6.031, over 7001.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:15:25,407 INFO [train.py:919] (4/6) Start epoch 847 +2024-03-15 15:17:57,100 INFO [train.py:527] (4/6) Epoch 847, batch 46, global_batch_idx: 104950, batch size: 42, loss[discriminator_loss=2.714, discriminator_real_loss=1.412, discriminator_fake_loss=1.302, generator_loss=29.27, generator_mel_loss=18.22, generator_kl_loss=1.37, generator_dur_loss=1.644, generator_adv_loss=1.949, generator_feat_match_loss=6.09, over 42.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.351, discriminator_fake_loss=1.318, generator_loss=29.16, generator_mel_loss=17.81, generator_kl_loss=1.466, generator_dur_loss=1.744, generator_adv_loss=2.035, generator_feat_match_loss=6.109, over 2680.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:20:17,166 INFO [train.py:527] (4/6) Epoch 847, batch 96, global_batch_idx: 105000, batch size: 56, loss[discriminator_loss=2.66, discriminator_real_loss=1.342, discriminator_fake_loss=1.319, generator_loss=28.83, generator_mel_loss=17.69, generator_kl_loss=1.282, generator_dur_loss=1.713, generator_adv_loss=1.955, generator_feat_match_loss=6.181, over 56.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.357, discriminator_fake_loss=1.317, generator_loss=29, generator_mel_loss=17.78, generator_kl_loss=1.436, generator_dur_loss=1.743, generator_adv_loss=2.021, generator_feat_match_loss=6.019, over 5545.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:20:17,167 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 15:20:26,203 INFO [train.py:591] (4/6) Epoch 847, validation: discriminator_loss=2.724, discriminator_real_loss=1.358, discriminator_fake_loss=1.366, generator_loss=27.32, generator_mel_loss=17.85, generator_kl_loss=1.251, generator_dur_loss=1.793, generator_adv_loss=1.83, generator_feat_match_loss=4.59, over 100.00 samples. +2024-03-15 15:20:26,204 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 15:21:42,806 INFO [train.py:919] (4/6) Start epoch 848 +2024-03-15 15:23:07,265 INFO [train.py:527] (4/6) Epoch 848, batch 22, global_batch_idx: 105050, batch size: 96, loss[discriminator_loss=2.708, discriminator_real_loss=1.423, discriminator_fake_loss=1.286, generator_loss=28.04, generator_mel_loss=17.51, generator_kl_loss=1.232, generator_dur_loss=1.844, generator_adv_loss=2.06, generator_feat_match_loss=5.393, over 96.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.342, discriminator_fake_loss=1.323, generator_loss=29.02, generator_mel_loss=17.79, generator_kl_loss=1.414, generator_dur_loss=1.742, generator_adv_loss=2.005, generator_feat_match_loss=6.069, over 1372.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:25:25,280 INFO [train.py:527] (4/6) Epoch 848, batch 72, global_batch_idx: 105100, batch size: 53, loss[discriminator_loss=2.643, discriminator_real_loss=1.338, discriminator_fake_loss=1.305, generator_loss=29.88, generator_mel_loss=18.19, generator_kl_loss=1.499, generator_dur_loss=1.653, generator_adv_loss=1.973, generator_feat_match_loss=6.559, over 53.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=28.93, generator_mel_loss=17.84, generator_kl_loss=1.405, generator_dur_loss=1.743, generator_adv_loss=2, generator_feat_match_loss=5.94, over 4284.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:27:45,020 INFO [train.py:527] (4/6) Epoch 848, batch 122, global_batch_idx: 105150, batch size: 70, loss[discriminator_loss=2.661, discriminator_real_loss=1.367, discriminator_fake_loss=1.294, generator_loss=29.92, generator_mel_loss=18.06, generator_kl_loss=1.234, generator_dur_loss=1.805, generator_adv_loss=2.031, generator_feat_match_loss=6.79, over 70.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.35, discriminator_fake_loss=1.322, generator_loss=29.02, generator_mel_loss=17.85, generator_kl_loss=1.421, generator_dur_loss=1.748, generator_adv_loss=2.001, generator_feat_match_loss=5.998, over 7261.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:27:49,976 INFO [train.py:919] (4/6) Start epoch 849 +2024-03-15 15:30:29,639 INFO [train.py:527] (4/6) Epoch 849, batch 48, global_batch_idx: 105200, batch size: 56, loss[discriminator_loss=2.68, discriminator_real_loss=1.418, discriminator_fake_loss=1.262, generator_loss=28.42, generator_mel_loss=17.53, generator_kl_loss=1.473, generator_dur_loss=1.738, generator_adv_loss=1.859, generator_feat_match_loss=5.815, over 56.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.347, discriminator_fake_loss=1.335, generator_loss=28.96, generator_mel_loss=17.78, generator_kl_loss=1.458, generator_dur_loss=1.725, generator_adv_loss=1.993, generator_feat_match_loss=6.007, over 2670.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:30:29,640 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 15:30:37,816 INFO [train.py:591] (4/6) Epoch 849, validation: discriminator_loss=2.723, discriminator_real_loss=1.264, discriminator_fake_loss=1.459, generator_loss=27.64, generator_mel_loss=18.21, generator_kl_loss=1.177, generator_dur_loss=1.8, generator_adv_loss=1.75, generator_feat_match_loss=4.702, over 100.00 samples. +2024-03-15 15:30:37,817 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 15:32:57,523 INFO [train.py:527] (4/6) Epoch 849, batch 98, global_batch_idx: 105250, batch size: 88, loss[discriminator_loss=2.649, discriminator_real_loss=1.283, discriminator_fake_loss=1.366, generator_loss=28.81, generator_mel_loss=17.78, generator_kl_loss=1.296, generator_dur_loss=1.797, generator_adv_loss=2.012, generator_feat_match_loss=5.923, over 88.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.351, discriminator_fake_loss=1.327, generator_loss=28.95, generator_mel_loss=17.77, generator_kl_loss=1.443, generator_dur_loss=1.733, generator_adv_loss=1.994, generator_feat_match_loss=6.014, over 5533.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:34:05,755 INFO [train.py:919] (4/6) Start epoch 850 +2024-03-15 15:35:36,260 INFO [train.py:527] (4/6) Epoch 850, batch 24, global_batch_idx: 105300, batch size: 53, loss[discriminator_loss=2.74, discriminator_real_loss=1.424, discriminator_fake_loss=1.316, generator_loss=27.8, generator_mel_loss=17.76, generator_kl_loss=1.276, generator_dur_loss=1.673, generator_adv_loss=2.004, generator_feat_match_loss=5.087, over 53.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.364, discriminator_fake_loss=1.33, generator_loss=28.74, generator_mel_loss=17.79, generator_kl_loss=1.407, generator_dur_loss=1.735, generator_adv_loss=1.991, generator_feat_match_loss=5.81, over 1546.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:37:54,298 INFO [train.py:527] (4/6) Epoch 850, batch 74, global_batch_idx: 105350, batch size: 72, loss[discriminator_loss=2.677, discriminator_real_loss=1.4, discriminator_fake_loss=1.277, generator_loss=28.34, generator_mel_loss=17.46, generator_kl_loss=1.311, generator_dur_loss=1.776, generator_adv_loss=1.996, generator_feat_match_loss=5.8, over 72.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.365, discriminator_fake_loss=1.322, generator_loss=28.9, generator_mel_loss=17.8, generator_kl_loss=1.425, generator_dur_loss=1.724, generator_adv_loss=2.003, generator_feat_match_loss=5.951, over 4313.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:40:12,296 INFO [train.py:919] (4/6) Start epoch 851 +2024-03-15 15:40:36,944 INFO [train.py:527] (4/6) Epoch 851, batch 0, global_batch_idx: 105400, batch size: 59, loss[discriminator_loss=2.644, discriminator_real_loss=1.277, discriminator_fake_loss=1.367, generator_loss=29.85, generator_mel_loss=17.98, generator_kl_loss=1.483, generator_dur_loss=1.75, generator_adv_loss=2.3, generator_feat_match_loss=6.336, over 59.00 samples.], tot_loss[discriminator_loss=2.644, discriminator_real_loss=1.277, discriminator_fake_loss=1.367, generator_loss=29.85, generator_mel_loss=17.98, generator_kl_loss=1.483, generator_dur_loss=1.75, generator_adv_loss=2.3, generator_feat_match_loss=6.336, over 59.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:40:36,947 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 15:40:45,127 INFO [train.py:591] (4/6) Epoch 851, validation: discriminator_loss=2.755, discriminator_real_loss=1.525, discriminator_fake_loss=1.23, generator_loss=28.27, generator_mel_loss=18.03, generator_kl_loss=1.212, generator_dur_loss=1.799, generator_adv_loss=2.133, generator_feat_match_loss=5.092, over 100.00 samples. +2024-03-15 15:40:45,130 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 15:43:04,977 INFO [train.py:527] (4/6) Epoch 851, batch 50, global_batch_idx: 105450, batch size: 66, loss[discriminator_loss=2.682, discriminator_real_loss=1.283, discriminator_fake_loss=1.399, generator_loss=29.84, generator_mel_loss=17.85, generator_kl_loss=1.284, generator_dur_loss=1.74, generator_adv_loss=1.975, generator_feat_match_loss=6.985, over 66.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=28.96, generator_mel_loss=17.77, generator_kl_loss=1.424, generator_dur_loss=1.733, generator_adv_loss=1.992, generator_feat_match_loss=6.039, over 2984.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:45:30,044 INFO [train.py:527] (4/6) Epoch 851, batch 100, global_batch_idx: 105500, batch size: 59, loss[discriminator_loss=2.621, discriminator_real_loss=1.254, discriminator_fake_loss=1.368, generator_loss=29.77, generator_mel_loss=18.26, generator_kl_loss=1.431, generator_dur_loss=1.754, generator_adv_loss=1.901, generator_feat_match_loss=6.417, over 59.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.326, generator_loss=29.07, generator_mel_loss=17.83, generator_kl_loss=1.441, generator_dur_loss=1.731, generator_adv_loss=2.002, generator_feat_match_loss=6.068, over 5697.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:46:30,898 INFO [train.py:919] (4/6) Start epoch 852 +2024-03-15 15:48:06,105 INFO [train.py:527] (4/6) Epoch 852, batch 26, global_batch_idx: 105550, batch size: 56, loss[discriminator_loss=2.654, discriminator_real_loss=1.313, discriminator_fake_loss=1.341, generator_loss=28.84, generator_mel_loss=17.3, generator_kl_loss=1.427, generator_dur_loss=1.758, generator_adv_loss=2.024, generator_feat_match_loss=6.33, over 56.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.355, discriminator_fake_loss=1.324, generator_loss=29.26, generator_mel_loss=17.88, generator_kl_loss=1.445, generator_dur_loss=1.709, generator_adv_loss=1.997, generator_feat_match_loss=6.228, over 1446.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:50:24,282 INFO [train.py:527] (4/6) Epoch 852, batch 76, global_batch_idx: 105600, batch size: 56, loss[discriminator_loss=2.715, discriminator_real_loss=1.428, discriminator_fake_loss=1.287, generator_loss=29.21, generator_mel_loss=17.6, generator_kl_loss=1.473, generator_dur_loss=1.661, generator_adv_loss=2.035, generator_feat_match_loss=6.445, over 56.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.352, discriminator_fake_loss=1.322, generator_loss=29.18, generator_mel_loss=17.81, generator_kl_loss=1.421, generator_dur_loss=1.732, generator_adv_loss=2.022, generator_feat_match_loss=6.195, over 4376.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:50:24,283 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 15:50:33,124 INFO [train.py:591] (4/6) Epoch 852, validation: discriminator_loss=2.749, discriminator_real_loss=1.44, discriminator_fake_loss=1.309, generator_loss=27.58, generator_mel_loss=17.73, generator_kl_loss=1.299, generator_dur_loss=1.807, generator_adv_loss=1.974, generator_feat_match_loss=4.766, over 100.00 samples. +2024-03-15 15:50:33,125 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 15:52:44,839 INFO [train.py:919] (4/6) Start epoch 853 +2024-03-15 15:53:14,672 INFO [train.py:527] (4/6) Epoch 853, batch 2, global_batch_idx: 105650, batch size: 59, loss[discriminator_loss=2.726, discriminator_real_loss=1.386, discriminator_fake_loss=1.34, generator_loss=29.17, generator_mel_loss=17.9, generator_kl_loss=1.347, generator_dur_loss=1.738, generator_adv_loss=2.092, generator_feat_match_loss=6.094, over 59.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.369, discriminator_fake_loss=1.337, generator_loss=28.97, generator_mel_loss=17.68, generator_kl_loss=1.418, generator_dur_loss=1.704, generator_adv_loss=1.999, generator_feat_match_loss=6.169, over 176.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:55:34,471 INFO [train.py:527] (4/6) Epoch 853, batch 52, global_batch_idx: 105700, batch size: 68, loss[discriminator_loss=2.705, discriminator_real_loss=1.424, discriminator_fake_loss=1.281, generator_loss=28.48, generator_mel_loss=17.46, generator_kl_loss=1.387, generator_dur_loss=1.786, generator_adv_loss=2.111, generator_feat_match_loss=5.732, over 68.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.351, discriminator_fake_loss=1.32, generator_loss=28.98, generator_mel_loss=17.77, generator_kl_loss=1.413, generator_dur_loss=1.741, generator_adv_loss=2.007, generator_feat_match_loss=6.051, over 3157.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:57:55,581 INFO [train.py:527] (4/6) Epoch 853, batch 102, global_batch_idx: 105750, batch size: 48, loss[discriminator_loss=2.635, discriminator_real_loss=1.352, discriminator_fake_loss=1.283, generator_loss=29.29, generator_mel_loss=17.72, generator_kl_loss=1.615, generator_dur_loss=1.685, generator_adv_loss=1.997, generator_feat_match_loss=6.272, over 48.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.35, discriminator_fake_loss=1.324, generator_loss=28.91, generator_mel_loss=17.77, generator_kl_loss=1.398, generator_dur_loss=1.745, generator_adv_loss=1.999, generator_feat_match_loss=6.007, over 6182.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:58:50,965 INFO [train.py:919] (4/6) Start epoch 854 +2024-03-15 16:00:31,948 INFO [train.py:527] (4/6) Epoch 854, batch 28, global_batch_idx: 105800, batch size: 42, loss[discriminator_loss=2.72, discriminator_real_loss=1.431, discriminator_fake_loss=1.29, generator_loss=28.73, generator_mel_loss=17.58, generator_kl_loss=1.491, generator_dur_loss=1.674, generator_adv_loss=1.915, generator_feat_match_loss=6.073, over 42.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.371, discriminator_fake_loss=1.31, generator_loss=28.78, generator_mel_loss=17.79, generator_kl_loss=1.431, generator_dur_loss=1.717, generator_adv_loss=2.02, generator_feat_match_loss=5.825, over 1518.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:00:31,950 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 16:00:40,053 INFO [train.py:591] (4/6) Epoch 854, validation: discriminator_loss=2.732, discriminator_real_loss=1.335, discriminator_fake_loss=1.397, generator_loss=27.9, generator_mel_loss=17.91, generator_kl_loss=1.349, generator_dur_loss=1.796, generator_adv_loss=1.837, generator_feat_match_loss=5.006, over 100.00 samples. +2024-03-15 16:00:40,054 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 16:03:01,604 INFO [train.py:527] (4/6) Epoch 854, batch 78, global_batch_idx: 105850, batch size: 70, loss[discriminator_loss=2.648, discriminator_real_loss=1.263, discriminator_fake_loss=1.386, generator_loss=30.65, generator_mel_loss=18.33, generator_kl_loss=1.334, generator_dur_loss=1.798, generator_adv_loss=2.023, generator_feat_match_loss=7.163, over 70.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.355, discriminator_fake_loss=1.316, generator_loss=28.99, generator_mel_loss=17.78, generator_kl_loss=1.417, generator_dur_loss=1.731, generator_adv_loss=2.018, generator_feat_match_loss=6.044, over 4426.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:05:09,217 INFO [train.py:919] (4/6) Start epoch 855 +2024-03-15 16:05:42,779 INFO [train.py:527] (4/6) Epoch 855, batch 4, global_batch_idx: 105900, batch size: 39, loss[discriminator_loss=2.715, discriminator_real_loss=1.419, discriminator_fake_loss=1.296, generator_loss=27.88, generator_mel_loss=17.91, generator_kl_loss=1.534, generator_dur_loss=1.628, generator_adv_loss=1.925, generator_feat_match_loss=4.876, over 39.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.369, discriminator_fake_loss=1.289, generator_loss=29.17, generator_mel_loss=17.81, generator_kl_loss=1.372, generator_dur_loss=1.727, generator_adv_loss=2.041, generator_feat_match_loss=6.217, over 298.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:08:01,170 INFO [train.py:527] (4/6) Epoch 855, batch 54, global_batch_idx: 105950, batch size: 77, loss[discriminator_loss=2.673, discriminator_real_loss=1.373, discriminator_fake_loss=1.301, generator_loss=29.26, generator_mel_loss=18.01, generator_kl_loss=1.384, generator_dur_loss=1.795, generator_adv_loss=2.03, generator_feat_match_loss=6.039, over 77.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.359, discriminator_fake_loss=1.319, generator_loss=28.96, generator_mel_loss=17.79, generator_kl_loss=1.468, generator_dur_loss=1.733, generator_adv_loss=2.008, generator_feat_match_loss=5.964, over 3017.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:10:21,498 INFO [train.py:527] (4/6) Epoch 855, batch 104, global_batch_idx: 106000, batch size: 88, loss[discriminator_loss=2.682, discriminator_real_loss=1.305, discriminator_fake_loss=1.377, generator_loss=28.58, generator_mel_loss=18.03, generator_kl_loss=1.163, generator_dur_loss=1.84, generator_adv_loss=1.921, generator_feat_match_loss=5.623, over 88.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.08, generator_mel_loss=17.87, generator_kl_loss=1.455, generator_dur_loss=1.736, generator_adv_loss=2, generator_feat_match_loss=6.022, over 5888.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:10:21,500 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 16:10:30,229 INFO [train.py:591] (4/6) Epoch 855, validation: discriminator_loss=2.738, discriminator_real_loss=1.342, discriminator_fake_loss=1.397, generator_loss=27.76, generator_mel_loss=18.26, generator_kl_loss=1.239, generator_dur_loss=1.803, generator_adv_loss=1.871, generator_feat_match_loss=4.589, over 100.00 samples. +2024-03-15 16:10:30,230 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 16:11:22,362 INFO [train.py:919] (4/6) Start epoch 856 +2024-03-15 16:13:09,351 INFO [train.py:527] (4/6) Epoch 856, batch 30, global_batch_idx: 106050, batch size: 62, loss[discriminator_loss=2.757, discriminator_real_loss=1.491, discriminator_fake_loss=1.266, generator_loss=28.14, generator_mel_loss=17.68, generator_kl_loss=1.38, generator_dur_loss=1.737, generator_adv_loss=2.045, generator_feat_match_loss=5.306, over 62.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.343, discriminator_fake_loss=1.325, generator_loss=29.11, generator_mel_loss=17.82, generator_kl_loss=1.413, generator_dur_loss=1.723, generator_adv_loss=2.072, generator_feat_match_loss=6.08, over 1603.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:15:28,347 INFO [train.py:527] (4/6) Epoch 856, batch 80, global_batch_idx: 106100, batch size: 36, loss[discriminator_loss=2.689, discriminator_real_loss=1.277, discriminator_fake_loss=1.412, generator_loss=28.55, generator_mel_loss=17.34, generator_kl_loss=1.498, generator_dur_loss=1.657, generator_adv_loss=2.013, generator_feat_match_loss=6.046, over 36.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.353, discriminator_fake_loss=1.322, generator_loss=28.96, generator_mel_loss=17.77, generator_kl_loss=1.415, generator_dur_loss=1.723, generator_adv_loss=2.032, generator_feat_match_loss=6.016, over 4396.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:17:28,882 INFO [train.py:919] (4/6) Start epoch 857 +2024-03-15 16:18:09,582 INFO [train.py:527] (4/6) Epoch 857, batch 6, global_batch_idx: 106150, batch size: 50, loss[discriminator_loss=2.697, discriminator_real_loss=1.311, discriminator_fake_loss=1.386, generator_loss=29.55, generator_mel_loss=18.04, generator_kl_loss=1.355, generator_dur_loss=1.635, generator_adv_loss=2.069, generator_feat_match_loss=6.449, over 50.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.379, discriminator_fake_loss=1.328, generator_loss=29.31, generator_mel_loss=17.87, generator_kl_loss=1.422, generator_dur_loss=1.695, generator_adv_loss=2.008, generator_feat_match_loss=6.314, over 371.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:20:27,306 INFO [train.py:527] (4/6) Epoch 857, batch 56, global_batch_idx: 106200, batch size: 96, loss[discriminator_loss=2.664, discriminator_real_loss=1.285, discriminator_fake_loss=1.38, generator_loss=29.3, generator_mel_loss=17.69, generator_kl_loss=1.403, generator_dur_loss=1.854, generator_adv_loss=1.989, generator_feat_match_loss=6.355, over 96.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.326, generator_loss=29.18, generator_mel_loss=17.84, generator_kl_loss=1.426, generator_dur_loss=1.738, generator_adv_loss=2.01, generator_feat_match_loss=6.169, over 3349.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:20:27,308 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 16:20:35,563 INFO [train.py:591] (4/6) Epoch 857, validation: discriminator_loss=2.751, discriminator_real_loss=1.439, discriminator_fake_loss=1.312, generator_loss=28.33, generator_mel_loss=18.37, generator_kl_loss=1.277, generator_dur_loss=1.797, generator_adv_loss=1.949, generator_feat_match_loss=4.945, over 100.00 samples. +2024-03-15 16:20:35,564 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 16:22:55,618 INFO [train.py:527] (4/6) Epoch 857, batch 106, global_batch_idx: 106250, batch size: 55, loss[discriminator_loss=2.65, discriminator_real_loss=1.333, discriminator_fake_loss=1.316, generator_loss=29.36, generator_mel_loss=17.65, generator_kl_loss=1.484, generator_dur_loss=1.665, generator_adv_loss=2.074, generator_feat_match_loss=6.494, over 55.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.03, generator_mel_loss=17.79, generator_kl_loss=1.425, generator_dur_loss=1.737, generator_adv_loss=2.007, generator_feat_match_loss=6.077, over 6246.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:23:42,601 INFO [train.py:919] (4/6) Start epoch 858 +2024-03-15 16:25:35,135 INFO [train.py:527] (4/6) Epoch 858, batch 32, global_batch_idx: 106300, batch size: 25, loss[discriminator_loss=2.649, discriminator_real_loss=1.333, discriminator_fake_loss=1.316, generator_loss=29.61, generator_mel_loss=17.77, generator_kl_loss=1.683, generator_dur_loss=1.563, generator_adv_loss=2.065, generator_feat_match_loss=6.528, over 25.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.323, generator_loss=29.13, generator_mel_loss=17.85, generator_kl_loss=1.436, generator_dur_loss=1.753, generator_adv_loss=1.999, generator_feat_match_loss=6.092, over 1953.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:27:50,705 INFO [train.py:527] (4/6) Epoch 858, batch 82, global_batch_idx: 106350, batch size: 25, loss[discriminator_loss=2.743, discriminator_real_loss=1.426, discriminator_fake_loss=1.317, generator_loss=28.21, generator_mel_loss=17.63, generator_kl_loss=1.695, generator_dur_loss=1.575, generator_adv_loss=1.976, generator_feat_match_loss=5.334, over 25.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.355, discriminator_fake_loss=1.33, generator_loss=29.15, generator_mel_loss=17.88, generator_kl_loss=1.445, generator_dur_loss=1.745, generator_adv_loss=1.998, generator_feat_match_loss=6.082, over 4814.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:29:48,913 INFO [train.py:919] (4/6) Start epoch 859 +2024-03-15 16:30:36,465 INFO [train.py:527] (4/6) Epoch 859, batch 8, global_batch_idx: 106400, batch size: 59, loss[discriminator_loss=2.76, discriminator_real_loss=1.41, discriminator_fake_loss=1.35, generator_loss=29.52, generator_mel_loss=18.01, generator_kl_loss=1.485, generator_dur_loss=1.717, generator_adv_loss=1.971, generator_feat_match_loss=6.341, over 59.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.38, discriminator_fake_loss=1.311, generator_loss=29, generator_mel_loss=17.86, generator_kl_loss=1.468, generator_dur_loss=1.725, generator_adv_loss=2.021, generator_feat_match_loss=5.928, over 469.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:30:36,468 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 16:30:44,310 INFO [train.py:591] (4/6) Epoch 859, validation: discriminator_loss=2.728, discriminator_real_loss=1.436, discriminator_fake_loss=1.292, generator_loss=29.1, generator_mel_loss=18.67, generator_kl_loss=1.303, generator_dur_loss=1.801, generator_adv_loss=1.93, generator_feat_match_loss=5.4, over 100.00 samples. +2024-03-15 16:30:44,313 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 16:33:02,189 INFO [train.py:527] (4/6) Epoch 859, batch 58, global_batch_idx: 106450, batch size: 72, loss[discriminator_loss=2.683, discriminator_real_loss=1.341, discriminator_fake_loss=1.341, generator_loss=29.03, generator_mel_loss=17.72, generator_kl_loss=1.315, generator_dur_loss=1.758, generator_adv_loss=1.998, generator_feat_match_loss=6.243, over 72.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.02, generator_mel_loss=17.8, generator_kl_loss=1.436, generator_dur_loss=1.728, generator_adv_loss=2.013, generator_feat_match_loss=6.044, over 3201.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:35:20,915 INFO [train.py:527] (4/6) Epoch 859, batch 108, global_batch_idx: 106500, batch size: 39, loss[discriminator_loss=2.662, discriminator_real_loss=1.442, discriminator_fake_loss=1.22, generator_loss=28.96, generator_mel_loss=17.96, generator_kl_loss=1.536, generator_dur_loss=1.669, generator_adv_loss=1.855, generator_feat_match_loss=5.945, over 39.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.354, discriminator_fake_loss=1.323, generator_loss=29.04, generator_mel_loss=17.8, generator_kl_loss=1.447, generator_dur_loss=1.724, generator_adv_loss=2.014, generator_feat_match_loss=6.062, over 6009.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:36:03,594 INFO [train.py:919] (4/6) Start epoch 860 +2024-03-15 16:38:00,634 INFO [train.py:527] (4/6) Epoch 860, batch 34, global_batch_idx: 106550, batch size: 77, loss[discriminator_loss=2.67, discriminator_real_loss=1.396, discriminator_fake_loss=1.274, generator_loss=29.67, generator_mel_loss=17.87, generator_kl_loss=1.517, generator_dur_loss=1.812, generator_adv_loss=1.896, generator_feat_match_loss=6.576, over 77.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.347, discriminator_fake_loss=1.318, generator_loss=28.95, generator_mel_loss=17.78, generator_kl_loss=1.446, generator_dur_loss=1.734, generator_adv_loss=2.008, generator_feat_match_loss=5.988, over 2092.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:40:16,738 INFO [train.py:527] (4/6) Epoch 860, batch 84, global_batch_idx: 106600, batch size: 42, loss[discriminator_loss=2.637, discriminator_real_loss=1.317, discriminator_fake_loss=1.32, generator_loss=30.42, generator_mel_loss=18.54, generator_kl_loss=1.634, generator_dur_loss=1.711, generator_adv_loss=2.035, generator_feat_match_loss=6.492, over 42.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.35, discriminator_fake_loss=1.318, generator_loss=29.13, generator_mel_loss=17.84, generator_kl_loss=1.462, generator_dur_loss=1.721, generator_adv_loss=2.013, generator_feat_match_loss=6.089, over 4533.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:40:16,739 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 16:40:25,518 INFO [train.py:591] (4/6) Epoch 860, validation: discriminator_loss=2.708, discriminator_real_loss=1.478, discriminator_fake_loss=1.23, generator_loss=28.1, generator_mel_loss=17.94, generator_kl_loss=1.235, generator_dur_loss=1.818, generator_adv_loss=2.011, generator_feat_match_loss=5.09, over 100.00 samples. +2024-03-15 16:40:25,519 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 16:42:19,404 INFO [train.py:919] (4/6) Start epoch 861 +2024-03-15 16:43:12,706 INFO [train.py:527] (4/6) Epoch 861, batch 10, global_batch_idx: 106650, batch size: 59, loss[discriminator_loss=2.709, discriminator_real_loss=1.399, discriminator_fake_loss=1.31, generator_loss=28.46, generator_mel_loss=17.77, generator_kl_loss=1.433, generator_dur_loss=1.75, generator_adv_loss=2.004, generator_feat_match_loss=5.507, over 59.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.385, discriminator_fake_loss=1.306, generator_loss=29.14, generator_mel_loss=17.76, generator_kl_loss=1.445, generator_dur_loss=1.72, generator_adv_loss=2.024, generator_feat_match_loss=6.187, over 552.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:45:32,205 INFO [train.py:527] (4/6) Epoch 861, batch 60, global_batch_idx: 106700, batch size: 56, loss[discriminator_loss=2.703, discriminator_real_loss=1.351, discriminator_fake_loss=1.352, generator_loss=28.95, generator_mel_loss=17.73, generator_kl_loss=1.494, generator_dur_loss=1.758, generator_adv_loss=1.953, generator_feat_match_loss=6.01, over 56.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.352, discriminator_fake_loss=1.325, generator_loss=29.23, generator_mel_loss=17.82, generator_kl_loss=1.453, generator_dur_loss=1.742, generator_adv_loss=2.015, generator_feat_match_loss=6.2, over 3365.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:47:49,919 INFO [train.py:527] (4/6) Epoch 861, batch 110, global_batch_idx: 106750, batch size: 36, loss[discriminator_loss=2.685, discriminator_real_loss=1.389, discriminator_fake_loss=1.296, generator_loss=28.36, generator_mel_loss=17.58, generator_kl_loss=1.579, generator_dur_loss=1.687, generator_adv_loss=1.926, generator_feat_match_loss=5.587, over 36.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=29.19, generator_mel_loss=17.85, generator_kl_loss=1.452, generator_dur_loss=1.739, generator_adv_loss=2.009, generator_feat_match_loss=6.138, over 6109.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:48:28,451 INFO [train.py:919] (4/6) Start epoch 862 +2024-03-15 16:50:33,813 INFO [train.py:527] (4/6) Epoch 862, batch 36, global_batch_idx: 106800, batch size: 74, loss[discriminator_loss=2.601, discriminator_real_loss=1.365, discriminator_fake_loss=1.236, generator_loss=30.32, generator_mel_loss=17.67, generator_kl_loss=1.326, generator_dur_loss=1.795, generator_adv_loss=2.03, generator_feat_match_loss=7.498, over 74.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.365, discriminator_fake_loss=1.327, generator_loss=29.01, generator_mel_loss=17.77, generator_kl_loss=1.45, generator_dur_loss=1.741, generator_adv_loss=2.003, generator_feat_match_loss=6.046, over 2075.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:50:33,814 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 16:50:41,933 INFO [train.py:591] (4/6) Epoch 862, validation: discriminator_loss=2.686, discriminator_real_loss=1.377, discriminator_fake_loss=1.309, generator_loss=26.97, generator_mel_loss=17.55, generator_kl_loss=1.282, generator_dur_loss=1.803, generator_adv_loss=1.825, generator_feat_match_loss=4.515, over 100.00 samples. +2024-03-15 16:50:41,934 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 16:53:01,122 INFO [train.py:527] (4/6) Epoch 862, batch 86, global_batch_idx: 106850, batch size: 52, loss[discriminator_loss=2.752, discriminator_real_loss=1.373, discriminator_fake_loss=1.379, generator_loss=28.02, generator_mel_loss=17.45, generator_kl_loss=1.439, generator_dur_loss=1.662, generator_adv_loss=2.061, generator_feat_match_loss=5.404, over 52.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.359, discriminator_fake_loss=1.331, generator_loss=28.92, generator_mel_loss=17.75, generator_kl_loss=1.43, generator_dur_loss=1.755, generator_adv_loss=1.999, generator_feat_match_loss=5.985, over 5150.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:54:43,070 INFO [train.py:919] (4/6) Start epoch 863 +2024-03-15 16:55:40,733 INFO [train.py:527] (4/6) Epoch 863, batch 12, global_batch_idx: 106900, batch size: 72, loss[discriminator_loss=2.657, discriminator_real_loss=1.3, discriminator_fake_loss=1.356, generator_loss=28.98, generator_mel_loss=17.5, generator_kl_loss=1.346, generator_dur_loss=1.778, generator_adv_loss=2.019, generator_feat_match_loss=6.329, over 72.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.347, discriminator_fake_loss=1.319, generator_loss=28.92, generator_mel_loss=17.68, generator_kl_loss=1.393, generator_dur_loss=1.767, generator_adv_loss=2.027, generator_feat_match_loss=6.059, over 807.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:58:01,586 INFO [train.py:527] (4/6) Epoch 863, batch 62, global_batch_idx: 106950, batch size: 68, loss[discriminator_loss=2.678, discriminator_real_loss=1.297, discriminator_fake_loss=1.381, generator_loss=28.83, generator_mel_loss=18.13, generator_kl_loss=1.327, generator_dur_loss=1.796, generator_adv_loss=1.907, generator_feat_match_loss=5.665, over 68.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.351, discriminator_fake_loss=1.326, generator_loss=29.04, generator_mel_loss=17.77, generator_kl_loss=1.45, generator_dur_loss=1.746, generator_adv_loss=1.999, generator_feat_match_loss=6.067, over 3563.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:00:22,700 INFO [train.py:527] (4/6) Epoch 863, batch 112, global_batch_idx: 107000, batch size: 48, loss[discriminator_loss=2.664, discriminator_real_loss=1.366, discriminator_fake_loss=1.298, generator_loss=30.59, generator_mel_loss=18.66, generator_kl_loss=1.623, generator_dur_loss=1.689, generator_adv_loss=2.024, generator_feat_match_loss=6.596, over 48.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.349, discriminator_fake_loss=1.329, generator_loss=29.13, generator_mel_loss=17.84, generator_kl_loss=1.447, generator_dur_loss=1.741, generator_adv_loss=2.002, generator_feat_match_loss=6.099, over 6365.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:00:22,701 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 17:00:31,749 INFO [train.py:591] (4/6) Epoch 863, validation: discriminator_loss=2.749, discriminator_real_loss=1.467, discriminator_fake_loss=1.282, generator_loss=27.81, generator_mel_loss=17.96, generator_kl_loss=1.232, generator_dur_loss=1.805, generator_adv_loss=1.926, generator_feat_match_loss=4.884, over 100.00 samples. +2024-03-15 17:00:31,749 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 17:01:03,064 INFO [train.py:919] (4/6) Start epoch 864 +2024-03-15 17:03:14,729 INFO [train.py:527] (4/6) Epoch 864, batch 38, global_batch_idx: 107050, batch size: 47, loss[discriminator_loss=2.709, discriminator_real_loss=1.278, discriminator_fake_loss=1.431, generator_loss=28.99, generator_mel_loss=17.59, generator_kl_loss=1.584, generator_dur_loss=1.675, generator_adv_loss=2.002, generator_feat_match_loss=6.137, over 47.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.353, discriminator_fake_loss=1.329, generator_loss=29.14, generator_mel_loss=17.84, generator_kl_loss=1.473, generator_dur_loss=1.728, generator_adv_loss=1.998, generator_feat_match_loss=6.095, over 2200.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:05:35,856 INFO [train.py:527] (4/6) Epoch 864, batch 88, global_batch_idx: 107100, batch size: 59, loss[discriminator_loss=2.7, discriminator_real_loss=1.323, discriminator_fake_loss=1.377, generator_loss=29.47, generator_mel_loss=18.16, generator_kl_loss=1.513, generator_dur_loss=1.692, generator_adv_loss=2.06, generator_feat_match_loss=6.048, over 59.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.363, discriminator_fake_loss=1.318, generator_loss=28.98, generator_mel_loss=17.78, generator_kl_loss=1.464, generator_dur_loss=1.73, generator_adv_loss=2.009, generator_feat_match_loss=5.995, over 5152.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:07:15,641 INFO [train.py:919] (4/6) Start epoch 865 +2024-03-15 17:08:19,441 INFO [train.py:527] (4/6) Epoch 865, batch 14, global_batch_idx: 107150, batch size: 88, loss[discriminator_loss=2.693, discriminator_real_loss=1.352, discriminator_fake_loss=1.341, generator_loss=29.78, generator_mel_loss=18.1, generator_kl_loss=1.405, generator_dur_loss=1.819, generator_adv_loss=1.897, generator_feat_match_loss=6.557, over 88.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.357, discriminator_fake_loss=1.332, generator_loss=29.04, generator_mel_loss=17.79, generator_kl_loss=1.393, generator_dur_loss=1.761, generator_adv_loss=2.004, generator_feat_match_loss=6.09, over 1006.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:10:41,778 INFO [train.py:527] (4/6) Epoch 865, batch 64, global_batch_idx: 107200, batch size: 56, loss[discriminator_loss=2.617, discriminator_real_loss=1.325, discriminator_fake_loss=1.293, generator_loss=29.37, generator_mel_loss=17.58, generator_kl_loss=1.23, generator_dur_loss=1.705, generator_adv_loss=2.122, generator_feat_match_loss=6.727, over 56.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.347, discriminator_fake_loss=1.326, generator_loss=29.22, generator_mel_loss=17.79, generator_kl_loss=1.419, generator_dur_loss=1.742, generator_adv_loss=2.019, generator_feat_match_loss=6.252, over 3886.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:10:41,780 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 17:10:49,760 INFO [train.py:591] (4/6) Epoch 865, validation: discriminator_loss=2.681, discriminator_real_loss=1.411, discriminator_fake_loss=1.27, generator_loss=27.54, generator_mel_loss=18.04, generator_kl_loss=1.312, generator_dur_loss=1.8, generator_adv_loss=1.97, generator_feat_match_loss=4.413, over 100.00 samples. +2024-03-15 17:10:49,760 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 17:13:13,561 INFO [train.py:527] (4/6) Epoch 865, batch 114, global_batch_idx: 107250, batch size: 88, loss[discriminator_loss=2.656, discriminator_real_loss=1.4, discriminator_fake_loss=1.256, generator_loss=28.85, generator_mel_loss=17.59, generator_kl_loss=1.313, generator_dur_loss=1.886, generator_adv_loss=2.064, generator_feat_match_loss=5.995, over 88.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.12, generator_mel_loss=17.79, generator_kl_loss=1.43, generator_dur_loss=1.741, generator_adv_loss=2.016, generator_feat_match_loss=6.14, over 6753.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:13:40,776 INFO [train.py:919] (4/6) Start epoch 866 +2024-03-15 17:15:59,851 INFO [train.py:527] (4/6) Epoch 866, batch 40, global_batch_idx: 107300, batch size: 39, loss[discriminator_loss=2.661, discriminator_real_loss=1.32, discriminator_fake_loss=1.341, generator_loss=30.84, generator_mel_loss=18.01, generator_kl_loss=1.741, generator_dur_loss=1.602, generator_adv_loss=1.949, generator_feat_match_loss=7.546, over 39.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.348, discriminator_fake_loss=1.319, generator_loss=29.17, generator_mel_loss=17.81, generator_kl_loss=1.458, generator_dur_loss=1.719, generator_adv_loss=2.025, generator_feat_match_loss=6.161, over 2160.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:18:21,243 INFO [train.py:527] (4/6) Epoch 866, batch 90, global_batch_idx: 107350, batch size: 50, loss[discriminator_loss=2.616, discriminator_real_loss=1.324, discriminator_fake_loss=1.292, generator_loss=27.99, generator_mel_loss=17.53, generator_kl_loss=1.387, generator_dur_loss=1.651, generator_adv_loss=2.109, generator_feat_match_loss=5.316, over 50.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.35, discriminator_fake_loss=1.319, generator_loss=29.05, generator_mel_loss=17.75, generator_kl_loss=1.451, generator_dur_loss=1.728, generator_adv_loss=2.017, generator_feat_match_loss=6.106, over 4993.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:19:50,488 INFO [train.py:919] (4/6) Start epoch 867 +2024-03-15 17:20:58,853 INFO [train.py:527] (4/6) Epoch 867, batch 16, global_batch_idx: 107400, batch size: 83, loss[discriminator_loss=2.579, discriminator_real_loss=1.317, discriminator_fake_loss=1.261, generator_loss=29.58, generator_mel_loss=17.88, generator_kl_loss=1.452, generator_dur_loss=1.832, generator_adv_loss=1.967, generator_feat_match_loss=6.453, over 83.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.332, discriminator_fake_loss=1.332, generator_loss=29.25, generator_mel_loss=17.89, generator_kl_loss=1.409, generator_dur_loss=1.728, generator_adv_loss=1.997, generator_feat_match_loss=6.233, over 976.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:20:58,854 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 17:21:06,797 INFO [train.py:591] (4/6) Epoch 867, validation: discriminator_loss=2.686, discriminator_real_loss=1.307, discriminator_fake_loss=1.379, generator_loss=27.67, generator_mel_loss=17.74, generator_kl_loss=1.298, generator_dur_loss=1.802, generator_adv_loss=1.89, generator_feat_match_loss=4.94, over 100.00 samples. +2024-03-15 17:21:06,798 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 17:23:24,438 INFO [train.py:527] (4/6) Epoch 867, batch 66, global_batch_idx: 107450, batch size: 77, loss[discriminator_loss=2.687, discriminator_real_loss=1.325, discriminator_fake_loss=1.361, generator_loss=28.82, generator_mel_loss=17.81, generator_kl_loss=1.259, generator_dur_loss=1.801, generator_adv_loss=2.187, generator_feat_match_loss=5.765, over 77.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.35, discriminator_fake_loss=1.325, generator_loss=28.98, generator_mel_loss=17.79, generator_kl_loss=1.411, generator_dur_loss=1.75, generator_adv_loss=2.005, generator_feat_match_loss=6.027, over 4089.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:25:42,113 INFO [train.py:527] (4/6) Epoch 867, batch 116, global_batch_idx: 107500, batch size: 31, loss[discriminator_loss=2.752, discriminator_real_loss=1.386, discriminator_fake_loss=1.366, generator_loss=28.88, generator_mel_loss=17.84, generator_kl_loss=1.602, generator_dur_loss=1.619, generator_adv_loss=2.093, generator_feat_match_loss=5.731, over 31.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=29, generator_mel_loss=17.76, generator_kl_loss=1.426, generator_dur_loss=1.744, generator_adv_loss=2.006, generator_feat_match_loss=6.066, over 6775.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:26:04,331 INFO [train.py:919] (4/6) Start epoch 868 +2024-03-15 17:28:24,434 INFO [train.py:527] (4/6) Epoch 868, batch 42, global_batch_idx: 107550, batch size: 62, loss[discriminator_loss=2.682, discriminator_real_loss=1.278, discriminator_fake_loss=1.405, generator_loss=28.54, generator_mel_loss=17.42, generator_kl_loss=1.416, generator_dur_loss=1.73, generator_adv_loss=2.109, generator_feat_match_loss=5.864, over 62.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.349, discriminator_fake_loss=1.322, generator_loss=29.15, generator_mel_loss=17.79, generator_kl_loss=1.431, generator_dur_loss=1.751, generator_adv_loss=2.008, generator_feat_match_loss=6.177, over 2463.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:30:40,951 INFO [train.py:527] (4/6) Epoch 868, batch 92, global_batch_idx: 107600, batch size: 55, loss[discriminator_loss=2.599, discriminator_real_loss=1.284, discriminator_fake_loss=1.315, generator_loss=29.35, generator_mel_loss=17.7, generator_kl_loss=1.528, generator_dur_loss=1.702, generator_adv_loss=2.023, generator_feat_match_loss=6.402, over 55.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.349, discriminator_fake_loss=1.324, generator_loss=29.2, generator_mel_loss=17.84, generator_kl_loss=1.437, generator_dur_loss=1.74, generator_adv_loss=2.008, generator_feat_match_loss=6.18, over 5217.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:30:40,952 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 17:30:49,714 INFO [train.py:591] (4/6) Epoch 868, validation: discriminator_loss=2.713, discriminator_real_loss=1.39, discriminator_fake_loss=1.323, generator_loss=28.26, generator_mel_loss=18.19, generator_kl_loss=1.222, generator_dur_loss=1.804, generator_adv_loss=1.966, generator_feat_match_loss=5.076, over 100.00 samples. +2024-03-15 17:30:49,715 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 17:32:19,764 INFO [train.py:919] (4/6) Start epoch 869 +2024-03-15 17:33:34,669 INFO [train.py:527] (4/6) Epoch 869, batch 18, global_batch_idx: 107650, batch size: 31, loss[discriminator_loss=2.701, discriminator_real_loss=1.354, discriminator_fake_loss=1.347, generator_loss=29.2, generator_mel_loss=17.98, generator_kl_loss=1.618, generator_dur_loss=1.579, generator_adv_loss=1.949, generator_feat_match_loss=6.071, over 31.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.365, discriminator_fake_loss=1.317, generator_loss=29.29, generator_mel_loss=17.85, generator_kl_loss=1.387, generator_dur_loss=1.762, generator_adv_loss=2.015, generator_feat_match_loss=6.278, over 1199.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:35:53,043 INFO [train.py:527] (4/6) Epoch 869, batch 68, global_batch_idx: 107700, batch size: 58, loss[discriminator_loss=2.626, discriminator_real_loss=1.294, discriminator_fake_loss=1.332, generator_loss=28.94, generator_mel_loss=17.71, generator_kl_loss=1.338, generator_dur_loss=1.729, generator_adv_loss=1.971, generator_feat_match_loss=6.187, over 58.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.323, generator_loss=29.19, generator_mel_loss=17.81, generator_kl_loss=1.424, generator_dur_loss=1.753, generator_adv_loss=2.011, generator_feat_match_loss=6.198, over 4219.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:38:10,261 INFO [train.py:527] (4/6) Epoch 869, batch 118, global_batch_idx: 107750, batch size: 16, loss[discriminator_loss=2.658, discriminator_real_loss=1.412, discriminator_fake_loss=1.247, generator_loss=28.81, generator_mel_loss=17.22, generator_kl_loss=1.7, generator_dur_loss=1.532, generator_adv_loss=1.944, generator_feat_match_loss=6.415, over 16.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.357, discriminator_fake_loss=1.322, generator_loss=29, generator_mel_loss=17.76, generator_kl_loss=1.422, generator_dur_loss=1.755, generator_adv_loss=2.011, generator_feat_match_loss=6.05, over 7095.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:38:25,831 INFO [train.py:919] (4/6) Start epoch 870 +2024-03-15 17:40:55,591 INFO [train.py:527] (4/6) Epoch 870, batch 44, global_batch_idx: 107800, batch size: 56, loss[discriminator_loss=2.7, discriminator_real_loss=1.346, discriminator_fake_loss=1.354, generator_loss=28.85, generator_mel_loss=17.56, generator_kl_loss=1.327, generator_dur_loss=1.741, generator_adv_loss=1.96, generator_feat_match_loss=6.263, over 56.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=28.85, generator_mel_loss=17.7, generator_kl_loss=1.399, generator_dur_loss=1.765, generator_adv_loss=1.994, generator_feat_match_loss=5.994, over 2918.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:40:55,593 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 17:41:03,898 INFO [train.py:591] (4/6) Epoch 870, validation: discriminator_loss=2.776, discriminator_real_loss=1.387, discriminator_fake_loss=1.389, generator_loss=28, generator_mel_loss=18.17, generator_kl_loss=1.305, generator_dur_loss=1.815, generator_adv_loss=1.815, generator_feat_match_loss=4.895, over 100.00 samples. +2024-03-15 17:41:03,899 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 17:43:23,788 INFO [train.py:527] (4/6) Epoch 870, batch 94, global_batch_idx: 107850, batch size: 68, loss[discriminator_loss=2.63, discriminator_real_loss=1.257, discriminator_fake_loss=1.373, generator_loss=29.37, generator_mel_loss=17.96, generator_kl_loss=1.384, generator_dur_loss=1.826, generator_adv_loss=2.168, generator_feat_match_loss=6.039, over 68.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=28.93, generator_mel_loss=17.76, generator_kl_loss=1.401, generator_dur_loss=1.761, generator_adv_loss=1.998, generator_feat_match_loss=6.01, over 5899.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:44:43,334 INFO [train.py:919] (4/6) Start epoch 871 +2024-03-15 17:46:03,813 INFO [train.py:527] (4/6) Epoch 871, batch 20, global_batch_idx: 107900, batch size: 61, loss[discriminator_loss=2.703, discriminator_real_loss=1.364, discriminator_fake_loss=1.339, generator_loss=28.57, generator_mel_loss=17.68, generator_kl_loss=1.512, generator_dur_loss=1.718, generator_adv_loss=1.809, generator_feat_match_loss=5.853, over 61.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.326, generator_loss=28.86, generator_mel_loss=17.71, generator_kl_loss=1.399, generator_dur_loss=1.771, generator_adv_loss=2.007, generator_feat_match_loss=5.973, over 1432.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:48:24,325 INFO [train.py:527] (4/6) Epoch 871, batch 70, global_batch_idx: 107950, batch size: 80, loss[discriminator_loss=2.7, discriminator_real_loss=1.427, discriminator_fake_loss=1.273, generator_loss=29.58, generator_mel_loss=17.91, generator_kl_loss=1.304, generator_dur_loss=1.821, generator_adv_loss=2.005, generator_feat_match_loss=6.544, over 80.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.356, discriminator_fake_loss=1.325, generator_loss=29.05, generator_mel_loss=17.8, generator_kl_loss=1.427, generator_dur_loss=1.755, generator_adv_loss=2.003, generator_feat_match_loss=6.059, over 4280.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:50:40,122 INFO [train.py:527] (4/6) Epoch 871, batch 120, global_batch_idx: 108000, batch size: 62, loss[discriminator_loss=2.63, discriminator_real_loss=1.305, discriminator_fake_loss=1.324, generator_loss=28.94, generator_mel_loss=17.77, generator_kl_loss=1.484, generator_dur_loss=1.755, generator_adv_loss=2.057, generator_feat_match_loss=5.873, over 62.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=29.1, generator_mel_loss=17.82, generator_kl_loss=1.431, generator_dur_loss=1.746, generator_adv_loss=2.007, generator_feat_match_loss=6.088, over 6993.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:50:40,123 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 17:50:49,222 INFO [train.py:591] (4/6) Epoch 871, validation: discriminator_loss=2.693, discriminator_real_loss=1.437, discriminator_fake_loss=1.256, generator_loss=27.69, generator_mel_loss=17.98, generator_kl_loss=1.284, generator_dur_loss=1.814, generator_adv_loss=1.927, generator_feat_match_loss=4.692, over 100.00 samples. +2024-03-15 17:50:49,222 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 17:50:58,931 INFO [train.py:919] (4/6) Start epoch 872 +2024-03-15 17:53:28,740 INFO [train.py:527] (4/6) Epoch 872, batch 46, global_batch_idx: 108050, batch size: 45, loss[discriminator_loss=2.697, discriminator_real_loss=1.457, discriminator_fake_loss=1.241, generator_loss=28.51, generator_mel_loss=17.73, generator_kl_loss=1.623, generator_dur_loss=1.662, generator_adv_loss=1.829, generator_feat_match_loss=5.662, over 45.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.358, discriminator_fake_loss=1.316, generator_loss=29.16, generator_mel_loss=17.81, generator_kl_loss=1.482, generator_dur_loss=1.72, generator_adv_loss=2.02, generator_feat_match_loss=6.125, over 2414.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:55:49,379 INFO [train.py:527] (4/6) Epoch 872, batch 96, global_batch_idx: 108100, batch size: 14, loss[discriminator_loss=2.515, discriminator_real_loss=1.225, discriminator_fake_loss=1.29, generator_loss=31.71, generator_mel_loss=18.03, generator_kl_loss=2.071, generator_dur_loss=1.487, generator_adv_loss=2.009, generator_feat_match_loss=8.111, over 14.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.349, discriminator_fake_loss=1.32, generator_loss=29.1, generator_mel_loss=17.78, generator_kl_loss=1.471, generator_dur_loss=1.731, generator_adv_loss=2.01, generator_feat_match_loss=6.113, over 5157.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:57:05,214 INFO [train.py:919] (4/6) Start epoch 873 +2024-03-15 17:58:29,769 INFO [train.py:527] (4/6) Epoch 873, batch 22, global_batch_idx: 108150, batch size: 61, loss[discriminator_loss=2.665, discriminator_real_loss=1.431, discriminator_fake_loss=1.234, generator_loss=29.34, generator_mel_loss=17.72, generator_kl_loss=1.45, generator_dur_loss=1.733, generator_adv_loss=2.133, generator_feat_match_loss=6.305, over 61.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.346, discriminator_fake_loss=1.342, generator_loss=29.05, generator_mel_loss=17.66, generator_kl_loss=1.445, generator_dur_loss=1.746, generator_adv_loss=2.058, generator_feat_match_loss=6.141, over 1429.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:00:46,087 INFO [train.py:527] (4/6) Epoch 873, batch 72, global_batch_idx: 108200, batch size: 52, loss[discriminator_loss=2.654, discriminator_real_loss=1.403, discriminator_fake_loss=1.251, generator_loss=28.69, generator_mel_loss=17.58, generator_kl_loss=1.595, generator_dur_loss=1.706, generator_adv_loss=2.059, generator_feat_match_loss=5.745, over 52.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.351, discriminator_fake_loss=1.331, generator_loss=29.08, generator_mel_loss=17.75, generator_kl_loss=1.452, generator_dur_loss=1.729, generator_adv_loss=2.028, generator_feat_match_loss=6.117, over 4108.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:00:46,089 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 18:00:54,110 INFO [train.py:591] (4/6) Epoch 873, validation: discriminator_loss=2.741, discriminator_real_loss=1.426, discriminator_fake_loss=1.315, generator_loss=27.49, generator_mel_loss=17.82, generator_kl_loss=1.225, generator_dur_loss=1.815, generator_adv_loss=1.95, generator_feat_match_loss=4.677, over 100.00 samples. +2024-03-15 18:00:54,111 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 18:03:12,114 INFO [train.py:527] (4/6) Epoch 873, batch 122, global_batch_idx: 108250, batch size: 17, loss[discriminator_loss=2.739, discriminator_real_loss=1.381, discriminator_fake_loss=1.358, generator_loss=30.48, generator_mel_loss=19.07, generator_kl_loss=1.889, generator_dur_loss=1.621, generator_adv_loss=1.944, generator_feat_match_loss=5.953, over 17.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=29.05, generator_mel_loss=17.75, generator_kl_loss=1.445, generator_dur_loss=1.74, generator_adv_loss=2.02, generator_feat_match_loss=6.103, over 6998.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:03:16,464 INFO [train.py:919] (4/6) Start epoch 874 +2024-03-15 18:05:52,501 INFO [train.py:527] (4/6) Epoch 874, batch 48, global_batch_idx: 108300, batch size: 31, loss[discriminator_loss=2.704, discriminator_real_loss=1.376, discriminator_fake_loss=1.328, generator_loss=29.38, generator_mel_loss=18.63, generator_kl_loss=1.55, generator_dur_loss=1.655, generator_adv_loss=2.063, generator_feat_match_loss=5.486, over 31.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.35, discriminator_fake_loss=1.327, generator_loss=28.9, generator_mel_loss=17.75, generator_kl_loss=1.436, generator_dur_loss=1.738, generator_adv_loss=1.996, generator_feat_match_loss=5.974, over 2783.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:08:12,162 INFO [train.py:527] (4/6) Epoch 874, batch 98, global_batch_idx: 108350, batch size: 64, loss[discriminator_loss=2.644, discriminator_real_loss=1.192, discriminator_fake_loss=1.452, generator_loss=29.15, generator_mel_loss=17.76, generator_kl_loss=1.332, generator_dur_loss=1.781, generator_adv_loss=2.247, generator_feat_match_loss=6.032, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=29.01, generator_mel_loss=17.79, generator_kl_loss=1.454, generator_dur_loss=1.736, generator_adv_loss=2.002, generator_feat_match_loss=6.03, over 5546.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:09:22,202 INFO [train.py:919] (4/6) Start epoch 875 +2024-03-15 18:10:53,783 INFO [train.py:527] (4/6) Epoch 875, batch 24, global_batch_idx: 108400, batch size: 70, loss[discriminator_loss=2.631, discriminator_real_loss=1.271, discriminator_fake_loss=1.359, generator_loss=30.56, generator_mel_loss=18.35, generator_kl_loss=1.436, generator_dur_loss=1.799, generator_adv_loss=1.984, generator_feat_match_loss=6.989, over 70.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.349, discriminator_fake_loss=1.327, generator_loss=29.28, generator_mel_loss=17.79, generator_kl_loss=1.437, generator_dur_loss=1.762, generator_adv_loss=2.024, generator_feat_match_loss=6.268, over 1490.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:10:53,785 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 18:11:01,727 INFO [train.py:591] (4/6) Epoch 875, validation: discriminator_loss=2.644, discriminator_real_loss=1.344, discriminator_fake_loss=1.3, generator_loss=27.54, generator_mel_loss=17.71, generator_kl_loss=1.262, generator_dur_loss=1.802, generator_adv_loss=1.912, generator_feat_match_loss=4.86, over 100.00 samples. +2024-03-15 18:11:01,728 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 18:13:20,859 INFO [train.py:527] (4/6) Epoch 875, batch 74, global_batch_idx: 108450, batch size: 56, loss[discriminator_loss=2.584, discriminator_real_loss=1.306, discriminator_fake_loss=1.278, generator_loss=29.5, generator_mel_loss=17.66, generator_kl_loss=1.394, generator_dur_loss=1.713, generator_adv_loss=2.126, generator_feat_match_loss=6.613, over 56.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.345, discriminator_fake_loss=1.327, generator_loss=29.16, generator_mel_loss=17.81, generator_kl_loss=1.427, generator_dur_loss=1.746, generator_adv_loss=2.014, generator_feat_match_loss=6.159, over 4348.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:15:38,377 INFO [train.py:919] (4/6) Start epoch 876 +2024-03-15 18:16:02,420 INFO [train.py:527] (4/6) Epoch 876, batch 0, global_batch_idx: 108500, batch size: 72, loss[discriminator_loss=2.668, discriminator_real_loss=1.385, discriminator_fake_loss=1.283, generator_loss=29.23, generator_mel_loss=18.05, generator_kl_loss=1.356, generator_dur_loss=1.811, generator_adv_loss=1.967, generator_feat_match_loss=6.048, over 72.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.385, discriminator_fake_loss=1.283, generator_loss=29.23, generator_mel_loss=18.05, generator_kl_loss=1.356, generator_dur_loss=1.811, generator_adv_loss=1.967, generator_feat_match_loss=6.048, over 72.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:18:19,657 INFO [train.py:527] (4/6) Epoch 876, batch 50, global_batch_idx: 108550, batch size: 68, loss[discriminator_loss=2.7, discriminator_real_loss=1.294, discriminator_fake_loss=1.406, generator_loss=29.34, generator_mel_loss=17.58, generator_kl_loss=1.556, generator_dur_loss=1.717, generator_adv_loss=1.971, generator_feat_match_loss=6.522, over 68.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.356, discriminator_fake_loss=1.332, generator_loss=29.11, generator_mel_loss=17.8, generator_kl_loss=1.446, generator_dur_loss=1.742, generator_adv_loss=2.009, generator_feat_match_loss=6.117, over 2930.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:20:40,524 INFO [train.py:527] (4/6) Epoch 876, batch 100, global_batch_idx: 108600, batch size: 36, loss[discriminator_loss=2.648, discriminator_real_loss=1.484, discriminator_fake_loss=1.163, generator_loss=29.13, generator_mel_loss=18.08, generator_kl_loss=1.455, generator_dur_loss=1.669, generator_adv_loss=2.058, generator_feat_match_loss=5.862, over 36.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.329, generator_loss=29.07, generator_mel_loss=17.77, generator_kl_loss=1.441, generator_dur_loss=1.737, generator_adv_loss=2.013, generator_feat_match_loss=6.11, over 5673.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:20:40,525 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 18:20:49,299 INFO [train.py:591] (4/6) Epoch 876, validation: discriminator_loss=2.681, discriminator_real_loss=1.37, discriminator_fake_loss=1.31, generator_loss=27.59, generator_mel_loss=17.93, generator_kl_loss=1.287, generator_dur_loss=1.799, generator_adv_loss=1.908, generator_feat_match_loss=4.673, over 100.00 samples. +2024-03-15 18:20:49,300 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 18:21:52,909 INFO [train.py:919] (4/6) Start epoch 877 +2024-03-15 18:23:24,957 INFO [train.py:527] (4/6) Epoch 877, batch 26, global_batch_idx: 108650, batch size: 58, loss[discriminator_loss=2.679, discriminator_real_loss=1.295, discriminator_fake_loss=1.384, generator_loss=28.83, generator_mel_loss=17.64, generator_kl_loss=1.333, generator_dur_loss=1.724, generator_adv_loss=2.024, generator_feat_match_loss=6.11, over 58.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.375, discriminator_fake_loss=1.316, generator_loss=28.94, generator_mel_loss=17.81, generator_kl_loss=1.44, generator_dur_loss=1.714, generator_adv_loss=1.987, generator_feat_match_loss=5.986, over 1361.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:25:44,599 INFO [train.py:527] (4/6) Epoch 877, batch 76, global_batch_idx: 108700, batch size: 39, loss[discriminator_loss=2.66, discriminator_real_loss=1.346, discriminator_fake_loss=1.313, generator_loss=30.12, generator_mel_loss=17.96, generator_kl_loss=1.588, generator_dur_loss=1.646, generator_adv_loss=2.056, generator_feat_match_loss=6.871, over 39.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.35, discriminator_fake_loss=1.319, generator_loss=29.16, generator_mel_loss=17.82, generator_kl_loss=1.437, generator_dur_loss=1.731, generator_adv_loss=2, generator_feat_match_loss=6.17, over 4128.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:27:55,976 INFO [train.py:919] (4/6) Start epoch 878 +2024-03-15 18:28:20,934 INFO [train.py:527] (4/6) Epoch 878, batch 2, global_batch_idx: 108750, batch size: 47, loss[discriminator_loss=2.66, discriminator_real_loss=1.425, discriminator_fake_loss=1.236, generator_loss=28.41, generator_mel_loss=17.78, generator_kl_loss=1.477, generator_dur_loss=1.64, generator_adv_loss=1.964, generator_feat_match_loss=5.546, over 47.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.401, discriminator_fake_loss=1.286, generator_loss=28.57, generator_mel_loss=17.71, generator_kl_loss=1.433, generator_dur_loss=1.686, generator_adv_loss=1.995, generator_feat_match_loss=5.739, over 171.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:30:42,793 INFO [train.py:527] (4/6) Epoch 878, batch 52, global_batch_idx: 108800, batch size: 47, loss[discriminator_loss=2.646, discriminator_real_loss=1.349, discriminator_fake_loss=1.297, generator_loss=28.96, generator_mel_loss=17.74, generator_kl_loss=1.492, generator_dur_loss=1.658, generator_adv_loss=2.138, generator_feat_match_loss=5.934, over 47.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.363, discriminator_fake_loss=1.319, generator_loss=29.03, generator_mel_loss=17.78, generator_kl_loss=1.452, generator_dur_loss=1.711, generator_adv_loss=2.015, generator_feat_match_loss=6.072, over 3006.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:30:42,794 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 18:30:51,152 INFO [train.py:591] (4/6) Epoch 878, validation: discriminator_loss=2.73, discriminator_real_loss=1.464, discriminator_fake_loss=1.267, generator_loss=27.14, generator_mel_loss=17.6, generator_kl_loss=1.343, generator_dur_loss=1.804, generator_adv_loss=2.048, generator_feat_match_loss=4.35, over 100.00 samples. +2024-03-15 18:30:51,153 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 18:33:07,784 INFO [train.py:527] (4/6) Epoch 878, batch 102, global_batch_idx: 108850, batch size: 74, loss[discriminator_loss=2.623, discriminator_real_loss=1.303, discriminator_fake_loss=1.32, generator_loss=28.88, generator_mel_loss=17.9, generator_kl_loss=1.361, generator_dur_loss=1.799, generator_adv_loss=2.101, generator_feat_match_loss=5.714, over 74.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.357, discriminator_fake_loss=1.321, generator_loss=29.05, generator_mel_loss=17.79, generator_kl_loss=1.445, generator_dur_loss=1.725, generator_adv_loss=2.026, generator_feat_match_loss=6.066, over 5928.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:34:08,135 INFO [train.py:919] (4/6) Start epoch 879 +2024-03-15 18:35:51,337 INFO [train.py:527] (4/6) Epoch 879, batch 28, global_batch_idx: 108900, batch size: 64, loss[discriminator_loss=2.673, discriminator_real_loss=1.362, discriminator_fake_loss=1.312, generator_loss=27.85, generator_mel_loss=17.4, generator_kl_loss=1.323, generator_dur_loss=1.77, generator_adv_loss=2.027, generator_feat_match_loss=5.339, over 64.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.364, discriminator_fake_loss=1.321, generator_loss=28.93, generator_mel_loss=17.78, generator_kl_loss=1.435, generator_dur_loss=1.736, generator_adv_loss=2.005, generator_feat_match_loss=5.973, over 1731.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:38:13,273 INFO [train.py:527] (4/6) Epoch 879, batch 78, global_batch_idx: 108950, batch size: 39, loss[discriminator_loss=2.659, discriminator_real_loss=1.373, discriminator_fake_loss=1.286, generator_loss=29.27, generator_mel_loss=18.25, generator_kl_loss=1.467, generator_dur_loss=1.592, generator_adv_loss=2.005, generator_feat_match_loss=5.951, over 39.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=29.01, generator_mel_loss=17.81, generator_kl_loss=1.445, generator_dur_loss=1.729, generator_adv_loss=2, generator_feat_match_loss=6.022, over 4319.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:40:18,518 INFO [train.py:919] (4/6) Start epoch 880 +2024-03-15 18:40:52,053 INFO [train.py:527] (4/6) Epoch 880, batch 4, global_batch_idx: 109000, batch size: 45, loss[discriminator_loss=2.707, discriminator_real_loss=1.344, discriminator_fake_loss=1.363, generator_loss=28.88, generator_mel_loss=17.6, generator_kl_loss=1.469, generator_dur_loss=1.656, generator_adv_loss=2.147, generator_feat_match_loss=6.002, over 45.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.338, discriminator_fake_loss=1.356, generator_loss=29.64, generator_mel_loss=17.87, generator_kl_loss=1.55, generator_dur_loss=1.7, generator_adv_loss=2.034, generator_feat_match_loss=6.48, over 238.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:40:52,056 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 18:40:59,967 INFO [train.py:591] (4/6) Epoch 880, validation: discriminator_loss=2.772, discriminator_real_loss=1.501, discriminator_fake_loss=1.271, generator_loss=27.81, generator_mel_loss=17.71, generator_kl_loss=1.226, generator_dur_loss=1.799, generator_adv_loss=2.008, generator_feat_match_loss=5.066, over 100.00 samples. +2024-03-15 18:40:59,970 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 18:43:19,239 INFO [train.py:527] (4/6) Epoch 880, batch 54, global_batch_idx: 109050, batch size: 25, loss[discriminator_loss=2.581, discriminator_real_loss=1.281, discriminator_fake_loss=1.299, generator_loss=31.37, generator_mel_loss=18.2, generator_kl_loss=1.853, generator_dur_loss=1.544, generator_adv_loss=2.047, generator_feat_match_loss=7.722, over 25.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.354, discriminator_fake_loss=1.331, generator_loss=29, generator_mel_loss=17.74, generator_kl_loss=1.438, generator_dur_loss=1.754, generator_adv_loss=2.001, generator_feat_match_loss=6.069, over 3172.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:45:38,303 INFO [train.py:527] (4/6) Epoch 880, batch 104, global_batch_idx: 109100, batch size: 31, loss[discriminator_loss=2.765, discriminator_real_loss=1.357, discriminator_fake_loss=1.408, generator_loss=28.22, generator_mel_loss=18.03, generator_kl_loss=1.719, generator_dur_loss=1.567, generator_adv_loss=1.898, generator_feat_match_loss=5.011, over 31.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.353, discriminator_fake_loss=1.326, generator_loss=28.99, generator_mel_loss=17.76, generator_kl_loss=1.445, generator_dur_loss=1.743, generator_adv_loss=2, generator_feat_match_loss=6.047, over 5969.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:46:32,653 INFO [train.py:919] (4/6) Start epoch 881 +2024-03-15 18:48:21,093 INFO [train.py:527] (4/6) Epoch 881, batch 30, global_batch_idx: 109150, batch size: 58, loss[discriminator_loss=2.74, discriminator_real_loss=1.389, discriminator_fake_loss=1.351, generator_loss=28.79, generator_mel_loss=17.7, generator_kl_loss=1.521, generator_dur_loss=1.692, generator_adv_loss=2.022, generator_feat_match_loss=5.85, over 58.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.345, discriminator_fake_loss=1.326, generator_loss=29.14, generator_mel_loss=17.78, generator_kl_loss=1.405, generator_dur_loss=1.749, generator_adv_loss=2.024, generator_feat_match_loss=6.185, over 1925.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:50:40,775 INFO [train.py:527] (4/6) Epoch 881, batch 80, global_batch_idx: 109200, batch size: 77, loss[discriminator_loss=2.64, discriminator_real_loss=1.31, discriminator_fake_loss=1.331, generator_loss=29.01, generator_mel_loss=18.14, generator_kl_loss=1.277, generator_dur_loss=1.814, generator_adv_loss=1.912, generator_feat_match_loss=5.87, over 77.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.348, discriminator_fake_loss=1.328, generator_loss=29.06, generator_mel_loss=17.78, generator_kl_loss=1.415, generator_dur_loss=1.741, generator_adv_loss=2.01, generator_feat_match_loss=6.109, over 4826.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:50:40,776 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 18:50:49,494 INFO [train.py:591] (4/6) Epoch 881, validation: discriminator_loss=2.706, discriminator_real_loss=1.309, discriminator_fake_loss=1.397, generator_loss=27.62, generator_mel_loss=18.22, generator_kl_loss=1.217, generator_dur_loss=1.809, generator_adv_loss=1.813, generator_feat_match_loss=4.559, over 100.00 samples. +2024-03-15 18:50:49,494 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 18:52:50,073 INFO [train.py:919] (4/6) Start epoch 882 +2024-03-15 18:53:31,118 INFO [train.py:527] (4/6) Epoch 882, batch 6, global_batch_idx: 109250, batch size: 36, loss[discriminator_loss=2.738, discriminator_real_loss=1.308, discriminator_fake_loss=1.43, generator_loss=28.95, generator_mel_loss=17.98, generator_kl_loss=1.491, generator_dur_loss=1.664, generator_adv_loss=1.972, generator_feat_match_loss=5.85, over 36.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.343, discriminator_fake_loss=1.344, generator_loss=29.01, generator_mel_loss=17.85, generator_kl_loss=1.439, generator_dur_loss=1.737, generator_adv_loss=2.019, generator_feat_match_loss=5.961, over 368.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:55:50,504 INFO [train.py:527] (4/6) Epoch 882, batch 56, global_batch_idx: 109300, batch size: 53, loss[discriminator_loss=2.706, discriminator_real_loss=1.425, discriminator_fake_loss=1.281, generator_loss=28.03, generator_mel_loss=17.32, generator_kl_loss=1.427, generator_dur_loss=1.702, generator_adv_loss=2.033, generator_feat_match_loss=5.548, over 53.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.354, discriminator_fake_loss=1.315, generator_loss=29.09, generator_mel_loss=17.79, generator_kl_loss=1.432, generator_dur_loss=1.741, generator_adv_loss=2.016, generator_feat_match_loss=6.113, over 3198.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:58:09,411 INFO [train.py:527] (4/6) Epoch 882, batch 106, global_batch_idx: 109350, batch size: 77, loss[discriminator_loss=2.7, discriminator_real_loss=1.315, discriminator_fake_loss=1.386, generator_loss=29, generator_mel_loss=17.75, generator_kl_loss=1.388, generator_dur_loss=1.767, generator_adv_loss=1.928, generator_feat_match_loss=6.168, over 77.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.12, generator_mel_loss=17.81, generator_kl_loss=1.434, generator_dur_loss=1.731, generator_adv_loss=2.011, generator_feat_match_loss=6.134, over 5992.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:58:57,541 INFO [train.py:919] (4/6) Start epoch 883 +2024-03-15 19:00:51,721 INFO [train.py:527] (4/6) Epoch 883, batch 32, global_batch_idx: 109400, batch size: 42, loss[discriminator_loss=2.652, discriminator_real_loss=1.272, discriminator_fake_loss=1.379, generator_loss=29.98, generator_mel_loss=18.12, generator_kl_loss=1.5, generator_dur_loss=1.657, generator_adv_loss=2.103, generator_feat_match_loss=6.603, over 42.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.345, discriminator_fake_loss=1.316, generator_loss=29.01, generator_mel_loss=17.74, generator_kl_loss=1.426, generator_dur_loss=1.747, generator_adv_loss=2.029, generator_feat_match_loss=6.074, over 1914.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:00:51,723 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 19:00:59,684 INFO [train.py:591] (4/6) Epoch 883, validation: discriminator_loss=2.722, discriminator_real_loss=1.435, discriminator_fake_loss=1.287, generator_loss=28.51, generator_mel_loss=17.87, generator_kl_loss=1.288, generator_dur_loss=1.781, generator_adv_loss=2.111, generator_feat_match_loss=5.462, over 100.00 samples. +2024-03-15 19:00:59,685 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 19:03:19,612 INFO [train.py:527] (4/6) Epoch 883, batch 82, global_batch_idx: 109450, batch size: 70, loss[discriminator_loss=2.656, discriminator_real_loss=1.303, discriminator_fake_loss=1.353, generator_loss=28.83, generator_mel_loss=17.54, generator_kl_loss=1.264, generator_dur_loss=1.789, generator_adv_loss=2.064, generator_feat_match_loss=6.176, over 70.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.35, discriminator_fake_loss=1.314, generator_loss=29.01, generator_mel_loss=17.74, generator_kl_loss=1.427, generator_dur_loss=1.736, generator_adv_loss=2.021, generator_feat_match_loss=6.078, over 4876.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:05:12,674 INFO [train.py:919] (4/6) Start epoch 884 +2024-03-15 19:05:58,868 INFO [train.py:527] (4/6) Epoch 884, batch 8, global_batch_idx: 109500, batch size: 96, loss[discriminator_loss=2.662, discriminator_real_loss=1.388, discriminator_fake_loss=1.274, generator_loss=27.98, generator_mel_loss=17.08, generator_kl_loss=1.225, generator_dur_loss=1.846, generator_adv_loss=2.149, generator_feat_match_loss=5.676, over 96.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.355, discriminator_fake_loss=1.312, generator_loss=29.26, generator_mel_loss=17.76, generator_kl_loss=1.488, generator_dur_loss=1.706, generator_adv_loss=2.058, generator_feat_match_loss=6.249, over 445.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:08:17,071 INFO [train.py:527] (4/6) Epoch 884, batch 58, global_batch_idx: 109550, batch size: 70, loss[discriminator_loss=2.691, discriminator_real_loss=1.459, discriminator_fake_loss=1.232, generator_loss=28.66, generator_mel_loss=17.59, generator_kl_loss=1.412, generator_dur_loss=1.777, generator_adv_loss=2.006, generator_feat_match_loss=5.88, over 70.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.344, discriminator_fake_loss=1.315, generator_loss=29.14, generator_mel_loss=17.74, generator_kl_loss=1.437, generator_dur_loss=1.727, generator_adv_loss=2.023, generator_feat_match_loss=6.209, over 3202.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:10:35,863 INFO [train.py:527] (4/6) Epoch 884, batch 108, global_batch_idx: 109600, batch size: 61, loss[discriminator_loss=2.68, discriminator_real_loss=1.382, discriminator_fake_loss=1.297, generator_loss=29.79, generator_mel_loss=17.89, generator_kl_loss=1.472, generator_dur_loss=1.656, generator_adv_loss=2.172, generator_feat_match_loss=6.606, over 61.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.13, generator_mel_loss=17.75, generator_kl_loss=1.44, generator_dur_loss=1.722, generator_adv_loss=2.018, generator_feat_match_loss=6.193, over 5963.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:10:35,864 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 19:10:44,693 INFO [train.py:591] (4/6) Epoch 884, validation: discriminator_loss=2.719, discriminator_real_loss=1.486, discriminator_fake_loss=1.233, generator_loss=28.43, generator_mel_loss=18.2, generator_kl_loss=1.247, generator_dur_loss=1.776, generator_adv_loss=2.09, generator_feat_match_loss=5.123, over 100.00 samples. +2024-03-15 19:10:44,694 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 19:11:27,569 INFO [train.py:919] (4/6) Start epoch 885 +2024-03-15 19:13:25,342 INFO [train.py:527] (4/6) Epoch 885, batch 34, global_batch_idx: 109650, batch size: 96, loss[discriminator_loss=2.661, discriminator_real_loss=1.41, discriminator_fake_loss=1.251, generator_loss=29.38, generator_mel_loss=17.47, generator_kl_loss=1.383, generator_dur_loss=1.842, generator_adv_loss=2.015, generator_feat_match_loss=6.667, over 96.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.364, discriminator_fake_loss=1.311, generator_loss=29.14, generator_mel_loss=17.78, generator_kl_loss=1.386, generator_dur_loss=1.747, generator_adv_loss=2.046, generator_feat_match_loss=6.184, over 2033.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:15:43,316 INFO [train.py:527] (4/6) Epoch 885, batch 84, global_batch_idx: 109700, batch size: 70, loss[discriminator_loss=2.666, discriminator_real_loss=1.311, discriminator_fake_loss=1.355, generator_loss=30.57, generator_mel_loss=17.67, generator_kl_loss=1.409, generator_dur_loss=1.775, generator_adv_loss=2.051, generator_feat_match_loss=7.665, over 70.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.357, discriminator_fake_loss=1.33, generator_loss=29.05, generator_mel_loss=17.78, generator_kl_loss=1.409, generator_dur_loss=1.746, generator_adv_loss=2.019, generator_feat_match_loss=6.1, over 5104.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:17:31,571 INFO [train.py:919] (4/6) Start epoch 886 +2024-03-15 19:18:22,995 INFO [train.py:527] (4/6) Epoch 886, batch 10, global_batch_idx: 109750, batch size: 96, loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=28.96, generator_mel_loss=17.77, generator_kl_loss=1.256, generator_dur_loss=1.845, generator_adv_loss=1.906, generator_feat_match_loss=6.185, over 96.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.372, discriminator_fake_loss=1.311, generator_loss=28.71, generator_mel_loss=17.67, generator_kl_loss=1.438, generator_dur_loss=1.744, generator_adv_loss=2.005, generator_feat_match_loss=5.851, over 641.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:20:43,173 INFO [train.py:527] (4/6) Epoch 886, batch 60, global_batch_idx: 109800, batch size: 56, loss[discriminator_loss=2.597, discriminator_real_loss=1.288, discriminator_fake_loss=1.309, generator_loss=29.47, generator_mel_loss=17.8, generator_kl_loss=1.465, generator_dur_loss=1.722, generator_adv_loss=2.007, generator_feat_match_loss=6.483, over 56.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.364, discriminator_fake_loss=1.321, generator_loss=28.94, generator_mel_loss=17.71, generator_kl_loss=1.441, generator_dur_loss=1.737, generator_adv_loss=1.999, generator_feat_match_loss=6.049, over 3557.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:20:43,174 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 19:20:51,343 INFO [train.py:591] (4/6) Epoch 886, validation: discriminator_loss=2.7, discriminator_real_loss=1.398, discriminator_fake_loss=1.302, generator_loss=27.26, generator_mel_loss=17.68, generator_kl_loss=1.296, generator_dur_loss=1.808, generator_adv_loss=1.922, generator_feat_match_loss=4.554, over 100.00 samples. +2024-03-15 19:20:51,344 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 19:23:09,718 INFO [train.py:527] (4/6) Epoch 886, batch 110, global_batch_idx: 109850, batch size: 47, loss[discriminator_loss=2.682, discriminator_real_loss=1.313, discriminator_fake_loss=1.368, generator_loss=28.82, generator_mel_loss=17.88, generator_kl_loss=1.681, generator_dur_loss=1.652, generator_adv_loss=1.981, generator_feat_match_loss=5.626, over 47.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.359, discriminator_fake_loss=1.321, generator_loss=29, generator_mel_loss=17.77, generator_kl_loss=1.44, generator_dur_loss=1.731, generator_adv_loss=2, generator_feat_match_loss=6.057, over 6208.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:23:43,660 INFO [train.py:919] (4/6) Start epoch 887 +2024-03-15 19:25:52,450 INFO [train.py:527] (4/6) Epoch 887, batch 36, global_batch_idx: 109900, batch size: 74, loss[discriminator_loss=2.724, discriminator_real_loss=1.379, discriminator_fake_loss=1.345, generator_loss=28.84, generator_mel_loss=17.71, generator_kl_loss=1.404, generator_dur_loss=1.761, generator_adv_loss=2.046, generator_feat_match_loss=5.914, over 74.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.351, discriminator_fake_loss=1.331, generator_loss=29.02, generator_mel_loss=17.79, generator_kl_loss=1.426, generator_dur_loss=1.742, generator_adv_loss=2.002, generator_feat_match_loss=6.055, over 2186.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:28:10,920 INFO [train.py:527] (4/6) Epoch 887, batch 86, global_batch_idx: 109950, batch size: 55, loss[discriminator_loss=2.7, discriminator_real_loss=1.275, discriminator_fake_loss=1.426, generator_loss=29.02, generator_mel_loss=17.84, generator_kl_loss=1.49, generator_dur_loss=1.678, generator_adv_loss=2.093, generator_feat_match_loss=5.923, over 55.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.352, discriminator_fake_loss=1.33, generator_loss=29.06, generator_mel_loss=17.81, generator_kl_loss=1.435, generator_dur_loss=1.741, generator_adv_loss=2.002, generator_feat_match_loss=6.072, over 5052.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:29:50,969 INFO [train.py:919] (4/6) Start epoch 888 +2024-03-15 19:30:46,966 INFO [train.py:527] (4/6) Epoch 888, batch 12, global_batch_idx: 110000, batch size: 47, loss[discriminator_loss=2.768, discriminator_real_loss=1.452, discriminator_fake_loss=1.316, generator_loss=27.95, generator_mel_loss=18.01, generator_kl_loss=1.467, generator_dur_loss=1.707, generator_adv_loss=1.897, generator_feat_match_loss=4.86, over 47.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.367, discriminator_fake_loss=1.312, generator_loss=29.13, generator_mel_loss=17.88, generator_kl_loss=1.434, generator_dur_loss=1.758, generator_adv_loss=2.023, generator_feat_match_loss=6.037, over 821.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:30:46,968 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 19:30:54,875 INFO [train.py:591] (4/6) Epoch 888, validation: discriminator_loss=2.734, discriminator_real_loss=1.375, discriminator_fake_loss=1.358, generator_loss=27.63, generator_mel_loss=18.08, generator_kl_loss=1.163, generator_dur_loss=1.791, generator_adv_loss=1.857, generator_feat_match_loss=4.732, over 100.00 samples. +2024-03-15 19:30:54,875 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 19:33:14,063 INFO [train.py:527] (4/6) Epoch 888, batch 62, global_batch_idx: 110050, batch size: 80, loss[discriminator_loss=2.693, discriminator_real_loss=1.33, discriminator_fake_loss=1.362, generator_loss=28.37, generator_mel_loss=17.6, generator_kl_loss=1.285, generator_dur_loss=1.827, generator_adv_loss=2.071, generator_feat_match_loss=5.586, over 80.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.321, generator_loss=28.92, generator_mel_loss=17.72, generator_kl_loss=1.412, generator_dur_loss=1.752, generator_adv_loss=2.006, generator_feat_match_loss=6.032, over 3858.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:35:33,593 INFO [train.py:527] (4/6) Epoch 888, batch 112, global_batch_idx: 110100, batch size: 56, loss[discriminator_loss=2.714, discriminator_real_loss=1.412, discriminator_fake_loss=1.301, generator_loss=27.66, generator_mel_loss=17.77, generator_kl_loss=1.44, generator_dur_loss=1.698, generator_adv_loss=1.992, generator_feat_match_loss=4.756, over 56.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.364, discriminator_fake_loss=1.322, generator_loss=29.04, generator_mel_loss=17.78, generator_kl_loss=1.439, generator_dur_loss=1.739, generator_adv_loss=2.006, generator_feat_match_loss=6.075, over 6385.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:36:05,219 INFO [train.py:919] (4/6) Start epoch 889 +2024-03-15 19:38:16,338 INFO [train.py:527] (4/6) Epoch 889, batch 38, global_batch_idx: 110150, batch size: 61, loss[discriminator_loss=2.681, discriminator_real_loss=1.329, discriminator_fake_loss=1.353, generator_loss=28.51, generator_mel_loss=17.81, generator_kl_loss=1.275, generator_dur_loss=1.742, generator_adv_loss=2.035, generator_feat_match_loss=5.653, over 61.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.363, discriminator_fake_loss=1.323, generator_loss=28.95, generator_mel_loss=17.82, generator_kl_loss=1.433, generator_dur_loss=1.724, generator_adv_loss=2.005, generator_feat_match_loss=5.967, over 2229.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:40:33,950 INFO [train.py:527] (4/6) Epoch 889, batch 88, global_batch_idx: 110200, batch size: 48, loss[discriminator_loss=2.709, discriminator_real_loss=1.308, discriminator_fake_loss=1.401, generator_loss=29.83, generator_mel_loss=17.83, generator_kl_loss=1.518, generator_dur_loss=1.706, generator_adv_loss=1.981, generator_feat_match_loss=6.791, over 48.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.327, generator_loss=29, generator_mel_loss=17.79, generator_kl_loss=1.428, generator_dur_loss=1.732, generator_adv_loss=2.009, generator_feat_match_loss=6.04, over 5109.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:40:33,952 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 19:40:42,834 INFO [train.py:591] (4/6) Epoch 889, validation: discriminator_loss=2.754, discriminator_real_loss=1.343, discriminator_fake_loss=1.411, generator_loss=28.45, generator_mel_loss=18.43, generator_kl_loss=1.235, generator_dur_loss=1.779, generator_adv_loss=1.929, generator_feat_match_loss=5.07, over 100.00 samples. +2024-03-15 19:40:42,835 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 19:42:21,682 INFO [train.py:919] (4/6) Start epoch 890 +2024-03-15 19:43:21,667 INFO [train.py:527] (4/6) Epoch 890, batch 14, global_batch_idx: 110250, batch size: 48, loss[discriminator_loss=2.689, discriminator_real_loss=1.316, discriminator_fake_loss=1.373, generator_loss=29.66, generator_mel_loss=18.13, generator_kl_loss=1.718, generator_dur_loss=1.648, generator_adv_loss=1.977, generator_feat_match_loss=6.187, over 48.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.346, discriminator_fake_loss=1.328, generator_loss=29.27, generator_mel_loss=17.91, generator_kl_loss=1.485, generator_dur_loss=1.711, generator_adv_loss=2.013, generator_feat_match_loss=6.154, over 826.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:45:41,022 INFO [train.py:527] (4/6) Epoch 890, batch 64, global_batch_idx: 110300, batch size: 62, loss[discriminator_loss=2.634, discriminator_real_loss=1.366, discriminator_fake_loss=1.267, generator_loss=29.92, generator_mel_loss=17.97, generator_kl_loss=1.56, generator_dur_loss=1.755, generator_adv_loss=1.975, generator_feat_match_loss=6.663, over 62.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.32, generator_loss=28.96, generator_mel_loss=17.75, generator_kl_loss=1.436, generator_dur_loss=1.741, generator_adv_loss=2.007, generator_feat_match_loss=6.024, over 3821.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:48:00,262 INFO [train.py:527] (4/6) Epoch 890, batch 114, global_batch_idx: 110350, batch size: 88, loss[discriminator_loss=2.643, discriminator_real_loss=1.331, discriminator_fake_loss=1.312, generator_loss=29.01, generator_mel_loss=17.69, generator_kl_loss=1.378, generator_dur_loss=1.816, generator_adv_loss=2.098, generator_feat_match_loss=6.03, over 88.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.01, generator_mel_loss=17.75, generator_kl_loss=1.427, generator_dur_loss=1.747, generator_adv_loss=2.006, generator_feat_match_loss=6.082, over 6791.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:48:25,382 INFO [train.py:919] (4/6) Start epoch 891 +2024-03-15 19:50:39,283 INFO [train.py:527] (4/6) Epoch 891, batch 40, global_batch_idx: 110400, batch size: 39, loss[discriminator_loss=2.751, discriminator_real_loss=1.417, discriminator_fake_loss=1.334, generator_loss=28.65, generator_mel_loss=17.96, generator_kl_loss=1.688, generator_dur_loss=1.701, generator_adv_loss=1.933, generator_feat_match_loss=5.36, over 39.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.349, discriminator_fake_loss=1.31, generator_loss=29.18, generator_mel_loss=17.78, generator_kl_loss=1.447, generator_dur_loss=1.736, generator_adv_loss=2.022, generator_feat_match_loss=6.189, over 2128.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:50:39,285 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 19:50:47,091 INFO [train.py:591] (4/6) Epoch 891, validation: discriminator_loss=2.732, discriminator_real_loss=1.373, discriminator_fake_loss=1.359, generator_loss=27.53, generator_mel_loss=17.92, generator_kl_loss=1.284, generator_dur_loss=1.802, generator_adv_loss=1.847, generator_feat_match_loss=4.682, over 100.00 samples. +2024-03-15 19:50:47,092 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 19:53:05,848 INFO [train.py:527] (4/6) Epoch 891, batch 90, global_batch_idx: 110450, batch size: 55, loss[discriminator_loss=2.721, discriminator_real_loss=1.358, discriminator_fake_loss=1.363, generator_loss=29.42, generator_mel_loss=17.99, generator_kl_loss=1.514, generator_dur_loss=1.7, generator_adv_loss=1.947, generator_feat_match_loss=6.27, over 55.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.356, discriminator_fake_loss=1.313, generator_loss=29.11, generator_mel_loss=17.76, generator_kl_loss=1.449, generator_dur_loss=1.739, generator_adv_loss=2.018, generator_feat_match_loss=6.137, over 4989.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:54:37,981 INFO [train.py:919] (4/6) Start epoch 892 +2024-03-15 19:55:47,565 INFO [train.py:527] (4/6) Epoch 892, batch 16, global_batch_idx: 110500, batch size: 58, loss[discriminator_loss=2.66, discriminator_real_loss=1.283, discriminator_fake_loss=1.378, generator_loss=29.06, generator_mel_loss=17.74, generator_kl_loss=1.462, generator_dur_loss=1.748, generator_adv_loss=2.026, generator_feat_match_loss=6.091, over 58.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.343, discriminator_fake_loss=1.329, generator_loss=29.07, generator_mel_loss=17.77, generator_kl_loss=1.417, generator_dur_loss=1.745, generator_adv_loss=2.003, generator_feat_match_loss=6.132, over 1079.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:58:05,751 INFO [train.py:527] (4/6) Epoch 892, batch 66, global_batch_idx: 110550, batch size: 45, loss[discriminator_loss=2.714, discriminator_real_loss=1.301, discriminator_fake_loss=1.413, generator_loss=30.1, generator_mel_loss=18.34, generator_kl_loss=1.524, generator_dur_loss=1.636, generator_adv_loss=2.072, generator_feat_match_loss=6.524, over 45.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.1, generator_mel_loss=17.78, generator_kl_loss=1.442, generator_dur_loss=1.737, generator_adv_loss=2.013, generator_feat_match_loss=6.131, over 3852.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:00:25,568 INFO [train.py:527] (4/6) Epoch 892, batch 116, global_batch_idx: 110600, batch size: 36, loss[discriminator_loss=2.696, discriminator_real_loss=1.349, discriminator_fake_loss=1.347, generator_loss=29.62, generator_mel_loss=17.95, generator_kl_loss=1.557, generator_dur_loss=1.661, generator_adv_loss=2.078, generator_feat_match_loss=6.38, over 36.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.359, discriminator_fake_loss=1.319, generator_loss=29.07, generator_mel_loss=17.77, generator_kl_loss=1.458, generator_dur_loss=1.728, generator_adv_loss=2.01, generator_feat_match_loss=6.113, over 6457.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:00:25,569 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 20:00:34,392 INFO [train.py:591] (4/6) Epoch 892, validation: discriminator_loss=2.773, discriminator_real_loss=1.453, discriminator_fake_loss=1.32, generator_loss=27.71, generator_mel_loss=17.94, generator_kl_loss=1.333, generator_dur_loss=1.809, generator_adv_loss=1.964, generator_feat_match_loss=4.661, over 100.00 samples. +2024-03-15 20:00:34,393 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 20:00:55,773 INFO [train.py:919] (4/6) Start epoch 893 +2024-03-15 20:03:17,312 INFO [train.py:527] (4/6) Epoch 893, batch 42, global_batch_idx: 110650, batch size: 70, loss[discriminator_loss=2.739, discriminator_real_loss=1.308, discriminator_fake_loss=1.431, generator_loss=29.29, generator_mel_loss=17.71, generator_kl_loss=1.394, generator_dur_loss=1.807, generator_adv_loss=2.14, generator_feat_match_loss=6.242, over 70.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.365, discriminator_fake_loss=1.319, generator_loss=29.04, generator_mel_loss=17.74, generator_kl_loss=1.465, generator_dur_loss=1.745, generator_adv_loss=2.018, generator_feat_match_loss=6.067, over 2423.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:05:36,789 INFO [train.py:527] (4/6) Epoch 893, batch 92, global_batch_idx: 110700, batch size: 68, loss[discriminator_loss=2.649, discriminator_real_loss=1.346, discriminator_fake_loss=1.303, generator_loss=29.37, generator_mel_loss=18.14, generator_kl_loss=1.407, generator_dur_loss=1.756, generator_adv_loss=1.934, generator_feat_match_loss=6.141, over 68.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.36, discriminator_fake_loss=1.319, generator_loss=29.23, generator_mel_loss=17.8, generator_kl_loss=1.471, generator_dur_loss=1.742, generator_adv_loss=2.03, generator_feat_match_loss=6.189, over 5314.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:07:01,902 INFO [train.py:919] (4/6) Start epoch 894 +2024-03-15 20:08:15,003 INFO [train.py:527] (4/6) Epoch 894, batch 18, global_batch_idx: 110750, batch size: 42, loss[discriminator_loss=2.666, discriminator_real_loss=1.333, discriminator_fake_loss=1.333, generator_loss=30.36, generator_mel_loss=18.09, generator_kl_loss=1.734, generator_dur_loss=1.63, generator_adv_loss=1.985, generator_feat_match_loss=6.927, over 42.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.349, discriminator_fake_loss=1.328, generator_loss=29.28, generator_mel_loss=17.86, generator_kl_loss=1.449, generator_dur_loss=1.739, generator_adv_loss=1.99, generator_feat_match_loss=6.245, over 1079.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:10:34,300 INFO [train.py:527] (4/6) Epoch 894, batch 68, global_batch_idx: 110800, batch size: 53, loss[discriminator_loss=2.743, discriminator_real_loss=1.396, discriminator_fake_loss=1.347, generator_loss=28.16, generator_mel_loss=17.57, generator_kl_loss=1.5, generator_dur_loss=1.662, generator_adv_loss=1.844, generator_feat_match_loss=5.586, over 53.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=29.16, generator_mel_loss=17.79, generator_kl_loss=1.471, generator_dur_loss=1.733, generator_adv_loss=2.001, generator_feat_match_loss=6.165, over 3776.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:10:34,301 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 20:10:42,208 INFO [train.py:591] (4/6) Epoch 894, validation: discriminator_loss=2.762, discriminator_real_loss=1.361, discriminator_fake_loss=1.402, generator_loss=26.94, generator_mel_loss=17.53, generator_kl_loss=1.314, generator_dur_loss=1.805, generator_adv_loss=1.797, generator_feat_match_loss=4.494, over 100.00 samples. +2024-03-15 20:10:42,209 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 20:12:59,040 INFO [train.py:527] (4/6) Epoch 894, batch 118, global_batch_idx: 110850, batch size: 56, loss[discriminator_loss=2.681, discriminator_real_loss=1.32, discriminator_fake_loss=1.361, generator_loss=30.01, generator_mel_loss=18.16, generator_kl_loss=1.477, generator_dur_loss=1.709, generator_adv_loss=2.049, generator_feat_match_loss=6.615, over 56.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.354, discriminator_fake_loss=1.325, generator_loss=29.13, generator_mel_loss=17.76, generator_kl_loss=1.462, generator_dur_loss=1.735, generator_adv_loss=2.004, generator_feat_match_loss=6.161, over 6542.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:13:16,885 INFO [train.py:919] (4/6) Start epoch 895 +2024-03-15 20:15:43,983 INFO [train.py:527] (4/6) Epoch 895, batch 44, global_batch_idx: 110900, batch size: 39, loss[discriminator_loss=2.709, discriminator_real_loss=1.354, discriminator_fake_loss=1.355, generator_loss=30.88, generator_mel_loss=18.38, generator_kl_loss=1.459, generator_dur_loss=1.665, generator_adv_loss=2.118, generator_feat_match_loss=7.265, over 39.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.356, discriminator_fake_loss=1.322, generator_loss=29.26, generator_mel_loss=17.83, generator_kl_loss=1.473, generator_dur_loss=1.738, generator_adv_loss=2.024, generator_feat_match_loss=6.194, over 2527.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:18:04,616 INFO [train.py:527] (4/6) Epoch 895, batch 94, global_batch_idx: 110950, batch size: 52, loss[discriminator_loss=2.665, discriminator_real_loss=1.293, discriminator_fake_loss=1.372, generator_loss=29.41, generator_mel_loss=18.16, generator_kl_loss=1.429, generator_dur_loss=1.675, generator_adv_loss=2.135, generator_feat_match_loss=6.018, over 52.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.359, discriminator_fake_loss=1.321, generator_loss=29.12, generator_mel_loss=17.8, generator_kl_loss=1.442, generator_dur_loss=1.739, generator_adv_loss=2.018, generator_feat_match_loss=6.113, over 5390.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:19:26,973 INFO [train.py:919] (4/6) Start epoch 896 +2024-03-15 20:20:46,881 INFO [train.py:527] (4/6) Epoch 896, batch 20, global_batch_idx: 111000, batch size: 59, loss[discriminator_loss=2.637, discriminator_real_loss=1.329, discriminator_fake_loss=1.308, generator_loss=29.48, generator_mel_loss=17.9, generator_kl_loss=1.572, generator_dur_loss=1.689, generator_adv_loss=2.073, generator_feat_match_loss=6.247, over 59.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=29.16, generator_mel_loss=17.82, generator_kl_loss=1.453, generator_dur_loss=1.743, generator_adv_loss=2.013, generator_feat_match_loss=6.127, over 1223.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:20:46,883 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 20:20:54,706 INFO [train.py:591] (4/6) Epoch 896, validation: discriminator_loss=2.71, discriminator_real_loss=1.454, discriminator_fake_loss=1.256, generator_loss=27.7, generator_mel_loss=17.73, generator_kl_loss=1.333, generator_dur_loss=1.819, generator_adv_loss=1.953, generator_feat_match_loss=4.868, over 100.00 samples. +2024-03-15 20:20:54,708 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 20:23:10,963 INFO [train.py:527] (4/6) Epoch 896, batch 70, global_batch_idx: 111050, batch size: 66, loss[discriminator_loss=2.649, discriminator_real_loss=1.353, discriminator_fake_loss=1.296, generator_loss=29.5, generator_mel_loss=17.65, generator_kl_loss=1.62, generator_dur_loss=1.738, generator_adv_loss=2.102, generator_feat_match_loss=6.393, over 66.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.347, discriminator_fake_loss=1.327, generator_loss=29.21, generator_mel_loss=17.81, generator_kl_loss=1.446, generator_dur_loss=1.736, generator_adv_loss=2.009, generator_feat_match_loss=6.208, over 4013.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:25:30,799 INFO [train.py:527] (4/6) Epoch 896, batch 120, global_batch_idx: 111100, batch size: 83, loss[discriminator_loss=2.68, discriminator_real_loss=1.368, discriminator_fake_loss=1.312, generator_loss=28.73, generator_mel_loss=17.49, generator_kl_loss=1.158, generator_dur_loss=1.809, generator_adv_loss=1.976, generator_feat_match_loss=6.298, over 83.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.18, generator_mel_loss=17.79, generator_kl_loss=1.431, generator_dur_loss=1.746, generator_adv_loss=2.013, generator_feat_match_loss=6.204, over 7003.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:25:41,117 INFO [train.py:919] (4/6) Start epoch 897 +2024-03-15 20:28:12,077 INFO [train.py:527] (4/6) Epoch 897, batch 46, global_batch_idx: 111150, batch size: 42, loss[discriminator_loss=2.666, discriminator_real_loss=1.355, discriminator_fake_loss=1.311, generator_loss=28.23, generator_mel_loss=17.54, generator_kl_loss=1.566, generator_dur_loss=1.647, generator_adv_loss=1.968, generator_feat_match_loss=5.514, over 42.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=29.11, generator_mel_loss=17.81, generator_kl_loss=1.439, generator_dur_loss=1.751, generator_adv_loss=2.027, generator_feat_match_loss=6.082, over 2804.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:30:31,936 INFO [train.py:527] (4/6) Epoch 897, batch 96, global_batch_idx: 111200, batch size: 47, loss[discriminator_loss=2.693, discriminator_real_loss=1.341, discriminator_fake_loss=1.353, generator_loss=30.34, generator_mel_loss=18.28, generator_kl_loss=1.61, generator_dur_loss=1.665, generator_adv_loss=2.27, generator_feat_match_loss=6.516, over 47.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=29.15, generator_mel_loss=17.8, generator_kl_loss=1.454, generator_dur_loss=1.742, generator_adv_loss=2.018, generator_feat_match_loss=6.134, over 5452.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:30:31,938 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 20:30:40,685 INFO [train.py:591] (4/6) Epoch 897, validation: discriminator_loss=2.755, discriminator_real_loss=1.525, discriminator_fake_loss=1.23, generator_loss=28.06, generator_mel_loss=18, generator_kl_loss=1.329, generator_dur_loss=1.795, generator_adv_loss=2.099, generator_feat_match_loss=4.839, over 100.00 samples. +2024-03-15 20:30:40,687 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 20:31:58,591 INFO [train.py:919] (4/6) Start epoch 898 +2024-03-15 20:33:24,508 INFO [train.py:527] (4/6) Epoch 898, batch 22, global_batch_idx: 111250, batch size: 31, loss[discriminator_loss=2.732, discriminator_real_loss=1.347, discriminator_fake_loss=1.386, generator_loss=29.34, generator_mel_loss=18.33, generator_kl_loss=1.773, generator_dur_loss=1.603, generator_adv_loss=2.063, generator_feat_match_loss=5.579, over 31.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.377, discriminator_fake_loss=1.316, generator_loss=28.94, generator_mel_loss=17.71, generator_kl_loss=1.434, generator_dur_loss=1.74, generator_adv_loss=2.013, generator_feat_match_loss=6.043, over 1349.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:35:45,818 INFO [train.py:527] (4/6) Epoch 898, batch 72, global_batch_idx: 111300, batch size: 83, loss[discriminator_loss=2.629, discriminator_real_loss=1.35, discriminator_fake_loss=1.279, generator_loss=29.01, generator_mel_loss=17.54, generator_kl_loss=1.253, generator_dur_loss=1.849, generator_adv_loss=2.127, generator_feat_match_loss=6.233, over 83.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.36, discriminator_fake_loss=1.321, generator_loss=28.95, generator_mel_loss=17.68, generator_kl_loss=1.43, generator_dur_loss=1.746, generator_adv_loss=2.016, generator_feat_match_loss=6.084, over 4263.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:38:00,129 INFO [train.py:527] (4/6) Epoch 898, batch 122, global_batch_idx: 111350, batch size: 31, loss[discriminator_loss=2.678, discriminator_real_loss=1.416, discriminator_fake_loss=1.262, generator_loss=27.89, generator_mel_loss=17.49, generator_kl_loss=1.591, generator_dur_loss=1.617, generator_adv_loss=1.914, generator_feat_match_loss=5.28, over 31.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.361, discriminator_fake_loss=1.317, generator_loss=28.98, generator_mel_loss=17.71, generator_kl_loss=1.436, generator_dur_loss=1.741, generator_adv_loss=2.014, generator_feat_match_loss=6.086, over 6958.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:38:04,651 INFO [train.py:919] (4/6) Start epoch 899 +2024-03-15 20:40:44,571 INFO [train.py:527] (4/6) Epoch 899, batch 48, global_batch_idx: 111400, batch size: 14, loss[discriminator_loss=2.742, discriminator_real_loss=1.403, discriminator_fake_loss=1.338, generator_loss=29.93, generator_mel_loss=18.2, generator_kl_loss=1.845, generator_dur_loss=1.637, generator_adv_loss=2.08, generator_feat_match_loss=6.17, over 14.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.349, discriminator_fake_loss=1.319, generator_loss=29.14, generator_mel_loss=17.76, generator_kl_loss=1.456, generator_dur_loss=1.739, generator_adv_loss=2.009, generator_feat_match_loss=6.186, over 2805.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:40:44,572 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 20:40:52,600 INFO [train.py:591] (4/6) Epoch 899, validation: discriminator_loss=2.78, discriminator_real_loss=1.487, discriminator_fake_loss=1.293, generator_loss=27.47, generator_mel_loss=17.89, generator_kl_loss=1.261, generator_dur_loss=1.794, generator_adv_loss=1.97, generator_feat_match_loss=4.565, over 100.00 samples. +2024-03-15 20:40:52,601 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 20:43:10,003 INFO [train.py:527] (4/6) Epoch 899, batch 98, global_batch_idx: 111450, batch size: 70, loss[discriminator_loss=2.615, discriminator_real_loss=1.287, discriminator_fake_loss=1.328, generator_loss=30.05, generator_mel_loss=17.74, generator_kl_loss=1.347, generator_dur_loss=1.78, generator_adv_loss=2.015, generator_feat_match_loss=7.165, over 70.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.12, generator_mel_loss=17.73, generator_kl_loss=1.442, generator_dur_loss=1.743, generator_adv_loss=2.002, generator_feat_match_loss=6.201, over 5926.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:44:18,417 INFO [train.py:919] (4/6) Start epoch 900 +2024-03-15 20:45:47,533 INFO [train.py:527] (4/6) Epoch 900, batch 24, global_batch_idx: 111500, batch size: 44, loss[discriminator_loss=2.643, discriminator_real_loss=1.375, discriminator_fake_loss=1.268, generator_loss=29.88, generator_mel_loss=17.81, generator_kl_loss=1.526, generator_dur_loss=1.639, generator_adv_loss=2.035, generator_feat_match_loss=6.867, over 44.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.34, discriminator_fake_loss=1.321, generator_loss=29.29, generator_mel_loss=17.79, generator_kl_loss=1.466, generator_dur_loss=1.714, generator_adv_loss=2.016, generator_feat_match_loss=6.305, over 1320.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:48:02,743 INFO [train.py:527] (4/6) Epoch 900, batch 74, global_batch_idx: 111550, batch size: 48, loss[discriminator_loss=2.656, discriminator_real_loss=1.33, discriminator_fake_loss=1.325, generator_loss=29.46, generator_mel_loss=17.76, generator_kl_loss=1.508, generator_dur_loss=1.642, generator_adv_loss=2.093, generator_feat_match_loss=6.453, over 48.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.321, generator_loss=29.16, generator_mel_loss=17.82, generator_kl_loss=1.447, generator_dur_loss=1.734, generator_adv_loss=2.012, generator_feat_match_loss=6.155, over 4010.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:50:22,605 INFO [train.py:919] (4/6) Start epoch 901 +2024-03-15 20:50:46,741 INFO [train.py:527] (4/6) Epoch 901, batch 0, global_batch_idx: 111600, batch size: 61, loss[discriminator_loss=2.652, discriminator_real_loss=1.35, discriminator_fake_loss=1.302, generator_loss=28.79, generator_mel_loss=18.02, generator_kl_loss=1.523, generator_dur_loss=1.729, generator_adv_loss=1.909, generator_feat_match_loss=5.603, over 61.00 samples.], tot_loss[discriminator_loss=2.652, discriminator_real_loss=1.35, discriminator_fake_loss=1.302, generator_loss=28.79, generator_mel_loss=18.02, generator_kl_loss=1.523, generator_dur_loss=1.729, generator_adv_loss=1.909, generator_feat_match_loss=5.603, over 61.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:50:46,744 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 20:50:54,867 INFO [train.py:591] (4/6) Epoch 901, validation: discriminator_loss=2.681, discriminator_real_loss=1.361, discriminator_fake_loss=1.32, generator_loss=28.28, generator_mel_loss=17.82, generator_kl_loss=1.409, generator_dur_loss=1.8, generator_adv_loss=1.931, generator_feat_match_loss=5.321, over 100.00 samples. +2024-03-15 20:50:54,869 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 20:53:15,366 INFO [train.py:527] (4/6) Epoch 901, batch 50, global_batch_idx: 111650, batch size: 31, loss[discriminator_loss=2.656, discriminator_real_loss=1.43, discriminator_fake_loss=1.226, generator_loss=28.46, generator_mel_loss=18.22, generator_kl_loss=1.569, generator_dur_loss=1.572, generator_adv_loss=1.864, generator_feat_match_loss=5.232, over 31.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.36, discriminator_fake_loss=1.317, generator_loss=29.14, generator_mel_loss=17.76, generator_kl_loss=1.45, generator_dur_loss=1.731, generator_adv_loss=2.009, generator_feat_match_loss=6.191, over 2826.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:55:33,003 INFO [train.py:527] (4/6) Epoch 901, batch 100, global_batch_idx: 111700, batch size: 61, loss[discriminator_loss=2.712, discriminator_real_loss=1.354, discriminator_fake_loss=1.357, generator_loss=28.82, generator_mel_loss=17.58, generator_kl_loss=1.427, generator_dur_loss=1.736, generator_adv_loss=2.135, generator_feat_match_loss=5.941, over 61.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=29.11, generator_mel_loss=17.75, generator_kl_loss=1.458, generator_dur_loss=1.74, generator_adv_loss=2.011, generator_feat_match_loss=6.155, over 5842.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:56:40,211 INFO [train.py:919] (4/6) Start epoch 902 +2024-03-15 20:58:14,286 INFO [train.py:527] (4/6) Epoch 902, batch 26, global_batch_idx: 111750, batch size: 58, loss[discriminator_loss=2.653, discriminator_real_loss=1.364, discriminator_fake_loss=1.29, generator_loss=28.8, generator_mel_loss=17.85, generator_kl_loss=1.465, generator_dur_loss=1.77, generator_adv_loss=2.084, generator_feat_match_loss=5.627, over 58.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=29.25, generator_mel_loss=17.79, generator_kl_loss=1.457, generator_dur_loss=1.737, generator_adv_loss=2.021, generator_feat_match_loss=6.238, over 1496.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:00:32,595 INFO [train.py:527] (4/6) Epoch 902, batch 76, global_batch_idx: 111800, batch size: 48, loss[discriminator_loss=2.675, discriminator_real_loss=1.303, discriminator_fake_loss=1.372, generator_loss=29.39, generator_mel_loss=17.99, generator_kl_loss=1.638, generator_dur_loss=1.644, generator_adv_loss=2.245, generator_feat_match_loss=5.872, over 48.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.354, discriminator_fake_loss=1.318, generator_loss=29.13, generator_mel_loss=17.75, generator_kl_loss=1.45, generator_dur_loss=1.732, generator_adv_loss=2.023, generator_feat_match_loss=6.174, over 4219.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:00:32,596 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 21:00:41,437 INFO [train.py:591] (4/6) Epoch 902, validation: discriminator_loss=2.754, discriminator_real_loss=1.503, discriminator_fake_loss=1.252, generator_loss=28.64, generator_mel_loss=18.16, generator_kl_loss=1.335, generator_dur_loss=1.797, generator_adv_loss=2.019, generator_feat_match_loss=5.327, over 100.00 samples. +2024-03-15 21:00:41,438 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 21:02:53,678 INFO [train.py:919] (4/6) Start epoch 903 +2024-03-15 21:03:24,096 INFO [train.py:527] (4/6) Epoch 903, batch 2, global_batch_idx: 111850, batch size: 88, loss[discriminator_loss=2.664, discriminator_real_loss=1.288, discriminator_fake_loss=1.376, generator_loss=28.92, generator_mel_loss=17.62, generator_kl_loss=1.317, generator_dur_loss=1.824, generator_adv_loss=2.059, generator_feat_match_loss=6.106, over 88.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.327, discriminator_fake_loss=1.346, generator_loss=29.25, generator_mel_loss=17.81, generator_kl_loss=1.415, generator_dur_loss=1.738, generator_adv_loss=2.013, generator_feat_match_loss=6.272, over 180.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:05:40,773 INFO [train.py:527] (4/6) Epoch 903, batch 52, global_batch_idx: 111900, batch size: 66, loss[discriminator_loss=2.701, discriminator_real_loss=1.345, discriminator_fake_loss=1.356, generator_loss=28.92, generator_mel_loss=17.52, generator_kl_loss=1.364, generator_dur_loss=1.741, generator_adv_loss=2, generator_feat_match_loss=6.299, over 66.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.357, discriminator_fake_loss=1.328, generator_loss=29.03, generator_mel_loss=17.77, generator_kl_loss=1.399, generator_dur_loss=1.73, generator_adv_loss=2.015, generator_feat_match_loss=6.11, over 3101.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:08:00,017 INFO [train.py:527] (4/6) Epoch 903, batch 102, global_batch_idx: 111950, batch size: 64, loss[discriminator_loss=2.743, discriminator_real_loss=1.477, discriminator_fake_loss=1.266, generator_loss=29.14, generator_mel_loss=17.73, generator_kl_loss=1.51, generator_dur_loss=1.74, generator_adv_loss=1.832, generator_feat_match_loss=6.329, over 64.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.358, discriminator_fake_loss=1.329, generator_loss=29.01, generator_mel_loss=17.75, generator_kl_loss=1.416, generator_dur_loss=1.741, generator_adv_loss=2.005, generator_feat_match_loss=6.1, over 6157.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:08:59,429 INFO [train.py:919] (4/6) Start epoch 904 +2024-03-15 21:10:44,465 INFO [train.py:527] (4/6) Epoch 904, batch 28, global_batch_idx: 112000, batch size: 52, loss[discriminator_loss=2.645, discriminator_real_loss=1.37, discriminator_fake_loss=1.275, generator_loss=29.62, generator_mel_loss=18.18, generator_kl_loss=1.442, generator_dur_loss=1.689, generator_adv_loss=2.022, generator_feat_match_loss=6.279, over 52.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.367, discriminator_fake_loss=1.317, generator_loss=28.95, generator_mel_loss=17.74, generator_kl_loss=1.447, generator_dur_loss=1.724, generator_adv_loss=2.018, generator_feat_match_loss=6.021, over 1701.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:10:44,467 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 21:10:52,259 INFO [train.py:591] (4/6) Epoch 904, validation: discriminator_loss=2.746, discriminator_real_loss=1.417, discriminator_fake_loss=1.329, generator_loss=28.33, generator_mel_loss=17.63, generator_kl_loss=1.195, generator_dur_loss=1.788, generator_adv_loss=1.936, generator_feat_match_loss=5.781, over 100.00 samples. +2024-03-15 21:10:52,260 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 21:13:12,992 INFO [train.py:527] (4/6) Epoch 904, batch 78, global_batch_idx: 112050, batch size: 88, loss[discriminator_loss=2.666, discriminator_real_loss=1.38, discriminator_fake_loss=1.286, generator_loss=28.4, generator_mel_loss=17.62, generator_kl_loss=1.368, generator_dur_loss=1.823, generator_adv_loss=2.075, generator_feat_match_loss=5.519, over 88.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.366, discriminator_fake_loss=1.318, generator_loss=28.99, generator_mel_loss=17.78, generator_kl_loss=1.432, generator_dur_loss=1.735, generator_adv_loss=2.017, generator_feat_match_loss=6.027, over 4666.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:15:17,197 INFO [train.py:919] (4/6) Start epoch 905 +2024-03-15 21:15:52,511 INFO [train.py:527] (4/6) Epoch 905, batch 4, global_batch_idx: 112100, batch size: 45, loss[discriminator_loss=2.709, discriminator_real_loss=1.254, discriminator_fake_loss=1.455, generator_loss=30.47, generator_mel_loss=18.42, generator_kl_loss=1.406, generator_dur_loss=1.647, generator_adv_loss=2.105, generator_feat_match_loss=6.892, over 45.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.354, discriminator_fake_loss=1.338, generator_loss=29.05, generator_mel_loss=17.6, generator_kl_loss=1.421, generator_dur_loss=1.735, generator_adv_loss=1.993, generator_feat_match_loss=6.296, over 279.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:18:11,895 INFO [train.py:527] (4/6) Epoch 905, batch 54, global_batch_idx: 112150, batch size: 42, loss[discriminator_loss=2.664, discriminator_real_loss=1.312, discriminator_fake_loss=1.353, generator_loss=29.4, generator_mel_loss=17.75, generator_kl_loss=1.583, generator_dur_loss=1.686, generator_adv_loss=2.045, generator_feat_match_loss=6.33, over 42.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.36, discriminator_fake_loss=1.318, generator_loss=29.06, generator_mel_loss=17.75, generator_kl_loss=1.459, generator_dur_loss=1.74, generator_adv_loss=2.004, generator_feat_match_loss=6.1, over 3135.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:20:30,325 INFO [train.py:527] (4/6) Epoch 905, batch 104, global_batch_idx: 112200, batch size: 15, loss[discriminator_loss=2.705, discriminator_real_loss=1.276, discriminator_fake_loss=1.429, generator_loss=32.25, generator_mel_loss=18.54, generator_kl_loss=1.97, generator_dur_loss=1.489, generator_adv_loss=2.164, generator_feat_match_loss=8.093, over 15.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.352, discriminator_fake_loss=1.321, generator_loss=29.09, generator_mel_loss=17.74, generator_kl_loss=1.446, generator_dur_loss=1.743, generator_adv_loss=2.013, generator_feat_match_loss=6.147, over 6032.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:20:30,327 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 21:20:39,139 INFO [train.py:591] (4/6) Epoch 905, validation: discriminator_loss=2.711, discriminator_real_loss=1.395, discriminator_fake_loss=1.316, generator_loss=28.24, generator_mel_loss=18.03, generator_kl_loss=1.283, generator_dur_loss=1.802, generator_adv_loss=1.967, generator_feat_match_loss=5.154, over 100.00 samples. +2024-03-15 21:20:39,140 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 21:21:31,949 INFO [train.py:919] (4/6) Start epoch 906 +2024-03-15 21:23:20,358 INFO [train.py:527] (4/6) Epoch 906, batch 30, global_batch_idx: 112250, batch size: 31, loss[discriminator_loss=2.691, discriminator_real_loss=1.456, discriminator_fake_loss=1.235, generator_loss=27.94, generator_mel_loss=17.32, generator_kl_loss=1.512, generator_dur_loss=1.633, generator_adv_loss=2.011, generator_feat_match_loss=5.458, over 31.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.355, discriminator_fake_loss=1.331, generator_loss=29.06, generator_mel_loss=17.77, generator_kl_loss=1.453, generator_dur_loss=1.725, generator_adv_loss=2.001, generator_feat_match_loss=6.109, over 1743.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:25:40,535 INFO [train.py:527] (4/6) Epoch 906, batch 80, global_batch_idx: 112300, batch size: 66, loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.319, generator_loss=29.42, generator_mel_loss=17.74, generator_kl_loss=1.653, generator_dur_loss=1.769, generator_adv_loss=2.034, generator_feat_match_loss=6.22, over 66.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.357, discriminator_fake_loss=1.33, generator_loss=29.06, generator_mel_loss=17.76, generator_kl_loss=1.43, generator_dur_loss=1.741, generator_adv_loss=2.009, generator_feat_match_loss=6.12, over 4755.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:27:38,945 INFO [train.py:919] (4/6) Start epoch 907 +2024-03-15 21:28:22,275 INFO [train.py:527] (4/6) Epoch 907, batch 6, global_batch_idx: 112350, batch size: 96, loss[discriminator_loss=2.742, discriminator_real_loss=1.411, discriminator_fake_loss=1.331, generator_loss=28.77, generator_mel_loss=17.63, generator_kl_loss=1.287, generator_dur_loss=1.839, generator_adv_loss=2.08, generator_feat_match_loss=5.925, over 96.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.34, discriminator_fake_loss=1.351, generator_loss=29.15, generator_mel_loss=17.75, generator_kl_loss=1.437, generator_dur_loss=1.756, generator_adv_loss=2.061, generator_feat_match_loss=6.142, over 431.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:30:38,148 INFO [train.py:527] (4/6) Epoch 907, batch 56, global_batch_idx: 112400, batch size: 31, loss[discriminator_loss=2.578, discriminator_real_loss=1.326, discriminator_fake_loss=1.253, generator_loss=30.53, generator_mel_loss=18.42, generator_kl_loss=1.52, generator_dur_loss=1.616, generator_adv_loss=2.232, generator_feat_match_loss=6.744, over 31.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.354, discriminator_fake_loss=1.326, generator_loss=29.05, generator_mel_loss=17.71, generator_kl_loss=1.451, generator_dur_loss=1.734, generator_adv_loss=2.005, generator_feat_match_loss=6.147, over 3316.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:30:38,149 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 21:30:45,990 INFO [train.py:591] (4/6) Epoch 907, validation: discriminator_loss=2.664, discriminator_real_loss=1.416, discriminator_fake_loss=1.248, generator_loss=27.56, generator_mel_loss=17.79, generator_kl_loss=1.266, generator_dur_loss=1.794, generator_adv_loss=1.994, generator_feat_match_loss=4.718, over 100.00 samples. +2024-03-15 21:30:45,992 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 21:33:05,515 INFO [train.py:527] (4/6) Epoch 907, batch 106, global_batch_idx: 112450, batch size: 50, loss[discriminator_loss=2.691, discriminator_real_loss=1.355, discriminator_fake_loss=1.336, generator_loss=28.77, generator_mel_loss=17.5, generator_kl_loss=1.466, generator_dur_loss=1.71, generator_adv_loss=1.956, generator_feat_match_loss=6.143, over 50.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.352, discriminator_fake_loss=1.322, generator_loss=29.14, generator_mel_loss=17.73, generator_kl_loss=1.45, generator_dur_loss=1.736, generator_adv_loss=2.011, generator_feat_match_loss=6.205, over 6214.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:33:56,353 INFO [train.py:919] (4/6) Start epoch 908 +2024-03-15 21:35:50,341 INFO [train.py:527] (4/6) Epoch 908, batch 32, global_batch_idx: 112500, batch size: 50, loss[discriminator_loss=2.735, discriminator_real_loss=1.384, discriminator_fake_loss=1.35, generator_loss=28.69, generator_mel_loss=17.74, generator_kl_loss=1.39, generator_dur_loss=1.675, generator_adv_loss=2.099, generator_feat_match_loss=5.79, over 50.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.14, generator_mel_loss=17.8, generator_kl_loss=1.418, generator_dur_loss=1.724, generator_adv_loss=2.009, generator_feat_match_loss=6.19, over 1750.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:38:12,226 INFO [train.py:527] (4/6) Epoch 908, batch 82, global_batch_idx: 112550, batch size: 83, loss[discriminator_loss=2.691, discriminator_real_loss=1.366, discriminator_fake_loss=1.325, generator_loss=28.88, generator_mel_loss=17.88, generator_kl_loss=1.321, generator_dur_loss=1.802, generator_adv_loss=2.051, generator_feat_match_loss=5.824, over 83.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=29.23, generator_mel_loss=17.83, generator_kl_loss=1.425, generator_dur_loss=1.722, generator_adv_loss=2.01, generator_feat_match_loss=6.243, over 4656.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:40:05,623 INFO [train.py:919] (4/6) Start epoch 909 +2024-03-15 21:40:51,766 INFO [train.py:527] (4/6) Epoch 909, batch 8, global_batch_idx: 112600, batch size: 74, loss[discriminator_loss=2.679, discriminator_real_loss=1.353, discriminator_fake_loss=1.326, generator_loss=28.16, generator_mel_loss=17.51, generator_kl_loss=1.379, generator_dur_loss=1.81, generator_adv_loss=1.965, generator_feat_match_loss=5.496, over 74.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.37, discriminator_fake_loss=1.321, generator_loss=28.73, generator_mel_loss=17.55, generator_kl_loss=1.413, generator_dur_loss=1.747, generator_adv_loss=1.982, generator_feat_match_loss=6.039, over 569.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:40:51,769 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 21:40:59,652 INFO [train.py:591] (4/6) Epoch 909, validation: discriminator_loss=2.692, discriminator_real_loss=1.392, discriminator_fake_loss=1.3, generator_loss=28.19, generator_mel_loss=18.39, generator_kl_loss=1.205, generator_dur_loss=1.783, generator_adv_loss=1.917, generator_feat_match_loss=4.895, over 100.00 samples. +2024-03-15 21:40:59,655 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 21:43:17,131 INFO [train.py:527] (4/6) Epoch 909, batch 58, global_batch_idx: 112650, batch size: 66, loss[discriminator_loss=2.686, discriminator_real_loss=1.307, discriminator_fake_loss=1.379, generator_loss=30.03, generator_mel_loss=17.82, generator_kl_loss=1.549, generator_dur_loss=1.727, generator_adv_loss=2.058, generator_feat_match_loss=6.873, over 66.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.09, generator_mel_loss=17.76, generator_kl_loss=1.468, generator_dur_loss=1.716, generator_adv_loss=2.008, generator_feat_match_loss=6.14, over 3171.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:45:36,646 INFO [train.py:527] (4/6) Epoch 909, batch 108, global_batch_idx: 112700, batch size: 58, loss[discriminator_loss=2.697, discriminator_real_loss=1.35, discriminator_fake_loss=1.347, generator_loss=28.81, generator_mel_loss=17.76, generator_kl_loss=1.369, generator_dur_loss=1.755, generator_adv_loss=2.071, generator_feat_match_loss=5.857, over 58.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.358, discriminator_fake_loss=1.321, generator_loss=29.03, generator_mel_loss=17.73, generator_kl_loss=1.46, generator_dur_loss=1.723, generator_adv_loss=2.008, generator_feat_match_loss=6.11, over 5993.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:46:19,273 INFO [train.py:919] (4/6) Start epoch 910 +2024-03-15 21:48:18,738 INFO [train.py:527] (4/6) Epoch 910, batch 34, global_batch_idx: 112750, batch size: 58, loss[discriminator_loss=2.661, discriminator_real_loss=1.326, discriminator_fake_loss=1.335, generator_loss=29.05, generator_mel_loss=17.71, generator_kl_loss=1.358, generator_dur_loss=1.722, generator_adv_loss=2.189, generator_feat_match_loss=6.074, over 58.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.326, generator_loss=28.99, generator_mel_loss=17.68, generator_kl_loss=1.44, generator_dur_loss=1.733, generator_adv_loss=2.008, generator_feat_match_loss=6.127, over 1958.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:50:36,709 INFO [train.py:527] (4/6) Epoch 910, batch 84, global_batch_idx: 112800, batch size: 61, loss[discriminator_loss=2.653, discriminator_real_loss=1.415, discriminator_fake_loss=1.237, generator_loss=29.26, generator_mel_loss=17.58, generator_kl_loss=1.499, generator_dur_loss=1.69, generator_adv_loss=2.024, generator_feat_match_loss=6.469, over 61.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.319, generator_loss=29.04, generator_mel_loss=17.69, generator_kl_loss=1.438, generator_dur_loss=1.735, generator_adv_loss=2.009, generator_feat_match_loss=6.168, over 4773.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:50:36,711 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 21:50:45,552 INFO [train.py:591] (4/6) Epoch 910, validation: discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.2, generator_mel_loss=18.04, generator_kl_loss=1.346, generator_dur_loss=1.793, generator_adv_loss=1.908, generator_feat_match_loss=5.107, over 100.00 samples. +2024-03-15 21:50:45,554 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 21:52:33,713 INFO [train.py:919] (4/6) Start epoch 911 +2024-03-15 21:53:21,781 INFO [train.py:527] (4/6) Epoch 911, batch 10, global_batch_idx: 112850, batch size: 62, loss[discriminator_loss=2.612, discriminator_real_loss=1.29, discriminator_fake_loss=1.322, generator_loss=30.28, generator_mel_loss=18.42, generator_kl_loss=1.466, generator_dur_loss=1.69, generator_adv_loss=2.095, generator_feat_match_loss=6.61, over 62.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.362, discriminator_fake_loss=1.333, generator_loss=29.06, generator_mel_loss=17.82, generator_kl_loss=1.484, generator_dur_loss=1.731, generator_adv_loss=2.006, generator_feat_match_loss=6.015, over 615.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 21:55:41,965 INFO [train.py:527] (4/6) Epoch 911, batch 60, global_batch_idx: 112900, batch size: 53, loss[discriminator_loss=2.626, discriminator_real_loss=1.325, discriminator_fake_loss=1.3, generator_loss=29.39, generator_mel_loss=17.66, generator_kl_loss=1.454, generator_dur_loss=1.694, generator_adv_loss=2.076, generator_feat_match_loss=6.503, over 53.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.358, discriminator_fake_loss=1.332, generator_loss=29.05, generator_mel_loss=17.78, generator_kl_loss=1.445, generator_dur_loss=1.735, generator_adv_loss=2, generator_feat_match_loss=6.092, over 3549.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 21:58:01,561 INFO [train.py:527] (4/6) Epoch 911, batch 110, global_batch_idx: 112950, batch size: 44, loss[discriminator_loss=2.657, discriminator_real_loss=1.397, discriminator_fake_loss=1.26, generator_loss=29.28, generator_mel_loss=17.45, generator_kl_loss=1.638, generator_dur_loss=1.646, generator_adv_loss=1.91, generator_feat_match_loss=6.635, over 44.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=29.11, generator_mel_loss=17.78, generator_kl_loss=1.445, generator_dur_loss=1.738, generator_adv_loss=2.002, generator_feat_match_loss=6.145, over 6317.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 21:58:38,227 INFO [train.py:919] (4/6) Start epoch 912 +2024-03-15 22:00:41,270 INFO [train.py:527] (4/6) Epoch 912, batch 36, global_batch_idx: 113000, batch size: 72, loss[discriminator_loss=2.654, discriminator_real_loss=1.351, discriminator_fake_loss=1.304, generator_loss=29.12, generator_mel_loss=17.55, generator_kl_loss=1.47, generator_dur_loss=1.77, generator_adv_loss=1.921, generator_feat_match_loss=6.414, over 72.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.363, discriminator_fake_loss=1.309, generator_loss=29.12, generator_mel_loss=17.7, generator_kl_loss=1.493, generator_dur_loss=1.743, generator_adv_loss=2.01, generator_feat_match_loss=6.18, over 1984.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:00:41,272 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 22:00:49,099 INFO [train.py:591] (4/6) Epoch 912, validation: discriminator_loss=2.746, discriminator_real_loss=1.38, discriminator_fake_loss=1.366, generator_loss=27.78, generator_mel_loss=18.16, generator_kl_loss=1.293, generator_dur_loss=1.821, generator_adv_loss=1.836, generator_feat_match_loss=4.669, over 100.00 samples. +2024-03-15 22:00:49,100 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 22:03:06,563 INFO [train.py:527] (4/6) Epoch 912, batch 86, global_batch_idx: 113050, batch size: 53, loss[discriminator_loss=2.739, discriminator_real_loss=1.363, discriminator_fake_loss=1.376, generator_loss=28.56, generator_mel_loss=17.63, generator_kl_loss=1.42, generator_dur_loss=1.669, generator_adv_loss=1.908, generator_feat_match_loss=5.937, over 53.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.356, discriminator_fake_loss=1.308, generator_loss=29.1, generator_mel_loss=17.66, generator_kl_loss=1.463, generator_dur_loss=1.736, generator_adv_loss=2.013, generator_feat_match_loss=6.232, over 4828.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:04:50,345 INFO [train.py:919] (4/6) Start epoch 913 +2024-03-15 22:05:47,815 INFO [train.py:527] (4/6) Epoch 913, batch 12, global_batch_idx: 113100, batch size: 55, loss[discriminator_loss=2.683, discriminator_real_loss=1.368, discriminator_fake_loss=1.315, generator_loss=29.46, generator_mel_loss=17.48, generator_kl_loss=1.519, generator_dur_loss=1.686, generator_adv_loss=2.051, generator_feat_match_loss=6.723, over 55.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.319, discriminator_fake_loss=1.35, generator_loss=29.25, generator_mel_loss=17.66, generator_kl_loss=1.454, generator_dur_loss=1.746, generator_adv_loss=2.031, generator_feat_match_loss=6.353, over 765.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:08:07,280 INFO [train.py:527] (4/6) Epoch 913, batch 62, global_batch_idx: 113150, batch size: 83, loss[discriminator_loss=2.649, discriminator_real_loss=1.317, discriminator_fake_loss=1.331, generator_loss=29.6, generator_mel_loss=18.07, generator_kl_loss=1.364, generator_dur_loss=1.79, generator_adv_loss=2.074, generator_feat_match_loss=6.299, over 83.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.341, discriminator_fake_loss=1.331, generator_loss=29.42, generator_mel_loss=17.81, generator_kl_loss=1.452, generator_dur_loss=1.745, generator_adv_loss=2.055, generator_feat_match_loss=6.358, over 3711.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:10:23,157 INFO [train.py:527] (4/6) Epoch 913, batch 112, global_batch_idx: 113200, batch size: 64, loss[discriminator_loss=2.697, discriminator_real_loss=1.349, discriminator_fake_loss=1.348, generator_loss=27.98, generator_mel_loss=17.25, generator_kl_loss=1.372, generator_dur_loss=1.788, generator_adv_loss=1.979, generator_feat_match_loss=5.595, over 64.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.343, discriminator_fake_loss=1.331, generator_loss=29.29, generator_mel_loss=17.8, generator_kl_loss=1.463, generator_dur_loss=1.729, generator_adv_loss=2.029, generator_feat_match_loss=6.265, over 6295.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:10:23,158 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 22:10:32,026 INFO [train.py:591] (4/6) Epoch 913, validation: discriminator_loss=2.712, discriminator_real_loss=1.412, discriminator_fake_loss=1.3, generator_loss=27.69, generator_mel_loss=17.83, generator_kl_loss=1.352, generator_dur_loss=1.812, generator_adv_loss=1.869, generator_feat_match_loss=4.819, over 100.00 samples. +2024-03-15 22:10:32,027 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 22:11:03,260 INFO [train.py:919] (4/6) Start epoch 914 +2024-03-15 22:13:12,205 INFO [train.py:527] (4/6) Epoch 914, batch 38, global_batch_idx: 113250, batch size: 61, loss[discriminator_loss=2.71, discriminator_real_loss=1.403, discriminator_fake_loss=1.307, generator_loss=28.4, generator_mel_loss=17.36, generator_kl_loss=1.251, generator_dur_loss=1.72, generator_adv_loss=2.042, generator_feat_match_loss=6.028, over 61.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.365, discriminator_fake_loss=1.315, generator_loss=29.07, generator_mel_loss=17.71, generator_kl_loss=1.422, generator_dur_loss=1.768, generator_adv_loss=2.01, generator_feat_match_loss=6.162, over 2394.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:15:32,706 INFO [train.py:527] (4/6) Epoch 914, batch 88, global_batch_idx: 113300, batch size: 62, loss[discriminator_loss=2.624, discriminator_real_loss=1.296, discriminator_fake_loss=1.329, generator_loss=29.31, generator_mel_loss=17.72, generator_kl_loss=1.409, generator_dur_loss=1.758, generator_adv_loss=2.144, generator_feat_match_loss=6.285, over 62.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.318, generator_loss=29.09, generator_mel_loss=17.7, generator_kl_loss=1.438, generator_dur_loss=1.752, generator_adv_loss=2.015, generator_feat_match_loss=6.186, over 5191.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:17:09,758 INFO [train.py:919] (4/6) Start epoch 915 +2024-03-15 22:18:12,960 INFO [train.py:527] (4/6) Epoch 915, batch 14, global_batch_idx: 113350, batch size: 66, loss[discriminator_loss=2.646, discriminator_real_loss=1.278, discriminator_fake_loss=1.368, generator_loss=29.59, generator_mel_loss=18.22, generator_kl_loss=1.538, generator_dur_loss=1.753, generator_adv_loss=2.073, generator_feat_match_loss=6.001, over 66.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.344, discriminator_fake_loss=1.324, generator_loss=29.11, generator_mel_loss=17.84, generator_kl_loss=1.451, generator_dur_loss=1.756, generator_adv_loss=2.003, generator_feat_match_loss=6.063, over 888.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:20:30,731 INFO [train.py:527] (4/6) Epoch 915, batch 64, global_batch_idx: 113400, batch size: 44, loss[discriminator_loss=2.643, discriminator_real_loss=1.323, discriminator_fake_loss=1.32, generator_loss=29.83, generator_mel_loss=17.88, generator_kl_loss=1.77, generator_dur_loss=1.689, generator_adv_loss=2.01, generator_feat_match_loss=6.483, over 44.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.351, discriminator_fake_loss=1.316, generator_loss=29.07, generator_mel_loss=17.69, generator_kl_loss=1.484, generator_dur_loss=1.744, generator_adv_loss=2.007, generator_feat_match_loss=6.145, over 3724.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:20:30,733 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 22:20:38,829 INFO [train.py:591] (4/6) Epoch 915, validation: discriminator_loss=2.713, discriminator_real_loss=1.421, discriminator_fake_loss=1.292, generator_loss=28.16, generator_mel_loss=18.15, generator_kl_loss=1.297, generator_dur_loss=1.807, generator_adv_loss=1.971, generator_feat_match_loss=4.934, over 100.00 samples. +2024-03-15 22:20:38,830 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 22:22:58,949 INFO [train.py:527] (4/6) Epoch 915, batch 114, global_batch_idx: 113450, batch size: 64, loss[discriminator_loss=2.699, discriminator_real_loss=1.39, discriminator_fake_loss=1.309, generator_loss=29.24, generator_mel_loss=17.81, generator_kl_loss=1.562, generator_dur_loss=1.73, generator_adv_loss=2.115, generator_feat_match_loss=6.023, over 64.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.35, discriminator_fake_loss=1.315, generator_loss=29.15, generator_mel_loss=17.7, generator_kl_loss=1.496, generator_dur_loss=1.74, generator_adv_loss=2.016, generator_feat_match_loss=6.199, over 6554.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:23:25,256 INFO [train.py:919] (4/6) Start epoch 916 +2024-03-15 22:25:38,680 INFO [train.py:527] (4/6) Epoch 916, batch 40, global_batch_idx: 113500, batch size: 48, loss[discriminator_loss=2.712, discriminator_real_loss=1.338, discriminator_fake_loss=1.374, generator_loss=29.17, generator_mel_loss=18.06, generator_kl_loss=1.564, generator_dur_loss=1.667, generator_adv_loss=1.95, generator_feat_match_loss=5.929, over 48.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.352, discriminator_fake_loss=1.325, generator_loss=29.23, generator_mel_loss=17.79, generator_kl_loss=1.496, generator_dur_loss=1.713, generator_adv_loss=2.011, generator_feat_match_loss=6.223, over 2174.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:27:58,787 INFO [train.py:527] (4/6) Epoch 916, batch 90, global_batch_idx: 113550, batch size: 77, loss[discriminator_loss=2.659, discriminator_real_loss=1.319, discriminator_fake_loss=1.34, generator_loss=29.28, generator_mel_loss=17.94, generator_kl_loss=1.346, generator_dur_loss=1.766, generator_adv_loss=1.954, generator_feat_match_loss=6.271, over 77.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.352, discriminator_fake_loss=1.326, generator_loss=29.15, generator_mel_loss=17.75, generator_kl_loss=1.465, generator_dur_loss=1.732, generator_adv_loss=2.006, generator_feat_match_loss=6.199, over 5121.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:29:32,290 INFO [train.py:919] (4/6) Start epoch 917 +2024-03-15 22:30:40,773 INFO [train.py:527] (4/6) Epoch 917, batch 16, global_batch_idx: 113600, batch size: 50, loss[discriminator_loss=2.717, discriminator_real_loss=1.395, discriminator_fake_loss=1.322, generator_loss=28.83, generator_mel_loss=17.86, generator_kl_loss=1.479, generator_dur_loss=1.696, generator_adv_loss=1.913, generator_feat_match_loss=5.881, over 50.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.359, discriminator_fake_loss=1.321, generator_loss=29.03, generator_mel_loss=17.77, generator_kl_loss=1.465, generator_dur_loss=1.731, generator_adv_loss=2.016, generator_feat_match_loss=6.044, over 923.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:30:40,774 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 22:30:48,826 INFO [train.py:591] (4/6) Epoch 917, validation: discriminator_loss=2.752, discriminator_real_loss=1.322, discriminator_fake_loss=1.43, generator_loss=28, generator_mel_loss=17.95, generator_kl_loss=1.406, generator_dur_loss=1.809, generator_adv_loss=1.827, generator_feat_match_loss=5.009, over 100.00 samples. +2024-03-15 22:30:48,827 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 22:33:08,178 INFO [train.py:527] (4/6) Epoch 917, batch 66, global_batch_idx: 113650, batch size: 70, loss[discriminator_loss=2.604, discriminator_real_loss=1.333, discriminator_fake_loss=1.271, generator_loss=28.83, generator_mel_loss=17.82, generator_kl_loss=1.478, generator_dur_loss=1.758, generator_adv_loss=1.944, generator_feat_match_loss=5.832, over 70.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.362, discriminator_fake_loss=1.319, generator_loss=29.04, generator_mel_loss=17.76, generator_kl_loss=1.475, generator_dur_loss=1.73, generator_adv_loss=2.006, generator_feat_match_loss=6.065, over 3720.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:35:24,425 INFO [train.py:527] (4/6) Epoch 917, batch 116, global_batch_idx: 113700, batch size: 83, loss[discriminator_loss=2.688, discriminator_real_loss=1.281, discriminator_fake_loss=1.407, generator_loss=28.13, generator_mel_loss=17.21, generator_kl_loss=1.477, generator_dur_loss=1.836, generator_adv_loss=1.945, generator_feat_match_loss=5.656, over 83.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.352, discriminator_fake_loss=1.324, generator_loss=29.09, generator_mel_loss=17.77, generator_kl_loss=1.452, generator_dur_loss=1.738, generator_adv_loss=2.008, generator_feat_match_loss=6.123, over 6684.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:35:45,764 INFO [train.py:919] (4/6) Start epoch 918 +2024-03-15 22:38:06,190 INFO [train.py:527] (4/6) Epoch 918, batch 42, global_batch_idx: 113750, batch size: 59, loss[discriminator_loss=2.63, discriminator_real_loss=1.339, discriminator_fake_loss=1.291, generator_loss=29.31, generator_mel_loss=17.73, generator_kl_loss=1.449, generator_dur_loss=1.711, generator_adv_loss=1.915, generator_feat_match_loss=6.508, over 59.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.358, discriminator_fake_loss=1.322, generator_loss=29.08, generator_mel_loss=17.72, generator_kl_loss=1.439, generator_dur_loss=1.741, generator_adv_loss=2.002, generator_feat_match_loss=6.18, over 2559.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:40:23,942 INFO [train.py:527] (4/6) Epoch 918, batch 92, global_batch_idx: 113800, batch size: 25, loss[discriminator_loss=2.638, discriminator_real_loss=1.29, discriminator_fake_loss=1.349, generator_loss=28.32, generator_mel_loss=18.09, generator_kl_loss=1.519, generator_dur_loss=1.627, generator_adv_loss=1.949, generator_feat_match_loss=5.132, over 25.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.2, generator_mel_loss=17.76, generator_kl_loss=1.449, generator_dur_loss=1.733, generator_adv_loss=2.012, generator_feat_match_loss=6.252, over 5308.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:40:23,944 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 22:40:32,679 INFO [train.py:591] (4/6) Epoch 918, validation: discriminator_loss=2.722, discriminator_real_loss=1.411, discriminator_fake_loss=1.31, generator_loss=27.73, generator_mel_loss=18.28, generator_kl_loss=1.329, generator_dur_loss=1.804, generator_adv_loss=1.903, generator_feat_match_loss=4.414, over 100.00 samples. +2024-03-15 22:40:32,680 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 22:42:01,369 INFO [train.py:919] (4/6) Start epoch 919 +2024-03-15 22:43:15,233 INFO [train.py:527] (4/6) Epoch 919, batch 18, global_batch_idx: 113850, batch size: 56, loss[discriminator_loss=2.713, discriminator_real_loss=1.299, discriminator_fake_loss=1.414, generator_loss=29.07, generator_mel_loss=17.58, generator_kl_loss=1.47, generator_dur_loss=1.737, generator_adv_loss=2.103, generator_feat_match_loss=6.188, over 56.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.347, discriminator_fake_loss=1.337, generator_loss=29.03, generator_mel_loss=17.68, generator_kl_loss=1.423, generator_dur_loss=1.739, generator_adv_loss=2.037, generator_feat_match_loss=6.15, over 1183.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:45:35,807 INFO [train.py:527] (4/6) Epoch 919, batch 68, global_batch_idx: 113900, batch size: 74, loss[discriminator_loss=2.7, discriminator_real_loss=1.351, discriminator_fake_loss=1.349, generator_loss=29.76, generator_mel_loss=17.82, generator_kl_loss=1.362, generator_dur_loss=1.806, generator_adv_loss=2.06, generator_feat_match_loss=6.715, over 74.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.35, discriminator_fake_loss=1.325, generator_loss=29.08, generator_mel_loss=17.7, generator_kl_loss=1.425, generator_dur_loss=1.749, generator_adv_loss=2.016, generator_feat_match_loss=6.192, over 4251.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:47:55,611 INFO [train.py:527] (4/6) Epoch 919, batch 118, global_batch_idx: 113950, batch size: 31, loss[discriminator_loss=2.72, discriminator_real_loss=1.346, discriminator_fake_loss=1.374, generator_loss=27.84, generator_mel_loss=17.61, generator_kl_loss=1.418, generator_dur_loss=1.62, generator_adv_loss=2.041, generator_feat_match_loss=5.149, over 31.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.352, discriminator_fake_loss=1.327, generator_loss=29.08, generator_mel_loss=17.73, generator_kl_loss=1.441, generator_dur_loss=1.746, generator_adv_loss=2.008, generator_feat_match_loss=6.153, over 7004.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:48:11,430 INFO [train.py:919] (4/6) Start epoch 920 +2024-03-15 22:50:39,396 INFO [train.py:527] (4/6) Epoch 920, batch 44, global_batch_idx: 114000, batch size: 62, loss[discriminator_loss=2.625, discriminator_real_loss=1.34, discriminator_fake_loss=1.285, generator_loss=29.6, generator_mel_loss=17.64, generator_kl_loss=1.495, generator_dur_loss=1.745, generator_adv_loss=2.08, generator_feat_match_loss=6.646, over 62.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.365, discriminator_fake_loss=1.329, generator_loss=29.12, generator_mel_loss=17.73, generator_kl_loss=1.444, generator_dur_loss=1.746, generator_adv_loss=2.014, generator_feat_match_loss=6.186, over 2672.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:50:39,397 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 22:50:47,287 INFO [train.py:591] (4/6) Epoch 920, validation: discriminator_loss=2.653, discriminator_real_loss=1.397, discriminator_fake_loss=1.255, generator_loss=28.07, generator_mel_loss=17.81, generator_kl_loss=1.25, generator_dur_loss=1.811, generator_adv_loss=2.03, generator_feat_match_loss=5.167, over 100.00 samples. +2024-03-15 22:50:47,288 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 22:53:07,374 INFO [train.py:527] (4/6) Epoch 920, batch 94, global_batch_idx: 114050, batch size: 52, loss[discriminator_loss=2.668, discriminator_real_loss=1.342, discriminator_fake_loss=1.326, generator_loss=29.53, generator_mel_loss=18.1, generator_kl_loss=1.562, generator_dur_loss=1.721, generator_adv_loss=1.96, generator_feat_match_loss=6.191, over 52.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.357, discriminator_fake_loss=1.323, generator_loss=29.18, generator_mel_loss=17.72, generator_kl_loss=1.452, generator_dur_loss=1.748, generator_adv_loss=2.013, generator_feat_match_loss=6.247, over 5641.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:54:26,708 INFO [train.py:919] (4/6) Start epoch 921 +2024-03-15 22:55:45,845 INFO [train.py:527] (4/6) Epoch 921, batch 20, global_batch_idx: 114100, batch size: 53, loss[discriminator_loss=2.641, discriminator_real_loss=1.289, discriminator_fake_loss=1.351, generator_loss=28.71, generator_mel_loss=17.63, generator_kl_loss=1.435, generator_dur_loss=1.689, generator_adv_loss=2.047, generator_feat_match_loss=5.913, over 53.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.348, discriminator_fake_loss=1.334, generator_loss=29.2, generator_mel_loss=17.82, generator_kl_loss=1.427, generator_dur_loss=1.739, generator_adv_loss=2.008, generator_feat_match_loss=6.205, over 1190.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:58:02,014 INFO [train.py:527] (4/6) Epoch 921, batch 70, global_batch_idx: 114150, batch size: 59, loss[discriminator_loss=2.713, discriminator_real_loss=1.316, discriminator_fake_loss=1.397, generator_loss=28.17, generator_mel_loss=17.73, generator_kl_loss=1.471, generator_dur_loss=1.739, generator_adv_loss=2.034, generator_feat_match_loss=5.201, over 59.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.355, discriminator_fake_loss=1.329, generator_loss=29.12, generator_mel_loss=17.8, generator_kl_loss=1.432, generator_dur_loss=1.737, generator_adv_loss=2.006, generator_feat_match_loss=6.138, over 4065.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:00:22,594 INFO [train.py:527] (4/6) Epoch 921, batch 120, global_batch_idx: 114200, batch size: 52, loss[discriminator_loss=2.715, discriminator_real_loss=1.42, discriminator_fake_loss=1.295, generator_loss=28.82, generator_mel_loss=17.54, generator_kl_loss=1.311, generator_dur_loss=1.651, generator_adv_loss=2.081, generator_feat_match_loss=6.246, over 52.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.356, discriminator_fake_loss=1.33, generator_loss=29.1, generator_mel_loss=17.77, generator_kl_loss=1.418, generator_dur_loss=1.747, generator_adv_loss=2.019, generator_feat_match_loss=6.145, over 7249.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:00:22,596 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 23:00:31,611 INFO [train.py:591] (4/6) Epoch 921, validation: discriminator_loss=2.693, discriminator_real_loss=1.445, discriminator_fake_loss=1.247, generator_loss=27.78, generator_mel_loss=17.8, generator_kl_loss=1.218, generator_dur_loss=1.817, generator_adv_loss=2.007, generator_feat_match_loss=4.93, over 100.00 samples. +2024-03-15 23:00:31,612 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 23:00:40,980 INFO [train.py:919] (4/6) Start epoch 922 +2024-03-15 23:03:11,646 INFO [train.py:527] (4/6) Epoch 922, batch 46, global_batch_idx: 114250, batch size: 96, loss[discriminator_loss=2.614, discriminator_real_loss=1.322, discriminator_fake_loss=1.292, generator_loss=29.37, generator_mel_loss=17.89, generator_kl_loss=1.26, generator_dur_loss=1.791, generator_adv_loss=2.066, generator_feat_match_loss=6.362, over 96.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.357, discriminator_fake_loss=1.312, generator_loss=29.11, generator_mel_loss=17.78, generator_kl_loss=1.44, generator_dur_loss=1.745, generator_adv_loss=2.007, generator_feat_match_loss=6.138, over 2753.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:05:29,938 INFO [train.py:527] (4/6) Epoch 922, batch 96, global_batch_idx: 114300, batch size: 31, loss[discriminator_loss=2.726, discriminator_real_loss=1.35, discriminator_fake_loss=1.376, generator_loss=29.03, generator_mel_loss=17.54, generator_kl_loss=1.6, generator_dur_loss=1.634, generator_adv_loss=2.113, generator_feat_match_loss=6.142, over 31.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=29.11, generator_mel_loss=17.75, generator_kl_loss=1.445, generator_dur_loss=1.735, generator_adv_loss=2.004, generator_feat_match_loss=6.178, over 5513.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:06:48,261 INFO [train.py:919] (4/6) Start epoch 923 +2024-03-15 23:08:16,273 INFO [train.py:527] (4/6) Epoch 923, batch 22, global_batch_idx: 114350, batch size: 56, loss[discriminator_loss=2.689, discriminator_real_loss=1.344, discriminator_fake_loss=1.345, generator_loss=29.83, generator_mel_loss=18, generator_kl_loss=1.426, generator_dur_loss=1.712, generator_adv_loss=2.046, generator_feat_match_loss=6.64, over 56.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.35, discriminator_fake_loss=1.334, generator_loss=29.14, generator_mel_loss=17.78, generator_kl_loss=1.409, generator_dur_loss=1.733, generator_adv_loss=2.024, generator_feat_match_loss=6.188, over 1401.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:10:38,419 INFO [train.py:527] (4/6) Epoch 923, batch 72, global_batch_idx: 114400, batch size: 25, loss[discriminator_loss=2.662, discriminator_real_loss=1.344, discriminator_fake_loss=1.317, generator_loss=29.56, generator_mel_loss=17.69, generator_kl_loss=1.895, generator_dur_loss=1.543, generator_adv_loss=2.135, generator_feat_match_loss=6.297, over 25.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.352, discriminator_fake_loss=1.322, generator_loss=29.23, generator_mel_loss=17.78, generator_kl_loss=1.437, generator_dur_loss=1.73, generator_adv_loss=2.024, generator_feat_match_loss=6.263, over 4270.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:10:38,420 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 23:10:47,329 INFO [train.py:591] (4/6) Epoch 923, validation: discriminator_loss=2.766, discriminator_real_loss=1.488, discriminator_fake_loss=1.277, generator_loss=28.54, generator_mel_loss=18.39, generator_kl_loss=1.281, generator_dur_loss=1.764, generator_adv_loss=1.994, generator_feat_match_loss=5.106, over 100.00 samples. +2024-03-15 23:10:47,330 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 23:13:02,695 INFO [train.py:527] (4/6) Epoch 923, batch 122, global_batch_idx: 114450, batch size: 52, loss[discriminator_loss=2.628, discriminator_real_loss=1.388, discriminator_fake_loss=1.24, generator_loss=28.93, generator_mel_loss=17.98, generator_kl_loss=1.467, generator_dur_loss=1.649, generator_adv_loss=1.836, generator_feat_match_loss=5.989, over 52.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.351, discriminator_fake_loss=1.323, generator_loss=29.22, generator_mel_loss=17.77, generator_kl_loss=1.436, generator_dur_loss=1.724, generator_adv_loss=2.013, generator_feat_match_loss=6.278, over 7161.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:13:07,991 INFO [train.py:919] (4/6) Start epoch 924 +2024-03-15 23:15:46,509 INFO [train.py:527] (4/6) Epoch 924, batch 48, global_batch_idx: 114500, batch size: 77, loss[discriminator_loss=2.636, discriminator_real_loss=1.301, discriminator_fake_loss=1.336, generator_loss=28.83, generator_mel_loss=17.85, generator_kl_loss=1.297, generator_dur_loss=1.788, generator_adv_loss=2.057, generator_feat_match_loss=5.836, over 77.00 samples.], tot_loss[discriminator_loss=2.658, discriminator_real_loss=1.343, discriminator_fake_loss=1.316, generator_loss=29.26, generator_mel_loss=17.82, generator_kl_loss=1.432, generator_dur_loss=1.734, generator_adv_loss=2.013, generator_feat_match_loss=6.262, over 2904.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:18:06,625 INFO [train.py:527] (4/6) Epoch 924, batch 98, global_batch_idx: 114550, batch size: 62, loss[discriminator_loss=2.658, discriminator_real_loss=1.329, discriminator_fake_loss=1.329, generator_loss=28.5, generator_mel_loss=17.24, generator_kl_loss=1.277, generator_dur_loss=1.731, generator_adv_loss=2.085, generator_feat_match_loss=6.167, over 62.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.348, discriminator_fake_loss=1.318, generator_loss=29.15, generator_mel_loss=17.76, generator_kl_loss=1.422, generator_dur_loss=1.734, generator_adv_loss=2.014, generator_feat_match_loss=6.221, over 5741.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:19:16,264 INFO [train.py:919] (4/6) Start epoch 925 +2024-03-15 23:20:43,877 INFO [train.py:527] (4/6) Epoch 925, batch 24, global_batch_idx: 114600, batch size: 44, loss[discriminator_loss=2.71, discriminator_real_loss=1.345, discriminator_fake_loss=1.365, generator_loss=28.71, generator_mel_loss=17.98, generator_kl_loss=1.535, generator_dur_loss=1.691, generator_adv_loss=1.894, generator_feat_match_loss=5.608, over 44.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.355, discriminator_fake_loss=1.316, generator_loss=29.1, generator_mel_loss=17.7, generator_kl_loss=1.463, generator_dur_loss=1.738, generator_adv_loss=2.023, generator_feat_match_loss=6.173, over 1452.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:20:43,879 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 23:20:52,335 INFO [train.py:591] (4/6) Epoch 925, validation: discriminator_loss=2.675, discriminator_real_loss=1.297, discriminator_fake_loss=1.378, generator_loss=27.34, generator_mel_loss=17.87, generator_kl_loss=1.345, generator_dur_loss=1.785, generator_adv_loss=1.806, generator_feat_match_loss=4.529, over 100.00 samples. +2024-03-15 23:20:52,336 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 23:23:12,818 INFO [train.py:527] (4/6) Epoch 925, batch 74, global_batch_idx: 114650, batch size: 55, loss[discriminator_loss=2.685, discriminator_real_loss=1.32, discriminator_fake_loss=1.365, generator_loss=29.37, generator_mel_loss=18.27, generator_kl_loss=1.424, generator_dur_loss=1.663, generator_adv_loss=2.03, generator_feat_match_loss=5.984, over 55.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.02, generator_mel_loss=17.71, generator_kl_loss=1.443, generator_dur_loss=1.733, generator_adv_loss=2.011, generator_feat_match_loss=6.124, over 4300.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:25:30,425 INFO [train.py:919] (4/6) Start epoch 926 +2024-03-15 23:25:55,298 INFO [train.py:527] (4/6) Epoch 926, batch 0, global_batch_idx: 114700, batch size: 80, loss[discriminator_loss=2.664, discriminator_real_loss=1.363, discriminator_fake_loss=1.301, generator_loss=29.93, generator_mel_loss=17.88, generator_kl_loss=1.554, generator_dur_loss=1.773, generator_adv_loss=1.952, generator_feat_match_loss=6.774, over 80.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.363, discriminator_fake_loss=1.301, generator_loss=29.93, generator_mel_loss=17.88, generator_kl_loss=1.554, generator_dur_loss=1.773, generator_adv_loss=1.952, generator_feat_match_loss=6.774, over 80.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:28:12,627 INFO [train.py:527] (4/6) Epoch 926, batch 50, global_batch_idx: 114750, batch size: 64, loss[discriminator_loss=2.739, discriminator_real_loss=1.392, discriminator_fake_loss=1.346, generator_loss=29.29, generator_mel_loss=17.68, generator_kl_loss=1.318, generator_dur_loss=1.785, generator_adv_loss=1.923, generator_feat_match_loss=6.577, over 64.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.356, discriminator_fake_loss=1.317, generator_loss=29.21, generator_mel_loss=17.8, generator_kl_loss=1.427, generator_dur_loss=1.723, generator_adv_loss=2.019, generator_feat_match_loss=6.238, over 2777.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:30:32,458 INFO [train.py:527] (4/6) Epoch 926, batch 100, global_batch_idx: 114800, batch size: 70, loss[discriminator_loss=2.702, discriminator_real_loss=1.368, discriminator_fake_loss=1.334, generator_loss=28.98, generator_mel_loss=17.65, generator_kl_loss=1.279, generator_dur_loss=1.792, generator_adv_loss=2.02, generator_feat_match_loss=6.235, over 70.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.32, generator_loss=29.1, generator_mel_loss=17.76, generator_kl_loss=1.422, generator_dur_loss=1.731, generator_adv_loss=2.006, generator_feat_match_loss=6.18, over 5794.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:30:32,459 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 23:30:41,175 INFO [train.py:591] (4/6) Epoch 926, validation: discriminator_loss=2.776, discriminator_real_loss=1.398, discriminator_fake_loss=1.378, generator_loss=28.2, generator_mel_loss=18.08, generator_kl_loss=1.284, generator_dur_loss=1.779, generator_adv_loss=1.868, generator_feat_match_loss=5.192, over 100.00 samples. +2024-03-15 23:30:41,176 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 23:31:46,790 INFO [train.py:919] (4/6) Start epoch 927 +2024-03-15 23:33:20,867 INFO [train.py:527] (4/6) Epoch 927, batch 26, global_batch_idx: 114850, batch size: 77, loss[discriminator_loss=2.642, discriminator_real_loss=1.265, discriminator_fake_loss=1.377, generator_loss=29.51, generator_mel_loss=17.83, generator_kl_loss=1.372, generator_dur_loss=1.8, generator_adv_loss=2.078, generator_feat_match_loss=6.436, over 77.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.35, discriminator_fake_loss=1.325, generator_loss=29.22, generator_mel_loss=17.74, generator_kl_loss=1.442, generator_dur_loss=1.746, generator_adv_loss=2.026, generator_feat_match_loss=6.266, over 1584.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:35:40,891 INFO [train.py:527] (4/6) Epoch 927, batch 76, global_batch_idx: 114900, batch size: 66, loss[discriminator_loss=2.676, discriminator_real_loss=1.269, discriminator_fake_loss=1.407, generator_loss=29.18, generator_mel_loss=18.04, generator_kl_loss=1.414, generator_dur_loss=1.745, generator_adv_loss=1.938, generator_feat_match_loss=6.041, over 66.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.32, generator_loss=29.14, generator_mel_loss=17.75, generator_kl_loss=1.45, generator_dur_loss=1.736, generator_adv_loss=2.02, generator_feat_match_loss=6.188, over 4167.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:37:53,408 INFO [train.py:919] (4/6) Start epoch 928 +2024-03-15 23:38:21,881 INFO [train.py:527] (4/6) Epoch 928, batch 2, global_batch_idx: 114950, batch size: 45, loss[discriminator_loss=2.715, discriminator_real_loss=1.422, discriminator_fake_loss=1.293, generator_loss=28.79, generator_mel_loss=17.74, generator_kl_loss=1.663, generator_dur_loss=1.629, generator_adv_loss=1.917, generator_feat_match_loss=5.843, over 45.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.395, discriminator_fake_loss=1.286, generator_loss=28.79, generator_mel_loss=17.69, generator_kl_loss=1.53, generator_dur_loss=1.658, generator_adv_loss=2.019, generator_feat_match_loss=5.899, over 142.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:40:44,053 INFO [train.py:527] (4/6) Epoch 928, batch 52, global_batch_idx: 115000, batch size: 56, loss[discriminator_loss=2.7, discriminator_real_loss=1.47, discriminator_fake_loss=1.23, generator_loss=28.97, generator_mel_loss=17.66, generator_kl_loss=1.448, generator_dur_loss=1.687, generator_adv_loss=2.006, generator_feat_match_loss=6.162, over 56.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.36, discriminator_fake_loss=1.319, generator_loss=29.06, generator_mel_loss=17.69, generator_kl_loss=1.434, generator_dur_loss=1.732, generator_adv_loss=2.017, generator_feat_match_loss=6.181, over 2956.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:40:44,055 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 23:40:52,365 INFO [train.py:591] (4/6) Epoch 928, validation: discriminator_loss=2.757, discriminator_real_loss=1.4, discriminator_fake_loss=1.357, generator_loss=27.1, generator_mel_loss=17.86, generator_kl_loss=1.265, generator_dur_loss=1.808, generator_adv_loss=1.835, generator_feat_match_loss=4.331, over 100.00 samples. +2024-03-15 23:40:52,366 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 23:43:11,600 INFO [train.py:527] (4/6) Epoch 928, batch 102, global_batch_idx: 115050, batch size: 64, loss[discriminator_loss=2.675, discriminator_real_loss=1.405, discriminator_fake_loss=1.27, generator_loss=29.03, generator_mel_loss=17.69, generator_kl_loss=1.411, generator_dur_loss=1.732, generator_adv_loss=1.964, generator_feat_match_loss=6.229, over 64.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.365, discriminator_fake_loss=1.32, generator_loss=29.1, generator_mel_loss=17.73, generator_kl_loss=1.438, generator_dur_loss=1.739, generator_adv_loss=2.024, generator_feat_match_loss=6.166, over 5730.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:44:08,895 INFO [train.py:919] (4/6) Start epoch 929 +2024-03-15 23:45:49,399 INFO [train.py:527] (4/6) Epoch 929, batch 28, global_batch_idx: 115100, batch size: 66, loss[discriminator_loss=2.665, discriminator_real_loss=1.253, discriminator_fake_loss=1.412, generator_loss=29.91, generator_mel_loss=17.52, generator_kl_loss=1.528, generator_dur_loss=1.811, generator_adv_loss=2.06, generator_feat_match_loss=6.991, over 66.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.347, discriminator_fake_loss=1.323, generator_loss=28.87, generator_mel_loss=17.59, generator_kl_loss=1.409, generator_dur_loss=1.746, generator_adv_loss=2.004, generator_feat_match_loss=6.127, over 1739.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:48:09,891 INFO [train.py:527] (4/6) Epoch 929, batch 78, global_batch_idx: 115150, batch size: 48, loss[discriminator_loss=2.711, discriminator_real_loss=1.356, discriminator_fake_loss=1.355, generator_loss=29.34, generator_mel_loss=17.66, generator_kl_loss=1.614, generator_dur_loss=1.71, generator_adv_loss=1.924, generator_feat_match_loss=6.426, over 48.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.344, discriminator_fake_loss=1.324, generator_loss=29.13, generator_mel_loss=17.67, generator_kl_loss=1.442, generator_dur_loss=1.748, generator_adv_loss=2.002, generator_feat_match_loss=6.267, over 4645.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:50:17,401 INFO [train.py:919] (4/6) Start epoch 930 +2024-03-15 23:50:51,633 INFO [train.py:527] (4/6) Epoch 930, batch 4, global_batch_idx: 115200, batch size: 83, loss[discriminator_loss=2.675, discriminator_real_loss=1.434, discriminator_fake_loss=1.24, generator_loss=29.21, generator_mel_loss=17.64, generator_kl_loss=1.332, generator_dur_loss=1.806, generator_adv_loss=1.992, generator_feat_match_loss=6.439, over 83.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.357, discriminator_fake_loss=1.322, generator_loss=29.51, generator_mel_loss=17.79, generator_kl_loss=1.412, generator_dur_loss=1.748, generator_adv_loss=2.071, generator_feat_match_loss=6.491, over 312.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:50:51,636 INFO [train.py:581] (4/6) Computing validation loss +2024-03-15 23:50:59,572 INFO [train.py:591] (4/6) Epoch 930, validation: discriminator_loss=2.644, discriminator_real_loss=1.352, discriminator_fake_loss=1.292, generator_loss=28.36, generator_mel_loss=18.17, generator_kl_loss=1.304, generator_dur_loss=1.819, generator_adv_loss=1.924, generator_feat_match_loss=5.15, over 100.00 samples. +2024-03-15 23:50:59,573 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-15 23:53:20,114 INFO [train.py:527] (4/6) Epoch 930, batch 54, global_batch_idx: 115250, batch size: 16, loss[discriminator_loss=2.57, discriminator_real_loss=1.26, discriminator_fake_loss=1.311, generator_loss=31.24, generator_mel_loss=19.16, generator_kl_loss=1.923, generator_dur_loss=1.565, generator_adv_loss=2.127, generator_feat_match_loss=6.467, over 16.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.359, discriminator_fake_loss=1.313, generator_loss=29.17, generator_mel_loss=17.7, generator_kl_loss=1.486, generator_dur_loss=1.742, generator_adv_loss=2.046, generator_feat_match_loss=6.204, over 3001.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:55:39,475 INFO [train.py:527] (4/6) Epoch 930, batch 104, global_batch_idx: 115300, batch size: 47, loss[discriminator_loss=2.715, discriminator_real_loss=1.286, discriminator_fake_loss=1.428, generator_loss=29.67, generator_mel_loss=18.05, generator_kl_loss=1.527, generator_dur_loss=1.683, generator_adv_loss=1.967, generator_feat_match_loss=6.448, over 47.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.353, discriminator_fake_loss=1.316, generator_loss=29.21, generator_mel_loss=17.7, generator_kl_loss=1.493, generator_dur_loss=1.722, generator_adv_loss=2.037, generator_feat_match_loss=6.257, over 5585.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:56:31,848 INFO [train.py:919] (4/6) Start epoch 931 +2024-03-15 23:58:20,368 INFO [train.py:527] (4/6) Epoch 931, batch 30, global_batch_idx: 115350, batch size: 14, loss[discriminator_loss=2.633, discriminator_real_loss=1.468, discriminator_fake_loss=1.165, generator_loss=30.4, generator_mel_loss=17.7, generator_kl_loss=1.663, generator_dur_loss=1.625, generator_adv_loss=2.291, generator_feat_match_loss=7.127, over 14.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.352, discriminator_fake_loss=1.309, generator_loss=29.37, generator_mel_loss=17.72, generator_kl_loss=1.481, generator_dur_loss=1.736, generator_adv_loss=2.013, generator_feat_match_loss=6.419, over 1737.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:00:41,279 INFO [train.py:527] (4/6) Epoch 931, batch 80, global_batch_idx: 115400, batch size: 39, loss[discriminator_loss=2.668, discriminator_real_loss=1.335, discriminator_fake_loss=1.332, generator_loss=27.86, generator_mel_loss=16.84, generator_kl_loss=1.46, generator_dur_loss=1.672, generator_adv_loss=1.947, generator_feat_match_loss=5.944, over 39.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.353, discriminator_fake_loss=1.319, generator_loss=29.33, generator_mel_loss=17.73, generator_kl_loss=1.478, generator_dur_loss=1.725, generator_adv_loss=2.023, generator_feat_match_loss=6.372, over 4408.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:00:41,281 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 00:00:50,265 INFO [train.py:591] (4/6) Epoch 931, validation: discriminator_loss=2.687, discriminator_real_loss=1.389, discriminator_fake_loss=1.298, generator_loss=27.94, generator_mel_loss=17.9, generator_kl_loss=1.323, generator_dur_loss=1.792, generator_adv_loss=1.908, generator_feat_match_loss=5.023, over 100.00 samples. +2024-03-16 00:00:50,266 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 00:02:49,687 INFO [train.py:919] (4/6) Start epoch 932 +2024-03-16 00:03:29,992 INFO [train.py:527] (4/6) Epoch 932, batch 6, global_batch_idx: 115450, batch size: 15, loss[discriminator_loss=2.638, discriminator_real_loss=1.434, discriminator_fake_loss=1.204, generator_loss=29.12, generator_mel_loss=17.96, generator_kl_loss=1.619, generator_dur_loss=1.658, generator_adv_loss=2.039, generator_feat_match_loss=5.836, over 15.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.382, discriminator_fake_loss=1.303, generator_loss=29.18, generator_mel_loss=17.64, generator_kl_loss=1.49, generator_dur_loss=1.735, generator_adv_loss=2.011, generator_feat_match_loss=6.306, over 340.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:05:49,199 INFO [train.py:527] (4/6) Epoch 932, batch 56, global_batch_idx: 115500, batch size: 70, loss[discriminator_loss=2.707, discriminator_real_loss=1.409, discriminator_fake_loss=1.298, generator_loss=29.33, generator_mel_loss=17.75, generator_kl_loss=1.479, generator_dur_loss=1.738, generator_adv_loss=2.025, generator_feat_match_loss=6.335, over 70.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.356, discriminator_fake_loss=1.317, generator_loss=29.18, generator_mel_loss=17.75, generator_kl_loss=1.469, generator_dur_loss=1.743, generator_adv_loss=2.021, generator_feat_match_loss=6.193, over 3216.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:08:05,286 INFO [train.py:527] (4/6) Epoch 932, batch 106, global_batch_idx: 115550, batch size: 64, loss[discriminator_loss=2.677, discriminator_real_loss=1.413, discriminator_fake_loss=1.264, generator_loss=28.45, generator_mel_loss=17.27, generator_kl_loss=1.366, generator_dur_loss=1.712, generator_adv_loss=1.985, generator_feat_match_loss=6.122, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.319, generator_loss=29.2, generator_mel_loss=17.79, generator_kl_loss=1.464, generator_dur_loss=1.74, generator_adv_loss=2.015, generator_feat_match_loss=6.192, over 6135.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:08:55,207 INFO [train.py:919] (4/6) Start epoch 933 +2024-03-16 00:10:46,919 INFO [train.py:527] (4/6) Epoch 933, batch 32, global_batch_idx: 115600, batch size: 48, loss[discriminator_loss=2.686, discriminator_real_loss=1.327, discriminator_fake_loss=1.358, generator_loss=29.46, generator_mel_loss=17.57, generator_kl_loss=1.6, generator_dur_loss=1.714, generator_adv_loss=1.992, generator_feat_match_loss=6.585, over 48.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.35, discriminator_fake_loss=1.319, generator_loss=29.28, generator_mel_loss=17.67, generator_kl_loss=1.467, generator_dur_loss=1.729, generator_adv_loss=2.04, generator_feat_match_loss=6.382, over 1879.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:10:46,920 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 00:10:55,125 INFO [train.py:591] (4/6) Epoch 933, validation: discriminator_loss=2.75, discriminator_real_loss=1.426, discriminator_fake_loss=1.324, generator_loss=28.26, generator_mel_loss=18.06, generator_kl_loss=1.33, generator_dur_loss=1.797, generator_adv_loss=1.905, generator_feat_match_loss=5.164, over 100.00 samples. +2024-03-16 00:10:55,126 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 00:13:11,399 INFO [train.py:527] (4/6) Epoch 933, batch 82, global_batch_idx: 115650, batch size: 31, loss[discriminator_loss=2.721, discriminator_real_loss=1.456, discriminator_fake_loss=1.265, generator_loss=28.76, generator_mel_loss=17.69, generator_kl_loss=1.59, generator_dur_loss=1.621, generator_adv_loss=1.888, generator_feat_match_loss=5.974, over 31.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.321, generator_loss=29.28, generator_mel_loss=17.73, generator_kl_loss=1.48, generator_dur_loss=1.732, generator_adv_loss=2.03, generator_feat_match_loss=6.315, over 4912.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:15:07,556 INFO [train.py:919] (4/6) Start epoch 934 +2024-03-16 00:15:53,523 INFO [train.py:527] (4/6) Epoch 934, batch 8, global_batch_idx: 115700, batch size: 53, loss[discriminator_loss=2.648, discriminator_real_loss=1.343, discriminator_fake_loss=1.305, generator_loss=29.51, generator_mel_loss=17.75, generator_kl_loss=1.607, generator_dur_loss=1.681, generator_adv_loss=2.169, generator_feat_match_loss=6.299, over 53.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.433, discriminator_fake_loss=1.296, generator_loss=29.56, generator_mel_loss=17.83, generator_kl_loss=1.49, generator_dur_loss=1.731, generator_adv_loss=2.103, generator_feat_match_loss=6.408, over 503.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:18:14,914 INFO [train.py:527] (4/6) Epoch 934, batch 58, global_batch_idx: 115750, batch size: 70, loss[discriminator_loss=2.692, discriminator_real_loss=1.366, discriminator_fake_loss=1.326, generator_loss=29.25, generator_mel_loss=17.67, generator_kl_loss=1.396, generator_dur_loss=1.775, generator_adv_loss=2.064, generator_feat_match_loss=6.342, over 70.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.372, discriminator_fake_loss=1.305, generator_loss=29.16, generator_mel_loss=17.71, generator_kl_loss=1.449, generator_dur_loss=1.738, generator_adv_loss=2.039, generator_feat_match_loss=6.222, over 3426.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:20:34,070 INFO [train.py:527] (4/6) Epoch 934, batch 108, global_batch_idx: 115800, batch size: 62, loss[discriminator_loss=2.732, discriminator_real_loss=1.348, discriminator_fake_loss=1.384, generator_loss=28.87, generator_mel_loss=18.04, generator_kl_loss=1.48, generator_dur_loss=1.737, generator_adv_loss=2.003, generator_feat_match_loss=5.608, over 62.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.367, discriminator_fake_loss=1.315, generator_loss=29.07, generator_mel_loss=17.71, generator_kl_loss=1.45, generator_dur_loss=1.739, generator_adv_loss=2.024, generator_feat_match_loss=6.143, over 6315.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:20:34,072 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 00:20:42,783 INFO [train.py:591] (4/6) Epoch 934, validation: discriminator_loss=2.725, discriminator_real_loss=1.444, discriminator_fake_loss=1.281, generator_loss=27.39, generator_mel_loss=17.48, generator_kl_loss=1.291, generator_dur_loss=1.817, generator_adv_loss=1.973, generator_feat_match_loss=4.825, over 100.00 samples. +2024-03-16 00:20:42,784 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 00:21:20,843 INFO [train.py:919] (4/6) Start epoch 935 +2024-03-16 00:23:20,363 INFO [train.py:527] (4/6) Epoch 935, batch 34, global_batch_idx: 115850, batch size: 61, loss[discriminator_loss=2.668, discriminator_real_loss=1.394, discriminator_fake_loss=1.273, generator_loss=28.92, generator_mel_loss=17.66, generator_kl_loss=1.305, generator_dur_loss=1.778, generator_adv_loss=2.008, generator_feat_match_loss=6.177, over 61.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.352, discriminator_fake_loss=1.319, generator_loss=29.03, generator_mel_loss=17.64, generator_kl_loss=1.425, generator_dur_loss=1.76, generator_adv_loss=2.001, generator_feat_match_loss=6.204, over 2121.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:25:38,972 INFO [train.py:527] (4/6) Epoch 935, batch 84, global_batch_idx: 115900, batch size: 44, loss[discriminator_loss=2.631, discriminator_real_loss=1.239, discriminator_fake_loss=1.392, generator_loss=30.3, generator_mel_loss=17.78, generator_kl_loss=1.688, generator_dur_loss=1.647, generator_adv_loss=2.048, generator_feat_match_loss=7.141, over 44.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.351, discriminator_fake_loss=1.324, generator_loss=29.21, generator_mel_loss=17.7, generator_kl_loss=1.443, generator_dur_loss=1.743, generator_adv_loss=2.033, generator_feat_match_loss=6.294, over 4795.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:27:27,563 INFO [train.py:919] (4/6) Start epoch 936 +2024-03-16 00:28:20,023 INFO [train.py:527] (4/6) Epoch 936, batch 10, global_batch_idx: 115950, batch size: 88, loss[discriminator_loss=2.689, discriminator_real_loss=1.318, discriminator_fake_loss=1.37, generator_loss=30.05, generator_mel_loss=18.26, generator_kl_loss=1.273, generator_dur_loss=1.816, generator_adv_loss=1.989, generator_feat_match_loss=6.713, over 88.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.367, discriminator_fake_loss=1.325, generator_loss=29.54, generator_mel_loss=17.93, generator_kl_loss=1.393, generator_dur_loss=1.758, generator_adv_loss=2.029, generator_feat_match_loss=6.426, over 691.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:30:39,203 INFO [train.py:527] (4/6) Epoch 936, batch 60, global_batch_idx: 116000, batch size: 56, loss[discriminator_loss=2.704, discriminator_real_loss=1.442, discriminator_fake_loss=1.262, generator_loss=28.61, generator_mel_loss=17.5, generator_kl_loss=1.5, generator_dur_loss=1.731, generator_adv_loss=1.906, generator_feat_match_loss=5.981, over 56.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.358, discriminator_fake_loss=1.321, generator_loss=29.2, generator_mel_loss=17.75, generator_kl_loss=1.454, generator_dur_loss=1.737, generator_adv_loss=2.011, generator_feat_match_loss=6.249, over 3580.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:30:39,207 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 00:30:47,171 INFO [train.py:591] (4/6) Epoch 936, validation: discriminator_loss=2.758, discriminator_real_loss=1.362, discriminator_fake_loss=1.396, generator_loss=29.14, generator_mel_loss=18.57, generator_kl_loss=1.275, generator_dur_loss=1.804, generator_adv_loss=1.909, generator_feat_match_loss=5.584, over 100.00 samples. +2024-03-16 00:30:47,172 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 00:33:04,960 INFO [train.py:527] (4/6) Epoch 936, batch 110, global_batch_idx: 116050, batch size: 72, loss[discriminator_loss=2.713, discriminator_real_loss=1.436, discriminator_fake_loss=1.277, generator_loss=28.55, generator_mel_loss=17.51, generator_kl_loss=1.312, generator_dur_loss=1.845, generator_adv_loss=2.044, generator_feat_match_loss=5.842, over 72.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.36, discriminator_fake_loss=1.321, generator_loss=29.12, generator_mel_loss=17.74, generator_kl_loss=1.46, generator_dur_loss=1.741, generator_adv_loss=2.006, generator_feat_match_loss=6.181, over 6512.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:33:40,199 INFO [train.py:919] (4/6) Start epoch 937 +2024-03-16 00:35:43,927 INFO [train.py:527] (4/6) Epoch 937, batch 36, global_batch_idx: 116100, batch size: 39, loss[discriminator_loss=2.738, discriminator_real_loss=1.407, discriminator_fake_loss=1.331, generator_loss=29.14, generator_mel_loss=18.22, generator_kl_loss=1.684, generator_dur_loss=1.658, generator_adv_loss=1.921, generator_feat_match_loss=5.659, over 39.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.349, discriminator_fake_loss=1.32, generator_loss=29.12, generator_mel_loss=17.74, generator_kl_loss=1.48, generator_dur_loss=1.736, generator_adv_loss=1.999, generator_feat_match_loss=6.166, over 2118.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:38:02,963 INFO [train.py:527] (4/6) Epoch 937, batch 86, global_batch_idx: 116150, batch size: 77, loss[discriminator_loss=2.695, discriminator_real_loss=1.407, discriminator_fake_loss=1.289, generator_loss=28.42, generator_mel_loss=17.57, generator_kl_loss=1.409, generator_dur_loss=1.784, generator_adv_loss=1.835, generator_feat_match_loss=5.818, over 77.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.16, generator_mel_loss=17.73, generator_kl_loss=1.46, generator_dur_loss=1.737, generator_adv_loss=2.009, generator_feat_match_loss=6.222, over 4823.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:39:46,482 INFO [train.py:919] (4/6) Start epoch 938 +2024-03-16 00:40:43,664 INFO [train.py:527] (4/6) Epoch 938, batch 12, global_batch_idx: 116200, batch size: 74, loss[discriminator_loss=2.724, discriminator_real_loss=1.41, discriminator_fake_loss=1.314, generator_loss=29.35, generator_mel_loss=17.82, generator_kl_loss=1.361, generator_dur_loss=1.756, generator_adv_loss=2.009, generator_feat_match_loss=6.402, over 74.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.339, discriminator_fake_loss=1.318, generator_loss=29.22, generator_mel_loss=17.68, generator_kl_loss=1.429, generator_dur_loss=1.74, generator_adv_loss=2.02, generator_feat_match_loss=6.354, over 790.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:40:43,667 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 00:40:51,534 INFO [train.py:591] (4/6) Epoch 938, validation: discriminator_loss=2.751, discriminator_real_loss=1.432, discriminator_fake_loss=1.319, generator_loss=27.89, generator_mel_loss=18.17, generator_kl_loss=1.323, generator_dur_loss=1.794, generator_adv_loss=1.896, generator_feat_match_loss=4.709, over 100.00 samples. +2024-03-16 00:40:51,535 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 00:43:09,180 INFO [train.py:527] (4/6) Epoch 938, batch 62, global_batch_idx: 116250, batch size: 68, loss[discriminator_loss=2.684, discriminator_real_loss=1.409, discriminator_fake_loss=1.276, generator_loss=28.38, generator_mel_loss=17.32, generator_kl_loss=1.499, generator_dur_loss=1.789, generator_adv_loss=2.066, generator_feat_match_loss=5.701, over 68.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=29.09, generator_mel_loss=17.69, generator_kl_loss=1.451, generator_dur_loss=1.741, generator_adv_loss=2.009, generator_feat_match_loss=6.194, over 3704.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:45:29,117 INFO [train.py:527] (4/6) Epoch 938, batch 112, global_batch_idx: 116300, batch size: 53, loss[discriminator_loss=2.651, discriminator_real_loss=1.302, discriminator_fake_loss=1.349, generator_loss=29.88, generator_mel_loss=17.84, generator_kl_loss=1.571, generator_dur_loss=1.705, generator_adv_loss=2.059, generator_feat_match_loss=6.702, over 53.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.356, discriminator_fake_loss=1.323, generator_loss=29.09, generator_mel_loss=17.69, generator_kl_loss=1.451, generator_dur_loss=1.743, generator_adv_loss=2.007, generator_feat_match_loss=6.196, over 6451.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:46:01,164 INFO [train.py:919] (4/6) Start epoch 939 +2024-03-16 00:48:11,726 INFO [train.py:527] (4/6) Epoch 939, batch 38, global_batch_idx: 116350, batch size: 58, loss[discriminator_loss=2.62, discriminator_real_loss=1.301, discriminator_fake_loss=1.318, generator_loss=28.9, generator_mel_loss=17.33, generator_kl_loss=1.35, generator_dur_loss=1.751, generator_adv_loss=2.132, generator_feat_match_loss=6.339, over 58.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.32, generator_loss=29.05, generator_mel_loss=17.55, generator_kl_loss=1.445, generator_dur_loss=1.752, generator_adv_loss=2.01, generator_feat_match_loss=6.291, over 2366.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:50:30,555 INFO [train.py:527] (4/6) Epoch 939, batch 88, global_batch_idx: 116400, batch size: 64, loss[discriminator_loss=2.735, discriminator_real_loss=1.381, discriminator_fake_loss=1.353, generator_loss=28.05, generator_mel_loss=17.4, generator_kl_loss=1.413, generator_dur_loss=1.738, generator_adv_loss=1.991, generator_feat_match_loss=5.512, over 64.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.349, discriminator_fake_loss=1.327, generator_loss=29.09, generator_mel_loss=17.65, generator_kl_loss=1.444, generator_dur_loss=1.746, generator_adv_loss=2.006, generator_feat_match_loss=6.246, over 5141.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:50:30,557 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 00:50:39,490 INFO [train.py:591] (4/6) Epoch 939, validation: discriminator_loss=2.745, discriminator_real_loss=1.466, discriminator_fake_loss=1.279, generator_loss=28.39, generator_mel_loss=18.45, generator_kl_loss=1.304, generator_dur_loss=1.8, generator_adv_loss=1.906, generator_feat_match_loss=4.93, over 100.00 samples. +2024-03-16 00:50:39,491 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 00:52:14,518 INFO [train.py:919] (4/6) Start epoch 940 +2024-03-16 00:53:18,649 INFO [train.py:527] (4/6) Epoch 940, batch 14, global_batch_idx: 116450, batch size: 88, loss[discriminator_loss=2.673, discriminator_real_loss=1.339, discriminator_fake_loss=1.334, generator_loss=28.8, generator_mel_loss=17.44, generator_kl_loss=1.31, generator_dur_loss=1.833, generator_adv_loss=2.085, generator_feat_match_loss=6.131, over 88.00 samples.], tot_loss[discriminator_loss=2.648, discriminator_real_loss=1.34, discriminator_fake_loss=1.307, generator_loss=29.18, generator_mel_loss=17.73, generator_kl_loss=1.387, generator_dur_loss=1.754, generator_adv_loss=2.031, generator_feat_match_loss=6.284, over 969.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:55:37,645 INFO [train.py:527] (4/6) Epoch 940, batch 64, global_batch_idx: 116500, batch size: 74, loss[discriminator_loss=2.625, discriminator_real_loss=1.361, discriminator_fake_loss=1.263, generator_loss=29.25, generator_mel_loss=17.75, generator_kl_loss=1.269, generator_dur_loss=1.791, generator_adv_loss=2.141, generator_feat_match_loss=6.302, over 74.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.351, discriminator_fake_loss=1.313, generator_loss=29.24, generator_mel_loss=17.74, generator_kl_loss=1.442, generator_dur_loss=1.738, generator_adv_loss=2.029, generator_feat_match_loss=6.294, over 3675.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:57:56,284 INFO [train.py:527] (4/6) Epoch 940, batch 114, global_batch_idx: 116550, batch size: 42, loss[discriminator_loss=2.653, discriminator_real_loss=1.363, discriminator_fake_loss=1.29, generator_loss=29.21, generator_mel_loss=17.43, generator_kl_loss=1.571, generator_dur_loss=1.717, generator_adv_loss=2.038, generator_feat_match_loss=6.447, over 42.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.358, discriminator_fake_loss=1.314, generator_loss=29.19, generator_mel_loss=17.71, generator_kl_loss=1.448, generator_dur_loss=1.745, generator_adv_loss=2.026, generator_feat_match_loss=6.26, over 6697.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:58:23,855 INFO [train.py:919] (4/6) Start epoch 941 +2024-03-16 01:00:37,595 INFO [train.py:527] (4/6) Epoch 941, batch 40, global_batch_idx: 116600, batch size: 62, loss[discriminator_loss=2.709, discriminator_real_loss=1.319, discriminator_fake_loss=1.391, generator_loss=29.36, generator_mel_loss=17.66, generator_kl_loss=1.452, generator_dur_loss=1.715, generator_adv_loss=2.161, generator_feat_match_loss=6.378, over 62.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.338, discriminator_fake_loss=1.325, generator_loss=29.16, generator_mel_loss=17.74, generator_kl_loss=1.418, generator_dur_loss=1.736, generator_adv_loss=2.019, generator_feat_match_loss=6.253, over 2328.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:00:37,596 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 01:00:45,811 INFO [train.py:591] (4/6) Epoch 941, validation: discriminator_loss=2.752, discriminator_real_loss=1.539, discriminator_fake_loss=1.214, generator_loss=28.52, generator_mel_loss=17.67, generator_kl_loss=1.372, generator_dur_loss=1.795, generator_adv_loss=2.107, generator_feat_match_loss=5.573, over 100.00 samples. +2024-03-16 01:00:45,812 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 01:03:04,746 INFO [train.py:527] (4/6) Epoch 941, batch 90, global_batch_idx: 116650, batch size: 62, loss[discriminator_loss=2.762, discriminator_real_loss=1.405, discriminator_fake_loss=1.357, generator_loss=28.5, generator_mel_loss=17.84, generator_kl_loss=1.442, generator_dur_loss=1.706, generator_adv_loss=2.058, generator_feat_match_loss=5.46, over 62.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.07, generator_mel_loss=17.69, generator_kl_loss=1.424, generator_dur_loss=1.728, generator_adv_loss=2.019, generator_feat_match_loss=6.215, over 5187.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:04:36,951 INFO [train.py:919] (4/6) Start epoch 942 +2024-03-16 01:05:45,322 INFO [train.py:527] (4/6) Epoch 942, batch 16, global_batch_idx: 116700, batch size: 25, loss[discriminator_loss=2.675, discriminator_real_loss=1.42, discriminator_fake_loss=1.255, generator_loss=30.35, generator_mel_loss=18.18, generator_kl_loss=1.788, generator_dur_loss=1.598, generator_adv_loss=2.003, generator_feat_match_loss=6.78, over 25.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=29.37, generator_mel_loss=17.83, generator_kl_loss=1.46, generator_dur_loss=1.723, generator_adv_loss=2.016, generator_feat_match_loss=6.344, over 907.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:08:05,309 INFO [train.py:527] (4/6) Epoch 942, batch 66, global_batch_idx: 116750, batch size: 72, loss[discriminator_loss=2.66, discriminator_real_loss=1.416, discriminator_fake_loss=1.244, generator_loss=28.63, generator_mel_loss=17.68, generator_kl_loss=1.197, generator_dur_loss=1.8, generator_adv_loss=1.853, generator_feat_match_loss=6.09, over 72.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.358, discriminator_fake_loss=1.321, generator_loss=29.23, generator_mel_loss=17.72, generator_kl_loss=1.456, generator_dur_loss=1.727, generator_adv_loss=2.009, generator_feat_match_loss=6.317, over 3680.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:10:21,055 INFO [train.py:527] (4/6) Epoch 942, batch 116, global_batch_idx: 116800, batch size: 47, loss[discriminator_loss=2.679, discriminator_real_loss=1.403, discriminator_fake_loss=1.276, generator_loss=28.91, generator_mel_loss=18.02, generator_kl_loss=1.433, generator_dur_loss=1.714, generator_adv_loss=1.955, generator_feat_match_loss=5.786, over 47.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.359, discriminator_fake_loss=1.322, generator_loss=29.2, generator_mel_loss=17.74, generator_kl_loss=1.454, generator_dur_loss=1.727, generator_adv_loss=2.008, generator_feat_match_loss=6.269, over 6414.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:10:21,056 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 01:10:29,980 INFO [train.py:591] (4/6) Epoch 942, validation: discriminator_loss=2.664, discriminator_real_loss=1.33, discriminator_fake_loss=1.334, generator_loss=27.31, generator_mel_loss=17.59, generator_kl_loss=1.23, generator_dur_loss=1.826, generator_adv_loss=1.896, generator_feat_match_loss=4.766, over 100.00 samples. +2024-03-16 01:10:29,982 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 01:10:51,005 INFO [train.py:919] (4/6) Start epoch 943 +2024-03-16 01:13:12,569 INFO [train.py:527] (4/6) Epoch 943, batch 42, global_batch_idx: 116850, batch size: 80, loss[discriminator_loss=2.665, discriminator_real_loss=1.313, discriminator_fake_loss=1.352, generator_loss=28.93, generator_mel_loss=17.66, generator_kl_loss=1.326, generator_dur_loss=1.804, generator_adv_loss=2.01, generator_feat_match_loss=6.132, over 80.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=29.11, generator_mel_loss=17.76, generator_kl_loss=1.41, generator_dur_loss=1.745, generator_adv_loss=2.009, generator_feat_match_loss=6.184, over 2516.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:15:31,008 INFO [train.py:527] (4/6) Epoch 943, batch 92, global_batch_idx: 116900, batch size: 59, loss[discriminator_loss=2.693, discriminator_real_loss=1.409, discriminator_fake_loss=1.283, generator_loss=29.32, generator_mel_loss=17.58, generator_kl_loss=1.487, generator_dur_loss=1.729, generator_adv_loss=2.045, generator_feat_match_loss=6.478, over 59.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.18, generator_mel_loss=17.76, generator_kl_loss=1.405, generator_dur_loss=1.748, generator_adv_loss=2.021, generator_feat_match_loss=6.243, over 5561.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:16:57,364 INFO [train.py:919] (4/6) Start epoch 944 +2024-03-16 01:18:10,330 INFO [train.py:527] (4/6) Epoch 944, batch 18, global_batch_idx: 116950, batch size: 74, loss[discriminator_loss=2.656, discriminator_real_loss=1.25, discriminator_fake_loss=1.406, generator_loss=29.23, generator_mel_loss=17.73, generator_kl_loss=1.293, generator_dur_loss=1.815, generator_adv_loss=1.918, generator_feat_match_loss=6.478, over 74.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.346, discriminator_fake_loss=1.313, generator_loss=29.03, generator_mel_loss=17.7, generator_kl_loss=1.422, generator_dur_loss=1.743, generator_adv_loss=2.004, generator_feat_match_loss=6.164, over 1116.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:20:29,912 INFO [train.py:527] (4/6) Epoch 944, batch 68, global_batch_idx: 117000, batch size: 53, loss[discriminator_loss=2.69, discriminator_real_loss=1.314, discriminator_fake_loss=1.376, generator_loss=29.07, generator_mel_loss=17.72, generator_kl_loss=1.496, generator_dur_loss=1.685, generator_adv_loss=1.989, generator_feat_match_loss=6.183, over 53.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.351, discriminator_fake_loss=1.319, generator_loss=29.18, generator_mel_loss=17.72, generator_kl_loss=1.448, generator_dur_loss=1.738, generator_adv_loss=2.017, generator_feat_match_loss=6.25, over 3856.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:20:29,914 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 01:20:38,016 INFO [train.py:591] (4/6) Epoch 944, validation: discriminator_loss=2.674, discriminator_real_loss=1.303, discriminator_fake_loss=1.371, generator_loss=28.81, generator_mel_loss=18.27, generator_kl_loss=1.272, generator_dur_loss=1.814, generator_adv_loss=1.948, generator_feat_match_loss=5.504, over 100.00 samples. +2024-03-16 01:20:38,018 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 01:22:56,780 INFO [train.py:527] (4/6) Epoch 944, batch 118, global_batch_idx: 117050, batch size: 44, loss[discriminator_loss=2.698, discriminator_real_loss=1.379, discriminator_fake_loss=1.319, generator_loss=29.8, generator_mel_loss=17.76, generator_kl_loss=1.482, generator_dur_loss=1.659, generator_adv_loss=1.977, generator_feat_match_loss=6.927, over 44.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.352, discriminator_fake_loss=1.318, generator_loss=29.15, generator_mel_loss=17.73, generator_kl_loss=1.435, generator_dur_loss=1.743, generator_adv_loss=2.016, generator_feat_match_loss=6.221, over 6753.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:23:11,043 INFO [train.py:919] (4/6) Start epoch 945 +2024-03-16 01:25:37,157 INFO [train.py:527] (4/6) Epoch 945, batch 44, global_batch_idx: 117100, batch size: 72, loss[discriminator_loss=2.672, discriminator_real_loss=1.427, discriminator_fake_loss=1.245, generator_loss=29.15, generator_mel_loss=17.76, generator_kl_loss=1.448, generator_dur_loss=1.807, generator_adv_loss=2.111, generator_feat_match_loss=6.023, over 72.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.359, discriminator_fake_loss=1.32, generator_loss=29.37, generator_mel_loss=17.85, generator_kl_loss=1.451, generator_dur_loss=1.738, generator_adv_loss=2.008, generator_feat_match_loss=6.321, over 2480.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:27:55,848 INFO [train.py:527] (4/6) Epoch 945, batch 94, global_batch_idx: 117150, batch size: 39, loss[discriminator_loss=2.621, discriminator_real_loss=1.19, discriminator_fake_loss=1.43, generator_loss=29.78, generator_mel_loss=17.55, generator_kl_loss=1.532, generator_dur_loss=1.67, generator_adv_loss=2.195, generator_feat_match_loss=6.827, over 39.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=29.26, generator_mel_loss=17.77, generator_kl_loss=1.43, generator_dur_loss=1.745, generator_adv_loss=2.016, generator_feat_match_loss=6.293, over 5501.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:29:15,483 INFO [train.py:919] (4/6) Start epoch 946 +2024-03-16 01:30:35,123 INFO [train.py:527] (4/6) Epoch 946, batch 20, global_batch_idx: 117200, batch size: 62, loss[discriminator_loss=2.648, discriminator_real_loss=1.336, discriminator_fake_loss=1.312, generator_loss=28.72, generator_mel_loss=17.48, generator_kl_loss=1.513, generator_dur_loss=1.709, generator_adv_loss=2.064, generator_feat_match_loss=5.959, over 62.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.358, discriminator_fake_loss=1.304, generator_loss=29, generator_mel_loss=17.67, generator_kl_loss=1.478, generator_dur_loss=1.707, generator_adv_loss=2.023, generator_feat_match_loss=6.122, over 1162.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:30:35,124 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 01:30:43,284 INFO [train.py:591] (4/6) Epoch 946, validation: discriminator_loss=2.702, discriminator_real_loss=1.422, discriminator_fake_loss=1.279, generator_loss=27.95, generator_mel_loss=17.8, generator_kl_loss=1.297, generator_dur_loss=1.79, generator_adv_loss=2.012, generator_feat_match_loss=5.047, over 100.00 samples. +2024-03-16 01:30:43,296 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 01:33:02,952 INFO [train.py:527] (4/6) Epoch 946, batch 70, global_batch_idx: 117250, batch size: 68, loss[discriminator_loss=2.633, discriminator_real_loss=1.33, discriminator_fake_loss=1.304, generator_loss=29.46, generator_mel_loss=18.12, generator_kl_loss=1.256, generator_dur_loss=1.764, generator_adv_loss=1.956, generator_feat_match_loss=6.361, over 68.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.357, discriminator_fake_loss=1.315, generator_loss=29.13, generator_mel_loss=17.72, generator_kl_loss=1.45, generator_dur_loss=1.735, generator_adv_loss=2.008, generator_feat_match_loss=6.221, over 4133.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:35:20,510 INFO [train.py:527] (4/6) Epoch 946, batch 120, global_batch_idx: 117300, batch size: 25, loss[discriminator_loss=2.675, discriminator_real_loss=1.368, discriminator_fake_loss=1.308, generator_loss=29.61, generator_mel_loss=17.8, generator_kl_loss=1.736, generator_dur_loss=1.604, generator_adv_loss=2.095, generator_feat_match_loss=6.369, over 25.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.355, discriminator_fake_loss=1.316, generator_loss=29.13, generator_mel_loss=17.71, generator_kl_loss=1.455, generator_dur_loss=1.736, generator_adv_loss=2.013, generator_feat_match_loss=6.216, over 6887.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:35:30,464 INFO [train.py:919] (4/6) Start epoch 947 +2024-03-16 01:38:02,487 INFO [train.py:527] (4/6) Epoch 947, batch 46, global_batch_idx: 117350, batch size: 68, loss[discriminator_loss=2.638, discriminator_real_loss=1.318, discriminator_fake_loss=1.32, generator_loss=28.58, generator_mel_loss=17.55, generator_kl_loss=1.313, generator_dur_loss=1.813, generator_adv_loss=1.984, generator_feat_match_loss=5.916, over 68.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.347, discriminator_fake_loss=1.328, generator_loss=29.21, generator_mel_loss=17.71, generator_kl_loss=1.402, generator_dur_loss=1.762, generator_adv_loss=2.013, generator_feat_match_loss=6.319, over 2820.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:40:21,635 INFO [train.py:527] (4/6) Epoch 947, batch 96, global_batch_idx: 117400, batch size: 68, loss[discriminator_loss=2.647, discriminator_real_loss=1.312, discriminator_fake_loss=1.335, generator_loss=29.75, generator_mel_loss=17.86, generator_kl_loss=1.335, generator_dur_loss=1.784, generator_adv_loss=1.943, generator_feat_match_loss=6.821, over 68.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.347, discriminator_fake_loss=1.323, generator_loss=29.21, generator_mel_loss=17.76, generator_kl_loss=1.418, generator_dur_loss=1.75, generator_adv_loss=2.012, generator_feat_match_loss=6.268, over 5494.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:40:21,637 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 01:40:30,417 INFO [train.py:591] (4/6) Epoch 947, validation: discriminator_loss=2.748, discriminator_real_loss=1.341, discriminator_fake_loss=1.407, generator_loss=28.92, generator_mel_loss=18.29, generator_kl_loss=1.363, generator_dur_loss=1.822, generator_adv_loss=1.87, generator_feat_match_loss=5.571, over 100.00 samples. +2024-03-16 01:40:30,418 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 01:41:46,483 INFO [train.py:919] (4/6) Start epoch 948 +2024-03-16 01:43:10,258 INFO [train.py:527] (4/6) Epoch 948, batch 22, global_batch_idx: 117450, batch size: 56, loss[discriminator_loss=2.698, discriminator_real_loss=1.354, discriminator_fake_loss=1.344, generator_loss=29.47, generator_mel_loss=17.81, generator_kl_loss=1.493, generator_dur_loss=1.69, generator_adv_loss=2.007, generator_feat_match_loss=6.466, over 56.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.341, discriminator_fake_loss=1.324, generator_loss=29.36, generator_mel_loss=17.86, generator_kl_loss=1.461, generator_dur_loss=1.743, generator_adv_loss=2.004, generator_feat_match_loss=6.288, over 1250.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:45:27,493 INFO [train.py:527] (4/6) Epoch 948, batch 72, global_batch_idx: 117500, batch size: 66, loss[discriminator_loss=2.701, discriminator_real_loss=1.415, discriminator_fake_loss=1.287, generator_loss=28.2, generator_mel_loss=17.52, generator_kl_loss=1.45, generator_dur_loss=1.754, generator_adv_loss=1.903, generator_feat_match_loss=5.578, over 66.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.351, discriminator_fake_loss=1.317, generator_loss=29.23, generator_mel_loss=17.78, generator_kl_loss=1.465, generator_dur_loss=1.737, generator_adv_loss=2.014, generator_feat_match_loss=6.235, over 3906.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:47:44,934 INFO [train.py:527] (4/6) Epoch 948, batch 122, global_batch_idx: 117550, batch size: 96, loss[discriminator_loss=2.641, discriminator_real_loss=1.395, discriminator_fake_loss=1.246, generator_loss=28.8, generator_mel_loss=17.53, generator_kl_loss=1.302, generator_dur_loss=1.858, generator_adv_loss=2.003, generator_feat_match_loss=6.107, over 96.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.35, discriminator_fake_loss=1.314, generator_loss=29.19, generator_mel_loss=17.75, generator_kl_loss=1.441, generator_dur_loss=1.742, generator_adv_loss=2.015, generator_feat_match_loss=6.24, over 6973.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:47:50,800 INFO [train.py:919] (4/6) Start epoch 949 +2024-03-16 01:50:29,824 INFO [train.py:527] (4/6) Epoch 949, batch 48, global_batch_idx: 117600, batch size: 64, loss[discriminator_loss=2.668, discriminator_real_loss=1.408, discriminator_fake_loss=1.26, generator_loss=29.99, generator_mel_loss=18.18, generator_kl_loss=1.461, generator_dur_loss=1.726, generator_adv_loss=2.037, generator_feat_match_loss=6.577, over 64.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=29.07, generator_mel_loss=17.7, generator_kl_loss=1.433, generator_dur_loss=1.744, generator_adv_loss=2.005, generator_feat_match_loss=6.184, over 2943.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:50:29,827 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 01:50:37,970 INFO [train.py:591] (4/6) Epoch 949, validation: discriminator_loss=2.697, discriminator_real_loss=1.391, discriminator_fake_loss=1.306, generator_loss=28.1, generator_mel_loss=17.84, generator_kl_loss=1.36, generator_dur_loss=1.799, generator_adv_loss=1.956, generator_feat_match_loss=5.15, over 100.00 samples. +2024-03-16 01:50:37,972 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 01:52:58,064 INFO [train.py:527] (4/6) Epoch 949, batch 98, global_batch_idx: 117650, batch size: 53, loss[discriminator_loss=2.675, discriminator_real_loss=1.364, discriminator_fake_loss=1.312, generator_loss=28.9, generator_mel_loss=18.01, generator_kl_loss=1.452, generator_dur_loss=1.689, generator_adv_loss=2.072, generator_feat_match_loss=5.674, over 53.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=29.07, generator_mel_loss=17.71, generator_kl_loss=1.444, generator_dur_loss=1.739, generator_adv_loss=1.999, generator_feat_match_loss=6.169, over 5824.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:54:07,039 INFO [train.py:919] (4/6) Start epoch 950 +2024-03-16 01:55:37,236 INFO [train.py:527] (4/6) Epoch 950, batch 24, global_batch_idx: 117700, batch size: 62, loss[discriminator_loss=2.633, discriminator_real_loss=1.334, discriminator_fake_loss=1.299, generator_loss=31.24, generator_mel_loss=18.35, generator_kl_loss=1.481, generator_dur_loss=1.747, generator_adv_loss=2.1, generator_feat_match_loss=7.56, over 62.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.355, discriminator_fake_loss=1.316, generator_loss=29.45, generator_mel_loss=17.92, generator_kl_loss=1.451, generator_dur_loss=1.726, generator_adv_loss=2.007, generator_feat_match_loss=6.346, over 1345.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:58:01,105 INFO [train.py:527] (4/6) Epoch 950, batch 74, global_batch_idx: 117750, batch size: 96, loss[discriminator_loss=2.694, discriminator_real_loss=1.322, discriminator_fake_loss=1.371, generator_loss=27.27, generator_mel_loss=17.04, generator_kl_loss=1.199, generator_dur_loss=1.814, generator_adv_loss=1.996, generator_feat_match_loss=5.221, over 96.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.318, generator_loss=29.19, generator_mel_loss=17.78, generator_kl_loss=1.429, generator_dur_loss=1.745, generator_adv_loss=2.011, generator_feat_match_loss=6.216, over 4270.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:00:19,453 INFO [train.py:919] (4/6) Start epoch 951 +2024-03-16 02:00:44,349 INFO [train.py:527] (4/6) Epoch 951, batch 0, global_batch_idx: 117800, batch size: 72, loss[discriminator_loss=2.671, discriminator_real_loss=1.341, discriminator_fake_loss=1.33, generator_loss=28.92, generator_mel_loss=17.4, generator_kl_loss=1.339, generator_dur_loss=1.827, generator_adv_loss=2.108, generator_feat_match_loss=6.241, over 72.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.341, discriminator_fake_loss=1.33, generator_loss=28.92, generator_mel_loss=17.4, generator_kl_loss=1.339, generator_dur_loss=1.827, generator_adv_loss=2.108, generator_feat_match_loss=6.241, over 72.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:00:44,351 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 02:00:52,409 INFO [train.py:591] (4/6) Epoch 951, validation: discriminator_loss=2.731, discriminator_real_loss=1.498, discriminator_fake_loss=1.232, generator_loss=28.27, generator_mel_loss=18.11, generator_kl_loss=1.324, generator_dur_loss=1.81, generator_adv_loss=2.02, generator_feat_match_loss=5.008, over 100.00 samples. +2024-03-16 02:00:52,411 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 02:03:13,099 INFO [train.py:527] (4/6) Epoch 951, batch 50, global_batch_idx: 117850, batch size: 53, loss[discriminator_loss=2.756, discriminator_real_loss=1.42, discriminator_fake_loss=1.336, generator_loss=28.17, generator_mel_loss=17.32, generator_kl_loss=1.506, generator_dur_loss=1.698, generator_adv_loss=2.087, generator_feat_match_loss=5.554, over 53.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.354, discriminator_fake_loss=1.33, generator_loss=29.36, generator_mel_loss=17.83, generator_kl_loss=1.449, generator_dur_loss=1.748, generator_adv_loss=2.019, generator_feat_match_loss=6.318, over 2972.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:05:35,480 INFO [train.py:527] (4/6) Epoch 951, batch 100, global_batch_idx: 117900, batch size: 70, loss[discriminator_loss=2.715, discriminator_real_loss=1.456, discriminator_fake_loss=1.259, generator_loss=28.98, generator_mel_loss=17.89, generator_kl_loss=1.306, generator_dur_loss=1.8, generator_adv_loss=2.002, generator_feat_match_loss=5.984, over 70.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.353, discriminator_fake_loss=1.327, generator_loss=29.23, generator_mel_loss=17.8, generator_kl_loss=1.442, generator_dur_loss=1.742, generator_adv_loss=2.015, generator_feat_match_loss=6.236, over 5733.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:06:40,943 INFO [train.py:919] (4/6) Start epoch 952 +2024-03-16 02:08:20,370 INFO [train.py:527] (4/6) Epoch 952, batch 26, global_batch_idx: 117950, batch size: 31, loss[discriminator_loss=2.675, discriminator_real_loss=1.225, discriminator_fake_loss=1.451, generator_loss=29.74, generator_mel_loss=18.06, generator_kl_loss=1.659, generator_dur_loss=1.604, generator_adv_loss=2.19, generator_feat_match_loss=6.227, over 31.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.343, discriminator_fake_loss=1.322, generator_loss=29.2, generator_mel_loss=17.75, generator_kl_loss=1.424, generator_dur_loss=1.731, generator_adv_loss=2.006, generator_feat_match_loss=6.285, over 1642.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:10:40,418 INFO [train.py:527] (4/6) Epoch 952, batch 76, global_batch_idx: 118000, batch size: 36, loss[discriminator_loss=2.664, discriminator_real_loss=1.336, discriminator_fake_loss=1.328, generator_loss=28.97, generator_mel_loss=18.21, generator_kl_loss=1.337, generator_dur_loss=1.657, generator_adv_loss=1.996, generator_feat_match_loss=5.768, over 36.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.354, discriminator_fake_loss=1.317, generator_loss=29.22, generator_mel_loss=17.76, generator_kl_loss=1.417, generator_dur_loss=1.73, generator_adv_loss=2.013, generator_feat_match_loss=6.299, over 4410.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:10:40,421 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 02:10:49,139 INFO [train.py:591] (4/6) Epoch 952, validation: discriminator_loss=2.691, discriminator_real_loss=1.407, discriminator_fake_loss=1.284, generator_loss=28.35, generator_mel_loss=17.98, generator_kl_loss=1.225, generator_dur_loss=1.797, generator_adv_loss=1.939, generator_feat_match_loss=5.41, over 100.00 samples. +2024-03-16 02:10:49,140 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 02:13:03,319 INFO [train.py:919] (4/6) Start epoch 953 +2024-03-16 02:13:33,503 INFO [train.py:527] (4/6) Epoch 953, batch 2, global_batch_idx: 118050, batch size: 95, loss[discriminator_loss=2.675, discriminator_real_loss=1.33, discriminator_fake_loss=1.345, generator_loss=29.46, generator_mel_loss=17.76, generator_kl_loss=1.315, generator_dur_loss=1.822, generator_adv_loss=2.061, generator_feat_match_loss=6.503, over 95.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.356, discriminator_fake_loss=1.336, generator_loss=29.44, generator_mel_loss=17.86, generator_kl_loss=1.402, generator_dur_loss=1.773, generator_adv_loss=2.023, generator_feat_match_loss=6.384, over 201.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:15:54,650 INFO [train.py:527] (4/6) Epoch 953, batch 52, global_batch_idx: 118100, batch size: 53, loss[discriminator_loss=2.629, discriminator_real_loss=1.27, discriminator_fake_loss=1.359, generator_loss=29.2, generator_mel_loss=17.38, generator_kl_loss=1.376, generator_dur_loss=1.677, generator_adv_loss=2.071, generator_feat_match_loss=6.695, over 53.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.352, discriminator_fake_loss=1.316, generator_loss=29.39, generator_mel_loss=17.82, generator_kl_loss=1.469, generator_dur_loss=1.709, generator_adv_loss=2.025, generator_feat_match_loss=6.374, over 2626.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:18:17,821 INFO [train.py:527] (4/6) Epoch 953, batch 102, global_batch_idx: 118150, batch size: 66, loss[discriminator_loss=2.707, discriminator_real_loss=1.355, discriminator_fake_loss=1.352, generator_loss=28.23, generator_mel_loss=17.52, generator_kl_loss=1.411, generator_dur_loss=1.735, generator_adv_loss=2.057, generator_feat_match_loss=5.513, over 66.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.353, discriminator_fake_loss=1.314, generator_loss=29.31, generator_mel_loss=17.79, generator_kl_loss=1.452, generator_dur_loss=1.723, generator_adv_loss=2.023, generator_feat_match_loss=6.322, over 5480.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:19:16,693 INFO [train.py:919] (4/6) Start epoch 954 +2024-03-16 02:21:03,288 INFO [train.py:527] (4/6) Epoch 954, batch 28, global_batch_idx: 118200, batch size: 68, loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.343, generator_loss=29.66, generator_mel_loss=17.73, generator_kl_loss=1.476, generator_dur_loss=1.78, generator_adv_loss=2.065, generator_feat_match_loss=6.607, over 68.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.332, discriminator_fake_loss=1.34, generator_loss=29.24, generator_mel_loss=17.71, generator_kl_loss=1.428, generator_dur_loss=1.762, generator_adv_loss=2.004, generator_feat_match_loss=6.333, over 1685.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:21:03,290 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 02:21:11,359 INFO [train.py:591] (4/6) Epoch 954, validation: discriminator_loss=2.663, discriminator_real_loss=1.387, discriminator_fake_loss=1.276, generator_loss=27.74, generator_mel_loss=17.97, generator_kl_loss=1.348, generator_dur_loss=1.821, generator_adv_loss=1.95, generator_feat_match_loss=4.657, over 100.00 samples. +2024-03-16 02:21:11,360 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 02:23:33,120 INFO [train.py:527] (4/6) Epoch 954, batch 78, global_batch_idx: 118250, batch size: 52, loss[discriminator_loss=2.755, discriminator_real_loss=1.269, discriminator_fake_loss=1.486, generator_loss=29.07, generator_mel_loss=17.96, generator_kl_loss=1.444, generator_dur_loss=1.715, generator_adv_loss=2.238, generator_feat_match_loss=5.706, over 52.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.339, discriminator_fake_loss=1.329, generator_loss=29.27, generator_mel_loss=17.75, generator_kl_loss=1.435, generator_dur_loss=1.76, generator_adv_loss=2.01, generator_feat_match_loss=6.312, over 4712.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:25:41,550 INFO [train.py:919] (4/6) Start epoch 955 +2024-03-16 02:26:18,706 INFO [train.py:527] (4/6) Epoch 955, batch 4, global_batch_idx: 118300, batch size: 77, loss[discriminator_loss=2.711, discriminator_real_loss=1.33, discriminator_fake_loss=1.382, generator_loss=28.94, generator_mel_loss=17.69, generator_kl_loss=1.467, generator_dur_loss=1.793, generator_adv_loss=2.027, generator_feat_match_loss=5.956, over 77.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.331, discriminator_fake_loss=1.346, generator_loss=29.43, generator_mel_loss=17.64, generator_kl_loss=1.443, generator_dur_loss=1.794, generator_adv_loss=1.981, generator_feat_match_loss=6.569, over 323.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:28:40,861 INFO [train.py:527] (4/6) Epoch 955, batch 54, global_batch_idx: 118350, batch size: 36, loss[discriminator_loss=2.592, discriminator_real_loss=1.287, discriminator_fake_loss=1.305, generator_loss=30.46, generator_mel_loss=18.03, generator_kl_loss=1.523, generator_dur_loss=1.664, generator_adv_loss=2.15, generator_feat_match_loss=7.098, over 36.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.347, discriminator_fake_loss=1.325, generator_loss=29.28, generator_mel_loss=17.72, generator_kl_loss=1.417, generator_dur_loss=1.76, generator_adv_loss=2.022, generator_feat_match_loss=6.361, over 3350.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:31:04,429 INFO [train.py:527] (4/6) Epoch 955, batch 104, global_batch_idx: 118400, batch size: 12, loss[discriminator_loss=2.585, discriminator_real_loss=1.378, discriminator_fake_loss=1.207, generator_loss=30.65, generator_mel_loss=18.32, generator_kl_loss=1.901, generator_dur_loss=1.585, generator_adv_loss=2.162, generator_feat_match_loss=6.686, over 12.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.349, discriminator_fake_loss=1.329, generator_loss=29.15, generator_mel_loss=17.69, generator_kl_loss=1.428, generator_dur_loss=1.762, generator_adv_loss=2.004, generator_feat_match_loss=6.266, over 6411.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:31:04,431 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 02:31:13,205 INFO [train.py:591] (4/6) Epoch 955, validation: discriminator_loss=2.737, discriminator_real_loss=1.437, discriminator_fake_loss=1.3, generator_loss=28.33, generator_mel_loss=17.99, generator_kl_loss=1.248, generator_dur_loss=1.814, generator_adv_loss=2.029, generator_feat_match_loss=5.248, over 100.00 samples. +2024-03-16 02:31:13,206 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 02:32:05,809 INFO [train.py:919] (4/6) Start epoch 956 +2024-03-16 02:33:55,003 INFO [train.py:527] (4/6) Epoch 956, batch 30, global_batch_idx: 118450, batch size: 52, loss[discriminator_loss=2.633, discriminator_real_loss=1.342, discriminator_fake_loss=1.291, generator_loss=29.55, generator_mel_loss=17.87, generator_kl_loss=1.508, generator_dur_loss=1.646, generator_adv_loss=2.032, generator_feat_match_loss=6.495, over 52.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.356, discriminator_fake_loss=1.311, generator_loss=29.25, generator_mel_loss=17.72, generator_kl_loss=1.499, generator_dur_loss=1.719, generator_adv_loss=2.036, generator_feat_match_loss=6.278, over 1548.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:36:16,863 INFO [train.py:527] (4/6) Epoch 956, batch 80, global_batch_idx: 118500, batch size: 39, loss[discriminator_loss=2.674, discriminator_real_loss=1.421, discriminator_fake_loss=1.253, generator_loss=28.74, generator_mel_loss=17.82, generator_kl_loss=1.607, generator_dur_loss=1.646, generator_adv_loss=2.024, generator_feat_match_loss=5.638, over 39.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.354, discriminator_fake_loss=1.318, generator_loss=29.26, generator_mel_loss=17.72, generator_kl_loss=1.473, generator_dur_loss=1.73, generator_adv_loss=2.028, generator_feat_match_loss=6.309, over 4496.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:38:19,759 INFO [train.py:919] (4/6) Start epoch 957 +2024-03-16 02:39:00,609 INFO [train.py:527] (4/6) Epoch 957, batch 6, global_batch_idx: 118550, batch size: 88, loss[discriminator_loss=2.662, discriminator_real_loss=1.317, discriminator_fake_loss=1.345, generator_loss=28.77, generator_mel_loss=17.39, generator_kl_loss=1.361, generator_dur_loss=1.764, generator_adv_loss=2.079, generator_feat_match_loss=6.17, over 88.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.366, discriminator_fake_loss=1.313, generator_loss=29.29, generator_mel_loss=17.76, generator_kl_loss=1.403, generator_dur_loss=1.753, generator_adv_loss=2.039, generator_feat_match_loss=6.331, over 449.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:41:20,876 INFO [train.py:527] (4/6) Epoch 957, batch 56, global_batch_idx: 118600, batch size: 31, loss[discriminator_loss=2.618, discriminator_real_loss=1.354, discriminator_fake_loss=1.264, generator_loss=28.59, generator_mel_loss=17.63, generator_kl_loss=1.486, generator_dur_loss=1.62, generator_adv_loss=2.034, generator_feat_match_loss=5.821, over 31.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.348, discriminator_fake_loss=1.326, generator_loss=29.28, generator_mel_loss=17.82, generator_kl_loss=1.44, generator_dur_loss=1.735, generator_adv_loss=2.013, generator_feat_match_loss=6.277, over 3201.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:41:20,878 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 02:41:28,896 INFO [train.py:591] (4/6) Epoch 957, validation: discriminator_loss=2.701, discriminator_real_loss=1.379, discriminator_fake_loss=1.321, generator_loss=28.3, generator_mel_loss=18.2, generator_kl_loss=1.215, generator_dur_loss=1.81, generator_adv_loss=1.93, generator_feat_match_loss=5.143, over 100.00 samples. +2024-03-16 02:41:28,897 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 02:43:50,931 INFO [train.py:527] (4/6) Epoch 957, batch 106, global_batch_idx: 118650, batch size: 48, loss[discriminator_loss=2.677, discriminator_real_loss=1.33, discriminator_fake_loss=1.346, generator_loss=29.49, generator_mel_loss=17.77, generator_kl_loss=1.48, generator_dur_loss=1.7, generator_adv_loss=2.099, generator_feat_match_loss=6.44, over 48.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.26, generator_mel_loss=17.78, generator_kl_loss=1.451, generator_dur_loss=1.731, generator_adv_loss=2.011, generator_feat_match_loss=6.289, over 5991.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:44:40,605 INFO [train.py:919] (4/6) Start epoch 958 +2024-03-16 02:46:36,206 INFO [train.py:527] (4/6) Epoch 958, batch 32, global_batch_idx: 118700, batch size: 64, loss[discriminator_loss=2.684, discriminator_real_loss=1.341, discriminator_fake_loss=1.343, generator_loss=29.38, generator_mel_loss=17.55, generator_kl_loss=1.384, generator_dur_loss=1.809, generator_adv_loss=2.062, generator_feat_match_loss=6.577, over 64.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.349, discriminator_fake_loss=1.314, generator_loss=29.36, generator_mel_loss=17.69, generator_kl_loss=1.411, generator_dur_loss=1.745, generator_adv_loss=2.052, generator_feat_match_loss=6.462, over 1977.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:49:02,496 INFO [train.py:527] (4/6) Epoch 958, batch 82, global_batch_idx: 118750, batch size: 58, loss[discriminator_loss=2.661, discriminator_real_loss=1.35, discriminator_fake_loss=1.311, generator_loss=29.07, generator_mel_loss=17.5, generator_kl_loss=1.569, generator_dur_loss=1.739, generator_adv_loss=2.099, generator_feat_match_loss=6.167, over 58.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.35, discriminator_fake_loss=1.313, generator_loss=29.31, generator_mel_loss=17.69, generator_kl_loss=1.433, generator_dur_loss=1.748, generator_adv_loss=2.03, generator_feat_match_loss=6.407, over 4898.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:50:54,586 INFO [train.py:919] (4/6) Start epoch 959 +2024-03-16 02:51:42,678 INFO [train.py:527] (4/6) Epoch 959, batch 8, global_batch_idx: 118800, batch size: 80, loss[discriminator_loss=2.678, discriminator_real_loss=1.331, discriminator_fake_loss=1.347, generator_loss=29.02, generator_mel_loss=17.81, generator_kl_loss=1.569, generator_dur_loss=1.781, generator_adv_loss=1.932, generator_feat_match_loss=5.929, over 80.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.34, discriminator_fake_loss=1.325, generator_loss=29.36, generator_mel_loss=17.83, generator_kl_loss=1.495, generator_dur_loss=1.718, generator_adv_loss=1.998, generator_feat_match_loss=6.32, over 452.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:51:42,680 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 02:51:50,405 INFO [train.py:591] (4/6) Epoch 959, validation: discriminator_loss=2.75, discriminator_real_loss=1.37, discriminator_fake_loss=1.381, generator_loss=27.2, generator_mel_loss=17.62, generator_kl_loss=1.332, generator_dur_loss=1.811, generator_adv_loss=1.8, generator_feat_match_loss=4.637, over 100.00 samples. +2024-03-16 02:51:50,407 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 02:54:11,336 INFO [train.py:527] (4/6) Epoch 959, batch 58, global_batch_idx: 118850, batch size: 64, loss[discriminator_loss=2.664, discriminator_real_loss=1.417, discriminator_fake_loss=1.248, generator_loss=29.19, generator_mel_loss=17.5, generator_kl_loss=1.517, generator_dur_loss=1.748, generator_adv_loss=1.969, generator_feat_match_loss=6.462, over 64.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.26, generator_mel_loss=17.76, generator_kl_loss=1.46, generator_dur_loss=1.735, generator_adv_loss=2.011, generator_feat_match_loss=6.293, over 3213.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:56:33,593 INFO [train.py:527] (4/6) Epoch 959, batch 108, global_batch_idx: 118900, batch size: 77, loss[discriminator_loss=2.727, discriminator_real_loss=1.404, discriminator_fake_loss=1.323, generator_loss=28.5, generator_mel_loss=17.58, generator_kl_loss=1.417, generator_dur_loss=1.791, generator_adv_loss=1.974, generator_feat_match_loss=5.736, over 77.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.353, discriminator_fake_loss=1.32, generator_loss=29.2, generator_mel_loss=17.72, generator_kl_loss=1.45, generator_dur_loss=1.743, generator_adv_loss=2.014, generator_feat_match_loss=6.278, over 6213.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:57:14,958 INFO [train.py:919] (4/6) Start epoch 960 +2024-03-16 02:59:14,354 INFO [train.py:527] (4/6) Epoch 960, batch 34, global_batch_idx: 118950, batch size: 31, loss[discriminator_loss=2.586, discriminator_real_loss=1.258, discriminator_fake_loss=1.328, generator_loss=30.3, generator_mel_loss=17.7, generator_kl_loss=1.6, generator_dur_loss=1.593, generator_adv_loss=2.064, generator_feat_match_loss=7.344, over 31.00 samples.], tot_loss[discriminator_loss=2.648, discriminator_real_loss=1.34, discriminator_fake_loss=1.308, generator_loss=29.55, generator_mel_loss=17.73, generator_kl_loss=1.402, generator_dur_loss=1.755, generator_adv_loss=2.106, generator_feat_match_loss=6.56, over 2067.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:01:36,170 INFO [train.py:527] (4/6) Epoch 960, batch 84, global_batch_idx: 119000, batch size: 74, loss[discriminator_loss=2.713, discriminator_real_loss=1.365, discriminator_fake_loss=1.348, generator_loss=28.75, generator_mel_loss=17.29, generator_kl_loss=1.318, generator_dur_loss=1.809, generator_adv_loss=2.035, generator_feat_match_loss=6.303, over 74.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.348, discriminator_fake_loss=1.312, generator_loss=29.27, generator_mel_loss=17.68, generator_kl_loss=1.414, generator_dur_loss=1.753, generator_adv_loss=2.053, generator_feat_match_loss=6.375, over 5123.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:01:36,172 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 03:01:43,956 INFO [train.py:591] (4/6) Epoch 960, validation: discriminator_loss=2.745, discriminator_real_loss=1.395, discriminator_fake_loss=1.35, generator_loss=28.7, generator_mel_loss=18.48, generator_kl_loss=1.318, generator_dur_loss=1.817, generator_adv_loss=1.916, generator_feat_match_loss=5.172, over 100.00 samples. +2024-03-16 03:01:43,958 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 03:03:35,992 INFO [train.py:919] (4/6) Start epoch 961 +2024-03-16 03:04:32,454 INFO [train.py:527] (4/6) Epoch 961, batch 10, global_batch_idx: 119050, batch size: 96, loss[discriminator_loss=2.696, discriminator_real_loss=1.373, discriminator_fake_loss=1.322, generator_loss=29.43, generator_mel_loss=17.47, generator_kl_loss=1.36, generator_dur_loss=1.827, generator_adv_loss=2.049, generator_feat_match_loss=6.723, over 96.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.349, discriminator_fake_loss=1.333, generator_loss=29.14, generator_mel_loss=17.7, generator_kl_loss=1.412, generator_dur_loss=1.747, generator_adv_loss=2.008, generator_feat_match_loss=6.268, over 670.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:06:51,868 INFO [train.py:527] (4/6) Epoch 961, batch 60, global_batch_idx: 119100, batch size: 55, loss[discriminator_loss=2.615, discriminator_real_loss=1.285, discriminator_fake_loss=1.33, generator_loss=29.29, generator_mel_loss=17.7, generator_kl_loss=1.396, generator_dur_loss=1.686, generator_adv_loss=2.215, generator_feat_match_loss=6.289, over 55.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=29.05, generator_mel_loss=17.7, generator_kl_loss=1.443, generator_dur_loss=1.733, generator_adv_loss=2.015, generator_feat_match_loss=6.16, over 3430.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:09:12,549 INFO [train.py:527] (4/6) Epoch 961, batch 110, global_batch_idx: 119150, batch size: 66, loss[discriminator_loss=2.622, discriminator_real_loss=1.296, discriminator_fake_loss=1.325, generator_loss=30.7, generator_mel_loss=17.93, generator_kl_loss=1.59, generator_dur_loss=1.746, generator_adv_loss=2.056, generator_feat_match_loss=7.373, over 66.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.13, generator_mel_loss=17.7, generator_kl_loss=1.454, generator_dur_loss=1.736, generator_adv_loss=2.016, generator_feat_match_loss=6.218, over 6293.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:09:51,335 INFO [train.py:919] (4/6) Start epoch 962 +2024-03-16 03:11:56,457 INFO [train.py:527] (4/6) Epoch 962, batch 36, global_batch_idx: 119200, batch size: 74, loss[discriminator_loss=2.712, discriminator_real_loss=1.339, discriminator_fake_loss=1.373, generator_loss=29.11, generator_mel_loss=17.34, generator_kl_loss=1.428, generator_dur_loss=1.751, generator_adv_loss=2.011, generator_feat_match_loss=6.583, over 74.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=29.28, generator_mel_loss=17.8, generator_kl_loss=1.48, generator_dur_loss=1.718, generator_adv_loss=2.008, generator_feat_match_loss=6.275, over 1955.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:11:56,458 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 03:12:04,408 INFO [train.py:591] (4/6) Epoch 962, validation: discriminator_loss=2.732, discriminator_real_loss=1.453, discriminator_fake_loss=1.279, generator_loss=27.79, generator_mel_loss=17.87, generator_kl_loss=1.245, generator_dur_loss=1.798, generator_adv_loss=1.95, generator_feat_match_loss=4.929, over 100.00 samples. +2024-03-16 03:12:04,409 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 03:14:22,732 INFO [train.py:527] (4/6) Epoch 962, batch 86, global_batch_idx: 119250, batch size: 88, loss[discriminator_loss=2.699, discriminator_real_loss=1.263, discriminator_fake_loss=1.437, generator_loss=28.65, generator_mel_loss=17.35, generator_kl_loss=1.282, generator_dur_loss=1.854, generator_adv_loss=1.978, generator_feat_match_loss=6.187, over 88.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.348, discriminator_fake_loss=1.322, generator_loss=29.18, generator_mel_loss=17.71, generator_kl_loss=1.453, generator_dur_loss=1.726, generator_adv_loss=2.012, generator_feat_match_loss=6.28, over 4946.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:16:09,790 INFO [train.py:919] (4/6) Start epoch 963 +2024-03-16 03:17:08,188 INFO [train.py:527] (4/6) Epoch 963, batch 12, global_batch_idx: 119300, batch size: 72, loss[discriminator_loss=2.664, discriminator_real_loss=1.318, discriminator_fake_loss=1.346, generator_loss=29.08, generator_mel_loss=17.46, generator_kl_loss=1.409, generator_dur_loss=1.768, generator_adv_loss=2.016, generator_feat_match_loss=6.434, over 72.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.352, discriminator_fake_loss=1.31, generator_loss=29.44, generator_mel_loss=17.77, generator_kl_loss=1.48, generator_dur_loss=1.703, generator_adv_loss=2.025, generator_feat_match_loss=6.469, over 710.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:19:29,486 INFO [train.py:527] (4/6) Epoch 963, batch 62, global_batch_idx: 119350, batch size: 39, loss[discriminator_loss=2.59, discriminator_real_loss=1.324, discriminator_fake_loss=1.266, generator_loss=28.78, generator_mel_loss=17.53, generator_kl_loss=1.41, generator_dur_loss=1.691, generator_adv_loss=2.177, generator_feat_match_loss=5.976, over 39.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.346, discriminator_fake_loss=1.325, generator_loss=29.23, generator_mel_loss=17.76, generator_kl_loss=1.447, generator_dur_loss=1.728, generator_adv_loss=2.009, generator_feat_match_loss=6.281, over 3642.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:21:50,014 INFO [train.py:527] (4/6) Epoch 963, batch 112, global_batch_idx: 119400, batch size: 64, loss[discriminator_loss=2.589, discriminator_real_loss=1.335, discriminator_fake_loss=1.254, generator_loss=30.51, generator_mel_loss=18.28, generator_kl_loss=1.347, generator_dur_loss=1.771, generator_adv_loss=2.044, generator_feat_match_loss=7.07, over 64.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.351, discriminator_fake_loss=1.32, generator_loss=29.25, generator_mel_loss=17.77, generator_kl_loss=1.435, generator_dur_loss=1.73, generator_adv_loss=2.017, generator_feat_match_loss=6.299, over 6477.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:21:50,015 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 03:21:59,012 INFO [train.py:591] (4/6) Epoch 963, validation: discriminator_loss=2.679, discriminator_real_loss=1.417, discriminator_fake_loss=1.263, generator_loss=27.53, generator_mel_loss=17.95, generator_kl_loss=1.161, generator_dur_loss=1.807, generator_adv_loss=1.98, generator_feat_match_loss=4.633, over 100.00 samples. +2024-03-16 03:21:59,012 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 03:22:30,345 INFO [train.py:919] (4/6) Start epoch 964 +2024-03-16 03:24:45,616 INFO [train.py:527] (4/6) Epoch 964, batch 38, global_batch_idx: 119450, batch size: 83, loss[discriminator_loss=2.7, discriminator_real_loss=1.405, discriminator_fake_loss=1.294, generator_loss=28.26, generator_mel_loss=17.33, generator_kl_loss=1.421, generator_dur_loss=1.826, generator_adv_loss=2.054, generator_feat_match_loss=5.63, over 83.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.352, discriminator_fake_loss=1.316, generator_loss=29.07, generator_mel_loss=17.67, generator_kl_loss=1.415, generator_dur_loss=1.74, generator_adv_loss=2.017, generator_feat_match_loss=6.231, over 2261.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:27:09,240 INFO [train.py:527] (4/6) Epoch 964, batch 88, global_batch_idx: 119500, batch size: 36, loss[discriminator_loss=2.748, discriminator_real_loss=1.373, discriminator_fake_loss=1.375, generator_loss=28.29, generator_mel_loss=17.52, generator_kl_loss=1.477, generator_dur_loss=1.62, generator_adv_loss=1.977, generator_feat_match_loss=5.7, over 36.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.21, generator_mel_loss=17.71, generator_kl_loss=1.427, generator_dur_loss=1.743, generator_adv_loss=2.034, generator_feat_match_loss=6.303, over 5163.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:28:46,881 INFO [train.py:919] (4/6) Start epoch 965 +2024-03-16 03:29:50,758 INFO [train.py:527] (4/6) Epoch 965, batch 14, global_batch_idx: 119550, batch size: 74, loss[discriminator_loss=2.725, discriminator_real_loss=1.354, discriminator_fake_loss=1.371, generator_loss=28.1, generator_mel_loss=17.64, generator_kl_loss=1.314, generator_dur_loss=1.709, generator_adv_loss=2.021, generator_feat_match_loss=5.42, over 74.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.349, discriminator_fake_loss=1.316, generator_loss=29.38, generator_mel_loss=17.75, generator_kl_loss=1.481, generator_dur_loss=1.695, generator_adv_loss=2.034, generator_feat_match_loss=6.42, over 794.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:32:10,294 INFO [train.py:527] (4/6) Epoch 965, batch 64, global_batch_idx: 119600, batch size: 64, loss[discriminator_loss=2.723, discriminator_real_loss=1.32, discriminator_fake_loss=1.403, generator_loss=28.61, generator_mel_loss=17.05, generator_kl_loss=1.315, generator_dur_loss=1.718, generator_adv_loss=2.132, generator_feat_match_loss=6.398, over 64.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.351, discriminator_fake_loss=1.321, generator_loss=29.25, generator_mel_loss=17.74, generator_kl_loss=1.439, generator_dur_loss=1.721, generator_adv_loss=2.016, generator_feat_match_loss=6.335, over 3626.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:32:10,296 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 03:32:18,313 INFO [train.py:591] (4/6) Epoch 965, validation: discriminator_loss=2.703, discriminator_real_loss=1.36, discriminator_fake_loss=1.343, generator_loss=27.66, generator_mel_loss=18, generator_kl_loss=1.352, generator_dur_loss=1.796, generator_adv_loss=1.896, generator_feat_match_loss=4.611, over 100.00 samples. +2024-03-16 03:32:18,314 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 03:34:35,889 INFO [train.py:527] (4/6) Epoch 965, batch 114, global_batch_idx: 119650, batch size: 17, loss[discriminator_loss=2.752, discriminator_real_loss=1.54, discriminator_fake_loss=1.212, generator_loss=27.86, generator_mel_loss=17.49, generator_kl_loss=1.825, generator_dur_loss=1.529, generator_adv_loss=1.834, generator_feat_match_loss=5.184, over 17.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.351, discriminator_fake_loss=1.32, generator_loss=29.23, generator_mel_loss=17.72, generator_kl_loss=1.448, generator_dur_loss=1.722, generator_adv_loss=2.019, generator_feat_match_loss=6.318, over 6383.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:35:02,716 INFO [train.py:919] (4/6) Start epoch 966 +2024-03-16 03:37:17,014 INFO [train.py:527] (4/6) Epoch 966, batch 40, global_batch_idx: 119700, batch size: 36, loss[discriminator_loss=2.702, discriminator_real_loss=1.405, discriminator_fake_loss=1.297, generator_loss=28.78, generator_mel_loss=17.83, generator_kl_loss=1.362, generator_dur_loss=1.7, generator_adv_loss=1.871, generator_feat_match_loss=6.018, over 36.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.355, discriminator_fake_loss=1.313, generator_loss=29.15, generator_mel_loss=17.72, generator_kl_loss=1.458, generator_dur_loss=1.722, generator_adv_loss=2.018, generator_feat_match_loss=6.228, over 2194.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:39:35,424 INFO [train.py:527] (4/6) Epoch 966, batch 90, global_batch_idx: 119750, batch size: 53, loss[discriminator_loss=2.719, discriminator_real_loss=1.475, discriminator_fake_loss=1.243, generator_loss=26.53, generator_mel_loss=17.03, generator_kl_loss=1.488, generator_dur_loss=1.738, generator_adv_loss=1.941, generator_feat_match_loss=4.335, over 53.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.353, discriminator_fake_loss=1.318, generator_loss=29.18, generator_mel_loss=17.72, generator_kl_loss=1.457, generator_dur_loss=1.733, generator_adv_loss=2.02, generator_feat_match_loss=6.248, over 4999.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:41:10,846 INFO [train.py:919] (4/6) Start epoch 967 +2024-03-16 03:42:20,774 INFO [train.py:527] (4/6) Epoch 967, batch 16, global_batch_idx: 119800, batch size: 72, loss[discriminator_loss=2.664, discriminator_real_loss=1.324, discriminator_fake_loss=1.34, generator_loss=28.28, generator_mel_loss=17.15, generator_kl_loss=1.454, generator_dur_loss=1.821, generator_adv_loss=2.036, generator_feat_match_loss=5.822, over 72.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.321, generator_loss=29.26, generator_mel_loss=17.74, generator_kl_loss=1.436, generator_dur_loss=1.762, generator_adv_loss=2.01, generator_feat_match_loss=6.31, over 1077.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:42:20,776 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 03:42:29,728 INFO [train.py:591] (4/6) Epoch 967, validation: discriminator_loss=2.732, discriminator_real_loss=1.422, discriminator_fake_loss=1.311, generator_loss=27.33, generator_mel_loss=17.69, generator_kl_loss=1.272, generator_dur_loss=1.813, generator_adv_loss=2, generator_feat_match_loss=4.555, over 100.00 samples. +2024-03-16 03:42:29,729 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 03:44:48,637 INFO [train.py:527] (4/6) Epoch 967, batch 66, global_batch_idx: 119850, batch size: 50, loss[discriminator_loss=2.676, discriminator_real_loss=1.266, discriminator_fake_loss=1.41, generator_loss=28.94, generator_mel_loss=17.42, generator_kl_loss=1.493, generator_dur_loss=1.714, generator_adv_loss=1.811, generator_feat_match_loss=6.508, over 50.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.22, generator_mel_loss=17.75, generator_kl_loss=1.436, generator_dur_loss=1.75, generator_adv_loss=2.008, generator_feat_match_loss=6.276, over 4043.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:47:04,616 INFO [train.py:527] (4/6) Epoch 967, batch 116, global_batch_idx: 119900, batch size: 39, loss[discriminator_loss=2.642, discriminator_real_loss=1.364, discriminator_fake_loss=1.278, generator_loss=29.85, generator_mel_loss=17.88, generator_kl_loss=1.595, generator_dur_loss=1.645, generator_adv_loss=1.977, generator_feat_match_loss=6.754, over 39.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.357, discriminator_fake_loss=1.321, generator_loss=29.22, generator_mel_loss=17.74, generator_kl_loss=1.442, generator_dur_loss=1.739, generator_adv_loss=2.007, generator_feat_match_loss=6.295, over 6757.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:47:27,337 INFO [train.py:919] (4/6) Start epoch 968 +2024-03-16 03:49:48,365 INFO [train.py:527] (4/6) Epoch 968, batch 42, global_batch_idx: 119950, batch size: 55, loss[discriminator_loss=2.669, discriminator_real_loss=1.374, discriminator_fake_loss=1.295, generator_loss=28.53, generator_mel_loss=17.22, generator_kl_loss=1.484, generator_dur_loss=1.713, generator_adv_loss=1.943, generator_feat_match_loss=6.167, over 55.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.32, generator_loss=29.16, generator_mel_loss=17.61, generator_kl_loss=1.426, generator_dur_loss=1.733, generator_adv_loss=2.079, generator_feat_match_loss=6.309, over 2409.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:52:07,609 INFO [train.py:527] (4/6) Epoch 968, batch 92, global_batch_idx: 120000, batch size: 68, loss[discriminator_loss=2.673, discriminator_real_loss=1.363, discriminator_fake_loss=1.31, generator_loss=28.31, generator_mel_loss=17.57, generator_kl_loss=1.395, generator_dur_loss=1.781, generator_adv_loss=1.955, generator_feat_match_loss=5.607, over 68.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.356, discriminator_fake_loss=1.316, generator_loss=29.08, generator_mel_loss=17.63, generator_kl_loss=1.426, generator_dur_loss=1.736, generator_adv_loss=2.04, generator_feat_match_loss=6.249, over 5274.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:52:07,610 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 03:52:16,508 INFO [train.py:591] (4/6) Epoch 968, validation: discriminator_loss=2.664, discriminator_real_loss=1.324, discriminator_fake_loss=1.34, generator_loss=28.55, generator_mel_loss=18.05, generator_kl_loss=1.228, generator_dur_loss=1.812, generator_adv_loss=1.908, generator_feat_match_loss=5.552, over 100.00 samples. +2024-03-16 03:52:16,508 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 03:53:43,994 INFO [train.py:919] (4/6) Start epoch 969 +2024-03-16 03:54:57,285 INFO [train.py:527] (4/6) Epoch 969, batch 18, global_batch_idx: 120050, batch size: 36, loss[discriminator_loss=2.716, discriminator_real_loss=1.346, discriminator_fake_loss=1.37, generator_loss=28.79, generator_mel_loss=17.83, generator_kl_loss=1.518, generator_dur_loss=1.676, generator_adv_loss=1.91, generator_feat_match_loss=5.863, over 36.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=29.34, generator_mel_loss=17.81, generator_kl_loss=1.424, generator_dur_loss=1.75, generator_adv_loss=2.02, generator_feat_match_loss=6.334, over 1073.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:57:17,443 INFO [train.py:527] (4/6) Epoch 969, batch 68, global_batch_idx: 120100, batch size: 70, loss[discriminator_loss=2.714, discriminator_real_loss=1.418, discriminator_fake_loss=1.297, generator_loss=27.48, generator_mel_loss=17.16, generator_kl_loss=1.413, generator_dur_loss=1.756, generator_adv_loss=1.895, generator_feat_match_loss=5.253, over 70.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.352, discriminator_fake_loss=1.319, generator_loss=29.18, generator_mel_loss=17.72, generator_kl_loss=1.428, generator_dur_loss=1.749, generator_adv_loss=2.007, generator_feat_match_loss=6.277, over 4088.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:59:34,221 INFO [train.py:527] (4/6) Epoch 969, batch 118, global_batch_idx: 120150, batch size: 31, loss[discriminator_loss=2.586, discriminator_real_loss=1.314, discriminator_fake_loss=1.272, generator_loss=32.67, generator_mel_loss=18.48, generator_kl_loss=1.829, generator_dur_loss=1.669, generator_adv_loss=2.098, generator_feat_match_loss=8.589, over 31.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.321, generator_loss=29.18, generator_mel_loss=17.7, generator_kl_loss=1.436, generator_dur_loss=1.744, generator_adv_loss=2.008, generator_feat_match_loss=6.292, over 6867.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:59:49,705 INFO [train.py:919] (4/6) Start epoch 970 +2024-03-16 04:02:16,063 INFO [train.py:527] (4/6) Epoch 970, batch 44, global_batch_idx: 120200, batch size: 31, loss[discriminator_loss=2.796, discriminator_real_loss=1.412, discriminator_fake_loss=1.385, generator_loss=27.76, generator_mel_loss=17.55, generator_kl_loss=1.57, generator_dur_loss=1.648, generator_adv_loss=1.953, generator_feat_match_loss=5.038, over 31.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.35, discriminator_fake_loss=1.326, generator_loss=29.16, generator_mel_loss=17.78, generator_kl_loss=1.42, generator_dur_loss=1.748, generator_adv_loss=2.008, generator_feat_match_loss=6.208, over 2654.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:02:16,064 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 04:02:23,798 INFO [train.py:591] (4/6) Epoch 970, validation: discriminator_loss=2.721, discriminator_real_loss=1.439, discriminator_fake_loss=1.282, generator_loss=27.91, generator_mel_loss=17.65, generator_kl_loss=1.307, generator_dur_loss=1.813, generator_adv_loss=1.951, generator_feat_match_loss=5.194, over 100.00 samples. +2024-03-16 04:02:23,798 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 04:04:41,784 INFO [train.py:527] (4/6) Epoch 970, batch 94, global_batch_idx: 120250, batch size: 72, loss[discriminator_loss=2.68, discriminator_real_loss=1.293, discriminator_fake_loss=1.387, generator_loss=29.77, generator_mel_loss=17.96, generator_kl_loss=1.272, generator_dur_loss=1.802, generator_adv_loss=2.104, generator_feat_match_loss=6.632, over 72.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.348, discriminator_fake_loss=1.327, generator_loss=29.2, generator_mel_loss=17.75, generator_kl_loss=1.441, generator_dur_loss=1.74, generator_adv_loss=2.008, generator_feat_match_loss=6.264, over 5504.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:06:00,585 INFO [train.py:919] (4/6) Start epoch 971 +2024-03-16 04:07:16,845 INFO [train.py:527] (4/6) Epoch 971, batch 20, global_batch_idx: 120300, batch size: 61, loss[discriminator_loss=2.614, discriminator_real_loss=1.318, discriminator_fake_loss=1.296, generator_loss=29.17, generator_mel_loss=17.55, generator_kl_loss=1.463, generator_dur_loss=1.753, generator_adv_loss=2.166, generator_feat_match_loss=6.235, over 61.00 samples.], tot_loss[discriminator_loss=2.65, discriminator_real_loss=1.35, discriminator_fake_loss=1.3, generator_loss=28.99, generator_mel_loss=17.53, generator_kl_loss=1.487, generator_dur_loss=1.732, generator_adv_loss=2.051, generator_feat_match_loss=6.196, over 1209.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:09:35,043 INFO [train.py:527] (4/6) Epoch 971, batch 70, global_batch_idx: 120350, batch size: 77, loss[discriminator_loss=2.613, discriminator_real_loss=1.297, discriminator_fake_loss=1.317, generator_loss=29.17, generator_mel_loss=17.8, generator_kl_loss=1.387, generator_dur_loss=1.783, generator_adv_loss=1.995, generator_feat_match_loss=6.203, over 77.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.348, discriminator_fake_loss=1.309, generator_loss=29.16, generator_mel_loss=17.63, generator_kl_loss=1.466, generator_dur_loss=1.738, generator_adv_loss=2.02, generator_feat_match_loss=6.297, over 4185.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:11:53,991 INFO [train.py:527] (4/6) Epoch 971, batch 120, global_batch_idx: 120400, batch size: 80, loss[discriminator_loss=2.676, discriminator_real_loss=1.362, discriminator_fake_loss=1.314, generator_loss=29.28, generator_mel_loss=17.5, generator_kl_loss=1.502, generator_dur_loss=1.797, generator_adv_loss=2.004, generator_feat_match_loss=6.47, over 80.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.348, discriminator_fake_loss=1.311, generator_loss=29.24, generator_mel_loss=17.67, generator_kl_loss=1.476, generator_dur_loss=1.732, generator_adv_loss=2.018, generator_feat_match_loss=6.343, over 6661.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:11:53,993 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 04:12:02,759 INFO [train.py:591] (4/6) Epoch 971, validation: discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=27.71, generator_mel_loss=17.7, generator_kl_loss=1.189, generator_dur_loss=1.816, generator_adv_loss=1.942, generator_feat_match_loss=5.053, over 100.00 samples. +2024-03-16 04:12:02,760 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 04:12:11,956 INFO [train.py:919] (4/6) Start epoch 972 +2024-03-16 04:14:42,824 INFO [train.py:527] (4/6) Epoch 972, batch 46, global_batch_idx: 120450, batch size: 31, loss[discriminator_loss=2.693, discriminator_real_loss=1.389, discriminator_fake_loss=1.304, generator_loss=29.33, generator_mel_loss=17.55, generator_kl_loss=1.643, generator_dur_loss=1.592, generator_adv_loss=2.021, generator_feat_match_loss=6.533, over 31.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.347, discriminator_fake_loss=1.319, generator_loss=29.19, generator_mel_loss=17.65, generator_kl_loss=1.43, generator_dur_loss=1.741, generator_adv_loss=2.01, generator_feat_match_loss=6.365, over 2650.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:17:02,586 INFO [train.py:527] (4/6) Epoch 972, batch 96, global_batch_idx: 120500, batch size: 13, loss[discriminator_loss=2.61, discriminator_real_loss=1.401, discriminator_fake_loss=1.209, generator_loss=31.25, generator_mel_loss=18.98, generator_kl_loss=1.961, generator_dur_loss=1.595, generator_adv_loss=1.985, generator_feat_match_loss=6.727, over 13.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.349, discriminator_fake_loss=1.316, generator_loss=29.26, generator_mel_loss=17.67, generator_kl_loss=1.448, generator_dur_loss=1.736, generator_adv_loss=2.02, generator_feat_match_loss=6.378, over 5502.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:18:19,347 INFO [train.py:919] (4/6) Start epoch 973 +2024-03-16 04:19:42,373 INFO [train.py:527] (4/6) Epoch 973, batch 22, global_batch_idx: 120550, batch size: 62, loss[discriminator_loss=2.642, discriminator_real_loss=1.274, discriminator_fake_loss=1.369, generator_loss=29.6, generator_mel_loss=17.35, generator_kl_loss=1.469, generator_dur_loss=1.695, generator_adv_loss=2.277, generator_feat_match_loss=6.81, over 62.00 samples.], tot_loss[discriminator_loss=2.658, discriminator_real_loss=1.341, discriminator_fake_loss=1.318, generator_loss=29.32, generator_mel_loss=17.72, generator_kl_loss=1.422, generator_dur_loss=1.754, generator_adv_loss=2.023, generator_feat_match_loss=6.409, over 1355.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:22:01,940 INFO [train.py:527] (4/6) Epoch 973, batch 72, global_batch_idx: 120600, batch size: 50, loss[discriminator_loss=2.637, discriminator_real_loss=1.393, discriminator_fake_loss=1.245, generator_loss=30.79, generator_mel_loss=17.73, generator_kl_loss=1.526, generator_dur_loss=1.696, generator_adv_loss=1.992, generator_feat_match_loss=7.852, over 50.00 samples.], tot_loss[discriminator_loss=2.653, discriminator_real_loss=1.34, discriminator_fake_loss=1.313, generator_loss=29.37, generator_mel_loss=17.76, generator_kl_loss=1.425, generator_dur_loss=1.742, generator_adv_loss=2.026, generator_feat_match_loss=6.42, over 4290.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:22:01,941 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 04:22:10,204 INFO [train.py:591] (4/6) Epoch 973, validation: discriminator_loss=2.714, discriminator_real_loss=1.352, discriminator_fake_loss=1.362, generator_loss=28.38, generator_mel_loss=18.37, generator_kl_loss=1.242, generator_dur_loss=1.801, generator_adv_loss=1.845, generator_feat_match_loss=5.118, over 100.00 samples. +2024-03-16 04:22:10,206 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 04:24:28,802 INFO [train.py:527] (4/6) Epoch 973, batch 122, global_batch_idx: 120650, batch size: 88, loss[discriminator_loss=2.644, discriminator_real_loss=1.316, discriminator_fake_loss=1.328, generator_loss=29.17, generator_mel_loss=17.8, generator_kl_loss=1.265, generator_dur_loss=1.815, generator_adv_loss=2.002, generator_feat_match_loss=6.296, over 88.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.342, discriminator_fake_loss=1.32, generator_loss=29.36, generator_mel_loss=17.75, generator_kl_loss=1.431, generator_dur_loss=1.748, generator_adv_loss=2.013, generator_feat_match_loss=6.421, over 7282.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:24:35,283 INFO [train.py:919] (4/6) Start epoch 974 +2024-03-16 04:27:14,406 INFO [train.py:527] (4/6) Epoch 974, batch 48, global_batch_idx: 120700, batch size: 64, loss[discriminator_loss=2.55, discriminator_real_loss=1.338, discriminator_fake_loss=1.212, generator_loss=29.78, generator_mel_loss=17.74, generator_kl_loss=1.404, generator_dur_loss=1.76, generator_adv_loss=2.251, generator_feat_match_loss=6.626, over 64.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.349, discriminator_fake_loss=1.316, generator_loss=29.14, generator_mel_loss=17.63, generator_kl_loss=1.426, generator_dur_loss=1.753, generator_adv_loss=2.026, generator_feat_match_loss=6.306, over 2898.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:29:32,927 INFO [train.py:527] (4/6) Epoch 974, batch 98, global_batch_idx: 120750, batch size: 72, loss[discriminator_loss=2.685, discriminator_real_loss=1.318, discriminator_fake_loss=1.367, generator_loss=28.88, generator_mel_loss=17.37, generator_kl_loss=1.383, generator_dur_loss=1.792, generator_adv_loss=1.977, generator_feat_match_loss=6.356, over 72.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.353, discriminator_fake_loss=1.316, generator_loss=29.2, generator_mel_loss=17.67, generator_kl_loss=1.428, generator_dur_loss=1.748, generator_adv_loss=2.027, generator_feat_match_loss=6.326, over 5876.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:30:42,925 INFO [train.py:919] (4/6) Start epoch 975 +2024-03-16 04:32:14,154 INFO [train.py:527] (4/6) Epoch 975, batch 24, global_batch_idx: 120800, batch size: 58, loss[discriminator_loss=2.613, discriminator_real_loss=1.311, discriminator_fake_loss=1.302, generator_loss=29.81, generator_mel_loss=17.97, generator_kl_loss=1.386, generator_dur_loss=1.765, generator_adv_loss=2.128, generator_feat_match_loss=6.56, over 58.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.358, discriminator_fake_loss=1.317, generator_loss=29.3, generator_mel_loss=17.79, generator_kl_loss=1.432, generator_dur_loss=1.737, generator_adv_loss=2.006, generator_feat_match_loss=6.33, over 1454.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:32:14,155 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 04:32:22,022 INFO [train.py:591] (4/6) Epoch 975, validation: discriminator_loss=2.764, discriminator_real_loss=1.476, discriminator_fake_loss=1.287, generator_loss=28.07, generator_mel_loss=18.2, generator_kl_loss=1.347, generator_dur_loss=1.808, generator_adv_loss=1.986, generator_feat_match_loss=4.725, over 100.00 samples. +2024-03-16 04:32:22,023 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 04:34:43,102 INFO [train.py:527] (4/6) Epoch 975, batch 74, global_batch_idx: 120850, batch size: 96, loss[discriminator_loss=2.691, discriminator_real_loss=1.397, discriminator_fake_loss=1.294, generator_loss=29.23, generator_mel_loss=17.55, generator_kl_loss=1.352, generator_dur_loss=1.854, generator_adv_loss=2.054, generator_feat_match_loss=6.423, over 96.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.356, discriminator_fake_loss=1.316, generator_loss=29.25, generator_mel_loss=17.74, generator_kl_loss=1.435, generator_dur_loss=1.747, generator_adv_loss=2.01, generator_feat_match_loss=6.317, over 4550.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:36:58,655 INFO [train.py:919] (4/6) Start epoch 976 +2024-03-16 04:37:24,094 INFO [train.py:527] (4/6) Epoch 976, batch 0, global_batch_idx: 120900, batch size: 66, loss[discriminator_loss=2.649, discriminator_real_loss=1.321, discriminator_fake_loss=1.327, generator_loss=29.68, generator_mel_loss=17.8, generator_kl_loss=1.51, generator_dur_loss=1.744, generator_adv_loss=2.117, generator_feat_match_loss=6.517, over 66.00 samples.], tot_loss[discriminator_loss=2.649, discriminator_real_loss=1.321, discriminator_fake_loss=1.327, generator_loss=29.68, generator_mel_loss=17.8, generator_kl_loss=1.51, generator_dur_loss=1.744, generator_adv_loss=2.117, generator_feat_match_loss=6.517, over 66.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:39:43,146 INFO [train.py:527] (4/6) Epoch 976, batch 50, global_batch_idx: 120950, batch size: 25, loss[discriminator_loss=2.562, discriminator_real_loss=1.344, discriminator_fake_loss=1.218, generator_loss=31.62, generator_mel_loss=18, generator_kl_loss=1.726, generator_dur_loss=1.546, generator_adv_loss=2.077, generator_feat_match_loss=8.272, over 25.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.354, discriminator_fake_loss=1.318, generator_loss=29.47, generator_mel_loss=17.86, generator_kl_loss=1.444, generator_dur_loss=1.738, generator_adv_loss=2.048, generator_feat_match_loss=6.375, over 2882.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:42:03,697 INFO [train.py:527] (4/6) Epoch 976, batch 100, global_batch_idx: 121000, batch size: 50, loss[discriminator_loss=2.61, discriminator_real_loss=1.331, discriminator_fake_loss=1.278, generator_loss=29.11, generator_mel_loss=17.41, generator_kl_loss=1.448, generator_dur_loss=1.633, generator_adv_loss=1.99, generator_feat_match_loss=6.637, over 50.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.35, discriminator_fake_loss=1.318, generator_loss=29.35, generator_mel_loss=17.76, generator_kl_loss=1.447, generator_dur_loss=1.743, generator_adv_loss=2.038, generator_feat_match_loss=6.362, over 5828.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:42:03,698 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 04:42:12,325 INFO [train.py:591] (4/6) Epoch 976, validation: discriminator_loss=2.716, discriminator_real_loss=1.315, discriminator_fake_loss=1.401, generator_loss=28.04, generator_mel_loss=18.46, generator_kl_loss=1.27, generator_dur_loss=1.816, generator_adv_loss=1.744, generator_feat_match_loss=4.742, over 100.00 samples. +2024-03-16 04:42:12,326 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 04:43:17,444 INFO [train.py:919] (4/6) Start epoch 977 +2024-03-16 04:44:52,797 INFO [train.py:527] (4/6) Epoch 977, batch 26, global_batch_idx: 121050, batch size: 80, loss[discriminator_loss=2.653, discriminator_real_loss=1.342, discriminator_fake_loss=1.311, generator_loss=29.7, generator_mel_loss=18.04, generator_kl_loss=1.627, generator_dur_loss=1.838, generator_adv_loss=2.032, generator_feat_match_loss=6.153, over 80.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.355, discriminator_fake_loss=1.333, generator_loss=29.07, generator_mel_loss=17.66, generator_kl_loss=1.571, generator_dur_loss=1.765, generator_adv_loss=2, generator_feat_match_loss=6.071, over 1703.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:47:15,608 INFO [train.py:527] (4/6) Epoch 977, batch 76, global_batch_idx: 121100, batch size: 64, loss[discriminator_loss=2.654, discriminator_real_loss=1.29, discriminator_fake_loss=1.364, generator_loss=29.99, generator_mel_loss=17.8, generator_kl_loss=1.355, generator_dur_loss=1.764, generator_adv_loss=2.128, generator_feat_match_loss=6.941, over 64.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.345, discriminator_fake_loss=1.321, generator_loss=29.33, generator_mel_loss=17.73, generator_kl_loss=1.513, generator_dur_loss=1.76, generator_adv_loss=2.023, generator_feat_match_loss=6.307, over 4805.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:49:23,439 INFO [train.py:919] (4/6) Start epoch 978 +2024-03-16 04:49:52,935 INFO [train.py:527] (4/6) Epoch 978, batch 2, global_batch_idx: 121150, batch size: 62, loss[discriminator_loss=2.651, discriminator_real_loss=1.358, discriminator_fake_loss=1.293, generator_loss=28.86, generator_mel_loss=17.81, generator_kl_loss=1.497, generator_dur_loss=1.719, generator_adv_loss=1.95, generator_feat_match_loss=5.887, over 62.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.324, discriminator_fake_loss=1.365, generator_loss=29.68, generator_mel_loss=18.01, generator_kl_loss=1.638, generator_dur_loss=1.663, generator_adv_loss=2.021, generator_feat_match_loss=6.352, over 123.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:52:09,656 INFO [train.py:527] (4/6) Epoch 978, batch 52, global_batch_idx: 121200, batch size: 53, loss[discriminator_loss=2.711, discriminator_real_loss=1.357, discriminator_fake_loss=1.354, generator_loss=28.42, generator_mel_loss=17.63, generator_kl_loss=1.511, generator_dur_loss=1.711, generator_adv_loss=1.965, generator_feat_match_loss=5.61, over 53.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.326, generator_loss=29.19, generator_mel_loss=17.78, generator_kl_loss=1.472, generator_dur_loss=1.734, generator_adv_loss=2.022, generator_feat_match_loss=6.174, over 2937.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:52:09,657 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 04:52:17,761 INFO [train.py:591] (4/6) Epoch 978, validation: discriminator_loss=2.684, discriminator_real_loss=1.319, discriminator_fake_loss=1.365, generator_loss=27.33, generator_mel_loss=17.94, generator_kl_loss=1.208, generator_dur_loss=1.801, generator_adv_loss=1.836, generator_feat_match_loss=4.543, over 100.00 samples. +2024-03-16 04:52:17,762 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 04:54:33,280 INFO [train.py:527] (4/6) Epoch 978, batch 102, global_batch_idx: 121250, batch size: 59, loss[discriminator_loss=2.66, discriminator_real_loss=1.381, discriminator_fake_loss=1.279, generator_loss=29.71, generator_mel_loss=17.66, generator_kl_loss=1.495, generator_dur_loss=1.709, generator_adv_loss=2.033, generator_feat_match_loss=6.806, over 59.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.357, discriminator_fake_loss=1.319, generator_loss=29.19, generator_mel_loss=17.72, generator_kl_loss=1.463, generator_dur_loss=1.735, generator_adv_loss=2.017, generator_feat_match_loss=6.25, over 5752.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:55:35,719 INFO [train.py:919] (4/6) Start epoch 979 +2024-03-16 04:57:14,756 INFO [train.py:527] (4/6) Epoch 979, batch 28, global_batch_idx: 121300, batch size: 77, loss[discriminator_loss=2.698, discriminator_real_loss=1.336, discriminator_fake_loss=1.362, generator_loss=28.62, generator_mel_loss=17.78, generator_kl_loss=1.336, generator_dur_loss=1.827, generator_adv_loss=1.943, generator_feat_match_loss=5.736, over 77.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=29.18, generator_mel_loss=17.71, generator_kl_loss=1.425, generator_dur_loss=1.752, generator_adv_loss=1.998, generator_feat_match_loss=6.296, over 1788.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:59:31,937 INFO [train.py:527] (4/6) Epoch 979, batch 78, global_batch_idx: 121350, batch size: 64, loss[discriminator_loss=2.746, discriminator_real_loss=1.481, discriminator_fake_loss=1.265, generator_loss=29.31, generator_mel_loss=17.92, generator_kl_loss=1.476, generator_dur_loss=1.76, generator_adv_loss=1.942, generator_feat_match_loss=6.214, over 64.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.16, generator_mel_loss=17.71, generator_kl_loss=1.448, generator_dur_loss=1.732, generator_adv_loss=2, generator_feat_match_loss=6.263, over 4480.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:01:40,100 INFO [train.py:919] (4/6) Start epoch 980 +2024-03-16 05:02:15,996 INFO [train.py:527] (4/6) Epoch 980, batch 4, global_batch_idx: 121400, batch size: 96, loss[discriminator_loss=2.683, discriminator_real_loss=1.376, discriminator_fake_loss=1.308, generator_loss=28.48, generator_mel_loss=17.32, generator_kl_loss=1.279, generator_dur_loss=1.835, generator_adv_loss=1.884, generator_feat_match_loss=6.153, over 96.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.319, generator_loss=29.37, generator_mel_loss=17.75, generator_kl_loss=1.413, generator_dur_loss=1.749, generator_adv_loss=1.996, generator_feat_match_loss=6.459, over 326.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:02:15,998 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 05:02:23,771 INFO [train.py:591] (4/6) Epoch 980, validation: discriminator_loss=2.722, discriminator_real_loss=1.367, discriminator_fake_loss=1.355, generator_loss=27.75, generator_mel_loss=18.1, generator_kl_loss=1.305, generator_dur_loss=1.816, generator_adv_loss=1.841, generator_feat_match_loss=4.69, over 100.00 samples. +2024-03-16 05:02:23,774 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 05:04:44,159 INFO [train.py:527] (4/6) Epoch 980, batch 54, global_batch_idx: 121450, batch size: 52, loss[discriminator_loss=2.684, discriminator_real_loss=1.332, discriminator_fake_loss=1.352, generator_loss=28.62, generator_mel_loss=17.76, generator_kl_loss=1.518, generator_dur_loss=1.621, generator_adv_loss=2.027, generator_feat_match_loss=5.695, over 52.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.35, discriminator_fake_loss=1.318, generator_loss=29.14, generator_mel_loss=17.68, generator_kl_loss=1.428, generator_dur_loss=1.735, generator_adv_loss=2.011, generator_feat_match_loss=6.288, over 3261.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:07:02,447 INFO [train.py:527] (4/6) Epoch 980, batch 104, global_batch_idx: 121500, batch size: 36, loss[discriminator_loss=2.692, discriminator_real_loss=1.291, discriminator_fake_loss=1.401, generator_loss=29.21, generator_mel_loss=17.78, generator_kl_loss=1.623, generator_dur_loss=1.707, generator_adv_loss=2.078, generator_feat_match_loss=6.025, over 36.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.32, generator_loss=29.13, generator_mel_loss=17.68, generator_kl_loss=1.438, generator_dur_loss=1.734, generator_adv_loss=2.006, generator_feat_match_loss=6.274, over 6073.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:07:55,082 INFO [train.py:919] (4/6) Start epoch 981 +2024-03-16 05:09:42,433 INFO [train.py:527] (4/6) Epoch 981, batch 30, global_batch_idx: 121550, batch size: 16, loss[discriminator_loss=2.63, discriminator_real_loss=1.322, discriminator_fake_loss=1.308, generator_loss=30.9, generator_mel_loss=18.26, generator_kl_loss=1.974, generator_dur_loss=1.566, generator_adv_loss=2.124, generator_feat_match_loss=6.967, over 16.00 samples.], tot_loss[discriminator_loss=2.651, discriminator_real_loss=1.339, discriminator_fake_loss=1.312, generator_loss=29.32, generator_mel_loss=17.71, generator_kl_loss=1.467, generator_dur_loss=1.711, generator_adv_loss=2.029, generator_feat_match_loss=6.407, over 1625.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:12:00,825 INFO [train.py:527] (4/6) Epoch 981, batch 80, global_batch_idx: 121600, batch size: 64, loss[discriminator_loss=2.742, discriminator_real_loss=1.322, discriminator_fake_loss=1.421, generator_loss=27.65, generator_mel_loss=17.53, generator_kl_loss=1.383, generator_dur_loss=1.739, generator_adv_loss=2.146, generator_feat_match_loss=4.845, over 64.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.344, discriminator_fake_loss=1.323, generator_loss=29.25, generator_mel_loss=17.71, generator_kl_loss=1.459, generator_dur_loss=1.727, generator_adv_loss=2.011, generator_feat_match_loss=6.338, over 4595.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:12:00,827 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 05:12:09,456 INFO [train.py:591] (4/6) Epoch 981, validation: discriminator_loss=2.757, discriminator_real_loss=1.515, discriminator_fake_loss=1.243, generator_loss=29.08, generator_mel_loss=18.26, generator_kl_loss=1.352, generator_dur_loss=1.792, generator_adv_loss=2.119, generator_feat_match_loss=5.557, over 100.00 samples. +2024-03-16 05:12:09,457 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 05:14:13,261 INFO [train.py:919] (4/6) Start epoch 982 +2024-03-16 05:14:52,277 INFO [train.py:527] (4/6) Epoch 982, batch 6, global_batch_idx: 121650, batch size: 59, loss[discriminator_loss=2.731, discriminator_real_loss=1.374, discriminator_fake_loss=1.357, generator_loss=28.83, generator_mel_loss=17.49, generator_kl_loss=1.518, generator_dur_loss=1.693, generator_adv_loss=2.04, generator_feat_match_loss=6.093, over 59.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.402, discriminator_fake_loss=1.294, generator_loss=29.04, generator_mel_loss=17.72, generator_kl_loss=1.477, generator_dur_loss=1.72, generator_adv_loss=2.052, generator_feat_match_loss=6.075, over 391.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:17:09,849 INFO [train.py:527] (4/6) Epoch 982, batch 56, global_batch_idx: 121700, batch size: 47, loss[discriminator_loss=2.658, discriminator_real_loss=1.259, discriminator_fake_loss=1.399, generator_loss=29.48, generator_mel_loss=17.71, generator_kl_loss=1.473, generator_dur_loss=1.7, generator_adv_loss=2.069, generator_feat_match_loss=6.523, over 47.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.36, discriminator_fake_loss=1.316, generator_loss=29.22, generator_mel_loss=17.72, generator_kl_loss=1.449, generator_dur_loss=1.725, generator_adv_loss=2.027, generator_feat_match_loss=6.293, over 3208.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:19:30,306 INFO [train.py:527] (4/6) Epoch 982, batch 106, global_batch_idx: 121750, batch size: 83, loss[discriminator_loss=2.662, discriminator_real_loss=1.306, discriminator_fake_loss=1.355, generator_loss=29.99, generator_mel_loss=17.57, generator_kl_loss=1.306, generator_dur_loss=1.831, generator_adv_loss=2.169, generator_feat_match_loss=7.117, over 83.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.361, discriminator_fake_loss=1.316, generator_loss=29.28, generator_mel_loss=17.73, generator_kl_loss=1.443, generator_dur_loss=1.726, generator_adv_loss=2.029, generator_feat_match_loss=6.354, over 6110.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:20:20,719 INFO [train.py:919] (4/6) Start epoch 983 +2024-03-16 05:22:11,836 INFO [train.py:527] (4/6) Epoch 983, batch 32, global_batch_idx: 121800, batch size: 66, loss[discriminator_loss=2.682, discriminator_real_loss=1.345, discriminator_fake_loss=1.337, generator_loss=28.62, generator_mel_loss=17.59, generator_kl_loss=1.383, generator_dur_loss=1.721, generator_adv_loss=2.117, generator_feat_match_loss=5.81, over 66.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.362, discriminator_fake_loss=1.317, generator_loss=29.15, generator_mel_loss=17.76, generator_kl_loss=1.441, generator_dur_loss=1.731, generator_adv_loss=2.026, generator_feat_match_loss=6.193, over 1874.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:22:11,837 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 05:22:19,998 INFO [train.py:591] (4/6) Epoch 983, validation: discriminator_loss=2.797, discriminator_real_loss=1.532, discriminator_fake_loss=1.265, generator_loss=27.99, generator_mel_loss=17.87, generator_kl_loss=1.185, generator_dur_loss=1.803, generator_adv_loss=2.017, generator_feat_match_loss=5.114, over 100.00 samples. +2024-03-16 05:22:19,999 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 05:24:35,132 INFO [train.py:527] (4/6) Epoch 983, batch 82, global_batch_idx: 121850, batch size: 31, loss[discriminator_loss=2.687, discriminator_real_loss=1.384, discriminator_fake_loss=1.303, generator_loss=29.35, generator_mel_loss=18.08, generator_kl_loss=1.686, generator_dur_loss=1.629, generator_adv_loss=2.113, generator_feat_match_loss=5.84, over 31.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.32, generator_loss=29.23, generator_mel_loss=17.77, generator_kl_loss=1.437, generator_dur_loss=1.74, generator_adv_loss=2.024, generator_feat_match_loss=6.258, over 4681.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:26:30,145 INFO [train.py:919] (4/6) Start epoch 984 +2024-03-16 05:27:17,813 INFO [train.py:527] (4/6) Epoch 984, batch 8, global_batch_idx: 121900, batch size: 62, loss[discriminator_loss=2.707, discriminator_real_loss=1.341, discriminator_fake_loss=1.366, generator_loss=29.01, generator_mel_loss=17.95, generator_kl_loss=1.355, generator_dur_loss=1.744, generator_adv_loss=1.923, generator_feat_match_loss=6.038, over 62.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.357, discriminator_fake_loss=1.31, generator_loss=28.97, generator_mel_loss=17.58, generator_kl_loss=1.407, generator_dur_loss=1.762, generator_adv_loss=2.024, generator_feat_match_loss=6.195, over 595.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:29:35,372 INFO [train.py:527] (4/6) Epoch 984, batch 58, global_batch_idx: 121950, batch size: 48, loss[discriminator_loss=2.626, discriminator_real_loss=1.29, discriminator_fake_loss=1.336, generator_loss=29.8, generator_mel_loss=17.56, generator_kl_loss=1.492, generator_dur_loss=1.622, generator_adv_loss=2.111, generator_feat_match_loss=7.012, over 48.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.347, discriminator_fake_loss=1.323, generator_loss=29.26, generator_mel_loss=17.7, generator_kl_loss=1.458, generator_dur_loss=1.732, generator_adv_loss=2.013, generator_feat_match_loss=6.355, over 3421.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:31:53,516 INFO [train.py:527] (4/6) Epoch 984, batch 108, global_batch_idx: 122000, batch size: 25, loss[discriminator_loss=2.617, discriminator_real_loss=1.321, discriminator_fake_loss=1.296, generator_loss=31.22, generator_mel_loss=18.75, generator_kl_loss=1.665, generator_dur_loss=1.554, generator_adv_loss=2.063, generator_feat_match_loss=7.187, over 25.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.35, discriminator_fake_loss=1.324, generator_loss=29.21, generator_mel_loss=17.72, generator_kl_loss=1.439, generator_dur_loss=1.739, generator_adv_loss=2.007, generator_feat_match_loss=6.31, over 6440.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:31:53,517 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 05:32:02,175 INFO [train.py:591] (4/6) Epoch 984, validation: discriminator_loss=2.731, discriminator_real_loss=1.446, discriminator_fake_loss=1.286, generator_loss=27.98, generator_mel_loss=18.09, generator_kl_loss=1.356, generator_dur_loss=1.799, generator_adv_loss=1.933, generator_feat_match_loss=4.807, over 100.00 samples. +2024-03-16 05:32:02,176 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 05:32:44,826 INFO [train.py:919] (4/6) Start epoch 985 +2024-03-16 05:34:45,308 INFO [train.py:527] (4/6) Epoch 985, batch 34, global_batch_idx: 122050, batch size: 15, loss[discriminator_loss=2.679, discriminator_real_loss=1.409, discriminator_fake_loss=1.27, generator_loss=29.76, generator_mel_loss=17.69, generator_kl_loss=1.867, generator_dur_loss=1.488, generator_adv_loss=1.99, generator_feat_match_loss=6.727, over 15.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.352, discriminator_fake_loss=1.318, generator_loss=29.46, generator_mel_loss=17.8, generator_kl_loss=1.47, generator_dur_loss=1.727, generator_adv_loss=2.021, generator_feat_match_loss=6.436, over 1917.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:37:03,529 INFO [train.py:527] (4/6) Epoch 985, batch 84, global_batch_idx: 122100, batch size: 74, loss[discriminator_loss=2.652, discriminator_real_loss=1.415, discriminator_fake_loss=1.237, generator_loss=29.26, generator_mel_loss=17.3, generator_kl_loss=1.417, generator_dur_loss=1.811, generator_adv_loss=2.121, generator_feat_match_loss=6.615, over 74.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.353, discriminator_fake_loss=1.315, generator_loss=29.32, generator_mel_loss=17.73, generator_kl_loss=1.446, generator_dur_loss=1.742, generator_adv_loss=2.023, generator_feat_match_loss=6.38, over 4907.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:38:51,479 INFO [train.py:919] (4/6) Start epoch 986 +2024-03-16 05:39:43,687 INFO [train.py:527] (4/6) Epoch 986, batch 10, global_batch_idx: 122150, batch size: 52, loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.345, generator_loss=29.75, generator_mel_loss=17.75, generator_kl_loss=1.607, generator_dur_loss=1.635, generator_adv_loss=2.064, generator_feat_match_loss=6.691, over 52.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.37, discriminator_fake_loss=1.332, generator_loss=29.4, generator_mel_loss=17.77, generator_kl_loss=1.461, generator_dur_loss=1.74, generator_adv_loss=2.006, generator_feat_match_loss=6.421, over 600.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:42:01,283 INFO [train.py:527] (4/6) Epoch 986, batch 60, global_batch_idx: 122200, batch size: 59, loss[discriminator_loss=2.664, discriminator_real_loss=1.317, discriminator_fake_loss=1.347, generator_loss=29.26, generator_mel_loss=17.89, generator_kl_loss=1.536, generator_dur_loss=1.761, generator_adv_loss=2.013, generator_feat_match_loss=6.061, over 59.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.347, discriminator_fake_loss=1.326, generator_loss=29.29, generator_mel_loss=17.74, generator_kl_loss=1.467, generator_dur_loss=1.728, generator_adv_loss=2.01, generator_feat_match_loss=6.344, over 3291.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:42:01,285 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 05:42:09,353 INFO [train.py:591] (4/6) Epoch 986, validation: discriminator_loss=2.721, discriminator_real_loss=1.44, discriminator_fake_loss=1.281, generator_loss=28.95, generator_mel_loss=18.18, generator_kl_loss=1.218, generator_dur_loss=1.808, generator_adv_loss=1.993, generator_feat_match_loss=5.746, over 100.00 samples. +2024-03-16 05:42:09,354 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 05:44:30,361 INFO [train.py:527] (4/6) Epoch 986, batch 110, global_batch_idx: 122250, batch size: 48, loss[discriminator_loss=2.631, discriminator_real_loss=1.477, discriminator_fake_loss=1.154, generator_loss=28.75, generator_mel_loss=17.43, generator_kl_loss=1.595, generator_dur_loss=1.643, generator_adv_loss=1.915, generator_feat_match_loss=6.17, over 48.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.352, discriminator_fake_loss=1.318, generator_loss=29.26, generator_mel_loss=17.71, generator_kl_loss=1.463, generator_dur_loss=1.732, generator_adv_loss=2.011, generator_feat_match_loss=6.347, over 6236.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:45:07,519 INFO [train.py:919] (4/6) Start epoch 987 +2024-03-16 05:47:13,329 INFO [train.py:527] (4/6) Epoch 987, batch 36, global_batch_idx: 122300, batch size: 31, loss[discriminator_loss=2.679, discriminator_real_loss=1.431, discriminator_fake_loss=1.248, generator_loss=28.96, generator_mel_loss=17.82, generator_kl_loss=1.721, generator_dur_loss=1.645, generator_adv_loss=2.017, generator_feat_match_loss=5.756, over 31.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.25, generator_mel_loss=17.7, generator_kl_loss=1.464, generator_dur_loss=1.713, generator_adv_loss=2.035, generator_feat_match_loss=6.342, over 2160.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:49:36,758 INFO [train.py:527] (4/6) Epoch 987, batch 86, global_batch_idx: 122350, batch size: 80, loss[discriminator_loss=2.693, discriminator_real_loss=1.266, discriminator_fake_loss=1.427, generator_loss=28.9, generator_mel_loss=17.78, generator_kl_loss=1.446, generator_dur_loss=1.757, generator_adv_loss=1.954, generator_feat_match_loss=5.969, over 80.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=29.26, generator_mel_loss=17.67, generator_kl_loss=1.477, generator_dur_loss=1.723, generator_adv_loss=2.03, generator_feat_match_loss=6.355, over 4985.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:51:16,557 INFO [train.py:919] (4/6) Start epoch 988 +2024-03-16 05:52:15,269 INFO [train.py:527] (4/6) Epoch 988, batch 12, global_batch_idx: 122400, batch size: 66, loss[discriminator_loss=2.688, discriminator_real_loss=1.39, discriminator_fake_loss=1.298, generator_loss=28.86, generator_mel_loss=17.49, generator_kl_loss=1.439, generator_dur_loss=1.793, generator_adv_loss=2.079, generator_feat_match_loss=6.062, over 66.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.345, discriminator_fake_loss=1.312, generator_loss=29.1, generator_mel_loss=17.51, generator_kl_loss=1.482, generator_dur_loss=1.732, generator_adv_loss=2.021, generator_feat_match_loss=6.364, over 795.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:52:15,272 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 05:52:23,024 INFO [train.py:591] (4/6) Epoch 988, validation: discriminator_loss=2.709, discriminator_real_loss=1.433, discriminator_fake_loss=1.275, generator_loss=27.75, generator_mel_loss=17.91, generator_kl_loss=1.349, generator_dur_loss=1.8, generator_adv_loss=1.963, generator_feat_match_loss=4.722, over 100.00 samples. +2024-03-16 05:52:23,025 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 05:54:41,326 INFO [train.py:527] (4/6) Epoch 988, batch 62, global_batch_idx: 122450, batch size: 55, loss[discriminator_loss=2.652, discriminator_real_loss=1.381, discriminator_fake_loss=1.271, generator_loss=30.15, generator_mel_loss=17.84, generator_kl_loss=1.44, generator_dur_loss=1.669, generator_adv_loss=2.048, generator_feat_match_loss=7.147, over 55.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=29.2, generator_mel_loss=17.72, generator_kl_loss=1.441, generator_dur_loss=1.732, generator_adv_loss=2.016, generator_feat_match_loss=6.296, over 3614.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:57:00,750 INFO [train.py:527] (4/6) Epoch 988, batch 112, global_batch_idx: 122500, batch size: 36, loss[discriminator_loss=2.721, discriminator_real_loss=1.287, discriminator_fake_loss=1.434, generator_loss=29.16, generator_mel_loss=17.79, generator_kl_loss=1.676, generator_dur_loss=1.656, generator_adv_loss=2.058, generator_feat_match_loss=5.979, over 36.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.352, discriminator_fake_loss=1.321, generator_loss=29.18, generator_mel_loss=17.71, generator_kl_loss=1.448, generator_dur_loss=1.725, generator_adv_loss=2.018, generator_feat_match_loss=6.279, over 6227.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:57:31,457 INFO [train.py:919] (4/6) Start epoch 989 +2024-03-16 05:59:43,201 INFO [train.py:527] (4/6) Epoch 989, batch 38, global_batch_idx: 122550, batch size: 31, loss[discriminator_loss=2.732, discriminator_real_loss=1.373, discriminator_fake_loss=1.359, generator_loss=27.81, generator_mel_loss=17.25, generator_kl_loss=1.564, generator_dur_loss=1.602, generator_adv_loss=1.881, generator_feat_match_loss=5.516, over 31.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.357, discriminator_fake_loss=1.326, generator_loss=29.1, generator_mel_loss=17.68, generator_kl_loss=1.478, generator_dur_loss=1.709, generator_adv_loss=2.001, generator_feat_match_loss=6.228, over 2173.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:02:03,370 INFO [train.py:527] (4/6) Epoch 989, batch 88, global_batch_idx: 122600, batch size: 74, loss[discriminator_loss=2.697, discriminator_real_loss=1.245, discriminator_fake_loss=1.452, generator_loss=29.36, generator_mel_loss=17.8, generator_kl_loss=1.437, generator_dur_loss=1.773, generator_adv_loss=2.025, generator_feat_match_loss=6.319, over 74.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.356, discriminator_fake_loss=1.322, generator_loss=29.07, generator_mel_loss=17.67, generator_kl_loss=1.466, generator_dur_loss=1.718, generator_adv_loss=2.008, generator_feat_match_loss=6.203, over 5072.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:02:03,371 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 06:02:12,383 INFO [train.py:591] (4/6) Epoch 989, validation: discriminator_loss=2.731, discriminator_real_loss=1.433, discriminator_fake_loss=1.298, generator_loss=28.06, generator_mel_loss=17.94, generator_kl_loss=1.327, generator_dur_loss=1.798, generator_adv_loss=1.954, generator_feat_match_loss=5.038, over 100.00 samples. +2024-03-16 06:02:12,384 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 06:03:48,445 INFO [train.py:919] (4/6) Start epoch 990 +2024-03-16 06:04:49,268 INFO [train.py:527] (4/6) Epoch 990, batch 14, global_batch_idx: 122650, batch size: 53, loss[discriminator_loss=2.626, discriminator_real_loss=1.359, discriminator_fake_loss=1.267, generator_loss=28.72, generator_mel_loss=17.45, generator_kl_loss=1.374, generator_dur_loss=1.673, generator_adv_loss=2.273, generator_feat_match_loss=5.949, over 53.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.349, discriminator_fake_loss=1.324, generator_loss=29.38, generator_mel_loss=17.78, generator_kl_loss=1.422, generator_dur_loss=1.73, generator_adv_loss=2.027, generator_feat_match_loss=6.418, over 842.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:07:08,595 INFO [train.py:527] (4/6) Epoch 990, batch 64, global_batch_idx: 122700, batch size: 25, loss[discriminator_loss=2.698, discriminator_real_loss=1.33, discriminator_fake_loss=1.368, generator_loss=31.22, generator_mel_loss=18.31, generator_kl_loss=1.696, generator_dur_loss=1.588, generator_adv_loss=1.996, generator_feat_match_loss=7.629, over 25.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.353, discriminator_fake_loss=1.321, generator_loss=29.24, generator_mel_loss=17.74, generator_kl_loss=1.419, generator_dur_loss=1.739, generator_adv_loss=2.021, generator_feat_match_loss=6.326, over 3673.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:09:27,428 INFO [train.py:527] (4/6) Epoch 990, batch 114, global_batch_idx: 122750, batch size: 39, loss[discriminator_loss=2.633, discriminator_real_loss=1.329, discriminator_fake_loss=1.304, generator_loss=30.03, generator_mel_loss=18.37, generator_kl_loss=1.456, generator_dur_loss=1.649, generator_adv_loss=1.916, generator_feat_match_loss=6.636, over 39.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.357, discriminator_fake_loss=1.317, generator_loss=29.15, generator_mel_loss=17.69, generator_kl_loss=1.416, generator_dur_loss=1.741, generator_adv_loss=2.017, generator_feat_match_loss=6.283, over 6602.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:09:52,740 INFO [train.py:919] (4/6) Start epoch 991 +2024-03-16 06:12:07,886 INFO [train.py:527] (4/6) Epoch 991, batch 40, global_batch_idx: 122800, batch size: 44, loss[discriminator_loss=2.721, discriminator_real_loss=1.436, discriminator_fake_loss=1.286, generator_loss=28.91, generator_mel_loss=17.59, generator_kl_loss=1.518, generator_dur_loss=1.634, generator_adv_loss=1.945, generator_feat_match_loss=6.219, over 44.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.35, discriminator_fake_loss=1.324, generator_loss=29.2, generator_mel_loss=17.66, generator_kl_loss=1.418, generator_dur_loss=1.75, generator_adv_loss=2.021, generator_feat_match_loss=6.35, over 2513.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:12:07,888 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 06:12:15,878 INFO [train.py:591] (4/6) Epoch 991, validation: discriminator_loss=2.734, discriminator_real_loss=1.385, discriminator_fake_loss=1.349, generator_loss=28.05, generator_mel_loss=17.76, generator_kl_loss=1.38, generator_dur_loss=1.803, generator_adv_loss=1.889, generator_feat_match_loss=5.217, over 100.00 samples. +2024-03-16 06:12:15,879 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 06:14:34,121 INFO [train.py:527] (4/6) Epoch 991, batch 90, global_batch_idx: 122850, batch size: 72, loss[discriminator_loss=2.688, discriminator_real_loss=1.349, discriminator_fake_loss=1.339, generator_loss=28.96, generator_mel_loss=17.38, generator_kl_loss=1.409, generator_dur_loss=1.752, generator_adv_loss=2.13, generator_feat_match_loss=6.287, over 72.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.2, generator_mel_loss=17.66, generator_kl_loss=1.451, generator_dur_loss=1.729, generator_adv_loss=2.025, generator_feat_match_loss=6.33, over 5234.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:16:06,418 INFO [train.py:919] (4/6) Start epoch 992 +2024-03-16 06:17:12,723 INFO [train.py:527] (4/6) Epoch 992, batch 16, global_batch_idx: 122900, batch size: 74, loss[discriminator_loss=2.668, discriminator_real_loss=1.26, discriminator_fake_loss=1.409, generator_loss=30.54, generator_mel_loss=18.05, generator_kl_loss=1.617, generator_dur_loss=1.785, generator_adv_loss=2.04, generator_feat_match_loss=7.051, over 74.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=29.32, generator_mel_loss=17.76, generator_kl_loss=1.497, generator_dur_loss=1.714, generator_adv_loss=2.026, generator_feat_match_loss=6.326, over 911.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:19:33,879 INFO [train.py:527] (4/6) Epoch 992, batch 66, global_batch_idx: 122950, batch size: 88, loss[discriminator_loss=2.679, discriminator_real_loss=1.295, discriminator_fake_loss=1.384, generator_loss=29.76, generator_mel_loss=17.82, generator_kl_loss=1.323, generator_dur_loss=1.815, generator_adv_loss=1.947, generator_feat_match_loss=6.857, over 88.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=29.28, generator_mel_loss=17.74, generator_kl_loss=1.462, generator_dur_loss=1.729, generator_adv_loss=2.017, generator_feat_match_loss=6.324, over 3612.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:21:53,156 INFO [train.py:527] (4/6) Epoch 992, batch 116, global_batch_idx: 123000, batch size: 72, loss[discriminator_loss=2.635, discriminator_real_loss=1.252, discriminator_fake_loss=1.383, generator_loss=29.12, generator_mel_loss=17.56, generator_kl_loss=1.365, generator_dur_loss=1.794, generator_adv_loss=2.095, generator_feat_match_loss=6.304, over 72.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.323, generator_loss=29.24, generator_mel_loss=17.71, generator_kl_loss=1.454, generator_dur_loss=1.731, generator_adv_loss=2.02, generator_feat_match_loss=6.323, over 6436.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:21:53,157 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 06:22:02,304 INFO [train.py:591] (4/6) Epoch 992, validation: discriminator_loss=2.774, discriminator_real_loss=1.453, discriminator_fake_loss=1.321, generator_loss=28.94, generator_mel_loss=18.67, generator_kl_loss=1.351, generator_dur_loss=1.807, generator_adv_loss=1.995, generator_feat_match_loss=5.123, over 100.00 samples. +2024-03-16 06:22:02,305 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 06:22:21,509 INFO [train.py:919] (4/6) Start epoch 993 +2024-03-16 06:24:43,655 INFO [train.py:527] (4/6) Epoch 993, batch 42, global_batch_idx: 123050, batch size: 96, loss[discriminator_loss=2.693, discriminator_real_loss=1.378, discriminator_fake_loss=1.315, generator_loss=28.74, generator_mel_loss=17.49, generator_kl_loss=1.389, generator_dur_loss=1.842, generator_adv_loss=1.932, generator_feat_match_loss=6.095, over 96.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.354, discriminator_fake_loss=1.325, generator_loss=29.25, generator_mel_loss=17.74, generator_kl_loss=1.44, generator_dur_loss=1.741, generator_adv_loss=2.014, generator_feat_match_loss=6.314, over 2459.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:27:03,413 INFO [train.py:527] (4/6) Epoch 993, batch 92, global_batch_idx: 123100, batch size: 50, loss[discriminator_loss=2.669, discriminator_real_loss=1.4, discriminator_fake_loss=1.269, generator_loss=29.48, generator_mel_loss=17.95, generator_kl_loss=1.377, generator_dur_loss=1.701, generator_adv_loss=2.025, generator_feat_match_loss=6.43, over 50.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.352, discriminator_fake_loss=1.319, generator_loss=29.27, generator_mel_loss=17.72, generator_kl_loss=1.432, generator_dur_loss=1.751, generator_adv_loss=2.022, generator_feat_match_loss=6.343, over 5572.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:28:28,211 INFO [train.py:919] (4/6) Start epoch 994 +2024-03-16 06:29:43,574 INFO [train.py:527] (4/6) Epoch 994, batch 18, global_batch_idx: 123150, batch size: 58, loss[discriminator_loss=2.627, discriminator_real_loss=1.287, discriminator_fake_loss=1.34, generator_loss=29.55, generator_mel_loss=17.75, generator_kl_loss=1.51, generator_dur_loss=1.699, generator_adv_loss=1.945, generator_feat_match_loss=6.648, over 58.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.345, discriminator_fake_loss=1.325, generator_loss=29.37, generator_mel_loss=17.77, generator_kl_loss=1.454, generator_dur_loss=1.729, generator_adv_loss=2.015, generator_feat_match_loss=6.394, over 1056.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:32:04,819 INFO [train.py:527] (4/6) Epoch 994, batch 68, global_batch_idx: 123200, batch size: 42, loss[discriminator_loss=2.695, discriminator_real_loss=1.38, discriminator_fake_loss=1.314, generator_loss=29.29, generator_mel_loss=18.03, generator_kl_loss=1.466, generator_dur_loss=1.686, generator_adv_loss=2.022, generator_feat_match_loss=6.085, over 42.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.345, discriminator_fake_loss=1.32, generator_loss=29.3, generator_mel_loss=17.72, generator_kl_loss=1.45, generator_dur_loss=1.747, generator_adv_loss=2.01, generator_feat_match_loss=6.372, over 3987.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:32:04,821 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 06:32:12,729 INFO [train.py:591] (4/6) Epoch 994, validation: discriminator_loss=2.784, discriminator_real_loss=1.422, discriminator_fake_loss=1.362, generator_loss=28.28, generator_mel_loss=18.37, generator_kl_loss=1.264, generator_dur_loss=1.805, generator_adv_loss=1.849, generator_feat_match_loss=4.988, over 100.00 samples. +2024-03-16 06:32:12,730 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 06:34:30,957 INFO [train.py:527] (4/6) Epoch 994, batch 118, global_batch_idx: 123250, batch size: 56, loss[discriminator_loss=2.631, discriminator_real_loss=1.295, discriminator_fake_loss=1.337, generator_loss=30.03, generator_mel_loss=17.9, generator_kl_loss=1.471, generator_dur_loss=1.674, generator_adv_loss=2.161, generator_feat_match_loss=6.828, over 56.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.343, discriminator_fake_loss=1.321, generator_loss=29.24, generator_mel_loss=17.7, generator_kl_loss=1.448, generator_dur_loss=1.74, generator_adv_loss=2.013, generator_feat_match_loss=6.337, over 6893.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:34:46,257 INFO [train.py:919] (4/6) Start epoch 995 +2024-03-16 06:37:11,267 INFO [train.py:527] (4/6) Epoch 995, batch 44, global_batch_idx: 123300, batch size: 39, loss[discriminator_loss=2.604, discriminator_real_loss=1.257, discriminator_fake_loss=1.347, generator_loss=29.89, generator_mel_loss=18.27, generator_kl_loss=1.498, generator_dur_loss=1.682, generator_adv_loss=2.04, generator_feat_match_loss=6.402, over 39.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.353, discriminator_fake_loss=1.326, generator_loss=29.32, generator_mel_loss=17.75, generator_kl_loss=1.449, generator_dur_loss=1.743, generator_adv_loss=2.008, generator_feat_match_loss=6.376, over 2548.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:39:29,365 INFO [train.py:527] (4/6) Epoch 995, batch 94, global_batch_idx: 123350, batch size: 56, loss[discriminator_loss=2.67, discriminator_real_loss=1.367, discriminator_fake_loss=1.303, generator_loss=29.76, generator_mel_loss=17.56, generator_kl_loss=1.489, generator_dur_loss=1.709, generator_adv_loss=2.015, generator_feat_match_loss=6.989, over 56.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.35, generator_mel_loss=17.72, generator_kl_loss=1.443, generator_dur_loss=1.741, generator_adv_loss=2.031, generator_feat_match_loss=6.411, over 5386.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:40:52,770 INFO [train.py:919] (4/6) Start epoch 996 +2024-03-16 06:42:12,820 INFO [train.py:527] (4/6) Epoch 996, batch 20, global_batch_idx: 123400, batch size: 74, loss[discriminator_loss=2.644, discriminator_real_loss=1.27, discriminator_fake_loss=1.374, generator_loss=29.63, generator_mel_loss=17.75, generator_kl_loss=1.304, generator_dur_loss=1.779, generator_adv_loss=1.965, generator_feat_match_loss=6.826, over 74.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.364, discriminator_fake_loss=1.323, generator_loss=28.9, generator_mel_loss=17.66, generator_kl_loss=1.43, generator_dur_loss=1.749, generator_adv_loss=1.999, generator_feat_match_loss=6.066, over 1258.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:42:12,822 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 06:42:20,825 INFO [train.py:591] (4/6) Epoch 996, validation: discriminator_loss=2.751, discriminator_real_loss=1.435, discriminator_fake_loss=1.315, generator_loss=27.61, generator_mel_loss=17.61, generator_kl_loss=1.406, generator_dur_loss=1.802, generator_adv_loss=1.891, generator_feat_match_loss=4.899, over 100.00 samples. +2024-03-16 06:42:20,825 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 06:44:41,163 INFO [train.py:527] (4/6) Epoch 996, batch 70, global_batch_idx: 123450, batch size: 74, loss[discriminator_loss=2.696, discriminator_real_loss=1.359, discriminator_fake_loss=1.338, generator_loss=29.26, generator_mel_loss=17.54, generator_kl_loss=1.366, generator_dur_loss=1.775, generator_adv_loss=2.012, generator_feat_match_loss=6.568, over 74.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.348, discriminator_fake_loss=1.317, generator_loss=29.18, generator_mel_loss=17.69, generator_kl_loss=1.435, generator_dur_loss=1.748, generator_adv_loss=2.016, generator_feat_match_loss=6.293, over 4239.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:46:58,099 INFO [train.py:527] (4/6) Epoch 996, batch 120, global_batch_idx: 123500, batch size: 70, loss[discriminator_loss=2.727, discriminator_real_loss=1.43, discriminator_fake_loss=1.296, generator_loss=29.77, generator_mel_loss=17.84, generator_kl_loss=1.35, generator_dur_loss=1.767, generator_adv_loss=2.07, generator_feat_match_loss=6.737, over 70.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.351, discriminator_fake_loss=1.317, generator_loss=29.19, generator_mel_loss=17.69, generator_kl_loss=1.446, generator_dur_loss=1.742, generator_adv_loss=2.019, generator_feat_match_loss=6.286, over 6884.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:47:07,461 INFO [train.py:919] (4/6) Start epoch 997 +2024-03-16 06:49:38,288 INFO [train.py:527] (4/6) Epoch 997, batch 46, global_batch_idx: 123550, batch size: 72, loss[discriminator_loss=2.628, discriminator_real_loss=1.369, discriminator_fake_loss=1.259, generator_loss=29.86, generator_mel_loss=17.93, generator_kl_loss=1.211, generator_dur_loss=1.781, generator_adv_loss=1.92, generator_feat_match_loss=7.014, over 72.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.356, discriminator_fake_loss=1.322, generator_loss=29.34, generator_mel_loss=17.79, generator_kl_loss=1.46, generator_dur_loss=1.727, generator_adv_loss=2.017, generator_feat_match_loss=6.346, over 2540.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:51:52,873 INFO [train.py:527] (4/6) Epoch 997, batch 96, global_batch_idx: 123600, batch size: 53, loss[discriminator_loss=2.646, discriminator_real_loss=1.347, discriminator_fake_loss=1.299, generator_loss=28.37, generator_mel_loss=17.6, generator_kl_loss=1.372, generator_dur_loss=1.747, generator_adv_loss=2.155, generator_feat_match_loss=5.488, over 53.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.32, generator_loss=29.3, generator_mel_loss=17.73, generator_kl_loss=1.453, generator_dur_loss=1.73, generator_adv_loss=2.018, generator_feat_match_loss=6.371, over 5341.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:51:52,874 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 06:52:00,767 INFO [train.py:591] (4/6) Epoch 997, validation: discriminator_loss=2.713, discriminator_real_loss=1.463, discriminator_fake_loss=1.25, generator_loss=28.08, generator_mel_loss=18.1, generator_kl_loss=1.252, generator_dur_loss=1.795, generator_adv_loss=2.04, generator_feat_match_loss=4.895, over 100.00 samples. +2024-03-16 06:52:00,768 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 06:53:19,091 INFO [train.py:919] (4/6) Start epoch 998 +2024-03-16 06:54:45,955 INFO [train.py:527] (4/6) Epoch 998, batch 22, global_batch_idx: 123650, batch size: 44, loss[discriminator_loss=2.651, discriminator_real_loss=1.25, discriminator_fake_loss=1.401, generator_loss=29.75, generator_mel_loss=17.73, generator_kl_loss=1.614, generator_dur_loss=1.679, generator_adv_loss=2.043, generator_feat_match_loss=6.677, over 44.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.322, generator_loss=29.09, generator_mel_loss=17.75, generator_kl_loss=1.467, generator_dur_loss=1.715, generator_adv_loss=1.997, generator_feat_match_loss=6.162, over 1249.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:57:01,465 INFO [train.py:527] (4/6) Epoch 998, batch 72, global_batch_idx: 123700, batch size: 25, loss[discriminator_loss=2.656, discriminator_real_loss=1.324, discriminator_fake_loss=1.332, generator_loss=30.69, generator_mel_loss=18.01, generator_kl_loss=1.825, generator_dur_loss=1.57, generator_adv_loss=2.094, generator_feat_match_loss=7.193, over 25.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.351, discriminator_fake_loss=1.319, generator_loss=29.19, generator_mel_loss=17.72, generator_kl_loss=1.444, generator_dur_loss=1.722, generator_adv_loss=2.015, generator_feat_match_loss=6.281, over 4143.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:59:20,820 INFO [train.py:527] (4/6) Epoch 998, batch 122, global_batch_idx: 123750, batch size: 96, loss[discriminator_loss=2.685, discriminator_real_loss=1.385, discriminator_fake_loss=1.301, generator_loss=28.89, generator_mel_loss=17.79, generator_kl_loss=1.323, generator_dur_loss=1.832, generator_adv_loss=1.858, generator_feat_match_loss=6.09, over 96.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.346, discriminator_fake_loss=1.32, generator_loss=29.27, generator_mel_loss=17.74, generator_kl_loss=1.463, generator_dur_loss=1.725, generator_adv_loss=2.013, generator_feat_match_loss=6.333, over 6972.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:59:26,487 INFO [train.py:919] (4/6) Start epoch 999 +2024-03-16 07:02:04,330 INFO [train.py:527] (4/6) Epoch 999, batch 48, global_batch_idx: 123800, batch size: 61, loss[discriminator_loss=2.716, discriminator_real_loss=1.425, discriminator_fake_loss=1.291, generator_loss=28.33, generator_mel_loss=17.94, generator_kl_loss=1.263, generator_dur_loss=1.72, generator_adv_loss=1.898, generator_feat_match_loss=5.515, over 61.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.32, generator_loss=29.24, generator_mel_loss=17.69, generator_kl_loss=1.44, generator_dur_loss=1.74, generator_adv_loss=2.029, generator_feat_match_loss=6.345, over 2818.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 07:02:04,332 INFO [train.py:581] (4/6) Computing validation loss +2024-03-16 07:02:12,163 INFO [train.py:591] (4/6) Epoch 999, validation: discriminator_loss=2.747, discriminator_real_loss=1.323, discriminator_fake_loss=1.424, generator_loss=28.31, generator_mel_loss=18.07, generator_kl_loss=1.205, generator_dur_loss=1.805, generator_adv_loss=1.787, generator_feat_match_loss=5.439, over 100.00 samples. +2024-03-16 07:02:12,164 INFO [train.py:592] (4/6) Maximum memory allocated so far is 29062MB +2024-03-16 07:04:29,736 INFO [train.py:527] (4/6) Epoch 999, batch 98, global_batch_idx: 123850, batch size: 48, loss[discriminator_loss=2.701, discriminator_real_loss=1.294, discriminator_fake_loss=1.407, generator_loss=28.99, generator_mel_loss=17.31, generator_kl_loss=1.51, generator_dur_loss=1.708, generator_adv_loss=2.031, generator_feat_match_loss=6.427, over 48.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.325, generator_loss=29.22, generator_mel_loss=17.7, generator_kl_loss=1.438, generator_dur_loss=1.74, generator_adv_loss=2.017, generator_feat_match_loss=6.327, over 5620.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 07:05:43,113 INFO [train.py:919] (4/6) Start epoch 1000 +2024-03-16 07:07:13,383 INFO [train.py:527] (4/6) Epoch 1000, batch 24, global_batch_idx: 123900, batch size: 58, loss[discriminator_loss=2.637, discriminator_real_loss=1.302, discriminator_fake_loss=1.335, generator_loss=30.7, generator_mel_loss=18.68, generator_kl_loss=1.435, generator_dur_loss=1.754, generator_adv_loss=2.237, generator_feat_match_loss=6.592, over 58.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.34, discriminator_fake_loss=1.327, generator_loss=29.29, generator_mel_loss=17.8, generator_kl_loss=1.438, generator_dur_loss=1.743, generator_adv_loss=2.054, generator_feat_match_loss=6.253, over 1422.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 07:09:33,607 INFO [train.py:527] (4/6) Epoch 1000, batch 74, global_batch_idx: 123950, batch size: 58, loss[discriminator_loss=2.617, discriminator_real_loss=1.314, discriminator_fake_loss=1.303, generator_loss=30.1, generator_mel_loss=18.11, generator_kl_loss=1.505, generator_dur_loss=1.693, generator_adv_loss=2.049, generator_feat_match_loss=6.743, over 58.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.349, discriminator_fake_loss=1.316, generator_loss=29.2, generator_mel_loss=17.7, generator_kl_loss=1.428, generator_dur_loss=1.737, generator_adv_loss=2.034, generator_feat_match_loss=6.305, over 4444.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 07:11:48,607 INFO [train.py:977] (4/6) Done!