diff --git "a/exp/log/log-train-2024-03-11-23-34-29-5" "b/exp/log/log-train-2024-03-11-23-34-29-5" new file mode 100644--- /dev/null +++ "b/exp/log/log-train-2024-03-11-23-34-29-5" @@ -0,0 +1,5364 @@ +2024-03-11 23:34:29,685 INFO [train.py:805] (5/6) Training started +2024-03-11 23:34:29,685 INFO [train.py:815] (5/6) Device: cuda:5 +2024-03-11 23:34:29,686 INFO [tts_datamodule.py:322] (5/6) About to get train cuts +2024-03-11 23:34:29,689 INFO [tts_datamodule.py:337] (5/6) About to get speakers +2024-03-11 23:34:29,690 INFO [train.py:827] (5/6) {'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 23:34:29,690 INFO [train.py:829] (5/6) About to create model +2024-03-11 23:34:32,709 INFO [train.py:835] (5/6) Number of parameters in generator: 39004210 +2024-03-11 23:34:32,710 INFO [train.py:837] (5/6) Number of parameters in discriminator: 50974956 +2024-03-11 23:34:32,710 INFO [train.py:838] (5/6) Total number of parameters: 89979166 +2024-03-11 23:34:40,116 INFO [train.py:845] (5/6) Using DDP +2024-03-11 23:34:41,292 INFO [tts_datamodule.py:175] (5/6) About to create train dataset +2024-03-11 23:34:41,293 INFO [tts_datamodule.py:201] (5/6) Using DynamicBucketingSampler. +2024-03-11 23:34:42,526 INFO [tts_datamodule.py:218] (5/6) About to create train dataloader +2024-03-11 23:34:42,526 INFO [tts_datamodule.py:327] (5/6) About to get validation cuts +2024-03-11 23:34:42,528 INFO [tts_datamodule.py:241] (5/6) About to create dev dataset +2024-03-11 23:34:42,541 INFO [tts_datamodule.py:270] (5/6) About to create valid dataloader +2024-03-11 23:34:42,541 INFO [train.py:725] (5/6) 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] (5/6) Maximum memory allocated so far is 8385MB +2024-03-11 23:35:06,673 INFO [train.py:780] (5/6) Maximum memory allocated so far is 8661MB +2024-03-11 23:35:10,795 INFO [train.py:780] (5/6) Maximum memory allocated so far is 17986MB +2024-03-11 23:35:13,666 INFO [train.py:780] (5/6) Maximum memory allocated so far is 17991MB +2024-03-11 23:35:18,923 INFO [train.py:780] (5/6) Maximum memory allocated so far is 27254MB +2024-03-11 23:35:22,779 INFO [train.py:780] (5/6) Maximum memory allocated so far is 27256MB +2024-03-11 23:35:22,789 INFO [train.py:919] (5/6) Start epoch 1 +2024-03-11 23:35:44,735 INFO [train.py:527] (5/6) Epoch 1, batch 0, global_batch_idx: 0, batch size: 77, loss[discriminator_loss=6.107, discriminator_real_loss=6.106, discriminator_fake_loss=0.001034, generator_loss=1582, generator_mel_loss=103.5, generator_kl_loss=1472, generator_dur_loss=1.898, generator_adv_loss=4.809, generator_feat_match_loss=0.1818, over 77.00 samples.], tot_loss[discriminator_loss=6.107, discriminator_real_loss=6.106, discriminator_fake_loss=0.001034, generator_loss=1582, generator_mel_loss=103.5, generator_kl_loss=1472, generator_dur_loss=1.898, generator_adv_loss=4.809, generator_feat_match_loss=0.1818, over 77.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:35:44,737 INFO [train.py:581] (5/6) Computing validation loss +2024-03-11 23:35:53,001 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 27256MB +2024-03-11 23:38:13,691 INFO [train.py:527] (5/6) Epoch 1, batch 50, global_batch_idx: 50, batch size: 36, loss[discriminator_loss=2.75, discriminator_real_loss=1.459, discriminator_fake_loss=1.291, generator_loss=116, generator_mel_loss=50.13, generator_kl_loss=61.22, generator_dur_loss=1.617, generator_adv_loss=1.841, generator_feat_match_loss=1.242, over 36.00 samples.], tot_loss[discriminator_loss=3.122, discriminator_real_loss=1.743, discriminator_fake_loss=1.379, generator_loss=221.4, generator_mel_loss=62.09, generator_kl_loss=155.3, generator_dur_loss=1.656, generator_adv_loss=1.785, generator_feat_match_loss=0.5079, over 3040.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:40:30,836 INFO [train.py:527] (5/6) Epoch 1, batch 100, global_batch_idx: 100, batch size: 55, loss[discriminator_loss=2.801, discriminator_real_loss=1.678, discriminator_fake_loss=1.123, generator_loss=85.28, generator_mel_loss=46.52, generator_kl_loss=34.18, generator_dur_loss=1.678, generator_adv_loss=1.842, generator_feat_match_loss=1.057, over 55.00 samples.], tot_loss[discriminator_loss=2.946, discriminator_real_loss=1.636, discriminator_fake_loss=1.309, generator_loss=164.6, generator_mel_loss=55.4, generator_kl_loss=104.7, generator_dur_loss=1.674, generator_adv_loss=1.844, generator_feat_match_loss=0.9767, over 5697.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:41:34,968 INFO [train.py:919] (5/6) Start epoch 2 +2024-03-11 23:43:11,661 INFO [train.py:527] (5/6) Epoch 2, batch 26, global_batch_idx: 150, batch size: 96, loss[discriminator_loss=2.788, discriminator_real_loss=1.264, discriminator_fake_loss=1.523, generator_loss=71.37, generator_mel_loss=41.53, generator_kl_loss=24.8, generator_dur_loss=1.929, generator_adv_loss=1.698, generator_feat_match_loss=1.407, over 96.00 samples.], tot_loss[discriminator_loss=2.795, discriminator_real_loss=1.486, discriminator_fake_loss=1.309, generator_loss=76.25, generator_mel_loss=45.28, generator_kl_loss=25.65, generator_dur_loss=1.806, generator_adv_loss=1.916, generator_feat_match_loss=1.596, over 1431.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:45:31,870 INFO [train.py:527] (5/6) Epoch 2, batch 76, global_batch_idx: 200, batch size: 88, loss[discriminator_loss=2.804, discriminator_real_loss=1.483, discriminator_fake_loss=1.32, generator_loss=62.46, generator_mel_loss=39.41, generator_kl_loss=18.03, generator_dur_loss=1.926, generator_adv_loss=1.737, generator_feat_match_loss=1.353, over 88.00 samples.], tot_loss[discriminator_loss=2.805, discriminator_real_loss=1.483, discriminator_fake_loss=1.322, generator_loss=70.84, generator_mel_loss=43.56, generator_kl_loss=21.98, generator_dur_loss=1.842, generator_adv_loss=1.874, generator_feat_match_loss=1.58, over 4316.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:45:31,871 INFO [train.py:581] (5/6) Computing validation loss +2024-03-11 23:45:39,715 INFO [train.py:591] (5/6) 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 23:45:39,716 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28512MB +2024-03-11 23:47:45,152 INFO [train.py:919] (5/6) Start epoch 3 +2024-03-11 23:48:14,682 INFO [train.py:527] (5/6) Epoch 3, batch 2, global_batch_idx: 250, batch size: 14, loss[discriminator_loss=2.831, discriminator_real_loss=1.381, discriminator_fake_loss=1.45, generator_loss=60.79, generator_mel_loss=41.54, generator_kl_loss=14.6, generator_dur_loss=1.758, generator_adv_loss=1.632, generator_feat_match_loss=1.261, over 14.00 samples.], tot_loss[discriminator_loss=2.852, discriminator_real_loss=1.385, discriminator_fake_loss=1.467, generator_loss=56.96, generator_mel_loss=38.93, generator_kl_loss=13.38, generator_dur_loss=1.875, generator_adv_loss=1.739, generator_feat_match_loss=1.033, over 122.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:50:34,537 INFO [train.py:527] (5/6) Epoch 3, batch 52, global_batch_idx: 300, batch size: 56, loss[discriminator_loss=3.107, discriminator_real_loss=1.657, discriminator_fake_loss=1.45, generator_loss=49.69, generator_mel_loss=35.52, generator_kl_loss=9.625, generator_dur_loss=1.963, generator_adv_loss=1.857, generator_feat_match_loss=0.7205, over 56.00 samples.], tot_loss[discriminator_loss=2.878, discriminator_real_loss=1.468, discriminator_fake_loss=1.41, generator_loss=53.68, generator_mel_loss=37.16, generator_kl_loss=11.98, generator_dur_loss=1.905, generator_adv_loss=1.696, generator_feat_match_loss=0.9395, over 2947.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:52:54,830 INFO [train.py:527] (5/6) Epoch 3, batch 102, global_batch_idx: 350, batch size: 39, loss[discriminator_loss=2.884, discriminator_real_loss=1.734, discriminator_fake_loss=1.15, generator_loss=47.8, generator_mel_loss=34.93, generator_kl_loss=8.102, generator_dur_loss=1.965, generator_adv_loss=1.815, generator_feat_match_loss=0.9937, over 39.00 samples.], tot_loss[discriminator_loss=2.859, discriminator_real_loss=1.472, discriminator_fake_loss=1.387, generator_loss=51.78, generator_mel_loss=36.27, generator_kl_loss=10.81, generator_dur_loss=1.922, generator_adv_loss=1.727, generator_feat_match_loss=1.049, over 5935.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:53:55,195 INFO [train.py:919] (5/6) Start epoch 4 +2024-03-11 23:55:37,840 INFO [train.py:527] (5/6) Epoch 4, batch 28, global_batch_idx: 400, batch size: 31, loss[discriminator_loss=2.613, discriminator_real_loss=1.33, discriminator_fake_loss=1.283, generator_loss=46.39, generator_mel_loss=32.58, generator_kl_loss=8.376, generator_dur_loss=1.878, generator_adv_loss=1.807, generator_feat_match_loss=1.749, over 31.00 samples.], tot_loss[discriminator_loss=2.775, discriminator_real_loss=1.464, discriminator_fake_loss=1.311, generator_loss=46.47, generator_mel_loss=33.31, generator_kl_loss=7.834, generator_dur_loss=1.958, generator_adv_loss=1.888, generator_feat_match_loss=1.483, over 1820.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-11 23:55:37,842 INFO [train.py:581] (5/6) Computing validation loss +2024-03-11 23:55:46,451 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28807MB +2024-03-11 23:58:04,883 INFO [train.py:527] (5/6) Epoch 4, batch 78, global_batch_idx: 450, batch size: 53, loss[discriminator_loss=2.518, discriminator_real_loss=1.162, discriminator_fake_loss=1.356, generator_loss=45.77, generator_mel_loss=33.19, generator_kl_loss=6.373, generator_dur_loss=1.857, generator_adv_loss=1.962, generator_feat_match_loss=2.385, over 53.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.448, discriminator_fake_loss=1.287, generator_loss=45.42, generator_mel_loss=32.84, generator_kl_loss=7.131, generator_dur_loss=1.955, generator_adv_loss=1.886, generator_feat_match_loss=1.603, over 4630.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:00:09,407 INFO [train.py:919] (5/6) Start epoch 5 +2024-03-12 00:00:45,080 INFO [train.py:527] (5/6) Epoch 5, batch 4, global_batch_idx: 500, batch size: 48, loss[discriminator_loss=2.778, discriminator_real_loss=1.522, discriminator_fake_loss=1.257, generator_loss=42.83, generator_mel_loss=32.26, generator_kl_loss=5.418, generator_dur_loss=1.932, generator_adv_loss=1.898, generator_feat_match_loss=1.323, over 48.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.49, discriminator_fake_loss=1.276, generator_loss=43.81, generator_mel_loss=32.78, generator_kl_loss=5.636, generator_dur_loss=1.947, generator_adv_loss=1.846, generator_feat_match_loss=1.598, over 288.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:03:05,367 INFO [train.py:527] (5/6) Epoch 5, batch 54, global_batch_idx: 550, batch size: 77, loss[discriminator_loss=2.77, discriminator_real_loss=1.49, discriminator_fake_loss=1.28, generator_loss=40.69, generator_mel_loss=30.62, generator_kl_loss=4.511, generator_dur_loss=1.94, generator_adv_loss=2.023, generator_feat_match_loss=1.599, over 77.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.456, discriminator_fake_loss=1.313, generator_loss=41.17, generator_mel_loss=30.75, generator_kl_loss=5.073, generator_dur_loss=1.974, generator_adv_loss=1.832, generator_feat_match_loss=1.544, over 3326.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:05:22,101 INFO [train.py:527] (5/6) Epoch 5, batch 104, global_batch_idx: 600, batch size: 55, loss[discriminator_loss=2.648, discriminator_real_loss=1.483, discriminator_fake_loss=1.165, generator_loss=40.06, generator_mel_loss=30.49, generator_kl_loss=3.876, generator_dur_loss=1.933, generator_adv_loss=1.91, generator_feat_match_loss=1.854, over 55.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.446, discriminator_fake_loss=1.302, generator_loss=40.31, generator_mel_loss=30.11, generator_kl_loss=4.749, generator_dur_loss=1.965, generator_adv_loss=1.856, generator_feat_match_loss=1.627, over 6381.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:05:22,103 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 00:05:31,117 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 00:06:24,143 INFO [train.py:919] (5/6) Start epoch 6 +2024-03-12 00:08:08,937 INFO [train.py:527] (5/6) Epoch 6, batch 30, global_batch_idx: 650, batch size: 44, loss[discriminator_loss=2.749, discriminator_real_loss=1.38, discriminator_fake_loss=1.369, generator_loss=39.55, generator_mel_loss=30.27, generator_kl_loss=3.808, generator_dur_loss=1.912, generator_adv_loss=1.743, generator_feat_match_loss=1.814, over 44.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.468, discriminator_fake_loss=1.295, generator_loss=38.69, generator_mel_loss=29.15, generator_kl_loss=3.862, generator_dur_loss=1.965, generator_adv_loss=1.916, generator_feat_match_loss=1.794, over 1707.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:10:28,030 INFO [train.py:527] (5/6) Epoch 6, batch 80, global_batch_idx: 700, batch size: 44, loss[discriminator_loss=2.759, discriminator_real_loss=1.22, discriminator_fake_loss=1.538, generator_loss=39.12, generator_mel_loss=29.38, generator_kl_loss=3.643, generator_dur_loss=1.871, generator_adv_loss=2.156, generator_feat_match_loss=2.066, over 44.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.432, discriminator_fake_loss=1.296, generator_loss=37.99, generator_mel_loss=28.64, generator_kl_loss=3.664, generator_dur_loss=1.964, generator_adv_loss=1.907, generator_feat_match_loss=1.811, over 4562.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:12:30,277 INFO [train.py:919] (5/6) Start epoch 7 +2024-03-12 00:13:11,139 INFO [train.py:527] (5/6) Epoch 7, batch 6, global_batch_idx: 750, batch size: 77, loss[discriminator_loss=2.776, discriminator_real_loss=1.37, discriminator_fake_loss=1.406, generator_loss=33.84, generator_mel_loss=25.43, generator_kl_loss=2.849, generator_dur_loss=2.051, generator_adv_loss=1.963, generator_feat_match_loss=1.554, over 77.00 samples.], tot_loss[discriminator_loss=2.78, discriminator_real_loss=1.42, discriminator_fake_loss=1.36, generator_loss=36.1, generator_mel_loss=27.44, generator_kl_loss=2.962, generator_dur_loss=1.969, generator_adv_loss=1.938, generator_feat_match_loss=1.792, over 413.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:15:30,307 INFO [train.py:527] (5/6) Epoch 7, batch 56, global_batch_idx: 800, batch size: 88, loss[discriminator_loss=2.823, discriminator_real_loss=1.514, discriminator_fake_loss=1.308, generator_loss=36.36, generator_mel_loss=27.89, generator_kl_loss=2.94, generator_dur_loss=1.992, generator_adv_loss=1.967, generator_feat_match_loss=1.57, over 88.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.453, discriminator_fake_loss=1.31, generator_loss=36.23, generator_mel_loss=27.58, generator_kl_loss=2.947, generator_dur_loss=1.962, generator_adv_loss=1.915, generator_feat_match_loss=1.831, over 3299.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:15:30,309 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 00:15:38,549 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 00:17:56,798 INFO [train.py:527] (5/6) Epoch 7, batch 106, global_batch_idx: 850, batch size: 48, loss[discriminator_loss=2.65, discriminator_real_loss=1.397, discriminator_fake_loss=1.253, generator_loss=34.95, generator_mel_loss=26.63, generator_kl_loss=2.648, generator_dur_loss=1.836, generator_adv_loss=1.869, generator_feat_match_loss=1.969, over 48.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.444, discriminator_fake_loss=1.307, generator_loss=35.83, generator_mel_loss=27.36, generator_kl_loss=2.821, generator_dur_loss=1.963, generator_adv_loss=1.889, generator_feat_match_loss=1.8, over 6281.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:18:47,641 INFO [train.py:919] (5/6) Start epoch 8 +2024-03-12 00:20:42,531 INFO [train.py:527] (5/6) Epoch 8, batch 32, global_batch_idx: 900, batch size: 62, loss[discriminator_loss=2.769, discriminator_real_loss=1.455, discriminator_fake_loss=1.314, generator_loss=34.25, generator_mel_loss=26.45, generator_kl_loss=2.509, generator_dur_loss=1.94, generator_adv_loss=1.764, generator_feat_match_loss=1.582, over 62.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.404, discriminator_fake_loss=1.316, generator_loss=34.91, generator_mel_loss=26.68, generator_kl_loss=2.498, generator_dur_loss=1.959, generator_adv_loss=1.887, generator_feat_match_loss=1.878, over 1859.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:23:05,283 INFO [train.py:527] (5/6) Epoch 8, batch 82, global_batch_idx: 950, batch size: 66, loss[discriminator_loss=2.666, discriminator_real_loss=1.425, discriminator_fake_loss=1.242, generator_loss=34.69, generator_mel_loss=26.41, generator_kl_loss=2.229, generator_dur_loss=2.001, generator_adv_loss=1.887, generator_feat_match_loss=2.17, over 66.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.422, discriminator_fake_loss=1.312, generator_loss=34.65, generator_mel_loss=26.49, generator_kl_loss=2.399, generator_dur_loss=1.958, generator_adv_loss=1.913, generator_feat_match_loss=1.89, over 4695.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:24:59,025 INFO [train.py:919] (5/6) Start epoch 9 +2024-03-12 00:25:44,762 INFO [train.py:527] (5/6) Epoch 9, batch 8, global_batch_idx: 1000, batch size: 55, loss[discriminator_loss=2.616, discriminator_real_loss=1.443, discriminator_fake_loss=1.173, generator_loss=34.34, generator_mel_loss=26.25, generator_kl_loss=2.137, generator_dur_loss=1.917, generator_adv_loss=1.964, generator_feat_match_loss=2.071, over 55.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.397, discriminator_fake_loss=1.299, generator_loss=33.82, generator_mel_loss=25.9, generator_kl_loss=2.199, generator_dur_loss=1.947, generator_adv_loss=1.887, generator_feat_match_loss=1.888, over 454.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:25:44,765 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 00:25:52,726 INFO [train.py:591] (5/6) 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 00:25:52,728 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 00:28:16,628 INFO [train.py:527] (5/6) Epoch 9, batch 58, global_batch_idx: 1050, batch size: 25, loss[discriminator_loss=2.779, discriminator_real_loss=1.367, discriminator_fake_loss=1.412, generator_loss=35.2, generator_mel_loss=26.92, generator_kl_loss=2.234, generator_dur_loss=1.792, generator_adv_loss=2.184, generator_feat_match_loss=2.072, over 25.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.402, discriminator_fake_loss=1.294, generator_loss=34.2, generator_mel_loss=26.15, generator_kl_loss=2.164, generator_dur_loss=1.95, generator_adv_loss=1.917, generator_feat_match_loss=2.019, over 3305.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:30:36,560 INFO [train.py:527] (5/6) Epoch 9, batch 108, global_batch_idx: 1100, batch size: 56, loss[discriminator_loss=2.784, discriminator_real_loss=1.504, discriminator_fake_loss=1.281, generator_loss=33.33, generator_mel_loss=25.32, generator_kl_loss=1.928, generator_dur_loss=1.888, generator_adv_loss=2.278, generator_feat_match_loss=1.918, over 56.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.418, discriminator_fake_loss=1.29, generator_loss=33.9, generator_mel_loss=25.93, generator_kl_loss=2.114, generator_dur_loss=1.95, generator_adv_loss=1.911, generator_feat_match_loss=1.993, over 6238.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:31:17,528 INFO [train.py:919] (5/6) Start epoch 10 +2024-03-12 00:33:17,585 INFO [train.py:527] (5/6) Epoch 10, batch 34, global_batch_idx: 1150, batch size: 13, loss[discriminator_loss=2.531, discriminator_real_loss=1.351, discriminator_fake_loss=1.181, generator_loss=38.17, generator_mel_loss=29.66, generator_kl_loss=2.065, generator_dur_loss=1.828, generator_adv_loss=2.349, generator_feat_match_loss=2.272, over 13.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.377, discriminator_fake_loss=1.295, generator_loss=33.72, generator_mel_loss=25.78, generator_kl_loss=1.927, generator_dur_loss=1.915, generator_adv_loss=1.934, generator_feat_match_loss=2.158, over 2001.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:35:38,714 INFO [train.py:527] (5/6) Epoch 10, batch 84, global_batch_idx: 1200, batch size: 53, loss[discriminator_loss=2.655, discriminator_real_loss=1.393, discriminator_fake_loss=1.262, generator_loss=31.4, generator_mel_loss=24.27, generator_kl_loss=1.672, generator_dur_loss=1.89, generator_adv_loss=1.626, generator_feat_match_loss=1.943, over 53.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.396, discriminator_fake_loss=1.287, generator_loss=33.19, generator_mel_loss=25.38, generator_kl_loss=1.833, generator_dur_loss=1.924, generator_adv_loss=1.932, generator_feat_match_loss=2.122, over 5068.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:35:38,716 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 00:35:47,142 INFO [train.py:591] (5/6) 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 00:35:47,143 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 00:37:36,030 INFO [train.py:919] (5/6) Start epoch 11 +2024-03-12 00:38:30,467 INFO [train.py:527] (5/6) Epoch 11, batch 10, global_batch_idx: 1250, batch size: 14, loss[discriminator_loss=2.536, discriminator_real_loss=1.361, discriminator_fake_loss=1.174, generator_loss=34.33, generator_mel_loss=26.18, generator_kl_loss=1.839, generator_dur_loss=1.834, generator_adv_loss=2.001, generator_feat_match_loss=2.471, over 14.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.409, discriminator_fake_loss=1.296, generator_loss=33.07, generator_mel_loss=25.54, generator_kl_loss=1.662, generator_dur_loss=1.953, generator_adv_loss=1.918, generator_feat_match_loss=1.999, over 576.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:40:53,318 INFO [train.py:527] (5/6) Epoch 11, batch 60, global_batch_idx: 1300, batch size: 52, loss[discriminator_loss=2.672, discriminator_real_loss=1.399, discriminator_fake_loss=1.274, generator_loss=33.3, generator_mel_loss=25.35, generator_kl_loss=1.573, generator_dur_loss=1.856, generator_adv_loss=2.088, generator_feat_match_loss=2.427, over 52.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.407, discriminator_fake_loss=1.292, generator_loss=32.87, generator_mel_loss=25.21, generator_kl_loss=1.642, generator_dur_loss=1.958, generator_adv_loss=1.947, generator_feat_match_loss=2.112, over 3546.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:43:12,150 INFO [train.py:527] (5/6) Epoch 11, batch 110, global_batch_idx: 1350, batch size: 66, loss[discriminator_loss=2.608, discriminator_real_loss=1.307, discriminator_fake_loss=1.302, generator_loss=33.28, generator_mel_loss=25.39, generator_kl_loss=1.561, generator_dur_loss=1.967, generator_adv_loss=2.026, generator_feat_match_loss=2.335, over 66.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.399, discriminator_fake_loss=1.288, generator_loss=32.9, generator_mel_loss=25.22, generator_kl_loss=1.615, generator_dur_loss=1.954, generator_adv_loss=1.953, generator_feat_match_loss=2.16, over 6321.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:43:49,050 INFO [train.py:919] (5/6) Start epoch 12 +2024-03-12 00:45:56,715 INFO [train.py:527] (5/6) Epoch 12, batch 36, global_batch_idx: 1400, batch size: 47, loss[discriminator_loss=2.805, discriminator_real_loss=1.521, discriminator_fake_loss=1.284, generator_loss=32.5, generator_mel_loss=25.32, generator_kl_loss=1.614, generator_dur_loss=1.916, generator_adv_loss=1.675, generator_feat_match_loss=1.977, over 47.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.411, discriminator_fake_loss=1.326, generator_loss=32.8, generator_mel_loss=25.07, generator_kl_loss=1.536, generator_dur_loss=1.982, generator_adv_loss=2, generator_feat_match_loss=2.212, over 2199.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:45:56,716 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 00:46:04,727 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 00:48:26,191 INFO [train.py:527] (5/6) Epoch 12, batch 86, global_batch_idx: 1450, batch size: 88, loss[discriminator_loss=2.719, discriminator_real_loss=1.405, discriminator_fake_loss=1.314, generator_loss=31.7, generator_mel_loss=24.54, generator_kl_loss=1.413, generator_dur_loss=2.052, generator_adv_loss=1.732, generator_feat_match_loss=1.963, over 88.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.414, discriminator_fake_loss=1.323, generator_loss=32.5, generator_mel_loss=25, generator_kl_loss=1.51, generator_dur_loss=1.974, generator_adv_loss=1.936, generator_feat_match_loss=2.084, over 5076.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:50:11,549 INFO [train.py:919] (5/6) Start epoch 13 +2024-03-12 00:51:09,524 INFO [train.py:527] (5/6) Epoch 13, batch 12, global_batch_idx: 1500, batch size: 88, loss[discriminator_loss=2.675, discriminator_real_loss=1.343, discriminator_fake_loss=1.332, generator_loss=31.86, generator_mel_loss=24.2, generator_kl_loss=1.443, generator_dur_loss=2.038, generator_adv_loss=1.87, generator_feat_match_loss=2.308, over 88.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.397, discriminator_fake_loss=1.31, generator_loss=32.28, generator_mel_loss=24.66, generator_kl_loss=1.461, generator_dur_loss=1.989, generator_adv_loss=1.936, generator_feat_match_loss=2.234, over 785.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:53:26,366 INFO [train.py:527] (5/6) Epoch 13, batch 62, global_batch_idx: 1550, batch size: 70, loss[discriminator_loss=2.706, discriminator_real_loss=1.623, discriminator_fake_loss=1.083, generator_loss=31.72, generator_mel_loss=24.19, generator_kl_loss=1.373, generator_dur_loss=2.01, generator_adv_loss=1.894, generator_feat_match_loss=2.253, over 70.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.401, discriminator_fake_loss=1.317, generator_loss=32.36, generator_mel_loss=24.85, generator_kl_loss=1.455, generator_dur_loss=1.962, generator_adv_loss=1.925, generator_feat_match_loss=2.171, over 3464.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:55:42,387 INFO [train.py:527] (5/6) Epoch 13, batch 112, global_batch_idx: 1600, batch size: 96, loss[discriminator_loss=2.621, discriminator_real_loss=1.226, discriminator_fake_loss=1.396, generator_loss=32.49, generator_mel_loss=24.49, generator_kl_loss=1.532, generator_dur_loss=2.062, generator_adv_loss=1.989, generator_feat_match_loss=2.416, over 96.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.404, discriminator_fake_loss=1.319, generator_loss=32.13, generator_mel_loss=24.69, generator_kl_loss=1.438, generator_dur_loss=1.967, generator_adv_loss=1.909, generator_feat_match_loss=2.128, over 6219.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 00:55:42,389 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 00:55:51,192 INFO [train.py:591] (5/6) 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 00:55:51,193 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 00:56:23,413 INFO [train.py:919] (5/6) Start epoch 14 +2024-03-12 00:58:32,771 INFO [train.py:527] (5/6) Epoch 14, batch 38, global_batch_idx: 1650, batch size: 58, loss[discriminator_loss=2.785, discriminator_real_loss=1.506, discriminator_fake_loss=1.28, generator_loss=33.34, generator_mel_loss=25.87, generator_kl_loss=1.413, generator_dur_loss=1.913, generator_adv_loss=2.049, generator_feat_match_loss=2.092, over 58.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.403, discriminator_fake_loss=1.336, generator_loss=31.57, generator_mel_loss=24.2, generator_kl_loss=1.402, generator_dur_loss=1.99, generator_adv_loss=1.898, generator_feat_match_loss=2.073, over 2352.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:00:50,112 INFO [train.py:527] (5/6) Epoch 14, batch 88, global_batch_idx: 1700, batch size: 83, loss[discriminator_loss=2.711, discriminator_real_loss=1.297, discriminator_fake_loss=1.414, generator_loss=31.07, generator_mel_loss=23.92, generator_kl_loss=1.209, generator_dur_loss=2.026, generator_adv_loss=1.848, generator_feat_match_loss=2.067, over 83.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.417, discriminator_fake_loss=1.335, generator_loss=31.64, generator_mel_loss=24.31, generator_kl_loss=1.393, generator_dur_loss=1.986, generator_adv_loss=1.886, generator_feat_match_loss=2.061, over 5295.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:02:30,029 INFO [train.py:919] (5/6) Start epoch 15 +2024-03-12 01:03:32,374 INFO [train.py:527] (5/6) Epoch 15, batch 14, global_batch_idx: 1750, batch size: 68, loss[discriminator_loss=2.822, discriminator_real_loss=1.374, discriminator_fake_loss=1.448, generator_loss=30.99, generator_mel_loss=23.99, generator_kl_loss=1.348, generator_dur_loss=2.026, generator_adv_loss=1.864, generator_feat_match_loss=1.767, over 68.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.403, discriminator_fake_loss=1.322, generator_loss=32.11, generator_mel_loss=24.77, generator_kl_loss=1.38, generator_dur_loss=1.954, generator_adv_loss=1.89, generator_feat_match_loss=2.117, over 749.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:05:50,106 INFO [train.py:527] (5/6) Epoch 15, batch 64, global_batch_idx: 1800, batch size: 25, loss[discriminator_loss=2.731, discriminator_real_loss=1.502, discriminator_fake_loss=1.229, generator_loss=31.71, generator_mel_loss=24.28, generator_kl_loss=1.497, generator_dur_loss=1.733, generator_adv_loss=2.058, generator_feat_match_loss=2.148, over 25.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.422, discriminator_fake_loss=1.331, generator_loss=31.69, generator_mel_loss=24.44, generator_kl_loss=1.361, generator_dur_loss=1.958, generator_adv_loss=1.891, generator_feat_match_loss=2.046, over 3361.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:05:50,108 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 01:05:57,889 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 01:08:15,589 INFO [train.py:527] (5/6) Epoch 15, batch 114, global_batch_idx: 1850, batch size: 55, loss[discriminator_loss=2.733, discriminator_real_loss=1.613, discriminator_fake_loss=1.119, generator_loss=32.5, generator_mel_loss=25.37, generator_kl_loss=1.367, generator_dur_loss=1.919, generator_adv_loss=1.767, generator_feat_match_loss=2.081, over 55.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.423, discriminator_fake_loss=1.334, generator_loss=31.65, generator_mel_loss=24.4, generator_kl_loss=1.339, generator_dur_loss=1.965, generator_adv_loss=1.889, generator_feat_match_loss=2.062, over 6115.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:08:41,568 INFO [train.py:919] (5/6) Start epoch 16 +2024-03-12 01:10:58,021 INFO [train.py:527] (5/6) Epoch 16, batch 40, global_batch_idx: 1900, batch size: 44, loss[discriminator_loss=2.785, discriminator_real_loss=1.525, discriminator_fake_loss=1.26, generator_loss=32.11, generator_mel_loss=25.06, generator_kl_loss=1.323, generator_dur_loss=1.913, generator_adv_loss=1.585, generator_feat_match_loss=2.226, over 44.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.409, discriminator_fake_loss=1.34, generator_loss=31.17, generator_mel_loss=24.03, generator_kl_loss=1.288, generator_dur_loss=1.985, generator_adv_loss=1.862, generator_feat_match_loss=2.006, over 2414.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:13:14,057 INFO [train.py:527] (5/6) Epoch 16, batch 90, global_batch_idx: 1950, batch size: 45, loss[discriminator_loss=2.636, discriminator_real_loss=1.304, discriminator_fake_loss=1.332, generator_loss=33.42, generator_mel_loss=25.72, generator_kl_loss=1.214, generator_dur_loss=1.889, generator_adv_loss=1.925, generator_feat_match_loss=2.679, over 45.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.405, discriminator_fake_loss=1.345, generator_loss=31.33, generator_mel_loss=24.15, generator_kl_loss=1.291, generator_dur_loss=1.977, generator_adv_loss=1.863, generator_feat_match_loss=2.04, over 5104.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:14:46,746 INFO [train.py:919] (5/6) Start epoch 17 +2024-03-12 01:15:55,088 INFO [train.py:527] (5/6) Epoch 17, batch 16, global_batch_idx: 2000, batch size: 53, loss[discriminator_loss=2.849, discriminator_real_loss=1.399, discriminator_fake_loss=1.45, generator_loss=30.89, generator_mel_loss=24.41, generator_kl_loss=1.177, generator_dur_loss=1.989, generator_adv_loss=1.74, generator_feat_match_loss=1.571, over 53.00 samples.], tot_loss[discriminator_loss=2.863, discriminator_real_loss=1.481, discriminator_fake_loss=1.382, generator_loss=31.02, generator_mel_loss=24.04, generator_kl_loss=1.233, generator_dur_loss=1.973, generator_adv_loss=1.881, generator_feat_match_loss=1.896, over 920.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:15:55,090 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 01:16:02,945 INFO [train.py:591] (5/6) 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 01:16:02,946 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 01:18:23,013 INFO [train.py:527] (5/6) Epoch 17, batch 66, global_batch_idx: 2050, batch size: 88, loss[discriminator_loss=2.87, discriminator_real_loss=1.605, discriminator_fake_loss=1.265, generator_loss=33.09, generator_mel_loss=25.61, generator_kl_loss=1.195, generator_dur_loss=2.041, generator_adv_loss=2.124, generator_feat_match_loss=2.121, over 88.00 samples.], tot_loss[discriminator_loss=2.792, discriminator_real_loss=1.438, discriminator_fake_loss=1.354, generator_loss=30.82, generator_mel_loss=23.82, generator_kl_loss=1.269, generator_dur_loss=1.977, generator_adv_loss=1.842, generator_feat_match_loss=1.92, over 3893.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:20:38,078 INFO [train.py:527] (5/6) Epoch 17, batch 116, global_batch_idx: 2100, batch size: 58, loss[discriminator_loss=2.744, discriminator_real_loss=1.388, discriminator_fake_loss=1.356, generator_loss=31.44, generator_mel_loss=24.2, generator_kl_loss=1.29, generator_dur_loss=2.03, generator_adv_loss=1.824, generator_feat_match_loss=2.1, over 58.00 samples.], tot_loss[discriminator_loss=2.784, discriminator_real_loss=1.434, discriminator_fake_loss=1.35, generator_loss=30.87, generator_mel_loss=23.8, generator_kl_loss=1.278, generator_dur_loss=1.975, generator_adv_loss=1.847, generator_feat_match_loss=1.966, over 6700.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:20:57,554 INFO [train.py:919] (5/6) Start epoch 18 +2024-03-12 01:23:20,724 INFO [train.py:527] (5/6) Epoch 18, batch 42, global_batch_idx: 2150, batch size: 44, loss[discriminator_loss=2.9, discriminator_real_loss=1.656, discriminator_fake_loss=1.244, generator_loss=31.98, generator_mel_loss=24.92, generator_kl_loss=1.414, generator_dur_loss=1.961, generator_adv_loss=1.82, generator_feat_match_loss=1.87, over 44.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.427, discriminator_fake_loss=1.328, generator_loss=31.44, generator_mel_loss=24.08, generator_kl_loss=1.269, generator_dur_loss=1.978, generator_adv_loss=1.908, generator_feat_match_loss=2.196, over 2367.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:25:39,854 INFO [train.py:527] (5/6) Epoch 18, batch 92, global_batch_idx: 2200, batch size: 47, loss[discriminator_loss=2.796, discriminator_real_loss=1.296, discriminator_fake_loss=1.5, generator_loss=31.49, generator_mel_loss=24.06, generator_kl_loss=1.263, generator_dur_loss=1.97, generator_adv_loss=2.051, generator_feat_match_loss=2.142, over 47.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.425, discriminator_fake_loss=1.342, generator_loss=31.11, generator_mel_loss=23.88, generator_kl_loss=1.253, generator_dur_loss=1.982, generator_adv_loss=1.886, generator_feat_match_loss=2.117, over 5314.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:25:39,855 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 01:25:48,601 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 01:27:10,329 INFO [train.py:919] (5/6) Start epoch 19 +2024-03-12 01:28:25,344 INFO [train.py:527] (5/6) Epoch 19, batch 18, global_batch_idx: 2250, batch size: 72, loss[discriminator_loss=2.752, discriminator_real_loss=1.446, discriminator_fake_loss=1.306, generator_loss=30.32, generator_mel_loss=23.48, generator_kl_loss=1.104, generator_dur_loss=2.009, generator_adv_loss=1.734, generator_feat_match_loss=1.988, over 72.00 samples.], tot_loss[discriminator_loss=2.791, discriminator_real_loss=1.45, discriminator_fake_loss=1.341, generator_loss=30.94, generator_mel_loss=23.84, generator_kl_loss=1.207, generator_dur_loss=1.988, generator_adv_loss=1.89, generator_feat_match_loss=2.019, over 1144.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:30:45,095 INFO [train.py:527] (5/6) Epoch 19, batch 68, global_batch_idx: 2300, batch size: 47, loss[discriminator_loss=2.734, discriminator_real_loss=1.613, discriminator_fake_loss=1.121, generator_loss=33.65, generator_mel_loss=25.39, generator_kl_loss=1.327, generator_dur_loss=1.849, generator_adv_loss=2.523, generator_feat_match_loss=2.554, over 47.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.418, discriminator_fake_loss=1.333, generator_loss=31.15, generator_mel_loss=23.79, generator_kl_loss=1.24, generator_dur_loss=1.976, generator_adv_loss=1.927, generator_feat_match_loss=2.224, over 4019.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:33:04,829 INFO [train.py:527] (5/6) Epoch 19, batch 118, global_batch_idx: 2350, batch size: 47, loss[discriminator_loss=2.759, discriminator_real_loss=1.388, discriminator_fake_loss=1.371, generator_loss=30.56, generator_mel_loss=23.43, generator_kl_loss=1.321, generator_dur_loss=1.897, generator_adv_loss=1.819, generator_feat_match_loss=2.091, over 47.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.423, discriminator_fake_loss=1.335, generator_loss=31.1, generator_mel_loss=23.71, generator_kl_loss=1.25, generator_dur_loss=1.97, generator_adv_loss=1.943, generator_feat_match_loss=2.227, over 6662.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:33:20,849 INFO [train.py:919] (5/6) Start epoch 20 +2024-03-12 01:35:47,086 INFO [train.py:527] (5/6) Epoch 20, batch 44, global_batch_idx: 2400, batch size: 61, loss[discriminator_loss=2.74, discriminator_real_loss=1.384, discriminator_fake_loss=1.356, generator_loss=30.61, generator_mel_loss=23.56, generator_kl_loss=1.286, generator_dur_loss=1.924, generator_adv_loss=1.865, generator_feat_match_loss=1.973, over 61.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.407, discriminator_fake_loss=1.324, generator_loss=30.64, generator_mel_loss=23.37, generator_kl_loss=1.279, generator_dur_loss=1.973, generator_adv_loss=1.893, generator_feat_match_loss=2.129, over 2499.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:35:47,088 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 01:35:54,746 INFO [train.py:591] (5/6) 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 01:35:54,747 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 01:38:14,731 INFO [train.py:527] (5/6) Epoch 20, batch 94, global_batch_idx: 2450, batch size: 56, loss[discriminator_loss=2.712, discriminator_real_loss=1.358, discriminator_fake_loss=1.354, generator_loss=30.66, generator_mel_loss=23.16, generator_kl_loss=1.296, generator_dur_loss=1.938, generator_adv_loss=2.012, generator_feat_match_loss=2.253, over 56.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.407, discriminator_fake_loss=1.322, generator_loss=30.78, generator_mel_loss=23.47, generator_kl_loss=1.273, generator_dur_loss=1.976, generator_adv_loss=1.887, generator_feat_match_loss=2.172, over 5382.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:39:34,768 INFO [train.py:919] (5/6) Start epoch 21 +2024-03-12 01:41:48,459 INFO [train.py:527] (5/6) Epoch 21, batch 20, global_batch_idx: 2500, batch size: 74, loss[discriminator_loss=2.766, discriminator_real_loss=1.57, discriminator_fake_loss=1.196, generator_loss=30.68, generator_mel_loss=23.38, generator_kl_loss=1.33, generator_dur_loss=1.983, generator_adv_loss=1.891, generator_feat_match_loss=2.098, over 74.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.418, discriminator_fake_loss=1.334, generator_loss=30.52, generator_mel_loss=23.12, generator_kl_loss=1.289, generator_dur_loss=1.989, generator_adv_loss=1.899, generator_feat_match_loss=2.225, over 1327.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:44:07,471 INFO [train.py:527] (5/6) Epoch 21, batch 70, global_batch_idx: 2550, batch size: 56, loss[discriminator_loss=2.796, discriminator_real_loss=1.564, discriminator_fake_loss=1.231, generator_loss=31.23, generator_mel_loss=24.12, generator_kl_loss=1.257, generator_dur_loss=1.915, generator_adv_loss=1.779, generator_feat_match_loss=2.16, over 56.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.407, discriminator_fake_loss=1.328, generator_loss=30.73, generator_mel_loss=23.32, generator_kl_loss=1.279, generator_dur_loss=1.972, generator_adv_loss=1.904, generator_feat_match_loss=2.256, over 4010.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:46:24,416 INFO [train.py:527] (5/6) Epoch 21, batch 120, global_batch_idx: 2600, batch size: 25, loss[discriminator_loss=2.755, discriminator_real_loss=1.512, discriminator_fake_loss=1.243, generator_loss=30.59, generator_mel_loss=23.58, generator_kl_loss=1.447, generator_dur_loss=1.775, generator_adv_loss=1.985, generator_feat_match_loss=1.8, over 25.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.414, discriminator_fake_loss=1.329, generator_loss=30.75, generator_mel_loss=23.32, generator_kl_loss=1.282, generator_dur_loss=1.965, generator_adv_loss=1.921, generator_feat_match_loss=2.261, over 6644.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, +2024-03-12 01:46:24,418 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 01:46:33,223 INFO [train.py:591] (5/6) 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 01:46:33,224 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 01:46:43,560 INFO [train.py:919] (5/6) Start epoch 22 +2024-03-12 01:49:19,673 INFO [train.py:527] (5/6) Epoch 22, batch 46, global_batch_idx: 2650, batch size: 77, loss[discriminator_loss=2.658, discriminator_real_loss=1.337, discriminator_fake_loss=1.321, generator_loss=30.84, generator_mel_loss=23.09, generator_kl_loss=1.372, generator_dur_loss=1.954, generator_adv_loss=2, generator_feat_match_loss=2.423, over 77.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.412, discriminator_fake_loss=1.323, generator_loss=30.66, generator_mel_loss=23.19, generator_kl_loss=1.281, generator_dur_loss=1.982, generator_adv_loss=1.92, generator_feat_match_loss=2.279, over 2855.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:51:39,037 INFO [train.py:527] (5/6) Epoch 22, batch 96, global_batch_idx: 2700, batch size: 48, loss[discriminator_loss=2.695, discriminator_real_loss=1.327, discriminator_fake_loss=1.369, generator_loss=31.41, generator_mel_loss=23.65, generator_kl_loss=1.274, generator_dur_loss=1.882, generator_adv_loss=2.175, generator_feat_match_loss=2.433, over 48.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.41, discriminator_fake_loss=1.325, generator_loss=30.49, generator_mel_loss=23.06, generator_kl_loss=1.276, generator_dur_loss=1.98, generator_adv_loss=1.917, generator_feat_match_loss=2.259, over 5859.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:52:50,065 INFO [train.py:919] (5/6) Start epoch 23 +2024-03-12 01:54:13,248 INFO [train.py:527] (5/6) Epoch 23, batch 22, global_batch_idx: 2750, batch size: 36, loss[discriminator_loss=2.738, discriminator_real_loss=1.499, discriminator_fake_loss=1.239, generator_loss=30.03, generator_mel_loss=23.05, generator_kl_loss=1.087, generator_dur_loss=2.015, generator_adv_loss=1.853, generator_feat_match_loss=2.021, over 36.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.399, discriminator_fake_loss=1.361, generator_loss=30.52, generator_mel_loss=23.18, generator_kl_loss=1.299, generator_dur_loss=1.96, generator_adv_loss=1.886, generator_feat_match_loss=2.198, over 1273.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:56:31,410 INFO [train.py:527] (5/6) Epoch 23, batch 72, global_batch_idx: 2800, batch size: 59, loss[discriminator_loss=2.762, discriminator_real_loss=1.378, discriminator_fake_loss=1.385, generator_loss=30.35, generator_mel_loss=22.87, generator_kl_loss=1.156, generator_dur_loss=1.962, generator_adv_loss=2.151, generator_feat_match_loss=2.209, over 59.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.42, discriminator_fake_loss=1.346, generator_loss=30.57, generator_mel_loss=23.04, generator_kl_loss=1.271, generator_dur_loss=1.971, generator_adv_loss=1.962, generator_feat_match_loss=2.325, over 4244.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:56:31,411 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 01:56:39,133 INFO [train.py:591] (5/6) 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 01:56:39,134 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 01:58:56,574 INFO [train.py:527] (5/6) Epoch 23, batch 122, global_batch_idx: 2850, batch size: 31, loss[discriminator_loss=2.76, discriminator_real_loss=1.396, discriminator_fake_loss=1.364, generator_loss=29.05, generator_mel_loss=21.69, generator_kl_loss=1.366, generator_dur_loss=1.923, generator_adv_loss=1.909, generator_feat_match_loss=2.168, over 31.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.411, discriminator_fake_loss=1.344, generator_loss=30.52, generator_mel_loss=23.03, generator_kl_loss=1.268, generator_dur_loss=1.969, generator_adv_loss=1.943, generator_feat_match_loss=2.317, over 7009.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 01:59:02,120 INFO [train.py:919] (5/6) Start epoch 24 +2024-03-12 02:01:38,293 INFO [train.py:527] (5/6) Epoch 24, batch 48, global_batch_idx: 2900, batch size: 66, loss[discriminator_loss=2.697, discriminator_real_loss=1.308, discriminator_fake_loss=1.389, generator_loss=30.3, generator_mel_loss=22.66, generator_kl_loss=1.373, generator_dur_loss=2.009, generator_adv_loss=1.992, generator_feat_match_loss=2.261, over 66.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.41, discriminator_fake_loss=1.347, generator_loss=30.11, generator_mel_loss=22.76, generator_kl_loss=1.286, generator_dur_loss=1.983, generator_adv_loss=1.892, generator_feat_match_loss=2.199, over 2930.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:03:56,254 INFO [train.py:527] (5/6) Epoch 24, batch 98, global_batch_idx: 2950, batch size: 58, loss[discriminator_loss=2.692, discriminator_real_loss=1.426, discriminator_fake_loss=1.266, generator_loss=30.63, generator_mel_loss=23.1, generator_kl_loss=1.23, generator_dur_loss=1.913, generator_adv_loss=2.031, generator_feat_match_loss=2.364, over 58.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.412, discriminator_fake_loss=1.337, generator_loss=30.26, generator_mel_loss=22.8, generator_kl_loss=1.281, generator_dur_loss=1.975, generator_adv_loss=1.927, generator_feat_match_loss=2.28, over 5683.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:05:07,771 INFO [train.py:919] (5/6) Start epoch 25 +2024-03-12 02:06:39,780 INFO [train.py:527] (5/6) Epoch 25, batch 24, global_batch_idx: 3000, batch size: 61, loss[discriminator_loss=2.9, discriminator_real_loss=1.732, discriminator_fake_loss=1.168, generator_loss=29.59, generator_mel_loss=22.18, generator_kl_loss=1.292, generator_dur_loss=1.994, generator_adv_loss=2.096, generator_feat_match_loss=2.024, over 61.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.411, discriminator_fake_loss=1.351, generator_loss=30.13, generator_mel_loss=22.58, generator_kl_loss=1.236, generator_dur_loss=1.98, generator_adv_loss=1.968, generator_feat_match_loss=2.359, over 1438.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:06:39,781 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 02:06:47,851 INFO [train.py:591] (5/6) 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 02:06:47,852 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28807MB +2024-03-12 02:09:03,738 INFO [train.py:527] (5/6) Epoch 25, batch 74, global_batch_idx: 3050, batch size: 42, loss[discriminator_loss=2.75, discriminator_real_loss=1.281, discriminator_fake_loss=1.469, generator_loss=31.5, generator_mel_loss=23.26, generator_kl_loss=1.308, generator_dur_loss=1.97, generator_adv_loss=2.388, generator_feat_match_loss=2.574, over 42.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.404, discriminator_fake_loss=1.334, generator_loss=30.24, generator_mel_loss=22.75, generator_kl_loss=1.255, generator_dur_loss=1.963, generator_adv_loss=1.933, generator_feat_match_loss=2.335, over 4186.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:11:22,285 INFO [train.py:919] (5/6) Start epoch 26 +2024-03-12 02:11:45,844 INFO [train.py:527] (5/6) Epoch 26, batch 0, global_batch_idx: 3100, batch size: 64, loss[discriminator_loss=2.767, discriminator_real_loss=1.321, discriminator_fake_loss=1.446, generator_loss=28.81, generator_mel_loss=21.8, generator_kl_loss=1.247, generator_dur_loss=1.987, generator_adv_loss=1.79, generator_feat_match_loss=1.993, over 64.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.321, discriminator_fake_loss=1.446, generator_loss=28.81, generator_mel_loss=21.8, generator_kl_loss=1.247, generator_dur_loss=1.987, generator_adv_loss=1.79, generator_feat_match_loss=1.993, over 64.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:14:06,958 INFO [train.py:527] (5/6) Epoch 26, batch 50, global_batch_idx: 3150, batch size: 96, loss[discriminator_loss=2.667, discriminator_real_loss=1.269, discriminator_fake_loss=1.398, generator_loss=30.3, generator_mel_loss=22.77, generator_kl_loss=1.027, generator_dur_loss=2.058, generator_adv_loss=1.924, generator_feat_match_loss=2.518, over 96.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.403, discriminator_fake_loss=1.338, generator_loss=30.08, generator_mel_loss=22.58, generator_kl_loss=1.268, generator_dur_loss=1.97, generator_adv_loss=1.928, generator_feat_match_loss=2.335, over 3038.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:16:25,899 INFO [train.py:527] (5/6) Epoch 26, batch 100, global_batch_idx: 3200, batch size: 61, loss[discriminator_loss=2.719, discriminator_real_loss=1.446, discriminator_fake_loss=1.273, generator_loss=30.33, generator_mel_loss=22.88, generator_kl_loss=1.347, generator_dur_loss=1.951, generator_adv_loss=1.784, generator_feat_match_loss=2.375, over 61.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.4, discriminator_fake_loss=1.332, generator_loss=30.1, generator_mel_loss=22.62, generator_kl_loss=1.273, generator_dur_loss=1.967, generator_adv_loss=1.922, generator_feat_match_loss=2.325, over 5880.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:16:25,901 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 02:16:34,498 INFO [train.py:591] (5/6) 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 02:16:34,499 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 02:17:37,896 INFO [train.py:919] (5/6) Start epoch 27 +2024-03-12 02:19:15,678 INFO [train.py:527] (5/6) Epoch 27, batch 26, global_batch_idx: 3250, batch size: 15, loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=32.02, generator_mel_loss=24.12, generator_kl_loss=1.671, generator_dur_loss=1.802, generator_adv_loss=1.823, generator_feat_match_loss=2.599, over 15.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.427, discriminator_fake_loss=1.324, generator_loss=30.54, generator_mel_loss=22.89, generator_kl_loss=1.259, generator_dur_loss=1.979, generator_adv_loss=1.955, generator_feat_match_loss=2.453, over 1501.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:21:36,367 INFO [train.py:527] (5/6) Epoch 27, batch 76, global_batch_idx: 3300, batch size: 68, loss[discriminator_loss=2.72, discriminator_real_loss=1.35, discriminator_fake_loss=1.369, generator_loss=29.15, generator_mel_loss=21.54, generator_kl_loss=1.293, generator_dur_loss=1.934, generator_adv_loss=2.149, generator_feat_match_loss=2.229, over 68.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.414, discriminator_fake_loss=1.341, generator_loss=30.31, generator_mel_loss=22.7, generator_kl_loss=1.245, generator_dur_loss=1.964, generator_adv_loss=1.971, generator_feat_match_loss=2.434, over 4397.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:23:47,909 INFO [train.py:919] (5/6) Start epoch 28 +2024-03-12 02:24:17,394 INFO [train.py:527] (5/6) Epoch 28, batch 2, global_batch_idx: 3350, batch size: 19, loss[discriminator_loss=2.85, discriminator_real_loss=1.465, discriminator_fake_loss=1.385, generator_loss=31.21, generator_mel_loss=23.6, generator_kl_loss=1.368, generator_dur_loss=1.796, generator_adv_loss=1.889, generator_feat_match_loss=2.555, over 19.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.396, discriminator_fake_loss=1.316, generator_loss=30.18, generator_mel_loss=22.3, generator_kl_loss=1.269, generator_dur_loss=1.992, generator_adv_loss=2.118, generator_feat_match_loss=2.505, over 172.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:26:35,516 INFO [train.py:527] (5/6) Epoch 28, batch 52, global_batch_idx: 3400, batch size: 96, loss[discriminator_loss=2.763, discriminator_real_loss=1.262, discriminator_fake_loss=1.501, generator_loss=29.02, generator_mel_loss=21.92, generator_kl_loss=1.11, generator_dur_loss=2.024, generator_adv_loss=1.786, generator_feat_match_loss=2.181, over 96.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.417, discriminator_fake_loss=1.35, generator_loss=29.99, generator_mel_loss=22.48, generator_kl_loss=1.265, generator_dur_loss=1.956, generator_adv_loss=1.937, generator_feat_match_loss=2.349, over 2946.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:26:35,517 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 02:26:43,331 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 02:29:00,583 INFO [train.py:527] (5/6) Epoch 28, batch 102, global_batch_idx: 3450, batch size: 31, loss[discriminator_loss=2.66, discriminator_real_loss=1.466, discriminator_fake_loss=1.194, generator_loss=30.24, generator_mel_loss=22.4, generator_kl_loss=1.312, generator_dur_loss=1.863, generator_adv_loss=2.184, generator_feat_match_loss=2.486, over 31.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.402, discriminator_fake_loss=1.344, generator_loss=30.05, generator_mel_loss=22.55, generator_kl_loss=1.267, generator_dur_loss=1.955, generator_adv_loss=1.928, generator_feat_match_loss=2.347, over 5677.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:30:01,182 INFO [train.py:919] (5/6) Start epoch 29 +2024-03-12 02:31:42,452 INFO [train.py:527] (5/6) Epoch 29, batch 28, global_batch_idx: 3500, batch size: 74, loss[discriminator_loss=2.737, discriminator_real_loss=1.459, discriminator_fake_loss=1.279, generator_loss=27.84, generator_mel_loss=20.91, generator_kl_loss=1.055, generator_dur_loss=1.962, generator_adv_loss=1.933, generator_feat_match_loss=1.983, over 74.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.4, discriminator_fake_loss=1.322, generator_loss=29.68, generator_mel_loss=22.31, generator_kl_loss=1.215, generator_dur_loss=1.959, generator_adv_loss=1.901, generator_feat_match_loss=2.286, over 1727.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:34:01,814 INFO [train.py:527] (5/6) Epoch 29, batch 78, global_batch_idx: 3550, batch size: 59, loss[discriminator_loss=2.672, discriminator_real_loss=1.339, discriminator_fake_loss=1.333, generator_loss=29.81, generator_mel_loss=21.87, generator_kl_loss=1.185, generator_dur_loss=1.924, generator_adv_loss=2.151, generator_feat_match_loss=2.682, over 59.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.403, discriminator_fake_loss=1.33, generator_loss=29.81, generator_mel_loss=22.33, generator_kl_loss=1.229, generator_dur_loss=1.965, generator_adv_loss=1.929, generator_feat_match_loss=2.362, over 4745.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:36:07,154 INFO [train.py:919] (5/6) Start epoch 30 +2024-03-12 02:36:42,546 INFO [train.py:527] (5/6) Epoch 30, batch 4, global_batch_idx: 3600, batch size: 56, loss[discriminator_loss=2.68, discriminator_real_loss=1.516, discriminator_fake_loss=1.164, generator_loss=28.74, generator_mel_loss=21.44, generator_kl_loss=1.226, generator_dur_loss=1.976, generator_adv_loss=1.755, generator_feat_match_loss=2.34, over 56.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.424, discriminator_fake_loss=1.28, generator_loss=29.59, generator_mel_loss=22.07, generator_kl_loss=1.304, generator_dur_loss=1.967, generator_adv_loss=1.948, generator_feat_match_loss=2.305, over 307.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:36:42,549 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 02:36:50,362 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 02:39:06,976 INFO [train.py:527] (5/6) Epoch 30, batch 54, global_batch_idx: 3650, batch size: 56, loss[discriminator_loss=2.725, discriminator_real_loss=1.349, discriminator_fake_loss=1.376, generator_loss=28.42, generator_mel_loss=21.26, generator_kl_loss=1.289, generator_dur_loss=1.943, generator_adv_loss=1.874, generator_feat_match_loss=2.054, over 56.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.4, discriminator_fake_loss=1.326, generator_loss=29.9, generator_mel_loss=22.33, generator_kl_loss=1.245, generator_dur_loss=1.939, generator_adv_loss=1.959, generator_feat_match_loss=2.433, over 3098.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:41:24,056 INFO [train.py:527] (5/6) Epoch 30, batch 104, global_batch_idx: 3700, batch size: 72, loss[discriminator_loss=2.704, discriminator_real_loss=1.431, discriminator_fake_loss=1.273, generator_loss=29.13, generator_mel_loss=21.57, generator_kl_loss=1.014, generator_dur_loss=2.013, generator_adv_loss=1.847, generator_feat_match_loss=2.682, over 72.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.396, discriminator_fake_loss=1.333, generator_loss=29.83, generator_mel_loss=22.26, generator_kl_loss=1.25, generator_dur_loss=1.945, generator_adv_loss=1.952, generator_feat_match_loss=2.422, over 5944.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:42:20,044 INFO [train.py:919] (5/6) Start epoch 31 +2024-03-12 02:44:05,451 INFO [train.py:527] (5/6) Epoch 31, batch 30, global_batch_idx: 3750, batch size: 96, loss[discriminator_loss=2.82, discriminator_real_loss=1.453, discriminator_fake_loss=1.367, generator_loss=29.94, generator_mel_loss=21.71, generator_kl_loss=1.168, generator_dur_loss=2.088, generator_adv_loss=2.02, generator_feat_match_loss=2.957, over 96.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.409, discriminator_fake_loss=1.343, generator_loss=29.55, generator_mel_loss=21.95, generator_kl_loss=1.214, generator_dur_loss=1.956, generator_adv_loss=1.964, generator_feat_match_loss=2.464, over 1893.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:46:25,092 INFO [train.py:527] (5/6) Epoch 31, batch 80, global_batch_idx: 3800, batch size: 83, loss[discriminator_loss=2.747, discriminator_real_loss=1.269, discriminator_fake_loss=1.479, generator_loss=29.08, generator_mel_loss=21.97, generator_kl_loss=1.067, generator_dur_loss=2.005, generator_adv_loss=1.633, generator_feat_match_loss=2.403, over 83.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.417, discriminator_fake_loss=1.346, generator_loss=29.6, generator_mel_loss=22.11, generator_kl_loss=1.215, generator_dur_loss=1.953, generator_adv_loss=1.94, generator_feat_match_loss=2.388, over 4915.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:46:25,094 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 02:46:33,134 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 02:48:32,056 INFO [train.py:919] (5/6) Start epoch 32 +2024-03-12 02:49:14,906 INFO [train.py:527] (5/6) Epoch 32, batch 6, global_batch_idx: 3850, batch size: 42, loss[discriminator_loss=2.615, discriminator_real_loss=1.286, discriminator_fake_loss=1.329, generator_loss=30.55, generator_mel_loss=22.73, generator_kl_loss=1.22, generator_dur_loss=1.905, generator_adv_loss=1.851, generator_feat_match_loss=2.846, over 42.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.353, discriminator_fake_loss=1.362, generator_loss=29.75, generator_mel_loss=22.28, generator_kl_loss=1.301, generator_dur_loss=1.926, generator_adv_loss=1.902, generator_feat_match_loss=2.345, over 347.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:51:36,068 INFO [train.py:527] (5/6) Epoch 32, batch 56, global_batch_idx: 3900, batch size: 31, loss[discriminator_loss=2.768, discriminator_real_loss=1.405, discriminator_fake_loss=1.363, generator_loss=29.11, generator_mel_loss=21.88, generator_kl_loss=1.338, generator_dur_loss=1.864, generator_adv_loss=1.848, generator_feat_match_loss=2.183, over 31.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.397, discriminator_fake_loss=1.355, generator_loss=29.76, generator_mel_loss=22.24, generator_kl_loss=1.241, generator_dur_loss=1.937, generator_adv_loss=1.935, generator_feat_match_loss=2.4, over 3180.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:53:54,758 INFO [train.py:527] (5/6) Epoch 32, batch 106, global_batch_idx: 3950, batch size: 55, loss[discriminator_loss=2.8, discriminator_real_loss=1.452, discriminator_fake_loss=1.348, generator_loss=29.77, generator_mel_loss=22.32, generator_kl_loss=1.264, generator_dur_loss=1.936, generator_adv_loss=1.949, generator_feat_match_loss=2.304, over 55.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.396, discriminator_fake_loss=1.346, generator_loss=29.75, generator_mel_loss=22.23, generator_kl_loss=1.244, generator_dur_loss=1.939, generator_adv_loss=1.928, generator_feat_match_loss=2.407, over 6093.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:54:41,309 INFO [train.py:919] (5/6) Start epoch 33 +2024-03-12 02:56:35,258 INFO [train.py:527] (5/6) Epoch 33, batch 32, global_batch_idx: 4000, batch size: 58, loss[discriminator_loss=2.785, discriminator_real_loss=1.51, discriminator_fake_loss=1.275, generator_loss=30.84, generator_mel_loss=23.02, generator_kl_loss=1.33, generator_dur_loss=1.919, generator_adv_loss=1.834, generator_feat_match_loss=2.733, over 58.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.408, discriminator_fake_loss=1.332, generator_loss=29.83, generator_mel_loss=22.18, generator_kl_loss=1.226, generator_dur_loss=1.953, generator_adv_loss=1.974, generator_feat_match_loss=2.501, over 2049.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 02:56:35,259 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 02:56:43,047 INFO [train.py:591] (5/6) 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 02:56:43,048 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 02:59:01,969 INFO [train.py:527] (5/6) Epoch 33, batch 82, global_batch_idx: 4050, batch size: 48, loss[discriminator_loss=2.839, discriminator_real_loss=1.429, discriminator_fake_loss=1.41, generator_loss=28.83, generator_mel_loss=21.78, generator_kl_loss=1.17, generator_dur_loss=1.91, generator_adv_loss=2.094, generator_feat_match_loss=1.875, over 48.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.418, discriminator_fake_loss=1.337, generator_loss=29.7, generator_mel_loss=22.06, generator_kl_loss=1.239, generator_dur_loss=1.937, generator_adv_loss=1.981, generator_feat_match_loss=2.486, over 4724.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:00:56,934 INFO [train.py:919] (5/6) Start epoch 34 +2024-03-12 03:01:42,812 INFO [train.py:527] (5/6) Epoch 34, batch 8, global_batch_idx: 4100, batch size: 44, loss[discriminator_loss=2.665, discriminator_real_loss=1.247, discriminator_fake_loss=1.417, generator_loss=29.17, generator_mel_loss=21.52, generator_kl_loss=1.311, generator_dur_loss=1.799, generator_adv_loss=1.973, generator_feat_match_loss=2.569, over 44.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.391, discriminator_fake_loss=1.316, generator_loss=29.14, generator_mel_loss=21.76, generator_kl_loss=1.192, generator_dur_loss=1.93, generator_adv_loss=1.899, generator_feat_match_loss=2.359, over 502.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:04:02,942 INFO [train.py:527] (5/6) Epoch 34, batch 58, global_batch_idx: 4150, batch size: 39, loss[discriminator_loss=2.77, discriminator_real_loss=1.35, discriminator_fake_loss=1.419, generator_loss=29.25, generator_mel_loss=22.12, generator_kl_loss=1.375, generator_dur_loss=1.832, generator_adv_loss=1.834, generator_feat_match_loss=2.085, over 39.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.399, discriminator_fake_loss=1.34, generator_loss=29.62, generator_mel_loss=22.11, generator_kl_loss=1.222, generator_dur_loss=1.944, generator_adv_loss=1.922, generator_feat_match_loss=2.415, over 3543.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:06:21,464 INFO [train.py:527] (5/6) Epoch 34, batch 108, global_batch_idx: 4200, batch size: 77, loss[discriminator_loss=2.726, discriminator_real_loss=1.339, discriminator_fake_loss=1.387, generator_loss=29.2, generator_mel_loss=21.8, generator_kl_loss=1.084, generator_dur_loss=1.979, generator_adv_loss=2.024, generator_feat_match_loss=2.315, over 77.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.398, discriminator_fake_loss=1.341, generator_loss=29.66, generator_mel_loss=22.11, generator_kl_loss=1.228, generator_dur_loss=1.93, generator_adv_loss=1.951, generator_feat_match_loss=2.445, over 6296.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:06:21,465 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 03:06:30,178 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 03:07:11,320 INFO [train.py:919] (5/6) Start epoch 35 +2024-03-12 03:09:10,464 INFO [train.py:527] (5/6) Epoch 35, batch 34, global_batch_idx: 4250, batch size: 52, loss[discriminator_loss=2.735, discriminator_real_loss=1.322, discriminator_fake_loss=1.413, generator_loss=30.7, generator_mel_loss=23.02, generator_kl_loss=1.28, generator_dur_loss=1.833, generator_adv_loss=2.078, generator_feat_match_loss=2.495, over 52.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.398, discriminator_fake_loss=1.321, generator_loss=29.59, generator_mel_loss=22.11, generator_kl_loss=1.251, generator_dur_loss=1.917, generator_adv_loss=1.911, generator_feat_match_loss=2.406, over 1974.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:11:30,601 INFO [train.py:527] (5/6) Epoch 35, batch 84, global_batch_idx: 4300, batch size: 39, loss[discriminator_loss=2.902, discriminator_real_loss=1.261, discriminator_fake_loss=1.641, generator_loss=28.31, generator_mel_loss=21.06, generator_kl_loss=1.249, generator_dur_loss=1.922, generator_adv_loss=1.989, generator_feat_match_loss=2.09, over 39.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.407, discriminator_fake_loss=1.328, generator_loss=29.75, generator_mel_loss=22.18, generator_kl_loss=1.263, generator_dur_loss=1.905, generator_adv_loss=1.939, generator_feat_match_loss=2.461, over 4536.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:13:17,925 INFO [train.py:919] (5/6) Start epoch 36 +2024-03-12 03:14:10,388 INFO [train.py:527] (5/6) Epoch 36, batch 10, global_batch_idx: 4350, batch size: 80, loss[discriminator_loss=2.787, discriminator_real_loss=1.56, discriminator_fake_loss=1.227, generator_loss=28.16, generator_mel_loss=21.25, generator_kl_loss=1.059, generator_dur_loss=1.95, generator_adv_loss=1.83, generator_feat_match_loss=2.068, over 80.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.421, discriminator_fake_loss=1.335, generator_loss=29.27, generator_mel_loss=21.86, generator_kl_loss=1.219, generator_dur_loss=1.951, generator_adv_loss=1.919, generator_feat_match_loss=2.322, over 696.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:16:31,676 INFO [train.py:527] (5/6) Epoch 36, batch 60, global_batch_idx: 4400, batch size: 96, loss[discriminator_loss=2.71, discriminator_real_loss=1.287, discriminator_fake_loss=1.423, generator_loss=28.02, generator_mel_loss=20.67, generator_kl_loss=1.282, generator_dur_loss=2.054, generator_adv_loss=1.871, generator_feat_match_loss=2.139, over 96.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.4, discriminator_fake_loss=1.329, generator_loss=29.36, generator_mel_loss=21.87, generator_kl_loss=1.266, generator_dur_loss=1.901, generator_adv_loss=1.923, generator_feat_match_loss=2.402, over 3522.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:16:31,677 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 03:16:39,666 INFO [train.py:591] (5/6) 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 03:16:39,667 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 03:18:56,393 INFO [train.py:527] (5/6) Epoch 36, batch 110, global_batch_idx: 4450, batch size: 44, loss[discriminator_loss=2.666, discriminator_real_loss=1.422, discriminator_fake_loss=1.244, generator_loss=31.18, generator_mel_loss=23.18, generator_kl_loss=1.441, generator_dur_loss=1.801, generator_adv_loss=1.863, generator_feat_match_loss=2.901, over 44.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.399, discriminator_fake_loss=1.33, generator_loss=29.4, generator_mel_loss=21.89, generator_kl_loss=1.263, generator_dur_loss=1.896, generator_adv_loss=1.929, generator_feat_match_loss=2.428, over 6264.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:19:33,015 INFO [train.py:919] (5/6) Start epoch 37 +2024-03-12 03:21:34,205 INFO [train.py:527] (5/6) Epoch 37, batch 36, global_batch_idx: 4500, batch size: 42, loss[discriminator_loss=2.732, discriminator_real_loss=1.236, discriminator_fake_loss=1.496, generator_loss=30.37, generator_mel_loss=23.04, generator_kl_loss=1.281, generator_dur_loss=1.784, generator_adv_loss=1.788, generator_feat_match_loss=2.476, over 42.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.41, discriminator_fake_loss=1.345, generator_loss=29.42, generator_mel_loss=21.86, generator_kl_loss=1.268, generator_dur_loss=1.897, generator_adv_loss=1.939, generator_feat_match_loss=2.453, over 2083.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:23:54,401 INFO [train.py:527] (5/6) Epoch 37, batch 86, global_batch_idx: 4550, batch size: 53, loss[discriminator_loss=2.696, discriminator_real_loss=1.257, discriminator_fake_loss=1.439, generator_loss=30.41, generator_mel_loss=22.71, generator_kl_loss=1.216, generator_dur_loss=1.799, generator_adv_loss=1.871, generator_feat_match_loss=2.807, over 53.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.407, discriminator_fake_loss=1.339, generator_loss=29.17, generator_mel_loss=21.68, generator_kl_loss=1.254, generator_dur_loss=1.904, generator_adv_loss=1.93, generator_feat_match_loss=2.408, over 5083.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:25:40,577 INFO [train.py:919] (5/6) Start epoch 38 +2024-03-12 03:26:39,497 INFO [train.py:527] (5/6) Epoch 38, batch 12, global_batch_idx: 4600, batch size: 80, loss[discriminator_loss=2.711, discriminator_real_loss=1.488, discriminator_fake_loss=1.223, generator_loss=28.88, generator_mel_loss=21.56, generator_kl_loss=1.245, generator_dur_loss=1.953, generator_adv_loss=1.862, generator_feat_match_loss=2.26, over 80.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.406, discriminator_fake_loss=1.332, generator_loss=29.53, generator_mel_loss=21.93, generator_kl_loss=1.3, generator_dur_loss=1.916, generator_adv_loss=1.945, generator_feat_match_loss=2.439, over 811.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:26:39,500 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 03:26:47,323 INFO [train.py:591] (5/6) 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 03:26:47,324 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 03:29:05,392 INFO [train.py:527] (5/6) Epoch 38, batch 62, global_batch_idx: 4650, batch size: 25, loss[discriminator_loss=2.64, discriminator_real_loss=1.389, discriminator_fake_loss=1.251, generator_loss=31.44, generator_mel_loss=23.19, generator_kl_loss=1.569, generator_dur_loss=1.719, generator_adv_loss=2.219, generator_feat_match_loss=2.74, over 25.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.396, discriminator_fake_loss=1.337, generator_loss=29.39, generator_mel_loss=21.87, generator_kl_loss=1.282, generator_dur_loss=1.878, generator_adv_loss=1.918, generator_feat_match_loss=2.435, over 3337.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:31:25,942 INFO [train.py:527] (5/6) Epoch 38, batch 112, global_batch_idx: 4700, batch size: 39, loss[discriminator_loss=2.613, discriminator_real_loss=1.391, discriminator_fake_loss=1.221, generator_loss=30.07, generator_mel_loss=22.14, generator_kl_loss=1.314, generator_dur_loss=1.795, generator_adv_loss=1.844, generator_feat_match_loss=2.976, over 39.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.393, discriminator_fake_loss=1.344, generator_loss=29.29, generator_mel_loss=21.79, generator_kl_loss=1.257, generator_dur_loss=1.885, generator_adv_loss=1.918, generator_feat_match_loss=2.437, over 6211.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:31:59,610 INFO [train.py:919] (5/6) Start epoch 39 +2024-03-12 03:34:09,096 INFO [train.py:527] (5/6) Epoch 39, batch 38, global_batch_idx: 4750, batch size: 77, loss[discriminator_loss=2.725, discriminator_real_loss=1.217, discriminator_fake_loss=1.507, generator_loss=29.74, generator_mel_loss=22.13, generator_kl_loss=1.277, generator_dur_loss=1.929, generator_adv_loss=1.912, generator_feat_match_loss=2.492, over 77.00 samples.], tot_loss[discriminator_loss=2.783, discriminator_real_loss=1.431, discriminator_fake_loss=1.352, generator_loss=29.35, generator_mel_loss=21.71, generator_kl_loss=1.278, generator_dur_loss=1.897, generator_adv_loss=1.998, generator_feat_match_loss=2.469, over 2296.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:36:27,787 INFO [train.py:527] (5/6) Epoch 39, batch 88, global_batch_idx: 4800, batch size: 47, loss[discriminator_loss=2.712, discriminator_real_loss=1.343, discriminator_fake_loss=1.368, generator_loss=29.31, generator_mel_loss=21.92, generator_kl_loss=1.331, generator_dur_loss=1.869, generator_adv_loss=1.932, generator_feat_match_loss=2.257, over 47.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.421, discriminator_fake_loss=1.343, generator_loss=29.22, generator_mel_loss=21.65, generator_kl_loss=1.277, generator_dur_loss=1.889, generator_adv_loss=1.966, generator_feat_match_loss=2.435, over 5087.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:36:27,788 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 03:36:36,858 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 03:38:15,466 INFO [train.py:919] (5/6) Start epoch 40 +2024-03-12 03:39:17,971 INFO [train.py:527] (5/6) Epoch 40, batch 14, global_batch_idx: 4850, batch size: 31, loss[discriminator_loss=2.733, discriminator_real_loss=1.565, discriminator_fake_loss=1.168, generator_loss=27.88, generator_mel_loss=20.73, generator_kl_loss=1.253, generator_dur_loss=1.827, generator_adv_loss=1.963, generator_feat_match_loss=2.1, over 31.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.41, discriminator_fake_loss=1.314, generator_loss=29.42, generator_mel_loss=21.82, generator_kl_loss=1.251, generator_dur_loss=1.872, generator_adv_loss=1.935, generator_feat_match_loss=2.538, over 812.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:41:34,677 INFO [train.py:527] (5/6) Epoch 40, batch 64, global_batch_idx: 4900, batch size: 68, loss[discriminator_loss=2.586, discriminator_real_loss=1.362, discriminator_fake_loss=1.225, generator_loss=31.96, generator_mel_loss=22.9, generator_kl_loss=1.334, generator_dur_loss=1.917, generator_adv_loss=2.371, generator_feat_match_loss=3.436, over 68.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.403, discriminator_fake_loss=1.336, generator_loss=29.35, generator_mel_loss=21.75, generator_kl_loss=1.266, generator_dur_loss=1.881, generator_adv_loss=1.944, generator_feat_match_loss=2.503, over 3537.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:43:52,656 INFO [train.py:527] (5/6) Epoch 40, batch 114, global_batch_idx: 4950, batch size: 45, loss[discriminator_loss=2.679, discriminator_real_loss=1.381, discriminator_fake_loss=1.298, generator_loss=28.65, generator_mel_loss=21.47, generator_kl_loss=1.285, generator_dur_loss=1.789, generator_adv_loss=1.816, generator_feat_match_loss=2.287, over 45.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.418, discriminator_fake_loss=1.333, generator_loss=29.18, generator_mel_loss=21.56, generator_kl_loss=1.273, generator_dur_loss=1.892, generator_adv_loss=1.971, generator_feat_match_loss=2.487, over 6466.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:44:20,478 INFO [train.py:919] (5/6) Start epoch 41 +2024-03-12 03:46:42,187 INFO [train.py:527] (5/6) Epoch 41, batch 40, global_batch_idx: 5000, batch size: 68, loss[discriminator_loss=2.691, discriminator_real_loss=1.348, discriminator_fake_loss=1.343, generator_loss=30, generator_mel_loss=22.01, generator_kl_loss=1.321, generator_dur_loss=1.858, generator_adv_loss=1.941, generator_feat_match_loss=2.867, over 68.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.378, discriminator_fake_loss=1.333, generator_loss=29.17, generator_mel_loss=21.65, generator_kl_loss=1.279, generator_dur_loss=1.885, generator_adv_loss=1.894, generator_feat_match_loss=2.461, over 2289.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:46:42,189 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 03:46:50,133 INFO [train.py:591] (5/6) 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 03:46:50,134 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 03:49:08,368 INFO [train.py:527] (5/6) Epoch 41, batch 90, global_batch_idx: 5050, batch size: 15, loss[discriminator_loss=2.675, discriminator_real_loss=1.361, discriminator_fake_loss=1.314, generator_loss=29.75, generator_mel_loss=22.05, generator_kl_loss=1.662, generator_dur_loss=1.699, generator_adv_loss=1.931, generator_feat_match_loss=2.415, over 15.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.395, discriminator_fake_loss=1.331, generator_loss=29.01, generator_mel_loss=21.52, generator_kl_loss=1.265, generator_dur_loss=1.893, generator_adv_loss=1.901, generator_feat_match_loss=2.432, over 5235.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:50:41,897 INFO [train.py:919] (5/6) Start epoch 42 +2024-03-12 03:51:51,542 INFO [train.py:527] (5/6) Epoch 42, batch 16, global_batch_idx: 5100, batch size: 45, loss[discriminator_loss=2.614, discriminator_real_loss=1.294, discriminator_fake_loss=1.32, generator_loss=30.69, generator_mel_loss=22.76, generator_kl_loss=1.387, generator_dur_loss=1.888, generator_adv_loss=1.828, generator_feat_match_loss=2.828, over 45.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.394, discriminator_fake_loss=1.358, generator_loss=28.85, generator_mel_loss=21.37, generator_kl_loss=1.272, generator_dur_loss=1.887, generator_adv_loss=1.906, generator_feat_match_loss=2.419, over 923.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:54:10,642 INFO [train.py:527] (5/6) Epoch 42, batch 66, global_batch_idx: 5150, batch size: 64, loss[discriminator_loss=2.749, discriminator_real_loss=1.343, discriminator_fake_loss=1.405, generator_loss=27.78, generator_mel_loss=20.72, generator_kl_loss=1.13, generator_dur_loss=1.924, generator_adv_loss=1.803, generator_feat_match_loss=2.197, over 64.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.421, discriminator_fake_loss=1.341, generator_loss=28.72, generator_mel_loss=21.23, generator_kl_loss=1.266, generator_dur_loss=1.887, generator_adv_loss=1.943, generator_feat_match_loss=2.391, over 3853.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:56:26,588 INFO [train.py:527] (5/6) Epoch 42, batch 116, global_batch_idx: 5200, batch size: 13, loss[discriminator_loss=2.803, discriminator_real_loss=1.711, discriminator_fake_loss=1.092, generator_loss=30.95, generator_mel_loss=23.68, generator_kl_loss=1.315, generator_dur_loss=1.642, generator_adv_loss=2.009, generator_feat_match_loss=2.301, over 13.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.406, discriminator_fake_loss=1.339, generator_loss=28.8, generator_mel_loss=21.3, generator_kl_loss=1.275, generator_dur_loss=1.884, generator_adv_loss=1.927, generator_feat_match_loss=2.414, over 6717.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 03:56:26,589 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 03:56:35,582 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 03:56:56,509 INFO [train.py:919] (5/6) Start epoch 43 +2024-03-12 03:59:14,949 INFO [train.py:527] (5/6) Epoch 43, batch 42, global_batch_idx: 5250, batch size: 61, loss[discriminator_loss=2.742, discriminator_real_loss=1.446, discriminator_fake_loss=1.296, generator_loss=29.33, generator_mel_loss=21.82, generator_kl_loss=1.298, generator_dur_loss=1.873, generator_adv_loss=1.846, generator_feat_match_loss=2.496, over 61.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.399, discriminator_fake_loss=1.344, generator_loss=28.75, generator_mel_loss=21.3, generator_kl_loss=1.274, generator_dur_loss=1.88, generator_adv_loss=1.889, generator_feat_match_loss=2.412, over 2465.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:01:32,854 INFO [train.py:527] (5/6) Epoch 43, batch 92, global_batch_idx: 5300, batch size: 96, loss[discriminator_loss=2.846, discriminator_real_loss=1.456, discriminator_fake_loss=1.39, generator_loss=28.23, generator_mel_loss=20.88, generator_kl_loss=1.219, generator_dur_loss=2.018, generator_adv_loss=1.838, generator_feat_match_loss=2.268, over 96.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.407, discriminator_fake_loss=1.347, generator_loss=28.84, generator_mel_loss=21.29, generator_kl_loss=1.275, generator_dur_loss=1.881, generator_adv_loss=1.94, generator_feat_match_loss=2.454, over 5261.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:03:01,427 INFO [train.py:919] (5/6) Start epoch 44 +2024-03-12 04:04:18,538 INFO [train.py:527] (5/6) Epoch 44, batch 18, global_batch_idx: 5350, batch size: 74, loss[discriminator_loss=2.692, discriminator_real_loss=1.436, discriminator_fake_loss=1.256, generator_loss=29.18, generator_mel_loss=21.55, generator_kl_loss=1.254, generator_dur_loss=1.887, generator_adv_loss=1.839, generator_feat_match_loss=2.649, over 74.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.399, discriminator_fake_loss=1.334, generator_loss=28.91, generator_mel_loss=21.43, generator_kl_loss=1.289, generator_dur_loss=1.891, generator_adv_loss=1.914, generator_feat_match_loss=2.383, over 1143.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:06:38,358 INFO [train.py:527] (5/6) Epoch 44, batch 68, global_batch_idx: 5400, batch size: 56, loss[discriminator_loss=2.702, discriminator_real_loss=1.433, discriminator_fake_loss=1.269, generator_loss=28.85, generator_mel_loss=21.1, generator_kl_loss=1.286, generator_dur_loss=1.882, generator_adv_loss=2.003, generator_feat_match_loss=2.573, over 56.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.393, discriminator_fake_loss=1.35, generator_loss=28.82, generator_mel_loss=21.34, generator_kl_loss=1.301, generator_dur_loss=1.875, generator_adv_loss=1.892, generator_feat_match_loss=2.409, over 3983.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:06:38,360 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 04:06:46,212 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 04:09:04,585 INFO [train.py:527] (5/6) Epoch 44, batch 118, global_batch_idx: 5450, batch size: 68, loss[discriminator_loss=2.666, discriminator_real_loss=1.388, discriminator_fake_loss=1.278, generator_loss=28.88, generator_mel_loss=21.1, generator_kl_loss=1.114, generator_dur_loss=1.879, generator_adv_loss=2.268, generator_feat_match_loss=2.52, over 68.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.4, discriminator_fake_loss=1.346, generator_loss=28.8, generator_mel_loss=21.25, generator_kl_loss=1.292, generator_dur_loss=1.883, generator_adv_loss=1.916, generator_feat_match_loss=2.457, over 6940.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:09:19,431 INFO [train.py:919] (5/6) Start epoch 45 +2024-03-12 04:11:43,234 INFO [train.py:527] (5/6) Epoch 45, batch 44, global_batch_idx: 5500, batch size: 58, loss[discriminator_loss=2.815, discriminator_real_loss=1.567, discriminator_fake_loss=1.247, generator_loss=29.05, generator_mel_loss=21.56, generator_kl_loss=1.367, generator_dur_loss=1.84, generator_adv_loss=1.952, generator_feat_match_loss=2.322, over 58.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.387, discriminator_fake_loss=1.342, generator_loss=28.69, generator_mel_loss=21.18, generator_kl_loss=1.291, generator_dur_loss=1.866, generator_adv_loss=1.897, generator_feat_match_loss=2.453, over 2486.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:14:03,076 INFO [train.py:527] (5/6) Epoch 45, batch 94, global_batch_idx: 5550, batch size: 42, loss[discriminator_loss=2.794, discriminator_real_loss=1.361, discriminator_fake_loss=1.433, generator_loss=27.93, generator_mel_loss=20.46, generator_kl_loss=1.261, generator_dur_loss=1.84, generator_adv_loss=2.098, generator_feat_match_loss=2.27, over 42.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.399, discriminator_fake_loss=1.343, generator_loss=28.67, generator_mel_loss=21.15, generator_kl_loss=1.284, generator_dur_loss=1.877, generator_adv_loss=1.906, generator_feat_match_loss=2.453, over 5404.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:15:26,214 INFO [train.py:919] (5/6) Start epoch 46 +2024-03-12 04:16:45,585 INFO [train.py:527] (5/6) Epoch 46, batch 20, global_batch_idx: 5600, batch size: 50, loss[discriminator_loss=2.754, discriminator_real_loss=1.473, discriminator_fake_loss=1.28, generator_loss=28.62, generator_mel_loss=21.09, generator_kl_loss=1.426, generator_dur_loss=1.758, generator_adv_loss=1.836, generator_feat_match_loss=2.511, over 50.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.405, discriminator_fake_loss=1.341, generator_loss=28.42, generator_mel_loss=20.86, generator_kl_loss=1.275, generator_dur_loss=1.874, generator_adv_loss=1.928, generator_feat_match_loss=2.476, over 1220.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:16:45,587 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 04:16:53,515 INFO [train.py:591] (5/6) 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 04:16:53,516 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 04:19:10,762 INFO [train.py:527] (5/6) Epoch 46, batch 70, global_batch_idx: 5650, batch size: 59, loss[discriminator_loss=2.707, discriminator_real_loss=1.321, discriminator_fake_loss=1.386, generator_loss=28.61, generator_mel_loss=21, generator_kl_loss=1.246, generator_dur_loss=1.823, generator_adv_loss=1.948, generator_feat_match_loss=2.587, over 59.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.403, discriminator_fake_loss=1.342, generator_loss=28.66, generator_mel_loss=21.12, generator_kl_loss=1.293, generator_dur_loss=1.877, generator_adv_loss=1.907, generator_feat_match_loss=2.463, over 4285.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:21:29,309 INFO [train.py:527] (5/6) Epoch 46, batch 120, global_batch_idx: 5700, batch size: 83, loss[discriminator_loss=2.75, discriminator_real_loss=1.428, discriminator_fake_loss=1.322, generator_loss=27.84, generator_mel_loss=20.39, generator_kl_loss=1.401, generator_dur_loss=1.928, generator_adv_loss=1.738, generator_feat_match_loss=2.384, over 83.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.4, discriminator_fake_loss=1.343, generator_loss=28.62, generator_mel_loss=21.09, generator_kl_loss=1.294, generator_dur_loss=1.876, generator_adv_loss=1.897, generator_feat_match_loss=2.46, over 7227.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:21:39,716 INFO [train.py:919] (5/6) Start epoch 47 +2024-03-12 04:24:11,035 INFO [train.py:527] (5/6) Epoch 47, batch 46, global_batch_idx: 5750, batch size: 96, loss[discriminator_loss=2.813, discriminator_real_loss=1.488, discriminator_fake_loss=1.326, generator_loss=28.17, generator_mel_loss=20.52, generator_kl_loss=1.25, generator_dur_loss=1.976, generator_adv_loss=2.022, generator_feat_match_loss=2.408, over 96.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.408, discriminator_fake_loss=1.352, generator_loss=28.77, generator_mel_loss=21.14, generator_kl_loss=1.282, generator_dur_loss=1.858, generator_adv_loss=1.959, generator_feat_match_loss=2.529, over 2714.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:26:28,702 INFO [train.py:527] (5/6) Epoch 47, batch 96, global_batch_idx: 5800, batch size: 88, loss[discriminator_loss=2.722, discriminator_real_loss=1.447, discriminator_fake_loss=1.275, generator_loss=27.65, generator_mel_loss=19.96, generator_kl_loss=1.312, generator_dur_loss=1.961, generator_adv_loss=1.83, generator_feat_match_loss=2.582, over 88.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.399, discriminator_fake_loss=1.347, generator_loss=28.64, generator_mel_loss=21.06, generator_kl_loss=1.299, generator_dur_loss=1.859, generator_adv_loss=1.919, generator_feat_match_loss=2.501, over 5582.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:26:28,704 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 04:26:37,447 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 04:27:55,140 INFO [train.py:919] (5/6) Start epoch 48 +2024-03-12 04:29:21,049 INFO [train.py:527] (5/6) Epoch 48, batch 22, global_batch_idx: 5850, batch size: 44, loss[discriminator_loss=2.768, discriminator_real_loss=1.374, discriminator_fake_loss=1.394, generator_loss=28.9, generator_mel_loss=21.1, generator_kl_loss=1.368, generator_dur_loss=1.81, generator_adv_loss=1.971, generator_feat_match_loss=2.649, over 44.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.472, discriminator_fake_loss=1.296, generator_loss=28.93, generator_mel_loss=21.01, generator_kl_loss=1.318, generator_dur_loss=1.842, generator_adv_loss=2.085, generator_feat_match_loss=2.671, over 1302.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:31:38,658 INFO [train.py:527] (5/6) Epoch 48, batch 72, global_batch_idx: 5900, batch size: 44, loss[discriminator_loss=2.807, discriminator_real_loss=1.465, discriminator_fake_loss=1.342, generator_loss=28.42, generator_mel_loss=21.16, generator_kl_loss=1.393, generator_dur_loss=1.79, generator_adv_loss=1.94, generator_feat_match_loss=2.139, over 44.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.429, discriminator_fake_loss=1.341, generator_loss=28.72, generator_mel_loss=21.05, generator_kl_loss=1.328, generator_dur_loss=1.835, generator_adv_loss=1.984, generator_feat_match_loss=2.528, over 3965.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:33:57,494 INFO [train.py:527] (5/6) Epoch 48, batch 122, global_batch_idx: 5950, batch size: 42, loss[discriminator_loss=2.797, discriminator_real_loss=1.389, discriminator_fake_loss=1.408, generator_loss=27.97, generator_mel_loss=20.93, generator_kl_loss=1.262, generator_dur_loss=1.759, generator_adv_loss=1.827, generator_feat_match_loss=2.192, over 42.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.418, discriminator_fake_loss=1.339, generator_loss=28.58, generator_mel_loss=21.02, generator_kl_loss=1.31, generator_dur_loss=1.843, generator_adv_loss=1.937, generator_feat_match_loss=2.468, over 6817.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:34:02,439 INFO [train.py:919] (5/6) Start epoch 49 +2024-03-12 04:36:36,719 INFO [train.py:527] (5/6) Epoch 49, batch 48, global_batch_idx: 6000, batch size: 74, loss[discriminator_loss=2.785, discriminator_real_loss=1.397, discriminator_fake_loss=1.388, generator_loss=27.94, generator_mel_loss=20.29, generator_kl_loss=1.261, generator_dur_loss=1.898, generator_adv_loss=2.078, generator_feat_match_loss=2.414, over 74.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.395, discriminator_fake_loss=1.35, generator_loss=28.5, generator_mel_loss=20.99, generator_kl_loss=1.338, generator_dur_loss=1.838, generator_adv_loss=1.88, generator_feat_match_loss=2.454, over 2821.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:36:36,721 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 04:36:44,771 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 04:39:03,252 INFO [train.py:527] (5/6) Epoch 49, batch 98, global_batch_idx: 6050, batch size: 47, loss[discriminator_loss=2.754, discriminator_real_loss=1.278, discriminator_fake_loss=1.477, generator_loss=29.52, generator_mel_loss=21.6, generator_kl_loss=1.456, generator_dur_loss=1.806, generator_adv_loss=2.032, generator_feat_match_loss=2.634, over 47.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.393, discriminator_fake_loss=1.35, generator_loss=28.45, generator_mel_loss=20.92, generator_kl_loss=1.328, generator_dur_loss=1.847, generator_adv_loss=1.877, generator_feat_match_loss=2.476, over 5663.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:40:16,383 INFO [train.py:919] (5/6) Start epoch 50 +2024-03-12 04:41:46,370 INFO [train.py:527] (5/6) Epoch 50, batch 24, global_batch_idx: 6100, batch size: 58, loss[discriminator_loss=2.644, discriminator_real_loss=1.344, discriminator_fake_loss=1.3, generator_loss=29.64, generator_mel_loss=21.57, generator_kl_loss=1.217, generator_dur_loss=1.8, generator_adv_loss=1.932, generator_feat_match_loss=3.119, over 58.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.396, discriminator_fake_loss=1.353, generator_loss=28.48, generator_mel_loss=20.84, generator_kl_loss=1.303, generator_dur_loss=1.855, generator_adv_loss=1.927, generator_feat_match_loss=2.552, over 1340.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:44:05,964 INFO [train.py:527] (5/6) Epoch 50, batch 74, global_batch_idx: 6150, batch size: 53, loss[discriminator_loss=2.632, discriminator_real_loss=1.195, discriminator_fake_loss=1.437, generator_loss=28.56, generator_mel_loss=20.58, generator_kl_loss=1.259, generator_dur_loss=1.79, generator_adv_loss=2.23, generator_feat_match_loss=2.7, over 53.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.404, discriminator_fake_loss=1.349, generator_loss=28.41, generator_mel_loss=20.81, generator_kl_loss=1.332, generator_dur_loss=1.846, generator_adv_loss=1.916, generator_feat_match_loss=2.499, over 4099.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:46:24,379 INFO [train.py:919] (5/6) Start epoch 51 +2024-03-12 04:46:48,873 INFO [train.py:527] (5/6) Epoch 51, batch 0, global_batch_idx: 6200, batch size: 70, loss[discriminator_loss=2.713, discriminator_real_loss=1.285, discriminator_fake_loss=1.428, generator_loss=28.54, generator_mel_loss=20.84, generator_kl_loss=1.406, generator_dur_loss=1.878, generator_adv_loss=1.832, generator_feat_match_loss=2.589, over 70.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.285, discriminator_fake_loss=1.428, generator_loss=28.54, generator_mel_loss=20.84, generator_kl_loss=1.406, generator_dur_loss=1.878, generator_adv_loss=1.832, generator_feat_match_loss=2.589, over 70.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:46:48,957 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 04:46:56,727 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 04:49:14,941 INFO [train.py:527] (5/6) Epoch 51, batch 50, global_batch_idx: 6250, batch size: 39, loss[discriminator_loss=2.634, discriminator_real_loss=1.268, discriminator_fake_loss=1.366, generator_loss=30.23, generator_mel_loss=22.23, generator_kl_loss=1.336, generator_dur_loss=1.817, generator_adv_loss=1.923, generator_feat_match_loss=2.933, over 39.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.39, discriminator_fake_loss=1.341, generator_loss=28.45, generator_mel_loss=20.91, generator_kl_loss=1.315, generator_dur_loss=1.841, generator_adv_loss=1.89, generator_feat_match_loss=2.497, over 2771.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:51:35,335 INFO [train.py:527] (5/6) Epoch 51, batch 100, global_batch_idx: 6300, batch size: 58, loss[discriminator_loss=2.768, discriminator_real_loss=1.406, discriminator_fake_loss=1.361, generator_loss=27.69, generator_mel_loss=20.54, generator_kl_loss=1.302, generator_dur_loss=1.822, generator_adv_loss=1.91, generator_feat_match_loss=2.12, over 58.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.398, discriminator_fake_loss=1.346, generator_loss=28.38, generator_mel_loss=20.79, generator_kl_loss=1.317, generator_dur_loss=1.841, generator_adv_loss=1.916, generator_feat_match_loss=2.519, over 5545.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:52:40,189 INFO [train.py:919] (5/6) Start epoch 52 +2024-03-12 04:54:18,864 INFO [train.py:527] (5/6) Epoch 52, batch 26, global_batch_idx: 6350, batch size: 36, loss[discriminator_loss=2.7, discriminator_real_loss=1.291, discriminator_fake_loss=1.409, generator_loss=28.61, generator_mel_loss=20.95, generator_kl_loss=1.375, generator_dur_loss=1.766, generator_adv_loss=1.997, generator_feat_match_loss=2.519, over 36.00 samples.], tot_loss[discriminator_loss=2.788, discriminator_real_loss=1.438, discriminator_fake_loss=1.35, generator_loss=28.39, generator_mel_loss=20.42, generator_kl_loss=1.308, generator_dur_loss=1.878, generator_adv_loss=2.057, generator_feat_match_loss=2.723, over 1731.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:56:38,036 INFO [train.py:527] (5/6) Epoch 52, batch 76, global_batch_idx: 6400, batch size: 74, loss[discriminator_loss=2.672, discriminator_real_loss=1.385, discriminator_fake_loss=1.287, generator_loss=28.75, generator_mel_loss=20.82, generator_kl_loss=1.328, generator_dur_loss=1.915, generator_adv_loss=2.079, generator_feat_match_loss=2.606, over 74.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.424, discriminator_fake_loss=1.341, generator_loss=28.36, generator_mel_loss=20.65, generator_kl_loss=1.312, generator_dur_loss=1.85, generator_adv_loss=1.985, generator_feat_match_loss=2.559, over 4505.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 04:56:38,037 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 04:56:46,042 INFO [train.py:591] (5/6) 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 04:56:46,043 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 04:58:55,291 INFO [train.py:919] (5/6) Start epoch 53 +2024-03-12 04:59:25,787 INFO [train.py:527] (5/6) Epoch 53, batch 2, global_batch_idx: 6450, batch size: 70, loss[discriminator_loss=2.759, discriminator_real_loss=1.385, discriminator_fake_loss=1.373, generator_loss=28.32, generator_mel_loss=20.69, generator_kl_loss=1.334, generator_dur_loss=1.919, generator_adv_loss=2.045, generator_feat_match_loss=2.333, over 70.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.443, discriminator_fake_loss=1.301, generator_loss=28.27, generator_mel_loss=20.76, generator_kl_loss=1.325, generator_dur_loss=1.867, generator_adv_loss=1.977, generator_feat_match_loss=2.338, over 166.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:01:44,716 INFO [train.py:527] (5/6) Epoch 53, batch 52, global_batch_idx: 6500, batch size: 64, loss[discriminator_loss=2.741, discriminator_real_loss=1.415, discriminator_fake_loss=1.327, generator_loss=28.17, generator_mel_loss=20.71, generator_kl_loss=1.283, generator_dur_loss=1.785, generator_adv_loss=1.879, generator_feat_match_loss=2.518, over 64.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.393, discriminator_fake_loss=1.353, generator_loss=28.19, generator_mel_loss=20.64, generator_kl_loss=1.325, generator_dur_loss=1.844, generator_adv_loss=1.893, generator_feat_match_loss=2.49, over 3122.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:04:04,407 INFO [train.py:527] (5/6) Epoch 53, batch 102, global_batch_idx: 6550, batch size: 39, loss[discriminator_loss=2.739, discriminator_real_loss=1.374, discriminator_fake_loss=1.365, generator_loss=29.29, generator_mel_loss=21.45, generator_kl_loss=1.423, generator_dur_loss=1.758, generator_adv_loss=1.741, generator_feat_match_loss=2.908, over 39.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.392, discriminator_fake_loss=1.351, generator_loss=28.26, generator_mel_loss=20.69, generator_kl_loss=1.325, generator_dur_loss=1.843, generator_adv_loss=1.881, generator_feat_match_loss=2.519, over 5915.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:05:03,964 INFO [train.py:919] (5/6) Start epoch 54 +2024-03-12 05:06:44,475 INFO [train.py:527] (5/6) Epoch 54, batch 28, global_batch_idx: 6600, batch size: 56, loss[discriminator_loss=2.755, discriminator_real_loss=1.334, discriminator_fake_loss=1.421, generator_loss=26.93, generator_mel_loss=19.9, generator_kl_loss=1.219, generator_dur_loss=1.833, generator_adv_loss=1.85, generator_feat_match_loss=2.127, over 56.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.405, discriminator_fake_loss=1.356, generator_loss=27.81, generator_mel_loss=20.34, generator_kl_loss=1.295, generator_dur_loss=1.853, generator_adv_loss=1.862, generator_feat_match_loss=2.454, over 1816.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:06:44,476 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 05:06:52,265 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 05:09:08,123 INFO [train.py:527] (5/6) Epoch 54, batch 78, global_batch_idx: 6650, batch size: 96, loss[discriminator_loss=2.755, discriminator_real_loss=1.377, discriminator_fake_loss=1.378, generator_loss=27.14, generator_mel_loss=19.76, generator_kl_loss=1.161, generator_dur_loss=1.955, generator_adv_loss=1.94, generator_feat_match_loss=2.321, over 96.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.399, discriminator_fake_loss=1.349, generator_loss=27.97, generator_mel_loss=20.44, generator_kl_loss=1.312, generator_dur_loss=1.83, generator_adv_loss=1.894, generator_feat_match_loss=2.491, over 4361.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:11:16,453 INFO [train.py:919] (5/6) Start epoch 55 +2024-03-12 05:11:50,267 INFO [train.py:527] (5/6) Epoch 55, batch 4, global_batch_idx: 6700, batch size: 62, loss[discriminator_loss=2.777, discriminator_real_loss=1.506, discriminator_fake_loss=1.27, generator_loss=27.42, generator_mel_loss=20.18, generator_kl_loss=1.414, generator_dur_loss=1.796, generator_adv_loss=1.691, generator_feat_match_loss=2.337, over 62.00 samples.], tot_loss[discriminator_loss=2.791, discriminator_real_loss=1.44, discriminator_fake_loss=1.351, generator_loss=27.78, generator_mel_loss=20.4, generator_kl_loss=1.259, generator_dur_loss=1.836, generator_adv_loss=1.91, generator_feat_match_loss=2.383, over 325.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:14:11,429 INFO [train.py:527] (5/6) Epoch 55, batch 54, global_batch_idx: 6750, batch size: 36, loss[discriminator_loss=2.82, discriminator_real_loss=1.358, discriminator_fake_loss=1.462, generator_loss=28.46, generator_mel_loss=20.69, generator_kl_loss=1.433, generator_dur_loss=1.762, generator_adv_loss=1.901, generator_feat_match_loss=2.675, over 36.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=28.22, generator_mel_loss=20.53, generator_kl_loss=1.333, generator_dur_loss=1.842, generator_adv_loss=1.932, generator_feat_match_loss=2.589, over 3147.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:16:32,138 INFO [train.py:527] (5/6) Epoch 55, batch 104, global_batch_idx: 6800, batch size: 14, loss[discriminator_loss=2.639, discriminator_real_loss=1.294, discriminator_fake_loss=1.345, generator_loss=28.67, generator_mel_loss=20.54, generator_kl_loss=1.649, generator_dur_loss=1.69, generator_adv_loss=1.928, generator_feat_match_loss=2.865, over 14.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.4, discriminator_fake_loss=1.345, generator_loss=28.18, generator_mel_loss=20.54, generator_kl_loss=1.319, generator_dur_loss=1.833, generator_adv_loss=1.924, generator_feat_match_loss=2.569, over 5861.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:16:32,139 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 05:16:41,183 INFO [train.py:591] (5/6) 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 05:16:41,184 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 05:17:33,222 INFO [train.py:919] (5/6) Start epoch 56 +2024-03-12 05:19:21,180 INFO [train.py:527] (5/6) Epoch 56, batch 30, global_batch_idx: 6850, batch size: 72, loss[discriminator_loss=2.745, discriminator_real_loss=1.407, discriminator_fake_loss=1.338, generator_loss=28.32, generator_mel_loss=20.41, generator_kl_loss=1.388, generator_dur_loss=1.905, generator_adv_loss=2.016, generator_feat_match_loss=2.603, over 72.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.42, discriminator_fake_loss=1.333, generator_loss=28.1, generator_mel_loss=20.62, generator_kl_loss=1.314, generator_dur_loss=1.83, generator_adv_loss=1.876, generator_feat_match_loss=2.461, over 1780.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:21:42,375 INFO [train.py:527] (5/6) Epoch 56, batch 80, global_batch_idx: 6900, batch size: 36, loss[discriminator_loss=2.722, discriminator_real_loss=1.504, discriminator_fake_loss=1.218, generator_loss=28.32, generator_mel_loss=20.63, generator_kl_loss=1.42, generator_dur_loss=1.788, generator_adv_loss=1.858, generator_feat_match_loss=2.625, over 36.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.409, discriminator_fake_loss=1.344, generator_loss=28.13, generator_mel_loss=20.58, generator_kl_loss=1.327, generator_dur_loss=1.831, generator_adv_loss=1.878, generator_feat_match_loss=2.512, over 4577.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:23:45,013 INFO [train.py:919] (5/6) Start epoch 57 +2024-03-12 05:24:26,658 INFO [train.py:527] (5/6) Epoch 57, batch 6, global_batch_idx: 6950, batch size: 44, loss[discriminator_loss=2.719, discriminator_real_loss=1.319, discriminator_fake_loss=1.401, generator_loss=28.66, generator_mel_loss=20.95, generator_kl_loss=1.44, generator_dur_loss=1.805, generator_adv_loss=1.937, generator_feat_match_loss=2.527, over 44.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.424, discriminator_fake_loss=1.321, generator_loss=28.68, generator_mel_loss=20.97, generator_kl_loss=1.335, generator_dur_loss=1.868, generator_adv_loss=1.932, generator_feat_match_loss=2.575, over 445.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:26:48,034 INFO [train.py:527] (5/6) Epoch 57, batch 56, global_batch_idx: 7000, batch size: 68, loss[discriminator_loss=2.812, discriminator_real_loss=1.392, discriminator_fake_loss=1.42, generator_loss=26.67, generator_mel_loss=19.3, generator_kl_loss=1.312, generator_dur_loss=1.844, generator_adv_loss=1.869, generator_feat_match_loss=2.348, over 68.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.409, discriminator_fake_loss=1.349, generator_loss=28.3, generator_mel_loss=20.59, generator_kl_loss=1.334, generator_dur_loss=1.843, generator_adv_loss=1.919, generator_feat_match_loss=2.621, over 3296.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:26:48,035 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 05:26:56,057 INFO [train.py:591] (5/6) 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 05:26:56,058 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 05:29:16,520 INFO [train.py:527] (5/6) Epoch 57, batch 106, global_batch_idx: 7050, batch size: 53, loss[discriminator_loss=2.72, discriminator_real_loss=1.363, discriminator_fake_loss=1.357, generator_loss=27.47, generator_mel_loss=19.85, generator_kl_loss=1.485, generator_dur_loss=1.686, generator_adv_loss=1.998, generator_feat_match_loss=2.451, over 53.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.407, discriminator_fake_loss=1.349, generator_loss=28.24, generator_mel_loss=20.6, generator_kl_loss=1.321, generator_dur_loss=1.834, generator_adv_loss=1.91, generator_feat_match_loss=2.578, over 6165.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:30:06,546 INFO [train.py:919] (5/6) Start epoch 58 +2024-03-12 05:32:02,696 INFO [train.py:527] (5/6) Epoch 58, batch 32, global_batch_idx: 7100, batch size: 72, loss[discriminator_loss=2.787, discriminator_real_loss=1.441, discriminator_fake_loss=1.346, generator_loss=27.07, generator_mel_loss=19.71, generator_kl_loss=1.299, generator_dur_loss=1.846, generator_adv_loss=1.84, generator_feat_match_loss=2.372, over 72.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.407, discriminator_fake_loss=1.348, generator_loss=27.9, generator_mel_loss=20.36, generator_kl_loss=1.322, generator_dur_loss=1.843, generator_adv_loss=1.89, generator_feat_match_loss=2.495, over 1966.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:34:24,830 INFO [train.py:527] (5/6) Epoch 58, batch 82, global_batch_idx: 7150, batch size: 42, loss[discriminator_loss=2.696, discriminator_real_loss=1.459, discriminator_fake_loss=1.236, generator_loss=27.69, generator_mel_loss=20.03, generator_kl_loss=1.506, generator_dur_loss=1.77, generator_adv_loss=1.863, generator_feat_match_loss=2.527, over 42.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.409, discriminator_fake_loss=1.348, generator_loss=27.95, generator_mel_loss=20.4, generator_kl_loss=1.32, generator_dur_loss=1.839, generator_adv_loss=1.89, generator_feat_match_loss=2.506, over 4858.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:36:23,195 INFO [train.py:919] (5/6) Start epoch 59 +2024-03-12 05:37:09,922 INFO [train.py:527] (5/6) Epoch 59, batch 8, global_batch_idx: 7200, batch size: 31, loss[discriminator_loss=2.605, discriminator_real_loss=1.313, discriminator_fake_loss=1.292, generator_loss=30.29, generator_mel_loss=21.63, generator_kl_loss=1.514, generator_dur_loss=1.704, generator_adv_loss=2.147, generator_feat_match_loss=3.29, over 31.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=28.09, generator_mel_loss=20.27, generator_kl_loss=1.314, generator_dur_loss=1.861, generator_adv_loss=1.954, generator_feat_match_loss=2.693, over 569.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:37:09,925 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 05:37:17,482 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 05:39:37,633 INFO [train.py:527] (5/6) Epoch 59, batch 58, global_batch_idx: 7250, batch size: 66, loss[discriminator_loss=2.704, discriminator_real_loss=1.399, discriminator_fake_loss=1.305, generator_loss=28.22, generator_mel_loss=20.56, generator_kl_loss=1.28, generator_dur_loss=1.859, generator_adv_loss=1.945, generator_feat_match_loss=2.58, over 66.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.408, discriminator_fake_loss=1.347, generator_loss=27.95, generator_mel_loss=20.34, generator_kl_loss=1.294, generator_dur_loss=1.846, generator_adv_loss=1.917, generator_feat_match_loss=2.545, over 3622.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:42:00,743 INFO [train.py:527] (5/6) Epoch 59, batch 108, global_batch_idx: 7300, batch size: 15, loss[discriminator_loss=2.662, discriminator_real_loss=1.336, discriminator_fake_loss=1.326, generator_loss=28.46, generator_mel_loss=20.76, generator_kl_loss=1.546, generator_dur_loss=1.667, generator_adv_loss=1.78, generator_feat_match_loss=2.714, over 15.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.404, discriminator_fake_loss=1.346, generator_loss=27.93, generator_mel_loss=20.32, generator_kl_loss=1.304, generator_dur_loss=1.844, generator_adv_loss=1.905, generator_feat_match_loss=2.552, over 6629.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:42:44,601 INFO [train.py:919] (5/6) Start epoch 60 +2024-03-12 05:44:46,073 INFO [train.py:527] (5/6) Epoch 60, batch 34, global_batch_idx: 7350, batch size: 44, loss[discriminator_loss=2.838, discriminator_real_loss=1.505, discriminator_fake_loss=1.334, generator_loss=27.52, generator_mel_loss=20.32, generator_kl_loss=1.353, generator_dur_loss=1.745, generator_adv_loss=1.785, generator_feat_match_loss=2.316, over 44.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.408, discriminator_fake_loss=1.341, generator_loss=27.99, generator_mel_loss=20.41, generator_kl_loss=1.329, generator_dur_loss=1.827, generator_adv_loss=1.881, generator_feat_match_loss=2.54, over 1930.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:47:06,830 INFO [train.py:527] (5/6) Epoch 60, batch 84, global_batch_idx: 7400, batch size: 55, loss[discriminator_loss=2.804, discriminator_real_loss=1.501, discriminator_fake_loss=1.303, generator_loss=27.15, generator_mel_loss=20.32, generator_kl_loss=1.189, generator_dur_loss=1.799, generator_adv_loss=1.738, generator_feat_match_loss=2.113, over 55.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.416, discriminator_fake_loss=1.344, generator_loss=28.05, generator_mel_loss=20.43, generator_kl_loss=1.336, generator_dur_loss=1.816, generator_adv_loss=1.914, generator_feat_match_loss=2.552, over 4594.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:47:06,832 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 05:47:15,707 INFO [train.py:591] (5/6) 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 05:47:15,708 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 05:49:05,618 INFO [train.py:919] (5/6) Start epoch 61 +2024-03-12 05:50:53,652 INFO [train.py:527] (5/6) Epoch 61, batch 10, global_batch_idx: 7450, batch size: 80, loss[discriminator_loss=2.78, discriminator_real_loss=1.406, discriminator_fake_loss=1.374, generator_loss=27.98, generator_mel_loss=20.47, generator_kl_loss=1.215, generator_dur_loss=1.916, generator_adv_loss=1.81, generator_feat_match_loss=2.566, over 80.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.427, discriminator_fake_loss=1.313, generator_loss=27.84, generator_mel_loss=20.28, generator_kl_loss=1.359, generator_dur_loss=1.807, generator_adv_loss=1.886, generator_feat_match_loss=2.505, over 602.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:53:16,181 INFO [train.py:527] (5/6) Epoch 61, batch 60, global_batch_idx: 7500, batch size: 62, loss[discriminator_loss=2.714, discriminator_real_loss=1.319, discriminator_fake_loss=1.395, generator_loss=27.47, generator_mel_loss=19.98, generator_kl_loss=1.28, generator_dur_loss=1.831, generator_adv_loss=1.727, generator_feat_match_loss=2.651, over 62.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.408, discriminator_fake_loss=1.336, generator_loss=27.88, generator_mel_loss=20.22, generator_kl_loss=1.341, generator_dur_loss=1.819, generator_adv_loss=1.924, generator_feat_match_loss=2.57, over 3364.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:55:37,058 INFO [train.py:527] (5/6) Epoch 61, batch 110, global_batch_idx: 7550, batch size: 25, loss[discriminator_loss=2.646, discriminator_real_loss=1.401, discriminator_fake_loss=1.245, generator_loss=29.44, generator_mel_loss=21.19, generator_kl_loss=1.572, generator_dur_loss=1.673, generator_adv_loss=1.871, generator_feat_match_loss=3.137, over 25.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.4, discriminator_fake_loss=1.338, generator_loss=27.94, generator_mel_loss=20.26, generator_kl_loss=1.338, generator_dur_loss=1.821, generator_adv_loss=1.914, generator_feat_match_loss=2.605, over 6265.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, +2024-03-12 05:56:17,068 INFO [train.py:919] (5/6) Start epoch 62 +2024-03-12 05:58:26,355 INFO [train.py:527] (5/6) Epoch 62, batch 36, global_batch_idx: 7600, batch size: 44, loss[discriminator_loss=2.748, discriminator_real_loss=1.458, discriminator_fake_loss=1.29, generator_loss=28.55, generator_mel_loss=20.78, generator_kl_loss=1.347, generator_dur_loss=1.764, generator_adv_loss=1.818, generator_feat_match_loss=2.841, over 44.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.403, discriminator_fake_loss=1.361, generator_loss=27.83, generator_mel_loss=20.18, generator_kl_loss=1.358, generator_dur_loss=1.842, generator_adv_loss=1.878, generator_feat_match_loss=2.57, over 2216.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 05:58:26,357 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 05:58:34,415 INFO [train.py:591] (5/6) 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 05:58:34,419 INFO [train.py:592] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 06:00:56,922 INFO [train.py:527] (5/6) Epoch 62, batch 86, global_batch_idx: 7650, batch size: 56, loss[discriminator_loss=2.819, discriminator_real_loss=1.261, discriminator_fake_loss=1.558, generator_loss=28.03, generator_mel_loss=20.88, generator_kl_loss=1.29, generator_dur_loss=1.82, generator_adv_loss=1.753, generator_feat_match_loss=2.288, over 56.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.401, discriminator_fake_loss=1.367, generator_loss=27.95, generator_mel_loss=20.26, generator_kl_loss=1.36, generator_dur_loss=1.829, generator_adv_loss=1.899, generator_feat_match_loss=2.604, over 5043.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:02:41,597 INFO [train.py:919] (5/6) Start epoch 63 +2024-03-12 06:03:38,524 INFO [train.py:527] (5/6) Epoch 63, batch 12, global_batch_idx: 7700, batch size: 56, loss[discriminator_loss=2.847, discriminator_real_loss=1.356, discriminator_fake_loss=1.49, generator_loss=29.42, generator_mel_loss=21.17, generator_kl_loss=1.466, generator_dur_loss=1.686, generator_adv_loss=2.402, generator_feat_match_loss=2.695, over 56.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.376, discriminator_fake_loss=1.347, generator_loss=28.16, generator_mel_loss=20.37, generator_kl_loss=1.367, generator_dur_loss=1.775, generator_adv_loss=1.931, generator_feat_match_loss=2.71, over 663.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:05:54,465 INFO [train.py:527] (5/6) Epoch 63, batch 62, global_batch_idx: 7750, batch size: 53, loss[discriminator_loss=2.749, discriminator_real_loss=1.424, discriminator_fake_loss=1.325, generator_loss=28.36, generator_mel_loss=20.63, generator_kl_loss=1.297, generator_dur_loss=1.781, generator_adv_loss=1.952, generator_feat_match_loss=2.692, over 53.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.401, discriminator_fake_loss=1.356, generator_loss=27.82, generator_mel_loss=20.2, generator_kl_loss=1.368, generator_dur_loss=1.789, generator_adv_loss=1.887, generator_feat_match_loss=2.579, over 3245.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:08:16,506 INFO [train.py:527] (5/6) Epoch 63, batch 112, global_batch_idx: 7800, batch size: 68, loss[discriminator_loss=2.786, discriminator_real_loss=1.573, discriminator_fake_loss=1.213, generator_loss=27.73, generator_mel_loss=20.17, generator_kl_loss=1.273, generator_dur_loss=1.901, generator_adv_loss=1.821, generator_feat_match_loss=2.573, over 68.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.403, discriminator_fake_loss=1.355, generator_loss=27.78, generator_mel_loss=20.19, generator_kl_loss=1.344, generator_dur_loss=1.799, generator_adv_loss=1.883, generator_feat_match_loss=2.567, over 6156.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:08:16,507 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 06:08:25,394 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 06:08:58,114 INFO [train.py:919] (5/6) Start epoch 64 +2024-03-12 06:11:06,522 INFO [train.py:527] (5/6) Epoch 64, batch 38, global_batch_idx: 7850, batch size: 58, loss[discriminator_loss=2.785, discriminator_real_loss=1.318, discriminator_fake_loss=1.468, generator_loss=27.62, generator_mel_loss=20.27, generator_kl_loss=1.387, generator_dur_loss=1.71, generator_adv_loss=1.74, generator_feat_match_loss=2.518, over 58.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.386, discriminator_fake_loss=1.351, generator_loss=27.78, generator_mel_loss=20.12, generator_kl_loss=1.28, generator_dur_loss=1.848, generator_adv_loss=1.898, generator_feat_match_loss=2.638, over 2302.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:13:29,491 INFO [train.py:527] (5/6) Epoch 64, batch 88, global_batch_idx: 7900, batch size: 42, loss[discriminator_loss=2.776, discriminator_real_loss=1.445, discriminator_fake_loss=1.331, generator_loss=28.43, generator_mel_loss=20.78, generator_kl_loss=1.199, generator_dur_loss=1.772, generator_adv_loss=2.164, generator_feat_match_loss=2.516, over 42.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.387, discriminator_fake_loss=1.35, generator_loss=27.81, generator_mel_loss=20.12, generator_kl_loss=1.287, generator_dur_loss=1.845, generator_adv_loss=1.907, generator_feat_match_loss=2.647, over 5318.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:15:03,160 INFO [train.py:919] (5/6) Start epoch 65 +2024-03-12 06:16:05,145 INFO [train.py:527] (5/6) Epoch 65, batch 14, global_batch_idx: 7950, batch size: 64, loss[discriminator_loss=2.846, discriminator_real_loss=1.678, discriminator_fake_loss=1.167, generator_loss=28.12, generator_mel_loss=20.57, generator_kl_loss=1.32, generator_dur_loss=1.826, generator_adv_loss=1.532, generator_feat_match_loss=2.873, over 64.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.424, discriminator_fake_loss=1.318, generator_loss=27.83, generator_mel_loss=20.19, generator_kl_loss=1.31, generator_dur_loss=1.81, generator_adv_loss=1.891, generator_feat_match_loss=2.631, over 841.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:18:22,625 INFO [train.py:527] (5/6) Epoch 65, batch 64, global_batch_idx: 8000, batch size: 42, loss[discriminator_loss=2.65, discriminator_real_loss=1.365, discriminator_fake_loss=1.285, generator_loss=29.09, generator_mel_loss=20.66, generator_kl_loss=1.384, generator_dur_loss=1.694, generator_adv_loss=2.057, generator_feat_match_loss=3.297, over 42.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.401, discriminator_fake_loss=1.351, generator_loss=28.03, generator_mel_loss=20.24, generator_kl_loss=1.348, generator_dur_loss=1.813, generator_adv_loss=1.933, generator_feat_match_loss=2.689, over 3582.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:18:22,627 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 06:18:31,351 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 06:20:51,806 INFO [train.py:527] (5/6) Epoch 65, batch 114, global_batch_idx: 8050, batch size: 68, loss[discriminator_loss=2.832, discriminator_real_loss=1.611, discriminator_fake_loss=1.221, generator_loss=27.22, generator_mel_loss=19.84, generator_kl_loss=1.126, generator_dur_loss=1.875, generator_adv_loss=1.935, generator_feat_match_loss=2.445, over 68.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.408, discriminator_fake_loss=1.35, generator_loss=27.94, generator_mel_loss=20.16, generator_kl_loss=1.34, generator_dur_loss=1.815, generator_adv_loss=1.937, generator_feat_match_loss=2.688, over 6423.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:21:19,028 INFO [train.py:919] (5/6) Start epoch 66 +2024-03-12 06:23:34,272 INFO [train.py:527] (5/6) Epoch 66, batch 40, global_batch_idx: 8100, batch size: 59, loss[discriminator_loss=2.755, discriminator_real_loss=1.479, discriminator_fake_loss=1.276, generator_loss=27.38, generator_mel_loss=19.79, generator_kl_loss=1.413, generator_dur_loss=1.815, generator_adv_loss=1.772, generator_feat_match_loss=2.597, over 59.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.402, discriminator_fake_loss=1.342, generator_loss=27.68, generator_mel_loss=20.04, generator_kl_loss=1.343, generator_dur_loss=1.798, generator_adv_loss=1.883, generator_feat_match_loss=2.615, over 2189.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:25:54,592 INFO [train.py:527] (5/6) Epoch 66, batch 90, global_batch_idx: 8150, batch size: 60, loss[discriminator_loss=2.652, discriminator_real_loss=1.199, discriminator_fake_loss=1.453, generator_loss=28.25, generator_mel_loss=20.03, generator_kl_loss=1.415, generator_dur_loss=1.789, generator_adv_loss=2.092, generator_feat_match_loss=2.922, over 60.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.401, discriminator_fake_loss=1.354, generator_loss=27.68, generator_mel_loss=20.01, generator_kl_loss=1.323, generator_dur_loss=1.826, generator_adv_loss=1.895, generator_feat_match_loss=2.621, over 5276.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:27:27,386 INFO [train.py:919] (5/6) Start epoch 67 +2024-03-12 06:28:32,215 INFO [train.py:527] (5/6) Epoch 67, batch 16, global_batch_idx: 8200, batch size: 58, loss[discriminator_loss=2.8, discriminator_real_loss=1.44, discriminator_fake_loss=1.36, generator_loss=28.24, generator_mel_loss=20.43, generator_kl_loss=1.327, generator_dur_loss=1.784, generator_adv_loss=2.061, generator_feat_match_loss=2.641, over 58.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.4, discriminator_fake_loss=1.343, generator_loss=28.17, generator_mel_loss=20.36, generator_kl_loss=1.327, generator_dur_loss=1.828, generator_adv_loss=1.926, generator_feat_match_loss=2.73, over 995.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:28:32,217 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 06:28:40,207 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 06:30:59,945 INFO [train.py:527] (5/6) Epoch 67, batch 66, global_batch_idx: 8250, batch size: 53, loss[discriminator_loss=2.823, discriminator_real_loss=1.665, discriminator_fake_loss=1.158, generator_loss=26.44, generator_mel_loss=18.97, generator_kl_loss=1.397, generator_dur_loss=1.755, generator_adv_loss=1.799, generator_feat_match_loss=2.525, over 53.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.403, discriminator_fake_loss=1.35, generator_loss=27.92, generator_mel_loss=20.2, generator_kl_loss=1.346, generator_dur_loss=1.815, generator_adv_loss=1.898, generator_feat_match_loss=2.668, over 3903.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:33:19,325 INFO [train.py:527] (5/6) Epoch 67, batch 116, global_batch_idx: 8300, batch size: 56, loss[discriminator_loss=2.865, discriminator_real_loss=1.352, discriminator_fake_loss=1.512, generator_loss=27.81, generator_mel_loss=19.81, generator_kl_loss=1.38, generator_dur_loss=1.731, generator_adv_loss=2.182, generator_feat_match_loss=2.712, over 56.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.401, discriminator_fake_loss=1.36, generator_loss=27.85, generator_mel_loss=20.15, generator_kl_loss=1.35, generator_dur_loss=1.812, generator_adv_loss=1.898, generator_feat_match_loss=2.646, over 6630.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:33:37,940 INFO [train.py:919] (5/6) Start epoch 68 +2024-03-12 06:35:59,109 INFO [train.py:527] (5/6) Epoch 68, batch 42, global_batch_idx: 8350, batch size: 68, loss[discriminator_loss=2.713, discriminator_real_loss=1.222, discriminator_fake_loss=1.49, generator_loss=27.9, generator_mel_loss=19.8, generator_kl_loss=1.302, generator_dur_loss=1.901, generator_adv_loss=1.977, generator_feat_match_loss=2.923, over 68.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.394, discriminator_fake_loss=1.346, generator_loss=27.68, generator_mel_loss=19.94, generator_kl_loss=1.329, generator_dur_loss=1.831, generator_adv_loss=1.945, generator_feat_match_loss=2.63, over 2519.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:38:17,960 INFO [train.py:527] (5/6) Epoch 68, batch 92, global_batch_idx: 8400, batch size: 52, loss[discriminator_loss=2.743, discriminator_real_loss=1.19, discriminator_fake_loss=1.553, generator_loss=28.64, generator_mel_loss=20.15, generator_kl_loss=1.506, generator_dur_loss=1.768, generator_adv_loss=2.201, generator_feat_match_loss=3.007, over 52.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.399, discriminator_fake_loss=1.342, generator_loss=27.64, generator_mel_loss=19.92, generator_kl_loss=1.335, generator_dur_loss=1.838, generator_adv_loss=1.909, generator_feat_match_loss=2.628, over 5542.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:38:17,961 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 06:38:26,712 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 06:39:51,813 INFO [train.py:919] (5/6) Start epoch 69 +2024-03-12 06:41:07,173 INFO [train.py:527] (5/6) Epoch 69, batch 18, global_batch_idx: 8450, batch size: 15, loss[discriminator_loss=2.742, discriminator_real_loss=1.487, discriminator_fake_loss=1.255, generator_loss=28.67, generator_mel_loss=20.69, generator_kl_loss=1.629, generator_dur_loss=1.658, generator_adv_loss=1.968, generator_feat_match_loss=2.725, over 15.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.37, discriminator_fake_loss=1.374, generator_loss=28.24, generator_mel_loss=20.32, generator_kl_loss=1.412, generator_dur_loss=1.767, generator_adv_loss=1.965, generator_feat_match_loss=2.776, over 841.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:43:24,070 INFO [train.py:527] (5/6) Epoch 69, batch 68, global_batch_idx: 8500, batch size: 31, loss[discriminator_loss=2.707, discriminator_real_loss=1.38, discriminator_fake_loss=1.327, generator_loss=27.35, generator_mel_loss=19.86, generator_kl_loss=1.351, generator_dur_loss=1.717, generator_adv_loss=1.924, generator_feat_match_loss=2.501, over 31.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.395, discriminator_fake_loss=1.351, generator_loss=27.72, generator_mel_loss=20.02, generator_kl_loss=1.373, generator_dur_loss=1.781, generator_adv_loss=1.919, generator_feat_match_loss=2.626, over 3546.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:45:42,055 INFO [train.py:527] (5/6) Epoch 69, batch 118, global_batch_idx: 8550, batch size: 66, loss[discriminator_loss=2.737, discriminator_real_loss=1.372, discriminator_fake_loss=1.365, generator_loss=28.98, generator_mel_loss=20.66, generator_kl_loss=1.476, generator_dur_loss=1.8, generator_adv_loss=1.991, generator_feat_match_loss=3.053, over 66.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.394, discriminator_fake_loss=1.357, generator_loss=27.7, generator_mel_loss=20.02, generator_kl_loss=1.365, generator_dur_loss=1.788, generator_adv_loss=1.895, generator_feat_match_loss=2.638, over 6359.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:45:59,579 INFO [train.py:919] (5/6) Start epoch 70 +2024-03-12 06:48:24,328 INFO [train.py:527] (5/6) Epoch 70, batch 44, global_batch_idx: 8600, batch size: 45, loss[discriminator_loss=2.879, discriminator_real_loss=1.608, discriminator_fake_loss=1.27, generator_loss=28.08, generator_mel_loss=20.39, generator_kl_loss=1.503, generator_dur_loss=1.7, generator_adv_loss=1.821, generator_feat_match_loss=2.665, over 45.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.423, discriminator_fake_loss=1.347, generator_loss=28.18, generator_mel_loss=20.09, generator_kl_loss=1.343, generator_dur_loss=1.789, generator_adv_loss=2.056, generator_feat_match_loss=2.9, over 2430.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:48:24,329 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 06:48:32,172 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 06:50:51,389 INFO [train.py:527] (5/6) Epoch 70, batch 94, global_batch_idx: 8650, batch size: 25, loss[discriminator_loss=2.759, discriminator_real_loss=1.534, discriminator_fake_loss=1.225, generator_loss=27.23, generator_mel_loss=19.8, generator_kl_loss=1.63, generator_dur_loss=1.63, generator_adv_loss=1.93, generator_feat_match_loss=2.237, over 25.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.406, discriminator_fake_loss=1.348, generator_loss=27.81, generator_mel_loss=19.97, generator_kl_loss=1.334, generator_dur_loss=1.804, generator_adv_loss=1.964, generator_feat_match_loss=2.732, over 5435.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:52:11,771 INFO [train.py:919] (5/6) Start epoch 71 +2024-03-12 06:53:31,209 INFO [train.py:527] (5/6) Epoch 71, batch 20, global_batch_idx: 8700, batch size: 14, loss[discriminator_loss=2.772, discriminator_real_loss=1.531, discriminator_fake_loss=1.241, generator_loss=30, generator_mel_loss=22.36, generator_kl_loss=1.694, generator_dur_loss=1.678, generator_adv_loss=1.839, generator_feat_match_loss=2.42, over 14.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.409, discriminator_fake_loss=1.33, generator_loss=28.06, generator_mel_loss=20.29, generator_kl_loss=1.37, generator_dur_loss=1.754, generator_adv_loss=1.916, generator_feat_match_loss=2.736, over 1009.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:55:49,082 INFO [train.py:527] (5/6) Epoch 71, batch 70, global_batch_idx: 8750, batch size: 59, loss[discriminator_loss=2.802, discriminator_real_loss=1.481, discriminator_fake_loss=1.321, generator_loss=27.86, generator_mel_loss=20.11, generator_kl_loss=1.425, generator_dur_loss=1.786, generator_adv_loss=1.919, generator_feat_match_loss=2.621, over 59.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.41, discriminator_fake_loss=1.342, generator_loss=27.77, generator_mel_loss=20.05, generator_kl_loss=1.344, generator_dur_loss=1.794, generator_adv_loss=1.916, generator_feat_match_loss=2.671, over 3861.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:58:06,889 INFO [train.py:527] (5/6) Epoch 71, batch 120, global_batch_idx: 8800, batch size: 72, loss[discriminator_loss=2.729, discriminator_real_loss=1.522, discriminator_fake_loss=1.208, generator_loss=28.6, generator_mel_loss=20.17, generator_kl_loss=1.327, generator_dur_loss=1.805, generator_adv_loss=2.094, generator_feat_match_loss=3.205, over 72.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.405, discriminator_fake_loss=1.353, generator_loss=27.86, generator_mel_loss=20.06, generator_kl_loss=1.354, generator_dur_loss=1.795, generator_adv_loss=1.925, generator_feat_match_loss=2.724, over 6633.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 06:58:06,890 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 06:58:15,683 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 06:58:25,718 INFO [train.py:919] (5/6) Start epoch 72 +2024-03-12 07:00:55,663 INFO [train.py:527] (5/6) Epoch 72, batch 46, global_batch_idx: 8850, batch size: 26, loss[discriminator_loss=2.705, discriminator_real_loss=1.326, discriminator_fake_loss=1.379, generator_loss=26.79, generator_mel_loss=19.2, generator_kl_loss=1.383, generator_dur_loss=1.701, generator_adv_loss=2.011, generator_feat_match_loss=2.497, over 26.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.408, discriminator_fake_loss=1.359, generator_loss=27.64, generator_mel_loss=19.96, generator_kl_loss=1.348, generator_dur_loss=1.779, generator_adv_loss=1.902, generator_feat_match_loss=2.642, over 2545.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:03:14,396 INFO [train.py:527] (5/6) Epoch 72, batch 96, global_batch_idx: 8900, batch size: 96, loss[discriminator_loss=2.697, discriminator_real_loss=1.358, discriminator_fake_loss=1.338, generator_loss=27.59, generator_mel_loss=19.66, generator_kl_loss=1.294, generator_dur_loss=1.915, generator_adv_loss=1.815, generator_feat_match_loss=2.9, over 96.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.406, discriminator_fake_loss=1.35, generator_loss=27.59, generator_mel_loss=19.92, generator_kl_loss=1.352, generator_dur_loss=1.786, generator_adv_loss=1.89, generator_feat_match_loss=2.636, over 5500.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:04:31,353 INFO [train.py:919] (5/6) Start epoch 73 +2024-03-12 07:05:57,012 INFO [train.py:527] (5/6) Epoch 73, batch 22, global_batch_idx: 8950, batch size: 48, loss[discriminator_loss=2.965, discriminator_real_loss=1.538, discriminator_fake_loss=1.427, generator_loss=28.17, generator_mel_loss=20.66, generator_kl_loss=1.429, generator_dur_loss=1.719, generator_adv_loss=1.837, generator_feat_match_loss=2.519, over 48.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.401, discriminator_fake_loss=1.342, generator_loss=28.21, generator_mel_loss=20.05, generator_kl_loss=1.389, generator_dur_loss=1.763, generator_adv_loss=2.052, generator_feat_match_loss=2.952, over 1156.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:08:14,557 INFO [train.py:527] (5/6) Epoch 73, batch 72, global_batch_idx: 9000, batch size: 72, loss[discriminator_loss=2.765, discriminator_real_loss=1.331, discriminator_fake_loss=1.434, generator_loss=26.6, generator_mel_loss=19.33, generator_kl_loss=1.186, generator_dur_loss=1.858, generator_adv_loss=1.776, generator_feat_match_loss=2.454, over 72.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.419, discriminator_fake_loss=1.351, generator_loss=27.8, generator_mel_loss=19.89, generator_kl_loss=1.363, generator_dur_loss=1.784, generator_adv_loss=1.998, generator_feat_match_loss=2.767, over 3977.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:08:14,559 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 07:08:23,139 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 28859MB +2024-03-12 07:10:40,052 INFO [train.py:527] (5/6) Epoch 73, batch 122, global_batch_idx: 9050, batch size: 53, loss[discriminator_loss=2.683, discriminator_real_loss=1.412, discriminator_fake_loss=1.272, generator_loss=28.01, generator_mel_loss=20.35, generator_kl_loss=1.363, generator_dur_loss=1.725, generator_adv_loss=1.869, generator_feat_match_loss=2.699, over 53.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.402, discriminator_fake_loss=1.347, generator_loss=27.77, generator_mel_loss=19.95, generator_kl_loss=1.36, generator_dur_loss=1.787, generator_adv_loss=1.955, generator_feat_match_loss=2.716, over 6752.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:10:46,023 INFO [train.py:919] (5/6) Start epoch 74 +2024-03-12 07:13:21,553 INFO [train.py:527] (5/6) Epoch 74, batch 48, global_batch_idx: 9100, batch size: 45, loss[discriminator_loss=2.693, discriminator_real_loss=1.464, discriminator_fake_loss=1.229, generator_loss=29.16, generator_mel_loss=20.52, generator_kl_loss=1.584, generator_dur_loss=1.674, generator_adv_loss=2.105, generator_feat_match_loss=3.278, over 45.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.398, discriminator_fake_loss=1.359, generator_loss=27.56, generator_mel_loss=19.84, generator_kl_loss=1.362, generator_dur_loss=1.778, generator_adv_loss=1.891, generator_feat_match_loss=2.69, over 2726.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:15:39,573 INFO [train.py:527] (5/6) Epoch 74, batch 98, global_batch_idx: 9150, batch size: 47, loss[discriminator_loss=2.729, discriminator_real_loss=1.297, discriminator_fake_loss=1.431, generator_loss=27.42, generator_mel_loss=19.57, generator_kl_loss=1.409, generator_dur_loss=1.706, generator_adv_loss=2.122, generator_feat_match_loss=2.614, over 47.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.4, discriminator_fake_loss=1.36, generator_loss=27.59, generator_mel_loss=19.89, generator_kl_loss=1.378, generator_dur_loss=1.787, generator_adv_loss=1.883, generator_feat_match_loss=2.661, over 5361.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:16:51,153 INFO [train.py:919] (5/6) Start epoch 75 +2024-03-12 07:18:20,456 INFO [train.py:527] (5/6) Epoch 75, batch 24, global_batch_idx: 9200, batch size: 77, loss[discriminator_loss=2.76, discriminator_real_loss=1.402, discriminator_fake_loss=1.358, generator_loss=26.66, generator_mel_loss=19.28, generator_kl_loss=1.162, generator_dur_loss=1.872, generator_adv_loss=1.891, generator_feat_match_loss=2.453, over 77.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.401, discriminator_fake_loss=1.359, generator_loss=27.62, generator_mel_loss=19.86, generator_kl_loss=1.349, generator_dur_loss=1.79, generator_adv_loss=1.892, generator_feat_match_loss=2.729, over 1491.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:18:20,457 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 07:18:28,221 INFO [train.py:591] (5/6) 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 07:18:28,222 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 07:20:44,770 INFO [train.py:527] (5/6) Epoch 75, batch 74, global_batch_idx: 9250, batch size: 36, loss[discriminator_loss=2.818, discriminator_real_loss=1.42, discriminator_fake_loss=1.398, generator_loss=28.8, generator_mel_loss=20.92, generator_kl_loss=1.521, generator_dur_loss=1.779, generator_adv_loss=1.978, generator_feat_match_loss=2.595, over 36.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.4, discriminator_fake_loss=1.36, generator_loss=27.54, generator_mel_loss=19.83, generator_kl_loss=1.379, generator_dur_loss=1.796, generator_adv_loss=1.881, generator_feat_match_loss=2.653, over 4142.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:23:02,342 INFO [train.py:919] (5/6) Start epoch 76 +2024-03-12 07:23:25,952 INFO [train.py:527] (5/6) Epoch 76, batch 0, global_batch_idx: 9300, batch size: 68, loss[discriminator_loss=2.677, discriminator_real_loss=1.277, discriminator_fake_loss=1.4, generator_loss=27.31, generator_mel_loss=19.67, generator_kl_loss=1.276, generator_dur_loss=1.816, generator_adv_loss=1.781, generator_feat_match_loss=2.768, over 68.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.277, discriminator_fake_loss=1.4, generator_loss=27.31, generator_mel_loss=19.67, generator_kl_loss=1.276, generator_dur_loss=1.816, generator_adv_loss=1.781, generator_feat_match_loss=2.768, over 68.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:25:43,624 INFO [train.py:527] (5/6) Epoch 76, batch 50, global_batch_idx: 9350, batch size: 36, loss[discriminator_loss=2.63, discriminator_real_loss=1.332, discriminator_fake_loss=1.299, generator_loss=30.31, generator_mel_loss=21.82, generator_kl_loss=1.318, generator_dur_loss=1.726, generator_adv_loss=2.169, generator_feat_match_loss=3.273, over 36.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.39, discriminator_fake_loss=1.34, generator_loss=27.55, generator_mel_loss=19.76, generator_kl_loss=1.345, generator_dur_loss=1.799, generator_adv_loss=1.942, generator_feat_match_loss=2.704, over 2936.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:28:05,342 INFO [train.py:527] (5/6) Epoch 76, batch 100, global_batch_idx: 9400, batch size: 16, loss[discriminator_loss=2.655, discriminator_real_loss=1.4, discriminator_fake_loss=1.255, generator_loss=27.52, generator_mel_loss=19.39, generator_kl_loss=1.514, generator_dur_loss=1.681, generator_adv_loss=2.115, generator_feat_match_loss=2.822, over 16.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.392, discriminator_fake_loss=1.342, generator_loss=27.56, generator_mel_loss=19.75, generator_kl_loss=1.36, generator_dur_loss=1.799, generator_adv_loss=1.926, generator_feat_match_loss=2.725, over 5730.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:28:05,344 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 07:28:14,095 INFO [train.py:591] (5/6) 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 07:28:14,096 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 07:29:15,466 INFO [train.py:919] (5/6) Start epoch 77 +2024-03-12 07:30:53,598 INFO [train.py:527] (5/6) Epoch 77, batch 26, global_batch_idx: 9450, batch size: 58, loss[discriminator_loss=2.727, discriminator_real_loss=1.26, discriminator_fake_loss=1.468, generator_loss=28.35, generator_mel_loss=20.42, generator_kl_loss=1.393, generator_dur_loss=1.732, generator_adv_loss=1.878, generator_feat_match_loss=2.933, over 58.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.402, discriminator_fake_loss=1.341, generator_loss=27.54, generator_mel_loss=19.77, generator_kl_loss=1.375, generator_dur_loss=1.8, generator_adv_loss=1.888, generator_feat_match_loss=2.7, over 1611.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:33:12,752 INFO [train.py:527] (5/6) Epoch 77, batch 76, global_batch_idx: 9500, batch size: 61, loss[discriminator_loss=2.697, discriminator_real_loss=1.527, discriminator_fake_loss=1.17, generator_loss=27.4, generator_mel_loss=19.75, generator_kl_loss=1.359, generator_dur_loss=1.751, generator_adv_loss=1.867, generator_feat_match_loss=2.668, over 61.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.408, discriminator_fake_loss=1.353, generator_loss=27.58, generator_mel_loss=19.77, generator_kl_loss=1.362, generator_dur_loss=1.797, generator_adv_loss=1.924, generator_feat_match_loss=2.722, over 4496.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:35:22,528 INFO [train.py:919] (5/6) Start epoch 78 +2024-03-12 07:35:52,848 INFO [train.py:527] (5/6) Epoch 78, batch 2, global_batch_idx: 9550, batch size: 47, loss[discriminator_loss=2.719, discriminator_real_loss=1.362, discriminator_fake_loss=1.356, generator_loss=27.35, generator_mel_loss=19.51, generator_kl_loss=1.432, generator_dur_loss=1.704, generator_adv_loss=1.914, generator_feat_match_loss=2.797, over 47.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.384, discriminator_fake_loss=1.34, generator_loss=27.63, generator_mel_loss=19.65, generator_kl_loss=1.463, generator_dur_loss=1.708, generator_adv_loss=1.906, generator_feat_match_loss=2.903, over 125.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:38:10,265 INFO [train.py:527] (5/6) Epoch 78, batch 52, global_batch_idx: 9600, batch size: 48, loss[discriminator_loss=2.677, discriminator_real_loss=1.345, discriminator_fake_loss=1.332, generator_loss=28.77, generator_mel_loss=20.49, generator_kl_loss=1.549, generator_dur_loss=1.708, generator_adv_loss=1.969, generator_feat_match_loss=3.051, over 48.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.394, discriminator_fake_loss=1.353, generator_loss=27.53, generator_mel_loss=19.72, generator_kl_loss=1.38, generator_dur_loss=1.788, generator_adv_loss=1.873, generator_feat_match_loss=2.767, over 2947.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:38:10,266 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 07:38:18,167 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 07:40:37,224 INFO [train.py:527] (5/6) Epoch 78, batch 102, global_batch_idx: 9650, batch size: 59, loss[discriminator_loss=2.769, discriminator_real_loss=1.454, discriminator_fake_loss=1.315, generator_loss=26.87, generator_mel_loss=19.44, generator_kl_loss=1.406, generator_dur_loss=1.777, generator_adv_loss=1.632, generator_feat_match_loss=2.618, over 59.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.392, discriminator_fake_loss=1.354, generator_loss=27.55, generator_mel_loss=19.73, generator_kl_loss=1.365, generator_dur_loss=1.797, generator_adv_loss=1.879, generator_feat_match_loss=2.779, over 5827.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:41:36,931 INFO [train.py:919] (5/6) Start epoch 79 +2024-03-12 07:43:17,105 INFO [train.py:527] (5/6) Epoch 79, batch 28, global_batch_idx: 9700, batch size: 61, loss[discriminator_loss=2.753, discriminator_real_loss=1.49, discriminator_fake_loss=1.263, generator_loss=28.35, generator_mel_loss=20.42, generator_kl_loss=1.478, generator_dur_loss=1.771, generator_adv_loss=1.928, generator_feat_match_loss=2.749, over 61.00 samples.], tot_loss[discriminator_loss=2.774, discriminator_real_loss=1.418, discriminator_fake_loss=1.356, generator_loss=27.69, generator_mel_loss=19.88, generator_kl_loss=1.382, generator_dur_loss=1.769, generator_adv_loss=1.914, generator_feat_match_loss=2.744, over 1621.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:45:39,699 INFO [train.py:527] (5/6) Epoch 79, batch 78, global_batch_idx: 9750, batch size: 55, loss[discriminator_loss=2.73, discriminator_real_loss=1.389, discriminator_fake_loss=1.341, generator_loss=27.09, generator_mel_loss=19.82, generator_kl_loss=1.333, generator_dur_loss=1.73, generator_adv_loss=1.85, generator_feat_match_loss=2.363, over 55.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.413, discriminator_fake_loss=1.353, generator_loss=27.67, generator_mel_loss=19.87, generator_kl_loss=1.377, generator_dur_loss=1.774, generator_adv_loss=1.906, generator_feat_match_loss=2.749, over 4388.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:47:45,351 INFO [train.py:919] (5/6) Start epoch 80 +2024-03-12 07:48:20,374 INFO [train.py:527] (5/6) Epoch 80, batch 4, global_batch_idx: 9800, batch size: 62, loss[discriminator_loss=2.789, discriminator_real_loss=1.513, discriminator_fake_loss=1.275, generator_loss=26.74, generator_mel_loss=19.24, generator_kl_loss=1.352, generator_dur_loss=1.849, generator_adv_loss=1.865, generator_feat_match_loss=2.434, over 62.00 samples.], tot_loss[discriminator_loss=2.782, discriminator_real_loss=1.452, discriminator_fake_loss=1.33, generator_loss=27.62, generator_mel_loss=19.97, generator_kl_loss=1.359, generator_dur_loss=1.792, generator_adv_loss=1.888, generator_feat_match_loss=2.613, over 255.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:48:20,376 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 07:48:28,384 INFO [train.py:591] (5/6) 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 07:48:28,386 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 07:50:45,559 INFO [train.py:527] (5/6) Epoch 80, batch 54, global_batch_idx: 9850, batch size: 55, loss[discriminator_loss=2.809, discriminator_real_loss=1.429, discriminator_fake_loss=1.38, generator_loss=27.81, generator_mel_loss=20.26, generator_kl_loss=1.417, generator_dur_loss=1.749, generator_adv_loss=1.707, generator_feat_match_loss=2.684, over 55.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.406, discriminator_fake_loss=1.347, generator_loss=27.74, generator_mel_loss=19.93, generator_kl_loss=1.383, generator_dur_loss=1.773, generator_adv_loss=1.891, generator_feat_match_loss=2.766, over 2961.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:53:04,955 INFO [train.py:527] (5/6) Epoch 80, batch 104, global_batch_idx: 9900, batch size: 72, loss[discriminator_loss=2.81, discriminator_real_loss=1.575, discriminator_fake_loss=1.236, generator_loss=26.43, generator_mel_loss=19.11, generator_kl_loss=1.258, generator_dur_loss=1.817, generator_adv_loss=1.712, generator_feat_match_loss=2.533, over 72.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.402, discriminator_fake_loss=1.347, generator_loss=27.72, generator_mel_loss=19.9, generator_kl_loss=1.372, generator_dur_loss=1.785, generator_adv_loss=1.89, generator_feat_match_loss=2.776, over 5703.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:53:58,788 INFO [train.py:919] (5/6) Start epoch 81 +2024-03-12 07:56:20,696 INFO [train.py:527] (5/6) Epoch 81, batch 30, global_batch_idx: 9950, batch size: 17, loss[discriminator_loss=2.756, discriminator_real_loss=1.522, discriminator_fake_loss=1.235, generator_loss=28.66, generator_mel_loss=20.65, generator_kl_loss=1.625, generator_dur_loss=1.632, generator_adv_loss=1.993, generator_feat_match_loss=2.755, over 17.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.405, discriminator_fake_loss=1.358, generator_loss=27.69, generator_mel_loss=19.73, generator_kl_loss=1.359, generator_dur_loss=1.802, generator_adv_loss=1.953, generator_feat_match_loss=2.849, over 1648.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:58:38,408 INFO [train.py:527] (5/6) Epoch 81, batch 80, global_batch_idx: 10000, batch size: 48, loss[discriminator_loss=2.643, discriminator_real_loss=1.215, discriminator_fake_loss=1.428, generator_loss=27.79, generator_mel_loss=19.58, generator_kl_loss=1.353, generator_dur_loss=1.731, generator_adv_loss=1.846, generator_feat_match_loss=3.282, over 48.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.393, discriminator_fake_loss=1.357, generator_loss=27.68, generator_mel_loss=19.78, generator_kl_loss=1.362, generator_dur_loss=1.8, generator_adv_loss=1.915, generator_feat_match_loss=2.822, over 4496.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 07:58:38,410 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 07:58:46,879 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 08:00:49,180 INFO [train.py:919] (5/6) Start epoch 82 +2024-03-12 08:01:27,366 INFO [train.py:527] (5/6) Epoch 82, batch 6, global_batch_idx: 10050, batch size: 25, loss[discriminator_loss=2.642, discriminator_real_loss=1.266, discriminator_fake_loss=1.376, generator_loss=28.95, generator_mel_loss=20.57, generator_kl_loss=1.506, generator_dur_loss=1.631, generator_adv_loss=1.925, generator_feat_match_loss=3.321, over 25.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=28.06, generator_mel_loss=20, generator_kl_loss=1.294, generator_dur_loss=1.797, generator_adv_loss=1.945, generator_feat_match_loss=3.03, over 393.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:03:45,418 INFO [train.py:527] (5/6) Epoch 82, batch 56, global_batch_idx: 10100, batch size: 55, loss[discriminator_loss=2.699, discriminator_real_loss=1.377, discriminator_fake_loss=1.322, generator_loss=27.62, generator_mel_loss=19.77, generator_kl_loss=1.333, generator_dur_loss=1.745, generator_adv_loss=1.973, generator_feat_match_loss=2.794, over 55.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.378, discriminator_fake_loss=1.334, generator_loss=27.75, generator_mel_loss=19.77, generator_kl_loss=1.366, generator_dur_loss=1.777, generator_adv_loss=1.947, generator_feat_match_loss=2.891, over 3065.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:06:04,719 INFO [train.py:527] (5/6) Epoch 82, batch 106, global_batch_idx: 10150, batch size: 77, loss[discriminator_loss=2.788, discriminator_real_loss=1.525, discriminator_fake_loss=1.263, generator_loss=27.74, generator_mel_loss=20.09, generator_kl_loss=1.086, generator_dur_loss=1.836, generator_adv_loss=1.992, generator_feat_match_loss=2.738, over 77.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.389, discriminator_fake_loss=1.345, generator_loss=27.65, generator_mel_loss=19.74, generator_kl_loss=1.369, generator_dur_loss=1.785, generator_adv_loss=1.932, generator_feat_match_loss=2.821, over 5907.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:06:53,161 INFO [train.py:919] (5/6) Start epoch 83 +2024-03-12 08:08:44,644 INFO [train.py:527] (5/6) Epoch 83, batch 32, global_batch_idx: 10200, batch size: 52, loss[discriminator_loss=2.732, discriminator_real_loss=1.376, discriminator_fake_loss=1.356, generator_loss=27.8, generator_mel_loss=19.86, generator_kl_loss=1.372, generator_dur_loss=1.74, generator_adv_loss=1.915, generator_feat_match_loss=2.914, over 52.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.398, discriminator_fake_loss=1.37, generator_loss=27.47, generator_mel_loss=19.69, generator_kl_loss=1.387, generator_dur_loss=1.81, generator_adv_loss=1.86, generator_feat_match_loss=2.723, over 1976.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:08:44,645 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 08:08:52,606 INFO [train.py:591] (5/6) 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 08:08:52,607 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 08:11:12,329 INFO [train.py:527] (5/6) Epoch 83, batch 82, global_batch_idx: 10250, batch size: 74, loss[discriminator_loss=2.664, discriminator_real_loss=1.416, discriminator_fake_loss=1.248, generator_loss=28.01, generator_mel_loss=19.56, generator_kl_loss=1.305, generator_dur_loss=1.811, generator_adv_loss=2.169, generator_feat_match_loss=3.166, over 74.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.405, discriminator_fake_loss=1.356, generator_loss=27.41, generator_mel_loss=19.63, generator_kl_loss=1.347, generator_dur_loss=1.818, generator_adv_loss=1.885, generator_feat_match_loss=2.726, over 5025.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:13:04,966 INFO [train.py:919] (5/6) Start epoch 84 +2024-03-12 08:13:51,792 INFO [train.py:527] (5/6) Epoch 84, batch 8, global_batch_idx: 10300, batch size: 15, loss[discriminator_loss=2.646, discriminator_real_loss=1.395, discriminator_fake_loss=1.25, generator_loss=29.77, generator_mel_loss=21.36, generator_kl_loss=1.571, generator_dur_loss=1.623, generator_adv_loss=1.868, generator_feat_match_loss=3.355, over 15.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.421, discriminator_fake_loss=1.315, generator_loss=27.34, generator_mel_loss=19.54, generator_kl_loss=1.41, generator_dur_loss=1.755, generator_adv_loss=1.87, generator_feat_match_loss=2.764, over 468.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:16:09,615 INFO [train.py:527] (5/6) Epoch 84, batch 58, global_batch_idx: 10350, batch size: 77, loss[discriminator_loss=2.726, discriminator_real_loss=1.335, discriminator_fake_loss=1.391, generator_loss=27.3, generator_mel_loss=19.65, generator_kl_loss=1.154, generator_dur_loss=1.847, generator_adv_loss=1.933, generator_feat_match_loss=2.717, over 77.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.399, discriminator_fake_loss=1.345, generator_loss=27.4, generator_mel_loss=19.55, generator_kl_loss=1.366, generator_dur_loss=1.792, generator_adv_loss=1.904, generator_feat_match_loss=2.788, over 3260.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:18:30,350 INFO [train.py:527] (5/6) Epoch 84, batch 108, global_batch_idx: 10400, batch size: 50, loss[discriminator_loss=2.771, discriminator_real_loss=1.488, discriminator_fake_loss=1.283, generator_loss=27.84, generator_mel_loss=20.4, generator_kl_loss=1.457, generator_dur_loss=1.678, generator_adv_loss=1.562, generator_feat_match_loss=2.744, over 50.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.403, discriminator_fake_loss=1.35, generator_loss=27.38, generator_mel_loss=19.6, generator_kl_loss=1.357, generator_dur_loss=1.786, generator_adv_loss=1.89, generator_feat_match_loss=2.746, over 6276.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:18:30,352 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 08:18:39,356 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 08:19:18,428 INFO [train.py:919] (5/6) Start epoch 85 +2024-03-12 08:21:17,375 INFO [train.py:527] (5/6) Epoch 85, batch 34, global_batch_idx: 10450, batch size: 74, loss[discriminator_loss=2.715, discriminator_real_loss=1.353, discriminator_fake_loss=1.362, generator_loss=27.75, generator_mel_loss=20.02, generator_kl_loss=1.208, generator_dur_loss=1.81, generator_adv_loss=1.962, generator_feat_match_loss=2.752, over 74.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.416, discriminator_fake_loss=1.339, generator_loss=27.69, generator_mel_loss=19.69, generator_kl_loss=1.371, generator_dur_loss=1.782, generator_adv_loss=1.968, generator_feat_match_loss=2.873, over 2000.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:23:35,892 INFO [train.py:527] (5/6) Epoch 85, batch 84, global_batch_idx: 10500, batch size: 45, loss[discriminator_loss=2.667, discriminator_real_loss=1.422, discriminator_fake_loss=1.244, generator_loss=26.31, generator_mel_loss=18.72, generator_kl_loss=1.475, generator_dur_loss=1.679, generator_adv_loss=1.823, generator_feat_match_loss=2.616, over 45.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.409, discriminator_fake_loss=1.347, generator_loss=27.37, generator_mel_loss=19.53, generator_kl_loss=1.368, generator_dur_loss=1.772, generator_adv_loss=1.923, generator_feat_match_loss=2.773, over 4744.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:25:24,272 INFO [train.py:919] (5/6) Start epoch 86 +2024-03-12 08:26:14,446 INFO [train.py:527] (5/6) Epoch 86, batch 10, global_batch_idx: 10550, batch size: 68, loss[discriminator_loss=2.788, discriminator_real_loss=1.303, discriminator_fake_loss=1.485, generator_loss=27.12, generator_mel_loss=19.73, generator_kl_loss=1.325, generator_dur_loss=1.791, generator_adv_loss=1.701, generator_feat_match_loss=2.574, over 68.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.407, discriminator_fake_loss=1.361, generator_loss=27.31, generator_mel_loss=19.57, generator_kl_loss=1.394, generator_dur_loss=1.742, generator_adv_loss=1.897, generator_feat_match_loss=2.711, over 576.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:28:39,020 INFO [train.py:527] (5/6) Epoch 86, batch 60, global_batch_idx: 10600, batch size: 62, loss[discriminator_loss=2.722, discriminator_real_loss=1.213, discriminator_fake_loss=1.509, generator_loss=28.14, generator_mel_loss=19.77, generator_kl_loss=1.444, generator_dur_loss=1.786, generator_adv_loss=2.087, generator_feat_match_loss=3.049, over 62.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.388, discriminator_fake_loss=1.349, generator_loss=27.56, generator_mel_loss=19.64, generator_kl_loss=1.386, generator_dur_loss=1.762, generator_adv_loss=1.923, generator_feat_match_loss=2.854, over 3353.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:28:39,022 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 08:28:46,850 INFO [train.py:591] (5/6) 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 08:28:46,851 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 08:31:04,100 INFO [train.py:527] (5/6) Epoch 86, batch 110, global_batch_idx: 10650, batch size: 66, loss[discriminator_loss=2.75, discriminator_real_loss=1.432, discriminator_fake_loss=1.318, generator_loss=27.1, generator_mel_loss=19.52, generator_kl_loss=1.295, generator_dur_loss=1.851, generator_adv_loss=1.882, generator_feat_match_loss=2.55, over 66.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.396, discriminator_fake_loss=1.335, generator_loss=27.79, generator_mel_loss=19.6, generator_kl_loss=1.371, generator_dur_loss=1.776, generator_adv_loss=2.022, generator_feat_match_loss=3.028, over 6134.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:31:40,040 INFO [train.py:919] (5/6) Start epoch 87 +2024-03-12 08:33:42,590 INFO [train.py:527] (5/6) Epoch 87, batch 36, global_batch_idx: 10700, batch size: 62, loss[discriminator_loss=2.724, discriminator_real_loss=1.415, discriminator_fake_loss=1.309, generator_loss=26.8, generator_mel_loss=19.18, generator_kl_loss=1.33, generator_dur_loss=1.795, generator_adv_loss=1.819, generator_feat_match_loss=2.676, over 62.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.385, discriminator_fake_loss=1.336, generator_loss=27.39, generator_mel_loss=19.53, generator_kl_loss=1.388, generator_dur_loss=1.784, generator_adv_loss=1.899, generator_feat_match_loss=2.786, over 1915.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:36:02,700 INFO [train.py:527] (5/6) Epoch 87, batch 86, global_batch_idx: 10750, batch size: 88, loss[discriminator_loss=2.807, discriminator_real_loss=1.449, discriminator_fake_loss=1.358, generator_loss=27.29, generator_mel_loss=19.41, generator_kl_loss=1.252, generator_dur_loss=1.903, generator_adv_loss=1.755, generator_feat_match_loss=2.973, over 88.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.387, discriminator_fake_loss=1.342, generator_loss=27.49, generator_mel_loss=19.56, generator_kl_loss=1.381, generator_dur_loss=1.793, generator_adv_loss=1.906, generator_feat_match_loss=2.853, over 4768.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:37:45,862 INFO [train.py:919] (5/6) Start epoch 88 +2024-03-12 08:38:43,581 INFO [train.py:527] (5/6) Epoch 88, batch 12, global_batch_idx: 10800, batch size: 61, loss[discriminator_loss=2.747, discriminator_real_loss=1.321, discriminator_fake_loss=1.425, generator_loss=27.87, generator_mel_loss=20, generator_kl_loss=1.237, generator_dur_loss=1.778, generator_adv_loss=1.867, generator_feat_match_loss=2.982, over 61.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.384, discriminator_fake_loss=1.365, generator_loss=27.32, generator_mel_loss=19.51, generator_kl_loss=1.349, generator_dur_loss=1.792, generator_adv_loss=1.868, generator_feat_match_loss=2.8, over 771.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:38:43,584 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 08:38:51,180 INFO [train.py:591] (5/6) 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 08:38:51,181 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 08:41:09,910 INFO [train.py:527] (5/6) Epoch 88, batch 62, global_batch_idx: 10850, batch size: 45, loss[discriminator_loss=2.719, discriminator_real_loss=1.49, discriminator_fake_loss=1.229, generator_loss=25.7, generator_mel_loss=18.2, generator_kl_loss=1.322, generator_dur_loss=1.726, generator_adv_loss=1.724, generator_feat_match_loss=2.729, over 45.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.393, discriminator_fake_loss=1.358, generator_loss=27.29, generator_mel_loss=19.45, generator_kl_loss=1.351, generator_dur_loss=1.799, generator_adv_loss=1.881, generator_feat_match_loss=2.806, over 3759.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:43:28,411 INFO [train.py:527] (5/6) Epoch 88, batch 112, global_batch_idx: 10900, batch size: 31, loss[discriminator_loss=2.775, discriminator_real_loss=1.427, discriminator_fake_loss=1.348, generator_loss=28.29, generator_mel_loss=20.13, generator_kl_loss=1.583, generator_dur_loss=1.717, generator_adv_loss=1.871, generator_feat_match_loss=2.986, over 31.00 samples.], tot_loss[discriminator_loss=2.772, discriminator_real_loss=1.412, discriminator_fake_loss=1.361, generator_loss=27.39, generator_mel_loss=19.52, generator_kl_loss=1.361, generator_dur_loss=1.788, generator_adv_loss=1.921, generator_feat_match_loss=2.8, over 6413.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:44:01,926 INFO [train.py:919] (5/6) Start epoch 89 +2024-03-12 08:46:13,354 INFO [train.py:527] (5/6) Epoch 89, batch 38, global_batch_idx: 10950, batch size: 56, loss[discriminator_loss=2.736, discriminator_real_loss=1.327, discriminator_fake_loss=1.409, generator_loss=26.54, generator_mel_loss=18.76, generator_kl_loss=1.314, generator_dur_loss=1.757, generator_adv_loss=1.951, generator_feat_match_loss=2.759, over 56.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.381, discriminator_fake_loss=1.348, generator_loss=27.47, generator_mel_loss=19.55, generator_kl_loss=1.365, generator_dur_loss=1.769, generator_adv_loss=1.914, generator_feat_match_loss=2.87, over 2247.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:48:28,940 INFO [train.py:527] (5/6) Epoch 89, batch 88, global_batch_idx: 11000, batch size: 31, loss[discriminator_loss=2.816, discriminator_real_loss=1.59, discriminator_fake_loss=1.225, generator_loss=25.76, generator_mel_loss=18.53, generator_kl_loss=1.388, generator_dur_loss=1.599, generator_adv_loss=2.042, generator_feat_match_loss=2.206, over 31.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.388, discriminator_fake_loss=1.352, generator_loss=27.44, generator_mel_loss=19.53, generator_kl_loss=1.366, generator_dur_loss=1.787, generator_adv_loss=1.897, generator_feat_match_loss=2.859, over 5108.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:48:28,941 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 08:48:37,876 INFO [train.py:591] (5/6) 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 08:48:37,877 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 08:50:16,472 INFO [train.py:919] (5/6) Start epoch 90 +2024-03-12 08:51:22,960 INFO [train.py:527] (5/6) Epoch 90, batch 14, global_batch_idx: 11050, batch size: 64, loss[discriminator_loss=2.633, discriminator_real_loss=1.362, discriminator_fake_loss=1.271, generator_loss=28.38, generator_mel_loss=19.74, generator_kl_loss=1.413, generator_dur_loss=1.821, generator_adv_loss=2.097, generator_feat_match_loss=3.312, over 64.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.365, discriminator_fake_loss=1.357, generator_loss=27.58, generator_mel_loss=19.47, generator_kl_loss=1.391, generator_dur_loss=1.782, generator_adv_loss=1.94, generator_feat_match_loss=2.989, over 850.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:53:43,241 INFO [train.py:527] (5/6) Epoch 90, batch 64, global_batch_idx: 11100, batch size: 70, loss[discriminator_loss=2.748, discriminator_real_loss=1.256, discriminator_fake_loss=1.492, generator_loss=28.37, generator_mel_loss=20.16, generator_kl_loss=1.378, generator_dur_loss=1.843, generator_adv_loss=1.961, generator_feat_match_loss=3.021, over 70.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.395, discriminator_fake_loss=1.355, generator_loss=27.37, generator_mel_loss=19.46, generator_kl_loss=1.365, generator_dur_loss=1.797, generator_adv_loss=1.904, generator_feat_match_loss=2.839, over 3885.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:56:00,689 INFO [train.py:527] (5/6) Epoch 90, batch 114, global_batch_idx: 11150, batch size: 16, loss[discriminator_loss=2.656, discriminator_real_loss=1.439, discriminator_fake_loss=1.217, generator_loss=27.71, generator_mel_loss=19.64, generator_kl_loss=1.546, generator_dur_loss=1.605, generator_adv_loss=1.941, generator_feat_match_loss=2.98, over 16.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.386, discriminator_fake_loss=1.35, generator_loss=27.44, generator_mel_loss=19.49, generator_kl_loss=1.362, generator_dur_loss=1.797, generator_adv_loss=1.908, generator_feat_match_loss=2.881, over 6665.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:56:22,916 INFO [train.py:919] (5/6) Start epoch 91 +2024-03-12 08:58:38,237 INFO [train.py:527] (5/6) Epoch 91, batch 40, global_batch_idx: 11200, batch size: 52, loss[discriminator_loss=2.664, discriminator_real_loss=1.334, discriminator_fake_loss=1.331, generator_loss=27.91, generator_mel_loss=19.64, generator_kl_loss=1.464, generator_dur_loss=1.691, generator_adv_loss=1.907, generator_feat_match_loss=3.211, over 52.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.394, discriminator_fake_loss=1.358, generator_loss=27.55, generator_mel_loss=19.63, generator_kl_loss=1.389, generator_dur_loss=1.764, generator_adv_loss=1.901, generator_feat_match_loss=2.867, over 2172.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 08:58:38,238 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 08:58:46,107 INFO [train.py:591] (5/6) 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 08:58:46,108 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 09:01:04,262 INFO [train.py:527] (5/6) Epoch 91, batch 90, global_batch_idx: 11250, batch size: 56, loss[discriminator_loss=2.703, discriminator_real_loss=1.328, discriminator_fake_loss=1.375, generator_loss=27.92, generator_mel_loss=19.68, generator_kl_loss=1.459, generator_dur_loss=1.72, generator_adv_loss=1.841, generator_feat_match_loss=3.22, over 56.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.393, discriminator_fake_loss=1.35, generator_loss=27.47, generator_mel_loss=19.54, generator_kl_loss=1.393, generator_dur_loss=1.764, generator_adv_loss=1.895, generator_feat_match_loss=2.874, over 4999.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:02:38,003 INFO [train.py:919] (5/6) Start epoch 92 +2024-03-12 09:03:48,768 INFO [train.py:527] (5/6) Epoch 92, batch 16, global_batch_idx: 11300, batch size: 68, loss[discriminator_loss=2.698, discriminator_real_loss=1.381, discriminator_fake_loss=1.317, generator_loss=27.25, generator_mel_loss=18.95, generator_kl_loss=1.45, generator_dur_loss=1.836, generator_adv_loss=2.083, generator_feat_match_loss=2.922, over 68.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.423, discriminator_fake_loss=1.33, generator_loss=27.36, generator_mel_loss=19.53, generator_kl_loss=1.315, generator_dur_loss=1.793, generator_adv_loss=1.966, generator_feat_match_loss=2.75, over 1019.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:06:10,304 INFO [train.py:527] (5/6) Epoch 92, batch 66, global_batch_idx: 11350, batch size: 48, loss[discriminator_loss=2.71, discriminator_real_loss=1.271, discriminator_fake_loss=1.439, generator_loss=28.33, generator_mel_loss=19.87, generator_kl_loss=1.419, generator_dur_loss=1.687, generator_adv_loss=2.025, generator_feat_match_loss=3.324, over 48.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.396, discriminator_fake_loss=1.343, generator_loss=27.45, generator_mel_loss=19.53, generator_kl_loss=1.344, generator_dur_loss=1.801, generator_adv_loss=1.91, generator_feat_match_loss=2.862, over 4008.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:08:27,318 INFO [train.py:527] (5/6) Epoch 92, batch 116, global_batch_idx: 11400, batch size: 47, loss[discriminator_loss=2.769, discriminator_real_loss=1.467, discriminator_fake_loss=1.302, generator_loss=26.8, generator_mel_loss=18.86, generator_kl_loss=1.494, generator_dur_loss=1.649, generator_adv_loss=1.969, generator_feat_match_loss=2.834, over 47.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.397, discriminator_fake_loss=1.341, generator_loss=27.46, generator_mel_loss=19.5, generator_kl_loss=1.353, generator_dur_loss=1.78, generator_adv_loss=1.931, generator_feat_match_loss=2.897, over 6615.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:08:27,319 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 09:08:36,555 INFO [train.py:591] (5/6) 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 09:08:36,556 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 09:08:55,326 INFO [train.py:919] (5/6) Start epoch 93 +2024-03-12 09:11:18,245 INFO [train.py:527] (5/6) Epoch 93, batch 42, global_batch_idx: 11450, batch size: 31, loss[discriminator_loss=2.615, discriminator_real_loss=1.184, discriminator_fake_loss=1.431, generator_loss=29, generator_mel_loss=20.76, generator_kl_loss=1.431, generator_dur_loss=1.632, generator_adv_loss=1.916, generator_feat_match_loss=3.264, over 31.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.401, discriminator_fake_loss=1.353, generator_loss=27.32, generator_mel_loss=19.44, generator_kl_loss=1.385, generator_dur_loss=1.743, generator_adv_loss=1.905, generator_feat_match_loss=2.854, over 2418.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:13:34,440 INFO [train.py:527] (5/6) Epoch 93, batch 92, global_batch_idx: 11500, batch size: 58, loss[discriminator_loss=2.735, discriminator_real_loss=1.445, discriminator_fake_loss=1.289, generator_loss=26.19, generator_mel_loss=18.83, generator_kl_loss=1.232, generator_dur_loss=1.704, generator_adv_loss=1.784, generator_feat_match_loss=2.638, over 58.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.4, discriminator_fake_loss=1.35, generator_loss=27.37, generator_mel_loss=19.44, generator_kl_loss=1.395, generator_dur_loss=1.741, generator_adv_loss=1.915, generator_feat_match_loss=2.875, over 5231.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:15:01,480 INFO [train.py:919] (5/6) Start epoch 94 +2024-03-12 09:16:10,263 INFO [train.py:527] (5/6) Epoch 94, batch 18, global_batch_idx: 11550, batch size: 39, loss[discriminator_loss=2.604, discriminator_real_loss=1.332, discriminator_fake_loss=1.272, generator_loss=27.38, generator_mel_loss=18.98, generator_kl_loss=1.377, generator_dur_loss=1.66, generator_adv_loss=1.999, generator_feat_match_loss=3.367, over 39.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.342, generator_loss=27.32, generator_mel_loss=19.24, generator_kl_loss=1.361, generator_dur_loss=1.752, generator_adv_loss=1.95, generator_feat_match_loss=3.02, over 1117.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:18:29,019 INFO [train.py:527] (5/6) Epoch 94, batch 68, global_batch_idx: 11600, batch size: 48, loss[discriminator_loss=2.692, discriminator_real_loss=1.338, discriminator_fake_loss=1.354, generator_loss=28.46, generator_mel_loss=20.3, generator_kl_loss=1.498, generator_dur_loss=1.708, generator_adv_loss=2.046, generator_feat_match_loss=2.909, over 48.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.392, discriminator_fake_loss=1.344, generator_loss=27.28, generator_mel_loss=19.36, generator_kl_loss=1.362, generator_dur_loss=1.746, generator_adv_loss=1.918, generator_feat_match_loss=2.895, over 4075.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:18:29,020 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 09:18:37,811 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 09:20:57,132 INFO [train.py:527] (5/6) Epoch 94, batch 118, global_batch_idx: 11650, batch size: 58, loss[discriminator_loss=2.636, discriminator_real_loss=1.334, discriminator_fake_loss=1.301, generator_loss=28.57, generator_mel_loss=19.82, generator_kl_loss=1.424, generator_dur_loss=1.73, generator_adv_loss=2.012, generator_feat_match_loss=3.587, over 58.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.388, discriminator_fake_loss=1.34, generator_loss=27.38, generator_mel_loss=19.4, generator_kl_loss=1.377, generator_dur_loss=1.731, generator_adv_loss=1.928, generator_feat_match_loss=2.946, over 6843.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:21:13,813 INFO [train.py:919] (5/6) Start epoch 95 +2024-03-12 09:23:40,683 INFO [train.py:527] (5/6) Epoch 95, batch 44, global_batch_idx: 11700, batch size: 66, loss[discriminator_loss=2.814, discriminator_real_loss=1.451, discriminator_fake_loss=1.363, generator_loss=27.55, generator_mel_loss=19.86, generator_kl_loss=1.29, generator_dur_loss=1.772, generator_adv_loss=1.761, generator_feat_match_loss=2.871, over 66.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.4, discriminator_fake_loss=1.344, generator_loss=27.65, generator_mel_loss=19.62, generator_kl_loss=1.387, generator_dur_loss=1.739, generator_adv_loss=1.927, generator_feat_match_loss=2.981, over 2583.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:26:04,432 INFO [train.py:527] (5/6) Epoch 95, batch 94, global_batch_idx: 11750, batch size: 52, loss[discriminator_loss=2.729, discriminator_real_loss=1.235, discriminator_fake_loss=1.494, generator_loss=28.29, generator_mel_loss=20.02, generator_kl_loss=1.326, generator_dur_loss=1.734, generator_adv_loss=1.934, generator_feat_match_loss=3.284, over 52.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.395, discriminator_fake_loss=1.345, generator_loss=27.54, generator_mel_loss=19.56, generator_kl_loss=1.392, generator_dur_loss=1.73, generator_adv_loss=1.915, generator_feat_match_loss=2.943, over 5293.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:27:30,630 INFO [train.py:919] (5/6) Start epoch 96 +2024-03-12 09:28:50,155 INFO [train.py:527] (5/6) Epoch 96, batch 20, global_batch_idx: 11800, batch size: 42, loss[discriminator_loss=2.693, discriminator_real_loss=1.356, discriminator_fake_loss=1.337, generator_loss=27.95, generator_mel_loss=19.67, generator_kl_loss=1.289, generator_dur_loss=1.698, generator_adv_loss=1.967, generator_feat_match_loss=3.332, over 42.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.385, discriminator_fake_loss=1.342, generator_loss=27.43, generator_mel_loss=19.44, generator_kl_loss=1.375, generator_dur_loss=1.754, generator_adv_loss=1.917, generator_feat_match_loss=2.944, over 1118.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:28:50,157 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 09:28:58,042 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 09:31:21,632 INFO [train.py:527] (5/6) Epoch 96, batch 70, global_batch_idx: 11850, batch size: 64, loss[discriminator_loss=2.704, discriminator_real_loss=1.265, discriminator_fake_loss=1.439, generator_loss=28.06, generator_mel_loss=19.88, generator_kl_loss=1.224, generator_dur_loss=1.827, generator_adv_loss=1.972, generator_feat_match_loss=3.159, over 64.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.398, discriminator_fake_loss=1.363, generator_loss=27.44, generator_mel_loss=19.42, generator_kl_loss=1.378, generator_dur_loss=1.759, generator_adv_loss=1.95, generator_feat_match_loss=2.936, over 4002.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:33:43,331 INFO [train.py:527] (5/6) Epoch 96, batch 120, global_batch_idx: 11900, batch size: 55, loss[discriminator_loss=2.95, discriminator_real_loss=1.196, discriminator_fake_loss=1.754, generator_loss=27.84, generator_mel_loss=19.71, generator_kl_loss=1.439, generator_dur_loss=1.673, generator_adv_loss=1.84, generator_feat_match_loss=3.178, over 55.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.396, discriminator_fake_loss=1.357, generator_loss=27.39, generator_mel_loss=19.41, generator_kl_loss=1.377, generator_dur_loss=1.767, generator_adv_loss=1.937, generator_feat_match_loss=2.899, over 6969.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:33:54,738 INFO [train.py:919] (5/6) Start epoch 97 +2024-03-12 09:36:27,999 INFO [train.py:527] (5/6) Epoch 97, batch 46, global_batch_idx: 11950, batch size: 88, loss[discriminator_loss=2.814, discriminator_real_loss=1.443, discriminator_fake_loss=1.371, generator_loss=26.47, generator_mel_loss=18.78, generator_kl_loss=1.354, generator_dur_loss=1.917, generator_adv_loss=1.886, generator_feat_match_loss=2.526, over 88.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.385, discriminator_fake_loss=1.358, generator_loss=27.44, generator_mel_loss=19.47, generator_kl_loss=1.399, generator_dur_loss=1.762, generator_adv_loss=1.9, generator_feat_match_loss=2.915, over 2644.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:38:50,874 INFO [train.py:527] (5/6) Epoch 97, batch 96, global_batch_idx: 12000, batch size: 56, loss[discriminator_loss=2.75, discriminator_real_loss=1.287, discriminator_fake_loss=1.463, generator_loss=27.92, generator_mel_loss=19.54, generator_kl_loss=1.437, generator_dur_loss=1.743, generator_adv_loss=1.953, generator_feat_match_loss=3.25, over 56.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.387, discriminator_fake_loss=1.355, generator_loss=27.39, generator_mel_loss=19.4, generator_kl_loss=1.379, generator_dur_loss=1.788, generator_adv_loss=1.902, generator_feat_match_loss=2.918, over 5571.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:38:50,876 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 09:38:59,575 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 09:40:11,947 INFO [train.py:919] (5/6) Start epoch 98 +2024-03-12 09:41:40,345 INFO [train.py:527] (5/6) Epoch 98, batch 22, global_batch_idx: 12050, batch size: 25, loss[discriminator_loss=2.814, discriminator_real_loss=1.563, discriminator_fake_loss=1.251, generator_loss=27.29, generator_mel_loss=19.28, generator_kl_loss=1.675, generator_dur_loss=1.627, generator_adv_loss=1.697, generator_feat_match_loss=3.005, over 25.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.388, discriminator_fake_loss=1.352, generator_loss=27.34, generator_mel_loss=19.35, generator_kl_loss=1.404, generator_dur_loss=1.783, generator_adv_loss=1.894, generator_feat_match_loss=2.913, over 1312.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:44:00,468 INFO [train.py:527] (5/6) Epoch 98, batch 72, global_batch_idx: 12100, batch size: 72, loss[discriminator_loss=2.743, discriminator_real_loss=1.441, discriminator_fake_loss=1.301, generator_loss=27.34, generator_mel_loss=19.3, generator_kl_loss=1.36, generator_dur_loss=1.84, generator_adv_loss=1.817, generator_feat_match_loss=3.019, over 72.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.402, discriminator_fake_loss=1.345, generator_loss=27.6, generator_mel_loss=19.45, generator_kl_loss=1.395, generator_dur_loss=1.778, generator_adv_loss=1.969, generator_feat_match_loss=3.011, over 4169.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:46:23,181 INFO [train.py:527] (5/6) Epoch 98, batch 122, global_batch_idx: 12150, batch size: 25, loss[discriminator_loss=2.667, discriminator_real_loss=1.297, discriminator_fake_loss=1.37, generator_loss=28.33, generator_mel_loss=19.86, generator_kl_loss=1.576, generator_dur_loss=1.598, generator_adv_loss=1.938, generator_feat_match_loss=3.35, over 25.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.397, discriminator_fake_loss=1.343, generator_loss=27.47, generator_mel_loss=19.36, generator_kl_loss=1.384, generator_dur_loss=1.784, generator_adv_loss=1.954, generator_feat_match_loss=2.99, over 7060.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:46:27,931 INFO [train.py:919] (5/6) Start epoch 99 +2024-03-12 09:49:09,503 INFO [train.py:527] (5/6) Epoch 99, batch 48, global_batch_idx: 12200, batch size: 45, loss[discriminator_loss=2.731, discriminator_real_loss=1.533, discriminator_fake_loss=1.197, generator_loss=27.41, generator_mel_loss=19.34, generator_kl_loss=1.376, generator_dur_loss=1.685, generator_adv_loss=1.937, generator_feat_match_loss=3.07, over 45.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.391, discriminator_fake_loss=1.341, generator_loss=27.45, generator_mel_loss=19.42, generator_kl_loss=1.366, generator_dur_loss=1.762, generator_adv_loss=1.912, generator_feat_match_loss=2.988, over 2829.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:49:09,505 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 09:49:17,400 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 09:51:38,141 INFO [train.py:527] (5/6) Epoch 99, batch 98, global_batch_idx: 12250, batch size: 48, loss[discriminator_loss=2.741, discriminator_real_loss=1.348, discriminator_fake_loss=1.393, generator_loss=26.64, generator_mel_loss=18.84, generator_kl_loss=1.445, generator_dur_loss=1.725, generator_adv_loss=1.926, generator_feat_match_loss=2.703, over 48.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.398, discriminator_fake_loss=1.346, generator_loss=27.26, generator_mel_loss=19.32, generator_kl_loss=1.35, generator_dur_loss=1.793, generator_adv_loss=1.893, generator_feat_match_loss=2.897, over 5881.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:52:49,354 INFO [train.py:919] (5/6) Start epoch 100 +2024-03-12 09:54:22,505 INFO [train.py:527] (5/6) Epoch 100, batch 24, global_batch_idx: 12300, batch size: 53, loss[discriminator_loss=2.741, discriminator_real_loss=1.391, discriminator_fake_loss=1.35, generator_loss=27.51, generator_mel_loss=19.73, generator_kl_loss=1.379, generator_dur_loss=1.741, generator_adv_loss=1.944, generator_feat_match_loss=2.713, over 53.00 samples.], tot_loss[discriminator_loss=2.774, discriminator_real_loss=1.428, discriminator_fake_loss=1.347, generator_loss=27.36, generator_mel_loss=19.43, generator_kl_loss=1.379, generator_dur_loss=1.763, generator_adv_loss=1.891, generator_feat_match_loss=2.892, over 1375.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:56:41,932 INFO [train.py:527] (5/6) Epoch 100, batch 74, global_batch_idx: 12350, batch size: 77, loss[discriminator_loss=2.661, discriminator_real_loss=1.376, discriminator_fake_loss=1.285, generator_loss=27.87, generator_mel_loss=18.88, generator_kl_loss=1.23, generator_dur_loss=1.849, generator_adv_loss=2.327, generator_feat_match_loss=3.59, over 77.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.41, discriminator_fake_loss=1.349, generator_loss=27.33, generator_mel_loss=19.28, generator_kl_loss=1.374, generator_dur_loss=1.783, generator_adv_loss=1.915, generator_feat_match_loss=2.971, over 4376.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:59:04,068 INFO [train.py:919] (5/6) Start epoch 101 +2024-03-12 09:59:33,506 INFO [train.py:527] (5/6) Epoch 101, batch 0, global_batch_idx: 12400, batch size: 26, loss[discriminator_loss=2.707, discriminator_real_loss=1.394, discriminator_fake_loss=1.312, generator_loss=27.64, generator_mel_loss=19.67, generator_kl_loss=1.655, generator_dur_loss=1.569, generator_adv_loss=1.867, generator_feat_match_loss=2.875, over 26.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.394, discriminator_fake_loss=1.312, generator_loss=27.64, generator_mel_loss=19.67, generator_kl_loss=1.655, generator_dur_loss=1.569, generator_adv_loss=1.867, generator_feat_match_loss=2.875, over 26.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 09:59:33,510 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 09:59:41,315 INFO [train.py:591] (5/6) 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 09:59:41,317 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 10:02:00,912 INFO [train.py:527] (5/6) Epoch 101, batch 50, global_batch_idx: 12450, batch size: 96, loss[discriminator_loss=2.735, discriminator_real_loss=1.347, discriminator_fake_loss=1.388, generator_loss=26.27, generator_mel_loss=18.41, generator_kl_loss=1.29, generator_dur_loss=1.971, generator_adv_loss=1.835, generator_feat_match_loss=2.765, over 96.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.386, discriminator_fake_loss=1.366, generator_loss=27.25, generator_mel_loss=19.25, generator_kl_loss=1.378, generator_dur_loss=1.795, generator_adv_loss=1.894, generator_feat_match_loss=2.929, over 2954.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 10:04:19,967 INFO [train.py:527] (5/6) Epoch 101, batch 100, global_batch_idx: 12500, batch size: 47, loss[discriminator_loss=2.848, discriminator_real_loss=1.449, discriminator_fake_loss=1.398, generator_loss=27.3, generator_mel_loss=19.73, generator_kl_loss=1.37, generator_dur_loss=1.742, generator_adv_loss=1.708, generator_feat_match_loss=2.753, over 47.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.39, discriminator_fake_loss=1.357, generator_loss=27.17, generator_mel_loss=19.16, generator_kl_loss=1.382, generator_dur_loss=1.804, generator_adv_loss=1.901, generator_feat_match_loss=2.92, over 5890.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, +2024-03-12 10:05:28,308 INFO [train.py:919] (5/6) Start epoch 102 +2024-03-12 10:07:05,811 INFO [train.py:527] (5/6) Epoch 102, batch 26, global_batch_idx: 12550, batch size: 64, loss[discriminator_loss=2.766, discriminator_real_loss=1.505, discriminator_fake_loss=1.261, generator_loss=27.64, generator_mel_loss=19.69, generator_kl_loss=1.398, generator_dur_loss=1.809, generator_adv_loss=2.055, generator_feat_match_loss=2.69, over 64.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.392, discriminator_fake_loss=1.358, generator_loss=27.16, generator_mel_loss=19.15, generator_kl_loss=1.373, generator_dur_loss=1.796, generator_adv_loss=1.91, generator_feat_match_loss=2.927, over 1651.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:09:26,925 INFO [train.py:527] (5/6) Epoch 102, batch 76, global_batch_idx: 12600, batch size: 45, loss[discriminator_loss=2.721, discriminator_real_loss=1.371, discriminator_fake_loss=1.35, generator_loss=26.67, generator_mel_loss=18.94, generator_kl_loss=1.575, generator_dur_loss=1.7, generator_adv_loss=1.838, generator_feat_match_loss=2.619, over 45.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.391, discriminator_fake_loss=1.353, generator_loss=27.32, generator_mel_loss=19.23, generator_kl_loss=1.378, generator_dur_loss=1.783, generator_adv_loss=1.938, generator_feat_match_loss=2.986, over 4502.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:09:26,926 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 10:09:35,877 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 10:11:48,524 INFO [train.py:919] (5/6) Start epoch 103 +2024-03-12 10:12:19,602 INFO [train.py:527] (5/6) Epoch 103, batch 2, global_batch_idx: 12650, batch size: 68, loss[discriminator_loss=2.735, discriminator_real_loss=1.346, discriminator_fake_loss=1.389, generator_loss=27.2, generator_mel_loss=19.23, generator_kl_loss=1.24, generator_dur_loss=1.828, generator_adv_loss=1.845, generator_feat_match_loss=3.057, over 68.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.427, discriminator_fake_loss=1.333, generator_loss=27.34, generator_mel_loss=19.42, generator_kl_loss=1.304, generator_dur_loss=1.796, generator_adv_loss=1.909, generator_feat_match_loss=2.909, over 198.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:14:41,469 INFO [train.py:527] (5/6) Epoch 103, batch 52, global_batch_idx: 12700, batch size: 62, loss[discriminator_loss=2.794, discriminator_real_loss=1.4, discriminator_fake_loss=1.394, generator_loss=27.65, generator_mel_loss=19.79, generator_kl_loss=1.292, generator_dur_loss=1.702, generator_adv_loss=2.003, generator_feat_match_loss=2.855, over 62.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=27.36, generator_mel_loss=19.29, generator_kl_loss=1.398, generator_dur_loss=1.742, generator_adv_loss=1.928, generator_feat_match_loss=3.006, over 3048.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:17:01,097 INFO [train.py:527] (5/6) Epoch 103, batch 102, global_batch_idx: 12750, batch size: 58, loss[discriminator_loss=2.713, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=27.59, generator_mel_loss=19.3, generator_kl_loss=1.292, generator_dur_loss=1.708, generator_adv_loss=1.964, generator_feat_match_loss=3.325, over 58.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.39, discriminator_fake_loss=1.353, generator_loss=27.42, generator_mel_loss=19.33, generator_kl_loss=1.406, generator_dur_loss=1.734, generator_adv_loss=1.925, generator_feat_match_loss=3.027, over 5630.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:18:03,672 INFO [train.py:919] (5/6) Start epoch 104 +2024-03-12 10:19:45,547 INFO [train.py:527] (5/6) Epoch 104, batch 28, global_batch_idx: 12800, batch size: 16, loss[discriminator_loss=2.735, discriminator_real_loss=1.518, discriminator_fake_loss=1.217, generator_loss=28.64, generator_mel_loss=20.29, generator_kl_loss=1.729, generator_dur_loss=1.649, generator_adv_loss=1.971, generator_feat_match_loss=2.995, over 16.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.386, discriminator_fake_loss=1.366, generator_loss=27.33, generator_mel_loss=19.33, generator_kl_loss=1.399, generator_dur_loss=1.742, generator_adv_loss=1.886, generator_feat_match_loss=2.965, over 1526.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:19:45,549 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 10:19:53,816 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 10:22:18,400 INFO [train.py:527] (5/6) Epoch 104, batch 78, global_batch_idx: 12850, batch size: 39, loss[discriminator_loss=2.746, discriminator_real_loss=1.352, discriminator_fake_loss=1.394, generator_loss=26.28, generator_mel_loss=18.37, generator_kl_loss=1.488, generator_dur_loss=1.729, generator_adv_loss=1.834, generator_feat_match_loss=2.864, over 39.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.389, discriminator_fake_loss=1.364, generator_loss=27.29, generator_mel_loss=19.29, generator_kl_loss=1.397, generator_dur_loss=1.751, generator_adv_loss=1.895, generator_feat_match_loss=2.952, over 4264.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:24:25,050 INFO [train.py:919] (5/6) Start epoch 105 +2024-03-12 10:25:02,205 INFO [train.py:527] (5/6) Epoch 105, batch 4, global_batch_idx: 12900, batch size: 39, loss[discriminator_loss=2.776, discriminator_real_loss=1.373, discriminator_fake_loss=1.402, generator_loss=26.68, generator_mel_loss=18.96, generator_kl_loss=1.488, generator_dur_loss=1.683, generator_adv_loss=1.865, generator_feat_match_loss=2.685, over 39.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.37, discriminator_fake_loss=1.381, generator_loss=26.97, generator_mel_loss=19.16, generator_kl_loss=1.416, generator_dur_loss=1.713, generator_adv_loss=1.861, generator_feat_match_loss=2.821, over 249.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:27:25,786 INFO [train.py:527] (5/6) Epoch 105, batch 54, global_batch_idx: 12950, batch size: 47, loss[discriminator_loss=2.656, discriminator_real_loss=1.319, discriminator_fake_loss=1.337, generator_loss=28.61, generator_mel_loss=19.89, generator_kl_loss=1.693, generator_dur_loss=1.676, generator_adv_loss=1.923, generator_feat_match_loss=3.434, over 47.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.397, discriminator_fake_loss=1.354, generator_loss=27.29, generator_mel_loss=19.27, generator_kl_loss=1.402, generator_dur_loss=1.759, generator_adv_loss=1.893, generator_feat_match_loss=2.966, over 2964.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:29:48,021 INFO [train.py:527] (5/6) Epoch 105, batch 104, global_batch_idx: 13000, batch size: 96, loss[discriminator_loss=2.697, discriminator_real_loss=1.259, discriminator_fake_loss=1.438, generator_loss=27.91, generator_mel_loss=19.29, generator_kl_loss=1.266, generator_dur_loss=1.897, generator_adv_loss=1.861, generator_feat_match_loss=3.595, over 96.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.396, discriminator_fake_loss=1.355, generator_loss=27.31, generator_mel_loss=19.24, generator_kl_loss=1.412, generator_dur_loss=1.762, generator_adv_loss=1.897, generator_feat_match_loss=2.994, over 5713.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:29:48,022 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 10:29:57,124 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 10:30:50,569 INFO [train.py:919] (5/6) Start epoch 106 +2024-03-12 10:32:41,700 INFO [train.py:527] (5/6) Epoch 106, batch 30, global_batch_idx: 13050, batch size: 45, loss[discriminator_loss=2.661, discriminator_real_loss=1.385, discriminator_fake_loss=1.276, generator_loss=27.21, generator_mel_loss=18.99, generator_kl_loss=1.575, generator_dur_loss=1.706, generator_adv_loss=1.911, generator_feat_match_loss=3.028, over 45.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.388, discriminator_fake_loss=1.353, generator_loss=27.25, generator_mel_loss=19.25, generator_kl_loss=1.401, generator_dur_loss=1.761, generator_adv_loss=1.916, generator_feat_match_loss=2.929, over 1670.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:35:02,054 INFO [train.py:527] (5/6) Epoch 106, batch 80, global_batch_idx: 13100, batch size: 88, loss[discriminator_loss=2.787, discriminator_real_loss=1.27, discriminator_fake_loss=1.518, generator_loss=27.13, generator_mel_loss=19.01, generator_kl_loss=1.388, generator_dur_loss=1.812, generator_adv_loss=2.048, generator_feat_match_loss=2.863, over 88.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.396, discriminator_fake_loss=1.355, generator_loss=27.18, generator_mel_loss=19.18, generator_kl_loss=1.379, generator_dur_loss=1.773, generator_adv_loss=1.908, generator_feat_match_loss=2.937, over 4756.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:37:09,042 INFO [train.py:919] (5/6) Start epoch 107 +2024-03-12 10:37:52,321 INFO [train.py:527] (5/6) Epoch 107, batch 6, global_batch_idx: 13150, batch size: 96, loss[discriminator_loss=2.68, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=27.24, generator_mel_loss=19.07, generator_kl_loss=1.324, generator_dur_loss=1.811, generator_adv_loss=1.87, generator_feat_match_loss=3.162, over 96.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.348, discriminator_fake_loss=1.368, generator_loss=27.22, generator_mel_loss=19.14, generator_kl_loss=1.34, generator_dur_loss=1.791, generator_adv_loss=1.866, generator_feat_match_loss=3.087, over 503.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:40:17,585 INFO [train.py:527] (5/6) Epoch 107, batch 56, global_batch_idx: 13200, batch size: 42, loss[discriminator_loss=2.714, discriminator_real_loss=1.356, discriminator_fake_loss=1.358, generator_loss=26.43, generator_mel_loss=18.61, generator_kl_loss=1.395, generator_dur_loss=1.749, generator_adv_loss=2.051, generator_feat_match_loss=2.625, over 42.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.39, discriminator_fake_loss=1.345, generator_loss=27.3, generator_mel_loss=19.16, generator_kl_loss=1.39, generator_dur_loss=1.739, generator_adv_loss=1.927, generator_feat_match_loss=3.093, over 3169.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:40:17,586 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 10:40:25,396 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 10:42:42,999 INFO [train.py:527] (5/6) Epoch 107, batch 106, global_batch_idx: 13250, batch size: 72, loss[discriminator_loss=2.702, discriminator_real_loss=1.338, discriminator_fake_loss=1.365, generator_loss=27.83, generator_mel_loss=19.71, generator_kl_loss=1.24, generator_dur_loss=1.874, generator_adv_loss=1.939, generator_feat_match_loss=3.068, over 72.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.388, discriminator_fake_loss=1.343, generator_loss=27.39, generator_mel_loss=19.24, generator_kl_loss=1.391, generator_dur_loss=1.75, generator_adv_loss=1.912, generator_feat_match_loss=3.099, over 5979.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:43:31,578 INFO [train.py:919] (5/6) Start epoch 108 +2024-03-12 10:45:23,071 INFO [train.py:527] (5/6) Epoch 108, batch 32, global_batch_idx: 13300, batch size: 16, loss[discriminator_loss=2.581, discriminator_real_loss=1.183, discriminator_fake_loss=1.398, generator_loss=27.48, generator_mel_loss=18.94, generator_kl_loss=1.634, generator_dur_loss=1.659, generator_adv_loss=1.84, generator_feat_match_loss=3.403, over 16.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.379, discriminator_fake_loss=1.355, generator_loss=27.54, generator_mel_loss=19.27, generator_kl_loss=1.383, generator_dur_loss=1.783, generator_adv_loss=1.926, generator_feat_match_loss=3.172, over 2036.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:47:44,106 INFO [train.py:527] (5/6) Epoch 108, batch 82, global_batch_idx: 13350, batch size: 52, loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.338, generator_loss=28.2, generator_mel_loss=19.59, generator_kl_loss=1.482, generator_dur_loss=1.703, generator_adv_loss=1.85, generator_feat_match_loss=3.567, over 52.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.389, discriminator_fake_loss=1.352, generator_loss=27.37, generator_mel_loss=19.2, generator_kl_loss=1.386, generator_dur_loss=1.775, generator_adv_loss=1.913, generator_feat_match_loss=3.091, over 4922.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:49:42,798 INFO [train.py:919] (5/6) Start epoch 109 +2024-03-12 10:50:30,655 INFO [train.py:527] (5/6) Epoch 109, batch 8, global_batch_idx: 13400, batch size: 74, loss[discriminator_loss=2.825, discriminator_real_loss=1.657, discriminator_fake_loss=1.168, generator_loss=26.76, generator_mel_loss=18.48, generator_kl_loss=1.269, generator_dur_loss=1.853, generator_adv_loss=2.35, generator_feat_match_loss=2.809, over 74.00 samples.], tot_loss[discriminator_loss=2.819, discriminator_real_loss=1.441, discriminator_fake_loss=1.377, generator_loss=27.8, generator_mel_loss=19.35, generator_kl_loss=1.416, generator_dur_loss=1.771, generator_adv_loss=2.063, generator_feat_match_loss=3.203, over 522.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:50:30,658 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 10:50:38,652 INFO [train.py:591] (5/6) 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 10:50:38,655 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 10:53:02,523 INFO [train.py:527] (5/6) Epoch 109, batch 58, global_batch_idx: 13450, batch size: 16, loss[discriminator_loss=2.836, discriminator_real_loss=1.488, discriminator_fake_loss=1.348, generator_loss=28.2, generator_mel_loss=19.67, generator_kl_loss=1.601, generator_dur_loss=1.616, generator_adv_loss=2.144, generator_feat_match_loss=3.164, over 16.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.419, discriminator_fake_loss=1.348, generator_loss=27.35, generator_mel_loss=19.21, generator_kl_loss=1.39, generator_dur_loss=1.787, generator_adv_loss=1.973, generator_feat_match_loss=2.989, over 3479.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:55:20,979 INFO [train.py:527] (5/6) Epoch 109, batch 108, global_batch_idx: 13500, batch size: 50, loss[discriminator_loss=2.776, discriminator_real_loss=1.419, discriminator_fake_loss=1.356, generator_loss=26.61, generator_mel_loss=19.1, generator_kl_loss=1.355, generator_dur_loss=1.674, generator_adv_loss=1.795, generator_feat_match_loss=2.686, over 50.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.41, discriminator_fake_loss=1.356, generator_loss=27.2, generator_mel_loss=19.18, generator_kl_loss=1.394, generator_dur_loss=1.768, generator_adv_loss=1.926, generator_feat_match_loss=2.929, over 6378.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 10:56:05,001 INFO [train.py:919] (5/6) Start epoch 110 +2024-03-12 10:58:07,941 INFO [train.py:527] (5/6) Epoch 110, batch 34, global_batch_idx: 13550, batch size: 64, loss[discriminator_loss=2.735, discriminator_real_loss=1.325, discriminator_fake_loss=1.411, generator_loss=26.55, generator_mel_loss=18.54, generator_kl_loss=1.429, generator_dur_loss=1.8, generator_adv_loss=2.001, generator_feat_match_loss=2.781, over 64.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.391, discriminator_fake_loss=1.354, generator_loss=27.17, generator_mel_loss=19.15, generator_kl_loss=1.394, generator_dur_loss=1.759, generator_adv_loss=1.881, generator_feat_match_loss=2.989, over 1987.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:00:32,652 INFO [train.py:527] (5/6) Epoch 110, batch 84, global_batch_idx: 13600, batch size: 83, loss[discriminator_loss=2.665, discriminator_real_loss=1.368, discriminator_fake_loss=1.297, generator_loss=27.48, generator_mel_loss=19.07, generator_kl_loss=1.348, generator_dur_loss=1.883, generator_adv_loss=2.017, generator_feat_match_loss=3.164, over 83.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.392, discriminator_fake_loss=1.355, generator_loss=27.24, generator_mel_loss=19.16, generator_kl_loss=1.4, generator_dur_loss=1.77, generator_adv_loss=1.891, generator_feat_match_loss=3.02, over 4887.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:00:32,653 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 11:00:41,510 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 11:02:28,463 INFO [train.py:919] (5/6) Start epoch 111 +2024-03-12 11:03:24,137 INFO [train.py:527] (5/6) Epoch 111, batch 10, global_batch_idx: 13650, batch size: 25, loss[discriminator_loss=2.796, discriminator_real_loss=1.4, discriminator_fake_loss=1.396, generator_loss=28.02, generator_mel_loss=19.65, generator_kl_loss=1.571, generator_dur_loss=1.636, generator_adv_loss=1.959, generator_feat_match_loss=3.205, over 25.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.369, discriminator_fake_loss=1.364, generator_loss=27.42, generator_mel_loss=19.11, generator_kl_loss=1.389, generator_dur_loss=1.766, generator_adv_loss=1.927, generator_feat_match_loss=3.23, over 643.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:05:46,910 INFO [train.py:527] (5/6) Epoch 111, batch 60, global_batch_idx: 13700, batch size: 59, loss[discriminator_loss=2.726, discriminator_real_loss=1.425, discriminator_fake_loss=1.302, generator_loss=27.85, generator_mel_loss=19.38, generator_kl_loss=1.366, generator_dur_loss=1.718, generator_adv_loss=1.939, generator_feat_match_loss=3.444, over 59.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.398, discriminator_fake_loss=1.348, generator_loss=27.18, generator_mel_loss=19.11, generator_kl_loss=1.379, generator_dur_loss=1.762, generator_adv_loss=1.905, generator_feat_match_loss=3.027, over 3638.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:08:05,502 INFO [train.py:527] (5/6) Epoch 111, batch 110, global_batch_idx: 13750, batch size: 42, loss[discriminator_loss=2.792, discriminator_real_loss=1.357, discriminator_fake_loss=1.435, generator_loss=27.5, generator_mel_loss=19.65, generator_kl_loss=1.517, generator_dur_loss=1.695, generator_adv_loss=1.774, generator_feat_match_loss=2.866, over 42.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.396, discriminator_fake_loss=1.362, generator_loss=27.29, generator_mel_loss=19.16, generator_kl_loss=1.376, generator_dur_loss=1.767, generator_adv_loss=1.931, generator_feat_match_loss=3.051, over 6507.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:08:44,692 INFO [train.py:919] (5/6) Start epoch 112 +2024-03-12 11:10:51,344 INFO [train.py:527] (5/6) Epoch 112, batch 36, global_batch_idx: 13800, batch size: 64, loss[discriminator_loss=2.762, discriminator_real_loss=1.418, discriminator_fake_loss=1.344, generator_loss=26.63, generator_mel_loss=18.75, generator_kl_loss=1.328, generator_dur_loss=1.78, generator_adv_loss=1.843, generator_feat_match_loss=2.928, over 64.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.4, discriminator_fake_loss=1.339, generator_loss=27.16, generator_mel_loss=19, generator_kl_loss=1.396, generator_dur_loss=1.801, generator_adv_loss=1.916, generator_feat_match_loss=3.044, over 2172.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:10:51,346 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 11:10:59,109 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 11:13:21,090 INFO [train.py:527] (5/6) Epoch 112, batch 86, global_batch_idx: 13850, batch size: 45, loss[discriminator_loss=2.751, discriminator_real_loss=1.43, discriminator_fake_loss=1.321, generator_loss=27.45, generator_mel_loss=19.63, generator_kl_loss=1.339, generator_dur_loss=1.725, generator_adv_loss=1.978, generator_feat_match_loss=2.776, over 45.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.396, discriminator_fake_loss=1.341, generator_loss=27.2, generator_mel_loss=19.08, generator_kl_loss=1.395, generator_dur_loss=1.777, generator_adv_loss=1.902, generator_feat_match_loss=3.043, over 4929.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:15:04,484 INFO [train.py:919] (5/6) Start epoch 113 +2024-03-12 11:16:04,049 INFO [train.py:527] (5/6) Epoch 113, batch 12, global_batch_idx: 13900, batch size: 25, loss[discriminator_loss=2.478, discriminator_real_loss=1.231, discriminator_fake_loss=1.247, generator_loss=29.73, generator_mel_loss=20.07, generator_kl_loss=1.694, generator_dur_loss=1.595, generator_adv_loss=2.12, generator_feat_match_loss=4.252, over 25.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.391, discriminator_fake_loss=1.337, generator_loss=27.7, generator_mel_loss=19.26, generator_kl_loss=1.394, generator_dur_loss=1.783, generator_adv_loss=2.004, generator_feat_match_loss=3.256, over 765.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:18:23,840 INFO [train.py:527] (5/6) Epoch 113, batch 62, global_batch_idx: 13950, batch size: 56, loss[discriminator_loss=2.697, discriminator_real_loss=1.407, discriminator_fake_loss=1.29, generator_loss=27.97, generator_mel_loss=19.52, generator_kl_loss=1.47, generator_dur_loss=1.708, generator_adv_loss=1.921, generator_feat_match_loss=3.349, over 56.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.405, discriminator_fake_loss=1.357, generator_loss=27.27, generator_mel_loss=19.1, generator_kl_loss=1.391, generator_dur_loss=1.794, generator_adv_loss=1.957, generator_feat_match_loss=3.033, over 3851.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:20:48,921 INFO [train.py:527] (5/6) Epoch 113, batch 112, global_batch_idx: 14000, batch size: 83, loss[discriminator_loss=2.765, discriminator_real_loss=1.404, discriminator_fake_loss=1.362, generator_loss=25.77, generator_mel_loss=18.27, generator_kl_loss=1.129, generator_dur_loss=1.925, generator_adv_loss=1.836, generator_feat_match_loss=2.609, over 83.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.4, discriminator_fake_loss=1.353, generator_loss=27.26, generator_mel_loss=19.12, generator_kl_loss=1.389, generator_dur_loss=1.786, generator_adv_loss=1.93, generator_feat_match_loss=3.042, over 6632.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:20:48,923 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 11:20:57,872 INFO [train.py:591] (5/6) 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 11:20:57,873 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 11:21:28,692 INFO [train.py:919] (5/6) Start epoch 114 +2024-03-12 11:23:41,680 INFO [train.py:527] (5/6) Epoch 114, batch 38, global_batch_idx: 14050, batch size: 64, loss[discriminator_loss=2.768, discriminator_real_loss=1.396, discriminator_fake_loss=1.372, generator_loss=25.96, generator_mel_loss=18.33, generator_kl_loss=1.211, generator_dur_loss=1.74, generator_adv_loss=1.813, generator_feat_match_loss=2.868, over 64.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.387, discriminator_fake_loss=1.352, generator_loss=27.34, generator_mel_loss=19.19, generator_kl_loss=1.415, generator_dur_loss=1.748, generator_adv_loss=1.899, generator_feat_match_loss=3.086, over 2270.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:26:06,588 INFO [train.py:527] (5/6) Epoch 114, batch 88, global_batch_idx: 14100, batch size: 55, loss[discriminator_loss=2.757, discriminator_real_loss=1.403, discriminator_fake_loss=1.354, generator_loss=28.19, generator_mel_loss=20.09, generator_kl_loss=1.528, generator_dur_loss=1.671, generator_adv_loss=2.002, generator_feat_match_loss=2.903, over 55.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.391, discriminator_fake_loss=1.349, generator_loss=27.34, generator_mel_loss=19.18, generator_kl_loss=1.4, generator_dur_loss=1.745, generator_adv_loss=1.915, generator_feat_match_loss=3.103, over 5195.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:27:46,804 INFO [train.py:919] (5/6) Start epoch 115 +2024-03-12 11:28:53,551 INFO [train.py:527] (5/6) Epoch 115, batch 14, global_batch_idx: 14150, batch size: 95, loss[discriminator_loss=2.774, discriminator_real_loss=1.378, discriminator_fake_loss=1.396, generator_loss=27.5, generator_mel_loss=19.05, generator_kl_loss=1.267, generator_dur_loss=1.849, generator_adv_loss=2.137, generator_feat_match_loss=3.196, over 95.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.398, discriminator_fake_loss=1.367, generator_loss=27.32, generator_mel_loss=19.1, generator_kl_loss=1.403, generator_dur_loss=1.777, generator_adv_loss=1.972, generator_feat_match_loss=3.074, over 908.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:31:17,609 INFO [train.py:527] (5/6) Epoch 115, batch 64, global_batch_idx: 14200, batch size: 74, loss[discriminator_loss=2.715, discriminator_real_loss=1.442, discriminator_fake_loss=1.273, generator_loss=27.69, generator_mel_loss=19.35, generator_kl_loss=1.193, generator_dur_loss=1.774, generator_adv_loss=1.916, generator_feat_match_loss=3.459, over 74.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.394, discriminator_fake_loss=1.349, generator_loss=27.4, generator_mel_loss=19.12, generator_kl_loss=1.388, generator_dur_loss=1.776, generator_adv_loss=1.963, generator_feat_match_loss=3.152, over 3899.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:31:17,610 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 11:31:25,537 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 11:33:42,889 INFO [train.py:527] (5/6) Epoch 115, batch 114, global_batch_idx: 14250, batch size: 83, loss[discriminator_loss=2.756, discriminator_real_loss=1.371, discriminator_fake_loss=1.385, generator_loss=27.64, generator_mel_loss=19.53, generator_kl_loss=1.383, generator_dur_loss=1.834, generator_adv_loss=1.892, generator_feat_match_loss=3.001, over 83.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.389, discriminator_fake_loss=1.351, generator_loss=27.42, generator_mel_loss=19.15, generator_kl_loss=1.392, generator_dur_loss=1.775, generator_adv_loss=1.94, generator_feat_match_loss=3.159, over 6729.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:34:09,030 INFO [train.py:919] (5/6) Start epoch 116 +2024-03-12 11:36:27,838 INFO [train.py:527] (5/6) Epoch 116, batch 40, global_batch_idx: 14300, batch size: 42, loss[discriminator_loss=2.802, discriminator_real_loss=1.389, discriminator_fake_loss=1.413, generator_loss=27.62, generator_mel_loss=19.51, generator_kl_loss=1.504, generator_dur_loss=1.742, generator_adv_loss=1.923, generator_feat_match_loss=2.937, over 42.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.387, discriminator_fake_loss=1.352, generator_loss=27.12, generator_mel_loss=19.02, generator_kl_loss=1.379, generator_dur_loss=1.802, generator_adv_loss=1.89, generator_feat_match_loss=3.026, over 2550.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:38:53,133 INFO [train.py:527] (5/6) Epoch 116, batch 90, global_batch_idx: 14350, batch size: 36, loss[discriminator_loss=2.726, discriminator_real_loss=1.318, discriminator_fake_loss=1.408, generator_loss=27.58, generator_mel_loss=19.36, generator_kl_loss=1.419, generator_dur_loss=1.693, generator_adv_loss=2.023, generator_feat_match_loss=3.094, over 36.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.388, discriminator_fake_loss=1.358, generator_loss=27.13, generator_mel_loss=19.06, generator_kl_loss=1.38, generator_dur_loss=1.789, generator_adv_loss=1.89, generator_feat_match_loss=3.011, over 5542.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:40:23,410 INFO [train.py:919] (5/6) Start epoch 117 +2024-03-12 11:41:34,036 INFO [train.py:527] (5/6) Epoch 117, batch 16, global_batch_idx: 14400, batch size: 77, loss[discriminator_loss=2.643, discriminator_real_loss=1.276, discriminator_fake_loss=1.367, generator_loss=27.74, generator_mel_loss=19.25, generator_kl_loss=1.238, generator_dur_loss=1.884, generator_adv_loss=1.912, generator_feat_match_loss=3.45, over 77.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.373, discriminator_fake_loss=1.355, generator_loss=27.64, generator_mel_loss=19.35, generator_kl_loss=1.387, generator_dur_loss=1.802, generator_adv_loss=1.918, generator_feat_match_loss=3.189, over 986.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:41:34,038 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 11:41:42,045 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 11:44:05,222 INFO [train.py:527] (5/6) Epoch 117, batch 66, global_batch_idx: 14450, batch size: 56, loss[discriminator_loss=2.69, discriminator_real_loss=1.277, discriminator_fake_loss=1.413, generator_loss=26.96, generator_mel_loss=19.02, generator_kl_loss=1.332, generator_dur_loss=1.74, generator_adv_loss=1.99, generator_feat_match_loss=2.87, over 56.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.39, discriminator_fake_loss=1.351, generator_loss=27.35, generator_mel_loss=19.17, generator_kl_loss=1.387, generator_dur_loss=1.77, generator_adv_loss=1.906, generator_feat_match_loss=3.117, over 3945.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:46:21,784 INFO [train.py:527] (5/6) Epoch 117, batch 116, global_batch_idx: 14500, batch size: 52, loss[discriminator_loss=2.712, discriminator_real_loss=1.304, discriminator_fake_loss=1.408, generator_loss=26.74, generator_mel_loss=18.55, generator_kl_loss=1.403, generator_dur_loss=1.707, generator_adv_loss=1.931, generator_feat_match_loss=3.148, over 52.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.393, discriminator_fake_loss=1.356, generator_loss=27.24, generator_mel_loss=19.11, generator_kl_loss=1.382, generator_dur_loss=1.767, generator_adv_loss=1.911, generator_feat_match_loss=3.075, over 6853.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:46:41,185 INFO [train.py:919] (5/6) Start epoch 118 +2024-03-12 11:49:04,477 INFO [train.py:527] (5/6) Epoch 118, batch 42, global_batch_idx: 14550, batch size: 70, loss[discriminator_loss=2.671, discriminator_real_loss=1.388, discriminator_fake_loss=1.283, generator_loss=27.67, generator_mel_loss=19.59, generator_kl_loss=1.393, generator_dur_loss=1.774, generator_adv_loss=1.759, generator_feat_match_loss=3.152, over 70.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.397, discriminator_fake_loss=1.356, generator_loss=27.1, generator_mel_loss=19.04, generator_kl_loss=1.367, generator_dur_loss=1.78, generator_adv_loss=1.895, generator_feat_match_loss=3.019, over 2636.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:51:25,221 INFO [train.py:527] (5/6) Epoch 118, batch 92, global_batch_idx: 14600, batch size: 66, loss[discriminator_loss=2.772, discriminator_real_loss=1.368, discriminator_fake_loss=1.403, generator_loss=27.63, generator_mel_loss=19.48, generator_kl_loss=1.342, generator_dur_loss=1.814, generator_adv_loss=1.862, generator_feat_match_loss=3.133, over 66.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.395, discriminator_fake_loss=1.357, generator_loss=27.2, generator_mel_loss=19.09, generator_kl_loss=1.372, generator_dur_loss=1.786, generator_adv_loss=1.898, generator_feat_match_loss=3.051, over 5573.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:51:25,223 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 11:51:33,692 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 11:53:02,047 INFO [train.py:919] (5/6) Start epoch 119 +2024-03-12 11:54:18,453 INFO [train.py:527] (5/6) Epoch 119, batch 18, global_batch_idx: 14650, batch size: 66, loss[discriminator_loss=2.732, discriminator_real_loss=1.314, discriminator_fake_loss=1.418, generator_loss=26.92, generator_mel_loss=18.97, generator_kl_loss=1.199, generator_dur_loss=1.758, generator_adv_loss=1.819, generator_feat_match_loss=3.172, over 66.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.379, discriminator_fake_loss=1.346, generator_loss=27.45, generator_mel_loss=19.24, generator_kl_loss=1.403, generator_dur_loss=1.741, generator_adv_loss=1.926, generator_feat_match_loss=3.141, over 1039.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:56:42,133 INFO [train.py:527] (5/6) Epoch 119, batch 68, global_batch_idx: 14700, batch size: 53, loss[discriminator_loss=2.802, discriminator_real_loss=1.408, discriminator_fake_loss=1.394, generator_loss=27.23, generator_mel_loss=19.32, generator_kl_loss=1.443, generator_dur_loss=1.679, generator_adv_loss=1.907, generator_feat_match_loss=2.882, over 53.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.395, discriminator_fake_loss=1.354, generator_loss=27.31, generator_mel_loss=19.17, generator_kl_loss=1.379, generator_dur_loss=1.753, generator_adv_loss=1.9, generator_feat_match_loss=3.107, over 4035.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:58:56,867 INFO [train.py:527] (5/6) Epoch 119, batch 118, global_batch_idx: 14750, batch size: 48, loss[discriminator_loss=2.702, discriminator_real_loss=1.258, discriminator_fake_loss=1.444, generator_loss=28.88, generator_mel_loss=20.03, generator_kl_loss=1.551, generator_dur_loss=1.693, generator_adv_loss=1.909, generator_feat_match_loss=3.694, over 48.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.389, discriminator_fake_loss=1.356, generator_loss=27.3, generator_mel_loss=19.15, generator_kl_loss=1.391, generator_dur_loss=1.755, generator_adv_loss=1.89, generator_feat_match_loss=3.112, over 6778.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 11:59:12,617 INFO [train.py:919] (5/6) Start epoch 120 +2024-03-12 12:01:37,948 INFO [train.py:527] (5/6) Epoch 120, batch 44, global_batch_idx: 14800, batch size: 44, loss[discriminator_loss=2.737, discriminator_real_loss=1.446, discriminator_fake_loss=1.291, generator_loss=27.33, generator_mel_loss=19.05, generator_kl_loss=1.567, generator_dur_loss=1.672, generator_adv_loss=1.937, generator_feat_match_loss=3.108, over 44.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.401, discriminator_fake_loss=1.348, generator_loss=27.26, generator_mel_loss=19.09, generator_kl_loss=1.419, generator_dur_loss=1.735, generator_adv_loss=1.926, generator_feat_match_loss=3.096, over 2311.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:01:37,950 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 12:01:45,878 INFO [train.py:591] (5/6) 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 12:01:45,879 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 12:04:04,592 INFO [train.py:527] (5/6) Epoch 120, batch 94, global_batch_idx: 14850, batch size: 45, loss[discriminator_loss=2.896, discriminator_real_loss=1.734, discriminator_fake_loss=1.162, generator_loss=26.21, generator_mel_loss=18.62, generator_kl_loss=1.427, generator_dur_loss=1.673, generator_adv_loss=1.586, generator_feat_match_loss=2.897, over 45.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.399, discriminator_fake_loss=1.348, generator_loss=27.21, generator_mel_loss=19.07, generator_kl_loss=1.394, generator_dur_loss=1.744, generator_adv_loss=1.914, generator_feat_match_loss=3.083, over 5264.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:05:27,034 INFO [train.py:919] (5/6) Start epoch 121 +2024-03-12 12:06:45,350 INFO [train.py:527] (5/6) Epoch 121, batch 20, global_batch_idx: 14900, batch size: 53, loss[discriminator_loss=2.782, discriminator_real_loss=1.406, discriminator_fake_loss=1.376, generator_loss=28.08, generator_mel_loss=19.92, generator_kl_loss=1.291, generator_dur_loss=1.679, generator_adv_loss=1.915, generator_feat_match_loss=3.28, over 53.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.401, discriminator_fake_loss=1.365, generator_loss=27.27, generator_mel_loss=19.06, generator_kl_loss=1.383, generator_dur_loss=1.74, generator_adv_loss=1.951, generator_feat_match_loss=3.135, over 1192.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:09:06,753 INFO [train.py:527] (5/6) Epoch 121, batch 70, global_batch_idx: 14950, batch size: 15, loss[discriminator_loss=2.79, discriminator_real_loss=1.376, discriminator_fake_loss=1.414, generator_loss=28.2, generator_mel_loss=20.02, generator_kl_loss=1.567, generator_dur_loss=1.694, generator_adv_loss=1.852, generator_feat_match_loss=3.074, over 15.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.405, discriminator_fake_loss=1.355, generator_loss=27.34, generator_mel_loss=19.17, generator_kl_loss=1.386, generator_dur_loss=1.756, generator_adv_loss=1.918, generator_feat_match_loss=3.114, over 3964.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:11:25,484 INFO [train.py:527] (5/6) Epoch 121, batch 120, global_batch_idx: 15000, batch size: 68, loss[discriminator_loss=2.735, discriminator_real_loss=1.289, discriminator_fake_loss=1.445, generator_loss=28.43, generator_mel_loss=19.38, generator_kl_loss=1.495, generator_dur_loss=1.793, generator_adv_loss=1.972, generator_feat_match_loss=3.788, over 68.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.392, discriminator_fake_loss=1.355, generator_loss=27.33, generator_mel_loss=19.11, generator_kl_loss=1.384, generator_dur_loss=1.76, generator_adv_loss=1.918, generator_feat_match_loss=3.153, over 6950.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:11:25,486 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 12:11:34,415 INFO [train.py:591] (5/6) 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 12:11:34,416 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 12:11:43,731 INFO [train.py:919] (5/6) Start epoch 122 +2024-03-12 12:14:17,615 INFO [train.py:527] (5/6) Epoch 122, batch 46, global_batch_idx: 15050, batch size: 36, loss[discriminator_loss=2.794, discriminator_real_loss=1.485, discriminator_fake_loss=1.309, generator_loss=26.54, generator_mel_loss=18.37, generator_kl_loss=1.588, generator_dur_loss=1.715, generator_adv_loss=2.11, generator_feat_match_loss=2.759, over 36.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.401, discriminator_fake_loss=1.359, generator_loss=27.27, generator_mel_loss=19.08, generator_kl_loss=1.362, generator_dur_loss=1.732, generator_adv_loss=1.937, generator_feat_match_loss=3.162, over 2786.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:16:36,052 INFO [train.py:527] (5/6) Epoch 122, batch 96, global_batch_idx: 15100, batch size: 64, loss[discriminator_loss=2.689, discriminator_real_loss=1.332, discriminator_fake_loss=1.357, generator_loss=28.08, generator_mel_loss=19.76, generator_kl_loss=1.331, generator_dur_loss=1.776, generator_adv_loss=1.865, generator_feat_match_loss=3.352, over 64.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.403, discriminator_fake_loss=1.35, generator_loss=27.27, generator_mel_loss=19.07, generator_kl_loss=1.379, generator_dur_loss=1.749, generator_adv_loss=1.928, generator_feat_match_loss=3.147, over 5592.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:17:47,993 INFO [train.py:919] (5/6) Start epoch 123 +2024-03-12 12:19:14,197 INFO [train.py:527] (5/6) Epoch 123, batch 22, global_batch_idx: 15150, batch size: 68, loss[discriminator_loss=2.803, discriminator_real_loss=1.357, discriminator_fake_loss=1.447, generator_loss=26.16, generator_mel_loss=18.4, generator_kl_loss=1.365, generator_dur_loss=1.749, generator_adv_loss=2.008, generator_feat_match_loss=2.631, over 68.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.384, discriminator_fake_loss=1.366, generator_loss=27.05, generator_mel_loss=19, generator_kl_loss=1.418, generator_dur_loss=1.757, generator_adv_loss=1.881, generator_feat_match_loss=3.002, over 1313.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:21:34,496 INFO [train.py:527] (5/6) Epoch 123, batch 72, global_batch_idx: 15200, batch size: 55, loss[discriminator_loss=2.797, discriminator_real_loss=1.436, discriminator_fake_loss=1.361, generator_loss=25.97, generator_mel_loss=18.17, generator_kl_loss=1.261, generator_dur_loss=1.738, generator_adv_loss=1.868, generator_feat_match_loss=2.931, over 55.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.396, discriminator_fake_loss=1.364, generator_loss=27.07, generator_mel_loss=18.98, generator_kl_loss=1.383, generator_dur_loss=1.769, generator_adv_loss=1.885, generator_feat_match_loss=3.046, over 4224.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:21:34,497 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 12:21:42,581 INFO [train.py:591] (5/6) 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 12:21:42,582 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 12:24:00,584 INFO [train.py:527] (5/6) Epoch 123, batch 122, global_batch_idx: 15250, batch size: 45, loss[discriminator_loss=2.727, discriminator_real_loss=1.351, discriminator_fake_loss=1.376, generator_loss=25.85, generator_mel_loss=18.24, generator_kl_loss=1.383, generator_dur_loss=1.741, generator_adv_loss=1.902, generator_feat_match_loss=2.583, over 45.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.393, discriminator_fake_loss=1.358, generator_loss=27.11, generator_mel_loss=18.99, generator_kl_loss=1.382, generator_dur_loss=1.774, generator_adv_loss=1.889, generator_feat_match_loss=3.078, over 7234.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:24:05,804 INFO [train.py:919] (5/6) Start epoch 124 +2024-03-12 12:26:41,477 INFO [train.py:527] (5/6) Epoch 124, batch 48, global_batch_idx: 15300, batch size: 31, loss[discriminator_loss=2.807, discriminator_real_loss=1.508, discriminator_fake_loss=1.3, generator_loss=27.68, generator_mel_loss=19.58, generator_kl_loss=1.596, generator_dur_loss=1.632, generator_adv_loss=2.1, generator_feat_match_loss=2.77, over 31.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.399, discriminator_fake_loss=1.358, generator_loss=27.48, generator_mel_loss=19.01, generator_kl_loss=1.406, generator_dur_loss=1.786, generator_adv_loss=1.98, generator_feat_match_loss=3.299, over 2761.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:28:58,146 INFO [train.py:527] (5/6) Epoch 124, batch 98, global_batch_idx: 15350, batch size: 36, loss[discriminator_loss=2.738, discriminator_real_loss=1.416, discriminator_fake_loss=1.322, generator_loss=27.37, generator_mel_loss=19.27, generator_kl_loss=1.467, generator_dur_loss=1.737, generator_adv_loss=1.925, generator_feat_match_loss=2.968, over 36.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.399, discriminator_fake_loss=1.355, generator_loss=27.29, generator_mel_loss=19.01, generator_kl_loss=1.39, generator_dur_loss=1.776, generator_adv_loss=1.942, generator_feat_match_loss=3.167, over 5627.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:30:10,467 INFO [train.py:919] (5/6) Start epoch 125 +2024-03-12 12:31:40,999 INFO [train.py:527] (5/6) Epoch 125, batch 24, global_batch_idx: 15400, batch size: 68, loss[discriminator_loss=2.732, discriminator_real_loss=1.417, discriminator_fake_loss=1.315, generator_loss=27.16, generator_mel_loss=19.11, generator_kl_loss=1.365, generator_dur_loss=1.835, generator_adv_loss=1.748, generator_feat_match_loss=3.106, over 68.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.403, discriminator_fake_loss=1.345, generator_loss=27.13, generator_mel_loss=18.93, generator_kl_loss=1.379, generator_dur_loss=1.781, generator_adv_loss=1.896, generator_feat_match_loss=3.143, over 1397.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:31:41,000 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 12:31:49,013 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 12:34:10,600 INFO [train.py:527] (5/6) Epoch 125, batch 74, global_batch_idx: 15450, batch size: 62, loss[discriminator_loss=2.684, discriminator_real_loss=1.331, discriminator_fake_loss=1.354, generator_loss=27.74, generator_mel_loss=19.32, generator_kl_loss=1.319, generator_dur_loss=1.74, generator_adv_loss=2.077, generator_feat_match_loss=3.285, over 62.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.396, discriminator_fake_loss=1.346, generator_loss=27.23, generator_mel_loss=19.02, generator_kl_loss=1.386, generator_dur_loss=1.764, generator_adv_loss=1.901, generator_feat_match_loss=3.154, over 4265.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:36:26,956 INFO [train.py:919] (5/6) Start epoch 126 +2024-03-12 12:36:50,419 INFO [train.py:527] (5/6) Epoch 126, batch 0, global_batch_idx: 15500, batch size: 36, loss[discriminator_loss=2.77, discriminator_real_loss=1.446, discriminator_fake_loss=1.324, generator_loss=27.34, generator_mel_loss=19.29, generator_kl_loss=1.454, generator_dur_loss=1.702, generator_adv_loss=1.911, generator_feat_match_loss=2.982, over 36.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.446, discriminator_fake_loss=1.324, generator_loss=27.34, generator_mel_loss=19.29, generator_kl_loss=1.454, generator_dur_loss=1.702, generator_adv_loss=1.911, generator_feat_match_loss=2.982, over 36.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:39:10,055 INFO [train.py:527] (5/6) Epoch 126, batch 50, global_batch_idx: 15550, batch size: 68, loss[discriminator_loss=2.756, discriminator_real_loss=1.369, discriminator_fake_loss=1.386, generator_loss=27.24, generator_mel_loss=19, generator_kl_loss=1.328, generator_dur_loss=1.826, generator_adv_loss=1.823, generator_feat_match_loss=3.268, over 68.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.4, discriminator_fake_loss=1.351, generator_loss=27.14, generator_mel_loss=19.05, generator_kl_loss=1.379, generator_dur_loss=1.729, generator_adv_loss=1.905, generator_feat_match_loss=3.083, over 2876.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:41:27,696 INFO [train.py:527] (5/6) Epoch 126, batch 100, global_batch_idx: 15600, batch size: 74, loss[discriminator_loss=2.719, discriminator_real_loss=1.288, discriminator_fake_loss=1.431, generator_loss=26.93, generator_mel_loss=18.44, generator_kl_loss=1.374, generator_dur_loss=1.838, generator_adv_loss=2.273, generator_feat_match_loss=3.002, over 74.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.395, discriminator_fake_loss=1.351, generator_loss=27.17, generator_mel_loss=19, generator_kl_loss=1.384, generator_dur_loss=1.748, generator_adv_loss=1.909, generator_feat_match_loss=3.126, over 5658.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:41:27,697 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 12:41:36,595 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 12:42:40,901 INFO [train.py:919] (5/6) Start epoch 127 +2024-03-12 12:44:17,920 INFO [train.py:527] (5/6) Epoch 127, batch 26, global_batch_idx: 15650, batch size: 88, loss[discriminator_loss=2.741, discriminator_real_loss=1.415, discriminator_fake_loss=1.326, generator_loss=26.08, generator_mel_loss=18.41, generator_kl_loss=1.246, generator_dur_loss=1.864, generator_adv_loss=1.731, generator_feat_match_loss=2.837, over 88.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.401, discriminator_fake_loss=1.338, generator_loss=27.06, generator_mel_loss=18.92, generator_kl_loss=1.349, generator_dur_loss=1.762, generator_adv_loss=1.893, generator_feat_match_loss=3.139, over 1547.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:46:38,042 INFO [train.py:527] (5/6) Epoch 127, batch 76, global_batch_idx: 15700, batch size: 56, loss[discriminator_loss=2.702, discriminator_real_loss=1.425, discriminator_fake_loss=1.277, generator_loss=27.67, generator_mel_loss=19.13, generator_kl_loss=1.394, generator_dur_loss=1.68, generator_adv_loss=2.036, generator_feat_match_loss=3.429, over 56.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.396, discriminator_fake_loss=1.343, generator_loss=27.27, generator_mel_loss=18.98, generator_kl_loss=1.374, generator_dur_loss=1.755, generator_adv_loss=1.941, generator_feat_match_loss=3.218, over 4309.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:48:48,642 INFO [train.py:919] (5/6) Start epoch 128 +2024-03-12 12:49:17,609 INFO [train.py:527] (5/6) Epoch 128, batch 2, global_batch_idx: 15750, batch size: 36, loss[discriminator_loss=2.848, discriminator_real_loss=1.336, discriminator_fake_loss=1.512, generator_loss=27.61, generator_mel_loss=19.1, generator_kl_loss=1.362, generator_dur_loss=1.764, generator_adv_loss=2.045, generator_feat_match_loss=3.343, over 36.00 samples.], tot_loss[discriminator_loss=2.774, discriminator_real_loss=1.384, discriminator_fake_loss=1.389, generator_loss=27.39, generator_mel_loss=19.09, generator_kl_loss=1.381, generator_dur_loss=1.778, generator_adv_loss=1.864, generator_feat_match_loss=3.28, over 161.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:51:35,804 INFO [train.py:527] (5/6) Epoch 128, batch 52, global_batch_idx: 15800, batch size: 36, loss[discriminator_loss=2.836, discriminator_real_loss=1.379, discriminator_fake_loss=1.457, generator_loss=26.42, generator_mel_loss=18.62, generator_kl_loss=1.408, generator_dur_loss=1.721, generator_adv_loss=1.866, generator_feat_match_loss=2.802, over 36.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.387, discriminator_fake_loss=1.361, generator_loss=27.24, generator_mel_loss=19, generator_kl_loss=1.391, generator_dur_loss=1.77, generator_adv_loss=1.913, generator_feat_match_loss=3.163, over 3015.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:51:35,806 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 12:51:44,034 INFO [train.py:591] (5/6) 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 12:51:44,035 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 12:54:00,544 INFO [train.py:527] (5/6) Epoch 128, batch 102, global_batch_idx: 15850, batch size: 74, loss[discriminator_loss=2.762, discriminator_real_loss=1.472, discriminator_fake_loss=1.291, generator_loss=26.75, generator_mel_loss=19.01, generator_kl_loss=1.167, generator_dur_loss=1.858, generator_adv_loss=1.862, generator_feat_match_loss=2.852, over 74.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.4, discriminator_fake_loss=1.354, generator_loss=27.23, generator_mel_loss=19.05, generator_kl_loss=1.369, generator_dur_loss=1.775, generator_adv_loss=1.902, generator_feat_match_loss=3.129, over 5910.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:55:02,083 INFO [train.py:919] (5/6) Start epoch 129 +2024-03-12 12:56:44,543 INFO [train.py:527] (5/6) Epoch 129, batch 28, global_batch_idx: 15900, batch size: 80, loss[discriminator_loss=2.695, discriminator_real_loss=1.358, discriminator_fake_loss=1.337, generator_loss=27.84, generator_mel_loss=19.08, generator_kl_loss=1.406, generator_dur_loss=1.852, generator_adv_loss=1.955, generator_feat_match_loss=3.544, over 80.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.385, discriminator_fake_loss=1.358, generator_loss=27.36, generator_mel_loss=19.13, generator_kl_loss=1.414, generator_dur_loss=1.763, generator_adv_loss=1.892, generator_feat_match_loss=3.162, over 1666.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 12:59:02,162 INFO [train.py:527] (5/6) Epoch 129, batch 78, global_batch_idx: 15950, batch size: 64, loss[discriminator_loss=2.768, discriminator_real_loss=1.448, discriminator_fake_loss=1.32, generator_loss=27.23, generator_mel_loss=18.99, generator_kl_loss=1.423, generator_dur_loss=1.781, generator_adv_loss=1.8, generator_feat_match_loss=3.233, over 64.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.396, discriminator_fake_loss=1.356, generator_loss=27.28, generator_mel_loss=19.02, generator_kl_loss=1.404, generator_dur_loss=1.775, generator_adv_loss=1.912, generator_feat_match_loss=3.163, over 4625.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:01:07,559 INFO [train.py:919] (5/6) Start epoch 130 +2024-03-12 13:01:42,852 INFO [train.py:527] (5/6) Epoch 130, batch 4, global_batch_idx: 16000, batch size: 88, loss[discriminator_loss=2.712, discriminator_real_loss=1.313, discriminator_fake_loss=1.399, generator_loss=26.91, generator_mel_loss=18.82, generator_kl_loss=1.253, generator_dur_loss=1.884, generator_adv_loss=1.859, generator_feat_match_loss=3.095, over 88.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.326, discriminator_fake_loss=1.408, generator_loss=27.35, generator_mel_loss=19.11, generator_kl_loss=1.396, generator_dur_loss=1.818, generator_adv_loss=1.899, generator_feat_match_loss=3.122, over 265.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:01:42,855 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 13:01:50,755 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 13:04:09,453 INFO [train.py:527] (5/6) Epoch 130, batch 54, global_batch_idx: 16050, batch size: 62, loss[discriminator_loss=2.706, discriminator_real_loss=1.39, discriminator_fake_loss=1.317, generator_loss=27.72, generator_mel_loss=19.33, generator_kl_loss=1.383, generator_dur_loss=1.695, generator_adv_loss=1.908, generator_feat_match_loss=3.405, over 62.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.386, discriminator_fake_loss=1.356, generator_loss=27.22, generator_mel_loss=18.97, generator_kl_loss=1.363, generator_dur_loss=1.798, generator_adv_loss=1.904, generator_feat_match_loss=3.178, over 3413.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:06:28,551 INFO [train.py:527] (5/6) Epoch 130, batch 104, global_batch_idx: 16100, batch size: 59, loss[discriminator_loss=2.792, discriminator_real_loss=1.531, discriminator_fake_loss=1.261, generator_loss=26.86, generator_mel_loss=18.92, generator_kl_loss=1.438, generator_dur_loss=1.778, generator_adv_loss=1.739, generator_feat_match_loss=2.986, over 59.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.39, discriminator_fake_loss=1.358, generator_loss=27.22, generator_mel_loss=19, generator_kl_loss=1.373, generator_dur_loss=1.787, generator_adv_loss=1.899, generator_feat_match_loss=3.162, over 6551.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:07:19,702 INFO [train.py:919] (5/6) Start epoch 131 +2024-03-12 13:09:08,202 INFO [train.py:527] (5/6) Epoch 131, batch 30, global_batch_idx: 16150, batch size: 72, loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=26.01, generator_mel_loss=17.93, generator_kl_loss=1.316, generator_dur_loss=1.796, generator_adv_loss=1.902, generator_feat_match_loss=3.075, over 72.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.384, discriminator_fake_loss=1.342, generator_loss=27.3, generator_mel_loss=18.99, generator_kl_loss=1.387, generator_dur_loss=1.765, generator_adv_loss=1.922, generator_feat_match_loss=3.239, over 1820.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:11:30,139 INFO [train.py:527] (5/6) Epoch 131, batch 80, global_batch_idx: 16200, batch size: 88, loss[discriminator_loss=2.684, discriminator_real_loss=1.385, discriminator_fake_loss=1.3, generator_loss=27.36, generator_mel_loss=18.92, generator_kl_loss=1.226, generator_dur_loss=1.871, generator_adv_loss=1.978, generator_feat_match_loss=3.366, over 88.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.385, discriminator_fake_loss=1.352, generator_loss=27.17, generator_mel_loss=18.93, generator_kl_loss=1.365, generator_dur_loss=1.765, generator_adv_loss=1.912, generator_feat_match_loss=3.202, over 4928.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:11:30,140 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 13:11:39,152 INFO [train.py:591] (5/6) 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 13:11:39,152 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 13:13:34,512 INFO [train.py:919] (5/6) Start epoch 132 +2024-03-12 13:14:15,325 INFO [train.py:527] (5/6) Epoch 132, batch 6, global_batch_idx: 16250, batch size: 53, loss[discriminator_loss=2.977, discriminator_real_loss=1.723, discriminator_fake_loss=1.255, generator_loss=27.22, generator_mel_loss=19.19, generator_kl_loss=1.648, generator_dur_loss=1.663, generator_adv_loss=1.85, generator_feat_match_loss=2.862, over 53.00 samples.], tot_loss[discriminator_loss=2.951, discriminator_real_loss=1.576, discriminator_fake_loss=1.375, generator_loss=28.07, generator_mel_loss=19.27, generator_kl_loss=1.368, generator_dur_loss=1.754, generator_adv_loss=2.241, generator_feat_match_loss=3.437, over 415.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:16:32,865 INFO [train.py:527] (5/6) Epoch 132, batch 56, global_batch_idx: 16300, batch size: 39, loss[discriminator_loss=2.817, discriminator_real_loss=1.521, discriminator_fake_loss=1.296, generator_loss=28.31, generator_mel_loss=19.64, generator_kl_loss=1.676, generator_dur_loss=1.691, generator_adv_loss=1.73, generator_feat_match_loss=3.574, over 39.00 samples.], tot_loss[discriminator_loss=2.772, discriminator_real_loss=1.42, discriminator_fake_loss=1.352, generator_loss=27.68, generator_mel_loss=19.2, generator_kl_loss=1.409, generator_dur_loss=1.729, generator_adv_loss=2.012, generator_feat_match_loss=3.335, over 3033.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:18:53,760 INFO [train.py:527] (5/6) Epoch 132, batch 106, global_batch_idx: 16350, batch size: 55, loss[discriminator_loss=2.752, discriminator_real_loss=1.486, discriminator_fake_loss=1.266, generator_loss=26.98, generator_mel_loss=19.01, generator_kl_loss=1.399, generator_dur_loss=1.69, generator_adv_loss=1.931, generator_feat_match_loss=2.943, over 55.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.41, discriminator_fake_loss=1.358, generator_loss=27.46, generator_mel_loss=19.12, generator_kl_loss=1.396, generator_dur_loss=1.748, generator_adv_loss=1.956, generator_feat_match_loss=3.242, over 6035.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:19:43,239 INFO [train.py:919] (5/6) Start epoch 133 +2024-03-12 13:21:35,243 INFO [train.py:527] (5/6) Epoch 133, batch 32, global_batch_idx: 16400, batch size: 36, loss[discriminator_loss=2.86, discriminator_real_loss=1.513, discriminator_fake_loss=1.346, generator_loss=26.31, generator_mel_loss=18.54, generator_kl_loss=1.537, generator_dur_loss=1.692, generator_adv_loss=1.641, generator_feat_match_loss=2.893, over 36.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.399, discriminator_fake_loss=1.35, generator_loss=27.21, generator_mel_loss=18.99, generator_kl_loss=1.383, generator_dur_loss=1.732, generator_adv_loss=1.915, generator_feat_match_loss=3.194, over 1865.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:21:35,245 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 13:21:43,787 INFO [train.py:591] (5/6) 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 13:21:43,789 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 13:24:02,140 INFO [train.py:527] (5/6) Epoch 133, batch 82, global_batch_idx: 16450, batch size: 68, loss[discriminator_loss=2.731, discriminator_real_loss=1.467, discriminator_fake_loss=1.265, generator_loss=28.29, generator_mel_loss=19.33, generator_kl_loss=1.512, generator_dur_loss=1.733, generator_adv_loss=2.106, generator_feat_match_loss=3.609, over 68.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.399, discriminator_fake_loss=1.357, generator_loss=27.27, generator_mel_loss=19.02, generator_kl_loss=1.403, generator_dur_loss=1.744, generator_adv_loss=1.908, generator_feat_match_loss=3.195, over 4500.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:25:57,311 INFO [train.py:919] (5/6) Start epoch 134 +2024-03-12 13:26:43,185 INFO [train.py:527] (5/6) Epoch 134, batch 8, global_batch_idx: 16500, batch size: 31, loss[discriminator_loss=2.784, discriminator_real_loss=1.401, discriminator_fake_loss=1.382, generator_loss=28.17, generator_mel_loss=19.82, generator_kl_loss=1.578, generator_dur_loss=1.614, generator_adv_loss=1.932, generator_feat_match_loss=3.225, over 31.00 samples.], tot_loss[discriminator_loss=2.774, discriminator_real_loss=1.449, discriminator_fake_loss=1.325, generator_loss=27.52, generator_mel_loss=19.23, generator_kl_loss=1.361, generator_dur_loss=1.787, generator_adv_loss=1.967, generator_feat_match_loss=3.179, over 565.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:29:01,150 INFO [train.py:527] (5/6) Epoch 134, batch 58, global_batch_idx: 16550, batch size: 62, loss[discriminator_loss=2.73, discriminator_real_loss=1.457, discriminator_fake_loss=1.273, generator_loss=27.32, generator_mel_loss=19.03, generator_kl_loss=1.347, generator_dur_loss=1.723, generator_adv_loss=1.962, generator_feat_match_loss=3.258, over 62.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.405, discriminator_fake_loss=1.347, generator_loss=27.26, generator_mel_loss=18.98, generator_kl_loss=1.386, generator_dur_loss=1.758, generator_adv_loss=1.925, generator_feat_match_loss=3.209, over 3247.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:31:20,769 INFO [train.py:527] (5/6) Epoch 134, batch 108, global_batch_idx: 16600, batch size: 66, loss[discriminator_loss=2.76, discriminator_real_loss=1.349, discriminator_fake_loss=1.411, generator_loss=27.16, generator_mel_loss=19.01, generator_kl_loss=1.255, generator_dur_loss=1.762, generator_adv_loss=1.902, generator_feat_match_loss=3.227, over 66.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.397, discriminator_fake_loss=1.346, generator_loss=27.34, generator_mel_loss=19.03, generator_kl_loss=1.399, generator_dur_loss=1.756, generator_adv_loss=1.924, generator_feat_match_loss=3.226, over 5989.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:31:20,770 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 13:31:29,813 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 13:32:11,582 INFO [train.py:919] (5/6) Start epoch 135 +2024-03-12 13:34:10,377 INFO [train.py:527] (5/6) Epoch 135, batch 34, global_batch_idx: 16650, batch size: 80, loss[discriminator_loss=2.708, discriminator_real_loss=1.355, discriminator_fake_loss=1.353, generator_loss=26.87, generator_mel_loss=18.42, generator_kl_loss=1.263, generator_dur_loss=1.824, generator_adv_loss=1.988, generator_feat_match_loss=3.366, over 80.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.18, generator_mel_loss=18.88, generator_kl_loss=1.384, generator_dur_loss=1.77, generator_adv_loss=1.913, generator_feat_match_loss=3.231, over 2144.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:36:29,227 INFO [train.py:527] (5/6) Epoch 135, batch 84, global_batch_idx: 16700, batch size: 48, loss[discriminator_loss=2.679, discriminator_real_loss=1.345, discriminator_fake_loss=1.334, generator_loss=27.94, generator_mel_loss=19.58, generator_kl_loss=1.461, generator_dur_loss=1.653, generator_adv_loss=1.824, generator_feat_match_loss=3.429, over 48.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=27.53, generator_mel_loss=19.02, generator_kl_loss=1.386, generator_dur_loss=1.748, generator_adv_loss=1.997, generator_feat_match_loss=3.383, over 4890.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:38:16,459 INFO [train.py:919] (5/6) Start epoch 136 +2024-03-12 13:39:10,268 INFO [train.py:527] (5/6) Epoch 136, batch 10, global_batch_idx: 16750, batch size: 77, loss[discriminator_loss=2.807, discriminator_real_loss=1.406, discriminator_fake_loss=1.401, generator_loss=27.19, generator_mel_loss=18.98, generator_kl_loss=1.266, generator_dur_loss=1.818, generator_adv_loss=2.14, generator_feat_match_loss=2.991, over 77.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=27.19, generator_mel_loss=18.9, generator_kl_loss=1.33, generator_dur_loss=1.795, generator_adv_loss=1.95, generator_feat_match_loss=3.223, over 742.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:41:29,487 INFO [train.py:527] (5/6) Epoch 136, batch 60, global_batch_idx: 16800, batch size: 77, loss[discriminator_loss=2.732, discriminator_real_loss=1.456, discriminator_fake_loss=1.276, generator_loss=27.48, generator_mel_loss=19.35, generator_kl_loss=1.228, generator_dur_loss=1.771, generator_adv_loss=1.763, generator_feat_match_loss=3.361, over 77.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.397, discriminator_fake_loss=1.352, generator_loss=27.17, generator_mel_loss=18.98, generator_kl_loss=1.387, generator_dur_loss=1.751, generator_adv_loss=1.9, generator_feat_match_loss=3.152, over 3680.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:41:29,488 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 13:41:37,381 INFO [train.py:591] (5/6) 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 13:41:37,382 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 13:43:55,314 INFO [train.py:527] (5/6) Epoch 136, batch 110, global_batch_idx: 16850, batch size: 42, loss[discriminator_loss=2.901, discriminator_real_loss=1.383, discriminator_fake_loss=1.518, generator_loss=26.54, generator_mel_loss=18.47, generator_kl_loss=1.407, generator_dur_loss=1.714, generator_adv_loss=2.011, generator_feat_match_loss=2.934, over 42.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.392, discriminator_fake_loss=1.354, generator_loss=27.18, generator_mel_loss=18.96, generator_kl_loss=1.389, generator_dur_loss=1.757, generator_adv_loss=1.897, generator_feat_match_loss=3.178, over 6586.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:44:31,322 INFO [train.py:919] (5/6) Start epoch 137 +2024-03-12 13:46:36,210 INFO [train.py:527] (5/6) Epoch 137, batch 36, global_batch_idx: 16900, batch size: 56, loss[discriminator_loss=2.778, discriminator_real_loss=1.395, discriminator_fake_loss=1.383, generator_loss=25.55, generator_mel_loss=17.8, generator_kl_loss=1.397, generator_dur_loss=1.723, generator_adv_loss=1.932, generator_feat_match_loss=2.699, over 56.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.386, discriminator_fake_loss=1.355, generator_loss=27.11, generator_mel_loss=18.89, generator_kl_loss=1.389, generator_dur_loss=1.77, generator_adv_loss=1.896, generator_feat_match_loss=3.166, over 2074.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:48:57,985 INFO [train.py:527] (5/6) Epoch 137, batch 86, global_batch_idx: 16950, batch size: 44, loss[discriminator_loss=2.629, discriminator_real_loss=1.295, discriminator_fake_loss=1.334, generator_loss=27.8, generator_mel_loss=19, generator_kl_loss=1.47, generator_dur_loss=1.716, generator_adv_loss=1.912, generator_feat_match_loss=3.7, over 44.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.386, discriminator_fake_loss=1.352, generator_loss=27.3, generator_mel_loss=18.94, generator_kl_loss=1.395, generator_dur_loss=1.757, generator_adv_loss=1.93, generator_feat_match_loss=3.279, over 4809.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:50:39,841 INFO [train.py:919] (5/6) Start epoch 138 +2024-03-12 13:51:34,801 INFO [train.py:527] (5/6) Epoch 138, batch 12, global_batch_idx: 17000, batch size: 39, loss[discriminator_loss=2.69, discriminator_real_loss=1.379, discriminator_fake_loss=1.311, generator_loss=27.93, generator_mel_loss=19.44, generator_kl_loss=1.584, generator_dur_loss=1.711, generator_adv_loss=1.783, generator_feat_match_loss=3.416, over 39.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.384, discriminator_fake_loss=1.342, generator_loss=27.03, generator_mel_loss=18.83, generator_kl_loss=1.347, generator_dur_loss=1.773, generator_adv_loss=1.903, generator_feat_match_loss=3.179, over 753.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:51:34,804 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 13:51:42,684 INFO [train.py:591] (5/6) 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 13:51:42,686 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 13:54:03,171 INFO [train.py:527] (5/6) Epoch 138, batch 62, global_batch_idx: 17050, batch size: 62, loss[discriminator_loss=2.755, discriminator_real_loss=1.389, discriminator_fake_loss=1.367, generator_loss=27.92, generator_mel_loss=19.46, generator_kl_loss=1.395, generator_dur_loss=1.758, generator_adv_loss=1.818, generator_feat_match_loss=3.497, over 62.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.381, discriminator_fake_loss=1.347, generator_loss=27.29, generator_mel_loss=18.97, generator_kl_loss=1.377, generator_dur_loss=1.759, generator_adv_loss=1.907, generator_feat_match_loss=3.268, over 3549.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:56:19,500 INFO [train.py:527] (5/6) Epoch 138, batch 112, global_batch_idx: 17100, batch size: 77, loss[discriminator_loss=2.689, discriminator_real_loss=1.334, discriminator_fake_loss=1.355, generator_loss=26.99, generator_mel_loss=18.98, generator_kl_loss=1.364, generator_dur_loss=1.809, generator_adv_loss=1.879, generator_feat_match_loss=2.957, over 77.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.384, discriminator_fake_loss=1.352, generator_loss=27.31, generator_mel_loss=19.01, generator_kl_loss=1.379, generator_dur_loss=1.764, generator_adv_loss=1.908, generator_feat_match_loss=3.245, over 6463.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 13:56:50,548 INFO [train.py:919] (5/6) Start epoch 139 +2024-03-12 13:59:01,226 INFO [train.py:527] (5/6) Epoch 139, batch 38, global_batch_idx: 17150, batch size: 44, loss[discriminator_loss=2.755, discriminator_real_loss=1.521, discriminator_fake_loss=1.234, generator_loss=27.77, generator_mel_loss=19.54, generator_kl_loss=1.493, generator_dur_loss=1.753, generator_adv_loss=1.918, generator_feat_match_loss=3.063, over 44.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.404, discriminator_fake_loss=1.365, generator_loss=27.57, generator_mel_loss=19.12, generator_kl_loss=1.387, generator_dur_loss=1.781, generator_adv_loss=1.988, generator_feat_match_loss=3.293, over 2310.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:01:21,604 INFO [train.py:527] (5/6) Epoch 139, batch 88, global_batch_idx: 17200, batch size: 55, loss[discriminator_loss=2.689, discriminator_real_loss=1.396, discriminator_fake_loss=1.293, generator_loss=27.62, generator_mel_loss=19.04, generator_kl_loss=1.444, generator_dur_loss=1.705, generator_adv_loss=1.965, generator_feat_match_loss=3.467, over 55.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.398, discriminator_fake_loss=1.358, generator_loss=27.4, generator_mel_loss=19.03, generator_kl_loss=1.391, generator_dur_loss=1.766, generator_adv_loss=1.939, generator_feat_match_loss=3.269, over 5145.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:01:21,606 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 14:01:29,860 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 14:03:08,456 INFO [train.py:919] (5/6) Start epoch 140 +2024-03-12 14:04:12,709 INFO [train.py:527] (5/6) Epoch 140, batch 14, global_batch_idx: 17250, batch size: 72, loss[discriminator_loss=2.706, discriminator_real_loss=1.398, discriminator_fake_loss=1.308, generator_loss=27.13, generator_mel_loss=18.82, generator_kl_loss=1.273, generator_dur_loss=1.782, generator_adv_loss=1.862, generator_feat_match_loss=3.392, over 72.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.396, discriminator_fake_loss=1.357, generator_loss=27.27, generator_mel_loss=19.04, generator_kl_loss=1.33, generator_dur_loss=1.782, generator_adv_loss=1.885, generator_feat_match_loss=3.236, over 1006.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:06:31,518 INFO [train.py:527] (5/6) Epoch 140, batch 64, global_batch_idx: 17300, batch size: 70, loss[discriminator_loss=2.72, discriminator_real_loss=1.368, discriminator_fake_loss=1.352, generator_loss=27.24, generator_mel_loss=18.91, generator_kl_loss=1.246, generator_dur_loss=1.819, generator_adv_loss=1.834, generator_feat_match_loss=3.433, over 70.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.392, discriminator_fake_loss=1.355, generator_loss=27.29, generator_mel_loss=19.02, generator_kl_loss=1.359, generator_dur_loss=1.761, generator_adv_loss=1.896, generator_feat_match_loss=3.251, over 4083.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:08:49,319 INFO [train.py:527] (5/6) Epoch 140, batch 114, global_batch_idx: 17350, batch size: 70, loss[discriminator_loss=2.695, discriminator_real_loss=1.372, discriminator_fake_loss=1.322, generator_loss=27.2, generator_mel_loss=18.91, generator_kl_loss=1.281, generator_dur_loss=1.754, generator_adv_loss=2.159, generator_feat_match_loss=3.088, over 70.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.391, discriminator_fake_loss=1.354, generator_loss=27.38, generator_mel_loss=19.07, generator_kl_loss=1.375, generator_dur_loss=1.756, generator_adv_loss=1.905, generator_feat_match_loss=3.281, over 6877.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:09:15,076 INFO [train.py:919] (5/6) Start epoch 141 +2024-03-12 14:11:31,922 INFO [train.py:527] (5/6) Epoch 141, batch 40, global_batch_idx: 17400, batch size: 44, loss[discriminator_loss=2.764, discriminator_real_loss=1.301, discriminator_fake_loss=1.463, generator_loss=26.85, generator_mel_loss=18.67, generator_kl_loss=1.392, generator_dur_loss=1.684, generator_adv_loss=1.878, generator_feat_match_loss=3.23, over 44.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.394, discriminator_fake_loss=1.351, generator_loss=27.29, generator_mel_loss=18.96, generator_kl_loss=1.391, generator_dur_loss=1.746, generator_adv_loss=1.93, generator_feat_match_loss=3.266, over 2286.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:11:31,924 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 14:11:40,075 INFO [train.py:591] (5/6) 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 14:11:40,076 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 14:13:59,383 INFO [train.py:527] (5/6) Epoch 141, batch 90, global_batch_idx: 17450, batch size: 62, loss[discriminator_loss=2.729, discriminator_real_loss=1.38, discriminator_fake_loss=1.348, generator_loss=27.03, generator_mel_loss=19.04, generator_kl_loss=1.446, generator_dur_loss=1.701, generator_adv_loss=1.866, generator_feat_match_loss=2.975, over 62.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.398, discriminator_fake_loss=1.352, generator_loss=27.31, generator_mel_loss=19.02, generator_kl_loss=1.39, generator_dur_loss=1.75, generator_adv_loss=1.91, generator_feat_match_loss=3.242, over 5252.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:15:33,285 INFO [train.py:919] (5/6) Start epoch 142 +2024-03-12 14:16:41,490 INFO [train.py:527] (5/6) Epoch 142, batch 16, global_batch_idx: 17500, batch size: 72, loss[discriminator_loss=2.73, discriminator_real_loss=1.342, discriminator_fake_loss=1.388, generator_loss=27.31, generator_mel_loss=19.02, generator_kl_loss=1.254, generator_dur_loss=1.791, generator_adv_loss=1.884, generator_feat_match_loss=3.363, over 72.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.425, discriminator_fake_loss=1.338, generator_loss=27.13, generator_mel_loss=18.89, generator_kl_loss=1.385, generator_dur_loss=1.73, generator_adv_loss=1.885, generator_feat_match_loss=3.244, over 932.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:19:00,075 INFO [train.py:527] (5/6) Epoch 142, batch 66, global_batch_idx: 17550, batch size: 64, loss[discriminator_loss=2.698, discriminator_real_loss=1.411, discriminator_fake_loss=1.287, generator_loss=26.97, generator_mel_loss=18.88, generator_kl_loss=1.335, generator_dur_loss=1.743, generator_adv_loss=1.898, generator_feat_match_loss=3.114, over 64.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.4, discriminator_fake_loss=1.353, generator_loss=27.11, generator_mel_loss=18.86, generator_kl_loss=1.367, generator_dur_loss=1.758, generator_adv_loss=1.893, generator_feat_match_loss=3.234, over 4012.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:21:16,279 INFO [train.py:527] (5/6) Epoch 142, batch 116, global_batch_idx: 17600, batch size: 68, loss[discriminator_loss=3.225, discriminator_real_loss=1.612, discriminator_fake_loss=1.613, generator_loss=27.42, generator_mel_loss=19.12, generator_kl_loss=1.426, generator_dur_loss=1.82, generator_adv_loss=1.849, generator_feat_match_loss=3.206, over 68.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.393, discriminator_fake_loss=1.364, generator_loss=27.24, generator_mel_loss=18.92, generator_kl_loss=1.373, generator_dur_loss=1.761, generator_adv_loss=1.902, generator_feat_match_loss=3.284, over 6925.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, +2024-03-12 14:21:16,280 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 14:21:25,032 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 14:21:45,384 INFO [train.py:919] (5/6) Start epoch 143 +2024-03-12 14:24:07,021 INFO [train.py:527] (5/6) Epoch 143, batch 42, global_batch_idx: 17650, batch size: 53, loss[discriminator_loss=2.743, discriminator_real_loss=1.479, discriminator_fake_loss=1.264, generator_loss=28.09, generator_mel_loss=19.47, generator_kl_loss=1.443, generator_dur_loss=1.642, generator_adv_loss=1.984, generator_feat_match_loss=3.547, over 53.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.391, discriminator_fake_loss=1.349, generator_loss=27.25, generator_mel_loss=18.96, generator_kl_loss=1.36, generator_dur_loss=1.761, generator_adv_loss=1.936, generator_feat_match_loss=3.234, over 2814.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:26:24,732 INFO [train.py:527] (5/6) Epoch 143, batch 92, global_batch_idx: 17700, batch size: 96, loss[discriminator_loss=2.744, discriminator_real_loss=1.379, discriminator_fake_loss=1.365, generator_loss=27.9, generator_mel_loss=19.12, generator_kl_loss=1.52, generator_dur_loss=1.814, generator_adv_loss=1.952, generator_feat_match_loss=3.491, over 96.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.389, discriminator_fake_loss=1.352, generator_loss=27.34, generator_mel_loss=18.99, generator_kl_loss=1.382, generator_dur_loss=1.752, generator_adv_loss=1.924, generator_feat_match_loss=3.298, over 5733.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:27:50,712 INFO [train.py:919] (5/6) Start epoch 144 +2024-03-12 14:29:04,068 INFO [train.py:527] (5/6) Epoch 144, batch 18, global_batch_idx: 17750, batch size: 48, loss[discriminator_loss=2.952, discriminator_real_loss=1.711, discriminator_fake_loss=1.241, generator_loss=26.57, generator_mel_loss=18.8, generator_kl_loss=1.46, generator_dur_loss=1.734, generator_adv_loss=1.513, generator_feat_match_loss=3.059, over 48.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.414, discriminator_fake_loss=1.35, generator_loss=27.38, generator_mel_loss=19.04, generator_kl_loss=1.429, generator_dur_loss=1.741, generator_adv_loss=1.904, generator_feat_match_loss=3.261, over 1065.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:31:23,759 INFO [train.py:527] (5/6) Epoch 144, batch 68, global_batch_idx: 17800, batch size: 25, loss[discriminator_loss=2.748, discriminator_real_loss=1.347, discriminator_fake_loss=1.401, generator_loss=28.04, generator_mel_loss=19.62, generator_kl_loss=1.576, generator_dur_loss=1.586, generator_adv_loss=1.796, generator_feat_match_loss=3.46, over 25.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.401, discriminator_fake_loss=1.346, generator_loss=27.35, generator_mel_loss=18.94, generator_kl_loss=1.403, generator_dur_loss=1.77, generator_adv_loss=1.932, generator_feat_match_loss=3.302, over 3962.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:31:23,761 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 14:31:31,869 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 14:33:51,163 INFO [train.py:527] (5/6) Epoch 144, batch 118, global_batch_idx: 17850, batch size: 47, loss[discriminator_loss=2.734, discriminator_real_loss=1.332, discriminator_fake_loss=1.402, generator_loss=28.23, generator_mel_loss=19.4, generator_kl_loss=1.42, generator_dur_loss=1.685, generator_adv_loss=1.956, generator_feat_match_loss=3.77, over 47.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.397, discriminator_fake_loss=1.349, generator_loss=27.28, generator_mel_loss=18.92, generator_kl_loss=1.379, generator_dur_loss=1.778, generator_adv_loss=1.922, generator_feat_match_loss=3.276, over 6921.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:34:06,659 INFO [train.py:919] (5/6) Start epoch 145 +2024-03-12 14:36:30,906 INFO [train.py:527] (5/6) Epoch 145, batch 44, global_batch_idx: 17900, batch size: 83, loss[discriminator_loss=2.693, discriminator_real_loss=1.355, discriminator_fake_loss=1.338, generator_loss=26.45, generator_mel_loss=18.37, generator_kl_loss=1.14, generator_dur_loss=1.855, generator_adv_loss=1.857, generator_feat_match_loss=3.226, over 83.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.393, discriminator_fake_loss=1.357, generator_loss=27.18, generator_mel_loss=18.86, generator_kl_loss=1.411, generator_dur_loss=1.748, generator_adv_loss=1.914, generator_feat_match_loss=3.244, over 2407.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:38:48,764 INFO [train.py:527] (5/6) Epoch 145, batch 94, global_batch_idx: 17950, batch size: 68, loss[discriminator_loss=2.742, discriminator_real_loss=1.476, discriminator_fake_loss=1.266, generator_loss=27.07, generator_mel_loss=18.78, generator_kl_loss=1.434, generator_dur_loss=1.809, generator_adv_loss=1.864, generator_feat_match_loss=3.185, over 68.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.397, discriminator_fake_loss=1.356, generator_loss=27.19, generator_mel_loss=18.86, generator_kl_loss=1.412, generator_dur_loss=1.746, generator_adv_loss=1.919, generator_feat_match_loss=3.249, over 5083.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:40:11,033 INFO [train.py:919] (5/6) Start epoch 146 +2024-03-12 14:41:30,512 INFO [train.py:527] (5/6) Epoch 146, batch 20, global_batch_idx: 18000, batch size: 56, loss[discriminator_loss=2.75, discriminator_real_loss=1.454, discriminator_fake_loss=1.296, generator_loss=26.67, generator_mel_loss=18.64, generator_kl_loss=1.346, generator_dur_loss=1.665, generator_adv_loss=1.827, generator_feat_match_loss=3.184, over 56.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.403, discriminator_fake_loss=1.336, generator_loss=27.55, generator_mel_loss=19.14, generator_kl_loss=1.388, generator_dur_loss=1.749, generator_adv_loss=1.939, generator_feat_match_loss=3.339, over 1174.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:41:30,513 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 14:41:38,562 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 14:43:56,251 INFO [train.py:527] (5/6) Epoch 146, batch 70, global_batch_idx: 18050, batch size: 44, loss[discriminator_loss=2.747, discriminator_real_loss=1.432, discriminator_fake_loss=1.315, generator_loss=27.43, generator_mel_loss=19.11, generator_kl_loss=1.495, generator_dur_loss=1.706, generator_adv_loss=1.797, generator_feat_match_loss=3.316, over 44.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.391, discriminator_fake_loss=1.349, generator_loss=27.26, generator_mel_loss=18.9, generator_kl_loss=1.375, generator_dur_loss=1.767, generator_adv_loss=1.921, generator_feat_match_loss=3.292, over 3950.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:46:14,929 INFO [train.py:527] (5/6) Epoch 146, batch 120, global_batch_idx: 18100, batch size: 64, loss[discriminator_loss=2.779, discriminator_real_loss=1.323, discriminator_fake_loss=1.456, generator_loss=27.43, generator_mel_loss=19.14, generator_kl_loss=1.223, generator_dur_loss=1.781, generator_adv_loss=2.033, generator_feat_match_loss=3.258, over 64.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.391, discriminator_fake_loss=1.348, generator_loss=27.3, generator_mel_loss=18.93, generator_kl_loss=1.376, generator_dur_loss=1.775, generator_adv_loss=1.924, generator_feat_match_loss=3.297, over 6816.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:46:24,591 INFO [train.py:919] (5/6) Start epoch 147 +2024-03-12 14:48:55,209 INFO [train.py:527] (5/6) Epoch 147, batch 46, global_batch_idx: 18150, batch size: 58, loss[discriminator_loss=2.679, discriminator_real_loss=1.396, discriminator_fake_loss=1.282, generator_loss=27.82, generator_mel_loss=19.34, generator_kl_loss=1.294, generator_dur_loss=1.759, generator_adv_loss=2.08, generator_feat_match_loss=3.339, over 58.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.396, discriminator_fake_loss=1.342, generator_loss=27.43, generator_mel_loss=19, generator_kl_loss=1.408, generator_dur_loss=1.761, generator_adv_loss=1.929, generator_feat_match_loss=3.334, over 2524.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:51:14,675 INFO [train.py:527] (5/6) Epoch 147, batch 96, global_batch_idx: 18200, batch size: 72, loss[discriminator_loss=2.703, discriminator_real_loss=1.343, discriminator_fake_loss=1.36, generator_loss=27.48, generator_mel_loss=19.07, generator_kl_loss=1.245, generator_dur_loss=1.777, generator_adv_loss=1.959, generator_feat_match_loss=3.428, over 72.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.395, discriminator_fake_loss=1.345, generator_loss=27.37, generator_mel_loss=18.96, generator_kl_loss=1.406, generator_dur_loss=1.763, generator_adv_loss=1.919, generator_feat_match_loss=3.318, over 5350.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:51:14,676 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 14:51:22,538 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 14:52:35,050 INFO [train.py:919] (5/6) Start epoch 148 +2024-03-12 14:54:01,865 INFO [train.py:527] (5/6) Epoch 148, batch 22, global_batch_idx: 18250, batch size: 72, loss[discriminator_loss=2.717, discriminator_real_loss=1.267, discriminator_fake_loss=1.45, generator_loss=26.77, generator_mel_loss=18.55, generator_kl_loss=1.449, generator_dur_loss=1.807, generator_adv_loss=1.723, generator_feat_match_loss=3.247, over 72.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.373, discriminator_fake_loss=1.393, generator_loss=27.45, generator_mel_loss=18.95, generator_kl_loss=1.394, generator_dur_loss=1.772, generator_adv_loss=1.962, generator_feat_match_loss=3.368, over 1426.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:56:18,653 INFO [train.py:527] (5/6) Epoch 148, batch 72, global_batch_idx: 18300, batch size: 62, loss[discriminator_loss=2.738, discriminator_real_loss=1.422, discriminator_fake_loss=1.317, generator_loss=27.73, generator_mel_loss=19.21, generator_kl_loss=1.434, generator_dur_loss=1.769, generator_adv_loss=1.909, generator_feat_match_loss=3.415, over 62.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.386, discriminator_fake_loss=1.36, generator_loss=27.38, generator_mel_loss=18.93, generator_kl_loss=1.379, generator_dur_loss=1.774, generator_adv_loss=1.934, generator_feat_match_loss=3.364, over 4423.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:58:38,587 INFO [train.py:527] (5/6) Epoch 148, batch 122, global_batch_idx: 18350, batch size: 89, loss[discriminator_loss=2.702, discriminator_real_loss=1.42, discriminator_fake_loss=1.281, generator_loss=27.98, generator_mel_loss=19.32, generator_kl_loss=1.368, generator_dur_loss=1.824, generator_adv_loss=1.831, generator_feat_match_loss=3.636, over 89.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.388, discriminator_fake_loss=1.359, generator_loss=27.34, generator_mel_loss=18.92, generator_kl_loss=1.394, generator_dur_loss=1.769, generator_adv_loss=1.925, generator_feat_match_loss=3.334, over 7219.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 14:58:43,911 INFO [train.py:919] (5/6) Start epoch 149 +2024-03-12 15:01:23,303 INFO [train.py:527] (5/6) Epoch 149, batch 48, global_batch_idx: 18400, batch size: 42, loss[discriminator_loss=2.75, discriminator_real_loss=1.393, discriminator_fake_loss=1.356, generator_loss=28.22, generator_mel_loss=19.14, generator_kl_loss=1.738, generator_dur_loss=1.686, generator_adv_loss=2.043, generator_feat_match_loss=3.615, over 42.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.378, discriminator_fake_loss=1.362, generator_loss=27.3, generator_mel_loss=18.92, generator_kl_loss=1.372, generator_dur_loss=1.76, generator_adv_loss=1.928, generator_feat_match_loss=3.319, over 2743.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:01:23,305 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 15:01:31,251 INFO [train.py:591] (5/6) 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 15:01:31,252 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 15:03:52,675 INFO [train.py:527] (5/6) Epoch 149, batch 98, global_batch_idx: 18450, batch size: 61, loss[discriminator_loss=2.732, discriminator_real_loss=1.443, discriminator_fake_loss=1.289, generator_loss=26.95, generator_mel_loss=19.08, generator_kl_loss=1.164, generator_dur_loss=1.77, generator_adv_loss=1.685, generator_feat_match_loss=3.252, over 61.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.387, discriminator_fake_loss=1.358, generator_loss=27.39, generator_mel_loss=18.95, generator_kl_loss=1.383, generator_dur_loss=1.768, generator_adv_loss=1.926, generator_feat_match_loss=3.362, over 5699.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:05:02,844 INFO [train.py:919] (5/6) Start epoch 150 +2024-03-12 15:06:33,959 INFO [train.py:527] (5/6) Epoch 150, batch 24, global_batch_idx: 18500, batch size: 88, loss[discriminator_loss=2.711, discriminator_real_loss=1.417, discriminator_fake_loss=1.294, generator_loss=26.88, generator_mel_loss=18.67, generator_kl_loss=1.35, generator_dur_loss=1.839, generator_adv_loss=1.78, generator_feat_match_loss=3.239, over 88.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.34, generator_loss=27.42, generator_mel_loss=18.92, generator_kl_loss=1.376, generator_dur_loss=1.772, generator_adv_loss=1.929, generator_feat_match_loss=3.422, over 1461.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:08:53,886 INFO [train.py:527] (5/6) Epoch 150, batch 74, global_batch_idx: 18550, batch size: 83, loss[discriminator_loss=2.701, discriminator_real_loss=1.272, discriminator_fake_loss=1.43, generator_loss=27.16, generator_mel_loss=18.88, generator_kl_loss=1.248, generator_dur_loss=1.842, generator_adv_loss=2.02, generator_feat_match_loss=3.172, over 83.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.38, discriminator_fake_loss=1.356, generator_loss=27.4, generator_mel_loss=18.93, generator_kl_loss=1.381, generator_dur_loss=1.78, generator_adv_loss=1.919, generator_feat_match_loss=3.389, over 4631.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:11:08,314 INFO [train.py:919] (5/6) Start epoch 151 +2024-03-12 15:11:33,461 INFO [train.py:527] (5/6) Epoch 151, batch 0, global_batch_idx: 18600, batch size: 55, loss[discriminator_loss=2.7, discriminator_real_loss=1.321, discriminator_fake_loss=1.379, generator_loss=27.58, generator_mel_loss=19.11, generator_kl_loss=1.399, generator_dur_loss=1.718, generator_adv_loss=2.011, generator_feat_match_loss=3.346, over 55.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.321, discriminator_fake_loss=1.379, generator_loss=27.58, generator_mel_loss=19.11, generator_kl_loss=1.399, generator_dur_loss=1.718, generator_adv_loss=2.011, generator_feat_match_loss=3.346, over 55.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:11:33,463 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 15:11:41,536 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 15:13:59,582 INFO [train.py:527] (5/6) Epoch 151, batch 50, global_batch_idx: 18650, batch size: 55, loss[discriminator_loss=2.668, discriminator_real_loss=1.289, discriminator_fake_loss=1.38, generator_loss=28.06, generator_mel_loss=19.63, generator_kl_loss=1.208, generator_dur_loss=1.77, generator_adv_loss=2.066, generator_feat_match_loss=3.385, over 55.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.381, discriminator_fake_loss=1.357, generator_loss=27.36, generator_mel_loss=18.88, generator_kl_loss=1.356, generator_dur_loss=1.768, generator_adv_loss=1.935, generator_feat_match_loss=3.419, over 2987.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:16:18,435 INFO [train.py:527] (5/6) Epoch 151, batch 100, global_batch_idx: 18700, batch size: 83, loss[discriminator_loss=2.754, discriminator_real_loss=1.336, discriminator_fake_loss=1.417, generator_loss=27.24, generator_mel_loss=18.82, generator_kl_loss=1.277, generator_dur_loss=1.844, generator_adv_loss=1.95, generator_feat_match_loss=3.355, over 83.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.391, discriminator_fake_loss=1.353, generator_loss=27.37, generator_mel_loss=18.9, generator_kl_loss=1.38, generator_dur_loss=1.77, generator_adv_loss=1.932, generator_feat_match_loss=3.393, over 5837.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:17:23,671 INFO [train.py:919] (5/6) Start epoch 152 +2024-03-12 15:19:01,282 INFO [train.py:527] (5/6) Epoch 152, batch 26, global_batch_idx: 18750, batch size: 96, loss[discriminator_loss=2.694, discriminator_real_loss=1.364, discriminator_fake_loss=1.33, generator_loss=26.52, generator_mel_loss=18.49, generator_kl_loss=1.125, generator_dur_loss=1.874, generator_adv_loss=1.818, generator_feat_match_loss=3.222, over 96.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.389, discriminator_fake_loss=1.362, generator_loss=27.31, generator_mel_loss=18.97, generator_kl_loss=1.356, generator_dur_loss=1.768, generator_adv_loss=1.899, generator_feat_match_loss=3.32, over 1561.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:21:20,223 INFO [train.py:527] (5/6) Epoch 152, batch 76, global_batch_idx: 18800, batch size: 77, loss[discriminator_loss=2.703, discriminator_real_loss=1.289, discriminator_fake_loss=1.414, generator_loss=27.81, generator_mel_loss=19.06, generator_kl_loss=1.207, generator_dur_loss=1.797, generator_adv_loss=2.008, generator_feat_match_loss=3.738, over 77.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.391, discriminator_fake_loss=1.356, generator_loss=27.33, generator_mel_loss=18.92, generator_kl_loss=1.371, generator_dur_loss=1.762, generator_adv_loss=1.916, generator_feat_match_loss=3.355, over 4358.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:21:20,225 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 15:21:28,346 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 15:23:37,378 INFO [train.py:919] (5/6) Start epoch 153 +2024-03-12 15:24:09,002 INFO [train.py:527] (5/6) Epoch 153, batch 2, global_batch_idx: 18850, batch size: 96, loss[discriminator_loss=2.645, discriminator_real_loss=1.312, discriminator_fake_loss=1.333, generator_loss=28.44, generator_mel_loss=19.24, generator_kl_loss=1.359, generator_dur_loss=1.874, generator_adv_loss=1.932, generator_feat_match_loss=4.03, over 96.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.323, discriminator_fake_loss=1.349, generator_loss=27.95, generator_mel_loss=18.97, generator_kl_loss=1.316, generator_dur_loss=1.885, generator_adv_loss=2.037, generator_feat_match_loss=3.74, over 269.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:26:29,807 INFO [train.py:527] (5/6) Epoch 153, batch 52, global_batch_idx: 18900, batch size: 31, loss[discriminator_loss=2.67, discriminator_real_loss=1.437, discriminator_fake_loss=1.233, generator_loss=26.35, generator_mel_loss=18.43, generator_kl_loss=1.529, generator_dur_loss=1.61, generator_adv_loss=1.937, generator_feat_match_loss=2.849, over 31.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.373, discriminator_fake_loss=1.361, generator_loss=27.46, generator_mel_loss=18.89, generator_kl_loss=1.386, generator_dur_loss=1.767, generator_adv_loss=1.958, generator_feat_match_loss=3.461, over 3068.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:28:46,122 INFO [train.py:527] (5/6) Epoch 153, batch 102, global_batch_idx: 18950, batch size: 59, loss[discriminator_loss=2.664, discriminator_real_loss=1.268, discriminator_fake_loss=1.396, generator_loss=27.58, generator_mel_loss=18.87, generator_kl_loss=1.654, generator_dur_loss=1.776, generator_adv_loss=1.856, generator_feat_match_loss=3.427, over 59.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.377, discriminator_fake_loss=1.357, generator_loss=27.39, generator_mel_loss=18.86, generator_kl_loss=1.387, generator_dur_loss=1.769, generator_adv_loss=1.937, generator_feat_match_loss=3.431, over 5962.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:29:46,792 INFO [train.py:919] (5/6) Start epoch 154 +2024-03-12 15:31:26,247 INFO [train.py:527] (5/6) Epoch 154, batch 28, global_batch_idx: 19000, batch size: 56, loss[discriminator_loss=2.715, discriminator_real_loss=1.411, discriminator_fake_loss=1.305, generator_loss=27.45, generator_mel_loss=19.02, generator_kl_loss=1.374, generator_dur_loss=1.69, generator_adv_loss=1.883, generator_feat_match_loss=3.48, over 56.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.366, discriminator_fake_loss=1.349, generator_loss=27.42, generator_mel_loss=18.98, generator_kl_loss=1.403, generator_dur_loss=1.71, generator_adv_loss=1.905, generator_feat_match_loss=3.423, over 1671.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:31:26,248 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 15:31:34,130 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 15:33:53,006 INFO [train.py:527] (5/6) Epoch 154, batch 78, global_batch_idx: 19050, batch size: 62, loss[discriminator_loss=2.896, discriminator_real_loss=1.672, discriminator_fake_loss=1.224, generator_loss=27.73, generator_mel_loss=19.18, generator_kl_loss=1.356, generator_dur_loss=1.789, generator_adv_loss=1.739, generator_feat_match_loss=3.67, over 62.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.378, discriminator_fake_loss=1.348, generator_loss=27.47, generator_mel_loss=18.95, generator_kl_loss=1.398, generator_dur_loss=1.729, generator_adv_loss=1.915, generator_feat_match_loss=3.474, over 4559.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:36:01,629 INFO [train.py:919] (5/6) Start epoch 155 +2024-03-12 15:36:36,042 INFO [train.py:527] (5/6) Epoch 155, batch 4, global_batch_idx: 19100, batch size: 52, loss[discriminator_loss=2.721, discriminator_real_loss=1.425, discriminator_fake_loss=1.296, generator_loss=27.61, generator_mel_loss=19.35, generator_kl_loss=1.39, generator_dur_loss=1.689, generator_adv_loss=2.106, generator_feat_match_loss=3.069, over 52.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.416, discriminator_fake_loss=1.318, generator_loss=27.7, generator_mel_loss=19.15, generator_kl_loss=1.39, generator_dur_loss=1.701, generator_adv_loss=1.979, generator_feat_match_loss=3.476, over 249.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:38:53,486 INFO [train.py:527] (5/6) Epoch 155, batch 54, global_batch_idx: 19150, batch size: 62, loss[discriminator_loss=2.695, discriminator_real_loss=1.435, discriminator_fake_loss=1.261, generator_loss=26.45, generator_mel_loss=18.36, generator_kl_loss=1.233, generator_dur_loss=1.791, generator_adv_loss=1.869, generator_feat_match_loss=3.198, over 62.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.396, discriminator_fake_loss=1.348, generator_loss=27.31, generator_mel_loss=18.91, generator_kl_loss=1.39, generator_dur_loss=1.745, generator_adv_loss=1.922, generator_feat_match_loss=3.344, over 2981.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:41:13,948 INFO [train.py:527] (5/6) Epoch 155, batch 104, global_batch_idx: 19200, batch size: 58, loss[discriminator_loss=2.752, discriminator_real_loss=1.395, discriminator_fake_loss=1.357, generator_loss=27.27, generator_mel_loss=18.78, generator_kl_loss=1.476, generator_dur_loss=1.786, generator_adv_loss=2.013, generator_feat_match_loss=3.217, over 58.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.391, discriminator_fake_loss=1.343, generator_loss=27.37, generator_mel_loss=18.92, generator_kl_loss=1.382, generator_dur_loss=1.755, generator_adv_loss=1.938, generator_feat_match_loss=3.379, over 5894.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:41:13,949 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 15:41:22,940 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 15:42:18,007 INFO [train.py:919] (5/6) Start epoch 156 +2024-03-12 15:44:06,857 INFO [train.py:527] (5/6) Epoch 156, batch 30, global_batch_idx: 19250, batch size: 58, loss[discriminator_loss=2.676, discriminator_real_loss=1.293, discriminator_fake_loss=1.383, generator_loss=28.92, generator_mel_loss=19.89, generator_kl_loss=1.387, generator_dur_loss=1.662, generator_adv_loss=1.963, generator_feat_match_loss=4.022, over 58.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.375, discriminator_fake_loss=1.331, generator_loss=27.3, generator_mel_loss=18.76, generator_kl_loss=1.357, generator_dur_loss=1.745, generator_adv_loss=1.949, generator_feat_match_loss=3.486, over 1944.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:46:28,108 INFO [train.py:527] (5/6) Epoch 156, batch 80, global_batch_idx: 19300, batch size: 47, loss[discriminator_loss=2.624, discriminator_real_loss=1.404, discriminator_fake_loss=1.22, generator_loss=27.64, generator_mel_loss=19.17, generator_kl_loss=1.433, generator_dur_loss=1.646, generator_adv_loss=1.94, generator_feat_match_loss=3.446, over 47.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.382, discriminator_fake_loss=1.342, generator_loss=27.41, generator_mel_loss=18.87, generator_kl_loss=1.373, generator_dur_loss=1.746, generator_adv_loss=1.947, generator_feat_match_loss=3.478, over 4876.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:48:24,179 INFO [train.py:919] (5/6) Start epoch 157 +2024-03-12 15:49:07,913 INFO [train.py:527] (5/6) Epoch 157, batch 6, global_batch_idx: 19350, batch size: 62, loss[discriminator_loss=2.718, discriminator_real_loss=1.26, discriminator_fake_loss=1.457, generator_loss=27.75, generator_mel_loss=18.96, generator_kl_loss=1.465, generator_dur_loss=1.714, generator_adv_loss=2.023, generator_feat_match_loss=3.59, over 62.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.328, discriminator_fake_loss=1.426, generator_loss=27.13, generator_mel_loss=18.74, generator_kl_loss=1.372, generator_dur_loss=1.786, generator_adv_loss=1.881, generator_feat_match_loss=3.353, over 434.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:51:26,065 INFO [train.py:527] (5/6) Epoch 157, batch 56, global_batch_idx: 19400, batch size: 42, loss[discriminator_loss=2.716, discriminator_real_loss=1.326, discriminator_fake_loss=1.389, generator_loss=28.38, generator_mel_loss=19.41, generator_kl_loss=1.642, generator_dur_loss=1.664, generator_adv_loss=1.949, generator_feat_match_loss=3.717, over 42.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.387, discriminator_fake_loss=1.35, generator_loss=27.55, generator_mel_loss=18.99, generator_kl_loss=1.399, generator_dur_loss=1.747, generator_adv_loss=1.978, generator_feat_match_loss=3.436, over 3148.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:51:26,067 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 15:51:34,072 INFO [train.py:591] (5/6) 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 15:51:34,073 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 15:53:51,941 INFO [train.py:527] (5/6) Epoch 157, batch 106, global_batch_idx: 19450, batch size: 39, loss[discriminator_loss=2.671, discriminator_real_loss=1.369, discriminator_fake_loss=1.302, generator_loss=28.31, generator_mel_loss=19.38, generator_kl_loss=1.657, generator_dur_loss=1.663, generator_adv_loss=1.907, generator_feat_match_loss=3.709, over 39.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.396, discriminator_fake_loss=1.355, generator_loss=27.47, generator_mel_loss=18.95, generator_kl_loss=1.397, generator_dur_loss=1.743, generator_adv_loss=1.976, generator_feat_match_loss=3.405, over 5962.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:54:43,445 INFO [train.py:919] (5/6) Start epoch 158 +2024-03-12 15:56:35,697 INFO [train.py:527] (5/6) Epoch 158, batch 32, global_batch_idx: 19500, batch size: 36, loss[discriminator_loss=2.784, discriminator_real_loss=1.38, discriminator_fake_loss=1.404, generator_loss=27.16, generator_mel_loss=18.65, generator_kl_loss=1.477, generator_dur_loss=1.725, generator_adv_loss=1.941, generator_feat_match_loss=3.367, over 36.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.39, discriminator_fake_loss=1.363, generator_loss=26.99, generator_mel_loss=18.72, generator_kl_loss=1.396, generator_dur_loss=1.743, generator_adv_loss=1.902, generator_feat_match_loss=3.227, over 1828.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 15:58:57,682 INFO [train.py:527] (5/6) Epoch 158, batch 82, global_batch_idx: 19550, batch size: 74, loss[discriminator_loss=2.725, discriminator_real_loss=1.31, discriminator_fake_loss=1.415, generator_loss=27.98, generator_mel_loss=19.24, generator_kl_loss=1.38, generator_dur_loss=1.825, generator_adv_loss=2.139, generator_feat_match_loss=3.4, over 74.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.391, discriminator_fake_loss=1.355, generator_loss=27.25, generator_mel_loss=18.86, generator_kl_loss=1.378, generator_dur_loss=1.763, generator_adv_loss=1.92, generator_feat_match_loss=3.326, over 4853.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:00:50,126 INFO [train.py:919] (5/6) Start epoch 159 +2024-03-12 16:01:36,487 INFO [train.py:527] (5/6) Epoch 159, batch 8, global_batch_idx: 19600, batch size: 48, loss[discriminator_loss=2.673, discriminator_real_loss=1.414, discriminator_fake_loss=1.259, generator_loss=26.68, generator_mel_loss=18.23, generator_kl_loss=1.371, generator_dur_loss=1.67, generator_adv_loss=1.963, generator_feat_match_loss=3.454, over 48.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.376, discriminator_fake_loss=1.319, generator_loss=27.33, generator_mel_loss=18.86, generator_kl_loss=1.437, generator_dur_loss=1.701, generator_adv_loss=1.941, generator_feat_match_loss=3.388, over 407.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:01:36,489 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 16:01:44,546 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 16:04:06,448 INFO [train.py:527] (5/6) Epoch 159, batch 58, global_batch_idx: 19650, batch size: 56, loss[discriminator_loss=2.785, discriminator_real_loss=1.463, discriminator_fake_loss=1.322, generator_loss=26.77, generator_mel_loss=18.66, generator_kl_loss=1.402, generator_dur_loss=1.72, generator_adv_loss=1.918, generator_feat_match_loss=3.076, over 56.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.385, discriminator_fake_loss=1.351, generator_loss=27.43, generator_mel_loss=18.88, generator_kl_loss=1.401, generator_dur_loss=1.758, generator_adv_loss=1.931, generator_feat_match_loss=3.46, over 3450.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:06:25,385 INFO [train.py:527] (5/6) Epoch 159, batch 108, global_batch_idx: 19700, batch size: 31, loss[discriminator_loss=2.687, discriminator_real_loss=1.268, discriminator_fake_loss=1.419, generator_loss=27.87, generator_mel_loss=18.98, generator_kl_loss=1.586, generator_dur_loss=1.653, generator_adv_loss=2.188, generator_feat_match_loss=3.465, over 31.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.36, generator_mel_loss=18.86, generator_kl_loss=1.408, generator_dur_loss=1.75, generator_adv_loss=1.93, generator_feat_match_loss=3.41, over 6387.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:07:05,340 INFO [train.py:919] (5/6) Start epoch 160 +2024-03-12 16:09:05,166 INFO [train.py:527] (5/6) Epoch 160, batch 34, global_batch_idx: 19750, batch size: 83, loss[discriminator_loss=2.734, discriminator_real_loss=1.42, discriminator_fake_loss=1.314, generator_loss=27.47, generator_mel_loss=19.18, generator_kl_loss=1.367, generator_dur_loss=1.853, generator_adv_loss=1.869, generator_feat_match_loss=3.196, over 83.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.398, discriminator_fake_loss=1.361, generator_loss=27.1, generator_mel_loss=18.74, generator_kl_loss=1.353, generator_dur_loss=1.774, generator_adv_loss=1.934, generator_feat_match_loss=3.303, over 2135.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:11:25,013 INFO [train.py:527] (5/6) Epoch 160, batch 84, global_batch_idx: 19800, batch size: 77, loss[discriminator_loss=2.69, discriminator_real_loss=1.317, discriminator_fake_loss=1.373, generator_loss=27.7, generator_mel_loss=19.08, generator_kl_loss=1.372, generator_dur_loss=1.837, generator_adv_loss=1.897, generator_feat_match_loss=3.51, over 77.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.392, discriminator_fake_loss=1.353, generator_loss=27.27, generator_mel_loss=18.84, generator_kl_loss=1.382, generator_dur_loss=1.758, generator_adv_loss=1.922, generator_feat_match_loss=3.373, over 4824.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:11:25,015 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 16:11:32,894 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 16:13:22,911 INFO [train.py:919] (5/6) Start epoch 161 +2024-03-12 16:14:14,943 INFO [train.py:527] (5/6) Epoch 161, batch 10, global_batch_idx: 19850, batch size: 39, loss[discriminator_loss=2.811, discriminator_real_loss=1.473, discriminator_fake_loss=1.338, generator_loss=27.6, generator_mel_loss=19.31, generator_kl_loss=1.524, generator_dur_loss=1.694, generator_adv_loss=1.872, generator_feat_match_loss=3.199, over 39.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.403, discriminator_fake_loss=1.349, generator_loss=27.39, generator_mel_loss=19, generator_kl_loss=1.435, generator_dur_loss=1.727, generator_adv_loss=1.936, generator_feat_match_loss=3.298, over 590.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:16:33,457 INFO [train.py:527] (5/6) Epoch 161, batch 60, global_batch_idx: 19900, batch size: 62, loss[discriminator_loss=2.763, discriminator_real_loss=1.345, discriminator_fake_loss=1.418, generator_loss=28.36, generator_mel_loss=19.36, generator_kl_loss=1.506, generator_dur_loss=1.727, generator_adv_loss=1.966, generator_feat_match_loss=3.808, over 62.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.397, discriminator_fake_loss=1.346, generator_loss=27.42, generator_mel_loss=18.97, generator_kl_loss=1.393, generator_dur_loss=1.752, generator_adv_loss=1.93, generator_feat_match_loss=3.381, over 3389.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:18:52,703 INFO [train.py:527] (5/6) Epoch 161, batch 110, global_batch_idx: 19950, batch size: 58, loss[discriminator_loss=2.744, discriminator_real_loss=1.299, discriminator_fake_loss=1.445, generator_loss=27.33, generator_mel_loss=18.66, generator_kl_loss=1.422, generator_dur_loss=1.726, generator_adv_loss=2.125, generator_feat_match_loss=3.392, over 58.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.399, discriminator_fake_loss=1.348, generator_loss=27.33, generator_mel_loss=18.88, generator_kl_loss=1.388, generator_dur_loss=1.756, generator_adv_loss=1.929, generator_feat_match_loss=3.377, over 6232.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:19:29,334 INFO [train.py:919] (5/6) Start epoch 162 +2024-03-12 16:21:31,292 INFO [train.py:527] (5/6) Epoch 162, batch 36, global_batch_idx: 20000, batch size: 52, loss[discriminator_loss=2.725, discriminator_real_loss=1.341, discriminator_fake_loss=1.384, generator_loss=27.54, generator_mel_loss=19.09, generator_kl_loss=1.425, generator_dur_loss=1.659, generator_adv_loss=1.758, generator_feat_match_loss=3.604, over 52.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.39, discriminator_fake_loss=1.351, generator_loss=27.44, generator_mel_loss=18.92, generator_kl_loss=1.385, generator_dur_loss=1.755, generator_adv_loss=1.908, generator_feat_match_loss=3.473, over 2099.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:21:31,293 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 16:21:39,252 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 16:23:56,726 INFO [train.py:527] (5/6) Epoch 162, batch 86, global_batch_idx: 20050, batch size: 96, loss[discriminator_loss=2.793, discriminator_real_loss=1.28, discriminator_fake_loss=1.513, generator_loss=26.99, generator_mel_loss=18.52, generator_kl_loss=1.072, generator_dur_loss=1.888, generator_adv_loss=2.017, generator_feat_match_loss=3.49, over 96.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.389, discriminator_fake_loss=1.36, generator_loss=27.33, generator_mel_loss=18.87, generator_kl_loss=1.392, generator_dur_loss=1.745, generator_adv_loss=1.915, generator_feat_match_loss=3.409, over 4862.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:25:43,948 INFO [train.py:919] (5/6) Start epoch 163 +2024-03-12 16:26:42,824 INFO [train.py:527] (5/6) Epoch 163, batch 12, global_batch_idx: 20100, batch size: 42, loss[discriminator_loss=2.731, discriminator_real_loss=1.345, discriminator_fake_loss=1.386, generator_loss=28.68, generator_mel_loss=19.41, generator_kl_loss=1.548, generator_dur_loss=1.705, generator_adv_loss=2.038, generator_feat_match_loss=3.975, over 42.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.389, discriminator_fake_loss=1.343, generator_loss=27.29, generator_mel_loss=18.81, generator_kl_loss=1.393, generator_dur_loss=1.747, generator_adv_loss=1.928, generator_feat_match_loss=3.418, over 653.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:29:01,043 INFO [train.py:527] (5/6) Epoch 163, batch 62, global_batch_idx: 20150, batch size: 45, loss[discriminator_loss=2.744, discriminator_real_loss=1.369, discriminator_fake_loss=1.375, generator_loss=27.64, generator_mel_loss=18.82, generator_kl_loss=1.497, generator_dur_loss=1.682, generator_adv_loss=1.993, generator_feat_match_loss=3.645, over 45.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.393, discriminator_fake_loss=1.359, generator_loss=27.29, generator_mel_loss=18.83, generator_kl_loss=1.368, generator_dur_loss=1.766, generator_adv_loss=1.903, generator_feat_match_loss=3.424, over 3674.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:31:20,214 INFO [train.py:527] (5/6) Epoch 163, batch 112, global_batch_idx: 20200, batch size: 97, loss[discriminator_loss=2.829, discriminator_real_loss=1.456, discriminator_fake_loss=1.373, generator_loss=27.2, generator_mel_loss=18.92, generator_kl_loss=1.121, generator_dur_loss=1.83, generator_adv_loss=1.972, generator_feat_match_loss=3.356, over 97.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.399, discriminator_fake_loss=1.354, generator_loss=27.42, generator_mel_loss=18.86, generator_kl_loss=1.376, generator_dur_loss=1.749, generator_adv_loss=1.948, generator_feat_match_loss=3.493, over 6473.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:31:20,216 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 16:31:29,124 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 16:31:59,531 INFO [train.py:919] (5/6) Start epoch 164 +2024-03-12 16:34:11,309 INFO [train.py:527] (5/6) Epoch 164, batch 38, global_batch_idx: 20250, batch size: 25, loss[discriminator_loss=2.782, discriminator_real_loss=1.483, discriminator_fake_loss=1.299, generator_loss=27.54, generator_mel_loss=19.29, generator_kl_loss=1.793, generator_dur_loss=1.573, generator_adv_loss=1.836, generator_feat_match_loss=3.044, over 25.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.394, discriminator_fake_loss=1.351, generator_loss=27.31, generator_mel_loss=18.86, generator_kl_loss=1.357, generator_dur_loss=1.75, generator_adv_loss=1.934, generator_feat_match_loss=3.416, over 2190.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:36:29,674 INFO [train.py:527] (5/6) Epoch 164, batch 88, global_batch_idx: 20300, batch size: 45, loss[discriminator_loss=2.765, discriminator_real_loss=1.399, discriminator_fake_loss=1.366, generator_loss=27.52, generator_mel_loss=19.12, generator_kl_loss=1.563, generator_dur_loss=1.662, generator_adv_loss=1.755, generator_feat_match_loss=3.423, over 45.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.393, discriminator_fake_loss=1.348, generator_loss=27.33, generator_mel_loss=18.85, generator_kl_loss=1.387, generator_dur_loss=1.746, generator_adv_loss=1.919, generator_feat_match_loss=3.429, over 5183.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:38:06,093 INFO [train.py:919] (5/6) Start epoch 165 +2024-03-12 16:39:10,389 INFO [train.py:527] (5/6) Epoch 165, batch 14, global_batch_idx: 20350, batch size: 47, loss[discriminator_loss=2.714, discriminator_real_loss=1.237, discriminator_fake_loss=1.478, generator_loss=27.73, generator_mel_loss=18.83, generator_kl_loss=1.469, generator_dur_loss=1.665, generator_adv_loss=1.915, generator_feat_match_loss=3.846, over 47.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.375, discriminator_fake_loss=1.371, generator_loss=27.44, generator_mel_loss=18.78, generator_kl_loss=1.355, generator_dur_loss=1.744, generator_adv_loss=1.957, generator_feat_match_loss=3.601, over 926.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:41:30,675 INFO [train.py:527] (5/6) Epoch 165, batch 64, global_batch_idx: 20400, batch size: 44, loss[discriminator_loss=2.729, discriminator_real_loss=1.414, discriminator_fake_loss=1.315, generator_loss=26.51, generator_mel_loss=18.28, generator_kl_loss=1.5, generator_dur_loss=1.708, generator_adv_loss=1.853, generator_feat_match_loss=3.167, over 44.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.4, discriminator_fake_loss=1.355, generator_loss=27.31, generator_mel_loss=18.83, generator_kl_loss=1.395, generator_dur_loss=1.737, generator_adv_loss=1.933, generator_feat_match_loss=3.419, over 3696.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:41:30,676 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 16:41:38,610 INFO [train.py:591] (5/6) 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 16:41:38,610 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 16:43:59,077 INFO [train.py:527] (5/6) Epoch 165, batch 114, global_batch_idx: 20450, batch size: 83, loss[discriminator_loss=2.71, discriminator_real_loss=1.36, discriminator_fake_loss=1.35, generator_loss=27.09, generator_mel_loss=18.52, generator_kl_loss=1.379, generator_dur_loss=1.838, generator_adv_loss=1.77, generator_feat_match_loss=3.581, over 83.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.4, discriminator_fake_loss=1.353, generator_loss=27.21, generator_mel_loss=18.77, generator_kl_loss=1.39, generator_dur_loss=1.754, generator_adv_loss=1.918, generator_feat_match_loss=3.384, over 6780.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:44:25,538 INFO [train.py:919] (5/6) Start epoch 166 +2024-03-12 16:46:40,200 INFO [train.py:527] (5/6) Epoch 166, batch 40, global_batch_idx: 20500, batch size: 96, loss[discriminator_loss=2.803, discriminator_real_loss=1.518, discriminator_fake_loss=1.285, generator_loss=26.49, generator_mel_loss=18.71, generator_kl_loss=1.364, generator_dur_loss=1.835, generator_adv_loss=1.762, generator_feat_match_loss=2.82, over 96.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.389, discriminator_fake_loss=1.358, generator_loss=27.29, generator_mel_loss=18.84, generator_kl_loss=1.379, generator_dur_loss=1.749, generator_adv_loss=1.916, generator_feat_match_loss=3.405, over 2286.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:48:59,968 INFO [train.py:527] (5/6) Epoch 166, batch 90, global_batch_idx: 20550, batch size: 66, loss[discriminator_loss=2.704, discriminator_real_loss=1.362, discriminator_fake_loss=1.342, generator_loss=27.43, generator_mel_loss=19.08, generator_kl_loss=1.353, generator_dur_loss=1.685, generator_adv_loss=1.934, generator_feat_match_loss=3.378, over 66.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.386, discriminator_fake_loss=1.358, generator_loss=27.31, generator_mel_loss=18.87, generator_kl_loss=1.391, generator_dur_loss=1.716, generator_adv_loss=1.914, generator_feat_match_loss=3.417, over 5042.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:50:32,964 INFO [train.py:919] (5/6) Start epoch 167 +2024-03-12 16:51:41,466 INFO [train.py:527] (5/6) Epoch 167, batch 16, global_batch_idx: 20600, batch size: 80, loss[discriminator_loss=2.725, discriminator_real_loss=1.412, discriminator_fake_loss=1.313, generator_loss=26.66, generator_mel_loss=18.75, generator_kl_loss=1.242, generator_dur_loss=1.803, generator_adv_loss=1.762, generator_feat_match_loss=3.107, over 80.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.389, discriminator_fake_loss=1.348, generator_loss=27.41, generator_mel_loss=18.93, generator_kl_loss=1.389, generator_dur_loss=1.736, generator_adv_loss=1.92, generator_feat_match_loss=3.435, over 1001.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:51:41,468 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 16:51:49,247 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 16:54:07,893 INFO [train.py:527] (5/6) Epoch 167, batch 66, global_batch_idx: 20650, batch size: 77, loss[discriminator_loss=2.765, discriminator_real_loss=1.465, discriminator_fake_loss=1.3, generator_loss=27.29, generator_mel_loss=18.69, generator_kl_loss=1.274, generator_dur_loss=1.815, generator_adv_loss=1.969, generator_feat_match_loss=3.548, over 77.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.387, discriminator_fake_loss=1.351, generator_loss=27.46, generator_mel_loss=18.89, generator_kl_loss=1.392, generator_dur_loss=1.742, generator_adv_loss=1.928, generator_feat_match_loss=3.508, over 3937.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:56:25,085 INFO [train.py:527] (5/6) Epoch 167, batch 116, global_batch_idx: 20700, batch size: 48, loss[discriminator_loss=2.615, discriminator_real_loss=1.348, discriminator_fake_loss=1.267, generator_loss=28.62, generator_mel_loss=19.53, generator_kl_loss=1.554, generator_dur_loss=1.623, generator_adv_loss=1.788, generator_feat_match_loss=4.117, over 48.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.42, generator_mel_loss=18.86, generator_kl_loss=1.385, generator_dur_loss=1.744, generator_adv_loss=1.926, generator_feat_match_loss=3.507, over 6936.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 16:56:47,174 INFO [train.py:919] (5/6) Start epoch 168 +2024-03-12 16:59:06,668 INFO [train.py:527] (5/6) Epoch 168, batch 42, global_batch_idx: 20750, batch size: 31, loss[discriminator_loss=2.777, discriminator_real_loss=1.365, discriminator_fake_loss=1.412, generator_loss=27.84, generator_mel_loss=18.97, generator_kl_loss=1.506, generator_dur_loss=1.611, generator_adv_loss=2.031, generator_feat_match_loss=3.718, over 31.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.389, discriminator_fake_loss=1.358, generator_loss=27.19, generator_mel_loss=18.71, generator_kl_loss=1.394, generator_dur_loss=1.758, generator_adv_loss=1.917, generator_feat_match_loss=3.402, over 2415.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:01:25,297 INFO [train.py:527] (5/6) Epoch 168, batch 92, global_batch_idx: 20800, batch size: 74, loss[discriminator_loss=2.728, discriminator_real_loss=1.461, discriminator_fake_loss=1.267, generator_loss=27.92, generator_mel_loss=19.31, generator_kl_loss=1.348, generator_dur_loss=1.838, generator_adv_loss=1.886, generator_feat_match_loss=3.534, over 74.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.388, discriminator_fake_loss=1.351, generator_loss=27.24, generator_mel_loss=18.73, generator_kl_loss=1.393, generator_dur_loss=1.752, generator_adv_loss=1.907, generator_feat_match_loss=3.456, over 5411.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:01:25,298 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 17:01:33,558 INFO [train.py:591] (5/6) 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 17:01:33,559 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 17:03:00,321 INFO [train.py:919] (5/6) Start epoch 169 +2024-03-12 17:04:15,932 INFO [train.py:527] (5/6) Epoch 169, batch 18, global_batch_idx: 20850, batch size: 25, loss[discriminator_loss=2.739, discriminator_real_loss=1.271, discriminator_fake_loss=1.467, generator_loss=29.07, generator_mel_loss=19.78, generator_kl_loss=1.674, generator_dur_loss=1.574, generator_adv_loss=1.867, generator_feat_match_loss=4.174, over 25.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.38, discriminator_fake_loss=1.318, generator_loss=27.92, generator_mel_loss=18.9, generator_kl_loss=1.348, generator_dur_loss=1.746, generator_adv_loss=2.132, generator_feat_match_loss=3.788, over 1095.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:06:32,665 INFO [train.py:527] (5/6) Epoch 169, batch 68, global_batch_idx: 20900, batch size: 80, loss[discriminator_loss=2.755, discriminator_real_loss=1.4, discriminator_fake_loss=1.356, generator_loss=26.77, generator_mel_loss=18.43, generator_kl_loss=1.307, generator_dur_loss=1.814, generator_adv_loss=1.842, generator_feat_match_loss=3.38, over 80.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.392, discriminator_fake_loss=1.334, generator_loss=27.5, generator_mel_loss=18.83, generator_kl_loss=1.39, generator_dur_loss=1.732, generator_adv_loss=1.985, generator_feat_match_loss=3.563, over 3819.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:08:52,727 INFO [train.py:527] (5/6) Epoch 169, batch 118, global_batch_idx: 20950, batch size: 45, loss[discriminator_loss=2.728, discriminator_real_loss=1.417, discriminator_fake_loss=1.311, generator_loss=27.66, generator_mel_loss=19.27, generator_kl_loss=1.466, generator_dur_loss=1.719, generator_adv_loss=1.789, generator_feat_match_loss=3.418, over 45.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.39, discriminator_fake_loss=1.342, generator_loss=27.47, generator_mel_loss=18.83, generator_kl_loss=1.382, generator_dur_loss=1.751, generator_adv_loss=1.948, generator_feat_match_loss=3.551, over 6767.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:09:08,191 INFO [train.py:919] (5/6) Start epoch 170 +2024-03-12 17:11:33,950 INFO [train.py:527] (5/6) Epoch 170, batch 44, global_batch_idx: 21000, batch size: 31, loss[discriminator_loss=2.742, discriminator_real_loss=1.492, discriminator_fake_loss=1.251, generator_loss=26.68, generator_mel_loss=18.73, generator_kl_loss=1.523, generator_dur_loss=1.646, generator_adv_loss=1.728, generator_feat_match_loss=3.048, over 31.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.384, discriminator_fake_loss=1.359, generator_loss=27.33, generator_mel_loss=18.78, generator_kl_loss=1.399, generator_dur_loss=1.756, generator_adv_loss=1.897, generator_feat_match_loss=3.494, over 2468.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:11:33,952 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 17:11:41,705 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 17:14:00,404 INFO [train.py:527] (5/6) Epoch 170, batch 94, global_batch_idx: 21050, batch size: 48, loss[discriminator_loss=2.721, discriminator_real_loss=1.388, discriminator_fake_loss=1.333, generator_loss=28.21, generator_mel_loss=19.82, generator_kl_loss=1.467, generator_dur_loss=1.651, generator_adv_loss=1.885, generator_feat_match_loss=3.388, over 48.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.383, discriminator_fake_loss=1.355, generator_loss=27.38, generator_mel_loss=18.79, generator_kl_loss=1.391, generator_dur_loss=1.766, generator_adv_loss=1.908, generator_feat_match_loss=3.532, over 5465.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:15:20,372 INFO [train.py:919] (5/6) Start epoch 171 +2024-03-12 17:16:42,735 INFO [train.py:527] (5/6) Epoch 171, batch 20, global_batch_idx: 21100, batch size: 96, loss[discriminator_loss=2.757, discriminator_real_loss=1.43, discriminator_fake_loss=1.327, generator_loss=26.84, generator_mel_loss=18.13, generator_kl_loss=1.414, generator_dur_loss=1.87, generator_adv_loss=1.853, generator_feat_match_loss=3.575, over 96.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.372, discriminator_fake_loss=1.362, generator_loss=27.44, generator_mel_loss=18.77, generator_kl_loss=1.43, generator_dur_loss=1.754, generator_adv_loss=1.921, generator_feat_match_loss=3.56, over 1143.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:19:03,296 INFO [train.py:527] (5/6) Epoch 171, batch 70, global_batch_idx: 21150, batch size: 58, loss[discriminator_loss=2.831, discriminator_real_loss=1.506, discriminator_fake_loss=1.325, generator_loss=27.62, generator_mel_loss=19.14, generator_kl_loss=1.384, generator_dur_loss=1.747, generator_adv_loss=1.974, generator_feat_match_loss=3.376, over 58.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.385, discriminator_fake_loss=1.358, generator_loss=27.37, generator_mel_loss=18.8, generator_kl_loss=1.41, generator_dur_loss=1.752, generator_adv_loss=1.916, generator_feat_match_loss=3.487, over 4076.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:21:20,400 INFO [train.py:527] (5/6) Epoch 171, batch 120, global_batch_idx: 21200, batch size: 14, loss[discriminator_loss=2.804, discriminator_real_loss=1.47, discriminator_fake_loss=1.333, generator_loss=27.61, generator_mel_loss=19.3, generator_kl_loss=1.697, generator_dur_loss=1.621, generator_adv_loss=1.99, generator_feat_match_loss=2.999, over 14.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.397, discriminator_fake_loss=1.353, generator_loss=27.45, generator_mel_loss=18.79, generator_kl_loss=1.408, generator_dur_loss=1.753, generator_adv_loss=1.964, generator_feat_match_loss=3.534, over 6839.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:21:20,401 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 17:21:29,266 INFO [train.py:591] (5/6) 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 17:21:29,267 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 17:21:38,923 INFO [train.py:919] (5/6) Start epoch 172 +2024-03-12 17:24:09,802 INFO [train.py:527] (5/6) Epoch 172, batch 46, global_batch_idx: 21250, batch size: 39, loss[discriminator_loss=2.664, discriminator_real_loss=1.396, discriminator_fake_loss=1.268, generator_loss=26.62, generator_mel_loss=18.23, generator_kl_loss=1.374, generator_dur_loss=1.739, generator_adv_loss=1.935, generator_feat_match_loss=3.338, over 39.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.389, discriminator_fake_loss=1.348, generator_loss=27.14, generator_mel_loss=18.64, generator_kl_loss=1.355, generator_dur_loss=1.787, generator_adv_loss=1.897, generator_feat_match_loss=3.466, over 2837.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:26:25,247 INFO [train.py:527] (5/6) Epoch 172, batch 96, global_batch_idx: 21300, batch size: 25, loss[discriminator_loss=2.581, discriminator_real_loss=1.21, discriminator_fake_loss=1.371, generator_loss=29.3, generator_mel_loss=19.65, generator_kl_loss=1.587, generator_dur_loss=1.586, generator_adv_loss=2.126, generator_feat_match_loss=4.352, over 25.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.384, discriminator_fake_loss=1.341, generator_loss=27.22, generator_mel_loss=18.71, generator_kl_loss=1.366, generator_dur_loss=1.774, generator_adv_loss=1.903, generator_feat_match_loss=3.47, over 5544.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:27:42,914 INFO [train.py:919] (5/6) Start epoch 173 +2024-03-12 17:29:08,002 INFO [train.py:527] (5/6) Epoch 173, batch 22, global_batch_idx: 21350, batch size: 74, loss[discriminator_loss=2.745, discriminator_real_loss=1.374, discriminator_fake_loss=1.372, generator_loss=28, generator_mel_loss=18.76, generator_kl_loss=1.495, generator_dur_loss=1.794, generator_adv_loss=1.799, generator_feat_match_loss=4.154, over 74.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.389, discriminator_fake_loss=1.354, generator_loss=27.44, generator_mel_loss=18.87, generator_kl_loss=1.376, generator_dur_loss=1.782, generator_adv_loss=1.914, generator_feat_match_loss=3.492, over 1427.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:31:29,513 INFO [train.py:527] (5/6) Epoch 173, batch 72, global_batch_idx: 21400, batch size: 48, loss[discriminator_loss=2.674, discriminator_real_loss=1.447, discriminator_fake_loss=1.227, generator_loss=26.37, generator_mel_loss=18.22, generator_kl_loss=1.37, generator_dur_loss=1.716, generator_adv_loss=1.803, generator_feat_match_loss=3.254, over 48.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.381, discriminator_fake_loss=1.352, generator_loss=27.45, generator_mel_loss=18.84, generator_kl_loss=1.386, generator_dur_loss=1.774, generator_adv_loss=1.906, generator_feat_match_loss=3.543, over 4287.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:31:29,515 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 17:31:37,517 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 17:33:54,556 INFO [train.py:527] (5/6) Epoch 173, batch 122, global_batch_idx: 21450, batch size: 64, loss[discriminator_loss=2.757, discriminator_real_loss=1.494, discriminator_fake_loss=1.263, generator_loss=27.22, generator_mel_loss=18.97, generator_kl_loss=1.315, generator_dur_loss=1.762, generator_adv_loss=1.837, generator_feat_match_loss=3.332, over 64.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.38, discriminator_fake_loss=1.351, generator_loss=27.4, generator_mel_loss=18.78, generator_kl_loss=1.386, generator_dur_loss=1.775, generator_adv_loss=1.914, generator_feat_match_loss=3.539, over 7229.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:33:59,661 INFO [train.py:919] (5/6) Start epoch 174 +2024-03-12 17:36:37,050 INFO [train.py:527] (5/6) Epoch 174, batch 48, global_batch_idx: 21500, batch size: 96, loss[discriminator_loss=2.773, discriminator_real_loss=1.396, discriminator_fake_loss=1.377, generator_loss=26.97, generator_mel_loss=18.39, generator_kl_loss=1.395, generator_dur_loss=1.86, generator_adv_loss=1.856, generator_feat_match_loss=3.468, over 96.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.386, discriminator_fake_loss=1.36, generator_loss=27.24, generator_mel_loss=18.72, generator_kl_loss=1.395, generator_dur_loss=1.756, generator_adv_loss=1.905, generator_feat_match_loss=3.461, over 2955.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:38:55,064 INFO [train.py:527] (5/6) Epoch 174, batch 98, global_batch_idx: 21550, batch size: 83, loss[discriminator_loss=2.706, discriminator_real_loss=1.388, discriminator_fake_loss=1.319, generator_loss=27.26, generator_mel_loss=18.52, generator_kl_loss=1.351, generator_dur_loss=1.84, generator_adv_loss=1.809, generator_feat_match_loss=3.739, over 83.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.387, discriminator_fake_loss=1.363, generator_loss=27.24, generator_mel_loss=18.69, generator_kl_loss=1.381, generator_dur_loss=1.766, generator_adv_loss=1.912, generator_feat_match_loss=3.483, over 6054.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:40:01,787 INFO [train.py:919] (5/6) Start epoch 175 +2024-03-12 17:41:32,583 INFO [train.py:527] (5/6) Epoch 175, batch 24, global_batch_idx: 21600, batch size: 77, loss[discriminator_loss=2.805, discriminator_real_loss=1.281, discriminator_fake_loss=1.523, generator_loss=27.01, generator_mel_loss=18.37, generator_kl_loss=1.326, generator_dur_loss=1.812, generator_adv_loss=2.052, generator_feat_match_loss=3.449, over 77.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.385, discriminator_fake_loss=1.359, generator_loss=27.38, generator_mel_loss=18.82, generator_kl_loss=1.367, generator_dur_loss=1.767, generator_adv_loss=1.925, generator_feat_match_loss=3.502, over 1392.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:41:32,585 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 17:41:40,577 INFO [train.py:591] (5/6) 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 17:41:40,577 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 17:44:02,811 INFO [train.py:527] (5/6) Epoch 175, batch 74, global_batch_idx: 21650, batch size: 25, loss[discriminator_loss=2.779, discriminator_real_loss=1.532, discriminator_fake_loss=1.247, generator_loss=26.73, generator_mel_loss=18.81, generator_kl_loss=1.586, generator_dur_loss=1.551, generator_adv_loss=1.814, generator_feat_match_loss=2.97, over 25.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.387, discriminator_fake_loss=1.348, generator_loss=27.38, generator_mel_loss=18.8, generator_kl_loss=1.387, generator_dur_loss=1.755, generator_adv_loss=1.917, generator_feat_match_loss=3.521, over 4181.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:46:17,507 INFO [train.py:919] (5/6) Start epoch 176 +2024-03-12 17:46:42,002 INFO [train.py:527] (5/6) Epoch 176, batch 0, global_batch_idx: 21700, batch size: 52, loss[discriminator_loss=2.733, discriminator_real_loss=1.329, discriminator_fake_loss=1.404, generator_loss=27.56, generator_mel_loss=18.47, generator_kl_loss=1.536, generator_dur_loss=1.577, generator_adv_loss=2.169, generator_feat_match_loss=3.817, over 52.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.329, discriminator_fake_loss=1.404, generator_loss=27.56, generator_mel_loss=18.47, generator_kl_loss=1.536, generator_dur_loss=1.577, generator_adv_loss=2.169, generator_feat_match_loss=3.817, over 52.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:49:00,881 INFO [train.py:527] (5/6) Epoch 176, batch 50, global_batch_idx: 21750, batch size: 15, loss[discriminator_loss=2.726, discriminator_real_loss=1.455, discriminator_fake_loss=1.271, generator_loss=27.7, generator_mel_loss=18.56, generator_kl_loss=1.765, generator_dur_loss=1.652, generator_adv_loss=2.068, generator_feat_match_loss=3.651, over 15.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.381, discriminator_fake_loss=1.36, generator_loss=27.35, generator_mel_loss=18.73, generator_kl_loss=1.391, generator_dur_loss=1.761, generator_adv_loss=1.926, generator_feat_match_loss=3.538, over 2977.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:51:22,075 INFO [train.py:527] (5/6) Epoch 176, batch 100, global_batch_idx: 21800, batch size: 48, loss[discriminator_loss=2.803, discriminator_real_loss=1.525, discriminator_fake_loss=1.278, generator_loss=28.09, generator_mel_loss=19.32, generator_kl_loss=1.45, generator_dur_loss=1.666, generator_adv_loss=2.009, generator_feat_match_loss=3.645, over 48.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.384, discriminator_fake_loss=1.366, generator_loss=27.36, generator_mel_loss=18.77, generator_kl_loss=1.384, generator_dur_loss=1.767, generator_adv_loss=1.915, generator_feat_match_loss=3.53, over 5858.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:51:22,076 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 17:51:31,010 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 17:52:33,719 INFO [train.py:919] (5/6) Start epoch 177 +2024-03-12 17:54:10,049 INFO [train.py:527] (5/6) Epoch 177, batch 26, global_batch_idx: 21850, batch size: 45, loss[discriminator_loss=2.744, discriminator_real_loss=1.394, discriminator_fake_loss=1.35, generator_loss=25.92, generator_mel_loss=17.85, generator_kl_loss=1.375, generator_dur_loss=1.693, generator_adv_loss=1.995, generator_feat_match_loss=3.014, over 45.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.396, discriminator_fake_loss=1.331, generator_loss=27.15, generator_mel_loss=18.57, generator_kl_loss=1.361, generator_dur_loss=1.777, generator_adv_loss=1.946, generator_feat_match_loss=3.499, over 1683.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:56:28,949 INFO [train.py:527] (5/6) Epoch 177, batch 76, global_batch_idx: 21900, batch size: 61, loss[discriminator_loss=2.776, discriminator_real_loss=1.387, discriminator_fake_loss=1.389, generator_loss=27.57, generator_mel_loss=19.02, generator_kl_loss=1.391, generator_dur_loss=1.719, generator_adv_loss=1.968, generator_feat_match_loss=3.469, over 61.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.396, discriminator_fake_loss=1.342, generator_loss=27.24, generator_mel_loss=18.69, generator_kl_loss=1.376, generator_dur_loss=1.762, generator_adv_loss=1.923, generator_feat_match_loss=3.482, over 4646.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 17:58:40,589 INFO [train.py:919] (5/6) Start epoch 178 +2024-03-12 17:59:09,261 INFO [train.py:527] (5/6) Epoch 178, batch 2, global_batch_idx: 21950, batch size: 39, loss[discriminator_loss=2.766, discriminator_real_loss=1.428, discriminator_fake_loss=1.338, generator_loss=26.23, generator_mel_loss=18.06, generator_kl_loss=1.517, generator_dur_loss=1.679, generator_adv_loss=1.84, generator_feat_match_loss=3.131, over 39.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.406, discriminator_fake_loss=1.325, generator_loss=27.73, generator_mel_loss=18.92, generator_kl_loss=1.588, generator_dur_loss=1.68, generator_adv_loss=1.906, generator_feat_match_loss=3.629, over 116.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:01:30,753 INFO [train.py:527] (5/6) Epoch 178, batch 52, global_batch_idx: 22000, batch size: 70, loss[discriminator_loss=2.698, discriminator_real_loss=1.413, discriminator_fake_loss=1.285, generator_loss=27.41, generator_mel_loss=18.73, generator_kl_loss=1.421, generator_dur_loss=1.834, generator_adv_loss=1.859, generator_feat_match_loss=3.563, over 70.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.393, discriminator_fake_loss=1.342, generator_loss=27.43, generator_mel_loss=18.79, generator_kl_loss=1.431, generator_dur_loss=1.737, generator_adv_loss=1.9, generator_feat_match_loss=3.57, over 2847.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:01:30,754 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 18:01:38,780 INFO [train.py:591] (5/6) 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 18:01:38,780 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 18:03:57,076 INFO [train.py:527] (5/6) Epoch 178, batch 102, global_batch_idx: 22050, batch size: 83, loss[discriminator_loss=2.782, discriminator_real_loss=1.441, discriminator_fake_loss=1.341, generator_loss=27.52, generator_mel_loss=19.08, generator_kl_loss=1.388, generator_dur_loss=1.856, generator_adv_loss=1.654, generator_feat_match_loss=3.545, over 83.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.387, discriminator_fake_loss=1.348, generator_loss=27.4, generator_mel_loss=18.77, generator_kl_loss=1.41, generator_dur_loss=1.75, generator_adv_loss=1.904, generator_feat_match_loss=3.566, over 5882.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:04:55,561 INFO [train.py:919] (5/6) Start epoch 179 +2024-03-12 18:06:36,860 INFO [train.py:527] (5/6) Epoch 179, batch 28, global_batch_idx: 22100, batch size: 25, loss[discriminator_loss=2.876, discriminator_real_loss=1.605, discriminator_fake_loss=1.271, generator_loss=27, generator_mel_loss=18.89, generator_kl_loss=1.523, generator_dur_loss=1.585, generator_adv_loss=1.707, generator_feat_match_loss=3.298, over 25.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.377, discriminator_fake_loss=1.358, generator_loss=27.58, generator_mel_loss=18.85, generator_kl_loss=1.41, generator_dur_loss=1.763, generator_adv_loss=1.935, generator_feat_match_loss=3.618, over 1731.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:08:55,015 INFO [train.py:527] (5/6) Epoch 179, batch 78, global_batch_idx: 22150, batch size: 48, loss[discriminator_loss=2.688, discriminator_real_loss=1.346, discriminator_fake_loss=1.341, generator_loss=26.56, generator_mel_loss=18.51, generator_kl_loss=1.36, generator_dur_loss=1.698, generator_adv_loss=1.823, generator_feat_match_loss=3.176, over 48.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.392, discriminator_fake_loss=1.347, generator_loss=27.42, generator_mel_loss=18.76, generator_kl_loss=1.392, generator_dur_loss=1.759, generator_adv_loss=1.939, generator_feat_match_loss=3.565, over 4681.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:11:02,062 INFO [train.py:919] (5/6) Start epoch 180 +2024-03-12 18:11:35,655 INFO [train.py:527] (5/6) Epoch 180, batch 4, global_batch_idx: 22200, batch size: 47, loss[discriminator_loss=2.732, discriminator_real_loss=1.329, discriminator_fake_loss=1.402, generator_loss=26.99, generator_mel_loss=18.84, generator_kl_loss=1.464, generator_dur_loss=1.722, generator_adv_loss=1.722, generator_feat_match_loss=3.236, over 47.00 samples.], tot_loss[discriminator_loss=2.808, discriminator_real_loss=1.412, discriminator_fake_loss=1.396, generator_loss=27.06, generator_mel_loss=18.72, generator_kl_loss=1.407, generator_dur_loss=1.753, generator_adv_loss=1.826, generator_feat_match_loss=3.356, over 282.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:11:35,657 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 18:11:43,426 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 18:14:02,378 INFO [train.py:527] (5/6) Epoch 180, batch 54, global_batch_idx: 22250, batch size: 44, loss[discriminator_loss=2.719, discriminator_real_loss=1.311, discriminator_fake_loss=1.408, generator_loss=27.04, generator_mel_loss=18.22, generator_kl_loss=1.521, generator_dur_loss=1.705, generator_adv_loss=1.958, generator_feat_match_loss=3.639, over 44.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.388, discriminator_fake_loss=1.353, generator_loss=27.48, generator_mel_loss=18.83, generator_kl_loss=1.409, generator_dur_loss=1.742, generator_adv_loss=1.916, generator_feat_match_loss=3.583, over 3136.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:16:22,221 INFO [train.py:527] (5/6) Epoch 180, batch 104, global_batch_idx: 22300, batch size: 55, loss[discriminator_loss=2.758, discriminator_real_loss=1.406, discriminator_fake_loss=1.352, generator_loss=28.39, generator_mel_loss=19.47, generator_kl_loss=1.484, generator_dur_loss=1.709, generator_adv_loss=1.91, generator_feat_match_loss=3.822, over 55.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.386, discriminator_fake_loss=1.355, generator_loss=27.4, generator_mel_loss=18.77, generator_kl_loss=1.4, generator_dur_loss=1.751, generator_adv_loss=1.923, generator_feat_match_loss=3.559, over 5987.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:17:13,420 INFO [train.py:919] (5/6) Start epoch 181 +2024-03-12 18:19:03,240 INFO [train.py:527] (5/6) Epoch 181, batch 30, global_batch_idx: 22350, batch size: 96, loss[discriminator_loss=2.768, discriminator_real_loss=1.486, discriminator_fake_loss=1.282, generator_loss=27.29, generator_mel_loss=18.45, generator_kl_loss=1.209, generator_dur_loss=1.894, generator_adv_loss=2.024, generator_feat_match_loss=3.71, over 96.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.395, discriminator_fake_loss=1.346, generator_loss=27.18, generator_mel_loss=18.59, generator_kl_loss=1.408, generator_dur_loss=1.78, generator_adv_loss=1.917, generator_feat_match_loss=3.493, over 1716.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:21:23,380 INFO [train.py:527] (5/6) Epoch 181, batch 80, global_batch_idx: 22400, batch size: 62, loss[discriminator_loss=2.766, discriminator_real_loss=1.405, discriminator_fake_loss=1.361, generator_loss=27.13, generator_mel_loss=18.38, generator_kl_loss=1.451, generator_dur_loss=1.742, generator_adv_loss=1.953, generator_feat_match_loss=3.6, over 62.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.396, discriminator_fake_loss=1.346, generator_loss=27.36, generator_mel_loss=18.71, generator_kl_loss=1.397, generator_dur_loss=1.772, generator_adv_loss=1.946, generator_feat_match_loss=3.54, over 4536.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:21:23,381 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 18:21:31,724 INFO [train.py:591] (5/6) 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 18:21:31,724 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 18:23:28,316 INFO [train.py:919] (5/6) Start epoch 182 +2024-03-12 18:24:07,856 INFO [train.py:527] (5/6) Epoch 182, batch 6, global_batch_idx: 22450, batch size: 97, loss[discriminator_loss=2.81, discriminator_real_loss=1.495, discriminator_fake_loss=1.315, generator_loss=26.29, generator_mel_loss=18.22, generator_kl_loss=1.279, generator_dur_loss=1.883, generator_adv_loss=1.735, generator_feat_match_loss=3.18, over 97.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.427, discriminator_fake_loss=1.343, generator_loss=26.95, generator_mel_loss=18.56, generator_kl_loss=1.374, generator_dur_loss=1.771, generator_adv_loss=1.87, generator_feat_match_loss=3.371, over 427.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:26:28,659 INFO [train.py:527] (5/6) Epoch 182, batch 56, global_batch_idx: 22500, batch size: 45, loss[discriminator_loss=2.782, discriminator_real_loss=1.446, discriminator_fake_loss=1.336, generator_loss=28, generator_mel_loss=19.27, generator_kl_loss=1.59, generator_dur_loss=1.685, generator_adv_loss=1.868, generator_feat_match_loss=3.588, over 45.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.395, discriminator_fake_loss=1.363, generator_loss=27.27, generator_mel_loss=18.7, generator_kl_loss=1.39, generator_dur_loss=1.762, generator_adv_loss=1.909, generator_feat_match_loss=3.513, over 3422.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:28:47,526 INFO [train.py:527] (5/6) Epoch 182, batch 106, global_batch_idx: 22550, batch size: 47, loss[discriminator_loss=2.682, discriminator_real_loss=1.364, discriminator_fake_loss=1.318, generator_loss=27.99, generator_mel_loss=19.14, generator_kl_loss=1.54, generator_dur_loss=1.687, generator_adv_loss=1.848, generator_feat_match_loss=3.783, over 47.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.395, discriminator_fake_loss=1.353, generator_loss=27.35, generator_mel_loss=18.71, generator_kl_loss=1.393, generator_dur_loss=1.759, generator_adv_loss=1.923, generator_feat_match_loss=3.56, over 6211.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:29:34,283 INFO [train.py:919] (5/6) Start epoch 183 +2024-03-12 18:31:27,990 INFO [train.py:527] (5/6) Epoch 183, batch 32, global_batch_idx: 22600, batch size: 18, loss[discriminator_loss=2.607, discriminator_real_loss=1.331, discriminator_fake_loss=1.276, generator_loss=30.14, generator_mel_loss=20.17, generator_kl_loss=2, generator_dur_loss=1.604, generator_adv_loss=1.939, generator_feat_match_loss=4.425, over 18.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.389, discriminator_fake_loss=1.338, generator_loss=27.54, generator_mel_loss=18.85, generator_kl_loss=1.419, generator_dur_loss=1.763, generator_adv_loss=1.916, generator_feat_match_loss=3.586, over 1789.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:31:27,992 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 18:31:35,882 INFO [train.py:591] (5/6) 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 18:31:35,883 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 18:33:56,377 INFO [train.py:527] (5/6) Epoch 183, batch 82, global_batch_idx: 22650, batch size: 68, loss[discriminator_loss=2.698, discriminator_real_loss=1.301, discriminator_fake_loss=1.397, generator_loss=27.57, generator_mel_loss=18.26, generator_kl_loss=1.459, generator_dur_loss=1.803, generator_adv_loss=1.999, generator_feat_match_loss=4.056, over 68.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.393, discriminator_fake_loss=1.346, generator_loss=27.45, generator_mel_loss=18.8, generator_kl_loss=1.412, generator_dur_loss=1.762, generator_adv_loss=1.918, generator_feat_match_loss=3.553, over 4600.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, +2024-03-12 18:35:50,045 INFO [train.py:919] (5/6) Start epoch 184 +2024-03-12 18:36:35,650 INFO [train.py:527] (5/6) Epoch 184, batch 8, global_batch_idx: 22700, batch size: 72, loss[discriminator_loss=2.689, discriminator_real_loss=1.314, discriminator_fake_loss=1.374, generator_loss=27.84, generator_mel_loss=18.68, generator_kl_loss=1.293, generator_dur_loss=1.807, generator_adv_loss=1.939, generator_feat_match_loss=4.126, over 72.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.388, discriminator_fake_loss=1.331, generator_loss=27.3, generator_mel_loss=18.63, generator_kl_loss=1.406, generator_dur_loss=1.741, generator_adv_loss=1.927, generator_feat_match_loss=3.594, over 502.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:38:51,504 INFO [train.py:527] (5/6) Epoch 184, batch 58, global_batch_idx: 22750, batch size: 56, loss[discriminator_loss=2.768, discriminator_real_loss=1.432, discriminator_fake_loss=1.336, generator_loss=27.13, generator_mel_loss=18.3, generator_kl_loss=1.49, generator_dur_loss=1.719, generator_adv_loss=2.133, generator_feat_match_loss=3.49, over 56.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.388, discriminator_fake_loss=1.36, generator_loss=27.32, generator_mel_loss=18.71, generator_kl_loss=1.385, generator_dur_loss=1.751, generator_adv_loss=1.933, generator_feat_match_loss=3.546, over 3375.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:41:09,986 INFO [train.py:527] (5/6) Epoch 184, batch 108, global_batch_idx: 22800, batch size: 88, loss[discriminator_loss=2.778, discriminator_real_loss=1.317, discriminator_fake_loss=1.461, generator_loss=27.72, generator_mel_loss=19, generator_kl_loss=1.388, generator_dur_loss=1.894, generator_adv_loss=2.121, generator_feat_match_loss=3.318, over 88.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.388, discriminator_fake_loss=1.355, generator_loss=27.36, generator_mel_loss=18.74, generator_kl_loss=1.388, generator_dur_loss=1.756, generator_adv_loss=1.924, generator_feat_match_loss=3.551, over 6261.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:41:09,988 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 18:41:18,937 INFO [train.py:591] (5/6) 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 18:41:18,938 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 18:42:02,641 INFO [train.py:919] (5/6) Start epoch 185 +2024-03-12 18:44:00,317 INFO [train.py:527] (5/6) Epoch 185, batch 34, global_batch_idx: 22850, batch size: 68, loss[discriminator_loss=2.791, discriminator_real_loss=1.543, discriminator_fake_loss=1.249, generator_loss=27.45, generator_mel_loss=18.8, generator_kl_loss=1.388, generator_dur_loss=1.788, generator_adv_loss=1.971, generator_feat_match_loss=3.497, over 68.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.399, discriminator_fake_loss=1.355, generator_loss=27.39, generator_mel_loss=18.8, generator_kl_loss=1.361, generator_dur_loss=1.765, generator_adv_loss=1.921, generator_feat_match_loss=3.536, over 1974.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:46:18,239 INFO [train.py:527] (5/6) Epoch 185, batch 84, global_batch_idx: 22900, batch size: 45, loss[discriminator_loss=2.662, discriminator_real_loss=1.354, discriminator_fake_loss=1.308, generator_loss=27.15, generator_mel_loss=18.39, generator_kl_loss=1.648, generator_dur_loss=1.696, generator_adv_loss=1.71, generator_feat_match_loss=3.708, over 45.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.396, discriminator_fake_loss=1.354, generator_loss=27.39, generator_mel_loss=18.78, generator_kl_loss=1.392, generator_dur_loss=1.762, generator_adv_loss=1.904, generator_feat_match_loss=3.554, over 4773.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:48:08,823 INFO [train.py:919] (5/6) Start epoch 186 +2024-03-12 18:49:00,496 INFO [train.py:527] (5/6) Epoch 186, batch 10, global_batch_idx: 22950, batch size: 88, loss[discriminator_loss=2.73, discriminator_real_loss=1.454, discriminator_fake_loss=1.276, generator_loss=28.38, generator_mel_loss=18.86, generator_kl_loss=1.399, generator_dur_loss=1.842, generator_adv_loss=2.254, generator_feat_match_loss=4.02, over 88.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.364, discriminator_fake_loss=1.372, generator_loss=27.36, generator_mel_loss=18.67, generator_kl_loss=1.374, generator_dur_loss=1.801, generator_adv_loss=1.953, generator_feat_match_loss=3.561, over 722.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:51:17,372 INFO [train.py:527] (5/6) Epoch 186, batch 60, global_batch_idx: 23000, batch size: 70, loss[discriminator_loss=2.755, discriminator_real_loss=1.454, discriminator_fake_loss=1.301, generator_loss=27.42, generator_mel_loss=18.76, generator_kl_loss=1.275, generator_dur_loss=1.828, generator_adv_loss=1.822, generator_feat_match_loss=3.737, over 70.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.388, discriminator_fake_loss=1.346, generator_loss=27.36, generator_mel_loss=18.68, generator_kl_loss=1.373, generator_dur_loss=1.772, generator_adv_loss=1.94, generator_feat_match_loss=3.599, over 3647.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:51:17,373 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 18:51:25,374 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 18:53:42,606 INFO [train.py:527] (5/6) Epoch 186, batch 110, global_batch_idx: 23050, batch size: 74, loss[discriminator_loss=2.734, discriminator_real_loss=1.411, discriminator_fake_loss=1.323, generator_loss=27, generator_mel_loss=18.4, generator_kl_loss=1.369, generator_dur_loss=1.8, generator_adv_loss=1.837, generator_feat_match_loss=3.592, over 74.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=27.33, generator_mel_loss=18.71, generator_kl_loss=1.385, generator_dur_loss=1.761, generator_adv_loss=1.925, generator_feat_match_loss=3.553, over 6399.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:54:20,495 INFO [train.py:919] (5/6) Start epoch 187 +2024-03-12 18:56:22,572 INFO [train.py:527] (5/6) Epoch 187, batch 36, global_batch_idx: 23100, batch size: 47, loss[discriminator_loss=2.786, discriminator_real_loss=1.326, discriminator_fake_loss=1.46, generator_loss=26.29, generator_mel_loss=18.05, generator_kl_loss=1.23, generator_dur_loss=1.694, generator_adv_loss=2.071, generator_feat_match_loss=3.244, over 47.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.387, discriminator_fake_loss=1.358, generator_loss=27.45, generator_mel_loss=18.7, generator_kl_loss=1.422, generator_dur_loss=1.735, generator_adv_loss=1.936, generator_feat_match_loss=3.66, over 1894.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 18:58:38,486 INFO [train.py:527] (5/6) Epoch 187, batch 86, global_batch_idx: 23150, batch size: 50, loss[discriminator_loss=2.745, discriminator_real_loss=1.363, discriminator_fake_loss=1.382, generator_loss=26.93, generator_mel_loss=18.4, generator_kl_loss=1.435, generator_dur_loss=1.711, generator_adv_loss=1.963, generator_feat_match_loss=3.417, over 50.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.387, discriminator_fake_loss=1.345, generator_loss=27.38, generator_mel_loss=18.68, generator_kl_loss=1.421, generator_dur_loss=1.736, generator_adv_loss=1.924, generator_feat_match_loss=3.622, over 4607.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:00:23,424 INFO [train.py:919] (5/6) Start epoch 188 +2024-03-12 19:01:22,029 INFO [train.py:527] (5/6) Epoch 188, batch 12, global_batch_idx: 23200, batch size: 61, loss[discriminator_loss=2.746, discriminator_real_loss=1.39, discriminator_fake_loss=1.356, generator_loss=27.35, generator_mel_loss=18.76, generator_kl_loss=1.247, generator_dur_loss=1.736, generator_adv_loss=1.931, generator_feat_match_loss=3.684, over 61.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.386, discriminator_fake_loss=1.366, generator_loss=27.39, generator_mel_loss=18.76, generator_kl_loss=1.379, generator_dur_loss=1.729, generator_adv_loss=1.895, generator_feat_match_loss=3.62, over 725.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:01:22,032 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 19:01:30,004 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 19:03:48,379 INFO [train.py:527] (5/6) Epoch 188, batch 62, global_batch_idx: 23250, batch size: 62, loss[discriminator_loss=2.748, discriminator_real_loss=1.402, discriminator_fake_loss=1.346, generator_loss=26.15, generator_mel_loss=18.13, generator_kl_loss=1.32, generator_dur_loss=1.737, generator_adv_loss=1.914, generator_feat_match_loss=3.051, over 62.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.399, discriminator_fake_loss=1.355, generator_loss=27.25, generator_mel_loss=18.69, generator_kl_loss=1.371, generator_dur_loss=1.74, generator_adv_loss=1.911, generator_feat_match_loss=3.539, over 3700.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:06:09,547 INFO [train.py:527] (5/6) Epoch 188, batch 112, global_batch_idx: 23300, batch size: 58, loss[discriminator_loss=2.789, discriminator_real_loss=1.284, discriminator_fake_loss=1.505, generator_loss=27.14, generator_mel_loss=18.47, generator_kl_loss=1.388, generator_dur_loss=1.752, generator_adv_loss=2.031, generator_feat_match_loss=3.497, over 58.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.388, discriminator_fake_loss=1.356, generator_loss=27.31, generator_mel_loss=18.69, generator_kl_loss=1.386, generator_dur_loss=1.752, generator_adv_loss=1.916, generator_feat_match_loss=3.573, over 6577.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:06:41,034 INFO [train.py:919] (5/6) Start epoch 189 +2024-03-12 19:08:53,193 INFO [train.py:527] (5/6) Epoch 189, batch 38, global_batch_idx: 23350, batch size: 39, loss[discriminator_loss=2.747, discriminator_real_loss=1.405, discriminator_fake_loss=1.342, generator_loss=27.54, generator_mel_loss=18.95, generator_kl_loss=1.472, generator_dur_loss=1.764, generator_adv_loss=1.844, generator_feat_match_loss=3.512, over 39.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.41, discriminator_fake_loss=1.345, generator_loss=27.37, generator_mel_loss=18.71, generator_kl_loss=1.42, generator_dur_loss=1.774, generator_adv_loss=1.93, generator_feat_match_loss=3.533, over 2250.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:11:15,669 INFO [train.py:527] (5/6) Epoch 189, batch 88, global_batch_idx: 23400, batch size: 50, loss[discriminator_loss=2.723, discriminator_real_loss=1.378, discriminator_fake_loss=1.345, generator_loss=26.85, generator_mel_loss=18.37, generator_kl_loss=1.391, generator_dur_loss=1.688, generator_adv_loss=1.987, generator_feat_match_loss=3.417, over 50.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.4, discriminator_fake_loss=1.348, generator_loss=27.29, generator_mel_loss=18.67, generator_kl_loss=1.396, generator_dur_loss=1.77, generator_adv_loss=1.924, generator_feat_match_loss=3.524, over 5156.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:11:15,670 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 19:11:23,871 INFO [train.py:591] (5/6) 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 19:11:23,873 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 19:12:58,387 INFO [train.py:919] (5/6) Start epoch 190 +2024-03-12 19:14:01,702 INFO [train.py:527] (5/6) Epoch 190, batch 14, global_batch_idx: 23450, batch size: 62, loss[discriminator_loss=2.824, discriminator_real_loss=1.397, discriminator_fake_loss=1.426, generator_loss=27.65, generator_mel_loss=18.99, generator_kl_loss=1.599, generator_dur_loss=1.767, generator_adv_loss=1.989, generator_feat_match_loss=3.302, over 62.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.41, discriminator_fake_loss=1.358, generator_loss=27.38, generator_mel_loss=18.69, generator_kl_loss=1.388, generator_dur_loss=1.753, generator_adv_loss=1.956, generator_feat_match_loss=3.595, over 841.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:16:20,305 INFO [train.py:527] (5/6) Epoch 190, batch 64, global_batch_idx: 23500, batch size: 62, loss[discriminator_loss=2.666, discriminator_real_loss=1.402, discriminator_fake_loss=1.265, generator_loss=28.95, generator_mel_loss=19.49, generator_kl_loss=1.472, generator_dur_loss=1.723, generator_adv_loss=1.956, generator_feat_match_loss=4.307, over 62.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.399, discriminator_fake_loss=1.344, generator_loss=27.34, generator_mel_loss=18.69, generator_kl_loss=1.402, generator_dur_loss=1.748, generator_adv_loss=1.922, generator_feat_match_loss=3.586, over 3549.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:18:39,756 INFO [train.py:527] (5/6) Epoch 190, batch 114, global_batch_idx: 23550, batch size: 56, loss[discriminator_loss=2.73, discriminator_real_loss=1.358, discriminator_fake_loss=1.371, generator_loss=28.46, generator_mel_loss=19.16, generator_kl_loss=1.408, generator_dur_loss=1.738, generator_adv_loss=1.879, generator_feat_match_loss=4.275, over 56.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.393, discriminator_fake_loss=1.351, generator_loss=27.33, generator_mel_loss=18.69, generator_kl_loss=1.39, generator_dur_loss=1.759, generator_adv_loss=1.916, generator_feat_match_loss=3.582, over 6419.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:19:03,948 INFO [train.py:919] (5/6) Start epoch 191 +2024-03-12 19:21:21,795 INFO [train.py:527] (5/6) Epoch 191, batch 40, global_batch_idx: 23600, batch size: 44, loss[discriminator_loss=2.728, discriminator_real_loss=1.3, discriminator_fake_loss=1.427, generator_loss=28.63, generator_mel_loss=19.16, generator_kl_loss=1.45, generator_dur_loss=1.68, generator_adv_loss=2.097, generator_feat_match_loss=4.243, over 44.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=27.47, generator_mel_loss=18.76, generator_kl_loss=1.411, generator_dur_loss=1.694, generator_adv_loss=1.932, generator_feat_match_loss=3.671, over 2428.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:21:21,795 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 19:21:29,539 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 19:23:48,573 INFO [train.py:527] (5/6) Epoch 191, batch 90, global_batch_idx: 23650, batch size: 61, loss[discriminator_loss=2.748, discriminator_real_loss=1.457, discriminator_fake_loss=1.291, generator_loss=27.92, generator_mel_loss=18.85, generator_kl_loss=1.482, generator_dur_loss=1.764, generator_adv_loss=1.889, generator_feat_match_loss=3.939, over 61.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.391, discriminator_fake_loss=1.351, generator_loss=27.4, generator_mel_loss=18.72, generator_kl_loss=1.41, generator_dur_loss=1.721, generator_adv_loss=1.925, generator_feat_match_loss=3.627, over 5231.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:25:17,535 INFO [train.py:919] (5/6) Start epoch 192 +2024-03-12 19:26:26,503 INFO [train.py:527] (5/6) Epoch 192, batch 16, global_batch_idx: 23700, batch size: 36, loss[discriminator_loss=2.707, discriminator_real_loss=1.379, discriminator_fake_loss=1.328, generator_loss=27.6, generator_mel_loss=18.91, generator_kl_loss=1.344, generator_dur_loss=1.705, generator_adv_loss=2.061, generator_feat_match_loss=3.583, over 36.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.383, discriminator_fake_loss=1.353, generator_loss=27.32, generator_mel_loss=18.66, generator_kl_loss=1.385, generator_dur_loss=1.777, generator_adv_loss=1.927, generator_feat_match_loss=3.568, over 962.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:28:41,825 INFO [train.py:527] (5/6) Epoch 192, batch 66, global_batch_idx: 23750, batch size: 74, loss[discriminator_loss=2.696, discriminator_real_loss=1.378, discriminator_fake_loss=1.318, generator_loss=28.43, generator_mel_loss=19.08, generator_kl_loss=1.28, generator_dur_loss=1.841, generator_adv_loss=1.929, generator_feat_match_loss=4.3, over 74.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.379, discriminator_fake_loss=1.362, generator_loss=27.42, generator_mel_loss=18.72, generator_kl_loss=1.391, generator_dur_loss=1.766, generator_adv_loss=1.912, generator_feat_match_loss=3.637, over 3860.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:31:01,570 INFO [train.py:527] (5/6) Epoch 192, batch 116, global_batch_idx: 23800, batch size: 53, loss[discriminator_loss=2.717, discriminator_real_loss=1.425, discriminator_fake_loss=1.292, generator_loss=26.49, generator_mel_loss=17.72, generator_kl_loss=1.516, generator_dur_loss=1.676, generator_adv_loss=1.806, generator_feat_match_loss=3.768, over 53.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.385, discriminator_fake_loss=1.355, generator_loss=27.43, generator_mel_loss=18.71, generator_kl_loss=1.394, generator_dur_loss=1.763, generator_adv_loss=1.914, generator_feat_match_loss=3.651, over 6482.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:31:01,572 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 19:31:10,675 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 19:31:32,483 INFO [train.py:919] (5/6) Start epoch 193 +2024-03-12 19:33:52,749 INFO [train.py:527] (5/6) Epoch 193, batch 42, global_batch_idx: 23850, batch size: 42, loss[discriminator_loss=2.63, discriminator_real_loss=1.334, discriminator_fake_loss=1.296, generator_loss=27.88, generator_mel_loss=18.78, generator_kl_loss=1.51, generator_dur_loss=1.737, generator_adv_loss=1.854, generator_feat_match_loss=3.996, over 42.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.379, discriminator_fake_loss=1.35, generator_loss=27.46, generator_mel_loss=18.68, generator_kl_loss=1.409, generator_dur_loss=1.738, generator_adv_loss=1.925, generator_feat_match_loss=3.701, over 2356.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:36:10,069 INFO [train.py:527] (5/6) Epoch 193, batch 92, global_batch_idx: 23900, batch size: 45, loss[discriminator_loss=2.759, discriminator_real_loss=1.371, discriminator_fake_loss=1.388, generator_loss=27.01, generator_mel_loss=18.67, generator_kl_loss=1.346, generator_dur_loss=1.73, generator_adv_loss=1.866, generator_feat_match_loss=3.406, over 45.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.379, discriminator_fake_loss=1.349, generator_loss=27.42, generator_mel_loss=18.66, generator_kl_loss=1.407, generator_dur_loss=1.74, generator_adv_loss=1.925, generator_feat_match_loss=3.685, over 5142.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:37:38,529 INFO [train.py:919] (5/6) Start epoch 194 +2024-03-12 19:38:53,608 INFO [train.py:527] (5/6) Epoch 194, batch 18, global_batch_idx: 23950, batch size: 96, loss[discriminator_loss=2.811, discriminator_real_loss=1.29, discriminator_fake_loss=1.521, generator_loss=26.87, generator_mel_loss=18.45, generator_kl_loss=1.167, generator_dur_loss=1.888, generator_adv_loss=1.965, generator_feat_match_loss=3.397, over 96.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.371, discriminator_fake_loss=1.361, generator_loss=27.63, generator_mel_loss=18.83, generator_kl_loss=1.374, generator_dur_loss=1.753, generator_adv_loss=1.922, generator_feat_match_loss=3.755, over 1107.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:41:16,324 INFO [train.py:527] (5/6) Epoch 194, batch 68, global_batch_idx: 24000, batch size: 62, loss[discriminator_loss=2.8, discriminator_real_loss=1.358, discriminator_fake_loss=1.442, generator_loss=27.33, generator_mel_loss=18.93, generator_kl_loss=1.276, generator_dur_loss=1.731, generator_adv_loss=1.93, generator_feat_match_loss=3.464, over 62.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.386, discriminator_fake_loss=1.354, generator_loss=27.41, generator_mel_loss=18.72, generator_kl_loss=1.37, generator_dur_loss=1.756, generator_adv_loss=1.928, generator_feat_match_loss=3.636, over 4177.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:41:16,325 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 19:41:24,224 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 19:43:40,242 INFO [train.py:527] (5/6) Epoch 194, batch 118, global_batch_idx: 24050, batch size: 55, loss[discriminator_loss=2.757, discriminator_real_loss=1.482, discriminator_fake_loss=1.275, generator_loss=28.21, generator_mel_loss=19.38, generator_kl_loss=1.536, generator_dur_loss=1.715, generator_adv_loss=1.973, generator_feat_match_loss=3.608, over 55.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.386, discriminator_fake_loss=1.35, generator_loss=27.35, generator_mel_loss=18.68, generator_kl_loss=1.381, generator_dur_loss=1.739, generator_adv_loss=1.924, generator_feat_match_loss=3.623, over 6904.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:43:56,269 INFO [train.py:919] (5/6) Start epoch 195 +2024-03-12 19:46:20,728 INFO [train.py:527] (5/6) Epoch 195, batch 44, global_batch_idx: 24100, batch size: 47, loss[discriminator_loss=2.715, discriminator_real_loss=1.342, discriminator_fake_loss=1.373, generator_loss=27.79, generator_mel_loss=18.74, generator_kl_loss=1.445, generator_dur_loss=1.663, generator_adv_loss=1.806, generator_feat_match_loss=4.142, over 47.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.391, discriminator_fake_loss=1.36, generator_loss=27.63, generator_mel_loss=18.73, generator_kl_loss=1.416, generator_dur_loss=1.722, generator_adv_loss=2.023, generator_feat_match_loss=3.734, over 2421.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:48:38,948 INFO [train.py:527] (5/6) Epoch 195, batch 94, global_batch_idx: 24150, batch size: 50, loss[discriminator_loss=2.765, discriminator_real_loss=1.386, discriminator_fake_loss=1.379, generator_loss=26.74, generator_mel_loss=18.2, generator_kl_loss=1.481, generator_dur_loss=1.747, generator_adv_loss=1.898, generator_feat_match_loss=3.414, over 50.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.391, discriminator_fake_loss=1.35, generator_loss=27.53, generator_mel_loss=18.75, generator_kl_loss=1.395, generator_dur_loss=1.745, generator_adv_loss=1.974, generator_feat_match_loss=3.673, over 5297.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:50:03,389 INFO [train.py:919] (5/6) Start epoch 196 +2024-03-12 19:51:22,222 INFO [train.py:527] (5/6) Epoch 196, batch 20, global_batch_idx: 24200, batch size: 62, loss[discriminator_loss=2.735, discriminator_real_loss=1.364, discriminator_fake_loss=1.371, generator_loss=27.76, generator_mel_loss=18.83, generator_kl_loss=1.408, generator_dur_loss=1.736, generator_adv_loss=1.967, generator_feat_match_loss=3.822, over 62.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.404, discriminator_fake_loss=1.342, generator_loss=27.49, generator_mel_loss=18.76, generator_kl_loss=1.394, generator_dur_loss=1.75, generator_adv_loss=1.918, generator_feat_match_loss=3.676, over 1227.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:51:22,223 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 19:51:30,420 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 19:53:49,927 INFO [train.py:527] (5/6) Epoch 196, batch 70, global_batch_idx: 24250, batch size: 61, loss[discriminator_loss=2.773, discriminator_real_loss=1.398, discriminator_fake_loss=1.374, generator_loss=26.43, generator_mel_loss=18.05, generator_kl_loss=1.267, generator_dur_loss=1.797, generator_adv_loss=1.981, generator_feat_match_loss=3.336, over 61.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.395, discriminator_fake_loss=1.347, generator_loss=27.38, generator_mel_loss=18.68, generator_kl_loss=1.388, generator_dur_loss=1.758, generator_adv_loss=1.907, generator_feat_match_loss=3.642, over 4102.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:56:06,131 INFO [train.py:527] (5/6) Epoch 196, batch 120, global_batch_idx: 24300, batch size: 47, loss[discriminator_loss=2.721, discriminator_real_loss=1.365, discriminator_fake_loss=1.355, generator_loss=28.25, generator_mel_loss=19.27, generator_kl_loss=1.556, generator_dur_loss=1.696, generator_adv_loss=1.999, generator_feat_match_loss=3.729, over 47.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.393, discriminator_fake_loss=1.347, generator_loss=27.37, generator_mel_loss=18.69, generator_kl_loss=1.394, generator_dur_loss=1.761, generator_adv_loss=1.903, generator_feat_match_loss=3.62, over 6856.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 19:56:15,251 INFO [train.py:919] (5/6) Start epoch 197 +2024-03-12 19:58:46,751 INFO [train.py:527] (5/6) Epoch 197, batch 46, global_batch_idx: 24350, batch size: 62, loss[discriminator_loss=2.735, discriminator_real_loss=1.424, discriminator_fake_loss=1.311, generator_loss=27.53, generator_mel_loss=18.9, generator_kl_loss=1.327, generator_dur_loss=1.751, generator_adv_loss=1.933, generator_feat_match_loss=3.622, over 62.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.389, discriminator_fake_loss=1.358, generator_loss=27.37, generator_mel_loss=18.74, generator_kl_loss=1.398, generator_dur_loss=1.738, generator_adv_loss=1.929, generator_feat_match_loss=3.572, over 2679.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:01:06,592 INFO [train.py:527] (5/6) Epoch 197, batch 96, global_batch_idx: 24400, batch size: 88, loss[discriminator_loss=2.75, discriminator_real_loss=1.295, discriminator_fake_loss=1.455, generator_loss=26.81, generator_mel_loss=18, generator_kl_loss=1.401, generator_dur_loss=1.817, generator_adv_loss=1.958, generator_feat_match_loss=3.633, over 88.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.389, discriminator_fake_loss=1.356, generator_loss=27.34, generator_mel_loss=18.71, generator_kl_loss=1.395, generator_dur_loss=1.739, generator_adv_loss=1.913, generator_feat_match_loss=3.587, over 5338.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:01:06,594 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 20:01:15,297 INFO [train.py:591] (5/6) 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 20:01:15,298 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 20:02:32,955 INFO [train.py:919] (5/6) Start epoch 198 +2024-03-12 20:03:57,304 INFO [train.py:527] (5/6) Epoch 198, batch 22, global_batch_idx: 24450, batch size: 39, loss[discriminator_loss=2.704, discriminator_real_loss=1.279, discriminator_fake_loss=1.424, generator_loss=28.81, generator_mel_loss=19.73, generator_kl_loss=1.471, generator_dur_loss=1.66, generator_adv_loss=2.048, generator_feat_match_loss=3.905, over 39.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.393, discriminator_fake_loss=1.331, generator_loss=27.3, generator_mel_loss=18.64, generator_kl_loss=1.405, generator_dur_loss=1.703, generator_adv_loss=1.928, generator_feat_match_loss=3.62, over 1229.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:06:18,074 INFO [train.py:527] (5/6) Epoch 198, batch 72, global_batch_idx: 24500, batch size: 15, loss[discriminator_loss=2.814, discriminator_real_loss=1.396, discriminator_fake_loss=1.418, generator_loss=26.94, generator_mel_loss=18.11, generator_kl_loss=1.676, generator_dur_loss=1.558, generator_adv_loss=1.859, generator_feat_match_loss=3.734, over 15.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.398, discriminator_fake_loss=1.345, generator_loss=27.33, generator_mel_loss=18.68, generator_kl_loss=1.385, generator_dur_loss=1.741, generator_adv_loss=1.91, generator_feat_match_loss=3.621, over 4016.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:08:36,502 INFO [train.py:527] (5/6) Epoch 198, batch 122, global_batch_idx: 24550, batch size: 56, loss[discriminator_loss=2.745, discriminator_real_loss=1.303, discriminator_fake_loss=1.442, generator_loss=28.11, generator_mel_loss=19.02, generator_kl_loss=1.527, generator_dur_loss=1.71, generator_adv_loss=1.972, generator_feat_match_loss=3.886, over 56.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.394, discriminator_fake_loss=1.347, generator_loss=27.37, generator_mel_loss=18.7, generator_kl_loss=1.392, generator_dur_loss=1.74, generator_adv_loss=1.912, generator_feat_match_loss=3.627, over 6735.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:08:41,605 INFO [train.py:919] (5/6) Start epoch 199 +2024-03-12 20:11:17,807 INFO [train.py:527] (5/6) Epoch 199, batch 48, global_batch_idx: 24600, batch size: 36, loss[discriminator_loss=2.767, discriminator_real_loss=1.4, discriminator_fake_loss=1.368, generator_loss=26.86, generator_mel_loss=18.56, generator_kl_loss=1.438, generator_dur_loss=1.718, generator_adv_loss=1.829, generator_feat_match_loss=3.315, over 36.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.386, discriminator_fake_loss=1.354, generator_loss=27.25, generator_mel_loss=18.58, generator_kl_loss=1.4, generator_dur_loss=1.758, generator_adv_loss=1.914, generator_feat_match_loss=3.594, over 2748.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:11:17,808 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 20:11:25,592 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 20:13:45,126 INFO [train.py:527] (5/6) Epoch 199, batch 98, global_batch_idx: 24650, batch size: 56, loss[discriminator_loss=2.758, discriminator_real_loss=1.465, discriminator_fake_loss=1.294, generator_loss=26.99, generator_mel_loss=18.48, generator_kl_loss=1.296, generator_dur_loss=1.728, generator_adv_loss=1.802, generator_feat_match_loss=3.681, over 56.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.389, discriminator_fake_loss=1.346, generator_loss=27.32, generator_mel_loss=18.63, generator_kl_loss=1.402, generator_dur_loss=1.756, generator_adv_loss=1.923, generator_feat_match_loss=3.609, over 5586.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:14:55,772 INFO [train.py:919] (5/6) Start epoch 200 +2024-03-12 20:16:26,062 INFO [train.py:527] (5/6) Epoch 200, batch 24, global_batch_idx: 24700, batch size: 45, loss[discriminator_loss=2.696, discriminator_real_loss=1.234, discriminator_fake_loss=1.462, generator_loss=28.17, generator_mel_loss=19.25, generator_kl_loss=1.509, generator_dur_loss=1.66, generator_adv_loss=1.945, generator_feat_match_loss=3.808, over 45.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.394, discriminator_fake_loss=1.348, generator_loss=27.34, generator_mel_loss=18.66, generator_kl_loss=1.381, generator_dur_loss=1.743, generator_adv_loss=1.918, generator_feat_match_loss=3.64, over 1459.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:18:45,132 INFO [train.py:527] (5/6) Epoch 200, batch 74, global_batch_idx: 24750, batch size: 88, loss[discriminator_loss=2.703, discriminator_real_loss=1.352, discriminator_fake_loss=1.351, generator_loss=27.04, generator_mel_loss=18.48, generator_kl_loss=1.25, generator_dur_loss=1.833, generator_adv_loss=1.773, generator_feat_match_loss=3.709, over 88.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.397, discriminator_fake_loss=1.347, generator_loss=27.38, generator_mel_loss=18.71, generator_kl_loss=1.387, generator_dur_loss=1.732, generator_adv_loss=1.914, generator_feat_match_loss=3.632, over 4214.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:21:03,006 INFO [train.py:919] (5/6) Start epoch 201 +2024-03-12 20:21:28,074 INFO [train.py:527] (5/6) Epoch 201, batch 0, global_batch_idx: 24800, batch size: 77, loss[discriminator_loss=2.742, discriminator_real_loss=1.344, discriminator_fake_loss=1.398, generator_loss=27, generator_mel_loss=18.46, generator_kl_loss=1.356, generator_dur_loss=1.816, generator_adv_loss=1.823, generator_feat_match_loss=3.541, over 77.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.344, discriminator_fake_loss=1.398, generator_loss=27, generator_mel_loss=18.46, generator_kl_loss=1.356, generator_dur_loss=1.816, generator_adv_loss=1.823, generator_feat_match_loss=3.541, over 77.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:21:28,077 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 20:21:35,805 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 20:23:53,779 INFO [train.py:527] (5/6) Epoch 201, batch 50, global_batch_idx: 24850, batch size: 80, loss[discriminator_loss=2.738, discriminator_real_loss=1.367, discriminator_fake_loss=1.371, generator_loss=27.46, generator_mel_loss=18.73, generator_kl_loss=1.352, generator_dur_loss=1.802, generator_adv_loss=1.904, generator_feat_match_loss=3.67, over 80.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.391, discriminator_fake_loss=1.345, generator_loss=27.31, generator_mel_loss=18.61, generator_kl_loss=1.394, generator_dur_loss=1.74, generator_adv_loss=1.909, generator_feat_match_loss=3.652, over 2868.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:26:11,228 INFO [train.py:527] (5/6) Epoch 201, batch 100, global_batch_idx: 24900, batch size: 88, loss[discriminator_loss=2.655, discriminator_real_loss=1.299, discriminator_fake_loss=1.357, generator_loss=27.83, generator_mel_loss=18.66, generator_kl_loss=1.247, generator_dur_loss=1.857, generator_adv_loss=2.032, generator_feat_match_loss=4.034, over 88.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.387, discriminator_fake_loss=1.346, generator_loss=27.37, generator_mel_loss=18.62, generator_kl_loss=1.382, generator_dur_loss=1.743, generator_adv_loss=1.938, generator_feat_match_loss=3.689, over 5496.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:27:18,180 INFO [train.py:919] (5/6) Start epoch 202 +2024-03-12 20:28:54,309 INFO [train.py:527] (5/6) Epoch 202, batch 26, global_batch_idx: 24950, batch size: 58, loss[discriminator_loss=2.763, discriminator_real_loss=1.484, discriminator_fake_loss=1.278, generator_loss=27.59, generator_mel_loss=18.78, generator_kl_loss=1.376, generator_dur_loss=1.757, generator_adv_loss=1.786, generator_feat_match_loss=3.89, over 58.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.38, discriminator_fake_loss=1.342, generator_loss=27.49, generator_mel_loss=18.7, generator_kl_loss=1.382, generator_dur_loss=1.738, generator_adv_loss=1.926, generator_feat_match_loss=3.745, over 1429.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:31:15,255 INFO [train.py:527] (5/6) Epoch 202, batch 76, global_batch_idx: 25000, batch size: 61, loss[discriminator_loss=2.748, discriminator_real_loss=1.363, discriminator_fake_loss=1.385, generator_loss=28.57, generator_mel_loss=19.18, generator_kl_loss=1.53, generator_dur_loss=1.711, generator_adv_loss=2.04, generator_feat_match_loss=4.117, over 61.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.382, discriminator_fake_loss=1.349, generator_loss=27.38, generator_mel_loss=18.63, generator_kl_loss=1.395, generator_dur_loss=1.753, generator_adv_loss=1.918, generator_feat_match_loss=3.685, over 4192.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:31:15,256 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 20:31:23,178 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 20:33:34,269 INFO [train.py:919] (5/6) Start epoch 203 +2024-03-12 20:34:05,351 INFO [train.py:527] (5/6) Epoch 203, batch 2, global_batch_idx: 25050, batch size: 25, loss[discriminator_loss=2.66, discriminator_real_loss=1.197, discriminator_fake_loss=1.462, generator_loss=29.21, generator_mel_loss=19.4, generator_kl_loss=1.692, generator_dur_loss=1.572, generator_adv_loss=2.142, generator_feat_match_loss=4.404, over 25.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.372, discriminator_fake_loss=1.352, generator_loss=27.45, generator_mel_loss=18.82, generator_kl_loss=1.423, generator_dur_loss=1.751, generator_adv_loss=1.873, generator_feat_match_loss=3.582, over 141.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:36:24,658 INFO [train.py:527] (5/6) Epoch 203, batch 52, global_batch_idx: 25100, batch size: 60, loss[discriminator_loss=2.655, discriminator_real_loss=1.319, discriminator_fake_loss=1.336, generator_loss=27.47, generator_mel_loss=18.62, generator_kl_loss=1.242, generator_dur_loss=1.69, generator_adv_loss=2.105, generator_feat_match_loss=3.809, over 60.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=27.41, generator_mel_loss=18.7, generator_kl_loss=1.379, generator_dur_loss=1.762, generator_adv_loss=1.917, generator_feat_match_loss=3.645, over 3056.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:38:42,991 INFO [train.py:527] (5/6) Epoch 203, batch 102, global_batch_idx: 25150, batch size: 96, loss[discriminator_loss=2.759, discriminator_real_loss=1.319, discriminator_fake_loss=1.44, generator_loss=26.65, generator_mel_loss=18.13, generator_kl_loss=1.208, generator_dur_loss=1.868, generator_adv_loss=1.896, generator_feat_match_loss=3.549, over 96.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.384, discriminator_fake_loss=1.351, generator_loss=27.38, generator_mel_loss=18.65, generator_kl_loss=1.379, generator_dur_loss=1.75, generator_adv_loss=1.941, generator_feat_match_loss=3.662, over 6038.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:39:43,008 INFO [train.py:919] (5/6) Start epoch 204 +2024-03-12 20:41:27,054 INFO [train.py:527] (5/6) Epoch 204, batch 28, global_batch_idx: 25200, batch size: 88, loss[discriminator_loss=2.774, discriminator_real_loss=1.315, discriminator_fake_loss=1.459, generator_loss=27.96, generator_mel_loss=18.92, generator_kl_loss=1.275, generator_dur_loss=1.854, generator_adv_loss=1.982, generator_feat_match_loss=3.923, over 88.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.388, discriminator_fake_loss=1.351, generator_loss=27.46, generator_mel_loss=18.71, generator_kl_loss=1.405, generator_dur_loss=1.753, generator_adv_loss=1.92, generator_feat_match_loss=3.669, over 1594.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:41:27,056 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 20:41:34,969 INFO [train.py:591] (5/6) 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 20:41:34,969 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 20:43:55,131 INFO [train.py:527] (5/6) Epoch 204, batch 78, global_batch_idx: 25250, batch size: 50, loss[discriminator_loss=2.758, discriminator_real_loss=1.395, discriminator_fake_loss=1.362, generator_loss=25.94, generator_mel_loss=18.13, generator_kl_loss=1.401, generator_dur_loss=1.678, generator_adv_loss=1.834, generator_feat_match_loss=2.894, over 50.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.39, discriminator_fake_loss=1.352, generator_loss=27.42, generator_mel_loss=18.7, generator_kl_loss=1.373, generator_dur_loss=1.757, generator_adv_loss=1.912, generator_feat_match_loss=3.68, over 4473.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:45:59,602 INFO [train.py:919] (5/6) Start epoch 205 +2024-03-12 20:46:34,521 INFO [train.py:527] (5/6) Epoch 205, batch 4, global_batch_idx: 25300, batch size: 62, loss[discriminator_loss=2.76, discriminator_real_loss=1.464, discriminator_fake_loss=1.296, generator_loss=27.88, generator_mel_loss=18.83, generator_kl_loss=1.39, generator_dur_loss=1.745, generator_adv_loss=1.891, generator_feat_match_loss=4.028, over 62.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.386, discriminator_fake_loss=1.348, generator_loss=27.3, generator_mel_loss=18.49, generator_kl_loss=1.361, generator_dur_loss=1.793, generator_adv_loss=1.913, generator_feat_match_loss=3.743, over 352.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:48:55,180 INFO [train.py:527] (5/6) Epoch 205, batch 54, global_batch_idx: 25350, batch size: 39, loss[discriminator_loss=2.699, discriminator_real_loss=1.486, discriminator_fake_loss=1.213, generator_loss=28.63, generator_mel_loss=18.87, generator_kl_loss=1.674, generator_dur_loss=1.609, generator_adv_loss=2.109, generator_feat_match_loss=4.374, over 39.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.387, discriminator_fake_loss=1.347, generator_loss=27.78, generator_mel_loss=18.64, generator_kl_loss=1.391, generator_dur_loss=1.744, generator_adv_loss=2.037, generator_feat_match_loss=3.964, over 3108.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:51:12,543 INFO [train.py:527] (5/6) Epoch 205, batch 104, global_batch_idx: 25400, batch size: 45, loss[discriminator_loss=2.746, discriminator_real_loss=1.262, discriminator_fake_loss=1.484, generator_loss=27.21, generator_mel_loss=18.3, generator_kl_loss=1.553, generator_dur_loss=1.65, generator_adv_loss=1.931, generator_feat_match_loss=3.77, over 45.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.393, discriminator_fake_loss=1.349, generator_loss=27.63, generator_mel_loss=18.69, generator_kl_loss=1.388, generator_dur_loss=1.748, generator_adv_loss=1.999, generator_feat_match_loss=3.804, over 5876.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:51:12,544 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 20:51:21,444 INFO [train.py:591] (5/6) 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 20:51:21,445 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 20:52:14,287 INFO [train.py:919] (5/6) Start epoch 206 +2024-03-12 20:54:02,092 INFO [train.py:527] (5/6) Epoch 206, batch 30, global_batch_idx: 25450, batch size: 88, loss[discriminator_loss=2.713, discriminator_real_loss=1.351, discriminator_fake_loss=1.362, generator_loss=26.95, generator_mel_loss=18.22, generator_kl_loss=1.339, generator_dur_loss=1.882, generator_adv_loss=1.91, generator_feat_match_loss=3.599, over 88.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.387, discriminator_fake_loss=1.355, generator_loss=27.43, generator_mel_loss=18.72, generator_kl_loss=1.35, generator_dur_loss=1.785, generator_adv_loss=1.889, generator_feat_match_loss=3.691, over 1824.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:56:20,981 INFO [train.py:527] (5/6) Epoch 206, batch 80, global_batch_idx: 25500, batch size: 15, loss[discriminator_loss=2.776, discriminator_real_loss=1.523, discriminator_fake_loss=1.254, generator_loss=28.3, generator_mel_loss=18.85, generator_kl_loss=1.864, generator_dur_loss=1.596, generator_adv_loss=1.833, generator_feat_match_loss=4.158, over 15.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.386, discriminator_fake_loss=1.346, generator_loss=27.38, generator_mel_loss=18.64, generator_kl_loss=1.366, generator_dur_loss=1.768, generator_adv_loss=1.897, generator_feat_match_loss=3.707, over 4608.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 20:58:19,617 INFO [train.py:919] (5/6) Start epoch 207 +2024-03-12 20:58:57,490 INFO [train.py:527] (5/6) Epoch 207, batch 6, global_batch_idx: 25550, batch size: 36, loss[discriminator_loss=2.79, discriminator_real_loss=1.408, discriminator_fake_loss=1.382, generator_loss=27.55, generator_mel_loss=18.66, generator_kl_loss=1.55, generator_dur_loss=1.722, generator_adv_loss=1.715, generator_feat_match_loss=3.899, over 36.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.35, discriminator_fake_loss=1.392, generator_loss=27.14, generator_mel_loss=18.48, generator_kl_loss=1.324, generator_dur_loss=1.784, generator_adv_loss=1.921, generator_feat_match_loss=3.635, over 371.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:01:18,363 INFO [train.py:527] (5/6) Epoch 207, batch 56, global_batch_idx: 25600, batch size: 56, loss[discriminator_loss=2.904, discriminator_real_loss=1.489, discriminator_fake_loss=1.415, generator_loss=28.17, generator_mel_loss=18.98, generator_kl_loss=1.409, generator_dur_loss=1.75, generator_adv_loss=2.173, generator_feat_match_loss=3.855, over 56.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.376, discriminator_fake_loss=1.361, generator_loss=27.43, generator_mel_loss=18.62, generator_kl_loss=1.377, generator_dur_loss=1.776, generator_adv_loss=1.929, generator_feat_match_loss=3.726, over 3250.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:01:18,364 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 21:01:26,533 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 21:03:49,613 INFO [train.py:527] (5/6) Epoch 207, batch 106, global_batch_idx: 25650, batch size: 15, loss[discriminator_loss=2.864, discriminator_real_loss=1.608, discriminator_fake_loss=1.256, generator_loss=29.15, generator_mel_loss=20.78, generator_kl_loss=1.794, generator_dur_loss=1.594, generator_adv_loss=1.626, generator_feat_match_loss=3.358, over 15.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.388, discriminator_fake_loss=1.348, generator_loss=27.35, generator_mel_loss=18.58, generator_kl_loss=1.363, generator_dur_loss=1.775, generator_adv_loss=1.938, generator_feat_match_loss=3.692, over 6229.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:04:33,437 INFO [train.py:919] (5/6) Start epoch 208 +2024-03-12 21:06:24,996 INFO [train.py:527] (5/6) Epoch 208, batch 32, global_batch_idx: 25700, batch size: 61, loss[discriminator_loss=2.696, discriminator_real_loss=1.425, discriminator_fake_loss=1.271, generator_loss=27.39, generator_mel_loss=18.25, generator_kl_loss=1.36, generator_dur_loss=1.799, generator_adv_loss=2.031, generator_feat_match_loss=3.952, over 61.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.386, discriminator_fake_loss=1.343, generator_loss=27.38, generator_mel_loss=18.56, generator_kl_loss=1.376, generator_dur_loss=1.762, generator_adv_loss=1.926, generator_feat_match_loss=3.755, over 1959.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:08:44,931 INFO [train.py:527] (5/6) Epoch 208, batch 82, global_batch_idx: 25750, batch size: 68, loss[discriminator_loss=2.714, discriminator_real_loss=1.365, discriminator_fake_loss=1.349, generator_loss=27.47, generator_mel_loss=18.77, generator_kl_loss=1.163, generator_dur_loss=1.832, generator_adv_loss=1.826, generator_feat_match_loss=3.881, over 68.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.385, discriminator_fake_loss=1.344, generator_loss=27.47, generator_mel_loss=18.61, generator_kl_loss=1.395, generator_dur_loss=1.77, generator_adv_loss=1.922, generator_feat_match_loss=3.772, over 4877.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:10:39,921 INFO [train.py:919] (5/6) Start epoch 209 +2024-03-12 21:11:24,668 INFO [train.py:527] (5/6) Epoch 209, batch 8, global_batch_idx: 25800, batch size: 56, loss[discriminator_loss=2.749, discriminator_real_loss=1.483, discriminator_fake_loss=1.266, generator_loss=26.56, generator_mel_loss=18.23, generator_kl_loss=1.447, generator_dur_loss=1.724, generator_adv_loss=1.857, generator_feat_match_loss=3.297, over 56.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.431, discriminator_fake_loss=1.329, generator_loss=26.72, generator_mel_loss=18.3, generator_kl_loss=1.344, generator_dur_loss=1.765, generator_adv_loss=1.908, generator_feat_match_loss=3.404, over 568.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:11:24,670 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 21:11:32,763 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 21:13:50,578 INFO [train.py:527] (5/6) Epoch 209, batch 58, global_batch_idx: 25850, batch size: 88, loss[discriminator_loss=2.804, discriminator_real_loss=1.263, discriminator_fake_loss=1.541, generator_loss=26.78, generator_mel_loss=18.25, generator_kl_loss=1.314, generator_dur_loss=1.814, generator_adv_loss=2.041, generator_feat_match_loss=3.359, over 88.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.391, discriminator_fake_loss=1.354, generator_loss=27.12, generator_mel_loss=18.45, generator_kl_loss=1.352, generator_dur_loss=1.756, generator_adv_loss=1.914, generator_feat_match_loss=3.64, over 3591.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:16:09,421 INFO [train.py:527] (5/6) Epoch 209, batch 108, global_batch_idx: 25900, batch size: 42, loss[discriminator_loss=2.635, discriminator_real_loss=1.319, discriminator_fake_loss=1.316, generator_loss=27.7, generator_mel_loss=18.46, generator_kl_loss=1.518, generator_dur_loss=1.655, generator_adv_loss=1.902, generator_feat_match_loss=4.163, over 42.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.388, discriminator_fake_loss=1.357, generator_loss=27.24, generator_mel_loss=18.52, generator_kl_loss=1.382, generator_dur_loss=1.741, generator_adv_loss=1.913, generator_feat_match_loss=3.69, over 6125.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:16:51,415 INFO [train.py:919] (5/6) Start epoch 210 +2024-03-12 21:18:49,183 INFO [train.py:527] (5/6) Epoch 210, batch 34, global_batch_idx: 25950, batch size: 53, loss[discriminator_loss=2.765, discriminator_real_loss=1.372, discriminator_fake_loss=1.394, generator_loss=26.97, generator_mel_loss=18.6, generator_kl_loss=1.37, generator_dur_loss=1.698, generator_adv_loss=1.936, generator_feat_match_loss=3.37, over 53.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.382, discriminator_fake_loss=1.359, generator_loss=27.38, generator_mel_loss=18.62, generator_kl_loss=1.387, generator_dur_loss=1.754, generator_adv_loss=1.904, generator_feat_match_loss=3.715, over 2085.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:21:09,236 INFO [train.py:527] (5/6) Epoch 210, batch 84, global_batch_idx: 26000, batch size: 88, loss[discriminator_loss=2.78, discriminator_real_loss=1.488, discriminator_fake_loss=1.292, generator_loss=26.84, generator_mel_loss=18.01, generator_kl_loss=1.302, generator_dur_loss=1.884, generator_adv_loss=1.761, generator_feat_match_loss=3.887, over 88.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.383, discriminator_fake_loss=1.352, generator_loss=27.39, generator_mel_loss=18.61, generator_kl_loss=1.382, generator_dur_loss=1.755, generator_adv_loss=1.908, generator_feat_match_loss=3.727, over 4966.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:21:09,238 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 21:21:17,006 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 21:23:06,806 INFO [train.py:919] (5/6) Start epoch 211 +2024-03-12 21:23:58,846 INFO [train.py:527] (5/6) Epoch 211, batch 10, global_batch_idx: 26050, batch size: 25, loss[discriminator_loss=2.717, discriminator_real_loss=1.396, discriminator_fake_loss=1.321, generator_loss=28.77, generator_mel_loss=19.44, generator_kl_loss=1.733, generator_dur_loss=1.555, generator_adv_loss=1.782, generator_feat_match_loss=4.265, over 25.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.377, discriminator_fake_loss=1.371, generator_loss=27.46, generator_mel_loss=18.53, generator_kl_loss=1.401, generator_dur_loss=1.747, generator_adv_loss=1.931, generator_feat_match_loss=3.851, over 585.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:26:18,204 INFO [train.py:527] (5/6) Epoch 211, batch 60, global_batch_idx: 26100, batch size: 80, loss[discriminator_loss=2.7, discriminator_real_loss=1.398, discriminator_fake_loss=1.303, generator_loss=27.12, generator_mel_loss=18.38, generator_kl_loss=1.227, generator_dur_loss=1.802, generator_adv_loss=1.9, generator_feat_match_loss=3.804, over 80.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.384, discriminator_fake_loss=1.353, generator_loss=27.38, generator_mel_loss=18.59, generator_kl_loss=1.364, generator_dur_loss=1.746, generator_adv_loss=1.913, generator_feat_match_loss=3.765, over 3566.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:28:36,091 INFO [train.py:527] (5/6) Epoch 211, batch 110, global_batch_idx: 26150, batch size: 47, loss[discriminator_loss=2.707, discriminator_real_loss=1.445, discriminator_fake_loss=1.262, generator_loss=26.83, generator_mel_loss=18.59, generator_kl_loss=1.214, generator_dur_loss=1.712, generator_adv_loss=1.974, generator_feat_match_loss=3.337, over 47.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.389, discriminator_fake_loss=1.354, generator_loss=27.36, generator_mel_loss=18.6, generator_kl_loss=1.367, generator_dur_loss=1.743, generator_adv_loss=1.91, generator_feat_match_loss=3.735, over 6428.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:29:13,157 INFO [train.py:919] (5/6) Start epoch 212 +2024-03-12 21:31:21,587 INFO [train.py:527] (5/6) Epoch 212, batch 36, global_batch_idx: 26200, batch size: 56, loss[discriminator_loss=2.722, discriminator_real_loss=1.231, discriminator_fake_loss=1.491, generator_loss=27.04, generator_mel_loss=18.42, generator_kl_loss=1.317, generator_dur_loss=1.735, generator_adv_loss=2.038, generator_feat_match_loss=3.53, over 56.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.377, discriminator_fake_loss=1.351, generator_loss=27.32, generator_mel_loss=18.48, generator_kl_loss=1.381, generator_dur_loss=1.77, generator_adv_loss=1.941, generator_feat_match_loss=3.743, over 2163.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:31:21,588 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 21:31:29,921 INFO [train.py:591] (5/6) 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 21:31:29,922 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 21:33:49,157 INFO [train.py:527] (5/6) Epoch 212, batch 86, global_batch_idx: 26250, batch size: 66, loss[discriminator_loss=2.786, discriminator_real_loss=1.378, discriminator_fake_loss=1.407, generator_loss=27.13, generator_mel_loss=18.41, generator_kl_loss=1.375, generator_dur_loss=1.708, generator_adv_loss=1.979, generator_feat_match_loss=3.65, over 66.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=27.46, generator_mel_loss=18.58, generator_kl_loss=1.374, generator_dur_loss=1.758, generator_adv_loss=1.947, generator_feat_match_loss=3.798, over 5034.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:35:33,265 INFO [train.py:919] (5/6) Start epoch 213 +2024-03-12 21:36:29,150 INFO [train.py:527] (5/6) Epoch 213, batch 12, global_batch_idx: 26300, batch size: 61, loss[discriminator_loss=2.722, discriminator_real_loss=1.374, discriminator_fake_loss=1.348, generator_loss=26.9, generator_mel_loss=18.39, generator_kl_loss=1.429, generator_dur_loss=1.732, generator_adv_loss=1.845, generator_feat_match_loss=3.51, over 61.00 samples.], tot_loss[discriminator_loss=2.775, discriminator_real_loss=1.425, discriminator_fake_loss=1.349, generator_loss=27.05, generator_mel_loss=18.44, generator_kl_loss=1.404, generator_dur_loss=1.763, generator_adv_loss=1.897, generator_feat_match_loss=3.545, over 741.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:38:50,001 INFO [train.py:527] (5/6) Epoch 213, batch 62, global_batch_idx: 26350, batch size: 39, loss[discriminator_loss=2.723, discriminator_real_loss=1.462, discriminator_fake_loss=1.261, generator_loss=27.49, generator_mel_loss=18.77, generator_kl_loss=1.368, generator_dur_loss=1.725, generator_adv_loss=1.915, generator_feat_match_loss=3.716, over 39.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.407, discriminator_fake_loss=1.351, generator_loss=27.34, generator_mel_loss=18.59, generator_kl_loss=1.399, generator_dur_loss=1.754, generator_adv_loss=1.932, generator_feat_match_loss=3.663, over 3539.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:41:08,799 INFO [train.py:527] (5/6) Epoch 213, batch 112, global_batch_idx: 26400, batch size: 45, loss[discriminator_loss=2.802, discriminator_real_loss=1.489, discriminator_fake_loss=1.312, generator_loss=26.6, generator_mel_loss=18.42, generator_kl_loss=1.555, generator_dur_loss=1.678, generator_adv_loss=1.835, generator_feat_match_loss=3.12, over 45.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.396, discriminator_fake_loss=1.347, generator_loss=27.41, generator_mel_loss=18.59, generator_kl_loss=1.393, generator_dur_loss=1.767, generator_adv_loss=1.933, generator_feat_match_loss=3.729, over 6349.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:41:08,800 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 21:41:17,868 INFO [train.py:591] (5/6) 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 21:41:17,869 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 21:41:48,455 INFO [train.py:919] (5/6) Start epoch 214 +2024-03-12 21:43:55,355 INFO [train.py:527] (5/6) Epoch 214, batch 38, global_batch_idx: 26450, batch size: 45, loss[discriminator_loss=2.798, discriminator_real_loss=1.583, discriminator_fake_loss=1.215, generator_loss=26.04, generator_mel_loss=17.96, generator_kl_loss=1.424, generator_dur_loss=1.705, generator_adv_loss=1.559, generator_feat_match_loss=3.385, over 45.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.391, discriminator_fake_loss=1.346, generator_loss=27.35, generator_mel_loss=18.58, generator_kl_loss=1.368, generator_dur_loss=1.777, generator_adv_loss=1.91, generator_feat_match_loss=3.718, over 2386.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:46:16,921 INFO [train.py:527] (5/6) Epoch 214, batch 88, global_batch_idx: 26500, batch size: 95, loss[discriminator_loss=2.761, discriminator_real_loss=1.467, discriminator_fake_loss=1.294, generator_loss=26.49, generator_mel_loss=18.19, generator_kl_loss=1.201, generator_dur_loss=1.891, generator_adv_loss=1.746, generator_feat_match_loss=3.472, over 95.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.397, discriminator_fake_loss=1.343, generator_loss=27.26, generator_mel_loss=18.53, generator_kl_loss=1.364, generator_dur_loss=1.778, generator_adv_loss=1.91, generator_feat_match_loss=3.678, over 5469.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:47:54,538 INFO [train.py:919] (5/6) Start epoch 215 +2024-03-12 21:48:58,706 INFO [train.py:527] (5/6) Epoch 215, batch 14, global_batch_idx: 26550, batch size: 36, loss[discriminator_loss=2.786, discriminator_real_loss=1.283, discriminator_fake_loss=1.503, generator_loss=27.72, generator_mel_loss=18.64, generator_kl_loss=1.499, generator_dur_loss=1.71, generator_adv_loss=1.972, generator_feat_match_loss=3.903, over 36.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.384, discriminator_fake_loss=1.364, generator_loss=27.42, generator_mel_loss=18.66, generator_kl_loss=1.355, generator_dur_loss=1.769, generator_adv_loss=1.897, generator_feat_match_loss=3.739, over 875.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:51:17,895 INFO [train.py:527] (5/6) Epoch 215, batch 64, global_batch_idx: 26600, batch size: 58, loss[discriminator_loss=2.734, discriminator_real_loss=1.454, discriminator_fake_loss=1.281, generator_loss=27.37, generator_mel_loss=18.56, generator_kl_loss=1.37, generator_dur_loss=1.78, generator_adv_loss=1.902, generator_feat_match_loss=3.764, over 58.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.392, discriminator_fake_loss=1.339, generator_loss=27.42, generator_mel_loss=18.57, generator_kl_loss=1.366, generator_dur_loss=1.779, generator_adv_loss=1.952, generator_feat_match_loss=3.753, over 3972.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:51:17,896 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 21:51:25,980 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 21:53:43,274 INFO [train.py:527] (5/6) Epoch 215, batch 114, global_batch_idx: 26650, batch size: 68, loss[discriminator_loss=2.66, discriminator_real_loss=1.36, discriminator_fake_loss=1.3, generator_loss=27.69, generator_mel_loss=18.69, generator_kl_loss=1.344, generator_dur_loss=1.774, generator_adv_loss=1.924, generator_feat_match_loss=3.962, over 68.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.391, discriminator_fake_loss=1.342, generator_loss=27.36, generator_mel_loss=18.55, generator_kl_loss=1.368, generator_dur_loss=1.77, generator_adv_loss=1.939, generator_feat_match_loss=3.734, over 6765.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:54:09,153 INFO [train.py:919] (5/6) Start epoch 216 +2024-03-12 21:56:25,342 INFO [train.py:527] (5/6) Epoch 216, batch 40, global_batch_idx: 26700, batch size: 68, loss[discriminator_loss=2.718, discriminator_real_loss=1.39, discriminator_fake_loss=1.328, generator_loss=27.75, generator_mel_loss=18.93, generator_kl_loss=1.434, generator_dur_loss=1.751, generator_adv_loss=1.839, generator_feat_match_loss=3.795, over 68.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.397, discriminator_fake_loss=1.342, generator_loss=27.43, generator_mel_loss=18.65, generator_kl_loss=1.382, generator_dur_loss=1.749, generator_adv_loss=1.911, generator_feat_match_loss=3.739, over 2196.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 21:58:46,841 INFO [train.py:527] (5/6) Epoch 216, batch 90, global_batch_idx: 26750, batch size: 48, loss[discriminator_loss=2.75, discriminator_real_loss=1.424, discriminator_fake_loss=1.325, generator_loss=26.96, generator_mel_loss=18.6, generator_kl_loss=1.358, generator_dur_loss=1.768, generator_adv_loss=1.954, generator_feat_match_loss=3.281, over 48.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.396, discriminator_fake_loss=1.343, generator_loss=27.43, generator_mel_loss=18.61, generator_kl_loss=1.379, generator_dur_loss=1.759, generator_adv_loss=1.938, generator_feat_match_loss=3.746, over 5339.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:00:15,973 INFO [train.py:919] (5/6) Start epoch 217 +2024-03-12 22:01:21,234 INFO [train.py:527] (5/6) Epoch 217, batch 16, global_batch_idx: 26800, batch size: 25, loss[discriminator_loss=2.661, discriminator_real_loss=1.247, discriminator_fake_loss=1.414, generator_loss=28.07, generator_mel_loss=18.95, generator_kl_loss=1.522, generator_dur_loss=1.544, generator_adv_loss=2.143, generator_feat_match_loss=3.911, over 25.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.407, discriminator_fake_loss=1.34, generator_loss=27.45, generator_mel_loss=18.67, generator_kl_loss=1.384, generator_dur_loss=1.745, generator_adv_loss=1.909, generator_feat_match_loss=3.735, over 887.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:01:21,236 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 22:01:29,067 INFO [train.py:591] (5/6) 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 22:01:29,068 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 22:03:48,068 INFO [train.py:527] (5/6) Epoch 217, batch 66, global_batch_idx: 26850, batch size: 74, loss[discriminator_loss=2.768, discriminator_real_loss=1.387, discriminator_fake_loss=1.381, generator_loss=27.45, generator_mel_loss=19.11, generator_kl_loss=1.192, generator_dur_loss=1.771, generator_adv_loss=1.827, generator_feat_match_loss=3.543, over 74.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.397, discriminator_fake_loss=1.352, generator_loss=27.35, generator_mel_loss=18.56, generator_kl_loss=1.381, generator_dur_loss=1.745, generator_adv_loss=1.902, generator_feat_match_loss=3.761, over 3708.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:06:09,893 INFO [train.py:527] (5/6) Epoch 217, batch 116, global_batch_idx: 26900, batch size: 68, loss[discriminator_loss=2.738, discriminator_real_loss=1.412, discriminator_fake_loss=1.326, generator_loss=26.67, generator_mel_loss=18.09, generator_kl_loss=1.434, generator_dur_loss=1.735, generator_adv_loss=1.818, generator_feat_match_loss=3.591, over 68.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=27.38, generator_mel_loss=18.58, generator_kl_loss=1.394, generator_dur_loss=1.745, generator_adv_loss=1.908, generator_feat_match_loss=3.754, over 6512.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:06:33,389 INFO [train.py:919] (5/6) Start epoch 218 +2024-03-12 22:08:55,562 INFO [train.py:527] (5/6) Epoch 218, batch 42, global_batch_idx: 26950, batch size: 96, loss[discriminator_loss=2.742, discriminator_real_loss=1.379, discriminator_fake_loss=1.363, generator_loss=26.54, generator_mel_loss=17.99, generator_kl_loss=1.18, generator_dur_loss=1.841, generator_adv_loss=2.075, generator_feat_match_loss=3.461, over 96.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.377, discriminator_fake_loss=1.349, generator_loss=27.54, generator_mel_loss=18.56, generator_kl_loss=1.372, generator_dur_loss=1.778, generator_adv_loss=1.93, generator_feat_match_loss=3.897, over 2727.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:11:14,218 INFO [train.py:527] (5/6) Epoch 218, batch 92, global_batch_idx: 27000, batch size: 55, loss[discriminator_loss=2.811, discriminator_real_loss=1.433, discriminator_fake_loss=1.378, generator_loss=27.89, generator_mel_loss=18.85, generator_kl_loss=1.595, generator_dur_loss=1.62, generator_adv_loss=1.742, generator_feat_match_loss=4.086, over 55.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.389, discriminator_fake_loss=1.348, generator_loss=27.43, generator_mel_loss=18.58, generator_kl_loss=1.379, generator_dur_loss=1.756, generator_adv_loss=1.922, generator_feat_match_loss=3.796, over 5548.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:11:14,220 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 22:11:22,207 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 22:12:46,816 INFO [train.py:919] (5/6) Start epoch 219 +2024-03-12 22:14:00,064 INFO [train.py:527] (5/6) Epoch 219, batch 18, global_batch_idx: 27050, batch size: 36, loss[discriminator_loss=2.761, discriminator_real_loss=1.571, discriminator_fake_loss=1.191, generator_loss=27.5, generator_mel_loss=18.59, generator_kl_loss=1.409, generator_dur_loss=1.703, generator_adv_loss=2.049, generator_feat_match_loss=3.75, over 36.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.42, discriminator_fake_loss=1.349, generator_loss=28.49, generator_mel_loss=18.81, generator_kl_loss=1.401, generator_dur_loss=1.77, generator_adv_loss=2.186, generator_feat_match_loss=4.317, over 1060.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:16:19,132 INFO [train.py:527] (5/6) Epoch 219, batch 68, global_batch_idx: 27100, batch size: 48, loss[discriminator_loss=2.66, discriminator_real_loss=1.323, discriminator_fake_loss=1.337, generator_loss=27.6, generator_mel_loss=18.47, generator_kl_loss=1.465, generator_dur_loss=1.737, generator_adv_loss=1.859, generator_feat_match_loss=4.076, over 48.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.393, discriminator_fake_loss=1.342, generator_loss=27.6, generator_mel_loss=18.58, generator_kl_loss=1.392, generator_dur_loss=1.765, generator_adv_loss=1.99, generator_feat_match_loss=3.869, over 3858.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:18:34,756 INFO [train.py:527] (5/6) Epoch 219, batch 118, global_batch_idx: 27150, batch size: 31, loss[discriminator_loss=2.743, discriminator_real_loss=1.441, discriminator_fake_loss=1.302, generator_loss=27.99, generator_mel_loss=19.02, generator_kl_loss=1.534, generator_dur_loss=1.637, generator_adv_loss=2.006, generator_feat_match_loss=3.792, over 31.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.389, discriminator_fake_loss=1.346, generator_loss=27.47, generator_mel_loss=18.54, generator_kl_loss=1.4, generator_dur_loss=1.756, generator_adv_loss=1.953, generator_feat_match_loss=3.818, over 6626.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:18:49,832 INFO [train.py:919] (5/6) Start epoch 220 +2024-03-12 22:21:10,709 INFO [train.py:527] (5/6) Epoch 220, batch 44, global_batch_idx: 27200, batch size: 14, loss[discriminator_loss=2.669, discriminator_real_loss=1.348, discriminator_fake_loss=1.321, generator_loss=29.2, generator_mel_loss=18.9, generator_kl_loss=1.781, generator_dur_loss=1.599, generator_adv_loss=1.907, generator_feat_match_loss=5.009, over 14.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.385, discriminator_fake_loss=1.34, generator_loss=27.38, generator_mel_loss=18.52, generator_kl_loss=1.392, generator_dur_loss=1.748, generator_adv_loss=1.923, generator_feat_match_loss=3.79, over 2425.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:21:10,710 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 22:21:18,865 INFO [train.py:591] (5/6) 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 22:21:18,866 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 22:23:37,439 INFO [train.py:527] (5/6) Epoch 220, batch 94, global_batch_idx: 27250, batch size: 36, loss[discriminator_loss=2.688, discriminator_real_loss=1.272, discriminator_fake_loss=1.416, generator_loss=27.58, generator_mel_loss=18.56, generator_kl_loss=1.494, generator_dur_loss=1.691, generator_adv_loss=2.005, generator_feat_match_loss=3.833, over 36.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.39, discriminator_fake_loss=1.341, generator_loss=27.32, generator_mel_loss=18.51, generator_kl_loss=1.386, generator_dur_loss=1.749, generator_adv_loss=1.92, generator_feat_match_loss=3.755, over 5264.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:25:02,018 INFO [train.py:919] (5/6) Start epoch 221 +2024-03-12 22:26:20,686 INFO [train.py:527] (5/6) Epoch 221, batch 20, global_batch_idx: 27300, batch size: 61, loss[discriminator_loss=2.781, discriminator_real_loss=1.466, discriminator_fake_loss=1.315, generator_loss=26.59, generator_mel_loss=18.22, generator_kl_loss=1.425, generator_dur_loss=1.78, generator_adv_loss=1.78, generator_feat_match_loss=3.383, over 61.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.378, discriminator_fake_loss=1.359, generator_loss=27.37, generator_mel_loss=18.65, generator_kl_loss=1.377, generator_dur_loss=1.77, generator_adv_loss=1.894, generator_feat_match_loss=3.68, over 1241.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:28:38,538 INFO [train.py:527] (5/6) Epoch 221, batch 70, global_batch_idx: 27350, batch size: 52, loss[discriminator_loss=2.765, discriminator_real_loss=1.353, discriminator_fake_loss=1.413, generator_loss=27.31, generator_mel_loss=18.56, generator_kl_loss=1.418, generator_dur_loss=1.742, generator_adv_loss=2.027, generator_feat_match_loss=3.562, over 52.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.377, discriminator_fake_loss=1.359, generator_loss=27.48, generator_mel_loss=18.61, generator_kl_loss=1.371, generator_dur_loss=1.768, generator_adv_loss=1.923, generator_feat_match_loss=3.809, over 4033.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:30:57,902 INFO [train.py:527] (5/6) Epoch 221, batch 120, global_batch_idx: 27400, batch size: 88, loss[discriminator_loss=2.669, discriminator_real_loss=1.288, discriminator_fake_loss=1.381, generator_loss=27.09, generator_mel_loss=18.18, generator_kl_loss=1.384, generator_dur_loss=1.837, generator_adv_loss=1.93, generator_feat_match_loss=3.76, over 88.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.382, discriminator_fake_loss=1.355, generator_loss=27.44, generator_mel_loss=18.59, generator_kl_loss=1.384, generator_dur_loss=1.771, generator_adv_loss=1.918, generator_feat_match_loss=3.78, over 6652.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:30:57,904 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 22:31:06,763 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 22:31:17,899 INFO [train.py:919] (5/6) Start epoch 222 +2024-03-12 22:33:52,055 INFO [train.py:527] (5/6) Epoch 222, batch 46, global_batch_idx: 27450, batch size: 61, loss[discriminator_loss=2.738, discriminator_real_loss=1.298, discriminator_fake_loss=1.441, generator_loss=27.87, generator_mel_loss=18.37, generator_kl_loss=1.463, generator_dur_loss=1.762, generator_adv_loss=2.009, generator_feat_match_loss=4.266, over 61.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=27.31, generator_mel_loss=18.54, generator_kl_loss=1.351, generator_dur_loss=1.757, generator_adv_loss=1.91, generator_feat_match_loss=3.755, over 2832.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:36:11,898 INFO [train.py:527] (5/6) Epoch 222, batch 96, global_batch_idx: 27500, batch size: 16, loss[discriminator_loss=2.68, discriminator_real_loss=1.336, discriminator_fake_loss=1.343, generator_loss=29.77, generator_mel_loss=19.41, generator_kl_loss=1.746, generator_dur_loss=1.589, generator_adv_loss=1.979, generator_feat_match_loss=5.044, over 16.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.391, discriminator_fake_loss=1.349, generator_loss=27.3, generator_mel_loss=18.51, generator_kl_loss=1.376, generator_dur_loss=1.766, generator_adv_loss=1.904, generator_feat_match_loss=3.743, over 5692.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:37:23,763 INFO [train.py:919] (5/6) Start epoch 223 +2024-03-12 22:38:48,922 INFO [train.py:527] (5/6) Epoch 223, batch 22, global_batch_idx: 27550, batch size: 96, loss[discriminator_loss=2.807, discriminator_real_loss=1.433, discriminator_fake_loss=1.374, generator_loss=26.68, generator_mel_loss=17.98, generator_kl_loss=1.22, generator_dur_loss=1.839, generator_adv_loss=1.912, generator_feat_match_loss=3.73, over 96.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.389, discriminator_fake_loss=1.358, generator_loss=27.36, generator_mel_loss=18.67, generator_kl_loss=1.385, generator_dur_loss=1.726, generator_adv_loss=1.904, generator_feat_match_loss=3.673, over 1244.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:41:08,779 INFO [train.py:527] (5/6) Epoch 223, batch 72, global_batch_idx: 27600, batch size: 55, loss[discriminator_loss=2.704, discriminator_real_loss=1.371, discriminator_fake_loss=1.333, generator_loss=28.6, generator_mel_loss=19.21, generator_kl_loss=1.459, generator_dur_loss=1.737, generator_adv_loss=1.901, generator_feat_match_loss=4.299, over 55.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.381, discriminator_fake_loss=1.349, generator_loss=27.42, generator_mel_loss=18.57, generator_kl_loss=1.383, generator_dur_loss=1.75, generator_adv_loss=1.915, generator_feat_match_loss=3.8, over 4252.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:41:08,781 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 22:41:16,881 INFO [train.py:591] (5/6) 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 22:41:16,882 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 22:43:34,935 INFO [train.py:527] (5/6) Epoch 223, batch 122, global_batch_idx: 27650, batch size: 39, loss[discriminator_loss=2.798, discriminator_real_loss=1.475, discriminator_fake_loss=1.323, generator_loss=27.72, generator_mel_loss=18.78, generator_kl_loss=1.583, generator_dur_loss=1.602, generator_adv_loss=1.894, generator_feat_match_loss=3.861, over 39.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.383, discriminator_fake_loss=1.351, generator_loss=27.49, generator_mel_loss=18.61, generator_kl_loss=1.392, generator_dur_loss=1.747, generator_adv_loss=1.917, generator_feat_match_loss=3.829, over 7042.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:43:39,701 INFO [train.py:919] (5/6) Start epoch 224 +2024-03-12 22:46:18,876 INFO [train.py:527] (5/6) Epoch 224, batch 48, global_batch_idx: 27700, batch size: 56, loss[discriminator_loss=2.769, discriminator_real_loss=1.464, discriminator_fake_loss=1.305, generator_loss=27.24, generator_mel_loss=18.51, generator_kl_loss=1.482, generator_dur_loss=1.74, generator_adv_loss=1.894, generator_feat_match_loss=3.609, over 56.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.39, discriminator_fake_loss=1.345, generator_loss=27.45, generator_mel_loss=18.55, generator_kl_loss=1.376, generator_dur_loss=1.741, generator_adv_loss=1.949, generator_feat_match_loss=3.834, over 2683.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:48:41,994 INFO [train.py:527] (5/6) Epoch 224, batch 98, global_batch_idx: 27750, batch size: 26, loss[discriminator_loss=2.727, discriminator_real_loss=1.263, discriminator_fake_loss=1.463, generator_loss=27.14, generator_mel_loss=18.53, generator_kl_loss=1.624, generator_dur_loss=1.573, generator_adv_loss=1.912, generator_feat_match_loss=3.508, over 26.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.388, discriminator_fake_loss=1.347, generator_loss=27.43, generator_mel_loss=18.54, generator_kl_loss=1.373, generator_dur_loss=1.749, generator_adv_loss=1.939, generator_feat_match_loss=3.83, over 5572.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, +2024-03-12 22:49:49,751 INFO [train.py:919] (5/6) Start epoch 225 +2024-03-12 22:51:18,701 INFO [train.py:527] (5/6) Epoch 225, batch 24, global_batch_idx: 27800, batch size: 74, loss[discriminator_loss=2.741, discriminator_real_loss=1.372, discriminator_fake_loss=1.369, generator_loss=26.69, generator_mel_loss=18.39, generator_kl_loss=1.31, generator_dur_loss=1.82, generator_adv_loss=1.818, generator_feat_match_loss=3.352, over 74.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.404, discriminator_fake_loss=1.349, generator_loss=27.45, generator_mel_loss=18.59, generator_kl_loss=1.404, generator_dur_loss=1.734, generator_adv_loss=1.935, generator_feat_match_loss=3.791, over 1389.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 22:51:18,702 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 22:51:26,433 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 22:53:48,647 INFO [train.py:527] (5/6) Epoch 225, batch 74, global_batch_idx: 27850, batch size: 25, loss[discriminator_loss=3.019, discriminator_real_loss=1.417, discriminator_fake_loss=1.602, generator_loss=28.05, generator_mel_loss=19.26, generator_kl_loss=1.695, generator_dur_loss=1.53, generator_adv_loss=1.858, generator_feat_match_loss=3.714, over 25.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.395, discriminator_fake_loss=1.346, generator_loss=27.63, generator_mel_loss=18.6, generator_kl_loss=1.393, generator_dur_loss=1.74, generator_adv_loss=1.986, generator_feat_match_loss=3.905, over 4143.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 22:56:01,220 INFO [train.py:919] (5/6) Start epoch 226 +2024-03-12 22:56:23,991 INFO [train.py:527] (5/6) Epoch 226, batch 0, global_batch_idx: 27900, batch size: 47, loss[discriminator_loss=2.69, discriminator_real_loss=1.384, discriminator_fake_loss=1.306, generator_loss=26.36, generator_mel_loss=17.93, generator_kl_loss=1.32, generator_dur_loss=1.778, generator_adv_loss=1.88, generator_feat_match_loss=3.46, over 47.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.384, discriminator_fake_loss=1.306, generator_loss=26.36, generator_mel_loss=17.93, generator_kl_loss=1.32, generator_dur_loss=1.778, generator_adv_loss=1.88, generator_feat_match_loss=3.46, over 47.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 22:58:40,628 INFO [train.py:527] (5/6) Epoch 226, batch 50, global_batch_idx: 27950, batch size: 25, loss[discriminator_loss=2.797, discriminator_real_loss=1.413, discriminator_fake_loss=1.384, generator_loss=27.88, generator_mel_loss=18.7, generator_kl_loss=1.526, generator_dur_loss=1.573, generator_adv_loss=1.793, generator_feat_match_loss=4.289, over 25.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=27.53, generator_mel_loss=18.59, generator_kl_loss=1.39, generator_dur_loss=1.747, generator_adv_loss=1.92, generator_feat_match_loss=3.881, over 2803.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:00:58,237 INFO [train.py:527] (5/6) Epoch 226, batch 100, global_batch_idx: 28000, batch size: 53, loss[discriminator_loss=2.81, discriminator_real_loss=1.409, discriminator_fake_loss=1.401, generator_loss=27.58, generator_mel_loss=18.53, generator_kl_loss=1.568, generator_dur_loss=1.693, generator_adv_loss=1.982, generator_feat_match_loss=3.804, over 53.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.387, discriminator_fake_loss=1.348, generator_loss=27.46, generator_mel_loss=18.57, generator_kl_loss=1.403, generator_dur_loss=1.754, generator_adv_loss=1.918, generator_feat_match_loss=3.812, over 5714.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:00:58,239 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 23:01:07,116 INFO [train.py:591] (5/6) 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 23:01:07,117 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 23:02:12,678 INFO [train.py:919] (5/6) Start epoch 227 +2024-03-12 23:03:46,707 INFO [train.py:527] (5/6) Epoch 227, batch 26, global_batch_idx: 28050, batch size: 36, loss[discriminator_loss=2.734, discriminator_real_loss=1.48, discriminator_fake_loss=1.254, generator_loss=26.93, generator_mel_loss=18.13, generator_kl_loss=1.476, generator_dur_loss=1.74, generator_adv_loss=1.86, generator_feat_match_loss=3.72, over 36.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.392, discriminator_fake_loss=1.341, generator_loss=27.54, generator_mel_loss=18.62, generator_kl_loss=1.399, generator_dur_loss=1.759, generator_adv_loss=1.912, generator_feat_match_loss=3.846, over 1479.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:06:09,293 INFO [train.py:527] (5/6) Epoch 227, batch 76, global_batch_idx: 28100, batch size: 72, loss[discriminator_loss=2.801, discriminator_real_loss=1.406, discriminator_fake_loss=1.395, generator_loss=26.85, generator_mel_loss=18.51, generator_kl_loss=1.237, generator_dur_loss=1.802, generator_adv_loss=2.009, generator_feat_match_loss=3.29, over 72.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.383, discriminator_fake_loss=1.348, generator_loss=27.47, generator_mel_loss=18.55, generator_kl_loss=1.397, generator_dur_loss=1.777, generator_adv_loss=1.923, generator_feat_match_loss=3.818, over 4403.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:08:18,213 INFO [train.py:919] (5/6) Start epoch 228 +2024-03-12 23:08:47,037 INFO [train.py:527] (5/6) Epoch 228, batch 2, global_batch_idx: 28150, batch size: 83, loss[discriminator_loss=2.657, discriminator_real_loss=1.352, discriminator_fake_loss=1.306, generator_loss=27.45, generator_mel_loss=18.38, generator_kl_loss=1.459, generator_dur_loss=1.814, generator_adv_loss=1.902, generator_feat_match_loss=3.899, over 83.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.373, discriminator_fake_loss=1.307, generator_loss=27.57, generator_mel_loss=18.49, generator_kl_loss=1.504, generator_dur_loss=1.767, generator_adv_loss=1.942, generator_feat_match_loss=3.87, over 158.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:11:05,150 INFO [train.py:527] (5/6) Epoch 228, batch 52, global_batch_idx: 28200, batch size: 88, loss[discriminator_loss=2.73, discriminator_real_loss=1.33, discriminator_fake_loss=1.4, generator_loss=26.91, generator_mel_loss=18.15, generator_kl_loss=1.229, generator_dur_loss=1.902, generator_adv_loss=2.021, generator_feat_match_loss=3.611, over 88.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.389, discriminator_fake_loss=1.341, generator_loss=27.33, generator_mel_loss=18.48, generator_kl_loss=1.411, generator_dur_loss=1.753, generator_adv_loss=1.926, generator_feat_match_loss=3.759, over 2890.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:11:05,151 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 23:11:13,162 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 23:13:35,306 INFO [train.py:527] (5/6) Epoch 228, batch 102, global_batch_idx: 28250, batch size: 44, loss[discriminator_loss=2.807, discriminator_real_loss=1.287, discriminator_fake_loss=1.52, generator_loss=27.73, generator_mel_loss=18.78, generator_kl_loss=1.458, generator_dur_loss=1.742, generator_adv_loss=1.852, generator_feat_match_loss=3.897, over 44.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.394, discriminator_fake_loss=1.348, generator_loss=27.37, generator_mel_loss=18.54, generator_kl_loss=1.409, generator_dur_loss=1.754, generator_adv_loss=1.921, generator_feat_match_loss=3.744, over 5652.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:14:38,393 INFO [train.py:919] (5/6) Start epoch 229 +2024-03-12 23:16:23,464 INFO [train.py:527] (5/6) Epoch 229, batch 28, global_batch_idx: 28300, batch size: 72, loss[discriminator_loss=2.696, discriminator_real_loss=1.289, discriminator_fake_loss=1.406, generator_loss=28.09, generator_mel_loss=18.61, generator_kl_loss=1.262, generator_dur_loss=1.781, generator_adv_loss=2.165, generator_feat_match_loss=4.266, over 72.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.376, discriminator_fake_loss=1.362, generator_loss=27.43, generator_mel_loss=18.58, generator_kl_loss=1.346, generator_dur_loss=1.783, generator_adv_loss=1.898, generator_feat_match_loss=3.825, over 1848.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:18:44,414 INFO [train.py:527] (5/6) Epoch 229, batch 78, global_batch_idx: 28350, batch size: 42, loss[discriminator_loss=2.707, discriminator_real_loss=1.4, discriminator_fake_loss=1.307, generator_loss=27.18, generator_mel_loss=18.45, generator_kl_loss=1.54, generator_dur_loss=1.732, generator_adv_loss=1.918, generator_feat_match_loss=3.544, over 42.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.378, discriminator_fake_loss=1.345, generator_loss=27.41, generator_mel_loss=18.5, generator_kl_loss=1.379, generator_dur_loss=1.757, generator_adv_loss=1.935, generator_feat_match_loss=3.846, over 4613.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:20:53,727 INFO [train.py:919] (5/6) Start epoch 230 +2024-03-12 23:21:31,254 INFO [train.py:527] (5/6) Epoch 230, batch 4, global_batch_idx: 28400, batch size: 53, loss[discriminator_loss=2.731, discriminator_real_loss=1.47, discriminator_fake_loss=1.261, generator_loss=26.76, generator_mel_loss=18.22, generator_kl_loss=1.411, generator_dur_loss=1.685, generator_adv_loss=1.935, generator_feat_match_loss=3.507, over 53.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.438, discriminator_fake_loss=1.326, generator_loss=27.04, generator_mel_loss=18.57, generator_kl_loss=1.404, generator_dur_loss=1.733, generator_adv_loss=1.925, generator_feat_match_loss=3.409, over 281.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:21:31,257 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 23:21:39,257 INFO [train.py:591] (5/6) 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 23:21:39,259 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 23:24:02,477 INFO [train.py:527] (5/6) Epoch 230, batch 54, global_batch_idx: 28450, batch size: 17, loss[discriminator_loss=2.652, discriminator_real_loss=1.318, discriminator_fake_loss=1.335, generator_loss=29.58, generator_mel_loss=19.89, generator_kl_loss=1.876, generator_dur_loss=1.608, generator_adv_loss=1.925, generator_feat_match_loss=4.288, over 17.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.384, discriminator_fake_loss=1.348, generator_loss=27.24, generator_mel_loss=18.44, generator_kl_loss=1.364, generator_dur_loss=1.762, generator_adv_loss=1.917, generator_feat_match_loss=3.755, over 3220.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:26:23,586 INFO [train.py:527] (5/6) Epoch 230, batch 104, global_batch_idx: 28500, batch size: 45, loss[discriminator_loss=2.711, discriminator_real_loss=1.404, discriminator_fake_loss=1.307, generator_loss=28.11, generator_mel_loss=18.88, generator_kl_loss=1.544, generator_dur_loss=1.683, generator_adv_loss=1.826, generator_feat_match_loss=4.178, over 45.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.381, discriminator_fake_loss=1.348, generator_loss=27.32, generator_mel_loss=18.49, generator_kl_loss=1.371, generator_dur_loss=1.752, generator_adv_loss=1.917, generator_feat_match_loss=3.789, over 6084.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:27:17,363 INFO [train.py:919] (5/6) Start epoch 231 +2024-03-12 23:29:07,597 INFO [train.py:527] (5/6) Epoch 231, batch 30, global_batch_idx: 28550, batch size: 50, loss[discriminator_loss=2.709, discriminator_real_loss=1.308, discriminator_fake_loss=1.401, generator_loss=27.52, generator_mel_loss=18.71, generator_kl_loss=1.558, generator_dur_loss=1.684, generator_adv_loss=1.886, generator_feat_match_loss=3.683, over 50.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.382, discriminator_fake_loss=1.336, generator_loss=27.51, generator_mel_loss=18.56, generator_kl_loss=1.401, generator_dur_loss=1.741, generator_adv_loss=1.931, generator_feat_match_loss=3.876, over 1769.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:31:26,531 INFO [train.py:527] (5/6) Epoch 231, batch 80, global_batch_idx: 28600, batch size: 59, loss[discriminator_loss=2.711, discriminator_real_loss=1.464, discriminator_fake_loss=1.247, generator_loss=26.36, generator_mel_loss=17.79, generator_kl_loss=1.378, generator_dur_loss=1.753, generator_adv_loss=1.91, generator_feat_match_loss=3.531, over 59.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.387, discriminator_fake_loss=1.342, generator_loss=27.49, generator_mel_loss=18.58, generator_kl_loss=1.397, generator_dur_loss=1.751, generator_adv_loss=1.915, generator_feat_match_loss=3.846, over 4508.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:31:26,532 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 23:31:34,637 INFO [train.py:591] (5/6) 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 23:31:34,651 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 23:33:36,932 INFO [train.py:919] (5/6) Start epoch 232 +2024-03-12 23:34:17,972 INFO [train.py:527] (5/6) Epoch 232, batch 6, global_batch_idx: 28650, batch size: 42, loss[discriminator_loss=2.641, discriminator_real_loss=1.206, discriminator_fake_loss=1.435, generator_loss=28.45, generator_mel_loss=18.95, generator_kl_loss=1.48, generator_dur_loss=1.695, generator_adv_loss=1.948, generator_feat_match_loss=4.381, over 42.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.314, discriminator_fake_loss=1.362, generator_loss=27.58, generator_mel_loss=18.53, generator_kl_loss=1.404, generator_dur_loss=1.743, generator_adv_loss=1.938, generator_feat_match_loss=3.968, over 358.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:36:36,749 INFO [train.py:527] (5/6) Epoch 232, batch 56, global_batch_idx: 28700, batch size: 39, loss[discriminator_loss=2.717, discriminator_real_loss=1.31, discriminator_fake_loss=1.407, generator_loss=27.23, generator_mel_loss=18.59, generator_kl_loss=1.473, generator_dur_loss=1.669, generator_adv_loss=1.883, generator_feat_match_loss=3.621, over 39.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.376, discriminator_fake_loss=1.35, generator_loss=27.51, generator_mel_loss=18.57, generator_kl_loss=1.392, generator_dur_loss=1.753, generator_adv_loss=1.925, generator_feat_match_loss=3.866, over 3222.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:38:58,075 INFO [train.py:527] (5/6) Epoch 232, batch 106, global_batch_idx: 28750, batch size: 68, loss[discriminator_loss=2.762, discriminator_real_loss=1.299, discriminator_fake_loss=1.463, generator_loss=26.85, generator_mel_loss=18.31, generator_kl_loss=1.221, generator_dur_loss=1.835, generator_adv_loss=1.803, generator_feat_match_loss=3.681, over 68.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.377, discriminator_fake_loss=1.354, generator_loss=27.45, generator_mel_loss=18.54, generator_kl_loss=1.389, generator_dur_loss=1.742, generator_adv_loss=1.921, generator_feat_match_loss=3.861, over 6163.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:39:48,891 INFO [train.py:919] (5/6) Start epoch 233 +2024-03-12 23:41:41,843 INFO [train.py:527] (5/6) Epoch 233, batch 32, global_batch_idx: 28800, batch size: 50, loss[discriminator_loss=2.671, discriminator_real_loss=1.383, discriminator_fake_loss=1.288, generator_loss=28.09, generator_mel_loss=18.83, generator_kl_loss=1.687, generator_dur_loss=1.667, generator_adv_loss=1.862, generator_feat_match_loss=4.045, over 50.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.389, discriminator_fake_loss=1.342, generator_loss=27.37, generator_mel_loss=18.48, generator_kl_loss=1.376, generator_dur_loss=1.745, generator_adv_loss=1.925, generator_feat_match_loss=3.844, over 1878.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:41:41,845 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 23:41:49,972 INFO [train.py:591] (5/6) 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 23:41:49,973 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 23:44:12,438 INFO [train.py:527] (5/6) Epoch 233, batch 82, global_batch_idx: 28850, batch size: 53, loss[discriminator_loss=2.627, discriminator_real_loss=1.292, discriminator_fake_loss=1.336, generator_loss=28.03, generator_mel_loss=18.53, generator_kl_loss=1.552, generator_dur_loss=1.624, generator_adv_loss=1.996, generator_feat_match_loss=4.334, over 53.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.384, discriminator_fake_loss=1.356, generator_loss=27.29, generator_mel_loss=18.44, generator_kl_loss=1.396, generator_dur_loss=1.739, generator_adv_loss=1.921, generator_feat_match_loss=3.799, over 4883.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:46:10,033 INFO [train.py:919] (5/6) Start epoch 234 +2024-03-12 23:46:57,797 INFO [train.py:527] (5/6) Epoch 234, batch 8, global_batch_idx: 28900, batch size: 88, loss[discriminator_loss=2.761, discriminator_real_loss=1.414, discriminator_fake_loss=1.347, generator_loss=27.06, generator_mel_loss=18.42, generator_kl_loss=1.272, generator_dur_loss=1.853, generator_adv_loss=1.729, generator_feat_match_loss=3.79, over 88.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.394, discriminator_fake_loss=1.345, generator_loss=27.64, generator_mel_loss=18.66, generator_kl_loss=1.341, generator_dur_loss=1.76, generator_adv_loss=1.927, generator_feat_match_loss=3.945, over 559.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:49:18,874 INFO [train.py:527] (5/6) Epoch 234, batch 58, global_batch_idx: 28950, batch size: 68, loss[discriminator_loss=2.741, discriminator_real_loss=1.397, discriminator_fake_loss=1.345, generator_loss=27.55, generator_mel_loss=18.8, generator_kl_loss=1.35, generator_dur_loss=1.745, generator_adv_loss=2.06, generator_feat_match_loss=3.593, over 68.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.389, discriminator_fake_loss=1.345, generator_loss=27.57, generator_mel_loss=18.56, generator_kl_loss=1.386, generator_dur_loss=1.764, generator_adv_loss=1.951, generator_feat_match_loss=3.905, over 3500.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:51:40,469 INFO [train.py:527] (5/6) Epoch 234, batch 108, global_batch_idx: 29000, batch size: 64, loss[discriminator_loss=2.707, discriminator_real_loss=1.427, discriminator_fake_loss=1.281, generator_loss=27.26, generator_mel_loss=18.59, generator_kl_loss=1.392, generator_dur_loss=1.763, generator_adv_loss=1.952, generator_feat_match_loss=3.566, over 64.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.387, discriminator_fake_loss=1.347, generator_loss=27.52, generator_mel_loss=18.52, generator_kl_loss=1.393, generator_dur_loss=1.765, generator_adv_loss=1.942, generator_feat_match_loss=3.9, over 6177.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:51:40,471 INFO [train.py:581] (5/6) Computing validation loss +2024-03-12 23:51:49,175 INFO [train.py:591] (5/6) 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 23:51:49,176 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-12 23:52:33,624 INFO [train.py:919] (5/6) Start epoch 235 +2024-03-12 23:54:37,291 INFO [train.py:527] (5/6) Epoch 235, batch 34, global_batch_idx: 29050, batch size: 64, loss[discriminator_loss=2.712, discriminator_real_loss=1.357, discriminator_fake_loss=1.355, generator_loss=27.7, generator_mel_loss=18.5, generator_kl_loss=1.539, generator_dur_loss=1.712, generator_adv_loss=2.041, generator_feat_match_loss=3.911, over 64.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.391, discriminator_fake_loss=1.344, generator_loss=27.27, generator_mel_loss=18.37, generator_kl_loss=1.399, generator_dur_loss=1.765, generator_adv_loss=1.929, generator_feat_match_loss=3.807, over 2175.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:57:01,717 INFO [train.py:527] (5/6) Epoch 235, batch 84, global_batch_idx: 29100, batch size: 50, loss[discriminator_loss=2.695, discriminator_real_loss=1.302, discriminator_fake_loss=1.393, generator_loss=27.68, generator_mel_loss=18.48, generator_kl_loss=1.533, generator_dur_loss=1.733, generator_adv_loss=1.846, generator_feat_match_loss=4.084, over 50.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.391, discriminator_fake_loss=1.348, generator_loss=27.28, generator_mel_loss=18.4, generator_kl_loss=1.383, generator_dur_loss=1.779, generator_adv_loss=1.916, generator_feat_match_loss=3.802, over 5324.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-12 23:58:51,602 INFO [train.py:919] (5/6) Start epoch 236 +2024-03-12 23:59:45,910 INFO [train.py:527] (5/6) Epoch 236, batch 10, global_batch_idx: 29150, batch size: 66, loss[discriminator_loss=2.724, discriminator_real_loss=1.363, discriminator_fake_loss=1.361, generator_loss=28.42, generator_mel_loss=19.18, generator_kl_loss=1.404, generator_dur_loss=1.786, generator_adv_loss=1.936, generator_feat_match_loss=4.117, over 66.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.363, discriminator_fake_loss=1.371, generator_loss=27.92, generator_mel_loss=18.84, generator_kl_loss=1.395, generator_dur_loss=1.755, generator_adv_loss=1.868, generator_feat_match_loss=4.062, over 637.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:02:09,688 INFO [train.py:527] (5/6) Epoch 236, batch 60, global_batch_idx: 29200, batch size: 47, loss[discriminator_loss=2.739, discriminator_real_loss=1.252, discriminator_fake_loss=1.487, generator_loss=27.56, generator_mel_loss=18.44, generator_kl_loss=1.337, generator_dur_loss=1.727, generator_adv_loss=2.001, generator_feat_match_loss=4.051, over 47.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.38, discriminator_fake_loss=1.352, generator_loss=27.52, generator_mel_loss=18.56, generator_kl_loss=1.372, generator_dur_loss=1.781, generator_adv_loss=1.912, generator_feat_match_loss=3.889, over 3540.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:02:09,690 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 00:02:17,718 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 00:04:37,636 INFO [train.py:527] (5/6) Epoch 236, batch 110, global_batch_idx: 29250, batch size: 64, loss[discriminator_loss=2.752, discriminator_real_loss=1.32, discriminator_fake_loss=1.432, generator_loss=27.64, generator_mel_loss=18.37, generator_kl_loss=1.191, generator_dur_loss=1.814, generator_adv_loss=2.035, generator_feat_match_loss=4.231, over 64.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.382, discriminator_fake_loss=1.348, generator_loss=27.52, generator_mel_loss=18.57, generator_kl_loss=1.391, generator_dur_loss=1.77, generator_adv_loss=1.916, generator_feat_match_loss=3.876, over 6278.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:05:15,608 INFO [train.py:919] (5/6) Start epoch 237 +2024-03-13 00:07:22,756 INFO [train.py:527] (5/6) Epoch 237, batch 36, global_batch_idx: 29300, batch size: 62, loss[discriminator_loss=2.704, discriminator_real_loss=1.37, discriminator_fake_loss=1.334, generator_loss=27.53, generator_mel_loss=18.53, generator_kl_loss=1.309, generator_dur_loss=1.78, generator_adv_loss=1.855, generator_feat_match_loss=4.059, over 62.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.397, discriminator_fake_loss=1.34, generator_loss=27.4, generator_mel_loss=18.46, generator_kl_loss=1.393, generator_dur_loss=1.77, generator_adv_loss=1.926, generator_feat_match_loss=3.852, over 2102.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:09:43,825 INFO [train.py:527] (5/6) Epoch 237, batch 86, global_batch_idx: 29350, batch size: 64, loss[discriminator_loss=2.72, discriminator_real_loss=1.377, discriminator_fake_loss=1.343, generator_loss=27.89, generator_mel_loss=19.04, generator_kl_loss=1.272, generator_dur_loss=1.792, generator_adv_loss=1.865, generator_feat_match_loss=3.921, over 64.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.393, discriminator_fake_loss=1.352, generator_loss=27.36, generator_mel_loss=18.45, generator_kl_loss=1.379, generator_dur_loss=1.778, generator_adv_loss=1.919, generator_feat_match_loss=3.834, over 5013.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:11:31,999 INFO [train.py:919] (5/6) Start epoch 238 +2024-03-13 00:12:28,660 INFO [train.py:527] (5/6) Epoch 238, batch 12, global_batch_idx: 29400, batch size: 31, loss[discriminator_loss=2.664, discriminator_real_loss=1.414, discriminator_fake_loss=1.25, generator_loss=28.81, generator_mel_loss=19.38, generator_kl_loss=1.374, generator_dur_loss=1.721, generator_adv_loss=2.065, generator_feat_match_loss=4.264, over 31.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.384, discriminator_fake_loss=1.337, generator_loss=27.36, generator_mel_loss=18.39, generator_kl_loss=1.401, generator_dur_loss=1.744, generator_adv_loss=1.925, generator_feat_match_loss=3.893, over 642.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:12:28,663 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 00:12:36,532 INFO [train.py:591] (5/6) 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 00:12:36,534 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 00:14:57,986 INFO [train.py:527] (5/6) Epoch 238, batch 62, global_batch_idx: 29450, batch size: 68, loss[discriminator_loss=2.816, discriminator_real_loss=1.426, discriminator_fake_loss=1.39, generator_loss=27.4, generator_mel_loss=18.48, generator_kl_loss=1.448, generator_dur_loss=1.841, generator_adv_loss=1.955, generator_feat_match_loss=3.683, over 68.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.384, discriminator_fake_loss=1.354, generator_loss=27.37, generator_mel_loss=18.4, generator_kl_loss=1.39, generator_dur_loss=1.792, generator_adv_loss=1.936, generator_feat_match_loss=3.849, over 3667.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:17:20,346 INFO [train.py:527] (5/6) Epoch 238, batch 112, global_batch_idx: 29500, batch size: 42, loss[discriminator_loss=2.72, discriminator_real_loss=1.498, discriminator_fake_loss=1.222, generator_loss=27.74, generator_mel_loss=18.8, generator_kl_loss=1.551, generator_dur_loss=1.714, generator_adv_loss=1.995, generator_feat_match_loss=3.686, over 42.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.39, discriminator_fake_loss=1.356, generator_loss=27.34, generator_mel_loss=18.41, generator_kl_loss=1.382, generator_dur_loss=1.787, generator_adv_loss=1.934, generator_feat_match_loss=3.828, over 6583.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:17:51,104 INFO [train.py:919] (5/6) Start epoch 239 +2024-03-13 00:20:03,252 INFO [train.py:527] (5/6) Epoch 239, batch 38, global_batch_idx: 29550, batch size: 70, loss[discriminator_loss=2.78, discriminator_real_loss=1.283, discriminator_fake_loss=1.497, generator_loss=27.69, generator_mel_loss=18.8, generator_kl_loss=1.377, generator_dur_loss=1.832, generator_adv_loss=1.962, generator_feat_match_loss=3.72, over 70.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.389, discriminator_fake_loss=1.357, generator_loss=27.43, generator_mel_loss=18.52, generator_kl_loss=1.399, generator_dur_loss=1.74, generator_adv_loss=1.918, generator_feat_match_loss=3.858, over 2049.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:22:24,382 INFO [train.py:527] (5/6) Epoch 239, batch 88, global_batch_idx: 29600, batch size: 83, loss[discriminator_loss=2.659, discriminator_real_loss=1.256, discriminator_fake_loss=1.404, generator_loss=27.84, generator_mel_loss=18.78, generator_kl_loss=1.263, generator_dur_loss=1.867, generator_adv_loss=1.942, generator_feat_match_loss=3.987, over 83.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.383, discriminator_fake_loss=1.355, generator_loss=27.41, generator_mel_loss=18.47, generator_kl_loss=1.398, generator_dur_loss=1.763, generator_adv_loss=1.915, generator_feat_match_loss=3.858, over 4934.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:22:24,384 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 00:22:33,354 INFO [train.py:591] (5/6) 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 00:22:33,354 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 00:24:15,039 INFO [train.py:919] (5/6) Start epoch 240 +2024-03-13 00:25:20,322 INFO [train.py:527] (5/6) Epoch 240, batch 14, global_batch_idx: 29650, batch size: 47, loss[discriminator_loss=2.73, discriminator_real_loss=1.339, discriminator_fake_loss=1.391, generator_loss=27.82, generator_mel_loss=19, generator_kl_loss=1.462, generator_dur_loss=1.713, generator_adv_loss=1.961, generator_feat_match_loss=3.679, over 47.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.342, generator_loss=27.87, generator_mel_loss=18.76, generator_kl_loss=1.399, generator_dur_loss=1.74, generator_adv_loss=1.937, generator_feat_match_loss=4.033, over 738.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:27:41,148 INFO [train.py:527] (5/6) Epoch 240, batch 64, global_batch_idx: 29700, batch size: 72, loss[discriminator_loss=2.746, discriminator_real_loss=1.32, discriminator_fake_loss=1.426, generator_loss=28.53, generator_mel_loss=18.84, generator_kl_loss=1.347, generator_dur_loss=1.817, generator_adv_loss=2.003, generator_feat_match_loss=4.523, over 72.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.58, generator_mel_loss=18.54, generator_kl_loss=1.417, generator_dur_loss=1.741, generator_adv_loss=1.92, generator_feat_match_loss=3.962, over 3484.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:30:00,491 INFO [train.py:527] (5/6) Epoch 240, batch 114, global_batch_idx: 29750, batch size: 77, loss[discriminator_loss=2.742, discriminator_real_loss=1.3, discriminator_fake_loss=1.441, generator_loss=27.16, generator_mel_loss=18.14, generator_kl_loss=1.36, generator_dur_loss=1.839, generator_adv_loss=2, generator_feat_match_loss=3.812, over 77.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.381, discriminator_fake_loss=1.348, generator_loss=27.48, generator_mel_loss=18.51, generator_kl_loss=1.406, generator_dur_loss=1.752, generator_adv_loss=1.922, generator_feat_match_loss=3.896, over 6156.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:30:28,537 INFO [train.py:919] (5/6) Start epoch 241 +2024-03-13 00:32:44,526 INFO [train.py:527] (5/6) Epoch 241, batch 40, global_batch_idx: 29800, batch size: 52, loss[discriminator_loss=2.686, discriminator_real_loss=1.333, discriminator_fake_loss=1.353, generator_loss=27.89, generator_mel_loss=18.57, generator_kl_loss=1.288, generator_dur_loss=1.747, generator_adv_loss=2.17, generator_feat_match_loss=4.116, over 52.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.39, discriminator_fake_loss=1.351, generator_loss=27.44, generator_mel_loss=18.44, generator_kl_loss=1.372, generator_dur_loss=1.795, generator_adv_loss=1.952, generator_feat_match_loss=3.884, over 2373.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:32:44,527 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 00:32:52,458 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 00:35:13,526 INFO [train.py:527] (5/6) Epoch 241, batch 90, global_batch_idx: 29850, batch size: 80, loss[discriminator_loss=2.682, discriminator_real_loss=1.419, discriminator_fake_loss=1.263, generator_loss=27.31, generator_mel_loss=18.42, generator_kl_loss=1.352, generator_dur_loss=1.836, generator_adv_loss=1.867, generator_feat_match_loss=3.837, over 80.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.393, discriminator_fake_loss=1.347, generator_loss=27.53, generator_mel_loss=18.51, generator_kl_loss=1.392, generator_dur_loss=1.778, generator_adv_loss=1.941, generator_feat_match_loss=3.904, over 5185.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:36:43,078 INFO [train.py:919] (5/6) Start epoch 242 +2024-03-13 00:37:54,036 INFO [train.py:527] (5/6) Epoch 242, batch 16, global_batch_idx: 29900, batch size: 48, loss[discriminator_loss=2.745, discriminator_real_loss=1.368, discriminator_fake_loss=1.376, generator_loss=27.77, generator_mel_loss=18.77, generator_kl_loss=1.432, generator_dur_loss=1.712, generator_adv_loss=1.97, generator_feat_match_loss=3.89, over 48.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.377, discriminator_fake_loss=1.331, generator_loss=27.52, generator_mel_loss=18.47, generator_kl_loss=1.428, generator_dur_loss=1.764, generator_adv_loss=1.941, generator_feat_match_loss=3.918, over 923.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:40:15,149 INFO [train.py:527] (5/6) Epoch 242, batch 66, global_batch_idx: 29950, batch size: 61, loss[discriminator_loss=2.696, discriminator_real_loss=1.334, discriminator_fake_loss=1.362, generator_loss=27.9, generator_mel_loss=18.47, generator_kl_loss=1.338, generator_dur_loss=1.78, generator_adv_loss=2.218, generator_feat_match_loss=4.093, over 61.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.378, discriminator_fake_loss=1.354, generator_loss=27.5, generator_mel_loss=18.48, generator_kl_loss=1.404, generator_dur_loss=1.775, generator_adv_loss=1.939, generator_feat_match_loss=3.901, over 3837.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:42:32,721 INFO [train.py:527] (5/6) Epoch 242, batch 116, global_batch_idx: 30000, batch size: 66, loss[discriminator_loss=2.778, discriminator_real_loss=1.404, discriminator_fake_loss=1.374, generator_loss=26.67, generator_mel_loss=17.92, generator_kl_loss=1.354, generator_dur_loss=1.782, generator_adv_loss=1.988, generator_feat_match_loss=3.633, over 66.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.4, generator_mel_loss=18.44, generator_kl_loss=1.403, generator_dur_loss=1.765, generator_adv_loss=1.924, generator_feat_match_loss=3.861, over 6666.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:42:32,723 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 00:42:41,231 INFO [train.py:591] (5/6) 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 00:42:41,233 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 00:43:01,453 INFO [train.py:919] (5/6) Start epoch 243 +2024-03-13 00:45:22,467 INFO [train.py:527] (5/6) Epoch 243, batch 42, global_batch_idx: 30050, batch size: 55, loss[discriminator_loss=2.731, discriminator_real_loss=1.465, discriminator_fake_loss=1.266, generator_loss=26.94, generator_mel_loss=18.14, generator_kl_loss=1.588, generator_dur_loss=1.661, generator_adv_loss=1.79, generator_feat_match_loss=3.763, over 55.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.67, generator_mel_loss=18.6, generator_kl_loss=1.431, generator_dur_loss=1.732, generator_adv_loss=1.921, generator_feat_match_loss=3.987, over 2268.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:47:41,901 INFO [train.py:527] (5/6) Epoch 243, batch 92, global_batch_idx: 30100, batch size: 42, loss[discriminator_loss=2.761, discriminator_real_loss=1.412, discriminator_fake_loss=1.348, generator_loss=27.38, generator_mel_loss=18.44, generator_kl_loss=1.481, generator_dur_loss=1.688, generator_adv_loss=1.821, generator_feat_match_loss=3.953, over 42.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.378, discriminator_fake_loss=1.343, generator_loss=27.57, generator_mel_loss=18.51, generator_kl_loss=1.41, generator_dur_loss=1.748, generator_adv_loss=1.941, generator_feat_match_loss=3.957, over 5248.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:49:08,232 INFO [train.py:919] (5/6) Start epoch 244 +2024-03-13 00:50:23,944 INFO [train.py:527] (5/6) Epoch 244, batch 18, global_batch_idx: 30150, batch size: 53, loss[discriminator_loss=2.729, discriminator_real_loss=1.257, discriminator_fake_loss=1.472, generator_loss=27.62, generator_mel_loss=18.44, generator_kl_loss=1.163, generator_dur_loss=1.729, generator_adv_loss=2.055, generator_feat_match_loss=4.233, over 53.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.374, discriminator_fake_loss=1.352, generator_loss=27.44, generator_mel_loss=18.5, generator_kl_loss=1.401, generator_dur_loss=1.76, generator_adv_loss=1.917, generator_feat_match_loss=3.856, over 1022.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:52:43,018 INFO [train.py:527] (5/6) Epoch 244, batch 68, global_batch_idx: 30200, batch size: 66, loss[discriminator_loss=2.73, discriminator_real_loss=1.358, discriminator_fake_loss=1.372, generator_loss=27.31, generator_mel_loss=18.34, generator_kl_loss=1.29, generator_dur_loss=1.787, generator_adv_loss=1.95, generator_feat_match_loss=3.946, over 66.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.389, discriminator_fake_loss=1.346, generator_loss=27.44, generator_mel_loss=18.47, generator_kl_loss=1.388, generator_dur_loss=1.774, generator_adv_loss=1.913, generator_feat_match_loss=3.895, over 3868.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:52:43,020 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 00:52:50,820 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 00:55:09,594 INFO [train.py:527] (5/6) Epoch 244, batch 118, global_batch_idx: 30250, batch size: 58, loss[discriminator_loss=2.753, discriminator_real_loss=1.263, discriminator_fake_loss=1.49, generator_loss=27.1, generator_mel_loss=18.23, generator_kl_loss=1.435, generator_dur_loss=1.812, generator_adv_loss=1.892, generator_feat_match_loss=3.725, over 58.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.382, discriminator_fake_loss=1.35, generator_loss=27.54, generator_mel_loss=18.53, generator_kl_loss=1.393, generator_dur_loss=1.777, generator_adv_loss=1.927, generator_feat_match_loss=3.914, over 6656.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 00:55:24,053 INFO [train.py:919] (5/6) Start epoch 245 +2024-03-13 00:57:48,093 INFO [train.py:527] (5/6) Epoch 245, batch 44, global_batch_idx: 30300, batch size: 68, loss[discriminator_loss=2.747, discriminator_real_loss=1.417, discriminator_fake_loss=1.33, generator_loss=27.33, generator_mel_loss=18.49, generator_kl_loss=1.37, generator_dur_loss=1.747, generator_adv_loss=1.903, generator_feat_match_loss=3.819, over 68.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.397, discriminator_fake_loss=1.347, generator_loss=27.46, generator_mel_loss=18.52, generator_kl_loss=1.399, generator_dur_loss=1.765, generator_adv_loss=1.906, generator_feat_match_loss=3.87, over 2481.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:00:05,246 INFO [train.py:527] (5/6) Epoch 245, batch 94, global_batch_idx: 30350, batch size: 56, loss[discriminator_loss=2.746, discriminator_real_loss=1.401, discriminator_fake_loss=1.346, generator_loss=27.72, generator_mel_loss=18.42, generator_kl_loss=1.502, generator_dur_loss=1.713, generator_adv_loss=2.097, generator_feat_match_loss=3.987, over 56.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.397, discriminator_fake_loss=1.344, generator_loss=27.42, generator_mel_loss=18.48, generator_kl_loss=1.401, generator_dur_loss=1.762, generator_adv_loss=1.917, generator_feat_match_loss=3.858, over 5227.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:01:28,938 INFO [train.py:919] (5/6) Start epoch 246 +2024-03-13 01:02:46,362 INFO [train.py:527] (5/6) Epoch 246, batch 20, global_batch_idx: 30400, batch size: 66, loss[discriminator_loss=2.785, discriminator_real_loss=1.507, discriminator_fake_loss=1.278, generator_loss=26.76, generator_mel_loss=18.04, generator_kl_loss=1.267, generator_dur_loss=1.819, generator_adv_loss=1.839, generator_feat_match_loss=3.791, over 66.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.411, discriminator_fake_loss=1.348, generator_loss=27.75, generator_mel_loss=18.68, generator_kl_loss=1.446, generator_dur_loss=1.757, generator_adv_loss=1.941, generator_feat_match_loss=3.926, over 1090.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:02:46,363 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 01:02:54,161 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 01:05:15,246 INFO [train.py:527] (5/6) Epoch 246, batch 70, global_batch_idx: 30450, batch size: 59, loss[discriminator_loss=2.725, discriminator_real_loss=1.405, discriminator_fake_loss=1.32, generator_loss=27.7, generator_mel_loss=18.58, generator_kl_loss=1.445, generator_dur_loss=1.755, generator_adv_loss=2.017, generator_feat_match_loss=3.9, over 59.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.394, discriminator_fake_loss=1.357, generator_loss=27.51, generator_mel_loss=18.51, generator_kl_loss=1.401, generator_dur_loss=1.761, generator_adv_loss=1.918, generator_feat_match_loss=3.924, over 3903.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:07:31,700 INFO [train.py:527] (5/6) Epoch 246, batch 120, global_batch_idx: 30500, batch size: 48, loss[discriminator_loss=2.681, discriminator_real_loss=1.4, discriminator_fake_loss=1.281, generator_loss=27.76, generator_mel_loss=18.52, generator_kl_loss=1.413, generator_dur_loss=1.748, generator_adv_loss=1.979, generator_feat_match_loss=4.1, over 48.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.388, discriminator_fake_loss=1.354, generator_loss=27.52, generator_mel_loss=18.52, generator_kl_loss=1.4, generator_dur_loss=1.757, generator_adv_loss=1.918, generator_feat_match_loss=3.916, over 6577.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:07:41,919 INFO [train.py:919] (5/6) Start epoch 247 +2024-03-13 01:10:17,042 INFO [train.py:527] (5/6) Epoch 247, batch 46, global_batch_idx: 30550, batch size: 77, loss[discriminator_loss=2.774, discriminator_real_loss=1.362, discriminator_fake_loss=1.412, generator_loss=27.64, generator_mel_loss=18.42, generator_kl_loss=1.329, generator_dur_loss=1.829, generator_adv_loss=1.926, generator_feat_match_loss=4.138, over 77.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.391, discriminator_fake_loss=1.354, generator_loss=27.4, generator_mel_loss=18.45, generator_kl_loss=1.373, generator_dur_loss=1.814, generator_adv_loss=1.904, generator_feat_match_loss=3.86, over 2933.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:12:34,193 INFO [train.py:527] (5/6) Epoch 247, batch 96, global_batch_idx: 30600, batch size: 64, loss[discriminator_loss=2.753, discriminator_real_loss=1.412, discriminator_fake_loss=1.341, generator_loss=27.22, generator_mel_loss=18.52, generator_kl_loss=1.406, generator_dur_loss=1.773, generator_adv_loss=1.712, generator_feat_match_loss=3.812, over 64.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.388, discriminator_fake_loss=1.355, generator_loss=27.52, generator_mel_loss=18.52, generator_kl_loss=1.391, generator_dur_loss=1.796, generator_adv_loss=1.92, generator_feat_match_loss=3.893, over 5676.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:12:34,194 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 01:12:43,015 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 01:13:59,267 INFO [train.py:919] (5/6) Start epoch 248 +2024-03-13 01:15:24,483 INFO [train.py:527] (5/6) Epoch 248, batch 22, global_batch_idx: 30650, batch size: 68, loss[discriminator_loss=2.74, discriminator_real_loss=1.364, discriminator_fake_loss=1.375, generator_loss=26.38, generator_mel_loss=17.95, generator_kl_loss=1.26, generator_dur_loss=1.741, generator_adv_loss=1.859, generator_feat_match_loss=3.576, over 68.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.389, discriminator_fake_loss=1.33, generator_loss=27.34, generator_mel_loss=18.31, generator_kl_loss=1.391, generator_dur_loss=1.757, generator_adv_loss=1.944, generator_feat_match_loss=3.937, over 1273.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:17:42,692 INFO [train.py:527] (5/6) Epoch 248, batch 72, global_batch_idx: 30700, batch size: 45, loss[discriminator_loss=2.679, discriminator_real_loss=1.419, discriminator_fake_loss=1.259, generator_loss=27.14, generator_mel_loss=18.45, generator_kl_loss=1.388, generator_dur_loss=1.704, generator_adv_loss=1.955, generator_feat_match_loss=3.643, over 45.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.376, discriminator_fake_loss=1.34, generator_loss=27.43, generator_mel_loss=18.42, generator_kl_loss=1.391, generator_dur_loss=1.763, generator_adv_loss=1.929, generator_feat_match_loss=3.932, over 4054.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:20:00,084 INFO [train.py:527] (5/6) Epoch 248, batch 122, global_batch_idx: 30750, batch size: 96, loss[discriminator_loss=2.744, discriminator_real_loss=1.384, discriminator_fake_loss=1.36, generator_loss=27.82, generator_mel_loss=18.41, generator_kl_loss=1.226, generator_dur_loss=1.884, generator_adv_loss=2.043, generator_feat_match_loss=4.26, over 96.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.378, discriminator_fake_loss=1.345, generator_loss=27.52, generator_mel_loss=18.48, generator_kl_loss=1.398, generator_dur_loss=1.757, generator_adv_loss=1.921, generator_feat_match_loss=3.964, over 6699.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:20:05,609 INFO [train.py:919] (5/6) Start epoch 249 +2024-03-13 01:22:42,443 INFO [train.py:527] (5/6) Epoch 249, batch 48, global_batch_idx: 30800, batch size: 45, loss[discriminator_loss=2.733, discriminator_real_loss=1.324, discriminator_fake_loss=1.408, generator_loss=28.73, generator_mel_loss=19.16, generator_kl_loss=1.565, generator_dur_loss=1.734, generator_adv_loss=1.944, generator_feat_match_loss=4.328, over 45.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.396, discriminator_fake_loss=1.358, generator_loss=27.44, generator_mel_loss=18.56, generator_kl_loss=1.395, generator_dur_loss=1.746, generator_adv_loss=1.896, generator_feat_match_loss=3.85, over 2678.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:22:42,445 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 01:22:50,507 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 01:25:08,810 INFO [train.py:527] (5/6) Epoch 249, batch 98, global_batch_idx: 30850, batch size: 25, loss[discriminator_loss=2.729, discriminator_real_loss=1.367, discriminator_fake_loss=1.361, generator_loss=28.26, generator_mel_loss=18.58, generator_kl_loss=1.71, generator_dur_loss=1.59, generator_adv_loss=1.973, generator_feat_match_loss=4.411, over 25.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.39, discriminator_fake_loss=1.355, generator_loss=27.41, generator_mel_loss=18.48, generator_kl_loss=1.4, generator_dur_loss=1.756, generator_adv_loss=1.903, generator_feat_match_loss=3.865, over 5398.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:26:20,210 INFO [train.py:919] (5/6) Start epoch 250 +2024-03-13 01:27:49,299 INFO [train.py:527] (5/6) Epoch 250, batch 24, global_batch_idx: 30900, batch size: 59, loss[discriminator_loss=2.671, discriminator_real_loss=1.458, discriminator_fake_loss=1.213, generator_loss=27.53, generator_mel_loss=18.44, generator_kl_loss=1.246, generator_dur_loss=1.778, generator_adv_loss=1.965, generator_feat_match_loss=4.099, over 59.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.396, discriminator_fake_loss=1.338, generator_loss=27.45, generator_mel_loss=18.51, generator_kl_loss=1.362, generator_dur_loss=1.78, generator_adv_loss=1.926, generator_feat_match_loss=3.869, over 1481.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:30:07,443 INFO [train.py:527] (5/6) Epoch 250, batch 74, global_batch_idx: 30950, batch size: 55, loss[discriminator_loss=2.702, discriminator_real_loss=1.474, discriminator_fake_loss=1.228, generator_loss=27.36, generator_mel_loss=18.17, generator_kl_loss=1.471, generator_dur_loss=1.705, generator_adv_loss=1.987, generator_feat_match_loss=4.026, over 55.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.397, discriminator_fake_loss=1.344, generator_loss=27.36, generator_mel_loss=18.46, generator_kl_loss=1.386, generator_dur_loss=1.759, generator_adv_loss=1.913, generator_feat_match_loss=3.847, over 4391.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:32:28,125 INFO [train.py:919] (5/6) Start epoch 251 +2024-03-13 01:32:48,818 INFO [train.py:527] (5/6) Epoch 251, batch 0, global_batch_idx: 31000, batch size: 45, loss[discriminator_loss=2.785, discriminator_real_loss=1.389, discriminator_fake_loss=1.396, generator_loss=28.09, generator_mel_loss=19, generator_kl_loss=1.462, generator_dur_loss=1.7, generator_adv_loss=1.854, generator_feat_match_loss=4.074, over 45.00 samples.], tot_loss[discriminator_loss=2.785, discriminator_real_loss=1.389, discriminator_fake_loss=1.396, generator_loss=28.09, generator_mel_loss=19, generator_kl_loss=1.462, generator_dur_loss=1.7, generator_adv_loss=1.854, generator_feat_match_loss=4.074, over 45.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:32:48,820 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 01:32:56,661 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 01:35:15,911 INFO [train.py:527] (5/6) Epoch 251, batch 50, global_batch_idx: 31050, batch size: 52, loss[discriminator_loss=2.692, discriminator_real_loss=1.414, discriminator_fake_loss=1.278, generator_loss=27.21, generator_mel_loss=18.18, generator_kl_loss=1.604, generator_dur_loss=1.693, generator_adv_loss=1.913, generator_feat_match_loss=3.822, over 52.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.385, discriminator_fake_loss=1.336, generator_loss=27.44, generator_mel_loss=18.48, generator_kl_loss=1.396, generator_dur_loss=1.743, generator_adv_loss=1.91, generator_feat_match_loss=3.912, over 2875.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:37:37,507 INFO [train.py:527] (5/6) Epoch 251, batch 100, global_batch_idx: 31100, batch size: 61, loss[discriminator_loss=2.802, discriminator_real_loss=1.367, discriminator_fake_loss=1.435, generator_loss=26.77, generator_mel_loss=18.5, generator_kl_loss=1.176, generator_dur_loss=1.735, generator_adv_loss=1.913, generator_feat_match_loss=3.449, over 61.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.388, discriminator_fake_loss=1.342, generator_loss=27.43, generator_mel_loss=18.47, generator_kl_loss=1.381, generator_dur_loss=1.757, generator_adv_loss=1.915, generator_feat_match_loss=3.908, over 5862.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:38:39,565 INFO [train.py:919] (5/6) Start epoch 252 +2024-03-13 01:40:18,000 INFO [train.py:527] (5/6) Epoch 252, batch 26, global_batch_idx: 31150, batch size: 66, loss[discriminator_loss=2.779, discriminator_real_loss=1.393, discriminator_fake_loss=1.385, generator_loss=27.6, generator_mel_loss=18.72, generator_kl_loss=1.195, generator_dur_loss=1.763, generator_adv_loss=1.962, generator_feat_match_loss=3.963, over 66.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.383, discriminator_fake_loss=1.348, generator_loss=27.59, generator_mel_loss=18.56, generator_kl_loss=1.418, generator_dur_loss=1.745, generator_adv_loss=1.903, generator_feat_match_loss=3.967, over 1568.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:42:36,550 INFO [train.py:527] (5/6) Epoch 252, batch 76, global_batch_idx: 31200, batch size: 83, loss[discriminator_loss=2.796, discriminator_real_loss=1.379, discriminator_fake_loss=1.416, generator_loss=27.53, generator_mel_loss=18.42, generator_kl_loss=1.254, generator_dur_loss=1.81, generator_adv_loss=1.985, generator_feat_match_loss=4.052, over 83.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.385, discriminator_fake_loss=1.345, generator_loss=27.46, generator_mel_loss=18.45, generator_kl_loss=1.399, generator_dur_loss=1.755, generator_adv_loss=1.917, generator_feat_match_loss=3.943, over 4488.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:42:36,552 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 01:42:44,415 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 01:44:53,802 INFO [train.py:919] (5/6) Start epoch 253 +2024-03-13 01:45:20,385 INFO [train.py:527] (5/6) Epoch 253, batch 2, global_batch_idx: 31250, batch size: 45, loss[discriminator_loss=2.746, discriminator_real_loss=1.414, discriminator_fake_loss=1.332, generator_loss=27.23, generator_mel_loss=18.32, generator_kl_loss=1.495, generator_dur_loss=1.716, generator_adv_loss=1.949, generator_feat_match_loss=3.754, over 45.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.374, discriminator_fake_loss=1.35, generator_loss=27.43, generator_mel_loss=18.46, generator_kl_loss=1.451, generator_dur_loss=1.74, generator_adv_loss=1.913, generator_feat_match_loss=3.858, over 164.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:47:37,775 INFO [train.py:527] (5/6) Epoch 253, batch 52, global_batch_idx: 31300, batch size: 88, loss[discriminator_loss=2.721, discriminator_real_loss=1.454, discriminator_fake_loss=1.267, generator_loss=27.22, generator_mel_loss=17.85, generator_kl_loss=1.223, generator_dur_loss=1.835, generator_adv_loss=2.114, generator_feat_match_loss=4.195, over 88.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.373, discriminator_fake_loss=1.359, generator_loss=27.71, generator_mel_loss=18.49, generator_kl_loss=1.375, generator_dur_loss=1.764, generator_adv_loss=2, generator_feat_match_loss=4.089, over 3141.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:49:55,112 INFO [train.py:527] (5/6) Epoch 253, batch 102, global_batch_idx: 31350, batch size: 72, loss[discriminator_loss=2.716, discriminator_real_loss=1.389, discriminator_fake_loss=1.328, generator_loss=27.55, generator_mel_loss=18.55, generator_kl_loss=1.383, generator_dur_loss=1.804, generator_adv_loss=1.91, generator_feat_match_loss=3.902, over 72.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.38, discriminator_fake_loss=1.35, generator_loss=27.52, generator_mel_loss=18.43, generator_kl_loss=1.381, generator_dur_loss=1.755, generator_adv_loss=1.962, generator_feat_match_loss=3.99, over 5885.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:50:56,569 INFO [train.py:919] (5/6) Start epoch 254 +2024-03-13 01:52:38,353 INFO [train.py:527] (5/6) Epoch 254, batch 28, global_batch_idx: 31400, batch size: 48, loss[discriminator_loss=2.809, discriminator_real_loss=1.317, discriminator_fake_loss=1.491, generator_loss=26.65, generator_mel_loss=18.09, generator_kl_loss=1.537, generator_dur_loss=1.747, generator_adv_loss=1.937, generator_feat_match_loss=3.338, over 48.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.378, discriminator_fake_loss=1.366, generator_loss=27.6, generator_mel_loss=18.58, generator_kl_loss=1.39, generator_dur_loss=1.769, generator_adv_loss=1.883, generator_feat_match_loss=3.986, over 1617.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:52:38,355 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 01:52:46,336 INFO [train.py:591] (5/6) 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 01:52:46,338 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 01:55:03,435 INFO [train.py:527] (5/6) Epoch 254, batch 78, global_batch_idx: 31450, batch size: 70, loss[discriminator_loss=2.716, discriminator_real_loss=1.352, discriminator_fake_loss=1.365, generator_loss=27.16, generator_mel_loss=18.19, generator_kl_loss=1.362, generator_dur_loss=1.732, generator_adv_loss=1.883, generator_feat_match_loss=3.999, over 70.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.378, discriminator_fake_loss=1.355, generator_loss=27.51, generator_mel_loss=18.46, generator_kl_loss=1.379, generator_dur_loss=1.765, generator_adv_loss=1.913, generator_feat_match_loss=3.998, over 4574.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 01:57:12,575 INFO [train.py:919] (5/6) Start epoch 255 +2024-03-13 01:57:47,746 INFO [train.py:527] (5/6) Epoch 255, batch 4, global_batch_idx: 31500, batch size: 88, loss[discriminator_loss=2.703, discriminator_real_loss=1.357, discriminator_fake_loss=1.346, generator_loss=28.08, generator_mel_loss=18.53, generator_kl_loss=1.443, generator_dur_loss=1.824, generator_adv_loss=1.902, generator_feat_match_loss=4.379, over 88.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.378, discriminator_fake_loss=1.345, generator_loss=27.7, generator_mel_loss=18.53, generator_kl_loss=1.374, generator_dur_loss=1.78, generator_adv_loss=1.925, generator_feat_match_loss=4.097, over 372.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:00:05,952 INFO [train.py:527] (5/6) Epoch 255, batch 54, global_batch_idx: 31550, batch size: 68, loss[discriminator_loss=2.715, discriminator_real_loss=1.392, discriminator_fake_loss=1.323, generator_loss=26.84, generator_mel_loss=18.19, generator_kl_loss=1.401, generator_dur_loss=1.775, generator_adv_loss=1.747, generator_feat_match_loss=3.727, over 68.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=27.41, generator_mel_loss=18.4, generator_kl_loss=1.368, generator_dur_loss=1.762, generator_adv_loss=1.921, generator_feat_match_loss=3.959, over 3333.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:02:24,336 INFO [train.py:527] (5/6) Epoch 255, batch 104, global_batch_idx: 31600, batch size: 53, loss[discriminator_loss=2.793, discriminator_real_loss=1.42, discriminator_fake_loss=1.373, generator_loss=26.47, generator_mel_loss=18.03, generator_kl_loss=1.522, generator_dur_loss=1.722, generator_adv_loss=1.825, generator_feat_match_loss=3.371, over 53.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.376, discriminator_fake_loss=1.344, generator_loss=27.52, generator_mel_loss=18.43, generator_kl_loss=1.373, generator_dur_loss=1.767, generator_adv_loss=1.961, generator_feat_match_loss=3.986, over 6378.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:02:24,338 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 02:02:33,142 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 02:03:26,447 INFO [train.py:919] (5/6) Start epoch 256 +2024-03-13 02:05:13,709 INFO [train.py:527] (5/6) Epoch 256, batch 30, global_batch_idx: 31650, batch size: 15, loss[discriminator_loss=2.876, discriminator_real_loss=1.61, discriminator_fake_loss=1.266, generator_loss=27.36, generator_mel_loss=18.82, generator_kl_loss=1.601, generator_dur_loss=1.609, generator_adv_loss=1.732, generator_feat_match_loss=3.599, over 15.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.383, discriminator_fake_loss=1.357, generator_loss=27.59, generator_mel_loss=18.49, generator_kl_loss=1.385, generator_dur_loss=1.781, generator_adv_loss=1.931, generator_feat_match_loss=4.002, over 1757.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:07:33,012 INFO [train.py:527] (5/6) Epoch 256, batch 80, global_batch_idx: 31700, batch size: 50, loss[discriminator_loss=2.672, discriminator_real_loss=1.438, discriminator_fake_loss=1.233, generator_loss=27.19, generator_mel_loss=18.06, generator_kl_loss=1.413, generator_dur_loss=1.665, generator_adv_loss=1.92, generator_feat_match_loss=4.134, over 50.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=27.38, generator_mel_loss=18.4, generator_kl_loss=1.368, generator_dur_loss=1.78, generator_adv_loss=1.917, generator_feat_match_loss=3.92, over 4729.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:09:31,586 INFO [train.py:919] (5/6) Start epoch 257 +2024-03-13 02:10:12,128 INFO [train.py:527] (5/6) Epoch 257, batch 6, global_batch_idx: 31750, batch size: 80, loss[discriminator_loss=2.775, discriminator_real_loss=1.438, discriminator_fake_loss=1.337, generator_loss=26.97, generator_mel_loss=18.2, generator_kl_loss=1.274, generator_dur_loss=1.819, generator_adv_loss=1.84, generator_feat_match_loss=3.837, over 80.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.393, discriminator_fake_loss=1.354, generator_loss=27.47, generator_mel_loss=18.61, generator_kl_loss=1.355, generator_dur_loss=1.756, generator_adv_loss=1.901, generator_feat_match_loss=3.851, over 398.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:12:30,468 INFO [train.py:527] (5/6) Epoch 257, batch 56, global_batch_idx: 31800, batch size: 59, loss[discriminator_loss=2.707, discriminator_real_loss=1.361, discriminator_fake_loss=1.345, generator_loss=27.85, generator_mel_loss=18.65, generator_kl_loss=1.462, generator_dur_loss=1.759, generator_adv_loss=1.99, generator_feat_match_loss=3.989, over 59.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.391, discriminator_fake_loss=1.36, generator_loss=27.25, generator_mel_loss=18.35, generator_kl_loss=1.385, generator_dur_loss=1.753, generator_adv_loss=1.898, generator_feat_match_loss=3.867, over 3179.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:12:30,469 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 02:12:38,372 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 02:14:57,217 INFO [train.py:527] (5/6) Epoch 257, batch 106, global_batch_idx: 31850, batch size: 47, loss[discriminator_loss=2.742, discriminator_real_loss=1.304, discriminator_fake_loss=1.438, generator_loss=27.76, generator_mel_loss=18.6, generator_kl_loss=1.513, generator_dur_loss=1.681, generator_adv_loss=1.879, generator_feat_match_loss=4.086, over 47.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.378, discriminator_fake_loss=1.358, generator_loss=27.41, generator_mel_loss=18.39, generator_kl_loss=1.38, generator_dur_loss=1.769, generator_adv_loss=1.905, generator_feat_match_loss=3.961, over 6348.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:15:44,101 INFO [train.py:919] (5/6) Start epoch 258 +2024-03-13 02:17:39,475 INFO [train.py:527] (5/6) Epoch 258, batch 32, global_batch_idx: 31900, batch size: 53, loss[discriminator_loss=2.777, discriminator_real_loss=1.461, discriminator_fake_loss=1.316, generator_loss=27.18, generator_mel_loss=18.18, generator_kl_loss=1.519, generator_dur_loss=1.774, generator_adv_loss=1.84, generator_feat_match_loss=3.859, over 53.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.385, discriminator_fake_loss=1.352, generator_loss=27.36, generator_mel_loss=18.41, generator_kl_loss=1.38, generator_dur_loss=1.742, generator_adv_loss=1.905, generator_feat_match_loss=3.932, over 1855.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:19:57,436 INFO [train.py:527] (5/6) Epoch 258, batch 82, global_batch_idx: 31950, batch size: 50, loss[discriminator_loss=2.771, discriminator_real_loss=1.412, discriminator_fake_loss=1.359, generator_loss=27.04, generator_mel_loss=18.5, generator_kl_loss=1.409, generator_dur_loss=1.739, generator_adv_loss=1.937, generator_feat_match_loss=3.459, over 50.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.382, discriminator_fake_loss=1.347, generator_loss=27.47, generator_mel_loss=18.41, generator_kl_loss=1.391, generator_dur_loss=1.767, generator_adv_loss=1.936, generator_feat_match_loss=3.97, over 4747.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:21:52,177 INFO [train.py:919] (5/6) Start epoch 259 +2024-03-13 02:22:39,483 INFO [train.py:527] (5/6) Epoch 259, batch 8, global_batch_idx: 32000, batch size: 55, loss[discriminator_loss=2.75, discriminator_real_loss=1.469, discriminator_fake_loss=1.281, generator_loss=27.85, generator_mel_loss=18.94, generator_kl_loss=1.414, generator_dur_loss=1.713, generator_adv_loss=1.836, generator_feat_match_loss=3.948, over 55.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.414, discriminator_fake_loss=1.342, generator_loss=27.53, generator_mel_loss=18.55, generator_kl_loss=1.433, generator_dur_loss=1.72, generator_adv_loss=1.9, generator_feat_match_loss=3.928, over 459.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:22:39,486 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 02:22:47,455 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 02:25:08,186 INFO [train.py:527] (5/6) Epoch 259, batch 58, global_batch_idx: 32050, batch size: 16, loss[discriminator_loss=2.691, discriminator_real_loss=1.392, discriminator_fake_loss=1.299, generator_loss=28.25, generator_mel_loss=18.72, generator_kl_loss=1.7, generator_dur_loss=1.489, generator_adv_loss=1.829, generator_feat_match_loss=4.504, over 16.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.395, discriminator_fake_loss=1.347, generator_loss=27.43, generator_mel_loss=18.44, generator_kl_loss=1.392, generator_dur_loss=1.766, generator_adv_loss=1.919, generator_feat_match_loss=3.905, over 3288.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:27:26,512 INFO [train.py:527] (5/6) Epoch 259, batch 108, global_batch_idx: 32100, batch size: 66, loss[discriminator_loss=2.691, discriminator_real_loss=1.377, discriminator_fake_loss=1.314, generator_loss=27.25, generator_mel_loss=18.47, generator_kl_loss=1.346, generator_dur_loss=1.82, generator_adv_loss=1.929, generator_feat_match_loss=3.69, over 66.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.385, discriminator_fake_loss=1.351, generator_loss=27.52, generator_mel_loss=18.46, generator_kl_loss=1.406, generator_dur_loss=1.767, generator_adv_loss=1.922, generator_feat_match_loss=3.963, over 6171.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:28:09,816 INFO [train.py:919] (5/6) Start epoch 260 +2024-03-13 02:30:09,128 INFO [train.py:527] (5/6) Epoch 260, batch 34, global_batch_idx: 32150, batch size: 59, loss[discriminator_loss=2.707, discriminator_real_loss=1.389, discriminator_fake_loss=1.318, generator_loss=27.24, generator_mel_loss=18.07, generator_kl_loss=1.503, generator_dur_loss=1.821, generator_adv_loss=1.91, generator_feat_match_loss=3.942, over 59.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.387, discriminator_fake_loss=1.345, generator_loss=27.39, generator_mel_loss=18.42, generator_kl_loss=1.354, generator_dur_loss=1.781, generator_adv_loss=1.908, generator_feat_match_loss=3.929, over 2138.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:32:29,168 INFO [train.py:527] (5/6) Epoch 260, batch 84, global_batch_idx: 32200, batch size: 53, loss[discriminator_loss=2.716, discriminator_real_loss=1.304, discriminator_fake_loss=1.412, generator_loss=28.59, generator_mel_loss=18.85, generator_kl_loss=1.444, generator_dur_loss=1.727, generator_adv_loss=2.093, generator_feat_match_loss=4.469, over 53.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.39, discriminator_fake_loss=1.347, generator_loss=27.41, generator_mel_loss=18.41, generator_kl_loss=1.367, generator_dur_loss=1.779, generator_adv_loss=1.909, generator_feat_match_loss=3.953, over 4863.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:32:29,169 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 02:32:37,975 INFO [train.py:591] (5/6) 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 02:32:37,977 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 02:34:27,245 INFO [train.py:919] (5/6) Start epoch 261 +2024-03-13 02:35:21,371 INFO [train.py:527] (5/6) Epoch 261, batch 10, global_batch_idx: 32250, batch size: 48, loss[discriminator_loss=2.837, discriminator_real_loss=1.357, discriminator_fake_loss=1.48, generator_loss=26.71, generator_mel_loss=17.92, generator_kl_loss=1.455, generator_dur_loss=1.667, generator_adv_loss=1.963, generator_feat_match_loss=3.7, over 48.00 samples.], tot_loss[discriminator_loss=2.773, discriminator_real_loss=1.4, discriminator_fake_loss=1.373, generator_loss=27.35, generator_mel_loss=18.47, generator_kl_loss=1.377, generator_dur_loss=1.737, generator_adv_loss=1.903, generator_feat_match_loss=3.87, over 608.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:37:37,340 INFO [train.py:527] (5/6) Epoch 261, batch 60, global_batch_idx: 32300, batch size: 70, loss[discriminator_loss=2.739, discriminator_real_loss=1.4, discriminator_fake_loss=1.339, generator_loss=28.35, generator_mel_loss=18.98, generator_kl_loss=1.318, generator_dur_loss=1.824, generator_adv_loss=1.985, generator_feat_match_loss=4.236, over 70.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.386, discriminator_fake_loss=1.358, generator_loss=27.43, generator_mel_loss=18.39, generator_kl_loss=1.396, generator_dur_loss=1.755, generator_adv_loss=1.917, generator_feat_match_loss=3.974, over 3557.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:39:54,464 INFO [train.py:527] (5/6) Epoch 261, batch 110, global_batch_idx: 32350, batch size: 96, loss[discriminator_loss=2.766, discriminator_real_loss=1.361, discriminator_fake_loss=1.404, generator_loss=26.81, generator_mel_loss=18.2, generator_kl_loss=1.329, generator_dur_loss=1.861, generator_adv_loss=1.908, generator_feat_match_loss=3.508, over 96.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.383, discriminator_fake_loss=1.355, generator_loss=27.4, generator_mel_loss=18.4, generator_kl_loss=1.383, generator_dur_loss=1.766, generator_adv_loss=1.912, generator_feat_match_loss=3.944, over 6673.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:40:30,859 INFO [train.py:919] (5/6) Start epoch 262 +2024-03-13 02:42:32,855 INFO [train.py:527] (5/6) Epoch 262, batch 36, global_batch_idx: 32400, batch size: 56, loss[discriminator_loss=2.658, discriminator_real_loss=1.392, discriminator_fake_loss=1.266, generator_loss=27.68, generator_mel_loss=18.48, generator_kl_loss=1.237, generator_dur_loss=1.679, generator_adv_loss=1.972, generator_feat_match_loss=4.31, over 56.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.38, discriminator_fake_loss=1.351, generator_loss=27.58, generator_mel_loss=18.5, generator_kl_loss=1.405, generator_dur_loss=1.759, generator_adv_loss=1.912, generator_feat_match_loss=4, over 2146.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:42:32,856 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 02:42:41,013 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 02:45:03,109 INFO [train.py:527] (5/6) Epoch 262, batch 86, global_batch_idx: 32450, batch size: 39, loss[discriminator_loss=2.719, discriminator_real_loss=1.367, discriminator_fake_loss=1.352, generator_loss=27.21, generator_mel_loss=18.36, generator_kl_loss=1.635, generator_dur_loss=1.695, generator_adv_loss=2.021, generator_feat_match_loss=3.5, over 39.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.55, generator_mel_loss=18.43, generator_kl_loss=1.394, generator_dur_loss=1.766, generator_adv_loss=1.936, generator_feat_match_loss=4.024, over 5123.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:46:47,953 INFO [train.py:919] (5/6) Start epoch 263 +2024-03-13 02:47:44,538 INFO [train.py:527] (5/6) Epoch 263, batch 12, global_batch_idx: 32500, batch size: 64, loss[discriminator_loss=2.745, discriminator_real_loss=1.385, discriminator_fake_loss=1.36, generator_loss=28.35, generator_mel_loss=18.96, generator_kl_loss=1.458, generator_dur_loss=1.791, generator_adv_loss=1.825, generator_feat_match_loss=4.312, over 64.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.361, discriminator_fake_loss=1.347, generator_loss=27.65, generator_mel_loss=18.48, generator_kl_loss=1.364, generator_dur_loss=1.757, generator_adv_loss=1.918, generator_feat_match_loss=4.133, over 822.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:50:03,561 INFO [train.py:527] (5/6) Epoch 263, batch 62, global_batch_idx: 32550, batch size: 80, loss[discriminator_loss=2.745, discriminator_real_loss=1.487, discriminator_fake_loss=1.258, generator_loss=26.13, generator_mel_loss=18.09, generator_kl_loss=1.275, generator_dur_loss=1.825, generator_adv_loss=1.741, generator_feat_match_loss=3.205, over 80.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.394, discriminator_fake_loss=1.341, generator_loss=27.49, generator_mel_loss=18.44, generator_kl_loss=1.4, generator_dur_loss=1.751, generator_adv_loss=1.927, generator_feat_match_loss=3.97, over 3641.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:52:22,820 INFO [train.py:527] (5/6) Epoch 263, batch 112, global_batch_idx: 32600, batch size: 62, loss[discriminator_loss=2.801, discriminator_real_loss=1.505, discriminator_fake_loss=1.296, generator_loss=27.3, generator_mel_loss=18.33, generator_kl_loss=1.291, generator_dur_loss=1.776, generator_adv_loss=1.877, generator_feat_match_loss=4.025, over 62.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.391, discriminator_fake_loss=1.348, generator_loss=27.45, generator_mel_loss=18.42, generator_kl_loss=1.399, generator_dur_loss=1.757, generator_adv_loss=1.916, generator_feat_match_loss=3.963, over 6477.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:52:22,822 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 02:52:31,661 INFO [train.py:591] (5/6) 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 02:52:31,662 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 02:53:03,848 INFO [train.py:919] (5/6) Start epoch 264 +2024-03-13 02:55:15,666 INFO [train.py:527] (5/6) Epoch 264, batch 38, global_batch_idx: 32650, batch size: 45, loss[discriminator_loss=2.724, discriminator_real_loss=1.432, discriminator_fake_loss=1.293, generator_loss=27.26, generator_mel_loss=18.29, generator_kl_loss=1.544, generator_dur_loss=1.682, generator_adv_loss=2.031, generator_feat_match_loss=3.712, over 45.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.393, discriminator_fake_loss=1.351, generator_loss=27.39, generator_mel_loss=18.39, generator_kl_loss=1.393, generator_dur_loss=1.761, generator_adv_loss=1.926, generator_feat_match_loss=3.913, over 2230.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:57:36,766 INFO [train.py:527] (5/6) Epoch 264, batch 88, global_batch_idx: 32700, batch size: 55, loss[discriminator_loss=2.798, discriminator_real_loss=1.482, discriminator_fake_loss=1.316, generator_loss=26.17, generator_mel_loss=18.15, generator_kl_loss=1.374, generator_dur_loss=1.694, generator_adv_loss=1.766, generator_feat_match_loss=3.188, over 55.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.391, discriminator_fake_loss=1.352, generator_loss=27.35, generator_mel_loss=18.37, generator_kl_loss=1.379, generator_dur_loss=1.759, generator_adv_loss=1.926, generator_feat_match_loss=3.913, over 5118.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 02:59:13,340 INFO [train.py:919] (5/6) Start epoch 265 +2024-03-13 03:00:17,346 INFO [train.py:527] (5/6) Epoch 265, batch 14, global_batch_idx: 32750, batch size: 31, loss[discriminator_loss=2.754, discriminator_real_loss=1.471, discriminator_fake_loss=1.282, generator_loss=26.36, generator_mel_loss=17.85, generator_kl_loss=1.332, generator_dur_loss=1.648, generator_adv_loss=1.965, generator_feat_match_loss=3.563, over 31.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.396, discriminator_fake_loss=1.365, generator_loss=27.18, generator_mel_loss=18.33, generator_kl_loss=1.347, generator_dur_loss=1.763, generator_adv_loss=1.875, generator_feat_match_loss=3.873, over 946.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 03:02:39,891 INFO [train.py:527] (5/6) Epoch 265, batch 64, global_batch_idx: 32800, batch size: 64, loss[discriminator_loss=2.752, discriminator_real_loss=1.359, discriminator_fake_loss=1.393, generator_loss=26.87, generator_mel_loss=18.38, generator_kl_loss=1.267, generator_dur_loss=1.771, generator_adv_loss=1.853, generator_feat_match_loss=3.604, over 64.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.38, discriminator_fake_loss=1.356, generator_loss=27.4, generator_mel_loss=18.39, generator_kl_loss=1.366, generator_dur_loss=1.761, generator_adv_loss=1.908, generator_feat_match_loss=3.976, over 3887.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 03:02:39,893 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 03:02:48,050 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 03:05:03,301 INFO [train.py:527] (5/6) Epoch 265, batch 114, global_batch_idx: 32850, batch size: 48, loss[discriminator_loss=2.672, discriminator_real_loss=1.391, discriminator_fake_loss=1.281, generator_loss=27.21, generator_mel_loss=18.29, generator_kl_loss=1.531, generator_dur_loss=1.704, generator_adv_loss=1.881, generator_feat_match_loss=3.804, over 48.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.39, generator_mel_loss=18.37, generator_kl_loss=1.379, generator_dur_loss=1.759, generator_adv_loss=1.915, generator_feat_match_loss=3.971, over 6746.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, +2024-03-13 03:05:29,183 INFO [train.py:919] (5/6) Start epoch 266 +2024-03-13 03:07:44,019 INFO [train.py:527] (5/6) Epoch 266, batch 40, global_batch_idx: 32900, batch size: 96, loss[discriminator_loss=2.717, discriminator_real_loss=1.402, discriminator_fake_loss=1.315, generator_loss=27.7, generator_mel_loss=18.7, generator_kl_loss=1.223, generator_dur_loss=1.87, generator_adv_loss=1.86, generator_feat_match_loss=4.049, over 96.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.383, discriminator_fake_loss=1.337, generator_loss=27.57, generator_mel_loss=18.47, generator_kl_loss=1.397, generator_dur_loss=1.749, generator_adv_loss=1.929, generator_feat_match_loss=4.024, over 2492.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:10:05,511 INFO [train.py:527] (5/6) Epoch 266, batch 90, global_batch_idx: 32950, batch size: 83, loss[discriminator_loss=2.723, discriminator_real_loss=1.441, discriminator_fake_loss=1.282, generator_loss=27.59, generator_mel_loss=18.14, generator_kl_loss=1.213, generator_dur_loss=1.878, generator_adv_loss=2.103, generator_feat_match_loss=4.254, over 83.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.383, discriminator_fake_loss=1.349, generator_loss=27.47, generator_mel_loss=18.38, generator_kl_loss=1.378, generator_dur_loss=1.762, generator_adv_loss=1.928, generator_feat_match_loss=4.021, over 5499.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:11:35,658 INFO [train.py:919] (5/6) Start epoch 267 +2024-03-13 03:12:46,534 INFO [train.py:527] (5/6) Epoch 267, batch 16, global_batch_idx: 33000, batch size: 48, loss[discriminator_loss=2.761, discriminator_real_loss=1.406, discriminator_fake_loss=1.354, generator_loss=27.44, generator_mel_loss=18.61, generator_kl_loss=1.32, generator_dur_loss=1.676, generator_adv_loss=1.914, generator_feat_match_loss=3.921, over 48.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.383, discriminator_fake_loss=1.35, generator_loss=27.7, generator_mel_loss=18.51, generator_kl_loss=1.372, generator_dur_loss=1.775, generator_adv_loss=1.922, generator_feat_match_loss=4.127, over 1032.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:12:46,535 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 03:12:54,395 INFO [train.py:591] (5/6) 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 03:12:54,396 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 03:15:11,773 INFO [train.py:527] (5/6) Epoch 267, batch 66, global_batch_idx: 33050, batch size: 83, loss[discriminator_loss=2.738, discriminator_real_loss=1.343, discriminator_fake_loss=1.395, generator_loss=28.06, generator_mel_loss=18.54, generator_kl_loss=1.34, generator_dur_loss=1.863, generator_adv_loss=1.96, generator_feat_match_loss=4.35, over 83.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.384, discriminator_fake_loss=1.341, generator_loss=27.59, generator_mel_loss=18.44, generator_kl_loss=1.397, generator_dur_loss=1.764, generator_adv_loss=1.918, generator_feat_match_loss=4.071, over 3786.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:17:28,109 INFO [train.py:527] (5/6) Epoch 267, batch 116, global_batch_idx: 33100, batch size: 17, loss[discriminator_loss=2.723, discriminator_real_loss=1.339, discriminator_fake_loss=1.384, generator_loss=28.62, generator_mel_loss=19.03, generator_kl_loss=1.811, generator_dur_loss=1.459, generator_adv_loss=1.904, generator_feat_match_loss=4.419, over 17.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.382, discriminator_fake_loss=1.343, generator_loss=27.6, generator_mel_loss=18.44, generator_kl_loss=1.399, generator_dur_loss=1.764, generator_adv_loss=1.923, generator_feat_match_loss=4.071, over 6757.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:17:51,130 INFO [train.py:919] (5/6) Start epoch 268 +2024-03-13 03:20:12,501 INFO [train.py:527] (5/6) Epoch 268, batch 42, global_batch_idx: 33150, batch size: 56, loss[discriminator_loss=2.71, discriminator_real_loss=1.282, discriminator_fake_loss=1.428, generator_loss=27.99, generator_mel_loss=18.82, generator_kl_loss=1.448, generator_dur_loss=1.686, generator_adv_loss=2.062, generator_feat_match_loss=3.97, over 56.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.382, discriminator_fake_loss=1.33, generator_loss=27.52, generator_mel_loss=18.37, generator_kl_loss=1.428, generator_dur_loss=1.767, generator_adv_loss=1.963, generator_feat_match_loss=3.99, over 2385.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:22:32,498 INFO [train.py:527] (5/6) Epoch 268, batch 92, global_batch_idx: 33200, batch size: 42, loss[discriminator_loss=2.647, discriminator_real_loss=1.386, discriminator_fake_loss=1.261, generator_loss=28.56, generator_mel_loss=18.65, generator_kl_loss=1.468, generator_dur_loss=1.679, generator_adv_loss=1.948, generator_feat_match_loss=4.811, over 42.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.385, discriminator_fake_loss=1.337, generator_loss=27.46, generator_mel_loss=18.34, generator_kl_loss=1.419, generator_dur_loss=1.775, generator_adv_loss=1.938, generator_feat_match_loss=3.985, over 5334.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:22:32,500 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 03:22:40,300 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 03:24:07,773 INFO [train.py:919] (5/6) Start epoch 269 +2024-03-13 03:25:22,506 INFO [train.py:527] (5/6) Epoch 269, batch 18, global_batch_idx: 33250, batch size: 45, loss[discriminator_loss=2.734, discriminator_real_loss=1.299, discriminator_fake_loss=1.435, generator_loss=28.13, generator_mel_loss=18.24, generator_kl_loss=1.476, generator_dur_loss=1.669, generator_adv_loss=1.902, generator_feat_match_loss=4.849, over 45.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.375, discriminator_fake_loss=1.375, generator_loss=27.42, generator_mel_loss=18.31, generator_kl_loss=1.363, generator_dur_loss=1.778, generator_adv_loss=1.912, generator_feat_match_loss=4.052, over 1162.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:27:42,957 INFO [train.py:527] (5/6) Epoch 269, batch 68, global_batch_idx: 33300, batch size: 36, loss[discriminator_loss=2.704, discriminator_real_loss=1.351, discriminator_fake_loss=1.352, generator_loss=27.45, generator_mel_loss=17.97, generator_kl_loss=1.45, generator_dur_loss=1.71, generator_adv_loss=1.92, generator_feat_match_loss=4.4, over 36.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.385, discriminator_fake_loss=1.363, generator_loss=27.39, generator_mel_loss=18.32, generator_kl_loss=1.365, generator_dur_loss=1.776, generator_adv_loss=1.922, generator_feat_match_loss=4.009, over 4044.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:30:02,499 INFO [train.py:527] (5/6) Epoch 269, batch 118, global_batch_idx: 33350, batch size: 66, loss[discriminator_loss=2.802, discriminator_real_loss=1.399, discriminator_fake_loss=1.403, generator_loss=27.41, generator_mel_loss=18.4, generator_kl_loss=1.289, generator_dur_loss=1.764, generator_adv_loss=1.776, generator_feat_match_loss=4.177, over 66.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.382, discriminator_fake_loss=1.363, generator_loss=27.42, generator_mel_loss=18.35, generator_kl_loss=1.366, generator_dur_loss=1.772, generator_adv_loss=1.919, generator_feat_match_loss=4.011, over 6730.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:30:19,116 INFO [train.py:919] (5/6) Start epoch 270 +2024-03-13 03:32:45,215 INFO [train.py:527] (5/6) Epoch 270, batch 44, global_batch_idx: 33400, batch size: 64, loss[discriminator_loss=2.667, discriminator_real_loss=1.362, discriminator_fake_loss=1.305, generator_loss=28.69, generator_mel_loss=18.51, generator_kl_loss=1.296, generator_dur_loss=1.726, generator_adv_loss=2.052, generator_feat_match_loss=5.11, over 64.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.38, discriminator_fake_loss=1.351, generator_loss=27.66, generator_mel_loss=18.49, generator_kl_loss=1.426, generator_dur_loss=1.734, generator_adv_loss=1.918, generator_feat_match_loss=4.095, over 2479.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:32:45,217 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 03:32:53,132 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 03:35:15,402 INFO [train.py:527] (5/6) Epoch 270, batch 94, global_batch_idx: 33450, batch size: 66, loss[discriminator_loss=2.797, discriminator_real_loss=1.418, discriminator_fake_loss=1.379, generator_loss=27.8, generator_mel_loss=18.34, generator_kl_loss=1.458, generator_dur_loss=1.81, generator_adv_loss=1.969, generator_feat_match_loss=4.216, over 66.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.378, discriminator_fake_loss=1.349, generator_loss=27.61, generator_mel_loss=18.4, generator_kl_loss=1.424, generator_dur_loss=1.741, generator_adv_loss=1.943, generator_feat_match_loss=4.108, over 5405.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:36:34,423 INFO [train.py:919] (5/6) Start epoch 271 +2024-03-13 03:37:54,868 INFO [train.py:527] (5/6) Epoch 271, batch 20, global_batch_idx: 33500, batch size: 72, loss[discriminator_loss=2.775, discriminator_real_loss=1.463, discriminator_fake_loss=1.312, generator_loss=27.07, generator_mel_loss=18.16, generator_kl_loss=1.37, generator_dur_loss=1.737, generator_adv_loss=1.89, generator_feat_match_loss=3.912, over 72.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.393, discriminator_fake_loss=1.343, generator_loss=27.39, generator_mel_loss=18.31, generator_kl_loss=1.394, generator_dur_loss=1.77, generator_adv_loss=1.926, generator_feat_match_loss=3.994, over 1255.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:40:09,442 INFO [train.py:527] (5/6) Epoch 271, batch 70, global_batch_idx: 33550, batch size: 61, loss[discriminator_loss=2.746, discriminator_real_loss=1.287, discriminator_fake_loss=1.459, generator_loss=26.42, generator_mel_loss=17.97, generator_kl_loss=1.454, generator_dur_loss=1.726, generator_adv_loss=1.947, generator_feat_match_loss=3.322, over 61.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.383, discriminator_fake_loss=1.353, generator_loss=27.45, generator_mel_loss=18.4, generator_kl_loss=1.384, generator_dur_loss=1.756, generator_adv_loss=1.908, generator_feat_match_loss=3.996, over 4243.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:42:31,239 INFO [train.py:527] (5/6) Epoch 271, batch 120, global_batch_idx: 33600, batch size: 25, loss[discriminator_loss=2.75, discriminator_real_loss=1.35, discriminator_fake_loss=1.4, generator_loss=28.82, generator_mel_loss=18.68, generator_kl_loss=1.789, generator_dur_loss=1.545, generator_adv_loss=2.191, generator_feat_match_loss=4.608, over 25.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.384, discriminator_fake_loss=1.354, generator_loss=27.46, generator_mel_loss=18.39, generator_kl_loss=1.388, generator_dur_loss=1.751, generator_adv_loss=1.911, generator_feat_match_loss=4.02, over 7131.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:42:31,240 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 03:42:40,082 INFO [train.py:591] (5/6) 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 03:42:40,082 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 03:42:49,512 INFO [train.py:919] (5/6) Start epoch 272 +2024-03-13 03:45:20,028 INFO [train.py:527] (5/6) Epoch 272, batch 46, global_batch_idx: 33650, batch size: 68, loss[discriminator_loss=2.747, discriminator_real_loss=1.523, discriminator_fake_loss=1.224, generator_loss=26.58, generator_mel_loss=18, generator_kl_loss=1.232, generator_dur_loss=1.851, generator_adv_loss=1.828, generator_feat_match_loss=3.664, over 68.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.379, discriminator_fake_loss=1.343, generator_loss=27.52, generator_mel_loss=18.43, generator_kl_loss=1.425, generator_dur_loss=1.752, generator_adv_loss=1.922, generator_feat_match_loss=3.989, over 2568.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:47:40,106 INFO [train.py:527] (5/6) Epoch 272, batch 96, global_batch_idx: 33700, batch size: 25, loss[discriminator_loss=2.787, discriminator_real_loss=1.363, discriminator_fake_loss=1.423, generator_loss=26.72, generator_mel_loss=18.11, generator_kl_loss=1.693, generator_dur_loss=1.603, generator_adv_loss=1.944, generator_feat_match_loss=3.372, over 25.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.385, discriminator_fake_loss=1.345, generator_loss=27.47, generator_mel_loss=18.36, generator_kl_loss=1.419, generator_dur_loss=1.764, generator_adv_loss=1.924, generator_feat_match_loss=4.008, over 5367.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:48:57,001 INFO [train.py:919] (5/6) Start epoch 273 +2024-03-13 03:50:21,935 INFO [train.py:527] (5/6) Epoch 273, batch 22, global_batch_idx: 33750, batch size: 55, loss[discriminator_loss=2.744, discriminator_real_loss=1.432, discriminator_fake_loss=1.311, generator_loss=27.55, generator_mel_loss=18.5, generator_kl_loss=1.303, generator_dur_loss=1.697, generator_adv_loss=1.849, generator_feat_match_loss=4.204, over 55.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.384, discriminator_fake_loss=1.335, generator_loss=27.78, generator_mel_loss=18.5, generator_kl_loss=1.426, generator_dur_loss=1.763, generator_adv_loss=1.953, generator_feat_match_loss=4.133, over 1259.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:52:43,518 INFO [train.py:527] (5/6) Epoch 273, batch 72, global_batch_idx: 33800, batch size: 77, loss[discriminator_loss=2.741, discriminator_real_loss=1.414, discriminator_fake_loss=1.327, generator_loss=26.95, generator_mel_loss=18.12, generator_kl_loss=1.375, generator_dur_loss=1.862, generator_adv_loss=1.791, generator_feat_match_loss=3.799, over 77.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=27.67, generator_mel_loss=18.43, generator_kl_loss=1.413, generator_dur_loss=1.787, generator_adv_loss=1.929, generator_feat_match_loss=4.116, over 4216.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:52:43,519 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 03:52:51,232 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 03:55:07,339 INFO [train.py:527] (5/6) Epoch 273, batch 122, global_batch_idx: 33850, batch size: 45, loss[discriminator_loss=2.742, discriminator_real_loss=1.38, discriminator_fake_loss=1.362, generator_loss=27.54, generator_mel_loss=18.51, generator_kl_loss=1.443, generator_dur_loss=1.692, generator_adv_loss=1.882, generator_feat_match_loss=4.014, over 45.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.379, discriminator_fake_loss=1.344, generator_loss=27.64, generator_mel_loss=18.42, generator_kl_loss=1.414, generator_dur_loss=1.781, generator_adv_loss=1.929, generator_feat_match_loss=4.093, over 6901.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 03:55:12,259 INFO [train.py:919] (5/6) Start epoch 274 +2024-03-13 03:57:47,010 INFO [train.py:527] (5/6) Epoch 274, batch 48, global_batch_idx: 33900, batch size: 77, loss[discriminator_loss=2.67, discriminator_real_loss=1.311, discriminator_fake_loss=1.359, generator_loss=27.27, generator_mel_loss=18.08, generator_kl_loss=1.405, generator_dur_loss=1.811, generator_adv_loss=1.977, generator_feat_match_loss=3.993, over 77.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.372, discriminator_fake_loss=1.35, generator_loss=27.59, generator_mel_loss=18.41, generator_kl_loss=1.382, generator_dur_loss=1.782, generator_adv_loss=1.923, generator_feat_match_loss=4.086, over 2748.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:00:05,816 INFO [train.py:527] (5/6) Epoch 274, batch 98, global_batch_idx: 33950, batch size: 25, loss[discriminator_loss=2.766, discriminator_real_loss=1.369, discriminator_fake_loss=1.397, generator_loss=27.45, generator_mel_loss=18.28, generator_kl_loss=1.727, generator_dur_loss=1.618, generator_adv_loss=1.867, generator_feat_match_loss=3.956, over 25.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.379, discriminator_fake_loss=1.346, generator_loss=27.57, generator_mel_loss=18.38, generator_kl_loss=1.398, generator_dur_loss=1.783, generator_adv_loss=1.929, generator_feat_match_loss=4.073, over 5509.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:01:18,152 INFO [train.py:919] (5/6) Start epoch 275 +2024-03-13 04:02:49,778 INFO [train.py:527] (5/6) Epoch 275, batch 24, global_batch_idx: 34000, batch size: 77, loss[discriminator_loss=2.771, discriminator_real_loss=1.326, discriminator_fake_loss=1.445, generator_loss=27.55, generator_mel_loss=18.4, generator_kl_loss=1.209, generator_dur_loss=1.851, generator_adv_loss=1.96, generator_feat_match_loss=4.129, over 77.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.369, discriminator_fake_loss=1.35, generator_loss=27.46, generator_mel_loss=18.32, generator_kl_loss=1.359, generator_dur_loss=1.809, generator_adv_loss=1.906, generator_feat_match_loss=4.072, over 1664.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:02:49,780 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 04:02:57,801 INFO [train.py:591] (5/6) 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 04:02:57,802 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 04:05:15,994 INFO [train.py:527] (5/6) Epoch 275, batch 74, global_batch_idx: 34050, batch size: 25, loss[discriminator_loss=2.678, discriminator_real_loss=1.508, discriminator_fake_loss=1.17, generator_loss=29.08, generator_mel_loss=19.05, generator_kl_loss=1.705, generator_dur_loss=1.575, generator_adv_loss=1.988, generator_feat_match_loss=4.759, over 25.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.393, discriminator_fake_loss=1.356, generator_loss=27.58, generator_mel_loss=18.34, generator_kl_loss=1.38, generator_dur_loss=1.796, generator_adv_loss=1.968, generator_feat_match_loss=4.096, over 4721.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:07:31,824 INFO [train.py:919] (5/6) Start epoch 276 +2024-03-13 04:07:54,853 INFO [train.py:527] (5/6) Epoch 276, batch 0, global_batch_idx: 34100, batch size: 39, loss[discriminator_loss=2.715, discriminator_real_loss=1.453, discriminator_fake_loss=1.263, generator_loss=27.48, generator_mel_loss=18.89, generator_kl_loss=1.583, generator_dur_loss=1.678, generator_adv_loss=1.743, generator_feat_match_loss=3.585, over 39.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.453, discriminator_fake_loss=1.263, generator_loss=27.48, generator_mel_loss=18.89, generator_kl_loss=1.583, generator_dur_loss=1.678, generator_adv_loss=1.743, generator_feat_match_loss=3.585, over 39.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:10:12,681 INFO [train.py:527] (5/6) Epoch 276, batch 50, global_batch_idx: 34150, batch size: 36, loss[discriminator_loss=2.685, discriminator_real_loss=1.389, discriminator_fake_loss=1.296, generator_loss=27.73, generator_mel_loss=18.45, generator_kl_loss=1.628, generator_dur_loss=1.734, generator_adv_loss=1.773, generator_feat_match_loss=4.144, over 36.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.377, discriminator_fake_loss=1.352, generator_loss=27.52, generator_mel_loss=18.39, generator_kl_loss=1.409, generator_dur_loss=1.774, generator_adv_loss=1.915, generator_feat_match_loss=4.031, over 2916.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:12:31,959 INFO [train.py:527] (5/6) Epoch 276, batch 100, global_batch_idx: 34200, batch size: 44, loss[discriminator_loss=2.724, discriminator_real_loss=1.396, discriminator_fake_loss=1.328, generator_loss=27.53, generator_mel_loss=18.3, generator_kl_loss=1.543, generator_dur_loss=1.689, generator_adv_loss=1.916, generator_feat_match_loss=4.086, over 44.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.377, discriminator_fake_loss=1.357, generator_loss=27.5, generator_mel_loss=18.36, generator_kl_loss=1.415, generator_dur_loss=1.771, generator_adv_loss=1.917, generator_feat_match_loss=4.042, over 5672.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:12:31,961 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 04:12:41,121 INFO [train.py:591] (5/6) 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 04:12:41,122 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 04:13:45,752 INFO [train.py:919] (5/6) Start epoch 277 +2024-03-13 04:15:22,402 INFO [train.py:527] (5/6) Epoch 277, batch 26, global_batch_idx: 34250, batch size: 88, loss[discriminator_loss=2.718, discriminator_real_loss=1.389, discriminator_fake_loss=1.329, generator_loss=28.16, generator_mel_loss=18.53, generator_kl_loss=1.352, generator_dur_loss=1.88, generator_adv_loss=1.992, generator_feat_match_loss=4.408, over 88.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=27.65, generator_mel_loss=18.39, generator_kl_loss=1.392, generator_dur_loss=1.789, generator_adv_loss=1.932, generator_feat_match_loss=4.147, over 1640.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:17:41,362 INFO [train.py:527] (5/6) Epoch 277, batch 76, global_batch_idx: 34300, batch size: 39, loss[discriminator_loss=2.636, discriminator_real_loss=1.397, discriminator_fake_loss=1.238, generator_loss=28.21, generator_mel_loss=18.95, generator_kl_loss=1.582, generator_dur_loss=1.784, generator_adv_loss=1.921, generator_feat_match_loss=3.973, over 39.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.38, discriminator_fake_loss=1.35, generator_loss=27.65, generator_mel_loss=18.48, generator_kl_loss=1.399, generator_dur_loss=1.767, generator_adv_loss=1.92, generator_feat_match_loss=4.078, over 4340.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:19:55,829 INFO [train.py:919] (5/6) Start epoch 278 +2024-03-13 04:20:25,027 INFO [train.py:527] (5/6) Epoch 278, batch 2, global_batch_idx: 34350, batch size: 58, loss[discriminator_loss=2.77, discriminator_real_loss=1.396, discriminator_fake_loss=1.374, generator_loss=27.33, generator_mel_loss=18.26, generator_kl_loss=1.538, generator_dur_loss=1.755, generator_adv_loss=2.1, generator_feat_match_loss=3.678, over 58.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.412, discriminator_fake_loss=1.354, generator_loss=27.62, generator_mel_loss=18.49, generator_kl_loss=1.433, generator_dur_loss=1.796, generator_adv_loss=1.938, generator_feat_match_loss=3.969, over 212.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:22:44,514 INFO [train.py:527] (5/6) Epoch 278, batch 52, global_batch_idx: 34400, batch size: 88, loss[discriminator_loss=2.758, discriminator_real_loss=1.327, discriminator_fake_loss=1.432, generator_loss=27.78, generator_mel_loss=18.58, generator_kl_loss=1.343, generator_dur_loss=1.823, generator_adv_loss=1.799, generator_feat_match_loss=4.229, over 88.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.377, discriminator_fake_loss=1.36, generator_loss=27.71, generator_mel_loss=18.46, generator_kl_loss=1.398, generator_dur_loss=1.775, generator_adv_loss=1.955, generator_feat_match_loss=4.118, over 3082.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:22:44,516 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 04:22:52,579 INFO [train.py:591] (5/6) 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 04:22:52,580 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 04:25:11,129 INFO [train.py:527] (5/6) Epoch 278, batch 102, global_batch_idx: 34450, batch size: 47, loss[discriminator_loss=2.621, discriminator_real_loss=1.199, discriminator_fake_loss=1.423, generator_loss=27.99, generator_mel_loss=18.21, generator_kl_loss=1.517, generator_dur_loss=1.638, generator_adv_loss=2.014, generator_feat_match_loss=4.606, over 47.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.377, discriminator_fake_loss=1.355, generator_loss=27.64, generator_mel_loss=18.41, generator_kl_loss=1.395, generator_dur_loss=1.785, generator_adv_loss=1.951, generator_feat_match_loss=4.103, over 6226.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:26:11,814 INFO [train.py:919] (5/6) Start epoch 279 +2024-03-13 04:27:52,088 INFO [train.py:527] (5/6) Epoch 279, batch 28, global_batch_idx: 34500, batch size: 59, loss[discriminator_loss=2.778, discriminator_real_loss=1.513, discriminator_fake_loss=1.264, generator_loss=28.69, generator_mel_loss=19.14, generator_kl_loss=1.235, generator_dur_loss=1.741, generator_adv_loss=2.054, generator_feat_match_loss=4.522, over 59.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.393, discriminator_fake_loss=1.34, generator_loss=27.45, generator_mel_loss=18.27, generator_kl_loss=1.416, generator_dur_loss=1.782, generator_adv_loss=1.934, generator_feat_match_loss=4.051, over 1729.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:30:10,929 INFO [train.py:527] (5/6) Epoch 279, batch 78, global_batch_idx: 34550, batch size: 77, loss[discriminator_loss=2.706, discriminator_real_loss=1.366, discriminator_fake_loss=1.34, generator_loss=27.7, generator_mel_loss=18.74, generator_kl_loss=1.147, generator_dur_loss=1.848, generator_adv_loss=1.914, generator_feat_match_loss=4.053, over 77.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.381, discriminator_fake_loss=1.344, generator_loss=27.52, generator_mel_loss=18.34, generator_kl_loss=1.39, generator_dur_loss=1.772, generator_adv_loss=1.93, generator_feat_match_loss=4.089, over 4637.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:32:16,911 INFO [train.py:919] (5/6) Start epoch 280 +2024-03-13 04:32:52,433 INFO [train.py:527] (5/6) Epoch 280, batch 4, global_batch_idx: 34600, batch size: 55, loss[discriminator_loss=2.718, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=26.29, generator_mel_loss=17.59, generator_kl_loss=1.489, generator_dur_loss=1.734, generator_adv_loss=1.813, generator_feat_match_loss=3.659, over 55.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.342, discriminator_fake_loss=1.353, generator_loss=27.62, generator_mel_loss=18.45, generator_kl_loss=1.426, generator_dur_loss=1.749, generator_adv_loss=1.895, generator_feat_match_loss=4.1, over 304.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:32:52,435 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 04:33:00,125 INFO [train.py:591] (5/6) 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 04:33:00,148 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 04:35:18,201 INFO [train.py:527] (5/6) Epoch 280, batch 54, global_batch_idx: 34650, batch size: 72, loss[discriminator_loss=2.739, discriminator_real_loss=1.402, discriminator_fake_loss=1.337, generator_loss=27.14, generator_mel_loss=17.93, generator_kl_loss=1.365, generator_dur_loss=1.799, generator_adv_loss=2.042, generator_feat_match_loss=4.01, over 72.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.45, generator_mel_loss=18.36, generator_kl_loss=1.389, generator_dur_loss=1.764, generator_adv_loss=1.914, generator_feat_match_loss=4.026, over 3143.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:37:37,889 INFO [train.py:527] (5/6) Epoch 280, batch 104, global_batch_idx: 34700, batch size: 25, loss[discriminator_loss=2.787, discriminator_real_loss=1.422, discriminator_fake_loss=1.364, generator_loss=28.55, generator_mel_loss=19.34, generator_kl_loss=1.632, generator_dur_loss=1.557, generator_adv_loss=1.809, generator_feat_match_loss=4.213, over 25.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.386, discriminator_fake_loss=1.348, generator_loss=27.49, generator_mel_loss=18.38, generator_kl_loss=1.379, generator_dur_loss=1.771, generator_adv_loss=1.914, generator_feat_match_loss=4.042, over 6026.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:38:30,616 INFO [train.py:919] (5/6) Start epoch 281 +2024-03-13 04:40:20,051 INFO [train.py:527] (5/6) Epoch 281, batch 30, global_batch_idx: 34750, batch size: 45, loss[discriminator_loss=2.798, discriminator_real_loss=1.498, discriminator_fake_loss=1.301, generator_loss=27.07, generator_mel_loss=18.15, generator_kl_loss=1.466, generator_dur_loss=1.677, generator_adv_loss=1.87, generator_feat_match_loss=3.905, over 45.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.384, discriminator_fake_loss=1.347, generator_loss=27.37, generator_mel_loss=18.29, generator_kl_loss=1.371, generator_dur_loss=1.764, generator_adv_loss=1.934, generator_feat_match_loss=4.012, over 1789.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:42:40,533 INFO [train.py:527] (5/6) Epoch 281, batch 80, global_batch_idx: 34800, batch size: 31, loss[discriminator_loss=2.747, discriminator_real_loss=1.443, discriminator_fake_loss=1.304, generator_loss=28.42, generator_mel_loss=18.77, generator_kl_loss=1.606, generator_dur_loss=1.657, generator_adv_loss=1.94, generator_feat_match_loss=4.442, over 31.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.388, discriminator_fake_loss=1.348, generator_loss=27.4, generator_mel_loss=18.31, generator_kl_loss=1.386, generator_dur_loss=1.77, generator_adv_loss=1.922, generator_feat_match_loss=4.014, over 4527.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:42:40,535 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 04:42:48,868 INFO [train.py:591] (5/6) 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 04:42:48,869 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 04:44:47,456 INFO [train.py:919] (5/6) Start epoch 282 +2024-03-13 04:45:27,843 INFO [train.py:527] (5/6) Epoch 282, batch 6, global_batch_idx: 34850, batch size: 53, loss[discriminator_loss=2.778, discriminator_real_loss=1.406, discriminator_fake_loss=1.372, generator_loss=27.65, generator_mel_loss=18.63, generator_kl_loss=1.334, generator_dur_loss=1.705, generator_adv_loss=2.005, generator_feat_match_loss=3.985, over 53.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.394, discriminator_fake_loss=1.312, generator_loss=27.58, generator_mel_loss=18.43, generator_kl_loss=1.322, generator_dur_loss=1.782, generator_adv_loss=1.957, generator_feat_match_loss=4.094, over 455.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:47:45,490 INFO [train.py:527] (5/6) Epoch 282, batch 56, global_batch_idx: 34900, batch size: 47, loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=28.4, generator_mel_loss=18.51, generator_kl_loss=1.509, generator_dur_loss=1.706, generator_adv_loss=2.006, generator_feat_match_loss=4.663, over 47.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.38, discriminator_fake_loss=1.347, generator_loss=27.62, generator_mel_loss=18.41, generator_kl_loss=1.405, generator_dur_loss=1.756, generator_adv_loss=1.925, generator_feat_match_loss=4.123, over 3199.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:50:06,611 INFO [train.py:527] (5/6) Epoch 282, batch 106, global_batch_idx: 34950, batch size: 25, loss[discriminator_loss=2.794, discriminator_real_loss=1.473, discriminator_fake_loss=1.321, generator_loss=26.41, generator_mel_loss=17.96, generator_kl_loss=1.575, generator_dur_loss=1.627, generator_adv_loss=1.775, generator_feat_match_loss=3.472, over 25.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.378, discriminator_fake_loss=1.354, generator_loss=27.56, generator_mel_loss=18.35, generator_kl_loss=1.398, generator_dur_loss=1.759, generator_adv_loss=1.929, generator_feat_match_loss=4.118, over 6242.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:50:53,806 INFO [train.py:919] (5/6) Start epoch 283 +2024-03-13 04:52:47,958 INFO [train.py:527] (5/6) Epoch 283, batch 32, global_batch_idx: 35000, batch size: 66, loss[discriminator_loss=2.74, discriminator_real_loss=1.399, discriminator_fake_loss=1.341, generator_loss=26.42, generator_mel_loss=17.6, generator_kl_loss=1.27, generator_dur_loss=1.75, generator_adv_loss=1.925, generator_feat_match_loss=3.882, over 66.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.387, discriminator_fake_loss=1.349, generator_loss=27.64, generator_mel_loss=18.49, generator_kl_loss=1.381, generator_dur_loss=1.759, generator_adv_loss=1.923, generator_feat_match_loss=4.087, over 1903.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:52:47,960 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 04:52:56,062 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 04:55:17,618 INFO [train.py:527] (5/6) Epoch 283, batch 82, global_batch_idx: 35050, batch size: 68, loss[discriminator_loss=2.677, discriminator_real_loss=1.393, discriminator_fake_loss=1.284, generator_loss=27.54, generator_mel_loss=18.04, generator_kl_loss=1.301, generator_dur_loss=1.789, generator_adv_loss=1.948, generator_feat_match_loss=4.462, over 68.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.386, discriminator_fake_loss=1.345, generator_loss=27.53, generator_mel_loss=18.36, generator_kl_loss=1.392, generator_dur_loss=1.768, generator_adv_loss=1.925, generator_feat_match_loss=4.083, over 4728.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 04:57:08,606 INFO [train.py:919] (5/6) Start epoch 284 +2024-03-13 04:57:56,570 INFO [train.py:527] (5/6) Epoch 284, batch 8, global_batch_idx: 35100, batch size: 64, loss[discriminator_loss=2.78, discriminator_real_loss=1.292, discriminator_fake_loss=1.488, generator_loss=27.97, generator_mel_loss=18.78, generator_kl_loss=1.211, generator_dur_loss=1.789, generator_adv_loss=1.946, generator_feat_match_loss=4.245, over 64.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.373, discriminator_fake_loss=1.351, generator_loss=27.94, generator_mel_loss=18.45, generator_kl_loss=1.39, generator_dur_loss=1.762, generator_adv_loss=1.954, generator_feat_match_loss=4.389, over 482.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:00:13,799 INFO [train.py:527] (5/6) Epoch 284, batch 58, global_batch_idx: 35150, batch size: 31, loss[discriminator_loss=2.404, discriminator_real_loss=1.141, discriminator_fake_loss=1.263, generator_loss=30.06, generator_mel_loss=19.07, generator_kl_loss=1.441, generator_dur_loss=1.688, generator_adv_loss=2.461, generator_feat_match_loss=5.395, over 31.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.376, discriminator_fake_loss=1.354, generator_loss=27.74, generator_mel_loss=18.39, generator_kl_loss=1.416, generator_dur_loss=1.748, generator_adv_loss=1.959, generator_feat_match_loss=4.228, over 3124.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:02:32,490 INFO [train.py:527] (5/6) Epoch 284, batch 108, global_batch_idx: 35200, batch size: 68, loss[discriminator_loss=2.762, discriminator_real_loss=1.306, discriminator_fake_loss=1.456, generator_loss=27.55, generator_mel_loss=18.74, generator_kl_loss=1.356, generator_dur_loss=1.794, generator_adv_loss=1.938, generator_feat_match_loss=3.715, over 68.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.374, discriminator_fake_loss=1.35, generator_loss=27.65, generator_mel_loss=18.35, generator_kl_loss=1.396, generator_dur_loss=1.757, generator_adv_loss=1.968, generator_feat_match_loss=4.178, over 6169.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:02:32,491 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 05:02:41,050 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 05:03:23,886 INFO [train.py:919] (5/6) Start epoch 285 +2024-03-13 05:05:22,038 INFO [train.py:527] (5/6) Epoch 285, batch 34, global_batch_idx: 35250, batch size: 48, loss[discriminator_loss=2.704, discriminator_real_loss=1.42, discriminator_fake_loss=1.284, generator_loss=27.84, generator_mel_loss=18.76, generator_kl_loss=1.393, generator_dur_loss=1.673, generator_adv_loss=1.883, generator_feat_match_loss=4.134, over 48.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.386, discriminator_fake_loss=1.344, generator_loss=27.6, generator_mel_loss=18.44, generator_kl_loss=1.408, generator_dur_loss=1.755, generator_adv_loss=1.907, generator_feat_match_loss=4.093, over 1946.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:07:39,956 INFO [train.py:527] (5/6) Epoch 285, batch 84, global_batch_idx: 35300, batch size: 74, loss[discriminator_loss=2.747, discriminator_real_loss=1.372, discriminator_fake_loss=1.375, generator_loss=27.22, generator_mel_loss=18.17, generator_kl_loss=1.404, generator_dur_loss=1.808, generator_adv_loss=1.9, generator_feat_match_loss=3.941, over 74.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.387, discriminator_fake_loss=1.344, generator_loss=27.61, generator_mel_loss=18.42, generator_kl_loss=1.41, generator_dur_loss=1.755, generator_adv_loss=1.918, generator_feat_match_loss=4.105, over 4868.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:09:32,993 INFO [train.py:919] (5/6) Start epoch 286 +2024-03-13 05:10:24,753 INFO [train.py:527] (5/6) Epoch 286, batch 10, global_batch_idx: 35350, batch size: 66, loss[discriminator_loss=2.733, discriminator_real_loss=1.451, discriminator_fake_loss=1.282, generator_loss=27.49, generator_mel_loss=18.27, generator_kl_loss=1.404, generator_dur_loss=1.775, generator_adv_loss=1.794, generator_feat_match_loss=4.251, over 66.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.372, discriminator_fake_loss=1.385, generator_loss=27.59, generator_mel_loss=18.48, generator_kl_loss=1.396, generator_dur_loss=1.786, generator_adv_loss=1.859, generator_feat_match_loss=4.063, over 704.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:12:44,988 INFO [train.py:527] (5/6) Epoch 286, batch 60, global_batch_idx: 35400, batch size: 61, loss[discriminator_loss=2.754, discriminator_real_loss=1.34, discriminator_fake_loss=1.414, generator_loss=27, generator_mel_loss=18.11, generator_kl_loss=1.28, generator_dur_loss=1.795, generator_adv_loss=1.942, generator_feat_match_loss=3.869, over 61.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.377, discriminator_fake_loss=1.366, generator_loss=27.53, generator_mel_loss=18.36, generator_kl_loss=1.373, generator_dur_loss=1.779, generator_adv_loss=1.911, generator_feat_match_loss=4.109, over 3445.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:12:44,990 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 05:12:52,913 INFO [train.py:591] (5/6) 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 05:12:52,914 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 05:15:11,533 INFO [train.py:527] (5/6) Epoch 286, batch 110, global_batch_idx: 35450, batch size: 31, loss[discriminator_loss=2.772, discriminator_real_loss=1.435, discriminator_fake_loss=1.337, generator_loss=26.64, generator_mel_loss=18.08, generator_kl_loss=1.556, generator_dur_loss=1.66, generator_adv_loss=1.839, generator_feat_match_loss=3.507, over 31.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.382, discriminator_fake_loss=1.354, generator_loss=27.5, generator_mel_loss=18.32, generator_kl_loss=1.398, generator_dur_loss=1.761, generator_adv_loss=1.917, generator_feat_match_loss=4.104, over 6167.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:15:49,938 INFO [train.py:919] (5/6) Start epoch 287 +2024-03-13 05:17:54,319 INFO [train.py:527] (5/6) Epoch 287, batch 36, global_batch_idx: 35500, batch size: 66, loss[discriminator_loss=2.69, discriminator_real_loss=1.355, discriminator_fake_loss=1.335, generator_loss=27.32, generator_mel_loss=18.2, generator_kl_loss=1.322, generator_dur_loss=1.769, generator_adv_loss=1.858, generator_feat_match_loss=4.17, over 66.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.381, discriminator_fake_loss=1.347, generator_loss=27.6, generator_mel_loss=18.37, generator_kl_loss=1.459, generator_dur_loss=1.719, generator_adv_loss=1.932, generator_feat_match_loss=4.119, over 2001.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:20:15,399 INFO [train.py:527] (5/6) Epoch 287, batch 86, global_batch_idx: 35550, batch size: 45, loss[discriminator_loss=2.693, discriminator_real_loss=1.387, discriminator_fake_loss=1.306, generator_loss=28.21, generator_mel_loss=18.87, generator_kl_loss=1.491, generator_dur_loss=1.662, generator_adv_loss=1.945, generator_feat_match_loss=4.241, over 45.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.379, discriminator_fake_loss=1.348, generator_loss=27.52, generator_mel_loss=18.32, generator_kl_loss=1.402, generator_dur_loss=1.757, generator_adv_loss=1.925, generator_feat_match_loss=4.114, over 5058.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:21:56,619 INFO [train.py:919] (5/6) Start epoch 288 +2024-03-13 05:22:55,026 INFO [train.py:527] (5/6) Epoch 288, batch 12, global_batch_idx: 35600, batch size: 58, loss[discriminator_loss=2.747, discriminator_real_loss=1.381, discriminator_fake_loss=1.366, generator_loss=26.96, generator_mel_loss=17.86, generator_kl_loss=1.356, generator_dur_loss=1.738, generator_adv_loss=2.029, generator_feat_match_loss=3.974, over 58.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.403, discriminator_fake_loss=1.331, generator_loss=27.62, generator_mel_loss=18.44, generator_kl_loss=1.455, generator_dur_loss=1.728, generator_adv_loss=1.928, generator_feat_match_loss=4.06, over 701.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:22:55,028 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 05:23:03,117 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 05:25:20,670 INFO [train.py:527] (5/6) Epoch 288, batch 62, global_batch_idx: 35650, batch size: 55, loss[discriminator_loss=2.738, discriminator_real_loss=1.5, discriminator_fake_loss=1.238, generator_loss=27.48, generator_mel_loss=18.4, generator_kl_loss=1.496, generator_dur_loss=1.725, generator_adv_loss=1.867, generator_feat_match_loss=3.993, over 55.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.386, discriminator_fake_loss=1.343, generator_loss=27.49, generator_mel_loss=18.3, generator_kl_loss=1.42, generator_dur_loss=1.74, generator_adv_loss=1.911, generator_feat_match_loss=4.119, over 3426.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:27:39,560 INFO [train.py:527] (5/6) Epoch 288, batch 112, global_batch_idx: 35700, batch size: 52, loss[discriminator_loss=2.665, discriminator_real_loss=1.413, discriminator_fake_loss=1.252, generator_loss=28.45, generator_mel_loss=18.73, generator_kl_loss=1.458, generator_dur_loss=1.671, generator_adv_loss=2.017, generator_feat_match_loss=4.577, over 52.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.378, discriminator_fake_loss=1.349, generator_loss=27.55, generator_mel_loss=18.3, generator_kl_loss=1.406, generator_dur_loss=1.757, generator_adv_loss=1.933, generator_feat_match_loss=4.152, over 6349.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:28:10,087 INFO [train.py:919] (5/6) Start epoch 289 +2024-03-13 05:30:22,433 INFO [train.py:527] (5/6) Epoch 289, batch 38, global_batch_idx: 35750, batch size: 72, loss[discriminator_loss=2.758, discriminator_real_loss=1.473, discriminator_fake_loss=1.285, generator_loss=27.02, generator_mel_loss=18.28, generator_kl_loss=1.3, generator_dur_loss=1.799, generator_adv_loss=1.863, generator_feat_match_loss=3.772, over 72.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.379, discriminator_fake_loss=1.35, generator_loss=27.61, generator_mel_loss=18.37, generator_kl_loss=1.361, generator_dur_loss=1.782, generator_adv_loss=1.929, generator_feat_match_loss=4.167, over 2297.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:32:42,190 INFO [train.py:527] (5/6) Epoch 289, batch 88, global_batch_idx: 35800, batch size: 68, loss[discriminator_loss=2.749, discriminator_real_loss=1.385, discriminator_fake_loss=1.364, generator_loss=27.07, generator_mel_loss=18.05, generator_kl_loss=1.448, generator_dur_loss=1.809, generator_adv_loss=1.995, generator_feat_match_loss=3.768, over 68.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.384, discriminator_fake_loss=1.355, generator_loss=27.52, generator_mel_loss=18.35, generator_kl_loss=1.375, generator_dur_loss=1.782, generator_adv_loss=1.924, generator_feat_match_loss=4.086, over 5258.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:32:42,192 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 05:32:50,893 INFO [train.py:591] (5/6) 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 05:32:50,894 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 05:34:27,320 INFO [train.py:919] (5/6) Start epoch 290 +2024-03-13 05:35:31,876 INFO [train.py:527] (5/6) Epoch 290, batch 14, global_batch_idx: 35850, batch size: 48, loss[discriminator_loss=2.695, discriminator_real_loss=1.395, discriminator_fake_loss=1.3, generator_loss=27.62, generator_mel_loss=18.51, generator_kl_loss=1.409, generator_dur_loss=1.691, generator_adv_loss=1.874, generator_feat_match_loss=4.14, over 48.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.388, discriminator_fake_loss=1.366, generator_loss=27.56, generator_mel_loss=18.43, generator_kl_loss=1.41, generator_dur_loss=1.76, generator_adv_loss=1.892, generator_feat_match_loss=4.066, over 907.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:37:53,291 INFO [train.py:527] (5/6) Epoch 290, batch 64, global_batch_idx: 35900, batch size: 56, loss[discriminator_loss=2.721, discriminator_real_loss=1.421, discriminator_fake_loss=1.3, generator_loss=26.47, generator_mel_loss=17.71, generator_kl_loss=1.449, generator_dur_loss=1.745, generator_adv_loss=1.823, generator_feat_match_loss=3.743, over 56.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.385, discriminator_fake_loss=1.347, generator_loss=27.62, generator_mel_loss=18.39, generator_kl_loss=1.415, generator_dur_loss=1.743, generator_adv_loss=1.939, generator_feat_match_loss=4.126, over 3790.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:40:10,217 INFO [train.py:527] (5/6) Epoch 290, batch 114, global_batch_idx: 35950, batch size: 70, loss[discriminator_loss=2.792, discriminator_real_loss=1.55, discriminator_fake_loss=1.243, generator_loss=27.68, generator_mel_loss=18.42, generator_kl_loss=1.359, generator_dur_loss=1.794, generator_adv_loss=1.774, generator_feat_match_loss=4.332, over 70.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.385, discriminator_fake_loss=1.344, generator_loss=27.57, generator_mel_loss=18.36, generator_kl_loss=1.42, generator_dur_loss=1.746, generator_adv_loss=1.935, generator_feat_match_loss=4.114, over 6688.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:40:36,157 INFO [train.py:919] (5/6) Start epoch 291 +2024-03-13 05:42:51,914 INFO [train.py:527] (5/6) Epoch 291, batch 40, global_batch_idx: 36000, batch size: 39, loss[discriminator_loss=2.694, discriminator_real_loss=1.382, discriminator_fake_loss=1.312, generator_loss=27.07, generator_mel_loss=18.33, generator_kl_loss=1.404, generator_dur_loss=1.769, generator_adv_loss=1.895, generator_feat_match_loss=3.671, over 39.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.382, discriminator_fake_loss=1.337, generator_loss=27.64, generator_mel_loss=18.32, generator_kl_loss=1.414, generator_dur_loss=1.762, generator_adv_loss=1.936, generator_feat_match_loss=4.208, over 2309.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:42:51,916 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 05:42:59,874 INFO [train.py:591] (5/6) 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 05:42:59,875 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 05:45:19,168 INFO [train.py:527] (5/6) Epoch 291, batch 90, global_batch_idx: 36050, batch size: 50, loss[discriminator_loss=2.743, discriminator_real_loss=1.406, discriminator_fake_loss=1.337, generator_loss=27.78, generator_mel_loss=18.6, generator_kl_loss=1.394, generator_dur_loss=1.668, generator_adv_loss=1.955, generator_feat_match_loss=4.166, over 50.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.379, discriminator_fake_loss=1.341, generator_loss=27.64, generator_mel_loss=18.34, generator_kl_loss=1.423, generator_dur_loss=1.749, generator_adv_loss=1.93, generator_feat_match_loss=4.202, over 5062.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:46:51,032 INFO [train.py:919] (5/6) Start epoch 292 +2024-03-13 05:47:55,713 INFO [train.py:527] (5/6) Epoch 292, batch 16, global_batch_idx: 36100, batch size: 66, loss[discriminator_loss=2.737, discriminator_real_loss=1.379, discriminator_fake_loss=1.358, generator_loss=26.88, generator_mel_loss=17.83, generator_kl_loss=1.359, generator_dur_loss=1.757, generator_adv_loss=1.947, generator_feat_match_loss=3.984, over 66.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.374, discriminator_fake_loss=1.359, generator_loss=27.56, generator_mel_loss=18.31, generator_kl_loss=1.441, generator_dur_loss=1.739, generator_adv_loss=1.925, generator_feat_match_loss=4.146, over 902.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:50:13,888 INFO [train.py:527] (5/6) Epoch 292, batch 66, global_batch_idx: 36150, batch size: 64, loss[discriminator_loss=2.759, discriminator_real_loss=1.383, discriminator_fake_loss=1.375, generator_loss=27.79, generator_mel_loss=18.4, generator_kl_loss=1.401, generator_dur_loss=1.75, generator_adv_loss=1.856, generator_feat_match_loss=4.376, over 64.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.338, generator_loss=27.69, generator_mel_loss=18.37, generator_kl_loss=1.408, generator_dur_loss=1.737, generator_adv_loss=1.955, generator_feat_match_loss=4.213, over 3637.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:52:35,020 INFO [train.py:527] (5/6) Epoch 292, batch 116, global_batch_idx: 36200, batch size: 52, loss[discriminator_loss=2.681, discriminator_real_loss=1.335, discriminator_fake_loss=1.346, generator_loss=26.58, generator_mel_loss=17.8, generator_kl_loss=1.365, generator_dur_loss=1.709, generator_adv_loss=1.869, generator_feat_match_loss=3.831, over 52.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.38, discriminator_fake_loss=1.339, generator_loss=27.59, generator_mel_loss=18.31, generator_kl_loss=1.397, generator_dur_loss=1.759, generator_adv_loss=1.942, generator_feat_match_loss=4.183, over 6732.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:52:35,022 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 05:52:43,684 INFO [train.py:591] (5/6) 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 05:52:43,685 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 05:53:04,567 INFO [train.py:919] (5/6) Start epoch 293 +2024-03-13 05:55:21,909 INFO [train.py:527] (5/6) Epoch 293, batch 42, global_batch_idx: 36250, batch size: 55, loss[discriminator_loss=2.759, discriminator_real_loss=1.329, discriminator_fake_loss=1.43, generator_loss=27.7, generator_mel_loss=18.24, generator_kl_loss=1.523, generator_dur_loss=1.667, generator_adv_loss=1.984, generator_feat_match_loss=4.284, over 55.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=27.71, generator_mel_loss=18.44, generator_kl_loss=1.413, generator_dur_loss=1.752, generator_adv_loss=1.934, generator_feat_match_loss=4.169, over 2335.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:57:42,457 INFO [train.py:527] (5/6) Epoch 293, batch 92, global_batch_idx: 36300, batch size: 68, loss[discriminator_loss=2.739, discriminator_real_loss=1.386, discriminator_fake_loss=1.353, generator_loss=26.74, generator_mel_loss=17.88, generator_kl_loss=1.293, generator_dur_loss=1.797, generator_adv_loss=1.959, generator_feat_match_loss=3.807, over 68.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.376, discriminator_fake_loss=1.34, generator_loss=27.67, generator_mel_loss=18.39, generator_kl_loss=1.414, generator_dur_loss=1.752, generator_adv_loss=1.928, generator_feat_match_loss=4.179, over 5123.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 05:59:11,579 INFO [train.py:919] (5/6) Start epoch 294 +2024-03-13 06:00:24,394 INFO [train.py:527] (5/6) Epoch 294, batch 18, global_batch_idx: 36350, batch size: 56, loss[discriminator_loss=2.704, discriminator_real_loss=1.376, discriminator_fake_loss=1.328, generator_loss=26.81, generator_mel_loss=17.61, generator_kl_loss=1.231, generator_dur_loss=1.781, generator_adv_loss=2.03, generator_feat_match_loss=4.166, over 56.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.343, generator_loss=27.59, generator_mel_loss=18.33, generator_kl_loss=1.411, generator_dur_loss=1.746, generator_adv_loss=1.894, generator_feat_match_loss=4.211, over 1089.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:02:44,345 INFO [train.py:527] (5/6) Epoch 294, batch 68, global_batch_idx: 36400, batch size: 96, loss[discriminator_loss=2.777, discriminator_real_loss=1.5, discriminator_fake_loss=1.277, generator_loss=25.92, generator_mel_loss=17.56, generator_kl_loss=1.272, generator_dur_loss=1.811, generator_adv_loss=1.857, generator_feat_match_loss=3.416, over 96.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=27.74, generator_mel_loss=18.35, generator_kl_loss=1.409, generator_dur_loss=1.743, generator_adv_loss=1.954, generator_feat_match_loss=4.286, over 3822.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:02:44,347 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 06:02:52,559 INFO [train.py:591] (5/6) 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 06:02:52,561 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 06:05:11,236 INFO [train.py:527] (5/6) Epoch 294, batch 118, global_batch_idx: 36450, batch size: 61, loss[discriminator_loss=2.708, discriminator_real_loss=1.43, discriminator_fake_loss=1.278, generator_loss=28.1, generator_mel_loss=18.73, generator_kl_loss=1.377, generator_dur_loss=1.762, generator_adv_loss=1.89, generator_feat_match_loss=4.348, over 61.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.379, discriminator_fake_loss=1.343, generator_loss=27.65, generator_mel_loss=18.32, generator_kl_loss=1.405, generator_dur_loss=1.754, generator_adv_loss=1.944, generator_feat_match_loss=4.226, over 6816.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:05:24,866 INFO [train.py:919] (5/6) Start epoch 295 +2024-03-13 06:07:52,726 INFO [train.py:527] (5/6) Epoch 295, batch 44, global_batch_idx: 36500, batch size: 74, loss[discriminator_loss=2.74, discriminator_real_loss=1.335, discriminator_fake_loss=1.405, generator_loss=27.35, generator_mel_loss=18.38, generator_kl_loss=1.38, generator_dur_loss=1.867, generator_adv_loss=1.951, generator_feat_match_loss=3.776, over 74.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.368, discriminator_fake_loss=1.353, generator_loss=27.5, generator_mel_loss=18.28, generator_kl_loss=1.405, generator_dur_loss=1.772, generator_adv_loss=1.916, generator_feat_match_loss=4.125, over 2637.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:10:12,724 INFO [train.py:527] (5/6) Epoch 295, batch 94, global_batch_idx: 36550, batch size: 58, loss[discriminator_loss=2.718, discriminator_real_loss=1.356, discriminator_fake_loss=1.362, generator_loss=27.65, generator_mel_loss=18.19, generator_kl_loss=1.532, generator_dur_loss=1.736, generator_adv_loss=1.908, generator_feat_match_loss=4.293, over 58.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.378, discriminator_fake_loss=1.351, generator_loss=27.55, generator_mel_loss=18.27, generator_kl_loss=1.402, generator_dur_loss=1.773, generator_adv_loss=1.918, generator_feat_match_loss=4.186, over 5564.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:11:31,688 INFO [train.py:919] (5/6) Start epoch 296 +2024-03-13 06:12:52,832 INFO [train.py:527] (5/6) Epoch 296, batch 20, global_batch_idx: 36600, batch size: 64, loss[discriminator_loss=2.775, discriminator_real_loss=1.371, discriminator_fake_loss=1.404, generator_loss=27.02, generator_mel_loss=18.08, generator_kl_loss=1.543, generator_dur_loss=1.77, generator_adv_loss=1.866, generator_feat_match_loss=3.761, over 64.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.391, discriminator_fake_loss=1.355, generator_loss=27.77, generator_mel_loss=18.45, generator_kl_loss=1.418, generator_dur_loss=1.755, generator_adv_loss=1.911, generator_feat_match_loss=4.237, over 1183.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:12:52,833 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 06:13:00,870 INFO [train.py:591] (5/6) 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 06:13:00,871 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 06:15:21,476 INFO [train.py:527] (5/6) Epoch 296, batch 70, global_batch_idx: 36650, batch size: 74, loss[discriminator_loss=2.683, discriminator_real_loss=1.4, discriminator_fake_loss=1.283, generator_loss=27.98, generator_mel_loss=18.87, generator_kl_loss=1.203, generator_dur_loss=1.792, generator_adv_loss=1.859, generator_feat_match_loss=4.253, over 74.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=27.73, generator_mel_loss=18.4, generator_kl_loss=1.389, generator_dur_loss=1.784, generator_adv_loss=1.925, generator_feat_match_loss=4.236, over 4221.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:17:34,846 INFO [train.py:527] (5/6) Epoch 296, batch 120, global_batch_idx: 36700, batch size: 53, loss[discriminator_loss=2.722, discriminator_real_loss=1.359, discriminator_fake_loss=1.363, generator_loss=26.71, generator_mel_loss=17.76, generator_kl_loss=1.606, generator_dur_loss=1.806, generator_adv_loss=1.888, generator_feat_match_loss=3.642, over 53.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.376, discriminator_fake_loss=1.351, generator_loss=27.65, generator_mel_loss=18.37, generator_kl_loss=1.401, generator_dur_loss=1.774, generator_adv_loss=1.92, generator_feat_match_loss=4.185, over 6983.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:17:45,222 INFO [train.py:919] (5/6) Start epoch 297 +2024-03-13 06:20:16,537 INFO [train.py:527] (5/6) Epoch 297, batch 46, global_batch_idx: 36750, batch size: 56, loss[discriminator_loss=2.774, discriminator_real_loss=1.472, discriminator_fake_loss=1.302, generator_loss=26.6, generator_mel_loss=18.2, generator_kl_loss=1.242, generator_dur_loss=1.736, generator_adv_loss=1.854, generator_feat_match_loss=3.564, over 56.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.385, discriminator_fake_loss=1.363, generator_loss=27.54, generator_mel_loss=18.33, generator_kl_loss=1.38, generator_dur_loss=1.786, generator_adv_loss=1.925, generator_feat_match_loss=4.124, over 2818.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:22:36,401 INFO [train.py:527] (5/6) Epoch 297, batch 96, global_batch_idx: 36800, batch size: 31, loss[discriminator_loss=2.7, discriminator_real_loss=1.366, discriminator_fake_loss=1.335, generator_loss=27.97, generator_mel_loss=18.57, generator_kl_loss=1.46, generator_dur_loss=1.629, generator_adv_loss=2.007, generator_feat_match_loss=4.3, over 31.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.378, discriminator_fake_loss=1.362, generator_loss=27.55, generator_mel_loss=18.32, generator_kl_loss=1.386, generator_dur_loss=1.791, generator_adv_loss=1.939, generator_feat_match_loss=4.12, over 5620.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:22:36,403 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 06:22:45,239 INFO [train.py:591] (5/6) 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 06:22:45,240 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 06:24:01,057 INFO [train.py:919] (5/6) Start epoch 298 +2024-03-13 06:25:24,675 INFO [train.py:527] (5/6) Epoch 298, batch 22, global_batch_idx: 36850, batch size: 39, loss[discriminator_loss=2.695, discriminator_real_loss=1.352, discriminator_fake_loss=1.343, generator_loss=27.96, generator_mel_loss=18.37, generator_kl_loss=1.684, generator_dur_loss=1.7, generator_adv_loss=1.958, generator_feat_match_loss=4.253, over 39.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=27.83, generator_mel_loss=18.48, generator_kl_loss=1.465, generator_dur_loss=1.73, generator_adv_loss=1.926, generator_feat_match_loss=4.226, over 998.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:27:44,427 INFO [train.py:527] (5/6) Epoch 298, batch 72, global_batch_idx: 36900, batch size: 45, loss[discriminator_loss=2.831, discriminator_real_loss=1.505, discriminator_fake_loss=1.327, generator_loss=27.98, generator_mel_loss=18.43, generator_kl_loss=1.535, generator_dur_loss=1.658, generator_adv_loss=1.801, generator_feat_match_loss=4.551, over 45.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.38, discriminator_fake_loss=1.348, generator_loss=27.64, generator_mel_loss=18.29, generator_kl_loss=1.406, generator_dur_loss=1.762, generator_adv_loss=1.938, generator_feat_match_loss=4.236, over 3937.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:30:01,636 INFO [train.py:527] (5/6) Epoch 298, batch 122, global_batch_idx: 36950, batch size: 66, loss[discriminator_loss=2.792, discriminator_real_loss=1.413, discriminator_fake_loss=1.379, generator_loss=27.55, generator_mel_loss=18.06, generator_kl_loss=1.39, generator_dur_loss=1.798, generator_adv_loss=1.858, generator_feat_match_loss=4.439, over 66.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=27.63, generator_mel_loss=18.32, generator_kl_loss=1.4, generator_dur_loss=1.763, generator_adv_loss=1.932, generator_feat_match_loss=4.21, over 6923.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:30:07,025 INFO [train.py:919] (5/6) Start epoch 299 +2024-03-13 06:32:45,597 INFO [train.py:527] (5/6) Epoch 299, batch 48, global_batch_idx: 37000, batch size: 26, loss[discriminator_loss=2.621, discriminator_real_loss=1.319, discriminator_fake_loss=1.302, generator_loss=28.44, generator_mel_loss=18.67, generator_kl_loss=1.525, generator_dur_loss=1.564, generator_adv_loss=1.954, generator_feat_match_loss=4.726, over 26.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.385, discriminator_fake_loss=1.352, generator_loss=27.5, generator_mel_loss=18.28, generator_kl_loss=1.412, generator_dur_loss=1.76, generator_adv_loss=1.922, generator_feat_match_loss=4.124, over 2740.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:32:45,598 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 06:32:53,799 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 06:35:15,114 INFO [train.py:527] (5/6) Epoch 299, batch 98, global_batch_idx: 37050, batch size: 77, loss[discriminator_loss=2.686, discriminator_real_loss=1.383, discriminator_fake_loss=1.303, generator_loss=28.08, generator_mel_loss=18.72, generator_kl_loss=1.346, generator_dur_loss=1.84, generator_adv_loss=1.862, generator_feat_match_loss=4.313, over 77.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.387, discriminator_fake_loss=1.348, generator_loss=27.45, generator_mel_loss=18.26, generator_kl_loss=1.393, generator_dur_loss=1.766, generator_adv_loss=1.922, generator_feat_match_loss=4.112, over 5631.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:36:23,659 INFO [train.py:919] (5/6) Start epoch 300 +2024-03-13 06:37:53,663 INFO [train.py:527] (5/6) Epoch 300, batch 24, global_batch_idx: 37100, batch size: 53, loss[discriminator_loss=2.743, discriminator_real_loss=1.393, discriminator_fake_loss=1.35, generator_loss=26.73, generator_mel_loss=17.75, generator_kl_loss=1.317, generator_dur_loss=1.653, generator_adv_loss=1.894, generator_feat_match_loss=4.122, over 53.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=27.8, generator_mel_loss=18.3, generator_kl_loss=1.404, generator_dur_loss=1.763, generator_adv_loss=1.993, generator_feat_match_loss=4.336, over 1458.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:40:10,445 INFO [train.py:527] (5/6) Epoch 300, batch 74, global_batch_idx: 37150, batch size: 56, loss[discriminator_loss=2.823, discriminator_real_loss=1.475, discriminator_fake_loss=1.349, generator_loss=27.03, generator_mel_loss=18.06, generator_kl_loss=1.37, generator_dur_loss=1.699, generator_adv_loss=1.644, generator_feat_match_loss=4.263, over 56.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=27.69, generator_mel_loss=18.31, generator_kl_loss=1.397, generator_dur_loss=1.762, generator_adv_loss=1.95, generator_feat_match_loss=4.264, over 4418.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:42:27,892 INFO [train.py:919] (5/6) Start epoch 301 +2024-03-13 06:42:51,351 INFO [train.py:527] (5/6) Epoch 301, batch 0, global_batch_idx: 37200, batch size: 72, loss[discriminator_loss=2.688, discriminator_real_loss=1.414, discriminator_fake_loss=1.274, generator_loss=28.16, generator_mel_loss=18.59, generator_kl_loss=1.328, generator_dur_loss=1.838, generator_adv_loss=2.031, generator_feat_match_loss=4.37, over 72.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.414, discriminator_fake_loss=1.274, generator_loss=28.16, generator_mel_loss=18.59, generator_kl_loss=1.328, generator_dur_loss=1.838, generator_adv_loss=2.031, generator_feat_match_loss=4.37, over 72.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:42:51,354 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 06:42:59,303 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 06:45:19,201 INFO [train.py:527] (5/6) Epoch 301, batch 50, global_batch_idx: 37250, batch size: 31, loss[discriminator_loss=2.677, discriminator_real_loss=1.331, discriminator_fake_loss=1.346, generator_loss=27.54, generator_mel_loss=18.64, generator_kl_loss=1.513, generator_dur_loss=1.679, generator_adv_loss=1.857, generator_feat_match_loss=3.852, over 31.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.382, discriminator_fake_loss=1.347, generator_loss=27.63, generator_mel_loss=18.32, generator_kl_loss=1.386, generator_dur_loss=1.784, generator_adv_loss=1.933, generator_feat_match_loss=4.211, over 2991.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:47:38,501 INFO [train.py:527] (5/6) Epoch 301, batch 100, global_batch_idx: 37300, batch size: 45, loss[discriminator_loss=2.685, discriminator_real_loss=1.339, discriminator_fake_loss=1.345, generator_loss=28.45, generator_mel_loss=18.75, generator_kl_loss=1.601, generator_dur_loss=1.672, generator_adv_loss=1.987, generator_feat_match_loss=4.436, over 45.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.38, discriminator_fake_loss=1.348, generator_loss=27.61, generator_mel_loss=18.29, generator_kl_loss=1.417, generator_dur_loss=1.766, generator_adv_loss=1.928, generator_feat_match_loss=4.204, over 5609.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:48:41,393 INFO [train.py:919] (5/6) Start epoch 302 +2024-03-13 06:50:16,557 INFO [train.py:527] (5/6) Epoch 302, batch 26, global_batch_idx: 37350, batch size: 31, loss[discriminator_loss=2.749, discriminator_real_loss=1.464, discriminator_fake_loss=1.284, generator_loss=27.17, generator_mel_loss=18.53, generator_kl_loss=1.534, generator_dur_loss=1.614, generator_adv_loss=1.884, generator_feat_match_loss=3.606, over 31.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.387, discriminator_fake_loss=1.343, generator_loss=27.54, generator_mel_loss=18.35, generator_kl_loss=1.41, generator_dur_loss=1.753, generator_adv_loss=1.931, generator_feat_match_loss=4.095, over 1520.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:52:36,073 INFO [train.py:527] (5/6) Epoch 302, batch 76, global_batch_idx: 37400, batch size: 68, loss[discriminator_loss=2.726, discriminator_real_loss=1.456, discriminator_fake_loss=1.27, generator_loss=28.26, generator_mel_loss=18.96, generator_kl_loss=1.283, generator_dur_loss=1.801, generator_adv_loss=1.79, generator_feat_match_loss=4.427, over 68.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.389, discriminator_fake_loss=1.35, generator_loss=27.54, generator_mel_loss=18.33, generator_kl_loss=1.395, generator_dur_loss=1.766, generator_adv_loss=1.92, generator_feat_match_loss=4.129, over 4405.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:52:36,075 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 06:52:44,010 INFO [train.py:591] (5/6) 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 06:52:44,011 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 06:54:55,302 INFO [train.py:919] (5/6) Start epoch 303 +2024-03-13 06:55:25,356 INFO [train.py:527] (5/6) Epoch 303, batch 2, global_batch_idx: 37450, batch size: 59, loss[discriminator_loss=3, discriminator_real_loss=1.242, discriminator_fake_loss=1.758, generator_loss=27.32, generator_mel_loss=18.72, generator_kl_loss=1.421, generator_dur_loss=1.788, generator_adv_loss=1.856, generator_feat_match_loss=3.531, over 59.00 samples.], tot_loss[discriminator_loss=2.971, discriminator_real_loss=1.511, discriminator_fake_loss=1.459, generator_loss=27.15, generator_mel_loss=18.29, generator_kl_loss=1.329, generator_dur_loss=1.828, generator_adv_loss=1.857, generator_feat_match_loss=3.85, over 216.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 06:57:42,837 INFO [train.py:527] (5/6) Epoch 303, batch 52, global_batch_idx: 37500, batch size: 88, loss[discriminator_loss=2.771, discriminator_real_loss=1.409, discriminator_fake_loss=1.363, generator_loss=27.78, generator_mel_loss=18.16, generator_kl_loss=1.215, generator_dur_loss=1.859, generator_adv_loss=2.186, generator_feat_match_loss=4.362, over 88.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.388, discriminator_fake_loss=1.353, generator_loss=27.46, generator_mel_loss=18.19, generator_kl_loss=1.387, generator_dur_loss=1.763, generator_adv_loss=1.929, generator_feat_match_loss=4.192, over 3180.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:00:01,168 INFO [train.py:527] (5/6) Epoch 303, batch 102, global_batch_idx: 37550, batch size: 55, loss[discriminator_loss=2.801, discriminator_real_loss=1.471, discriminator_fake_loss=1.33, generator_loss=26.68, generator_mel_loss=18.32, generator_kl_loss=1.483, generator_dur_loss=1.745, generator_adv_loss=1.762, generator_feat_match_loss=3.364, over 55.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.381, discriminator_fake_loss=1.351, generator_loss=27.63, generator_mel_loss=18.27, generator_kl_loss=1.415, generator_dur_loss=1.759, generator_adv_loss=1.95, generator_feat_match_loss=4.238, over 5909.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:01:00,566 INFO [train.py:919] (5/6) Start epoch 304 +2024-03-13 07:02:43,389 INFO [train.py:527] (5/6) Epoch 304, batch 28, global_batch_idx: 37600, batch size: 15, loss[discriminator_loss=2.671, discriminator_real_loss=1.225, discriminator_fake_loss=1.446, generator_loss=30.29, generator_mel_loss=19.72, generator_kl_loss=1.559, generator_dur_loss=1.631, generator_adv_loss=2.258, generator_feat_match_loss=5.121, over 15.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.382, discriminator_fake_loss=1.339, generator_loss=27.67, generator_mel_loss=18.39, generator_kl_loss=1.368, generator_dur_loss=1.774, generator_adv_loss=1.921, generator_feat_match_loss=4.212, over 1654.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:02:43,391 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 07:02:51,456 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 07:05:09,938 INFO [train.py:527] (5/6) Epoch 304, batch 78, global_batch_idx: 37650, batch size: 50, loss[discriminator_loss=2.699, discriminator_real_loss=1.451, discriminator_fake_loss=1.247, generator_loss=27.57, generator_mel_loss=18.38, generator_kl_loss=1.502, generator_dur_loss=1.644, generator_adv_loss=1.847, generator_feat_match_loss=4.201, over 50.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.387, discriminator_fake_loss=1.343, generator_loss=27.63, generator_mel_loss=18.35, generator_kl_loss=1.402, generator_dur_loss=1.775, generator_adv_loss=1.908, generator_feat_match_loss=4.192, over 4469.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:07:12,002 INFO [train.py:919] (5/6) Start epoch 305 +2024-03-13 07:07:47,431 INFO [train.py:527] (5/6) Epoch 305, batch 4, global_batch_idx: 37700, batch size: 64, loss[discriminator_loss=2.751, discriminator_real_loss=1.37, discriminator_fake_loss=1.381, generator_loss=27.63, generator_mel_loss=18.67, generator_kl_loss=1.271, generator_dur_loss=1.794, generator_adv_loss=1.897, generator_feat_match_loss=4.001, over 64.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.42, discriminator_fake_loss=1.351, generator_loss=27.71, generator_mel_loss=18.64, generator_kl_loss=1.361, generator_dur_loss=1.772, generator_adv_loss=1.878, generator_feat_match_loss=4.055, over 293.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:10:05,711 INFO [train.py:527] (5/6) Epoch 305, batch 54, global_batch_idx: 37750, batch size: 48, loss[discriminator_loss=2.697, discriminator_real_loss=1.423, discriminator_fake_loss=1.273, generator_loss=27.35, generator_mel_loss=18.31, generator_kl_loss=1.572, generator_dur_loss=1.723, generator_adv_loss=1.721, generator_feat_match_loss=4.024, over 48.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.345, generator_loss=27.53, generator_mel_loss=18.25, generator_kl_loss=1.396, generator_dur_loss=1.78, generator_adv_loss=1.924, generator_feat_match_loss=4.181, over 3170.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:12:24,959 INFO [train.py:527] (5/6) Epoch 305, batch 104, global_batch_idx: 37800, batch size: 70, loss[discriminator_loss=2.773, discriminator_real_loss=1.389, discriminator_fake_loss=1.383, generator_loss=27.03, generator_mel_loss=17.98, generator_kl_loss=1.271, generator_dur_loss=1.818, generator_adv_loss=1.898, generator_feat_match_loss=4.062, over 70.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=27.61, generator_mel_loss=18.28, generator_kl_loss=1.396, generator_dur_loss=1.78, generator_adv_loss=1.927, generator_feat_match_loss=4.228, over 6029.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:12:24,961 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 07:12:33,606 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 07:13:27,732 INFO [train.py:919] (5/6) Start epoch 306 +2024-03-13 07:15:14,967 INFO [train.py:527] (5/6) Epoch 306, batch 30, global_batch_idx: 37850, batch size: 39, loss[discriminator_loss=2.761, discriminator_real_loss=1.186, discriminator_fake_loss=1.575, generator_loss=27.04, generator_mel_loss=18.24, generator_kl_loss=1.574, generator_dur_loss=1.72, generator_adv_loss=1.81, generator_feat_match_loss=3.693, over 39.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.382, discriminator_fake_loss=1.35, generator_loss=27.55, generator_mel_loss=18.27, generator_kl_loss=1.376, generator_dur_loss=1.779, generator_adv_loss=1.935, generator_feat_match_loss=4.191, over 1739.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:17:33,458 INFO [train.py:527] (5/6) Epoch 306, batch 80, global_batch_idx: 37900, batch size: 68, loss[discriminator_loss=2.774, discriminator_real_loss=1.295, discriminator_fake_loss=1.479, generator_loss=27.99, generator_mel_loss=18.37, generator_kl_loss=1.409, generator_dur_loss=1.823, generator_adv_loss=1.911, generator_feat_match_loss=4.474, over 68.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.388, discriminator_fake_loss=1.347, generator_loss=27.6, generator_mel_loss=18.27, generator_kl_loss=1.375, generator_dur_loss=1.782, generator_adv_loss=1.96, generator_feat_match_loss=4.207, over 4799.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, +2024-03-13 07:19:33,260 INFO [train.py:919] (5/6) Start epoch 307 +2024-03-13 07:20:14,577 INFO [train.py:527] (5/6) Epoch 307, batch 6, global_batch_idx: 37950, batch size: 45, loss[discriminator_loss=2.75, discriminator_real_loss=1.344, discriminator_fake_loss=1.405, generator_loss=28.07, generator_mel_loss=18.3, generator_kl_loss=1.529, generator_dur_loss=1.682, generator_adv_loss=1.928, generator_feat_match_loss=4.631, over 45.00 samples.], tot_loss[discriminator_loss=2.791, discriminator_real_loss=1.417, discriminator_fake_loss=1.374, generator_loss=27.65, generator_mel_loss=18.31, generator_kl_loss=1.422, generator_dur_loss=1.787, generator_adv_loss=1.892, generator_feat_match_loss=4.244, over 458.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:22:34,409 INFO [train.py:527] (5/6) Epoch 307, batch 56, global_batch_idx: 38000, batch size: 68, loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=28.07, generator_mel_loss=18.38, generator_kl_loss=1.467, generator_dur_loss=1.87, generator_adv_loss=1.816, generator_feat_match_loss=4.539, over 68.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.39, discriminator_fake_loss=1.347, generator_loss=27.52, generator_mel_loss=18.26, generator_kl_loss=1.398, generator_dur_loss=1.774, generator_adv_loss=1.921, generator_feat_match_loss=4.175, over 3353.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:22:34,410 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 07:22:42,558 INFO [train.py:591] (5/6) 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 07:22:42,559 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 07:25:00,853 INFO [train.py:527] (5/6) Epoch 307, batch 106, global_batch_idx: 38050, batch size: 55, loss[discriminator_loss=2.723, discriminator_real_loss=1.261, discriminator_fake_loss=1.461, generator_loss=28.43, generator_mel_loss=18.73, generator_kl_loss=1.542, generator_dur_loss=1.695, generator_adv_loss=1.984, generator_feat_match_loss=4.483, over 55.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.385, discriminator_fake_loss=1.344, generator_loss=27.6, generator_mel_loss=18.26, generator_kl_loss=1.397, generator_dur_loss=1.775, generator_adv_loss=1.931, generator_feat_match_loss=4.239, over 6350.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:25:48,059 INFO [train.py:919] (5/6) Start epoch 308 +2024-03-13 07:27:39,180 INFO [train.py:527] (5/6) Epoch 308, batch 32, global_batch_idx: 38100, batch size: 39, loss[discriminator_loss=2.65, discriminator_real_loss=1.348, discriminator_fake_loss=1.302, generator_loss=28.13, generator_mel_loss=18.61, generator_kl_loss=1.639, generator_dur_loss=1.696, generator_adv_loss=1.828, generator_feat_match_loss=4.361, over 39.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.383, discriminator_fake_loss=1.364, generator_loss=27.75, generator_mel_loss=18.41, generator_kl_loss=1.397, generator_dur_loss=1.752, generator_adv_loss=1.93, generator_feat_match_loss=4.261, over 1869.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:29:59,294 INFO [train.py:527] (5/6) Epoch 308, batch 82, global_batch_idx: 38150, batch size: 80, loss[discriminator_loss=2.676, discriminator_real_loss=1.365, discriminator_fake_loss=1.311, generator_loss=28.28, generator_mel_loss=18.7, generator_kl_loss=1.43, generator_dur_loss=1.824, generator_adv_loss=1.996, generator_feat_match_loss=4.336, over 80.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.378, discriminator_fake_loss=1.353, generator_loss=27.68, generator_mel_loss=18.32, generator_kl_loss=1.378, generator_dur_loss=1.782, generator_adv_loss=1.937, generator_feat_match_loss=4.265, over 5098.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:31:53,213 INFO [train.py:919] (5/6) Start epoch 309 +2024-03-13 07:32:39,974 INFO [train.py:527] (5/6) Epoch 309, batch 8, global_batch_idx: 38200, batch size: 14, loss[discriminator_loss=2.695, discriminator_real_loss=1.385, discriminator_fake_loss=1.31, generator_loss=29.69, generator_mel_loss=19.41, generator_kl_loss=1.859, generator_dur_loss=1.579, generator_adv_loss=2.113, generator_feat_match_loss=4.724, over 14.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.346, generator_loss=27.76, generator_mel_loss=18.39, generator_kl_loss=1.372, generator_dur_loss=1.768, generator_adv_loss=1.958, generator_feat_match_loss=4.268, over 523.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:32:39,977 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 07:32:47,771 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 07:35:05,296 INFO [train.py:527] (5/6) Epoch 309, batch 58, global_batch_idx: 38250, batch size: 77, loss[discriminator_loss=2.751, discriminator_real_loss=1.424, discriminator_fake_loss=1.328, generator_loss=26.92, generator_mel_loss=17.64, generator_kl_loss=1.339, generator_dur_loss=1.822, generator_adv_loss=1.873, generator_feat_match_loss=4.244, over 77.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.381, discriminator_fake_loss=1.349, generator_loss=27.66, generator_mel_loss=18.28, generator_kl_loss=1.41, generator_dur_loss=1.771, generator_adv_loss=1.952, generator_feat_match_loss=4.25, over 3343.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:37:22,828 INFO [train.py:527] (5/6) Epoch 309, batch 108, global_batch_idx: 38300, batch size: 55, loss[discriminator_loss=2.692, discriminator_real_loss=1.267, discriminator_fake_loss=1.424, generator_loss=28.01, generator_mel_loss=18.26, generator_kl_loss=1.514, generator_dur_loss=1.635, generator_adv_loss=1.976, generator_feat_match_loss=4.628, over 55.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.379, discriminator_fake_loss=1.345, generator_loss=27.61, generator_mel_loss=18.26, generator_kl_loss=1.403, generator_dur_loss=1.768, generator_adv_loss=1.945, generator_feat_match_loss=4.229, over 6330.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:38:08,232 INFO [train.py:919] (5/6) Start epoch 310 +2024-03-13 07:40:05,817 INFO [train.py:527] (5/6) Epoch 310, batch 34, global_batch_idx: 38350, batch size: 70, loss[discriminator_loss=2.68, discriminator_real_loss=1.282, discriminator_fake_loss=1.398, generator_loss=27.82, generator_mel_loss=18.25, generator_kl_loss=1.211, generator_dur_loss=1.799, generator_adv_loss=2.187, generator_feat_match_loss=4.38, over 70.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.381, discriminator_fake_loss=1.35, generator_loss=27.58, generator_mel_loss=18.31, generator_kl_loss=1.387, generator_dur_loss=1.774, generator_adv_loss=1.935, generator_feat_match_loss=4.174, over 1999.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:42:24,905 INFO [train.py:527] (5/6) Epoch 310, batch 84, global_batch_idx: 38400, batch size: 42, loss[discriminator_loss=2.716, discriminator_real_loss=1.36, discriminator_fake_loss=1.356, generator_loss=27.98, generator_mel_loss=18.55, generator_kl_loss=1.585, generator_dur_loss=1.677, generator_adv_loss=1.961, generator_feat_match_loss=4.2, over 42.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.381, discriminator_fake_loss=1.348, generator_loss=27.53, generator_mel_loss=18.29, generator_kl_loss=1.386, generator_dur_loss=1.784, generator_adv_loss=1.923, generator_feat_match_loss=4.148, over 4968.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:42:24,907 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 07:42:33,708 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 07:44:23,006 INFO [train.py:919] (5/6) Start epoch 311 +2024-03-13 07:45:16,124 INFO [train.py:527] (5/6) Epoch 311, batch 10, global_batch_idx: 38450, batch size: 83, loss[discriminator_loss=2.703, discriminator_real_loss=1.438, discriminator_fake_loss=1.264, generator_loss=27.66, generator_mel_loss=18.49, generator_kl_loss=1.318, generator_dur_loss=1.843, generator_adv_loss=1.897, generator_feat_match_loss=4.107, over 83.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.396, discriminator_fake_loss=1.352, generator_loss=27.24, generator_mel_loss=18.1, generator_kl_loss=1.402, generator_dur_loss=1.755, generator_adv_loss=1.951, generator_feat_match_loss=4.029, over 600.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:47:35,299 INFO [train.py:527] (5/6) Epoch 311, batch 60, global_batch_idx: 38500, batch size: 59, loss[discriminator_loss=2.764, discriminator_real_loss=1.472, discriminator_fake_loss=1.293, generator_loss=25.33, generator_mel_loss=17.24, generator_kl_loss=1.325, generator_dur_loss=1.787, generator_adv_loss=1.878, generator_feat_match_loss=3.1, over 59.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.38, discriminator_fake_loss=1.351, generator_loss=27.52, generator_mel_loss=18.22, generator_kl_loss=1.406, generator_dur_loss=1.765, generator_adv_loss=1.918, generator_feat_match_loss=4.205, over 3516.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:49:52,809 INFO [train.py:527] (5/6) Epoch 311, batch 110, global_batch_idx: 38550, batch size: 62, loss[discriminator_loss=2.755, discriminator_real_loss=1.414, discriminator_fake_loss=1.341, generator_loss=27.23, generator_mel_loss=18.2, generator_kl_loss=1.433, generator_dur_loss=1.761, generator_adv_loss=1.833, generator_feat_match_loss=4, over 62.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.378, discriminator_fake_loss=1.351, generator_loss=27.56, generator_mel_loss=18.24, generator_kl_loss=1.416, generator_dur_loss=1.765, generator_adv_loss=1.917, generator_feat_match_loss=4.224, over 6479.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:50:29,225 INFO [train.py:919] (5/6) Start epoch 312 +2024-03-13 07:52:36,858 INFO [train.py:527] (5/6) Epoch 312, batch 36, global_batch_idx: 38600, batch size: 52, loss[discriminator_loss=2.745, discriminator_real_loss=1.323, discriminator_fake_loss=1.422, generator_loss=28.11, generator_mel_loss=18.47, generator_kl_loss=1.559, generator_dur_loss=1.705, generator_adv_loss=2.066, generator_feat_match_loss=4.304, over 52.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.383, discriminator_fake_loss=1.347, generator_loss=27.26, generator_mel_loss=18.15, generator_kl_loss=1.372, generator_dur_loss=1.768, generator_adv_loss=1.908, generator_feat_match_loss=4.057, over 2230.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:52:36,860 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 07:52:44,819 INFO [train.py:591] (5/6) 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 07:52:44,820 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 07:55:03,767 INFO [train.py:527] (5/6) Epoch 312, batch 86, global_batch_idx: 38650, batch size: 55, loss[discriminator_loss=2.725, discriminator_real_loss=1.331, discriminator_fake_loss=1.394, generator_loss=26.57, generator_mel_loss=18.2, generator_kl_loss=1.32, generator_dur_loss=1.699, generator_adv_loss=1.886, generator_feat_match_loss=3.464, over 55.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=27.5, generator_mel_loss=18.26, generator_kl_loss=1.393, generator_dur_loss=1.765, generator_adv_loss=1.914, generator_feat_match_loss=4.17, over 5080.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 07:56:43,239 INFO [train.py:919] (5/6) Start epoch 313 +2024-03-13 07:57:41,496 INFO [train.py:527] (5/6) Epoch 313, batch 12, global_batch_idx: 38700, batch size: 25, loss[discriminator_loss=2.671, discriminator_real_loss=1.302, discriminator_fake_loss=1.369, generator_loss=29.5, generator_mel_loss=18.86, generator_kl_loss=1.979, generator_dur_loss=1.502, generator_adv_loss=2.019, generator_feat_match_loss=5.141, over 25.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.389, discriminator_fake_loss=1.372, generator_loss=27.38, generator_mel_loss=18.15, generator_kl_loss=1.379, generator_dur_loss=1.793, generator_adv_loss=1.891, generator_feat_match_loss=4.166, over 806.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:00:02,070 INFO [train.py:527] (5/6) Epoch 313, batch 62, global_batch_idx: 38750, batch size: 68, loss[discriminator_loss=2.693, discriminator_real_loss=1.294, discriminator_fake_loss=1.399, generator_loss=26.73, generator_mel_loss=17.79, generator_kl_loss=1.458, generator_dur_loss=1.733, generator_adv_loss=1.948, generator_feat_match_loss=3.798, over 68.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.38, discriminator_fake_loss=1.351, generator_loss=27.48, generator_mel_loss=18.2, generator_kl_loss=1.402, generator_dur_loss=1.773, generator_adv_loss=1.913, generator_feat_match_loss=4.199, over 3503.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:02:21,438 INFO [train.py:527] (5/6) Epoch 313, batch 112, global_batch_idx: 38800, batch size: 68, loss[discriminator_loss=2.754, discriminator_real_loss=1.412, discriminator_fake_loss=1.341, generator_loss=27.45, generator_mel_loss=18.42, generator_kl_loss=1.433, generator_dur_loss=1.832, generator_adv_loss=1.898, generator_feat_match_loss=3.865, over 68.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.38, discriminator_fake_loss=1.351, generator_loss=27.51, generator_mel_loss=18.23, generator_kl_loss=1.399, generator_dur_loss=1.772, generator_adv_loss=1.921, generator_feat_match_loss=4.19, over 6375.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:02:21,440 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 08:02:30,293 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 08:03:00,335 INFO [train.py:919] (5/6) Start epoch 314 +2024-03-13 08:05:10,060 INFO [train.py:527] (5/6) Epoch 314, batch 38, global_batch_idx: 38850, batch size: 31, loss[discriminator_loss=2.835, discriminator_real_loss=1.408, discriminator_fake_loss=1.427, generator_loss=28.33, generator_mel_loss=19.02, generator_kl_loss=1.457, generator_dur_loss=1.623, generator_adv_loss=2.096, generator_feat_match_loss=4.131, over 31.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.377, discriminator_fake_loss=1.348, generator_loss=27.49, generator_mel_loss=18.17, generator_kl_loss=1.403, generator_dur_loss=1.766, generator_adv_loss=1.935, generator_feat_match_loss=4.208, over 2255.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:07:30,770 INFO [train.py:527] (5/6) Epoch 314, batch 88, global_batch_idx: 38900, batch size: 36, loss[discriminator_loss=2.767, discriminator_real_loss=1.31, discriminator_fake_loss=1.457, generator_loss=27.41, generator_mel_loss=18.71, generator_kl_loss=1.513, generator_dur_loss=1.745, generator_adv_loss=1.796, generator_feat_match_loss=3.644, over 36.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.379, discriminator_fake_loss=1.35, generator_loss=27.44, generator_mel_loss=18.18, generator_kl_loss=1.377, generator_dur_loss=1.767, generator_adv_loss=1.916, generator_feat_match_loss=4.2, over 5319.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:09:07,635 INFO [train.py:919] (5/6) Start epoch 315 +2024-03-13 08:10:09,720 INFO [train.py:527] (5/6) Epoch 315, batch 14, global_batch_idx: 38950, batch size: 52, loss[discriminator_loss=2.754, discriminator_real_loss=1.373, discriminator_fake_loss=1.381, generator_loss=27.75, generator_mel_loss=18.4, generator_kl_loss=1.4, generator_dur_loss=1.762, generator_adv_loss=1.884, generator_feat_match_loss=4.31, over 52.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.372, discriminator_fake_loss=1.366, generator_loss=27.65, generator_mel_loss=18.31, generator_kl_loss=1.427, generator_dur_loss=1.746, generator_adv_loss=1.895, generator_feat_match_loss=4.276, over 784.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:12:31,524 INFO [train.py:527] (5/6) Epoch 315, batch 64, global_batch_idx: 39000, batch size: 62, loss[discriminator_loss=2.743, discriminator_real_loss=1.413, discriminator_fake_loss=1.331, generator_loss=26.88, generator_mel_loss=17.83, generator_kl_loss=1.491, generator_dur_loss=1.753, generator_adv_loss=1.837, generator_feat_match_loss=3.966, over 62.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.373, discriminator_fake_loss=1.352, generator_loss=27.73, generator_mel_loss=18.31, generator_kl_loss=1.391, generator_dur_loss=1.762, generator_adv_loss=1.939, generator_feat_match_loss=4.333, over 3584.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:12:31,525 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 08:12:39,671 INFO [train.py:591] (5/6) 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 08:12:39,672 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 08:14:57,030 INFO [train.py:527] (5/6) Epoch 315, batch 114, global_batch_idx: 39050, batch size: 77, loss[discriminator_loss=2.688, discriminator_real_loss=1.315, discriminator_fake_loss=1.373, generator_loss=28.17, generator_mel_loss=18.36, generator_kl_loss=1.252, generator_dur_loss=1.829, generator_adv_loss=2.084, generator_feat_match_loss=4.642, over 77.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.378, discriminator_fake_loss=1.349, generator_loss=27.7, generator_mel_loss=18.29, generator_kl_loss=1.406, generator_dur_loss=1.76, generator_adv_loss=1.934, generator_feat_match_loss=4.308, over 6259.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:15:23,625 INFO [train.py:919] (5/6) Start epoch 316 +2024-03-13 08:17:41,137 INFO [train.py:527] (5/6) Epoch 316, batch 40, global_batch_idx: 39100, batch size: 39, loss[discriminator_loss=2.691, discriminator_real_loss=1.426, discriminator_fake_loss=1.264, generator_loss=27.27, generator_mel_loss=17.84, generator_kl_loss=1.593, generator_dur_loss=1.708, generator_adv_loss=1.901, generator_feat_match_loss=4.227, over 39.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=27.65, generator_mel_loss=18.25, generator_kl_loss=1.384, generator_dur_loss=1.781, generator_adv_loss=1.935, generator_feat_match_loss=4.292, over 2450.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:19:58,077 INFO [train.py:527] (5/6) Epoch 316, batch 90, global_batch_idx: 39150, batch size: 31, loss[discriminator_loss=2.701, discriminator_real_loss=1.37, discriminator_fake_loss=1.331, generator_loss=29.05, generator_mel_loss=18.98, generator_kl_loss=1.668, generator_dur_loss=1.628, generator_adv_loss=1.933, generator_feat_match_loss=4.844, over 31.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.71, generator_mel_loss=18.29, generator_kl_loss=1.403, generator_dur_loss=1.764, generator_adv_loss=1.934, generator_feat_match_loss=4.315, over 5255.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:21:29,564 INFO [train.py:919] (5/6) Start epoch 317 +2024-03-13 08:22:39,698 INFO [train.py:527] (5/6) Epoch 317, batch 16, global_batch_idx: 39200, batch size: 88, loss[discriminator_loss=2.732, discriminator_real_loss=1.461, discriminator_fake_loss=1.271, generator_loss=26.9, generator_mel_loss=18.1, generator_kl_loss=1.223, generator_dur_loss=1.837, generator_adv_loss=1.843, generator_feat_match_loss=3.898, over 88.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.392, discriminator_fake_loss=1.334, generator_loss=27.68, generator_mel_loss=18.35, generator_kl_loss=1.399, generator_dur_loss=1.753, generator_adv_loss=1.973, generator_feat_match_loss=4.206, over 895.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:22:39,699 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 08:22:47,857 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 08:25:08,372 INFO [train.py:527] (5/6) Epoch 317, batch 66, global_batch_idx: 39250, batch size: 36, loss[discriminator_loss=2.76, discriminator_real_loss=1.428, discriminator_fake_loss=1.332, generator_loss=28.06, generator_mel_loss=18.39, generator_kl_loss=1.614, generator_dur_loss=1.661, generator_adv_loss=1.731, generator_feat_match_loss=4.666, over 36.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.385, discriminator_fake_loss=1.351, generator_loss=27.59, generator_mel_loss=18.26, generator_kl_loss=1.418, generator_dur_loss=1.754, generator_adv_loss=1.931, generator_feat_match_loss=4.23, over 3752.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:27:27,180 INFO [train.py:527] (5/6) Epoch 317, batch 116, global_batch_idx: 39300, batch size: 83, loss[discriminator_loss=2.701, discriminator_real_loss=1.278, discriminator_fake_loss=1.423, generator_loss=27.61, generator_mel_loss=18.26, generator_kl_loss=1.403, generator_dur_loss=1.827, generator_adv_loss=1.946, generator_feat_match_loss=4.173, over 83.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.381, discriminator_fake_loss=1.349, generator_loss=27.68, generator_mel_loss=18.3, generator_kl_loss=1.421, generator_dur_loss=1.752, generator_adv_loss=1.93, generator_feat_match_loss=4.274, over 6437.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:27:47,659 INFO [train.py:919] (5/6) Start epoch 318 +2024-03-13 08:30:05,496 INFO [train.py:527] (5/6) Epoch 318, batch 42, global_batch_idx: 39350, batch size: 44, loss[discriminator_loss=2.675, discriminator_real_loss=1.381, discriminator_fake_loss=1.294, generator_loss=28.53, generator_mel_loss=18.35, generator_kl_loss=1.543, generator_dur_loss=1.685, generator_adv_loss=2.256, generator_feat_match_loss=4.695, over 44.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=27.78, generator_mel_loss=18.22, generator_kl_loss=1.424, generator_dur_loss=1.747, generator_adv_loss=1.989, generator_feat_match_loss=4.399, over 2388.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:32:24,874 INFO [train.py:527] (5/6) Epoch 318, batch 92, global_batch_idx: 39400, batch size: 52, loss[discriminator_loss=2.749, discriminator_real_loss=1.457, discriminator_fake_loss=1.292, generator_loss=26.47, generator_mel_loss=17.71, generator_kl_loss=1.45, generator_dur_loss=1.685, generator_adv_loss=2.013, generator_feat_match_loss=3.62, over 52.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.386, discriminator_fake_loss=1.342, generator_loss=27.62, generator_mel_loss=18.21, generator_kl_loss=1.423, generator_dur_loss=1.743, generator_adv_loss=1.959, generator_feat_match_loss=4.287, over 5243.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:32:24,875 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 08:32:33,727 INFO [train.py:591] (5/6) 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 08:32:33,728 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 08:34:00,256 INFO [train.py:919] (5/6) Start epoch 319 +2024-03-13 08:35:17,681 INFO [train.py:527] (5/6) Epoch 319, batch 18, global_batch_idx: 39450, batch size: 52, loss[discriminator_loss=2.712, discriminator_real_loss=1.338, discriminator_fake_loss=1.374, generator_loss=27.37, generator_mel_loss=18.17, generator_kl_loss=1.358, generator_dur_loss=1.675, generator_adv_loss=1.949, generator_feat_match_loss=4.224, over 52.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.386, discriminator_fake_loss=1.351, generator_loss=27.87, generator_mel_loss=18.42, generator_kl_loss=1.435, generator_dur_loss=1.756, generator_adv_loss=1.931, generator_feat_match_loss=4.328, over 995.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:37:36,150 INFO [train.py:527] (5/6) Epoch 319, batch 68, global_batch_idx: 39500, batch size: 68, loss[discriminator_loss=2.762, discriminator_real_loss=1.403, discriminator_fake_loss=1.359, generator_loss=27.65, generator_mel_loss=18.36, generator_kl_loss=1.512, generator_dur_loss=1.795, generator_adv_loss=1.853, generator_feat_match_loss=4.132, over 68.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.385, discriminator_fake_loss=1.343, generator_loss=27.74, generator_mel_loss=18.36, generator_kl_loss=1.413, generator_dur_loss=1.763, generator_adv_loss=1.937, generator_feat_match_loss=4.264, over 3667.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:39:52,982 INFO [train.py:527] (5/6) Epoch 319, batch 118, global_batch_idx: 39550, batch size: 62, loss[discriminator_loss=2.73, discriminator_real_loss=1.248, discriminator_fake_loss=1.482, generator_loss=29.09, generator_mel_loss=18.86, generator_kl_loss=1.47, generator_dur_loss=1.744, generator_adv_loss=2.297, generator_feat_match_loss=4.721, over 62.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=27.75, generator_mel_loss=18.37, generator_kl_loss=1.414, generator_dur_loss=1.77, generator_adv_loss=1.936, generator_feat_match_loss=4.264, over 6599.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:40:09,374 INFO [train.py:919] (5/6) Start epoch 320 +2024-03-13 08:42:30,487 INFO [train.py:527] (5/6) Epoch 320, batch 44, global_batch_idx: 39600, batch size: 59, loss[discriminator_loss=2.712, discriminator_real_loss=1.351, discriminator_fake_loss=1.361, generator_loss=27.66, generator_mel_loss=18.17, generator_kl_loss=1.629, generator_dur_loss=1.769, generator_adv_loss=2.034, generator_feat_match_loss=4.053, over 59.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.383, discriminator_fake_loss=1.342, generator_loss=27.5, generator_mel_loss=18.16, generator_kl_loss=1.398, generator_dur_loss=1.768, generator_adv_loss=1.949, generator_feat_match_loss=4.225, over 2657.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:42:30,489 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 08:42:38,338 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 08:44:58,540 INFO [train.py:527] (5/6) Epoch 320, batch 94, global_batch_idx: 39650, batch size: 42, loss[discriminator_loss=2.678, discriminator_real_loss=1.334, discriminator_fake_loss=1.344, generator_loss=27.88, generator_mel_loss=18.1, generator_kl_loss=1.555, generator_dur_loss=1.715, generator_adv_loss=2.05, generator_feat_match_loss=4.455, over 42.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.382, discriminator_fake_loss=1.345, generator_loss=27.61, generator_mel_loss=18.23, generator_kl_loss=1.421, generator_dur_loss=1.77, generator_adv_loss=1.937, generator_feat_match_loss=4.249, over 5577.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:46:19,280 INFO [train.py:919] (5/6) Start epoch 321 +2024-03-13 08:47:37,889 INFO [train.py:527] (5/6) Epoch 321, batch 20, global_batch_idx: 39700, batch size: 44, loss[discriminator_loss=2.759, discriminator_real_loss=1.447, discriminator_fake_loss=1.313, generator_loss=27.09, generator_mel_loss=18.16, generator_kl_loss=1.415, generator_dur_loss=1.678, generator_adv_loss=1.837, generator_feat_match_loss=3.992, over 44.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.391, discriminator_fake_loss=1.343, generator_loss=27.57, generator_mel_loss=18.26, generator_kl_loss=1.445, generator_dur_loss=1.75, generator_adv_loss=1.905, generator_feat_match_loss=4.211, over 1099.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:49:55,684 INFO [train.py:527] (5/6) Epoch 321, batch 70, global_batch_idx: 39750, batch size: 72, loss[discriminator_loss=2.739, discriminator_real_loss=1.355, discriminator_fake_loss=1.384, generator_loss=27.48, generator_mel_loss=18.09, generator_kl_loss=1.317, generator_dur_loss=1.822, generator_adv_loss=1.997, generator_feat_match_loss=4.251, over 72.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.387, discriminator_fake_loss=1.351, generator_loss=27.62, generator_mel_loss=18.28, generator_kl_loss=1.443, generator_dur_loss=1.754, generator_adv_loss=1.93, generator_feat_match_loss=4.21, over 3719.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:52:15,319 INFO [train.py:527] (5/6) Epoch 321, batch 120, global_batch_idx: 39800, batch size: 52, loss[discriminator_loss=2.654, discriminator_real_loss=1.414, discriminator_fake_loss=1.24, generator_loss=28.6, generator_mel_loss=18.95, generator_kl_loss=1.329, generator_dur_loss=1.744, generator_adv_loss=1.947, generator_feat_match_loss=4.626, over 52.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.385, discriminator_fake_loss=1.349, generator_loss=27.63, generator_mel_loss=18.26, generator_kl_loss=1.414, generator_dur_loss=1.774, generator_adv_loss=1.93, generator_feat_match_loss=4.25, over 6891.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:52:15,320 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 08:52:24,263 INFO [train.py:591] (5/6) 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 08:52:24,263 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 08:52:35,305 INFO [train.py:919] (5/6) Start epoch 322 +2024-03-13 08:55:05,029 INFO [train.py:527] (5/6) Epoch 322, batch 46, global_batch_idx: 39850, batch size: 44, loss[discriminator_loss=2.713, discriminator_real_loss=1.295, discriminator_fake_loss=1.417, generator_loss=27.34, generator_mel_loss=18.25, generator_kl_loss=1.379, generator_dur_loss=1.684, generator_adv_loss=2.027, generator_feat_match_loss=3.995, over 44.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.376, discriminator_fake_loss=1.348, generator_loss=27.66, generator_mel_loss=18.28, generator_kl_loss=1.413, generator_dur_loss=1.777, generator_adv_loss=1.924, generator_feat_match_loss=4.27, over 2643.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:57:20,916 INFO [train.py:527] (5/6) Epoch 322, batch 96, global_batch_idx: 39900, batch size: 70, loss[discriminator_loss=2.753, discriminator_real_loss=1.392, discriminator_fake_loss=1.361, generator_loss=27.18, generator_mel_loss=17.99, generator_kl_loss=1.232, generator_dur_loss=1.788, generator_adv_loss=2.062, generator_feat_match_loss=4.111, over 70.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.379, discriminator_fake_loss=1.346, generator_loss=27.68, generator_mel_loss=18.29, generator_kl_loss=1.404, generator_dur_loss=1.765, generator_adv_loss=1.929, generator_feat_match_loss=4.294, over 5443.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 08:58:37,595 INFO [train.py:919] (5/6) Start epoch 323 +2024-03-13 09:00:00,618 INFO [train.py:527] (5/6) Epoch 323, batch 22, global_batch_idx: 39950, batch size: 62, loss[discriminator_loss=2.701, discriminator_real_loss=1.397, discriminator_fake_loss=1.304, generator_loss=28.18, generator_mel_loss=18.58, generator_kl_loss=1.616, generator_dur_loss=1.705, generator_adv_loss=1.84, generator_feat_match_loss=4.433, over 62.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.385, discriminator_fake_loss=1.338, generator_loss=27.6, generator_mel_loss=18.24, generator_kl_loss=1.458, generator_dur_loss=1.708, generator_adv_loss=1.937, generator_feat_match_loss=4.252, over 1265.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:02:19,742 INFO [train.py:527] (5/6) Epoch 323, batch 72, global_batch_idx: 40000, batch size: 50, loss[discriminator_loss=2.777, discriminator_real_loss=1.413, discriminator_fake_loss=1.364, generator_loss=27.65, generator_mel_loss=18.4, generator_kl_loss=1.579, generator_dur_loss=1.695, generator_adv_loss=1.857, generator_feat_match_loss=4.127, over 50.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.384, discriminator_fake_loss=1.345, generator_loss=27.67, generator_mel_loss=18.3, generator_kl_loss=1.444, generator_dur_loss=1.731, generator_adv_loss=1.93, generator_feat_match_loss=4.264, over 4008.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:02:19,743 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 09:02:27,806 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 09:04:45,726 INFO [train.py:527] (5/6) Epoch 323, batch 122, global_batch_idx: 40050, batch size: 77, loss[discriminator_loss=2.724, discriminator_real_loss=1.392, discriminator_fake_loss=1.331, generator_loss=26.81, generator_mel_loss=18.01, generator_kl_loss=1.246, generator_dur_loss=1.856, generator_adv_loss=1.967, generator_feat_match_loss=3.728, over 77.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.385, discriminator_fake_loss=1.345, generator_loss=27.66, generator_mel_loss=18.29, generator_kl_loss=1.434, generator_dur_loss=1.743, generator_adv_loss=1.922, generator_feat_match_loss=4.267, over 6854.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:04:51,361 INFO [train.py:919] (5/6) Start epoch 324 +2024-03-13 09:07:28,017 INFO [train.py:527] (5/6) Epoch 324, batch 48, global_batch_idx: 40100, batch size: 56, loss[discriminator_loss=2.756, discriminator_real_loss=1.254, discriminator_fake_loss=1.502, generator_loss=26.77, generator_mel_loss=17.82, generator_kl_loss=1.374, generator_dur_loss=1.694, generator_adv_loss=2.012, generator_feat_match_loss=3.878, over 56.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.71, generator_mel_loss=18.24, generator_kl_loss=1.378, generator_dur_loss=1.774, generator_adv_loss=1.93, generator_feat_match_loss=4.381, over 3018.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:09:47,725 INFO [train.py:527] (5/6) Epoch 324, batch 98, global_batch_idx: 40150, batch size: 50, loss[discriminator_loss=2.724, discriminator_real_loss=1.351, discriminator_fake_loss=1.373, generator_loss=27.49, generator_mel_loss=18.39, generator_kl_loss=1.533, generator_dur_loss=1.652, generator_adv_loss=1.879, generator_feat_match_loss=4.035, over 50.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.38, discriminator_fake_loss=1.346, generator_loss=27.59, generator_mel_loss=18.19, generator_kl_loss=1.393, generator_dur_loss=1.772, generator_adv_loss=1.92, generator_feat_match_loss=4.31, over 6017.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:10:56,610 INFO [train.py:919] (5/6) Start epoch 325 +2024-03-13 09:12:27,284 INFO [train.py:527] (5/6) Epoch 325, batch 24, global_batch_idx: 40200, batch size: 60, loss[discriminator_loss=2.732, discriminator_real_loss=1.357, discriminator_fake_loss=1.375, generator_loss=27.11, generator_mel_loss=18.25, generator_kl_loss=1.326, generator_dur_loss=1.764, generator_adv_loss=1.809, generator_feat_match_loss=3.954, over 60.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.376, discriminator_fake_loss=1.354, generator_loss=27.63, generator_mel_loss=18.26, generator_kl_loss=1.42, generator_dur_loss=1.763, generator_adv_loss=1.918, generator_feat_match_loss=4.276, over 1516.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:12:27,286 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 09:12:35,411 INFO [train.py:591] (5/6) 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 09:12:35,412 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 09:14:54,048 INFO [train.py:527] (5/6) Epoch 325, batch 74, global_batch_idx: 40250, batch size: 44, loss[discriminator_loss=2.819, discriminator_real_loss=1.344, discriminator_fake_loss=1.475, generator_loss=26.76, generator_mel_loss=17.85, generator_kl_loss=1.569, generator_dur_loss=1.716, generator_adv_loss=1.845, generator_feat_match_loss=3.782, over 44.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=27.66, generator_mel_loss=18.26, generator_kl_loss=1.417, generator_dur_loss=1.75, generator_adv_loss=1.921, generator_feat_match_loss=4.315, over 4255.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:17:11,652 INFO [train.py:919] (5/6) Start epoch 326 +2024-03-13 09:17:34,884 INFO [train.py:527] (5/6) Epoch 326, batch 0, global_batch_idx: 40300, batch size: 62, loss[discriminator_loss=2.629, discriminator_real_loss=1.285, discriminator_fake_loss=1.345, generator_loss=28.13, generator_mel_loss=18.18, generator_kl_loss=1.415, generator_dur_loss=1.775, generator_adv_loss=2.124, generator_feat_match_loss=4.638, over 62.00 samples.], tot_loss[discriminator_loss=2.629, discriminator_real_loss=1.285, discriminator_fake_loss=1.345, generator_loss=28.13, generator_mel_loss=18.18, generator_kl_loss=1.415, generator_dur_loss=1.775, generator_adv_loss=2.124, generator_feat_match_loss=4.638, over 62.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:19:53,515 INFO [train.py:527] (5/6) Epoch 326, batch 50, global_batch_idx: 40350, batch size: 96, loss[discriminator_loss=2.771, discriminator_real_loss=1.472, discriminator_fake_loss=1.299, generator_loss=27.1, generator_mel_loss=18.02, generator_kl_loss=1.326, generator_dur_loss=1.897, generator_adv_loss=1.763, generator_feat_match_loss=4.099, over 96.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.375, discriminator_fake_loss=1.338, generator_loss=27.66, generator_mel_loss=18.26, generator_kl_loss=1.388, generator_dur_loss=1.78, generator_adv_loss=1.955, generator_feat_match_loss=4.281, over 3015.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:22:12,710 INFO [train.py:527] (5/6) Epoch 326, batch 100, global_batch_idx: 40400, batch size: 44, loss[discriminator_loss=2.64, discriminator_real_loss=1.418, discriminator_fake_loss=1.222, generator_loss=28.37, generator_mel_loss=18.42, generator_kl_loss=1.545, generator_dur_loss=1.642, generator_adv_loss=1.836, generator_feat_match_loss=4.932, over 44.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.379, discriminator_fake_loss=1.337, generator_loss=27.61, generator_mel_loss=18.23, generator_kl_loss=1.401, generator_dur_loss=1.77, generator_adv_loss=1.937, generator_feat_match_loss=4.271, over 5888.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:22:12,712 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 09:22:21,415 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 09:23:27,468 INFO [train.py:919] (5/6) Start epoch 327 +2024-03-13 09:25:03,103 INFO [train.py:527] (5/6) Epoch 327, batch 26, global_batch_idx: 40450, batch size: 36, loss[discriminator_loss=2.745, discriminator_real_loss=1.308, discriminator_fake_loss=1.437, generator_loss=28.06, generator_mel_loss=18.37, generator_kl_loss=1.496, generator_dur_loss=1.709, generator_adv_loss=2.03, generator_feat_match_loss=4.462, over 36.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.369, discriminator_fake_loss=1.343, generator_loss=27.74, generator_mel_loss=18.32, generator_kl_loss=1.402, generator_dur_loss=1.766, generator_adv_loss=1.941, generator_feat_match_loss=4.303, over 1516.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:27:22,191 INFO [train.py:527] (5/6) Epoch 327, batch 76, global_batch_idx: 40500, batch size: 96, loss[discriminator_loss=2.754, discriminator_real_loss=1.304, discriminator_fake_loss=1.45, generator_loss=27.59, generator_mel_loss=17.86, generator_kl_loss=1.244, generator_dur_loss=1.928, generator_adv_loss=2.077, generator_feat_match_loss=4.479, over 96.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.372, discriminator_fake_loss=1.349, generator_loss=27.72, generator_mel_loss=18.3, generator_kl_loss=1.4, generator_dur_loss=1.764, generator_adv_loss=1.921, generator_feat_match_loss=4.332, over 4300.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:29:34,386 INFO [train.py:919] (5/6) Start epoch 328 +2024-03-13 09:30:05,269 INFO [train.py:527] (5/6) Epoch 328, batch 2, global_batch_idx: 40550, batch size: 45, loss[discriminator_loss=2.747, discriminator_real_loss=1.37, discriminator_fake_loss=1.377, generator_loss=28.17, generator_mel_loss=18.72, generator_kl_loss=1.607, generator_dur_loss=1.698, generator_adv_loss=1.908, generator_feat_match_loss=4.238, over 45.00 samples.], tot_loss[discriminator_loss=2.771, discriminator_real_loss=1.424, discriminator_fake_loss=1.348, generator_loss=27.96, generator_mel_loss=18.35, generator_kl_loss=1.409, generator_dur_loss=1.834, generator_adv_loss=1.931, generator_feat_match_loss=4.428, over 229.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:32:23,455 INFO [train.py:527] (5/6) Epoch 328, batch 52, global_batch_idx: 40600, batch size: 44, loss[discriminator_loss=2.722, discriminator_real_loss=1.419, discriminator_fake_loss=1.303, generator_loss=27.27, generator_mel_loss=18.19, generator_kl_loss=1.447, generator_dur_loss=1.705, generator_adv_loss=1.929, generator_feat_match_loss=3.998, over 44.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.382, discriminator_fake_loss=1.354, generator_loss=27.85, generator_mel_loss=18.32, generator_kl_loss=1.411, generator_dur_loss=1.774, generator_adv_loss=1.929, generator_feat_match_loss=4.418, over 3249.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:32:23,456 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 09:32:31,661 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 09:34:49,844 INFO [train.py:527] (5/6) Epoch 328, batch 102, global_batch_idx: 40650, batch size: 70, loss[discriminator_loss=2.723, discriminator_real_loss=1.413, discriminator_fake_loss=1.309, generator_loss=27.9, generator_mel_loss=18.03, generator_kl_loss=1.326, generator_dur_loss=1.794, generator_adv_loss=1.979, generator_feat_match_loss=4.767, over 70.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.379, discriminator_fake_loss=1.356, generator_loss=27.81, generator_mel_loss=18.29, generator_kl_loss=1.416, generator_dur_loss=1.777, generator_adv_loss=1.929, generator_feat_match_loss=4.401, over 6153.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:35:48,816 INFO [train.py:919] (5/6) Start epoch 329 +2024-03-13 09:37:29,844 INFO [train.py:527] (5/6) Epoch 329, batch 28, global_batch_idx: 40700, batch size: 68, loss[discriminator_loss=2.775, discriminator_real_loss=1.556, discriminator_fake_loss=1.219, generator_loss=27.02, generator_mel_loss=17.88, generator_kl_loss=1.316, generator_dur_loss=1.774, generator_adv_loss=1.788, generator_feat_match_loss=4.267, over 68.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.376, discriminator_fake_loss=1.35, generator_loss=27.57, generator_mel_loss=18.14, generator_kl_loss=1.42, generator_dur_loss=1.778, generator_adv_loss=1.918, generator_feat_match_loss=4.307, over 1677.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:39:49,126 INFO [train.py:527] (5/6) Epoch 329, batch 78, global_batch_idx: 40750, batch size: 61, loss[discriminator_loss=2.708, discriminator_real_loss=1.419, discriminator_fake_loss=1.289, generator_loss=27.92, generator_mel_loss=18.68, generator_kl_loss=1.377, generator_dur_loss=1.833, generator_adv_loss=1.762, generator_feat_match_loss=4.267, over 61.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.377, discriminator_fake_loss=1.351, generator_loss=27.7, generator_mel_loss=18.24, generator_kl_loss=1.408, generator_dur_loss=1.769, generator_adv_loss=1.927, generator_feat_match_loss=4.357, over 4513.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:41:52,537 INFO [train.py:919] (5/6) Start epoch 330 +2024-03-13 09:42:27,531 INFO [train.py:527] (5/6) Epoch 330, batch 4, global_batch_idx: 40800, batch size: 55, loss[discriminator_loss=2.68, discriminator_real_loss=1.282, discriminator_fake_loss=1.398, generator_loss=28.41, generator_mel_loss=18.6, generator_kl_loss=1.457, generator_dur_loss=1.721, generator_adv_loss=2.005, generator_feat_match_loss=4.624, over 55.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.355, discriminator_fake_loss=1.365, generator_loss=27.83, generator_mel_loss=18.53, generator_kl_loss=1.378, generator_dur_loss=1.777, generator_adv_loss=1.928, generator_feat_match_loss=4.219, over 294.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:42:27,543 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 09:42:35,303 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 09:44:59,975 INFO [train.py:527] (5/6) Epoch 330, batch 54, global_batch_idx: 40850, batch size: 68, loss[discriminator_loss=2.759, discriminator_real_loss=1.267, discriminator_fake_loss=1.492, generator_loss=27.95, generator_mel_loss=18.14, generator_kl_loss=1.429, generator_dur_loss=1.799, generator_adv_loss=2.038, generator_feat_match_loss=4.541, over 68.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.373, discriminator_fake_loss=1.35, generator_loss=27.83, generator_mel_loss=18.36, generator_kl_loss=1.403, generator_dur_loss=1.76, generator_adv_loss=1.946, generator_feat_match_loss=4.357, over 3043.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:47:18,864 INFO [train.py:527] (5/6) Epoch 330, batch 104, global_batch_idx: 40900, batch size: 56, loss[discriminator_loss=2.719, discriminator_real_loss=1.43, discriminator_fake_loss=1.289, generator_loss=26.77, generator_mel_loss=17.8, generator_kl_loss=1.356, generator_dur_loss=1.771, generator_adv_loss=2.021, generator_feat_match_loss=3.826, over 56.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=27.72, generator_mel_loss=18.31, generator_kl_loss=1.385, generator_dur_loss=1.765, generator_adv_loss=1.937, generator_feat_match_loss=4.319, over 5954.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:48:10,620 INFO [train.py:919] (5/6) Start epoch 331 +2024-03-13 09:49:59,672 INFO [train.py:527] (5/6) Epoch 331, batch 30, global_batch_idx: 40950, batch size: 70, loss[discriminator_loss=2.664, discriminator_real_loss=1.357, discriminator_fake_loss=1.306, generator_loss=26.55, generator_mel_loss=17.59, generator_kl_loss=1.322, generator_dur_loss=1.805, generator_adv_loss=2.015, generator_feat_match_loss=3.821, over 70.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.382, discriminator_fake_loss=1.348, generator_loss=27.5, generator_mel_loss=18.16, generator_kl_loss=1.384, generator_dur_loss=1.773, generator_adv_loss=1.925, generator_feat_match_loss=4.26, over 1857.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:52:17,499 INFO [train.py:527] (5/6) Epoch 331, batch 80, global_batch_idx: 41000, batch size: 58, loss[discriminator_loss=2.72, discriminator_real_loss=1.272, discriminator_fake_loss=1.448, generator_loss=28.12, generator_mel_loss=18.11, generator_kl_loss=1.542, generator_dur_loss=1.76, generator_adv_loss=1.906, generator_feat_match_loss=4.802, over 58.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.379, discriminator_fake_loss=1.354, generator_loss=27.62, generator_mel_loss=18.22, generator_kl_loss=1.403, generator_dur_loss=1.758, generator_adv_loss=1.93, generator_feat_match_loss=4.309, over 4577.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:52:17,501 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 09:52:26,154 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 09:54:26,845 INFO [train.py:919] (5/6) Start epoch 332 +2024-03-13 09:55:07,455 INFO [train.py:527] (5/6) Epoch 332, batch 6, global_batch_idx: 41050, batch size: 25, loss[discriminator_loss=2.608, discriminator_real_loss=1.407, discriminator_fake_loss=1.202, generator_loss=28.37, generator_mel_loss=18.5, generator_kl_loss=1.571, generator_dur_loss=1.574, generator_adv_loss=2.088, generator_feat_match_loss=4.638, over 25.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.353, discriminator_fake_loss=1.347, generator_loss=27.94, generator_mel_loss=18.41, generator_kl_loss=1.415, generator_dur_loss=1.733, generator_adv_loss=1.969, generator_feat_match_loss=4.411, over 355.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:57:25,720 INFO [train.py:527] (5/6) Epoch 332, batch 56, global_batch_idx: 41100, batch size: 55, loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.32, generator_loss=26.94, generator_mel_loss=18.09, generator_kl_loss=1.378, generator_dur_loss=1.714, generator_adv_loss=1.894, generator_feat_match_loss=3.857, over 55.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.372, discriminator_fake_loss=1.347, generator_loss=27.77, generator_mel_loss=18.26, generator_kl_loss=1.414, generator_dur_loss=1.744, generator_adv_loss=1.938, generator_feat_match_loss=4.411, over 3215.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 09:59:44,635 INFO [train.py:527] (5/6) Epoch 332, batch 106, global_batch_idx: 41150, batch size: 42, loss[discriminator_loss=2.697, discriminator_real_loss=1.385, discriminator_fake_loss=1.312, generator_loss=27.58, generator_mel_loss=18.16, generator_kl_loss=1.475, generator_dur_loss=1.73, generator_adv_loss=1.97, generator_feat_match_loss=4.246, over 42.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.373, discriminator_fake_loss=1.35, generator_loss=27.67, generator_mel_loss=18.21, generator_kl_loss=1.408, generator_dur_loss=1.752, generator_adv_loss=1.933, generator_feat_match_loss=4.366, over 6151.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:00:33,447 INFO [train.py:919] (5/6) Start epoch 333 +2024-03-13 10:02:27,503 INFO [train.py:527] (5/6) Epoch 333, batch 32, global_batch_idx: 41200, batch size: 70, loss[discriminator_loss=2.728, discriminator_real_loss=1.33, discriminator_fake_loss=1.398, generator_loss=27.2, generator_mel_loss=18.27, generator_kl_loss=1.229, generator_dur_loss=1.819, generator_adv_loss=1.998, generator_feat_match_loss=3.887, over 70.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=27.57, generator_mel_loss=18.18, generator_kl_loss=1.389, generator_dur_loss=1.765, generator_adv_loss=1.944, generator_feat_match_loss=4.299, over 1925.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:02:27,505 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 10:02:35,685 INFO [train.py:591] (5/6) 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 10:02:35,686 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 10:04:55,347 INFO [train.py:527] (5/6) Epoch 333, batch 82, global_batch_idx: 41250, batch size: 47, loss[discriminator_loss=2.652, discriminator_real_loss=1.371, discriminator_fake_loss=1.28, generator_loss=27.63, generator_mel_loss=18.29, generator_kl_loss=1.446, generator_dur_loss=1.727, generator_adv_loss=1.797, generator_feat_match_loss=4.374, over 47.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.387, discriminator_fake_loss=1.34, generator_loss=27.67, generator_mel_loss=18.26, generator_kl_loss=1.41, generator_dur_loss=1.758, generator_adv_loss=1.933, generator_feat_match_loss=4.309, over 4612.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:06:51,878 INFO [train.py:919] (5/6) Start epoch 334 +2024-03-13 10:07:38,865 INFO [train.py:527] (5/6) Epoch 334, batch 8, global_batch_idx: 41300, batch size: 39, loss[discriminator_loss=2.75, discriminator_real_loss=1.388, discriminator_fake_loss=1.362, generator_loss=27.67, generator_mel_loss=18.28, generator_kl_loss=1.501, generator_dur_loss=1.709, generator_adv_loss=1.78, generator_feat_match_loss=4.399, over 39.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.397, discriminator_fake_loss=1.354, generator_loss=27.57, generator_mel_loss=18.13, generator_kl_loss=1.386, generator_dur_loss=1.763, generator_adv_loss=1.927, generator_feat_match_loss=4.362, over 537.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:09:55,678 INFO [train.py:527] (5/6) Epoch 334, batch 58, global_batch_idx: 41350, batch size: 50, loss[discriminator_loss=2.7, discriminator_real_loss=1.349, discriminator_fake_loss=1.351, generator_loss=27.65, generator_mel_loss=18.33, generator_kl_loss=1.469, generator_dur_loss=1.678, generator_adv_loss=1.763, generator_feat_match_loss=4.403, over 50.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.383, discriminator_fake_loss=1.352, generator_loss=27.65, generator_mel_loss=18.26, generator_kl_loss=1.395, generator_dur_loss=1.763, generator_adv_loss=1.927, generator_feat_match_loss=4.309, over 3286.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:12:16,016 INFO [train.py:527] (5/6) Epoch 334, batch 108, global_batch_idx: 41400, batch size: 58, loss[discriminator_loss=2.711, discriminator_real_loss=1.434, discriminator_fake_loss=1.277, generator_loss=27.6, generator_mel_loss=18.32, generator_kl_loss=1.307, generator_dur_loss=1.75, generator_adv_loss=1.746, generator_feat_match_loss=4.481, over 58.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.379, discriminator_fake_loss=1.347, generator_loss=27.69, generator_mel_loss=18.22, generator_kl_loss=1.398, generator_dur_loss=1.776, generator_adv_loss=1.932, generator_feat_match_loss=4.364, over 6312.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:12:16,018 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 10:12:24,660 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 10:13:07,799 INFO [train.py:919] (5/6) Start epoch 335 +2024-03-13 10:15:06,166 INFO [train.py:527] (5/6) Epoch 335, batch 34, global_batch_idx: 41450, batch size: 70, loss[discriminator_loss=2.718, discriminator_real_loss=1.463, discriminator_fake_loss=1.255, generator_loss=28.6, generator_mel_loss=18.47, generator_kl_loss=1.379, generator_dur_loss=1.794, generator_adv_loss=2.195, generator_feat_match_loss=4.764, over 70.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.377, discriminator_fake_loss=1.349, generator_loss=27.75, generator_mel_loss=18.2, generator_kl_loss=1.404, generator_dur_loss=1.792, generator_adv_loss=1.942, generator_feat_match_loss=4.408, over 2058.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:17:24,566 INFO [train.py:527] (5/6) Epoch 335, batch 84, global_batch_idx: 41500, batch size: 77, loss[discriminator_loss=2.738, discriminator_real_loss=1.334, discriminator_fake_loss=1.405, generator_loss=27.94, generator_mel_loss=18.22, generator_kl_loss=1.385, generator_dur_loss=1.811, generator_adv_loss=1.929, generator_feat_match_loss=4.599, over 77.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.381, discriminator_fake_loss=1.343, generator_loss=27.71, generator_mel_loss=18.19, generator_kl_loss=1.415, generator_dur_loss=1.772, generator_adv_loss=1.948, generator_feat_match_loss=4.383, over 4741.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:19:14,377 INFO [train.py:919] (5/6) Start epoch 336 +2024-03-13 10:20:06,626 INFO [train.py:527] (5/6) Epoch 336, batch 10, global_batch_idx: 41550, batch size: 52, loss[discriminator_loss=2.649, discriminator_real_loss=1.298, discriminator_fake_loss=1.352, generator_loss=28.3, generator_mel_loss=18.39, generator_kl_loss=1.4, generator_dur_loss=1.723, generator_adv_loss=2.002, generator_feat_match_loss=4.785, over 52.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.376, discriminator_fake_loss=1.348, generator_loss=27.38, generator_mel_loss=18.13, generator_kl_loss=1.432, generator_dur_loss=1.751, generator_adv_loss=1.896, generator_feat_match_loss=4.166, over 600.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:22:24,043 INFO [train.py:527] (5/6) Epoch 336, batch 60, global_batch_idx: 41600, batch size: 61, loss[discriminator_loss=2.726, discriminator_real_loss=1.226, discriminator_fake_loss=1.499, generator_loss=27.92, generator_mel_loss=18.52, generator_kl_loss=1.342, generator_dur_loss=1.76, generator_adv_loss=1.951, generator_feat_match_loss=4.356, over 61.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.342, generator_loss=27.66, generator_mel_loss=18.23, generator_kl_loss=1.416, generator_dur_loss=1.769, generator_adv_loss=1.929, generator_feat_match_loss=4.317, over 3498.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:22:24,044 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 10:22:32,326 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 10:24:50,779 INFO [train.py:527] (5/6) Epoch 336, batch 110, global_batch_idx: 41650, batch size: 39, loss[discriminator_loss=2.722, discriminator_real_loss=1.409, discriminator_fake_loss=1.314, generator_loss=28.42, generator_mel_loss=18.66, generator_kl_loss=1.499, generator_dur_loss=1.722, generator_adv_loss=1.95, generator_feat_match_loss=4.589, over 39.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.379, discriminator_fake_loss=1.344, generator_loss=27.73, generator_mel_loss=18.27, generator_kl_loss=1.404, generator_dur_loss=1.77, generator_adv_loss=1.943, generator_feat_match_loss=4.349, over 6301.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:25:28,198 INFO [train.py:919] (5/6) Start epoch 337 +2024-03-13 10:27:32,973 INFO [train.py:527] (5/6) Epoch 337, batch 36, global_batch_idx: 41700, batch size: 44, loss[discriminator_loss=2.751, discriminator_real_loss=1.378, discriminator_fake_loss=1.373, generator_loss=27.12, generator_mel_loss=17.64, generator_kl_loss=1.381, generator_dur_loss=1.673, generator_adv_loss=1.89, generator_feat_match_loss=4.53, over 44.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.384, discriminator_fake_loss=1.339, generator_loss=27.66, generator_mel_loss=18.24, generator_kl_loss=1.416, generator_dur_loss=1.751, generator_adv_loss=1.934, generator_feat_match_loss=4.316, over 2075.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:29:48,321 INFO [train.py:527] (5/6) Epoch 337, batch 86, global_batch_idx: 41750, batch size: 70, loss[discriminator_loss=2.771, discriminator_real_loss=1.354, discriminator_fake_loss=1.417, generator_loss=28.02, generator_mel_loss=18.47, generator_kl_loss=1.253, generator_dur_loss=1.83, generator_adv_loss=1.887, generator_feat_match_loss=4.574, over 70.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.379, discriminator_fake_loss=1.345, generator_loss=27.7, generator_mel_loss=18.24, generator_kl_loss=1.409, generator_dur_loss=1.76, generator_adv_loss=1.931, generator_feat_match_loss=4.355, over 4856.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:31:32,782 INFO [train.py:919] (5/6) Start epoch 338 +2024-03-13 10:32:32,773 INFO [train.py:527] (5/6) Epoch 338, batch 12, global_batch_idx: 41800, batch size: 74, loss[discriminator_loss=2.716, discriminator_real_loss=1.394, discriminator_fake_loss=1.323, generator_loss=28.29, generator_mel_loss=18.35, generator_kl_loss=1.392, generator_dur_loss=1.814, generator_adv_loss=2.004, generator_feat_match_loss=4.732, over 74.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.367, discriminator_fake_loss=1.349, generator_loss=27.72, generator_mel_loss=18.15, generator_kl_loss=1.438, generator_dur_loss=1.779, generator_adv_loss=1.947, generator_feat_match_loss=4.407, over 749.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:32:32,776 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 10:32:40,994 INFO [train.py:591] (5/6) 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 10:32:40,995 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 10:34:58,354 INFO [train.py:527] (5/6) Epoch 338, batch 62, global_batch_idx: 41850, batch size: 55, loss[discriminator_loss=2.685, discriminator_real_loss=1.424, discriminator_fake_loss=1.261, generator_loss=28.21, generator_mel_loss=18.53, generator_kl_loss=1.49, generator_dur_loss=1.749, generator_adv_loss=2.007, generator_feat_match_loss=4.437, over 55.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=27.79, generator_mel_loss=18.26, generator_kl_loss=1.438, generator_dur_loss=1.755, generator_adv_loss=1.936, generator_feat_match_loss=4.396, over 3326.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:37:18,480 INFO [train.py:527] (5/6) Epoch 338, batch 112, global_batch_idx: 41900, batch size: 66, loss[discriminator_loss=2.625, discriminator_real_loss=1.261, discriminator_fake_loss=1.364, generator_loss=29.36, generator_mel_loss=18.92, generator_kl_loss=1.287, generator_dur_loss=1.822, generator_adv_loss=2.121, generator_feat_match_loss=5.21, over 66.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.342, generator_loss=27.72, generator_mel_loss=18.21, generator_kl_loss=1.424, generator_dur_loss=1.767, generator_adv_loss=1.944, generator_feat_match_loss=4.374, over 6274.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:37:47,406 INFO [train.py:919] (5/6) Start epoch 339 +2024-03-13 10:39:57,970 INFO [train.py:527] (5/6) Epoch 339, batch 38, global_batch_idx: 41950, batch size: 36, loss[discriminator_loss=2.657, discriminator_real_loss=1.39, discriminator_fake_loss=1.267, generator_loss=27.46, generator_mel_loss=17.91, generator_kl_loss=1.65, generator_dur_loss=1.701, generator_adv_loss=1.977, generator_feat_match_loss=4.224, over 36.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.347, generator_loss=27.78, generator_mel_loss=18.25, generator_kl_loss=1.401, generator_dur_loss=1.779, generator_adv_loss=1.933, generator_feat_match_loss=4.422, over 2238.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:42:14,519 INFO [train.py:527] (5/6) Epoch 339, batch 88, global_batch_idx: 42000, batch size: 44, loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.341, generator_loss=28.28, generator_mel_loss=18.45, generator_kl_loss=1.51, generator_dur_loss=1.677, generator_adv_loss=2.051, generator_feat_match_loss=4.589, over 44.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.378, discriminator_fake_loss=1.348, generator_loss=27.72, generator_mel_loss=18.23, generator_kl_loss=1.395, generator_dur_loss=1.78, generator_adv_loss=1.937, generator_feat_match_loss=4.38, over 5126.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:42:14,520 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 10:42:23,426 INFO [train.py:591] (5/6) 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 10:42:23,427 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 10:44:02,676 INFO [train.py:919] (5/6) Start epoch 340 +2024-03-13 10:45:05,875 INFO [train.py:527] (5/6) Epoch 340, batch 14, global_batch_idx: 42050, batch size: 96, loss[discriminator_loss=2.664, discriminator_real_loss=1.329, discriminator_fake_loss=1.334, generator_loss=28.04, generator_mel_loss=18.33, generator_kl_loss=1.191, generator_dur_loss=1.847, generator_adv_loss=1.917, generator_feat_match_loss=4.755, over 96.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.357, discriminator_fake_loss=1.359, generator_loss=27.76, generator_mel_loss=18.26, generator_kl_loss=1.372, generator_dur_loss=1.791, generator_adv_loss=1.904, generator_feat_match_loss=4.432, over 857.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:47:26,163 INFO [train.py:527] (5/6) Epoch 340, batch 64, global_batch_idx: 42100, batch size: 26, loss[discriminator_loss=2.509, discriminator_real_loss=1.239, discriminator_fake_loss=1.27, generator_loss=29.84, generator_mel_loss=18.67, generator_kl_loss=1.672, generator_dur_loss=1.564, generator_adv_loss=2.107, generator_feat_match_loss=5.82, over 26.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.367, discriminator_fake_loss=1.351, generator_loss=27.68, generator_mel_loss=18.19, generator_kl_loss=1.387, generator_dur_loss=1.783, generator_adv_loss=1.925, generator_feat_match_loss=4.399, over 3806.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:49:42,720 INFO [train.py:527] (5/6) Epoch 340, batch 114, global_batch_idx: 42150, batch size: 55, loss[discriminator_loss=2.757, discriminator_real_loss=1.442, discriminator_fake_loss=1.315, generator_loss=26.13, generator_mel_loss=17.48, generator_kl_loss=1.546, generator_dur_loss=1.681, generator_adv_loss=1.837, generator_feat_match_loss=3.585, over 55.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.378, discriminator_fake_loss=1.348, generator_loss=27.84, generator_mel_loss=18.28, generator_kl_loss=1.398, generator_dur_loss=1.772, generator_adv_loss=1.96, generator_feat_match_loss=4.428, over 6626.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:50:09,175 INFO [train.py:919] (5/6) Start epoch 341 +2024-03-13 10:52:27,777 INFO [train.py:527] (5/6) Epoch 341, batch 40, global_batch_idx: 42200, batch size: 59, loss[discriminator_loss=2.655, discriminator_real_loss=1.293, discriminator_fake_loss=1.362, generator_loss=28.37, generator_mel_loss=18.59, generator_kl_loss=1.421, generator_dur_loss=1.762, generator_adv_loss=1.847, generator_feat_match_loss=4.749, over 59.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.376, discriminator_fake_loss=1.344, generator_loss=27.47, generator_mel_loss=18.08, generator_kl_loss=1.374, generator_dur_loss=1.76, generator_adv_loss=1.929, generator_feat_match_loss=4.324, over 2474.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:52:27,778 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 10:52:35,864 INFO [train.py:591] (5/6) 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 10:52:35,866 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 10:54:54,152 INFO [train.py:527] (5/6) Epoch 341, batch 90, global_batch_idx: 42250, batch size: 39, loss[discriminator_loss=2.665, discriminator_real_loss=1.348, discriminator_fake_loss=1.318, generator_loss=28.91, generator_mel_loss=18.79, generator_kl_loss=1.546, generator_dur_loss=1.704, generator_adv_loss=2.058, generator_feat_match_loss=4.807, over 39.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=27.51, generator_mel_loss=18.12, generator_kl_loss=1.398, generator_dur_loss=1.764, generator_adv_loss=1.926, generator_feat_match_loss=4.305, over 5224.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:56:23,780 INFO [train.py:919] (5/6) Start epoch 342 +2024-03-13 10:57:33,212 INFO [train.py:527] (5/6) Epoch 342, batch 16, global_batch_idx: 42300, batch size: 17, loss[discriminator_loss=2.77, discriminator_real_loss=1.51, discriminator_fake_loss=1.26, generator_loss=27.08, generator_mel_loss=18, generator_kl_loss=1.931, generator_dur_loss=1.543, generator_adv_loss=1.75, generator_feat_match_loss=3.857, over 17.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.395, discriminator_fake_loss=1.332, generator_loss=27.59, generator_mel_loss=18.26, generator_kl_loss=1.396, generator_dur_loss=1.759, generator_adv_loss=1.924, generator_feat_match_loss=4.257, over 978.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 10:59:51,284 INFO [train.py:527] (5/6) Epoch 342, batch 66, global_batch_idx: 42350, batch size: 48, loss[discriminator_loss=2.658, discriminator_real_loss=1.303, discriminator_fake_loss=1.354, generator_loss=28.34, generator_mel_loss=18.54, generator_kl_loss=1.504, generator_dur_loss=1.707, generator_adv_loss=1.967, generator_feat_match_loss=4.622, over 48.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.342, generator_loss=27.7, generator_mel_loss=18.15, generator_kl_loss=1.417, generator_dur_loss=1.766, generator_adv_loss=1.934, generator_feat_match_loss=4.437, over 3876.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:02:09,679 INFO [train.py:527] (5/6) Epoch 342, batch 116, global_batch_idx: 42400, batch size: 25, loss[discriminator_loss=2.675, discriminator_real_loss=1.34, discriminator_fake_loss=1.335, generator_loss=29.45, generator_mel_loss=19.03, generator_kl_loss=1.802, generator_dur_loss=1.599, generator_adv_loss=2.031, generator_feat_match_loss=4.982, over 25.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.344, generator_loss=27.69, generator_mel_loss=18.18, generator_kl_loss=1.412, generator_dur_loss=1.768, generator_adv_loss=1.93, generator_feat_match_loss=4.404, over 6577.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:02:09,680 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 11:02:18,384 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 11:02:38,940 INFO [train.py:919] (5/6) Start epoch 343 +2024-03-13 11:05:00,079 INFO [train.py:527] (5/6) Epoch 343, batch 42, global_batch_idx: 42450, batch size: 64, loss[discriminator_loss=2.757, discriminator_real_loss=1.438, discriminator_fake_loss=1.319, generator_loss=26.74, generator_mel_loss=17.79, generator_kl_loss=1.414, generator_dur_loss=1.787, generator_adv_loss=2.003, generator_feat_match_loss=3.741, over 64.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.372, discriminator_fake_loss=1.348, generator_loss=27.82, generator_mel_loss=18.24, generator_kl_loss=1.428, generator_dur_loss=1.76, generator_adv_loss=1.942, generator_feat_match_loss=4.453, over 2358.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:07:19,511 INFO [train.py:527] (5/6) Epoch 343, batch 92, global_batch_idx: 42500, batch size: 83, loss[discriminator_loss=2.775, discriminator_real_loss=1.405, discriminator_fake_loss=1.37, generator_loss=27.72, generator_mel_loss=18.61, generator_kl_loss=1.345, generator_dur_loss=1.807, generator_adv_loss=1.937, generator_feat_match_loss=4.022, over 83.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.78, generator_mel_loss=18.22, generator_kl_loss=1.419, generator_dur_loss=1.765, generator_adv_loss=1.944, generator_feat_match_loss=4.428, over 5169.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:08:48,149 INFO [train.py:919] (5/6) Start epoch 344 +2024-03-13 11:10:02,092 INFO [train.py:527] (5/6) Epoch 344, batch 18, global_batch_idx: 42550, batch size: 39, loss[discriminator_loss=2.756, discriminator_real_loss=1.506, discriminator_fake_loss=1.25, generator_loss=27.41, generator_mel_loss=18.57, generator_kl_loss=1.596, generator_dur_loss=1.698, generator_adv_loss=1.771, generator_feat_match_loss=3.769, over 39.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.372, discriminator_fake_loss=1.334, generator_loss=27.96, generator_mel_loss=18.34, generator_kl_loss=1.432, generator_dur_loss=1.764, generator_adv_loss=1.95, generator_feat_match_loss=4.478, over 1039.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:12:19,934 INFO [train.py:527] (5/6) Epoch 344, batch 68, global_batch_idx: 42600, batch size: 64, loss[discriminator_loss=2.703, discriminator_real_loss=1.334, discriminator_fake_loss=1.369, generator_loss=27.68, generator_mel_loss=18, generator_kl_loss=1.433, generator_dur_loss=1.748, generator_adv_loss=1.837, generator_feat_match_loss=4.659, over 64.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=27.81, generator_mel_loss=18.23, generator_kl_loss=1.417, generator_dur_loss=1.76, generator_adv_loss=1.935, generator_feat_match_loss=4.469, over 4181.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:12:19,935 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 11:12:27,977 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 11:14:44,643 INFO [train.py:527] (5/6) Epoch 344, batch 118, global_batch_idx: 42650, batch size: 68, loss[discriminator_loss=2.747, discriminator_real_loss=1.443, discriminator_fake_loss=1.304, generator_loss=27.35, generator_mel_loss=18.15, generator_kl_loss=1.385, generator_dur_loss=1.783, generator_adv_loss=1.731, generator_feat_match_loss=4.306, over 68.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=27.85, generator_mel_loss=18.26, generator_kl_loss=1.416, generator_dur_loss=1.759, generator_adv_loss=1.95, generator_feat_match_loss=4.463, over 6817.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:15:00,588 INFO [train.py:919] (5/6) Start epoch 345 +2024-03-13 11:17:24,181 INFO [train.py:527] (5/6) Epoch 345, batch 44, global_batch_idx: 42700, batch size: 45, loss[discriminator_loss=2.781, discriminator_real_loss=1.425, discriminator_fake_loss=1.356, generator_loss=26.67, generator_mel_loss=17.92, generator_kl_loss=1.544, generator_dur_loss=1.706, generator_adv_loss=1.697, generator_feat_match_loss=3.798, over 45.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.368, discriminator_fake_loss=1.346, generator_loss=27.72, generator_mel_loss=18.17, generator_kl_loss=1.4, generator_dur_loss=1.782, generator_adv_loss=1.918, generator_feat_match_loss=4.448, over 2738.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:19:44,560 INFO [train.py:527] (5/6) Epoch 345, batch 94, global_batch_idx: 42750, batch size: 52, loss[discriminator_loss=2.728, discriminator_real_loss=1.391, discriminator_fake_loss=1.336, generator_loss=26.94, generator_mel_loss=17.73, generator_kl_loss=1.421, generator_dur_loss=1.726, generator_adv_loss=1.993, generator_feat_match_loss=4.068, over 52.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.37, discriminator_fake_loss=1.35, generator_loss=27.74, generator_mel_loss=18.19, generator_kl_loss=1.396, generator_dur_loss=1.791, generator_adv_loss=1.925, generator_feat_match_loss=4.442, over 5767.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:21:03,580 INFO [train.py:919] (5/6) Start epoch 346 +2024-03-13 11:22:25,983 INFO [train.py:527] (5/6) Epoch 346, batch 20, global_batch_idx: 42800, batch size: 55, loss[discriminator_loss=2.691, discriminator_real_loss=1.271, discriminator_fake_loss=1.42, generator_loss=28.6, generator_mel_loss=18.18, generator_kl_loss=1.449, generator_dur_loss=1.69, generator_adv_loss=2.048, generator_feat_match_loss=5.229, over 55.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.385, discriminator_fake_loss=1.345, generator_loss=28.11, generator_mel_loss=18.51, generator_kl_loss=1.44, generator_dur_loss=1.758, generator_adv_loss=1.947, generator_feat_match_loss=4.456, over 1111.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:22:25,984 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 11:22:34,313 INFO [train.py:591] (5/6) 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 11:22:34,314 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 11:24:53,459 INFO [train.py:527] (5/6) Epoch 346, batch 70, global_batch_idx: 42850, batch size: 97, loss[discriminator_loss=2.736, discriminator_real_loss=1.27, discriminator_fake_loss=1.466, generator_loss=27.84, generator_mel_loss=18, generator_kl_loss=1.19, generator_dur_loss=1.824, generator_adv_loss=2.056, generator_feat_match_loss=4.764, over 97.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.377, discriminator_fake_loss=1.353, generator_loss=27.85, generator_mel_loss=18.3, generator_kl_loss=1.396, generator_dur_loss=1.765, generator_adv_loss=1.941, generator_feat_match_loss=4.449, over 4210.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:27:09,832 INFO [train.py:527] (5/6) Epoch 346, batch 120, global_batch_idx: 42900, batch size: 58, loss[discriminator_loss=2.82, discriminator_real_loss=1.462, discriminator_fake_loss=1.358, generator_loss=28.08, generator_mel_loss=18.54, generator_kl_loss=1.564, generator_dur_loss=1.738, generator_adv_loss=1.887, generator_feat_match_loss=4.358, over 58.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.381, discriminator_fake_loss=1.351, generator_loss=27.85, generator_mel_loss=18.3, generator_kl_loss=1.416, generator_dur_loss=1.759, generator_adv_loss=1.94, generator_feat_match_loss=4.436, over 6890.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:27:20,676 INFO [train.py:919] (5/6) Start epoch 347 +2024-03-13 11:29:52,483 INFO [train.py:527] (5/6) Epoch 347, batch 46, global_batch_idx: 42950, batch size: 39, loss[discriminator_loss=2.833, discriminator_real_loss=1.264, discriminator_fake_loss=1.568, generator_loss=27.7, generator_mel_loss=17.91, generator_kl_loss=1.632, generator_dur_loss=1.671, generator_adv_loss=2.096, generator_feat_match_loss=4.385, over 39.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.365, discriminator_fake_loss=1.347, generator_loss=27.78, generator_mel_loss=18.17, generator_kl_loss=1.424, generator_dur_loss=1.739, generator_adv_loss=1.946, generator_feat_match_loss=4.496, over 2643.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:32:12,681 INFO [train.py:527] (5/6) Epoch 347, batch 96, global_batch_idx: 43000, batch size: 88, loss[discriminator_loss=2.729, discriminator_real_loss=1.409, discriminator_fake_loss=1.32, generator_loss=28.09, generator_mel_loss=18.37, generator_kl_loss=1.393, generator_dur_loss=1.816, generator_adv_loss=2.266, generator_feat_match_loss=4.245, over 88.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.368, discriminator_fake_loss=1.35, generator_loss=27.8, generator_mel_loss=18.24, generator_kl_loss=1.407, generator_dur_loss=1.755, generator_adv_loss=1.944, generator_feat_match_loss=4.46, over 5682.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:32:12,683 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 11:32:21,588 INFO [train.py:591] (5/6) 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 11:32:21,589 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 11:33:37,376 INFO [train.py:919] (5/6) Start epoch 348 +2024-03-13 11:35:02,267 INFO [train.py:527] (5/6) Epoch 348, batch 22, global_batch_idx: 43050, batch size: 25, loss[discriminator_loss=2.64, discriminator_real_loss=1.237, discriminator_fake_loss=1.404, generator_loss=28.35, generator_mel_loss=18.51, generator_kl_loss=1.809, generator_dur_loss=1.624, generator_adv_loss=1.8, generator_feat_match_loss=4.602, over 25.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.52, generator_mel_loss=18.1, generator_kl_loss=1.4, generator_dur_loss=1.764, generator_adv_loss=1.925, generator_feat_match_loss=4.331, over 1187.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:37:23,088 INFO [train.py:527] (5/6) Epoch 348, batch 72, global_batch_idx: 43100, batch size: 45, loss[discriminator_loss=2.718, discriminator_real_loss=1.36, discriminator_fake_loss=1.358, generator_loss=27.29, generator_mel_loss=17.89, generator_kl_loss=1.579, generator_dur_loss=1.651, generator_adv_loss=1.848, generator_feat_match_loss=4.314, over 45.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.347, generator_loss=27.65, generator_mel_loss=18.17, generator_kl_loss=1.407, generator_dur_loss=1.758, generator_adv_loss=1.943, generator_feat_match_loss=4.381, over 4048.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:39:39,560 INFO [train.py:527] (5/6) Epoch 348, batch 122, global_batch_idx: 43150, batch size: 83, loss[discriminator_loss=2.74, discriminator_real_loss=1.428, discriminator_fake_loss=1.312, generator_loss=27.54, generator_mel_loss=18.07, generator_kl_loss=1.374, generator_dur_loss=1.833, generator_adv_loss=1.843, generator_feat_match_loss=4.418, over 83.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=27.73, generator_mel_loss=18.21, generator_kl_loss=1.413, generator_dur_loss=1.756, generator_adv_loss=1.936, generator_feat_match_loss=4.417, over 6912.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, +2024-03-13 11:39:44,294 INFO [train.py:919] (5/6) Start epoch 349 +2024-03-13 11:42:24,565 INFO [train.py:527] (5/6) Epoch 349, batch 48, global_batch_idx: 43200, batch size: 70, loss[discriminator_loss=2.716, discriminator_real_loss=1.293, discriminator_fake_loss=1.423, generator_loss=27.02, generator_mel_loss=17.59, generator_kl_loss=1.342, generator_dur_loss=1.763, generator_adv_loss=2.089, generator_feat_match_loss=4.234, over 70.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.374, discriminator_fake_loss=1.35, generator_loss=27.66, generator_mel_loss=18.16, generator_kl_loss=1.409, generator_dur_loss=1.741, generator_adv_loss=1.924, generator_feat_match_loss=4.424, over 2686.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:42:24,567 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 11:42:32,518 INFO [train.py:591] (5/6) 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 11:42:32,519 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 11:44:52,719 INFO [train.py:527] (5/6) Epoch 349, batch 98, global_batch_idx: 43250, batch size: 16, loss[discriminator_loss=2.764, discriminator_real_loss=1.516, discriminator_fake_loss=1.248, generator_loss=29.34, generator_mel_loss=18.47, generator_kl_loss=1.813, generator_dur_loss=1.596, generator_adv_loss=1.911, generator_feat_match_loss=5.551, over 16.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.345, generator_loss=27.71, generator_mel_loss=18.18, generator_kl_loss=1.426, generator_dur_loss=1.741, generator_adv_loss=1.93, generator_feat_match_loss=4.427, over 5451.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:46:04,050 INFO [train.py:919] (5/6) Start epoch 350 +2024-03-13 11:47:37,397 INFO [train.py:527] (5/6) Epoch 350, batch 24, global_batch_idx: 43300, batch size: 45, loss[discriminator_loss=2.747, discriminator_real_loss=1.497, discriminator_fake_loss=1.25, generator_loss=27.7, generator_mel_loss=18.26, generator_kl_loss=1.497, generator_dur_loss=1.677, generator_adv_loss=1.719, generator_feat_match_loss=4.553, over 45.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.376, discriminator_fake_loss=1.344, generator_loss=27.79, generator_mel_loss=18.2, generator_kl_loss=1.393, generator_dur_loss=1.764, generator_adv_loss=1.951, generator_feat_match_loss=4.482, over 1502.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:49:58,921 INFO [train.py:527] (5/6) Epoch 350, batch 74, global_batch_idx: 43350, batch size: 14, loss[discriminator_loss=2.777, discriminator_real_loss=1.404, discriminator_fake_loss=1.373, generator_loss=27.54, generator_mel_loss=18.09, generator_kl_loss=1.901, generator_dur_loss=1.601, generator_adv_loss=1.929, generator_feat_match_loss=4.025, over 14.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.375, discriminator_fake_loss=1.349, generator_loss=27.8, generator_mel_loss=18.23, generator_kl_loss=1.408, generator_dur_loss=1.755, generator_adv_loss=1.951, generator_feat_match_loss=4.448, over 4243.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:52:17,329 INFO [train.py:919] (5/6) Start epoch 351 +2024-03-13 11:52:41,799 INFO [train.py:527] (5/6) Epoch 351, batch 0, global_batch_idx: 43400, batch size: 68, loss[discriminator_loss=2.764, discriminator_real_loss=1.306, discriminator_fake_loss=1.458, generator_loss=27.75, generator_mel_loss=18.21, generator_kl_loss=1.387, generator_dur_loss=1.759, generator_adv_loss=1.976, generator_feat_match_loss=4.421, over 68.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.306, discriminator_fake_loss=1.458, generator_loss=27.75, generator_mel_loss=18.21, generator_kl_loss=1.387, generator_dur_loss=1.759, generator_adv_loss=1.976, generator_feat_match_loss=4.421, over 68.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:52:41,802 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 11:52:49,648 INFO [train.py:591] (5/6) 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 11:52:49,650 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 11:55:12,310 INFO [train.py:527] (5/6) Epoch 351, batch 50, global_batch_idx: 43450, batch size: 70, loss[discriminator_loss=2.721, discriminator_real_loss=1.384, discriminator_fake_loss=1.337, generator_loss=27.48, generator_mel_loss=18.13, generator_kl_loss=1.291, generator_dur_loss=1.827, generator_adv_loss=1.926, generator_feat_match_loss=4.303, over 70.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.382, discriminator_fake_loss=1.344, generator_loss=27.66, generator_mel_loss=18.19, generator_kl_loss=1.427, generator_dur_loss=1.765, generator_adv_loss=1.924, generator_feat_match_loss=4.35, over 2853.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:57:34,592 INFO [train.py:527] (5/6) Epoch 351, batch 100, global_batch_idx: 43500, batch size: 45, loss[discriminator_loss=2.787, discriminator_real_loss=1.326, discriminator_fake_loss=1.461, generator_loss=30.2, generator_mel_loss=19.24, generator_kl_loss=1.674, generator_dur_loss=1.698, generator_adv_loss=1.967, generator_feat_match_loss=5.614, over 45.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.378, discriminator_fake_loss=1.344, generator_loss=27.71, generator_mel_loss=18.19, generator_kl_loss=1.429, generator_dur_loss=1.758, generator_adv_loss=1.928, generator_feat_match_loss=4.404, over 5658.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 11:58:40,019 INFO [train.py:919] (5/6) Start epoch 352 +2024-03-13 12:00:19,319 INFO [train.py:527] (5/6) Epoch 352, batch 26, global_batch_idx: 43550, batch size: 25, loss[discriminator_loss=2.72, discriminator_real_loss=1.473, discriminator_fake_loss=1.248, generator_loss=28.02, generator_mel_loss=18.54, generator_kl_loss=1.792, generator_dur_loss=1.547, generator_adv_loss=1.876, generator_feat_match_loss=4.265, over 25.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.387, discriminator_fake_loss=1.341, generator_loss=27.89, generator_mel_loss=18.29, generator_kl_loss=1.427, generator_dur_loss=1.749, generator_adv_loss=1.936, generator_feat_match_loss=4.484, over 1453.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:02:40,173 INFO [train.py:527] (5/6) Epoch 352, batch 76, global_batch_idx: 43600, batch size: 64, loss[discriminator_loss=2.744, discriminator_real_loss=1.374, discriminator_fake_loss=1.37, generator_loss=26.68, generator_mel_loss=17.62, generator_kl_loss=1.313, generator_dur_loss=1.78, generator_adv_loss=1.919, generator_feat_match_loss=4.051, over 64.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.384, discriminator_fake_loss=1.347, generator_loss=27.73, generator_mel_loss=18.21, generator_kl_loss=1.424, generator_dur_loss=1.757, generator_adv_loss=1.929, generator_feat_match_loss=4.406, over 4321.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:02:40,174 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 12:02:48,185 INFO [train.py:591] (5/6) 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 12:02:48,187 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 12:05:03,747 INFO [train.py:919] (5/6) Start epoch 353 +2024-03-13 12:05:34,599 INFO [train.py:527] (5/6) Epoch 353, batch 2, global_batch_idx: 43650, batch size: 66, loss[discriminator_loss=2.673, discriminator_real_loss=1.312, discriminator_fake_loss=1.361, generator_loss=28.04, generator_mel_loss=18.21, generator_kl_loss=1.333, generator_dur_loss=1.831, generator_adv_loss=2.042, generator_feat_match_loss=4.633, over 66.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.395, discriminator_fake_loss=1.315, generator_loss=27.4, generator_mel_loss=18.01, generator_kl_loss=1.386, generator_dur_loss=1.803, generator_adv_loss=1.945, generator_feat_match_loss=4.263, over 188.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:07:55,310 INFO [train.py:527] (5/6) Epoch 353, batch 52, global_batch_idx: 43700, batch size: 88, loss[discriminator_loss=2.689, discriminator_real_loss=1.297, discriminator_fake_loss=1.392, generator_loss=27.57, generator_mel_loss=17.84, generator_kl_loss=1.219, generator_dur_loss=1.863, generator_adv_loss=1.985, generator_feat_match_loss=4.665, over 88.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.378, discriminator_fake_loss=1.343, generator_loss=27.82, generator_mel_loss=18.2, generator_kl_loss=1.426, generator_dur_loss=1.777, generator_adv_loss=1.938, generator_feat_match_loss=4.484, over 3036.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:10:16,736 INFO [train.py:527] (5/6) Epoch 353, batch 102, global_batch_idx: 43750, batch size: 45, loss[discriminator_loss=2.741, discriminator_real_loss=1.349, discriminator_fake_loss=1.392, generator_loss=28.84, generator_mel_loss=18.37, generator_kl_loss=1.554, generator_dur_loss=1.665, generator_adv_loss=1.965, generator_feat_match_loss=5.287, over 45.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.374, discriminator_fake_loss=1.351, generator_loss=27.76, generator_mel_loss=18.17, generator_kl_loss=1.413, generator_dur_loss=1.779, generator_adv_loss=1.933, generator_feat_match_loss=4.466, over 5869.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:11:16,583 INFO [train.py:919] (5/6) Start epoch 354 +2024-03-13 12:13:03,060 INFO [train.py:527] (5/6) Epoch 354, batch 28, global_batch_idx: 43800, batch size: 70, loss[discriminator_loss=2.701, discriminator_real_loss=1.305, discriminator_fake_loss=1.396, generator_loss=26.72, generator_mel_loss=17.82, generator_kl_loss=1.27, generator_dur_loss=1.827, generator_adv_loss=1.87, generator_feat_match_loss=3.938, over 70.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.381, discriminator_fake_loss=1.337, generator_loss=27.61, generator_mel_loss=18.09, generator_kl_loss=1.395, generator_dur_loss=1.79, generator_adv_loss=1.911, generator_feat_match_loss=4.424, over 1693.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:13:03,063 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 12:13:11,069 INFO [train.py:591] (5/6) 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 12:13:11,070 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 12:15:30,886 INFO [train.py:527] (5/6) Epoch 354, batch 78, global_batch_idx: 43850, batch size: 61, loss[discriminator_loss=2.704, discriminator_real_loss=1.361, discriminator_fake_loss=1.343, generator_loss=28.02, generator_mel_loss=18.29, generator_kl_loss=1.385, generator_dur_loss=1.812, generator_adv_loss=1.877, generator_feat_match_loss=4.66, over 61.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.72, generator_mel_loss=18.14, generator_kl_loss=1.418, generator_dur_loss=1.768, generator_adv_loss=1.924, generator_feat_match_loss=4.472, over 4465.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:17:37,964 INFO [train.py:919] (5/6) Start epoch 355 +2024-03-13 12:18:15,378 INFO [train.py:527] (5/6) Epoch 355, batch 4, global_batch_idx: 43900, batch size: 47, loss[discriminator_loss=2.681, discriminator_real_loss=1.242, discriminator_fake_loss=1.438, generator_loss=28.21, generator_mel_loss=18.09, generator_kl_loss=1.504, generator_dur_loss=1.713, generator_adv_loss=2.013, generator_feat_match_loss=4.887, over 47.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.373, discriminator_fake_loss=1.307, generator_loss=27.74, generator_mel_loss=18.04, generator_kl_loss=1.456, generator_dur_loss=1.795, generator_adv_loss=1.941, generator_feat_match_loss=4.515, over 345.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:20:37,075 INFO [train.py:527] (5/6) Epoch 355, batch 54, global_batch_idx: 43950, batch size: 62, loss[discriminator_loss=2.64, discriminator_real_loss=1.216, discriminator_fake_loss=1.424, generator_loss=28.58, generator_mel_loss=18.58, generator_kl_loss=1.375, generator_dur_loss=1.808, generator_adv_loss=1.819, generator_feat_match_loss=4.997, over 62.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.373, discriminator_fake_loss=1.339, generator_loss=27.76, generator_mel_loss=18.13, generator_kl_loss=1.405, generator_dur_loss=1.762, generator_adv_loss=1.933, generator_feat_match_loss=4.527, over 3269.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:23:01,864 INFO [train.py:527] (5/6) Epoch 355, batch 104, global_batch_idx: 44000, batch size: 58, loss[discriminator_loss=2.76, discriminator_real_loss=1.351, discriminator_fake_loss=1.409, generator_loss=27, generator_mel_loss=17.87, generator_kl_loss=1.415, generator_dur_loss=1.793, generator_adv_loss=2.076, generator_feat_match_loss=3.842, over 58.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=27.65, generator_mel_loss=18.1, generator_kl_loss=1.399, generator_dur_loss=1.773, generator_adv_loss=1.945, generator_feat_match_loss=4.443, over 6289.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:23:01,866 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 12:23:10,646 INFO [train.py:591] (5/6) 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 12:23:10,647 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 12:24:02,287 INFO [train.py:919] (5/6) Start epoch 356 +2024-03-13 12:25:51,542 INFO [train.py:527] (5/6) Epoch 356, batch 30, global_batch_idx: 44050, batch size: 25, loss[discriminator_loss=2.73, discriminator_real_loss=1.416, discriminator_fake_loss=1.314, generator_loss=28.25, generator_mel_loss=19.02, generator_kl_loss=1.595, generator_dur_loss=1.578, generator_adv_loss=1.842, generator_feat_match_loss=4.211, over 25.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.374, discriminator_fake_loss=1.353, generator_loss=27.66, generator_mel_loss=18.11, generator_kl_loss=1.415, generator_dur_loss=1.782, generator_adv_loss=1.928, generator_feat_match_loss=4.422, over 1821.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:28:09,437 INFO [train.py:527] (5/6) Epoch 356, batch 80, global_batch_idx: 44100, batch size: 66, loss[discriminator_loss=2.767, discriminator_real_loss=1.428, discriminator_fake_loss=1.338, generator_loss=27.71, generator_mel_loss=18.04, generator_kl_loss=1.327, generator_dur_loss=1.768, generator_adv_loss=1.746, generator_feat_match_loss=4.833, over 66.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.68, generator_mel_loss=18.12, generator_kl_loss=1.419, generator_dur_loss=1.762, generator_adv_loss=1.922, generator_feat_match_loss=4.461, over 4486.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:30:10,833 INFO [train.py:919] (5/6) Start epoch 357 +2024-03-13 12:30:50,354 INFO [train.py:527] (5/6) Epoch 357, batch 6, global_batch_idx: 44150, batch size: 68, loss[discriminator_loss=2.763, discriminator_real_loss=1.516, discriminator_fake_loss=1.247, generator_loss=26.37, generator_mel_loss=17.63, generator_kl_loss=1.397, generator_dur_loss=1.806, generator_adv_loss=1.865, generator_feat_match_loss=3.673, over 68.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.394, discriminator_fake_loss=1.349, generator_loss=27.38, generator_mel_loss=18.08, generator_kl_loss=1.382, generator_dur_loss=1.797, generator_adv_loss=1.924, generator_feat_match_loss=4.198, over 428.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:33:13,632 INFO [train.py:527] (5/6) Epoch 357, batch 56, global_batch_idx: 44200, batch size: 58, loss[discriminator_loss=2.726, discriminator_real_loss=1.324, discriminator_fake_loss=1.402, generator_loss=27.83, generator_mel_loss=18.17, generator_kl_loss=1.472, generator_dur_loss=1.717, generator_adv_loss=2.054, generator_feat_match_loss=4.422, over 58.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.377, discriminator_fake_loss=1.349, generator_loss=27.75, generator_mel_loss=18.23, generator_kl_loss=1.422, generator_dur_loss=1.755, generator_adv_loss=1.92, generator_feat_match_loss=4.428, over 3137.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:33:13,634 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 12:33:21,458 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 12:35:40,652 INFO [train.py:527] (5/6) Epoch 357, batch 106, global_batch_idx: 44250, batch size: 58, loss[discriminator_loss=2.777, discriminator_real_loss=1.274, discriminator_fake_loss=1.503, generator_loss=27.77, generator_mel_loss=17.99, generator_kl_loss=1.556, generator_dur_loss=1.795, generator_adv_loss=2.23, generator_feat_match_loss=4.207, over 58.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.375, discriminator_fake_loss=1.353, generator_loss=27.72, generator_mel_loss=18.19, generator_kl_loss=1.429, generator_dur_loss=1.758, generator_adv_loss=1.922, generator_feat_match_loss=4.429, over 5954.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:36:29,215 INFO [train.py:919] (5/6) Start epoch 358 +2024-03-13 12:38:26,626 INFO [train.py:527] (5/6) Epoch 358, batch 32, global_batch_idx: 44300, batch size: 36, loss[discriminator_loss=2.658, discriminator_real_loss=1.331, discriminator_fake_loss=1.327, generator_loss=29, generator_mel_loss=18.63, generator_kl_loss=1.355, generator_dur_loss=1.737, generator_adv_loss=1.958, generator_feat_match_loss=5.321, over 36.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.374, discriminator_fake_loss=1.35, generator_loss=27.83, generator_mel_loss=18.27, generator_kl_loss=1.401, generator_dur_loss=1.768, generator_adv_loss=1.924, generator_feat_match_loss=4.464, over 2037.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:40:47,948 INFO [train.py:527] (5/6) Epoch 358, batch 82, global_batch_idx: 44350, batch size: 68, loss[discriminator_loss=2.672, discriminator_real_loss=1.298, discriminator_fake_loss=1.374, generator_loss=28.65, generator_mel_loss=18.65, generator_kl_loss=1.507, generator_dur_loss=1.775, generator_adv_loss=1.946, generator_feat_match_loss=4.772, over 68.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.375, discriminator_fake_loss=1.353, generator_loss=27.72, generator_mel_loss=18.19, generator_kl_loss=1.411, generator_dur_loss=1.766, generator_adv_loss=1.914, generator_feat_match_loss=4.442, over 4890.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:42:42,961 INFO [train.py:919] (5/6) Start epoch 359 +2024-03-13 12:43:29,823 INFO [train.py:527] (5/6) Epoch 359, batch 8, global_batch_idx: 44400, batch size: 70, loss[discriminator_loss=2.757, discriminator_real_loss=1.342, discriminator_fake_loss=1.415, generator_loss=26.87, generator_mel_loss=17.74, generator_kl_loss=1.118, generator_dur_loss=1.789, generator_adv_loss=1.999, generator_feat_match_loss=4.227, over 70.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.393, discriminator_fake_loss=1.341, generator_loss=27.68, generator_mel_loss=18.14, generator_kl_loss=1.39, generator_dur_loss=1.741, generator_adv_loss=1.953, generator_feat_match_loss=4.449, over 508.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:43:29,825 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 12:43:37,892 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 12:45:59,769 INFO [train.py:527] (5/6) Epoch 359, batch 58, global_batch_idx: 44450, batch size: 83, loss[discriminator_loss=2.678, discriminator_real_loss=1.338, discriminator_fake_loss=1.34, generator_loss=27.58, generator_mel_loss=17.94, generator_kl_loss=1.326, generator_dur_loss=1.837, generator_adv_loss=1.894, generator_feat_match_loss=4.589, over 83.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.388, discriminator_fake_loss=1.343, generator_loss=27.64, generator_mel_loss=18.08, generator_kl_loss=1.41, generator_dur_loss=1.749, generator_adv_loss=1.934, generator_feat_match_loss=4.46, over 3318.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:48:21,857 INFO [train.py:527] (5/6) Epoch 359, batch 108, global_batch_idx: 44500, batch size: 96, loss[discriminator_loss=2.74, discriminator_real_loss=1.454, discriminator_fake_loss=1.287, generator_loss=27.2, generator_mel_loss=18.03, generator_kl_loss=1.352, generator_dur_loss=1.823, generator_adv_loss=1.884, generator_feat_match_loss=4.108, over 96.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.376, discriminator_fake_loss=1.344, generator_loss=27.67, generator_mel_loss=18.13, generator_kl_loss=1.407, generator_dur_loss=1.76, generator_adv_loss=1.93, generator_feat_match_loss=4.441, over 6427.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:49:04,205 INFO [train.py:919] (5/6) Start epoch 360 +2024-03-13 12:51:04,541 INFO [train.py:527] (5/6) Epoch 360, batch 34, global_batch_idx: 44550, batch size: 80, loss[discriminator_loss=2.724, discriminator_real_loss=1.38, discriminator_fake_loss=1.344, generator_loss=27.54, generator_mel_loss=18.02, generator_kl_loss=1.476, generator_dur_loss=1.797, generator_adv_loss=1.877, generator_feat_match_loss=4.377, over 80.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.365, discriminator_fake_loss=1.344, generator_loss=27.59, generator_mel_loss=18.03, generator_kl_loss=1.395, generator_dur_loss=1.76, generator_adv_loss=1.929, generator_feat_match_loss=4.469, over 2210.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:53:25,859 INFO [train.py:527] (5/6) Epoch 360, batch 84, global_batch_idx: 44600, batch size: 68, loss[discriminator_loss=2.726, discriminator_real_loss=1.525, discriminator_fake_loss=1.201, generator_loss=27.08, generator_mel_loss=17.92, generator_kl_loss=1.372, generator_dur_loss=1.777, generator_adv_loss=1.891, generator_feat_match_loss=4.112, over 68.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=27.71, generator_mel_loss=18.08, generator_kl_loss=1.416, generator_dur_loss=1.757, generator_adv_loss=1.932, generator_feat_match_loss=4.529, over 4978.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:53:25,860 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 12:53:34,615 INFO [train.py:591] (5/6) 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 12:53:34,616 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 12:55:24,654 INFO [train.py:919] (5/6) Start epoch 361 +2024-03-13 12:56:21,929 INFO [train.py:527] (5/6) Epoch 361, batch 10, global_batch_idx: 44650, batch size: 83, loss[discriminator_loss=2.756, discriminator_real_loss=1.311, discriminator_fake_loss=1.445, generator_loss=27.53, generator_mel_loss=18.25, generator_kl_loss=1.215, generator_dur_loss=1.848, generator_adv_loss=1.905, generator_feat_match_loss=4.311, over 83.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.373, discriminator_fake_loss=1.35, generator_loss=27.72, generator_mel_loss=18.26, generator_kl_loss=1.403, generator_dur_loss=1.774, generator_adv_loss=1.932, generator_feat_match_loss=4.345, over 676.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 12:58:43,143 INFO [train.py:527] (5/6) Epoch 361, batch 60, global_batch_idx: 44700, batch size: 66, loss[discriminator_loss=2.735, discriminator_real_loss=1.376, discriminator_fake_loss=1.359, generator_loss=27.72, generator_mel_loss=17.99, generator_kl_loss=1.378, generator_dur_loss=1.794, generator_adv_loss=2.009, generator_feat_match_loss=4.541, over 66.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.381, discriminator_fake_loss=1.343, generator_loss=27.69, generator_mel_loss=18.15, generator_kl_loss=1.41, generator_dur_loss=1.771, generator_adv_loss=1.94, generator_feat_match_loss=4.411, over 3552.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:01:03,608 INFO [train.py:527] (5/6) Epoch 361, batch 110, global_batch_idx: 44750, batch size: 32, loss[discriminator_loss=2.725, discriminator_real_loss=1.501, discriminator_fake_loss=1.224, generator_loss=28.14, generator_mel_loss=18.52, generator_kl_loss=1.706, generator_dur_loss=1.664, generator_adv_loss=1.947, generator_feat_match_loss=4.296, over 32.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.378, discriminator_fake_loss=1.343, generator_loss=27.73, generator_mel_loss=18.18, generator_kl_loss=1.411, generator_dur_loss=1.773, generator_adv_loss=1.932, generator_feat_match_loss=4.432, over 6555.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:01:41,414 INFO [train.py:919] (5/6) Start epoch 362 +2024-03-13 13:03:46,592 INFO [train.py:527] (5/6) Epoch 362, batch 36, global_batch_idx: 44800, batch size: 25, loss[discriminator_loss=2.707, discriminator_real_loss=1.374, discriminator_fake_loss=1.332, generator_loss=28.43, generator_mel_loss=18.52, generator_kl_loss=1.449, generator_dur_loss=1.559, generator_adv_loss=1.959, generator_feat_match_loss=4.948, over 25.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=27.89, generator_mel_loss=18.19, generator_kl_loss=1.44, generator_dur_loss=1.745, generator_adv_loss=1.93, generator_feat_match_loss=4.582, over 1875.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:03:46,594 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 13:03:54,776 INFO [train.py:591] (5/6) 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 13:03:54,778 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 13:06:14,435 INFO [train.py:527] (5/6) Epoch 362, batch 86, global_batch_idx: 44850, batch size: 66, loss[discriminator_loss=2.695, discriminator_real_loss=1.316, discriminator_fake_loss=1.379, generator_loss=27.46, generator_mel_loss=17.84, generator_kl_loss=1.518, generator_dur_loss=1.795, generator_adv_loss=2.012, generator_feat_match_loss=4.294, over 66.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.371, discriminator_fake_loss=1.348, generator_loss=27.74, generator_mel_loss=18.14, generator_kl_loss=1.402, generator_dur_loss=1.75, generator_adv_loss=1.927, generator_feat_match_loss=4.523, over 5035.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:08:02,306 INFO [train.py:919] (5/6) Start epoch 363 +2024-03-13 13:09:03,558 INFO [train.py:527] (5/6) Epoch 363, batch 12, global_batch_idx: 44900, batch size: 36, loss[discriminator_loss=2.72, discriminator_real_loss=1.282, discriminator_fake_loss=1.438, generator_loss=28.18, generator_mel_loss=18.22, generator_kl_loss=1.654, generator_dur_loss=1.651, generator_adv_loss=1.942, generator_feat_match_loss=4.71, over 36.00 samples.], tot_loss[discriminator_loss=2.772, discriminator_real_loss=1.437, discriminator_fake_loss=1.335, generator_loss=27.66, generator_mel_loss=18.18, generator_kl_loss=1.449, generator_dur_loss=1.726, generator_adv_loss=1.955, generator_feat_match_loss=4.349, over 719.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:11:29,864 INFO [train.py:527] (5/6) Epoch 363, batch 62, global_batch_idx: 44950, batch size: 52, loss[discriminator_loss=2.72, discriminator_real_loss=1.372, discriminator_fake_loss=1.348, generator_loss=28.32, generator_mel_loss=18.48, generator_kl_loss=1.389, generator_dur_loss=1.664, generator_adv_loss=2.025, generator_feat_match_loss=4.765, over 52.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.395, discriminator_fake_loss=1.345, generator_loss=27.77, generator_mel_loss=18.18, generator_kl_loss=1.452, generator_dur_loss=1.721, generator_adv_loss=1.941, generator_feat_match_loss=4.475, over 3515.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:13:48,389 INFO [train.py:527] (5/6) Epoch 363, batch 112, global_batch_idx: 45000, batch size: 58, loss[discriminator_loss=2.676, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=28.15, generator_mel_loss=18.31, generator_kl_loss=1.367, generator_dur_loss=1.751, generator_adv_loss=1.962, generator_feat_match_loss=4.755, over 58.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.379, discriminator_fake_loss=1.349, generator_loss=27.83, generator_mel_loss=18.18, generator_kl_loss=1.439, generator_dur_loss=1.729, generator_adv_loss=1.951, generator_feat_match_loss=4.534, over 6495.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:13:48,390 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 13:13:57,314 INFO [train.py:591] (5/6) 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 13:13:57,314 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 13:14:29,035 INFO [train.py:919] (5/6) Start epoch 364 +2024-03-13 13:16:41,250 INFO [train.py:527] (5/6) Epoch 364, batch 38, global_batch_idx: 45050, batch size: 72, loss[discriminator_loss=2.759, discriminator_real_loss=1.388, discriminator_fake_loss=1.37, generator_loss=27.04, generator_mel_loss=17.83, generator_kl_loss=1.366, generator_dur_loss=1.791, generator_adv_loss=1.868, generator_feat_match_loss=4.183, over 72.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.387, discriminator_fake_loss=1.35, generator_loss=27.55, generator_mel_loss=18.07, generator_kl_loss=1.401, generator_dur_loss=1.773, generator_adv_loss=1.931, generator_feat_match_loss=4.371, over 2401.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:19:04,714 INFO [train.py:527] (5/6) Epoch 364, batch 88, global_batch_idx: 45100, batch size: 31, loss[discriminator_loss=2.646, discriminator_real_loss=1.334, discriminator_fake_loss=1.311, generator_loss=28.32, generator_mel_loss=18.3, generator_kl_loss=1.526, generator_dur_loss=1.683, generator_adv_loss=2.051, generator_feat_match_loss=4.761, over 31.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.67, generator_mel_loss=18.11, generator_kl_loss=1.413, generator_dur_loss=1.763, generator_adv_loss=1.933, generator_feat_match_loss=4.447, over 5175.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:20:42,425 INFO [train.py:919] (5/6) Start epoch 365 +2024-03-13 13:21:47,487 INFO [train.py:527] (5/6) Epoch 365, batch 14, global_batch_idx: 45150, batch size: 80, loss[discriminator_loss=2.715, discriminator_real_loss=1.401, discriminator_fake_loss=1.313, generator_loss=28.03, generator_mel_loss=18.15, generator_kl_loss=1.387, generator_dur_loss=1.825, generator_adv_loss=1.982, generator_feat_match_loss=4.693, over 80.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.358, discriminator_fake_loss=1.359, generator_loss=27.89, generator_mel_loss=18.18, generator_kl_loss=1.415, generator_dur_loss=1.8, generator_adv_loss=1.94, generator_feat_match_loss=4.55, over 936.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:24:06,014 INFO [train.py:527] (5/6) Epoch 365, batch 64, global_batch_idx: 45200, batch size: 96, loss[discriminator_loss=2.686, discriminator_real_loss=1.322, discriminator_fake_loss=1.364, generator_loss=27.69, generator_mel_loss=17.94, generator_kl_loss=1.308, generator_dur_loss=1.916, generator_adv_loss=2.003, generator_feat_match_loss=4.529, over 96.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.378, discriminator_fake_loss=1.342, generator_loss=27.81, generator_mel_loss=18.2, generator_kl_loss=1.41, generator_dur_loss=1.774, generator_adv_loss=1.938, generator_feat_match_loss=4.489, over 3842.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:24:06,016 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 13:24:14,059 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 13:26:35,951 INFO [train.py:527] (5/6) Epoch 365, batch 114, global_batch_idx: 45250, batch size: 52, loss[discriminator_loss=2.77, discriminator_real_loss=1.395, discriminator_fake_loss=1.375, generator_loss=27.26, generator_mel_loss=17.98, generator_kl_loss=1.604, generator_dur_loss=1.702, generator_adv_loss=1.754, generator_feat_match_loss=4.213, over 52.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.379, discriminator_fake_loss=1.343, generator_loss=27.78, generator_mel_loss=18.2, generator_kl_loss=1.404, generator_dur_loss=1.773, generator_adv_loss=1.936, generator_feat_match_loss=4.468, over 6776.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:27:02,987 INFO [train.py:919] (5/6) Start epoch 366 +2024-03-13 13:29:19,159 INFO [train.py:527] (5/6) Epoch 366, batch 40, global_batch_idx: 45300, batch size: 77, loss[discriminator_loss=2.676, discriminator_real_loss=1.386, discriminator_fake_loss=1.29, generator_loss=27.61, generator_mel_loss=18.07, generator_kl_loss=1.223, generator_dur_loss=1.868, generator_adv_loss=1.909, generator_feat_match_loss=4.537, over 77.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.385, discriminator_fake_loss=1.338, generator_loss=27.7, generator_mel_loss=18.13, generator_kl_loss=1.412, generator_dur_loss=1.761, generator_adv_loss=1.934, generator_feat_match_loss=4.462, over 2324.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:31:38,437 INFO [train.py:527] (5/6) Epoch 366, batch 90, global_batch_idx: 45350, batch size: 44, loss[discriminator_loss=2.698, discriminator_real_loss=1.38, discriminator_fake_loss=1.318, generator_loss=28.59, generator_mel_loss=18.52, generator_kl_loss=1.538, generator_dur_loss=1.699, generator_adv_loss=1.954, generator_feat_match_loss=4.886, over 44.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.38, discriminator_fake_loss=1.339, generator_loss=27.77, generator_mel_loss=18.13, generator_kl_loss=1.408, generator_dur_loss=1.767, generator_adv_loss=1.946, generator_feat_match_loss=4.517, over 5165.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:33:13,068 INFO [train.py:919] (5/6) Start epoch 367 +2024-03-13 13:34:23,828 INFO [train.py:527] (5/6) Epoch 367, batch 16, global_batch_idx: 45400, batch size: 68, loss[discriminator_loss=2.742, discriminator_real_loss=1.442, discriminator_fake_loss=1.3, generator_loss=27.37, generator_mel_loss=18.16, generator_kl_loss=1.32, generator_dur_loss=1.777, generator_adv_loss=2.009, generator_feat_match_loss=4.105, over 68.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.381, discriminator_fake_loss=1.337, generator_loss=27.61, generator_mel_loss=18.09, generator_kl_loss=1.401, generator_dur_loss=1.747, generator_adv_loss=1.953, generator_feat_match_loss=4.422, over 955.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:34:23,829 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 13:34:31,912 INFO [train.py:591] (5/6) 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 13:34:31,913 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 13:36:53,497 INFO [train.py:527] (5/6) Epoch 367, batch 66, global_batch_idx: 45450, batch size: 62, loss[discriminator_loss=2.704, discriminator_real_loss=1.351, discriminator_fake_loss=1.353, generator_loss=28.65, generator_mel_loss=18.57, generator_kl_loss=1.401, generator_dur_loss=1.77, generator_adv_loss=1.918, generator_feat_match_loss=4.99, over 62.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.377, discriminator_fake_loss=1.343, generator_loss=27.7, generator_mel_loss=18.12, generator_kl_loss=1.409, generator_dur_loss=1.758, generator_adv_loss=1.951, generator_feat_match_loss=4.464, over 3751.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:39:09,396 INFO [train.py:527] (5/6) Epoch 367, batch 116, global_batch_idx: 45500, batch size: 15, loss[discriminator_loss=2.645, discriminator_real_loss=1.429, discriminator_fake_loss=1.216, generator_loss=27.77, generator_mel_loss=18.02, generator_kl_loss=1.732, generator_dur_loss=1.616, generator_adv_loss=1.878, generator_feat_match_loss=4.527, over 15.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.384, discriminator_fake_loss=1.341, generator_loss=27.69, generator_mel_loss=18.13, generator_kl_loss=1.413, generator_dur_loss=1.761, generator_adv_loss=1.941, generator_feat_match_loss=4.443, over 6535.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:39:32,559 INFO [train.py:919] (5/6) Start epoch 368 +2024-03-13 13:41:59,454 INFO [train.py:527] (5/6) Epoch 368, batch 42, global_batch_idx: 45550, batch size: 59, loss[discriminator_loss=2.745, discriminator_real_loss=1.385, discriminator_fake_loss=1.36, generator_loss=28.35, generator_mel_loss=18.78, generator_kl_loss=1.385, generator_dur_loss=1.723, generator_adv_loss=1.804, generator_feat_match_loss=4.65, over 59.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.378, discriminator_fake_loss=1.332, generator_loss=27.85, generator_mel_loss=18.17, generator_kl_loss=1.418, generator_dur_loss=1.765, generator_adv_loss=1.938, generator_feat_match_loss=4.56, over 2461.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:44:20,744 INFO [train.py:527] (5/6) Epoch 368, batch 92, global_batch_idx: 45600, batch size: 70, loss[discriminator_loss=2.68, discriminator_real_loss=1.381, discriminator_fake_loss=1.299, generator_loss=27.92, generator_mel_loss=18.11, generator_kl_loss=1.283, generator_dur_loss=1.845, generator_adv_loss=2.067, generator_feat_match_loss=4.619, over 70.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.381, discriminator_fake_loss=1.336, generator_loss=27.89, generator_mel_loss=18.21, generator_kl_loss=1.413, generator_dur_loss=1.747, generator_adv_loss=1.946, generator_feat_match_loss=4.571, over 5194.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:44:20,745 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 13:44:29,495 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 13:45:58,150 INFO [train.py:919] (5/6) Start epoch 369 +2024-03-13 13:47:12,364 INFO [train.py:527] (5/6) Epoch 369, batch 18, global_batch_idx: 45650, batch size: 55, loss[discriminator_loss=2.704, discriminator_real_loss=1.323, discriminator_fake_loss=1.38, generator_loss=28.95, generator_mel_loss=18.68, generator_kl_loss=1.582, generator_dur_loss=1.698, generator_adv_loss=1.984, generator_feat_match_loss=5, over 55.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.367, discriminator_fake_loss=1.346, generator_loss=27.94, generator_mel_loss=18.19, generator_kl_loss=1.462, generator_dur_loss=1.74, generator_adv_loss=1.948, generator_feat_match_loss=4.602, over 1025.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:49:31,582 INFO [train.py:527] (5/6) Epoch 369, batch 68, global_batch_idx: 45700, batch size: 74, loss[discriminator_loss=2.695, discriminator_real_loss=1.395, discriminator_fake_loss=1.3, generator_loss=27.72, generator_mel_loss=18.29, generator_kl_loss=1.313, generator_dur_loss=1.792, generator_adv_loss=1.826, generator_feat_match_loss=4.494, over 74.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.37, discriminator_fake_loss=1.347, generator_loss=27.78, generator_mel_loss=18.15, generator_kl_loss=1.418, generator_dur_loss=1.754, generator_adv_loss=1.923, generator_feat_match_loss=4.541, over 3877.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:51:54,962 INFO [train.py:527] (5/6) Epoch 369, batch 118, global_batch_idx: 45750, batch size: 68, loss[discriminator_loss=2.659, discriminator_real_loss=1.31, discriminator_fake_loss=1.349, generator_loss=27.81, generator_mel_loss=17.99, generator_kl_loss=1.36, generator_dur_loss=1.713, generator_adv_loss=2.034, generator_feat_match_loss=4.712, over 68.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.368, discriminator_fake_loss=1.35, generator_loss=27.78, generator_mel_loss=18.14, generator_kl_loss=1.42, generator_dur_loss=1.744, generator_adv_loss=1.932, generator_feat_match_loss=4.551, over 6608.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:52:11,718 INFO [train.py:919] (5/6) Start epoch 370 +2024-03-13 13:54:38,404 INFO [train.py:527] (5/6) Epoch 370, batch 44, global_batch_idx: 45800, batch size: 59, loss[discriminator_loss=2.758, discriminator_real_loss=1.389, discriminator_fake_loss=1.368, generator_loss=27.15, generator_mel_loss=18.05, generator_kl_loss=1.306, generator_dur_loss=1.791, generator_adv_loss=1.971, generator_feat_match_loss=4.03, over 59.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.376, discriminator_fake_loss=1.334, generator_loss=27.71, generator_mel_loss=18.09, generator_kl_loss=1.414, generator_dur_loss=1.748, generator_adv_loss=1.95, generator_feat_match_loss=4.512, over 2400.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:54:38,406 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 13:54:46,121 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 13:57:03,176 INFO [train.py:527] (5/6) Epoch 370, batch 94, global_batch_idx: 45850, batch size: 31, loss[discriminator_loss=2.715, discriminator_real_loss=1.453, discriminator_fake_loss=1.262, generator_loss=28.75, generator_mel_loss=18.55, generator_kl_loss=1.582, generator_dur_loss=1.58, generator_adv_loss=2.023, generator_feat_match_loss=5.017, over 31.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=27.8, generator_mel_loss=18.13, generator_kl_loss=1.41, generator_dur_loss=1.749, generator_adv_loss=1.95, generator_feat_match_loss=4.558, over 5397.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 13:58:22,610 INFO [train.py:919] (5/6) Start epoch 371 +2024-03-13 13:59:42,310 INFO [train.py:527] (5/6) Epoch 371, batch 20, global_batch_idx: 45900, batch size: 36, loss[discriminator_loss=2.766, discriminator_real_loss=1.301, discriminator_fake_loss=1.466, generator_loss=28.42, generator_mel_loss=18.72, generator_kl_loss=1.566, generator_dur_loss=1.709, generator_adv_loss=1.954, generator_feat_match_loss=4.473, over 36.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.377, discriminator_fake_loss=1.333, generator_loss=27.84, generator_mel_loss=18.21, generator_kl_loss=1.394, generator_dur_loss=1.754, generator_adv_loss=1.931, generator_feat_match_loss=4.555, over 1238.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:02:03,633 INFO [train.py:527] (5/6) Epoch 371, batch 70, global_batch_idx: 45950, batch size: 52, loss[discriminator_loss=2.726, discriminator_real_loss=1.411, discriminator_fake_loss=1.314, generator_loss=26.99, generator_mel_loss=17.81, generator_kl_loss=1.533, generator_dur_loss=1.678, generator_adv_loss=1.952, generator_feat_match_loss=4.014, over 52.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.385, discriminator_fake_loss=1.34, generator_loss=27.82, generator_mel_loss=18.18, generator_kl_loss=1.406, generator_dur_loss=1.738, generator_adv_loss=1.942, generator_feat_match_loss=4.554, over 4085.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:04:17,146 INFO [train.py:527] (5/6) Epoch 371, batch 120, global_batch_idx: 46000, batch size: 50, loss[discriminator_loss=2.787, discriminator_real_loss=1.452, discriminator_fake_loss=1.335, generator_loss=26.38, generator_mel_loss=17.41, generator_kl_loss=1.477, generator_dur_loss=1.688, generator_adv_loss=1.928, generator_feat_match_loss=3.88, over 50.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.38, discriminator_fake_loss=1.342, generator_loss=27.79, generator_mel_loss=18.16, generator_kl_loss=1.416, generator_dur_loss=1.73, generator_adv_loss=1.941, generator_feat_match_loss=4.548, over 6853.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:04:17,147 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 14:04:26,086 INFO [train.py:591] (5/6) 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 14:04:26,087 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 14:04:35,584 INFO [train.py:919] (5/6) Start epoch 372 +2024-03-13 14:07:06,460 INFO [train.py:527] (5/6) Epoch 372, batch 46, global_batch_idx: 46050, batch size: 64, loss[discriminator_loss=2.684, discriminator_real_loss=1.399, discriminator_fake_loss=1.285, generator_loss=27.5, generator_mel_loss=17.94, generator_kl_loss=1.372, generator_dur_loss=1.742, generator_adv_loss=1.93, generator_feat_match_loss=4.515, over 64.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.378, discriminator_fake_loss=1.354, generator_loss=27.82, generator_mel_loss=18.17, generator_kl_loss=1.442, generator_dur_loss=1.725, generator_adv_loss=1.928, generator_feat_match_loss=4.554, over 2450.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:09:23,744 INFO [train.py:527] (5/6) Epoch 372, batch 96, global_batch_idx: 46100, batch size: 66, loss[discriminator_loss=2.716, discriminator_real_loss=1.321, discriminator_fake_loss=1.395, generator_loss=28.52, generator_mel_loss=18.15, generator_kl_loss=1.451, generator_dur_loss=1.793, generator_adv_loss=1.929, generator_feat_match_loss=5.192, over 66.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.348, generator_loss=27.85, generator_mel_loss=18.19, generator_kl_loss=1.429, generator_dur_loss=1.739, generator_adv_loss=1.926, generator_feat_match_loss=4.56, over 5263.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:10:41,852 INFO [train.py:919] (5/6) Start epoch 373 +2024-03-13 14:12:07,420 INFO [train.py:527] (5/6) Epoch 373, batch 22, global_batch_idx: 46150, batch size: 48, loss[discriminator_loss=2.682, discriminator_real_loss=1.404, discriminator_fake_loss=1.278, generator_loss=27.73, generator_mel_loss=18.11, generator_kl_loss=1.476, generator_dur_loss=1.712, generator_adv_loss=1.957, generator_feat_match_loss=4.473, over 48.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.393, discriminator_fake_loss=1.352, generator_loss=27.75, generator_mel_loss=18.21, generator_kl_loss=1.402, generator_dur_loss=1.741, generator_adv_loss=1.979, generator_feat_match_loss=4.419, over 1287.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:14:28,033 INFO [train.py:527] (5/6) Epoch 373, batch 72, global_batch_idx: 46200, batch size: 58, loss[discriminator_loss=2.729, discriminator_real_loss=1.378, discriminator_fake_loss=1.351, generator_loss=28.95, generator_mel_loss=18.97, generator_kl_loss=1.367, generator_dur_loss=1.724, generator_adv_loss=1.97, generator_feat_match_loss=4.914, over 58.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.384, discriminator_fake_loss=1.343, generator_loss=27.75, generator_mel_loss=18.15, generator_kl_loss=1.383, generator_dur_loss=1.759, generator_adv_loss=1.952, generator_feat_match_loss=4.506, over 4241.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:14:28,035 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 14:14:36,415 INFO [train.py:591] (5/6) 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 14:14:36,416 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 14:16:53,650 INFO [train.py:527] (5/6) Epoch 373, batch 122, global_batch_idx: 46250, batch size: 26, loss[discriminator_loss=2.729, discriminator_real_loss=1.37, discriminator_fake_loss=1.36, generator_loss=29.93, generator_mel_loss=19.1, generator_kl_loss=1.578, generator_dur_loss=1.538, generator_adv_loss=2.125, generator_feat_match_loss=5.589, over 26.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.385, discriminator_fake_loss=1.343, generator_loss=27.75, generator_mel_loss=18.15, generator_kl_loss=1.401, generator_dur_loss=1.759, generator_adv_loss=1.941, generator_feat_match_loss=4.498, over 7029.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:16:58,224 INFO [train.py:919] (5/6) Start epoch 374 +2024-03-13 14:19:34,027 INFO [train.py:527] (5/6) Epoch 374, batch 48, global_batch_idx: 46300, batch size: 39, loss[discriminator_loss=2.729, discriminator_real_loss=1.388, discriminator_fake_loss=1.341, generator_loss=27.72, generator_mel_loss=18.25, generator_kl_loss=1.37, generator_dur_loss=1.727, generator_adv_loss=1.912, generator_feat_match_loss=4.465, over 39.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.377, discriminator_fake_loss=1.343, generator_loss=27.86, generator_mel_loss=18.17, generator_kl_loss=1.432, generator_dur_loss=1.769, generator_adv_loss=1.932, generator_feat_match_loss=4.558, over 2856.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:21:53,549 INFO [train.py:527] (5/6) Epoch 374, batch 98, global_batch_idx: 46350, batch size: 47, loss[discriminator_loss=2.775, discriminator_real_loss=1.434, discriminator_fake_loss=1.341, generator_loss=27.62, generator_mel_loss=18.36, generator_kl_loss=1.377, generator_dur_loss=1.703, generator_adv_loss=1.903, generator_feat_match_loss=4.279, over 47.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.347, generator_loss=27.86, generator_mel_loss=18.17, generator_kl_loss=1.431, generator_dur_loss=1.767, generator_adv_loss=1.93, generator_feat_match_loss=4.567, over 5833.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:23:03,592 INFO [train.py:919] (5/6) Start epoch 375 +2024-03-13 14:24:35,809 INFO [train.py:527] (5/6) Epoch 375, batch 24, global_batch_idx: 46400, batch size: 58, loss[discriminator_loss=2.704, discriminator_real_loss=1.452, discriminator_fake_loss=1.252, generator_loss=27.33, generator_mel_loss=17.92, generator_kl_loss=1.421, generator_dur_loss=1.691, generator_adv_loss=1.95, generator_feat_match_loss=4.346, over 58.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.395, discriminator_fake_loss=1.346, generator_loss=27.57, generator_mel_loss=18.04, generator_kl_loss=1.433, generator_dur_loss=1.746, generator_adv_loss=1.929, generator_feat_match_loss=4.424, over 1369.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:24:35,811 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 14:24:43,824 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 14:27:02,919 INFO [train.py:527] (5/6) Epoch 375, batch 74, global_batch_idx: 46450, batch size: 48, loss[discriminator_loss=2.653, discriminator_real_loss=1.305, discriminator_fake_loss=1.348, generator_loss=27.64, generator_mel_loss=17.76, generator_kl_loss=1.452, generator_dur_loss=1.706, generator_adv_loss=2.013, generator_feat_match_loss=4.714, over 48.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.384, discriminator_fake_loss=1.345, generator_loss=27.69, generator_mel_loss=18.07, generator_kl_loss=1.433, generator_dur_loss=1.761, generator_adv_loss=1.926, generator_feat_match_loss=4.494, over 4132.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:29:21,184 INFO [train.py:919] (5/6) Start epoch 376 +2024-03-13 14:29:45,041 INFO [train.py:527] (5/6) Epoch 376, batch 0, global_batch_idx: 46500, batch size: 39, loss[discriminator_loss=2.758, discriminator_real_loss=1.456, discriminator_fake_loss=1.302, generator_loss=27.43, generator_mel_loss=17.82, generator_kl_loss=1.542, generator_dur_loss=1.711, generator_adv_loss=1.775, generator_feat_match_loss=4.583, over 39.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.456, discriminator_fake_loss=1.302, generator_loss=27.43, generator_mel_loss=17.82, generator_kl_loss=1.542, generator_dur_loss=1.711, generator_adv_loss=1.775, generator_feat_match_loss=4.583, over 39.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:32:04,775 INFO [train.py:527] (5/6) Epoch 376, batch 50, global_batch_idx: 46550, batch size: 48, loss[discriminator_loss=2.71, discriminator_real_loss=1.282, discriminator_fake_loss=1.429, generator_loss=27.85, generator_mel_loss=18.53, generator_kl_loss=1.291, generator_dur_loss=1.746, generator_adv_loss=2.001, generator_feat_match_loss=4.28, over 48.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.378, discriminator_fake_loss=1.331, generator_loss=27.88, generator_mel_loss=18.15, generator_kl_loss=1.424, generator_dur_loss=1.754, generator_adv_loss=1.938, generator_feat_match_loss=4.609, over 2946.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:34:24,130 INFO [train.py:527] (5/6) Epoch 376, batch 100, global_batch_idx: 46600, batch size: 88, loss[discriminator_loss=2.693, discriminator_real_loss=1.326, discriminator_fake_loss=1.368, generator_loss=28.08, generator_mel_loss=18.07, generator_kl_loss=1.394, generator_dur_loss=1.909, generator_adv_loss=2.227, generator_feat_match_loss=4.481, over 88.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.377, discriminator_fake_loss=1.338, generator_loss=27.88, generator_mel_loss=18.15, generator_kl_loss=1.427, generator_dur_loss=1.757, generator_adv_loss=1.959, generator_feat_match_loss=4.593, over 5745.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:34:24,131 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 14:34:32,873 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 14:35:34,506 INFO [train.py:919] (5/6) Start epoch 377 +2024-03-13 14:37:11,573 INFO [train.py:527] (5/6) Epoch 377, batch 26, global_batch_idx: 46650, batch size: 96, loss[discriminator_loss=2.72, discriminator_real_loss=1.38, discriminator_fake_loss=1.341, generator_loss=27.49, generator_mel_loss=18.12, generator_kl_loss=1.367, generator_dur_loss=1.895, generator_adv_loss=1.988, generator_feat_match_loss=4.116, over 96.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=27.78, generator_mel_loss=18.17, generator_kl_loss=1.379, generator_dur_loss=1.799, generator_adv_loss=1.927, generator_feat_match_loss=4.508, over 1730.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:39:32,063 INFO [train.py:527] (5/6) Epoch 377, batch 76, global_batch_idx: 46700, batch size: 47, loss[discriminator_loss=2.7, discriminator_real_loss=1.331, discriminator_fake_loss=1.37, generator_loss=28.06, generator_mel_loss=18.28, generator_kl_loss=1.392, generator_dur_loss=1.65, generator_adv_loss=2.013, generator_feat_match_loss=4.731, over 47.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.371, discriminator_fake_loss=1.345, generator_loss=27.82, generator_mel_loss=18.14, generator_kl_loss=1.417, generator_dur_loss=1.773, generator_adv_loss=1.924, generator_feat_match_loss=4.568, over 4610.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:41:41,320 INFO [train.py:919] (5/6) Start epoch 378 +2024-03-13 14:42:11,541 INFO [train.py:527] (5/6) Epoch 378, batch 2, global_batch_idx: 46750, batch size: 64, loss[discriminator_loss=2.702, discriminator_real_loss=1.41, discriminator_fake_loss=1.292, generator_loss=27.75, generator_mel_loss=18.14, generator_kl_loss=1.315, generator_dur_loss=1.803, generator_adv_loss=1.865, generator_feat_match_loss=4.622, over 64.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.364, discriminator_fake_loss=1.343, generator_loss=27.96, generator_mel_loss=18.2, generator_kl_loss=1.301, generator_dur_loss=1.812, generator_adv_loss=1.924, generator_feat_match_loss=4.723, over 224.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:44:34,243 INFO [train.py:527] (5/6) Epoch 378, batch 52, global_batch_idx: 46800, batch size: 36, loss[discriminator_loss=2.707, discriminator_real_loss=1.317, discriminator_fake_loss=1.39, generator_loss=27.53, generator_mel_loss=18.09, generator_kl_loss=1.566, generator_dur_loss=1.676, generator_adv_loss=1.881, generator_feat_match_loss=4.316, over 36.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.377, discriminator_fake_loss=1.35, generator_loss=27.77, generator_mel_loss=18.08, generator_kl_loss=1.412, generator_dur_loss=1.766, generator_adv_loss=1.923, generator_feat_match_loss=4.586, over 3094.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:44:34,245 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 14:44:42,306 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 14:47:01,118 INFO [train.py:527] (5/6) Epoch 378, batch 102, global_batch_idx: 46850, batch size: 66, loss[discriminator_loss=2.683, discriminator_real_loss=1.377, discriminator_fake_loss=1.306, generator_loss=27.56, generator_mel_loss=18.02, generator_kl_loss=1.371, generator_dur_loss=1.793, generator_adv_loss=2.156, generator_feat_match_loss=4.212, over 66.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.375, discriminator_fake_loss=1.344, generator_loss=27.74, generator_mel_loss=18.09, generator_kl_loss=1.407, generator_dur_loss=1.762, generator_adv_loss=1.94, generator_feat_match_loss=4.544, over 6105.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:47:56,023 INFO [train.py:919] (5/6) Start epoch 379 +2024-03-13 14:49:40,452 INFO [train.py:527] (5/6) Epoch 379, batch 28, global_batch_idx: 46900, batch size: 50, loss[discriminator_loss=2.667, discriminator_real_loss=1.442, discriminator_fake_loss=1.225, generator_loss=28.9, generator_mel_loss=18.8, generator_kl_loss=1.46, generator_dur_loss=1.696, generator_adv_loss=1.949, generator_feat_match_loss=4.996, over 50.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.384, discriminator_fake_loss=1.333, generator_loss=27.75, generator_mel_loss=18.15, generator_kl_loss=1.429, generator_dur_loss=1.729, generator_adv_loss=1.924, generator_feat_match_loss=4.524, over 1467.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:51:57,952 INFO [train.py:527] (5/6) Epoch 379, batch 78, global_batch_idx: 46950, batch size: 36, loss[discriminator_loss=2.693, discriminator_real_loss=1.315, discriminator_fake_loss=1.378, generator_loss=28.74, generator_mel_loss=18.62, generator_kl_loss=1.737, generator_dur_loss=1.613, generator_adv_loss=1.909, generator_feat_match_loss=4.858, over 36.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.341, generator_loss=27.78, generator_mel_loss=18.16, generator_kl_loss=1.41, generator_dur_loss=1.761, generator_adv_loss=1.935, generator_feat_match_loss=4.519, over 4499.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:54:01,387 INFO [train.py:919] (5/6) Start epoch 380 +2024-03-13 14:54:36,718 INFO [train.py:527] (5/6) Epoch 380, batch 4, global_batch_idx: 47000, batch size: 62, loss[discriminator_loss=2.661, discriminator_real_loss=1.316, discriminator_fake_loss=1.345, generator_loss=29.04, generator_mel_loss=18.47, generator_kl_loss=1.406, generator_dur_loss=1.726, generator_adv_loss=2.139, generator_feat_match_loss=5.293, over 62.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.362, discriminator_fake_loss=1.318, generator_loss=28.05, generator_mel_loss=18.11, generator_kl_loss=1.364, generator_dur_loss=1.776, generator_adv_loss=1.975, generator_feat_match_loss=4.827, over 292.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:54:36,720 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 14:54:44,911 INFO [train.py:591] (5/6) 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 14:54:44,914 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 14:57:04,657 INFO [train.py:527] (5/6) Epoch 380, batch 54, global_batch_idx: 47050, batch size: 58, loss[discriminator_loss=2.694, discriminator_real_loss=1.337, discriminator_fake_loss=1.358, generator_loss=28.19, generator_mel_loss=18.13, generator_kl_loss=1.405, generator_dur_loss=1.693, generator_adv_loss=2.059, generator_feat_match_loss=4.904, over 58.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.376, discriminator_fake_loss=1.337, generator_loss=27.85, generator_mel_loss=18.13, generator_kl_loss=1.385, generator_dur_loss=1.767, generator_adv_loss=1.945, generator_feat_match_loss=4.623, over 3230.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 14:59:25,729 INFO [train.py:527] (5/6) Epoch 380, batch 104, global_batch_idx: 47100, batch size: 72, loss[discriminator_loss=2.717, discriminator_real_loss=1.412, discriminator_fake_loss=1.305, generator_loss=27.57, generator_mel_loss=18.12, generator_kl_loss=1.307, generator_dur_loss=1.804, generator_adv_loss=1.897, generator_feat_match_loss=4.441, over 72.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=27.75, generator_mel_loss=18.08, generator_kl_loss=1.383, generator_dur_loss=1.775, generator_adv_loss=1.936, generator_feat_match_loss=4.577, over 6247.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:00:16,800 INFO [train.py:919] (5/6) Start epoch 381 +2024-03-13 15:02:04,239 INFO [train.py:527] (5/6) Epoch 381, batch 30, global_batch_idx: 47150, batch size: 42, loss[discriminator_loss=2.714, discriminator_real_loss=1.378, discriminator_fake_loss=1.335, generator_loss=27.89, generator_mel_loss=17.95, generator_kl_loss=1.679, generator_dur_loss=1.719, generator_adv_loss=1.889, generator_feat_match_loss=4.65, over 42.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.37, discriminator_fake_loss=1.355, generator_loss=27.85, generator_mel_loss=18.1, generator_kl_loss=1.396, generator_dur_loss=1.79, generator_adv_loss=1.929, generator_feat_match_loss=4.63, over 1836.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:04:25,743 INFO [train.py:527] (5/6) Epoch 381, batch 80, global_batch_idx: 47200, batch size: 62, loss[discriminator_loss=2.678, discriminator_real_loss=1.444, discriminator_fake_loss=1.234, generator_loss=27.51, generator_mel_loss=18.03, generator_kl_loss=1.544, generator_dur_loss=1.762, generator_adv_loss=2.014, generator_feat_match_loss=4.164, over 62.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.343, generator_loss=27.97, generator_mel_loss=18.14, generator_kl_loss=1.424, generator_dur_loss=1.779, generator_adv_loss=1.984, generator_feat_match_loss=4.645, over 4786.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:04:25,744 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 15:04:34,804 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 15:06:34,353 INFO [train.py:919] (5/6) Start epoch 382 +2024-03-13 15:07:16,316 INFO [train.py:527] (5/6) Epoch 382, batch 6, global_batch_idx: 47250, batch size: 74, loss[discriminator_loss=2.678, discriminator_real_loss=1.339, discriminator_fake_loss=1.339, generator_loss=28.03, generator_mel_loss=18.1, generator_kl_loss=1.302, generator_dur_loss=1.826, generator_adv_loss=1.956, generator_feat_match_loss=4.847, over 74.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.34, discriminator_fake_loss=1.333, generator_loss=28.02, generator_mel_loss=18.05, generator_kl_loss=1.414, generator_dur_loss=1.799, generator_adv_loss=1.952, generator_feat_match_loss=4.801, over 428.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:09:36,768 INFO [train.py:527] (5/6) Epoch 382, batch 56, global_batch_idx: 47300, batch size: 53, loss[discriminator_loss=2.727, discriminator_real_loss=1.457, discriminator_fake_loss=1.27, generator_loss=27.77, generator_mel_loss=18, generator_kl_loss=1.582, generator_dur_loss=1.693, generator_adv_loss=1.723, generator_feat_match_loss=4.774, over 53.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.376, discriminator_fake_loss=1.331, generator_loss=27.85, generator_mel_loss=18.09, generator_kl_loss=1.433, generator_dur_loss=1.763, generator_adv_loss=1.943, generator_feat_match_loss=4.616, over 3195.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:11:58,778 INFO [train.py:527] (5/6) Epoch 382, batch 106, global_batch_idx: 47350, batch size: 62, loss[discriminator_loss=2.773, discriminator_real_loss=1.427, discriminator_fake_loss=1.345, generator_loss=27.4, generator_mel_loss=18.45, generator_kl_loss=1.247, generator_dur_loss=1.773, generator_adv_loss=1.854, generator_feat_match_loss=4.074, over 62.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=27.82, generator_mel_loss=18.12, generator_kl_loss=1.418, generator_dur_loss=1.77, generator_adv_loss=1.939, generator_feat_match_loss=4.565, over 6073.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:12:47,970 INFO [train.py:919] (5/6) Start epoch 383 +2024-03-13 15:14:45,964 INFO [train.py:527] (5/6) Epoch 383, batch 32, global_batch_idx: 47400, batch size: 55, loss[discriminator_loss=2.742, discriminator_real_loss=1.51, discriminator_fake_loss=1.231, generator_loss=28.11, generator_mel_loss=18.01, generator_kl_loss=1.427, generator_dur_loss=1.706, generator_adv_loss=1.978, generator_feat_match_loss=4.984, over 55.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.382, discriminator_fake_loss=1.343, generator_loss=27.75, generator_mel_loss=18.09, generator_kl_loss=1.415, generator_dur_loss=1.743, generator_adv_loss=1.937, generator_feat_match_loss=4.562, over 1860.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:14:45,966 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 15:14:54,034 INFO [train.py:591] (5/6) 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 15:14:54,035 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 15:17:15,718 INFO [train.py:527] (5/6) Epoch 383, batch 82, global_batch_idx: 47450, batch size: 72, loss[discriminator_loss=2.706, discriminator_real_loss=1.3, discriminator_fake_loss=1.405, generator_loss=28.3, generator_mel_loss=18.39, generator_kl_loss=1.276, generator_dur_loss=1.764, generator_adv_loss=2.013, generator_feat_match_loss=4.849, over 72.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.376, discriminator_fake_loss=1.348, generator_loss=27.8, generator_mel_loss=18.11, generator_kl_loss=1.424, generator_dur_loss=1.746, generator_adv_loss=1.941, generator_feat_match_loss=4.583, over 4651.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:19:12,195 INFO [train.py:919] (5/6) Start epoch 384 +2024-03-13 15:19:59,938 INFO [train.py:527] (5/6) Epoch 384, batch 8, global_batch_idx: 47500, batch size: 72, loss[discriminator_loss=2.695, discriminator_real_loss=1.346, discriminator_fake_loss=1.349, generator_loss=27.85, generator_mel_loss=18.24, generator_kl_loss=1.373, generator_dur_loss=1.82, generator_adv_loss=1.77, generator_feat_match_loss=4.654, over 72.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.412, discriminator_fake_loss=1.326, generator_loss=27.82, generator_mel_loss=18.2, generator_kl_loss=1.383, generator_dur_loss=1.744, generator_adv_loss=1.916, generator_feat_match_loss=4.581, over 446.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:22:23,530 INFO [train.py:527] (5/6) Epoch 384, batch 58, global_batch_idx: 47550, batch size: 42, loss[discriminator_loss=2.726, discriminator_real_loss=1.442, discriminator_fake_loss=1.285, generator_loss=27.35, generator_mel_loss=18.17, generator_kl_loss=1.504, generator_dur_loss=1.693, generator_adv_loss=1.927, generator_feat_match_loss=4.052, over 42.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.382, discriminator_fake_loss=1.345, generator_loss=27.85, generator_mel_loss=18.11, generator_kl_loss=1.43, generator_dur_loss=1.754, generator_adv_loss=1.934, generator_feat_match_loss=4.625, over 3281.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:24:42,676 INFO [train.py:527] (5/6) Epoch 384, batch 108, global_batch_idx: 47600, batch size: 56, loss[discriminator_loss=2.713, discriminator_real_loss=1.346, discriminator_fake_loss=1.366, generator_loss=27.25, generator_mel_loss=17.89, generator_kl_loss=1.456, generator_dur_loss=1.7, generator_adv_loss=1.932, generator_feat_match_loss=4.27, over 56.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.376, discriminator_fake_loss=1.344, generator_loss=27.86, generator_mel_loss=18.12, generator_kl_loss=1.425, generator_dur_loss=1.747, generator_adv_loss=1.938, generator_feat_match_loss=4.631, over 6269.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:24:42,677 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 15:24:51,611 INFO [train.py:591] (5/6) 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 15:24:51,612 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 15:25:32,940 INFO [train.py:919] (5/6) Start epoch 385 +2024-03-13 15:27:32,035 INFO [train.py:527] (5/6) Epoch 385, batch 34, global_batch_idx: 47650, batch size: 62, loss[discriminator_loss=2.702, discriminator_real_loss=1.386, discriminator_fake_loss=1.316, generator_loss=27.97, generator_mel_loss=18.27, generator_kl_loss=1.413, generator_dur_loss=1.727, generator_adv_loss=1.968, generator_feat_match_loss=4.584, over 62.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=27.86, generator_mel_loss=18.15, generator_kl_loss=1.391, generator_dur_loss=1.731, generator_adv_loss=1.943, generator_feat_match_loss=4.641, over 2014.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:29:55,393 INFO [train.py:527] (5/6) Epoch 385, batch 84, global_batch_idx: 47700, batch size: 58, loss[discriminator_loss=2.775, discriminator_real_loss=1.419, discriminator_fake_loss=1.356, generator_loss=27.33, generator_mel_loss=17.78, generator_kl_loss=1.469, generator_dur_loss=1.75, generator_adv_loss=1.816, generator_feat_match_loss=4.511, over 58.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.373, discriminator_fake_loss=1.348, generator_loss=27.85, generator_mel_loss=18.14, generator_kl_loss=1.392, generator_dur_loss=1.745, generator_adv_loss=1.95, generator_feat_match_loss=4.628, over 4948.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:31:46,957 INFO [train.py:919] (5/6) Start epoch 386 +2024-03-13 15:32:37,994 INFO [train.py:527] (5/6) Epoch 386, batch 10, global_batch_idx: 47750, batch size: 56, loss[discriminator_loss=2.827, discriminator_real_loss=1.244, discriminator_fake_loss=1.583, generator_loss=27.89, generator_mel_loss=18.38, generator_kl_loss=1.419, generator_dur_loss=1.739, generator_adv_loss=2.05, generator_feat_match_loss=4.303, over 56.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.389, discriminator_fake_loss=1.343, generator_loss=27.94, generator_mel_loss=18.22, generator_kl_loss=1.467, generator_dur_loss=1.732, generator_adv_loss=1.973, generator_feat_match_loss=4.557, over 563.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:34:59,626 INFO [train.py:527] (5/6) Epoch 386, batch 60, global_batch_idx: 47800, batch size: 80, loss[discriminator_loss=2.721, discriminator_real_loss=1.342, discriminator_fake_loss=1.379, generator_loss=28.31, generator_mel_loss=18.17, generator_kl_loss=1.415, generator_dur_loss=1.839, generator_adv_loss=1.886, generator_feat_match_loss=4.998, over 80.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.382, discriminator_fake_loss=1.339, generator_loss=27.89, generator_mel_loss=18.17, generator_kl_loss=1.436, generator_dur_loss=1.739, generator_adv_loss=1.94, generator_feat_match_loss=4.61, over 3391.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:34:59,627 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 15:35:07,966 INFO [train.py:591] (5/6) 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 15:35:07,967 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 15:37:28,613 INFO [train.py:527] (5/6) Epoch 386, batch 110, global_batch_idx: 47850, batch size: 64, loss[discriminator_loss=2.678, discriminator_real_loss=1.34, discriminator_fake_loss=1.337, generator_loss=27.86, generator_mel_loss=18.16, generator_kl_loss=1.364, generator_dur_loss=1.763, generator_adv_loss=1.805, generator_feat_match_loss=4.769, over 64.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.382, discriminator_fake_loss=1.341, generator_loss=27.85, generator_mel_loss=18.16, generator_kl_loss=1.432, generator_dur_loss=1.752, generator_adv_loss=1.937, generator_feat_match_loss=4.571, over 6282.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:38:05,425 INFO [train.py:919] (5/6) Start epoch 387 +2024-03-13 15:40:12,012 INFO [train.py:527] (5/6) Epoch 387, batch 36, global_batch_idx: 47900, batch size: 14, loss[discriminator_loss=2.579, discriminator_real_loss=1.152, discriminator_fake_loss=1.427, generator_loss=30.37, generator_mel_loss=19.45, generator_kl_loss=1.577, generator_dur_loss=1.51, generator_adv_loss=1.964, generator_feat_match_loss=5.873, over 14.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.392, discriminator_fake_loss=1.345, generator_loss=27.77, generator_mel_loss=18.14, generator_kl_loss=1.412, generator_dur_loss=1.751, generator_adv_loss=1.929, generator_feat_match_loss=4.537, over 2118.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:42:31,099 INFO [train.py:527] (5/6) Epoch 387, batch 86, global_batch_idx: 47950, batch size: 52, loss[discriminator_loss=2.723, discriminator_real_loss=1.285, discriminator_fake_loss=1.438, generator_loss=27.3, generator_mel_loss=17.58, generator_kl_loss=1.474, generator_dur_loss=1.651, generator_adv_loss=2.083, generator_feat_match_loss=4.517, over 52.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=27.8, generator_mel_loss=18.07, generator_kl_loss=1.424, generator_dur_loss=1.752, generator_adv_loss=1.939, generator_feat_match_loss=4.608, over 4911.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:44:16,124 INFO [train.py:919] (5/6) Start epoch 388 +2024-03-13 15:45:14,055 INFO [train.py:527] (5/6) Epoch 388, batch 12, global_batch_idx: 48000, batch size: 52, loss[discriminator_loss=2.694, discriminator_real_loss=1.347, discriminator_fake_loss=1.347, generator_loss=27.95, generator_mel_loss=17.8, generator_kl_loss=1.624, generator_dur_loss=1.66, generator_adv_loss=2.073, generator_feat_match_loss=4.787, over 52.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.372, discriminator_fake_loss=1.349, generator_loss=28, generator_mel_loss=18.2, generator_kl_loss=1.43, generator_dur_loss=1.748, generator_adv_loss=1.925, generator_feat_match_loss=4.695, over 756.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:45:14,058 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 15:45:21,913 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 15:47:41,617 INFO [train.py:527] (5/6) Epoch 388, batch 62, global_batch_idx: 48050, batch size: 88, loss[discriminator_loss=2.661, discriminator_real_loss=1.289, discriminator_fake_loss=1.372, generator_loss=27.13, generator_mel_loss=18.08, generator_kl_loss=1.249, generator_dur_loss=1.869, generator_adv_loss=1.803, generator_feat_match_loss=4.131, over 88.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.37, discriminator_fake_loss=1.339, generator_loss=27.96, generator_mel_loss=18.15, generator_kl_loss=1.412, generator_dur_loss=1.753, generator_adv_loss=1.946, generator_feat_match_loss=4.698, over 3554.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:50:02,390 INFO [train.py:527] (5/6) Epoch 388, batch 112, global_batch_idx: 48100, batch size: 48, loss[discriminator_loss=2.723, discriminator_real_loss=1.415, discriminator_fake_loss=1.309, generator_loss=28.46, generator_mel_loss=18.49, generator_kl_loss=1.528, generator_dur_loss=1.652, generator_adv_loss=1.955, generator_feat_match_loss=4.833, over 48.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.37, discriminator_fake_loss=1.341, generator_loss=27.94, generator_mel_loss=18.12, generator_kl_loss=1.418, generator_dur_loss=1.764, generator_adv_loss=1.942, generator_feat_match_loss=4.698, over 6484.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:50:35,065 INFO [train.py:919] (5/6) Start epoch 389 +2024-03-13 15:52:48,446 INFO [train.py:527] (5/6) Epoch 389, batch 38, global_batch_idx: 48150, batch size: 45, loss[discriminator_loss=2.764, discriminator_real_loss=1.421, discriminator_fake_loss=1.342, generator_loss=27.39, generator_mel_loss=17.61, generator_kl_loss=1.443, generator_dur_loss=1.72, generator_adv_loss=2.041, generator_feat_match_loss=4.576, over 45.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.373, discriminator_fake_loss=1.352, generator_loss=27.97, generator_mel_loss=18.22, generator_kl_loss=1.406, generator_dur_loss=1.771, generator_adv_loss=1.942, generator_feat_match_loss=4.627, over 2272.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:55:12,726 INFO [train.py:527] (5/6) Epoch 389, batch 88, global_batch_idx: 48200, batch size: 25, loss[discriminator_loss=2.8, discriminator_real_loss=1.622, discriminator_fake_loss=1.178, generator_loss=27.97, generator_mel_loss=18.62, generator_kl_loss=1.689, generator_dur_loss=1.597, generator_adv_loss=1.929, generator_feat_match_loss=4.139, over 25.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.379, discriminator_fake_loss=1.343, generator_loss=27.94, generator_mel_loss=18.2, generator_kl_loss=1.394, generator_dur_loss=1.779, generator_adv_loss=1.942, generator_feat_match_loss=4.624, over 5319.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 15:55:12,727 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 15:55:21,782 INFO [train.py:591] (5/6) 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 15:55:21,783 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 15:56:57,629 INFO [train.py:919] (5/6) Start epoch 390 +2024-03-13 15:58:02,144 INFO [train.py:527] (5/6) Epoch 390, batch 14, global_batch_idx: 48250, batch size: 70, loss[discriminator_loss=2.74, discriminator_real_loss=1.291, discriminator_fake_loss=1.45, generator_loss=27.45, generator_mel_loss=18, generator_kl_loss=1.317, generator_dur_loss=1.823, generator_adv_loss=1.977, generator_feat_match_loss=4.333, over 70.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.369, discriminator_fake_loss=1.364, generator_loss=27.85, generator_mel_loss=18.17, generator_kl_loss=1.406, generator_dur_loss=1.761, generator_adv_loss=1.931, generator_feat_match_loss=4.58, over 860.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 16:00:21,680 INFO [train.py:527] (5/6) Epoch 390, batch 64, global_batch_idx: 48300, batch size: 70, loss[discriminator_loss=2.69, discriminator_real_loss=1.422, discriminator_fake_loss=1.267, generator_loss=27.27, generator_mel_loss=18.08, generator_kl_loss=1.336, generator_dur_loss=1.803, generator_adv_loss=1.822, generator_feat_match_loss=4.226, over 70.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.369, discriminator_fake_loss=1.349, generator_loss=28, generator_mel_loss=18.15, generator_kl_loss=1.4, generator_dur_loss=1.76, generator_adv_loss=1.956, generator_feat_match_loss=4.728, over 3633.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 16:02:39,591 INFO [train.py:527] (5/6) Epoch 390, batch 114, global_batch_idx: 48350, batch size: 48, loss[discriminator_loss=2.777, discriminator_real_loss=1.444, discriminator_fake_loss=1.333, generator_loss=27.97, generator_mel_loss=18.18, generator_kl_loss=1.529, generator_dur_loss=1.746, generator_adv_loss=1.93, generator_feat_match_loss=4.583, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=28, generator_mel_loss=18.16, generator_kl_loss=1.4, generator_dur_loss=1.77, generator_adv_loss=1.965, generator_feat_match_loss=4.71, over 6609.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, +2024-03-13 16:03:05,797 INFO [train.py:919] (5/6) Start epoch 391 +2024-03-13 16:05:21,061 INFO [train.py:527] (5/6) Epoch 391, batch 40, global_batch_idx: 48400, batch size: 53, loss[discriminator_loss=2.821, discriminator_real_loss=1.361, discriminator_fake_loss=1.46, generator_loss=27.56, generator_mel_loss=18.05, generator_kl_loss=1.347, generator_dur_loss=1.703, generator_adv_loss=1.977, generator_feat_match_loss=4.49, over 53.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=28, generator_mel_loss=18.25, generator_kl_loss=1.421, generator_dur_loss=1.758, generator_adv_loss=1.941, generator_feat_match_loss=4.623, over 2350.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:05:21,063 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 16:05:28,904 INFO [train.py:591] (5/6) 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 16:05:28,905 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 16:07:47,806 INFO [train.py:527] (5/6) Epoch 391, batch 90, global_batch_idx: 48450, batch size: 48, loss[discriminator_loss=2.748, discriminator_real_loss=1.496, discriminator_fake_loss=1.251, generator_loss=27.7, generator_mel_loss=18.05, generator_kl_loss=1.409, generator_dur_loss=1.659, generator_adv_loss=1.785, generator_feat_match_loss=4.797, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=27.92, generator_mel_loss=18.14, generator_kl_loss=1.409, generator_dur_loss=1.755, generator_adv_loss=1.941, generator_feat_match_loss=4.678, over 5232.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:09:22,818 INFO [train.py:919] (5/6) Start epoch 392 +2024-03-13 16:10:32,249 INFO [train.py:527] (5/6) Epoch 392, batch 16, global_batch_idx: 48500, batch size: 80, loss[discriminator_loss=2.718, discriminator_real_loss=1.47, discriminator_fake_loss=1.247, generator_loss=27.86, generator_mel_loss=18.08, generator_kl_loss=1.425, generator_dur_loss=1.812, generator_adv_loss=1.841, generator_feat_match_loss=4.705, over 80.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.372, discriminator_fake_loss=1.337, generator_loss=28.07, generator_mel_loss=18.14, generator_kl_loss=1.432, generator_dur_loss=1.782, generator_adv_loss=1.942, generator_feat_match_loss=4.775, over 1090.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:12:50,661 INFO [train.py:527] (5/6) Epoch 392, batch 66, global_batch_idx: 48550, batch size: 48, loss[discriminator_loss=2.626, discriminator_real_loss=1.316, discriminator_fake_loss=1.31, generator_loss=27.94, generator_mel_loss=17.96, generator_kl_loss=1.471, generator_dur_loss=1.694, generator_adv_loss=2.159, generator_feat_match_loss=4.661, over 48.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.373, discriminator_fake_loss=1.335, generator_loss=27.98, generator_mel_loss=18.14, generator_kl_loss=1.415, generator_dur_loss=1.756, generator_adv_loss=1.95, generator_feat_match_loss=4.712, over 3864.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:15:11,527 INFO [train.py:527] (5/6) Epoch 392, batch 116, global_batch_idx: 48600, batch size: 77, loss[discriminator_loss=2.76, discriminator_real_loss=1.367, discriminator_fake_loss=1.393, generator_loss=27.31, generator_mel_loss=17.98, generator_kl_loss=1.275, generator_dur_loss=1.779, generator_adv_loss=1.877, generator_feat_match_loss=4.395, over 77.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=27.94, generator_mel_loss=18.12, generator_kl_loss=1.416, generator_dur_loss=1.748, generator_adv_loss=1.949, generator_feat_match_loss=4.707, over 6756.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:15:11,528 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 16:15:20,602 INFO [train.py:591] (5/6) 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 16:15:20,604 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 16:15:40,697 INFO [train.py:919] (5/6) Start epoch 393 +2024-03-13 16:18:01,779 INFO [train.py:527] (5/6) Epoch 393, batch 42, global_batch_idx: 48650, batch size: 74, loss[discriminator_loss=2.738, discriminator_real_loss=1.452, discriminator_fake_loss=1.285, generator_loss=28.33, generator_mel_loss=18.24, generator_kl_loss=1.372, generator_dur_loss=1.776, generator_adv_loss=2.005, generator_feat_match_loss=4.934, over 74.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.377, discriminator_fake_loss=1.34, generator_loss=27.95, generator_mel_loss=18.14, generator_kl_loss=1.387, generator_dur_loss=1.779, generator_adv_loss=1.972, generator_feat_match_loss=4.678, over 2523.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:20:18,273 INFO [train.py:527] (5/6) Epoch 393, batch 92, global_batch_idx: 48700, batch size: 48, loss[discriminator_loss=2.726, discriminator_real_loss=1.365, discriminator_fake_loss=1.361, generator_loss=28.13, generator_mel_loss=18.07, generator_kl_loss=1.513, generator_dur_loss=1.687, generator_adv_loss=1.944, generator_feat_match_loss=4.912, over 48.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.343, generator_loss=27.95, generator_mel_loss=18.12, generator_kl_loss=1.403, generator_dur_loss=1.761, generator_adv_loss=1.954, generator_feat_match_loss=4.706, over 5311.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:21:48,645 INFO [train.py:919] (5/6) Start epoch 394 +2024-03-13 16:23:03,836 INFO [train.py:527] (5/6) Epoch 394, batch 18, global_batch_idx: 48750, batch size: 88, loss[discriminator_loss=2.739, discriminator_real_loss=1.426, discriminator_fake_loss=1.313, generator_loss=27.86, generator_mel_loss=18.06, generator_kl_loss=1.229, generator_dur_loss=1.876, generator_adv_loss=1.901, generator_feat_match_loss=4.788, over 88.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.395, discriminator_fake_loss=1.328, generator_loss=27.88, generator_mel_loss=18.12, generator_kl_loss=1.403, generator_dur_loss=1.722, generator_adv_loss=1.952, generator_feat_match_loss=4.686, over 1063.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:25:26,072 INFO [train.py:527] (5/6) Epoch 394, batch 68, global_batch_idx: 48800, batch size: 53, loss[discriminator_loss=2.701, discriminator_real_loss=1.507, discriminator_fake_loss=1.195, generator_loss=27.52, generator_mel_loss=17.97, generator_kl_loss=1.447, generator_dur_loss=1.74, generator_adv_loss=1.967, generator_feat_match_loss=4.398, over 53.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=27.91, generator_mel_loss=18.11, generator_kl_loss=1.396, generator_dur_loss=1.752, generator_adv_loss=1.95, generator_feat_match_loss=4.7, over 3973.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:25:26,073 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 16:25:34,361 INFO [train.py:591] (5/6) 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 16:25:34,362 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 16:27:50,223 INFO [train.py:527] (5/6) Epoch 394, batch 118, global_batch_idx: 48850, batch size: 48, loss[discriminator_loss=2.673, discriminator_real_loss=1.311, discriminator_fake_loss=1.362, generator_loss=28.73, generator_mel_loss=18.21, generator_kl_loss=1.637, generator_dur_loss=1.677, generator_adv_loss=1.902, generator_feat_match_loss=5.307, over 48.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.37, discriminator_fake_loss=1.347, generator_loss=27.96, generator_mel_loss=18.15, generator_kl_loss=1.414, generator_dur_loss=1.756, generator_adv_loss=1.945, generator_feat_match_loss=4.694, over 6722.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:28:05,266 INFO [train.py:919] (5/6) Start epoch 395 +2024-03-13 16:30:31,076 INFO [train.py:527] (5/6) Epoch 395, batch 44, global_batch_idx: 48900, batch size: 48, loss[discriminator_loss=2.745, discriminator_real_loss=1.332, discriminator_fake_loss=1.413, generator_loss=28.42, generator_mel_loss=18.63, generator_kl_loss=1.466, generator_dur_loss=1.731, generator_adv_loss=1.919, generator_feat_match_loss=4.666, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.05, generator_mel_loss=18.18, generator_kl_loss=1.43, generator_dur_loss=1.747, generator_adv_loss=1.943, generator_feat_match_loss=4.753, over 2383.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:32:51,108 INFO [train.py:527] (5/6) Epoch 395, batch 94, global_batch_idx: 48950, batch size: 74, loss[discriminator_loss=2.683, discriminator_real_loss=1.319, discriminator_fake_loss=1.364, generator_loss=28.32, generator_mel_loss=18.39, generator_kl_loss=1.376, generator_dur_loss=1.827, generator_adv_loss=2.093, generator_feat_match_loss=4.626, over 74.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.02, generator_mel_loss=18.18, generator_kl_loss=1.417, generator_dur_loss=1.756, generator_adv_loss=1.946, generator_feat_match_loss=4.717, over 5385.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:34:12,418 INFO [train.py:919] (5/6) Start epoch 396 +2024-03-13 16:35:34,934 INFO [train.py:527] (5/6) Epoch 396, batch 20, global_batch_idx: 49000, batch size: 77, loss[discriminator_loss=2.723, discriminator_real_loss=1.388, discriminator_fake_loss=1.334, generator_loss=27.85, generator_mel_loss=18.02, generator_kl_loss=1.429, generator_dur_loss=1.835, generator_adv_loss=1.927, generator_feat_match_loss=4.636, over 77.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.361, discriminator_fake_loss=1.343, generator_loss=28.07, generator_mel_loss=18.17, generator_kl_loss=1.425, generator_dur_loss=1.8, generator_adv_loss=1.932, generator_feat_match_loss=4.737, over 1265.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:35:34,935 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 16:35:43,002 INFO [train.py:591] (5/6) 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 16:35:43,004 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 16:37:59,756 INFO [train.py:527] (5/6) Epoch 396, batch 70, global_batch_idx: 49050, batch size: 36, loss[discriminator_loss=2.76, discriminator_real_loss=1.506, discriminator_fake_loss=1.254, generator_loss=25.36, generator_mel_loss=16.98, generator_kl_loss=1.407, generator_dur_loss=1.663, generator_adv_loss=1.885, generator_feat_match_loss=3.419, over 36.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.364, discriminator_fake_loss=1.349, generator_loss=28.03, generator_mel_loss=18.19, generator_kl_loss=1.407, generator_dur_loss=1.778, generator_adv_loss=1.936, generator_feat_match_loss=4.72, over 4073.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:40:17,396 INFO [train.py:527] (5/6) Epoch 396, batch 120, global_batch_idx: 49100, batch size: 53, loss[discriminator_loss=2.632, discriminator_real_loss=1.291, discriminator_fake_loss=1.341, generator_loss=29.26, generator_mel_loss=18.25, generator_kl_loss=1.517, generator_dur_loss=1.687, generator_adv_loss=2.257, generator_feat_match_loss=5.544, over 53.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.363, discriminator_fake_loss=1.346, generator_loss=28, generator_mel_loss=18.16, generator_kl_loss=1.399, generator_dur_loss=1.77, generator_adv_loss=1.949, generator_feat_match_loss=4.727, over 7110.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:40:26,666 INFO [train.py:919] (5/6) Start epoch 397 +2024-03-13 16:42:57,344 INFO [train.py:527] (5/6) Epoch 397, batch 46, global_batch_idx: 49150, batch size: 83, loss[discriminator_loss=2.657, discriminator_real_loss=1.335, discriminator_fake_loss=1.322, generator_loss=28.15, generator_mel_loss=18.26, generator_kl_loss=1.379, generator_dur_loss=1.813, generator_adv_loss=1.909, generator_feat_match_loss=4.786, over 83.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.383, discriminator_fake_loss=1.33, generator_loss=27.82, generator_mel_loss=18.09, generator_kl_loss=1.424, generator_dur_loss=1.744, generator_adv_loss=1.952, generator_feat_match_loss=4.607, over 2752.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:45:16,663 INFO [train.py:527] (5/6) Epoch 397, batch 96, global_batch_idx: 49200, batch size: 52, loss[discriminator_loss=2.826, discriminator_real_loss=1.563, discriminator_fake_loss=1.263, generator_loss=27.67, generator_mel_loss=17.95, generator_kl_loss=1.299, generator_dur_loss=1.703, generator_adv_loss=1.737, generator_feat_match_loss=4.985, over 52.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=27.86, generator_mel_loss=18.1, generator_kl_loss=1.422, generator_dur_loss=1.737, generator_adv_loss=1.948, generator_feat_match_loss=4.655, over 5543.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:45:16,664 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 16:45:25,871 INFO [train.py:591] (5/6) 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 16:45:25,872 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 16:46:39,925 INFO [train.py:919] (5/6) Start epoch 398 +2024-03-13 16:48:06,619 INFO [train.py:527] (5/6) Epoch 398, batch 22, global_batch_idx: 49250, batch size: 88, loss[discriminator_loss=2.721, discriminator_real_loss=1.42, discriminator_fake_loss=1.302, generator_loss=27.69, generator_mel_loss=18.13, generator_kl_loss=1.485, generator_dur_loss=1.81, generator_adv_loss=2.015, generator_feat_match_loss=4.248, over 88.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.368, discriminator_fake_loss=1.329, generator_loss=27.95, generator_mel_loss=18.16, generator_kl_loss=1.382, generator_dur_loss=1.73, generator_adv_loss=1.982, generator_feat_match_loss=4.702, over 1284.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:50:27,761 INFO [train.py:527] (5/6) Epoch 398, batch 72, global_batch_idx: 49300, batch size: 74, loss[discriminator_loss=2.753, discriminator_real_loss=1.403, discriminator_fake_loss=1.35, generator_loss=27.38, generator_mel_loss=17.93, generator_kl_loss=1.309, generator_dur_loss=1.792, generator_adv_loss=1.929, generator_feat_match_loss=4.413, over 74.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.382, discriminator_fake_loss=1.336, generator_loss=27.73, generator_mel_loss=18.05, generator_kl_loss=1.405, generator_dur_loss=1.731, generator_adv_loss=1.953, generator_feat_match_loss=4.595, over 4125.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:52:46,033 INFO [train.py:527] (5/6) Epoch 398, batch 122, global_batch_idx: 49350, batch size: 95, loss[discriminator_loss=2.683, discriminator_real_loss=1.376, discriminator_fake_loss=1.307, generator_loss=27.75, generator_mel_loss=17.93, generator_kl_loss=1.22, generator_dur_loss=1.858, generator_adv_loss=1.931, generator_feat_match_loss=4.811, over 95.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.381, discriminator_fake_loss=1.339, generator_loss=27.81, generator_mel_loss=18.1, generator_kl_loss=1.415, generator_dur_loss=1.733, generator_adv_loss=1.943, generator_feat_match_loss=4.626, over 6754.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:52:51,078 INFO [train.py:919] (5/6) Start epoch 399 +2024-03-13 16:55:32,775 INFO [train.py:527] (5/6) Epoch 399, batch 48, global_batch_idx: 49400, batch size: 25, loss[discriminator_loss=2.724, discriminator_real_loss=1.449, discriminator_fake_loss=1.276, generator_loss=27.96, generator_mel_loss=18.41, generator_kl_loss=1.737, generator_dur_loss=1.58, generator_adv_loss=1.864, generator_feat_match_loss=4.364, over 25.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.373, discriminator_fake_loss=1.354, generator_loss=27.75, generator_mel_loss=18.05, generator_kl_loss=1.41, generator_dur_loss=1.732, generator_adv_loss=1.931, generator_feat_match_loss=4.631, over 2739.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:55:32,777 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 16:55:40,681 INFO [train.py:591] (5/6) 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 16:55:40,682 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 16:58:02,356 INFO [train.py:527] (5/6) Epoch 399, batch 98, global_batch_idx: 49450, batch size: 53, loss[discriminator_loss=2.709, discriminator_real_loss=1.311, discriminator_fake_loss=1.399, generator_loss=28.73, generator_mel_loss=18.57, generator_kl_loss=1.589, generator_dur_loss=1.64, generator_adv_loss=1.994, generator_feat_match_loss=4.94, over 53.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.378, discriminator_fake_loss=1.353, generator_loss=27.81, generator_mel_loss=18.07, generator_kl_loss=1.43, generator_dur_loss=1.745, generator_adv_loss=1.952, generator_feat_match_loss=4.613, over 5626.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 16:59:13,054 INFO [train.py:919] (5/6) Start epoch 400 +2024-03-13 17:00:45,616 INFO [train.py:527] (5/6) Epoch 400, batch 24, global_batch_idx: 49500, batch size: 68, loss[discriminator_loss=2.755, discriminator_real_loss=1.459, discriminator_fake_loss=1.296, generator_loss=27.9, generator_mel_loss=18.24, generator_kl_loss=1.514, generator_dur_loss=1.788, generator_adv_loss=1.82, generator_feat_match_loss=4.543, over 68.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.382, discriminator_fake_loss=1.341, generator_loss=27.95, generator_mel_loss=18.11, generator_kl_loss=1.396, generator_dur_loss=1.777, generator_adv_loss=1.939, generator_feat_match_loss=4.731, over 1584.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:03:05,344 INFO [train.py:527] (5/6) Epoch 400, batch 74, global_batch_idx: 49550, batch size: 66, loss[discriminator_loss=2.667, discriminator_real_loss=1.376, discriminator_fake_loss=1.292, generator_loss=27.91, generator_mel_loss=18.2, generator_kl_loss=1.376, generator_dur_loss=1.761, generator_adv_loss=2.018, generator_feat_match_loss=4.557, over 66.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.379, discriminator_fake_loss=1.345, generator_loss=27.83, generator_mel_loss=18.07, generator_kl_loss=1.387, generator_dur_loss=1.782, generator_adv_loss=1.936, generator_feat_match_loss=4.659, over 4706.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:05:20,673 INFO [train.py:919] (5/6) Start epoch 401 +2024-03-13 17:05:44,942 INFO [train.py:527] (5/6) Epoch 401, batch 0, global_batch_idx: 49600, batch size: 39, loss[discriminator_loss=2.736, discriminator_real_loss=1.482, discriminator_fake_loss=1.254, generator_loss=27.9, generator_mel_loss=18.35, generator_kl_loss=1.418, generator_dur_loss=1.723, generator_adv_loss=1.899, generator_feat_match_loss=4.512, over 39.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.482, discriminator_fake_loss=1.254, generator_loss=27.9, generator_mel_loss=18.35, generator_kl_loss=1.418, generator_dur_loss=1.723, generator_adv_loss=1.899, generator_feat_match_loss=4.512, over 39.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:05:44,945 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 17:05:52,962 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 17:08:10,609 INFO [train.py:527] (5/6) Epoch 401, batch 50, global_batch_idx: 49650, batch size: 62, loss[discriminator_loss=2.704, discriminator_real_loss=1.377, discriminator_fake_loss=1.327, generator_loss=28.17, generator_mel_loss=18.31, generator_kl_loss=1.449, generator_dur_loss=1.754, generator_adv_loss=2.01, generator_feat_match_loss=4.654, over 62.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.377, discriminator_fake_loss=1.348, generator_loss=27.87, generator_mel_loss=18.13, generator_kl_loss=1.43, generator_dur_loss=1.755, generator_adv_loss=1.949, generator_feat_match_loss=4.611, over 2807.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:10:29,519 INFO [train.py:527] (5/6) Epoch 401, batch 100, global_batch_idx: 49700, batch size: 74, loss[discriminator_loss=2.74, discriminator_real_loss=1.407, discriminator_fake_loss=1.333, generator_loss=28.18, generator_mel_loss=18.17, generator_kl_loss=1.269, generator_dur_loss=1.815, generator_adv_loss=1.966, generator_feat_match_loss=4.963, over 74.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.37, discriminator_fake_loss=1.349, generator_loss=27.91, generator_mel_loss=18.11, generator_kl_loss=1.412, generator_dur_loss=1.761, generator_adv_loss=1.939, generator_feat_match_loss=4.683, over 5836.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:11:35,530 INFO [train.py:919] (5/6) Start epoch 402 +2024-03-13 17:13:12,022 INFO [train.py:527] (5/6) Epoch 402, batch 26, global_batch_idx: 49750, batch size: 39, loss[discriminator_loss=2.823, discriminator_real_loss=1.6, discriminator_fake_loss=1.224, generator_loss=28.08, generator_mel_loss=18.11, generator_kl_loss=1.644, generator_dur_loss=1.691, generator_adv_loss=1.851, generator_feat_match_loss=4.786, over 39.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.345, discriminator_fake_loss=1.351, generator_loss=28.06, generator_mel_loss=18.14, generator_kl_loss=1.419, generator_dur_loss=1.761, generator_adv_loss=1.991, generator_feat_match_loss=4.755, over 1475.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:15:32,468 INFO [train.py:527] (5/6) Epoch 402, batch 76, global_batch_idx: 49800, batch size: 77, loss[discriminator_loss=2.77, discriminator_real_loss=1.465, discriminator_fake_loss=1.305, generator_loss=27.47, generator_mel_loss=18.14, generator_kl_loss=1.287, generator_dur_loss=1.8, generator_adv_loss=1.834, generator_feat_match_loss=4.408, over 77.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.377, discriminator_fake_loss=1.337, generator_loss=27.93, generator_mel_loss=18.11, generator_kl_loss=1.403, generator_dur_loss=1.766, generator_adv_loss=1.984, generator_feat_match_loss=4.665, over 4440.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:15:32,469 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 17:15:40,609 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 17:17:51,347 INFO [train.py:919] (5/6) Start epoch 403 +2024-03-13 17:18:19,573 INFO [train.py:527] (5/6) Epoch 403, batch 2, global_batch_idx: 49850, batch size: 53, loss[discriminator_loss=2.67, discriminator_real_loss=1.382, discriminator_fake_loss=1.288, generator_loss=27.09, generator_mel_loss=18.01, generator_kl_loss=1.454, generator_dur_loss=1.685, generator_adv_loss=1.961, generator_feat_match_loss=3.982, over 53.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=28.15, generator_mel_loss=18.21, generator_kl_loss=1.486, generator_dur_loss=1.701, generator_adv_loss=1.95, generator_feat_match_loss=4.805, over 167.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:20:38,887 INFO [train.py:527] (5/6) Epoch 403, batch 52, global_batch_idx: 49900, batch size: 47, loss[discriminator_loss=2.732, discriminator_real_loss=1.412, discriminator_fake_loss=1.32, generator_loss=29.4, generator_mel_loss=18.61, generator_kl_loss=1.535, generator_dur_loss=1.699, generator_adv_loss=2.026, generator_feat_match_loss=5.527, over 47.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.373, discriminator_fake_loss=1.339, generator_loss=27.93, generator_mel_loss=18.1, generator_kl_loss=1.418, generator_dur_loss=1.755, generator_adv_loss=1.938, generator_feat_match_loss=4.72, over 2873.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:22:55,488 INFO [train.py:527] (5/6) Epoch 403, batch 102, global_batch_idx: 49950, batch size: 77, loss[discriminator_loss=2.709, discriminator_real_loss=1.388, discriminator_fake_loss=1.321, generator_loss=27.44, generator_mel_loss=17.75, generator_kl_loss=1.389, generator_dur_loss=1.857, generator_adv_loss=1.958, generator_feat_match_loss=4.488, over 77.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=27.87, generator_mel_loss=18.08, generator_kl_loss=1.406, generator_dur_loss=1.76, generator_adv_loss=1.938, generator_feat_match_loss=4.679, over 5803.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:23:56,846 INFO [train.py:919] (5/6) Start epoch 404 +2024-03-13 17:25:39,404 INFO [train.py:527] (5/6) Epoch 404, batch 28, global_batch_idx: 50000, batch size: 55, loss[discriminator_loss=2.756, discriminator_real_loss=1.363, discriminator_fake_loss=1.393, generator_loss=27.83, generator_mel_loss=18.32, generator_kl_loss=1.374, generator_dur_loss=1.688, generator_adv_loss=1.869, generator_feat_match_loss=4.583, over 55.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.377, discriminator_fake_loss=1.34, generator_loss=27.79, generator_mel_loss=18.02, generator_kl_loss=1.441, generator_dur_loss=1.734, generator_adv_loss=1.923, generator_feat_match_loss=4.663, over 1546.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:25:39,405 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 17:25:47,417 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 17:28:05,604 INFO [train.py:527] (5/6) Epoch 404, batch 78, global_batch_idx: 50050, batch size: 72, loss[discriminator_loss=2.7, discriminator_real_loss=1.425, discriminator_fake_loss=1.275, generator_loss=27.92, generator_mel_loss=18.22, generator_kl_loss=1.359, generator_dur_loss=1.74, generator_adv_loss=1.989, generator_feat_match_loss=4.605, over 72.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=27.81, generator_mel_loss=18.04, generator_kl_loss=1.418, generator_dur_loss=1.735, generator_adv_loss=1.938, generator_feat_match_loss=4.676, over 4570.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:30:10,206 INFO [train.py:919] (5/6) Start epoch 405 +2024-03-13 17:30:43,989 INFO [train.py:527] (5/6) Epoch 405, batch 4, global_batch_idx: 50100, batch size: 77, loss[discriminator_loss=2.712, discriminator_real_loss=1.382, discriminator_fake_loss=1.33, generator_loss=27.78, generator_mel_loss=18.12, generator_kl_loss=1.279, generator_dur_loss=1.831, generator_adv_loss=1.917, generator_feat_match_loss=4.631, over 77.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.374, discriminator_fake_loss=1.33, generator_loss=27.47, generator_mel_loss=17.92, generator_kl_loss=1.395, generator_dur_loss=1.749, generator_adv_loss=1.923, generator_feat_match_loss=4.482, over 284.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:33:06,902 INFO [train.py:527] (5/6) Epoch 405, batch 54, global_batch_idx: 50150, batch size: 70, loss[discriminator_loss=2.718, discriminator_real_loss=1.4, discriminator_fake_loss=1.318, generator_loss=27.96, generator_mel_loss=18.09, generator_kl_loss=1.369, generator_dur_loss=1.786, generator_adv_loss=1.88, generator_feat_match_loss=4.839, over 70.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=27.92, generator_mel_loss=18.11, generator_kl_loss=1.409, generator_dur_loss=1.767, generator_adv_loss=1.932, generator_feat_match_loss=4.695, over 3375.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:35:24,914 INFO [train.py:527] (5/6) Epoch 405, batch 104, global_batch_idx: 50200, batch size: 80, loss[discriminator_loss=2.727, discriminator_real_loss=1.402, discriminator_fake_loss=1.325, generator_loss=28.19, generator_mel_loss=18.49, generator_kl_loss=1.369, generator_dur_loss=1.77, generator_adv_loss=1.952, generator_feat_match_loss=4.612, over 80.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=27.94, generator_mel_loss=18.13, generator_kl_loss=1.412, generator_dur_loss=1.766, generator_adv_loss=1.941, generator_feat_match_loss=4.686, over 6212.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:35:24,916 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 17:35:33,902 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 17:36:23,208 INFO [train.py:919] (5/6) Start epoch 406 +2024-03-13 17:38:09,540 INFO [train.py:527] (5/6) Epoch 406, batch 30, global_batch_idx: 50250, batch size: 52, loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=29.13, generator_mel_loss=18.35, generator_kl_loss=1.549, generator_dur_loss=1.648, generator_adv_loss=2.012, generator_feat_match_loss=5.575, over 52.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.375, discriminator_fake_loss=1.342, generator_loss=28.06, generator_mel_loss=18.2, generator_kl_loss=1.412, generator_dur_loss=1.735, generator_adv_loss=1.941, generator_feat_match_loss=4.776, over 1731.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:40:30,657 INFO [train.py:527] (5/6) Epoch 406, batch 80, global_batch_idx: 50300, batch size: 50, loss[discriminator_loss=2.699, discriminator_real_loss=1.387, discriminator_fake_loss=1.312, generator_loss=27.71, generator_mel_loss=18.16, generator_kl_loss=1.599, generator_dur_loss=1.702, generator_adv_loss=2.065, generator_feat_match_loss=4.184, over 50.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.37, discriminator_fake_loss=1.342, generator_loss=28.02, generator_mel_loss=18.14, generator_kl_loss=1.402, generator_dur_loss=1.758, generator_adv_loss=1.943, generator_feat_match_loss=4.768, over 4660.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:42:26,810 INFO [train.py:919] (5/6) Start epoch 407 +2024-03-13 17:43:09,000 INFO [train.py:527] (5/6) Epoch 407, batch 6, global_batch_idx: 50350, batch size: 31, loss[discriminator_loss=2.751, discriminator_real_loss=1.493, discriminator_fake_loss=1.258, generator_loss=27.78, generator_mel_loss=18.36, generator_kl_loss=1.478, generator_dur_loss=1.647, generator_adv_loss=1.926, generator_feat_match_loss=4.366, over 31.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.359, discriminator_fake_loss=1.358, generator_loss=28.17, generator_mel_loss=18.26, generator_kl_loss=1.318, generator_dur_loss=1.81, generator_adv_loss=1.965, generator_feat_match_loss=4.817, over 506.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:45:26,354 INFO [train.py:527] (5/6) Epoch 407, batch 56, global_batch_idx: 50400, batch size: 25, loss[discriminator_loss=2.687, discriminator_real_loss=1.319, discriminator_fake_loss=1.368, generator_loss=27.61, generator_mel_loss=18.09, generator_kl_loss=1.622, generator_dur_loss=1.552, generator_adv_loss=1.982, generator_feat_match_loss=4.363, over 25.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.381, discriminator_fake_loss=1.342, generator_loss=28.04, generator_mel_loss=18.19, generator_kl_loss=1.415, generator_dur_loss=1.749, generator_adv_loss=1.973, generator_feat_match_loss=4.708, over 3129.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:45:26,355 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 17:45:34,376 INFO [train.py:591] (5/6) 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 17:45:34,377 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 17:47:53,238 INFO [train.py:527] (5/6) Epoch 407, batch 106, global_batch_idx: 50450, batch size: 17, loss[discriminator_loss=2.719, discriminator_real_loss=1.457, discriminator_fake_loss=1.262, generator_loss=28.05, generator_mel_loss=18.59, generator_kl_loss=1.555, generator_dur_loss=1.638, generator_adv_loss=1.932, generator_feat_match_loss=4.329, over 17.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.338, generator_loss=27.92, generator_mel_loss=18.11, generator_kl_loss=1.414, generator_dur_loss=1.757, generator_adv_loss=1.962, generator_feat_match_loss=4.67, over 6082.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:48:40,652 INFO [train.py:919] (5/6) Start epoch 408 +2024-03-13 17:50:32,307 INFO [train.py:527] (5/6) Epoch 408, batch 32, global_batch_idx: 50500, batch size: 62, loss[discriminator_loss=2.751, discriminator_real_loss=1.409, discriminator_fake_loss=1.341, generator_loss=27.99, generator_mel_loss=18.1, generator_kl_loss=1.465, generator_dur_loss=1.732, generator_adv_loss=1.835, generator_feat_match_loss=4.856, over 62.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.383, discriminator_fake_loss=1.36, generator_loss=27.9, generator_mel_loss=18.14, generator_kl_loss=1.403, generator_dur_loss=1.756, generator_adv_loss=1.925, generator_feat_match_loss=4.679, over 1863.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:52:51,952 INFO [train.py:527] (5/6) Epoch 408, batch 82, global_batch_idx: 50550, batch size: 15, loss[discriminator_loss=2.622, discriminator_real_loss=1.309, discriminator_fake_loss=1.313, generator_loss=29.13, generator_mel_loss=18.66, generator_kl_loss=1.774, generator_dur_loss=1.55, generator_adv_loss=1.968, generator_feat_match_loss=5.178, over 15.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.38, discriminator_fake_loss=1.352, generator_loss=28, generator_mel_loss=18.16, generator_kl_loss=1.424, generator_dur_loss=1.75, generator_adv_loss=1.941, generator_feat_match_loss=4.728, over 4494.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:54:46,529 INFO [train.py:919] (5/6) Start epoch 409 +2024-03-13 17:55:34,344 INFO [train.py:527] (5/6) Epoch 409, batch 8, global_batch_idx: 50600, batch size: 31, loss[discriminator_loss=2.651, discriminator_real_loss=1.261, discriminator_fake_loss=1.39, generator_loss=28.08, generator_mel_loss=17.98, generator_kl_loss=1.597, generator_dur_loss=1.703, generator_adv_loss=2.056, generator_feat_match_loss=4.75, over 31.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.355, discriminator_fake_loss=1.356, generator_loss=28.28, generator_mel_loss=18.36, generator_kl_loss=1.459, generator_dur_loss=1.732, generator_adv_loss=1.925, generator_feat_match_loss=4.806, over 460.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 17:55:34,347 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 17:55:42,243 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 17:58:01,426 INFO [train.py:527] (5/6) Epoch 409, batch 58, global_batch_idx: 50650, batch size: 47, loss[discriminator_loss=2.672, discriminator_real_loss=1.362, discriminator_fake_loss=1.31, generator_loss=29.01, generator_mel_loss=18.46, generator_kl_loss=1.672, generator_dur_loss=1.71, generator_adv_loss=2.049, generator_feat_match_loss=5.117, over 47.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.367, discriminator_fake_loss=1.345, generator_loss=27.89, generator_mel_loss=18.06, generator_kl_loss=1.406, generator_dur_loss=1.767, generator_adv_loss=1.945, generator_feat_match_loss=4.711, over 3473.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:00:17,700 INFO [train.py:527] (5/6) Epoch 409, batch 108, global_batch_idx: 50700, batch size: 88, loss[discriminator_loss=2.623, discriminator_real_loss=1.294, discriminator_fake_loss=1.329, generator_loss=27.63, generator_mel_loss=17.87, generator_kl_loss=1.292, generator_dur_loss=1.865, generator_adv_loss=1.951, generator_feat_match_loss=4.655, over 88.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.369, discriminator_fake_loss=1.342, generator_loss=27.92, generator_mel_loss=18.06, generator_kl_loss=1.404, generator_dur_loss=1.769, generator_adv_loss=1.944, generator_feat_match_loss=4.75, over 6453.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:00:59,757 INFO [train.py:919] (5/6) Start epoch 410 +2024-03-13 18:02:59,006 INFO [train.py:527] (5/6) Epoch 410, batch 34, global_batch_idx: 50750, batch size: 72, loss[discriminator_loss=2.745, discriminator_real_loss=1.421, discriminator_fake_loss=1.324, generator_loss=28.56, generator_mel_loss=18.01, generator_kl_loss=1.315, generator_dur_loss=1.792, generator_adv_loss=2.115, generator_feat_match_loss=5.328, over 72.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.372, discriminator_fake_loss=1.35, generator_loss=28.07, generator_mel_loss=18.18, generator_kl_loss=1.411, generator_dur_loss=1.755, generator_adv_loss=1.958, generator_feat_match_loss=4.763, over 2031.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:05:17,679 INFO [train.py:527] (5/6) Epoch 410, batch 84, global_batch_idx: 50800, batch size: 74, loss[discriminator_loss=2.839, discriminator_real_loss=1.535, discriminator_fake_loss=1.304, generator_loss=26.97, generator_mel_loss=18.04, generator_kl_loss=1.405, generator_dur_loss=1.795, generator_adv_loss=1.772, generator_feat_match_loss=3.957, over 74.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.341, generator_loss=27.94, generator_mel_loss=18.09, generator_kl_loss=1.418, generator_dur_loss=1.752, generator_adv_loss=1.96, generator_feat_match_loss=4.727, over 4817.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:05:17,680 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 18:05:26,738 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 18:07:15,282 INFO [train.py:919] (5/6) Start epoch 411 +2024-03-13 18:08:06,451 INFO [train.py:527] (5/6) Epoch 411, batch 10, global_batch_idx: 50850, batch size: 62, loss[discriminator_loss=2.67, discriminator_real_loss=1.344, discriminator_fake_loss=1.327, generator_loss=28.5, generator_mel_loss=18.21, generator_kl_loss=1.582, generator_dur_loss=1.714, generator_adv_loss=2.132, generator_feat_match_loss=4.863, over 62.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.378, discriminator_fake_loss=1.341, generator_loss=27.85, generator_mel_loss=18.12, generator_kl_loss=1.445, generator_dur_loss=1.761, generator_adv_loss=1.96, generator_feat_match_loss=4.563, over 657.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:10:22,413 INFO [train.py:527] (5/6) Epoch 411, batch 60, global_batch_idx: 50900, batch size: 72, loss[discriminator_loss=2.738, discriminator_real_loss=1.407, discriminator_fake_loss=1.331, generator_loss=27.76, generator_mel_loss=17.95, generator_kl_loss=1.362, generator_dur_loss=1.76, generator_adv_loss=1.941, generator_feat_match_loss=4.746, over 72.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=27.91, generator_mel_loss=18.15, generator_kl_loss=1.43, generator_dur_loss=1.731, generator_adv_loss=1.956, generator_feat_match_loss=4.644, over 3326.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:12:40,482 INFO [train.py:527] (5/6) Epoch 411, batch 110, global_batch_idx: 50950, batch size: 66, loss[discriminator_loss=2.722, discriminator_real_loss=1.437, discriminator_fake_loss=1.284, generator_loss=27.46, generator_mel_loss=17.79, generator_kl_loss=1.341, generator_dur_loss=1.733, generator_adv_loss=2.065, generator_feat_match_loss=4.526, over 66.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.369, discriminator_fake_loss=1.344, generator_loss=27.91, generator_mel_loss=18.12, generator_kl_loss=1.413, generator_dur_loss=1.737, generator_adv_loss=1.957, generator_feat_match_loss=4.681, over 6308.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:13:19,865 INFO [train.py:919] (5/6) Start epoch 412 +2024-03-13 18:15:23,103 INFO [train.py:527] (5/6) Epoch 412, batch 36, global_batch_idx: 51000, batch size: 39, loss[discriminator_loss=2.779, discriminator_real_loss=1.412, discriminator_fake_loss=1.366, generator_loss=27.2, generator_mel_loss=17.82, generator_kl_loss=1.432, generator_dur_loss=1.716, generator_adv_loss=1.792, generator_feat_match_loss=4.44, over 39.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.367, discriminator_fake_loss=1.35, generator_loss=27.8, generator_mel_loss=17.98, generator_kl_loss=1.365, generator_dur_loss=1.779, generator_adv_loss=1.929, generator_feat_match_loss=4.75, over 2423.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:15:23,104 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 18:15:30,954 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 18:17:48,779 INFO [train.py:527] (5/6) Epoch 412, batch 86, global_batch_idx: 51050, batch size: 68, loss[discriminator_loss=2.699, discriminator_real_loss=1.313, discriminator_fake_loss=1.386, generator_loss=28.02, generator_mel_loss=18.36, generator_kl_loss=1.363, generator_dur_loss=1.778, generator_adv_loss=1.915, generator_feat_match_loss=4.612, over 68.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.37, discriminator_fake_loss=1.342, generator_loss=27.89, generator_mel_loss=18.01, generator_kl_loss=1.397, generator_dur_loss=1.76, generator_adv_loss=1.941, generator_feat_match_loss=4.779, over 5185.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:19:29,985 INFO [train.py:919] (5/6) Start epoch 413 +2024-03-13 18:20:26,268 INFO [train.py:527] (5/6) Epoch 413, batch 12, global_batch_idx: 51100, batch size: 62, loss[discriminator_loss=2.701, discriminator_real_loss=1.442, discriminator_fake_loss=1.259, generator_loss=28.96, generator_mel_loss=18.57, generator_kl_loss=1.442, generator_dur_loss=1.744, generator_adv_loss=2.005, generator_feat_match_loss=5.199, over 62.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.362, discriminator_fake_loss=1.334, generator_loss=28.18, generator_mel_loss=18.17, generator_kl_loss=1.413, generator_dur_loss=1.761, generator_adv_loss=2.007, generator_feat_match_loss=4.831, over 811.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:22:47,504 INFO [train.py:527] (5/6) Epoch 413, batch 62, global_batch_idx: 51150, batch size: 59, loss[discriminator_loss=2.686, discriminator_real_loss=1.407, discriminator_fake_loss=1.28, generator_loss=28.28, generator_mel_loss=18.48, generator_kl_loss=1.317, generator_dur_loss=1.752, generator_adv_loss=1.941, generator_feat_match_loss=4.799, over 59.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.373, discriminator_fake_loss=1.332, generator_loss=28.16, generator_mel_loss=18.17, generator_kl_loss=1.427, generator_dur_loss=1.765, generator_adv_loss=1.97, generator_feat_match_loss=4.834, over 3805.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:25:05,887 INFO [train.py:527] (5/6) Epoch 413, batch 112, global_batch_idx: 51200, batch size: 55, loss[discriminator_loss=2.688, discriminator_real_loss=1.346, discriminator_fake_loss=1.342, generator_loss=28.39, generator_mel_loss=18.13, generator_kl_loss=1.558, generator_dur_loss=1.69, generator_adv_loss=2.032, generator_feat_match_loss=4.979, over 55.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=28.06, generator_mel_loss=18.14, generator_kl_loss=1.416, generator_dur_loss=1.765, generator_adv_loss=1.963, generator_feat_match_loss=4.775, over 6868.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:25:05,889 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 18:25:14,604 INFO [train.py:591] (5/6) 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 18:25:14,605 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 18:25:44,304 INFO [train.py:919] (5/6) Start epoch 414 +2024-03-13 18:27:51,394 INFO [train.py:527] (5/6) Epoch 414, batch 38, global_batch_idx: 51250, batch size: 58, loss[discriminator_loss=2.718, discriminator_real_loss=1.362, discriminator_fake_loss=1.357, generator_loss=28.24, generator_mel_loss=18.33, generator_kl_loss=1.526, generator_dur_loss=1.703, generator_adv_loss=1.897, generator_feat_match_loss=4.792, over 58.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.371, discriminator_fake_loss=1.346, generator_loss=28.06, generator_mel_loss=18.16, generator_kl_loss=1.397, generator_dur_loss=1.744, generator_adv_loss=1.942, generator_feat_match_loss=4.822, over 2197.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:30:07,676 INFO [train.py:527] (5/6) Epoch 414, batch 88, global_batch_idx: 51300, batch size: 62, loss[discriminator_loss=2.76, discriminator_real_loss=1.513, discriminator_fake_loss=1.248, generator_loss=27.03, generator_mel_loss=17.78, generator_kl_loss=1.57, generator_dur_loss=1.732, generator_adv_loss=1.873, generator_feat_match_loss=4.078, over 62.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.341, generator_loss=27.97, generator_mel_loss=18.11, generator_kl_loss=1.427, generator_dur_loss=1.741, generator_adv_loss=1.939, generator_feat_match_loss=4.75, over 5178.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:31:45,179 INFO [train.py:919] (5/6) Start epoch 415 +2024-03-13 18:32:48,728 INFO [train.py:527] (5/6) Epoch 415, batch 14, global_batch_idx: 51350, batch size: 66, loss[discriminator_loss=2.765, discriminator_real_loss=1.423, discriminator_fake_loss=1.342, generator_loss=27.82, generator_mel_loss=18.47, generator_kl_loss=1.375, generator_dur_loss=1.786, generator_adv_loss=1.906, generator_feat_match_loss=4.284, over 66.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.404, discriminator_fake_loss=1.323, generator_loss=27.98, generator_mel_loss=18.21, generator_kl_loss=1.392, generator_dur_loss=1.76, generator_adv_loss=1.966, generator_feat_match_loss=4.649, over 805.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:35:05,724 INFO [train.py:527] (5/6) Epoch 415, batch 64, global_batch_idx: 51400, batch size: 25, loss[discriminator_loss=2.634, discriminator_real_loss=1.288, discriminator_fake_loss=1.346, generator_loss=29.94, generator_mel_loss=18.74, generator_kl_loss=1.815, generator_dur_loss=1.594, generator_adv_loss=2.09, generator_feat_match_loss=5.705, over 25.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.378, discriminator_fake_loss=1.331, generator_loss=28.02, generator_mel_loss=18.15, generator_kl_loss=1.42, generator_dur_loss=1.754, generator_adv_loss=1.947, generator_feat_match_loss=4.753, over 3531.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:35:05,725 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 18:35:13,673 INFO [train.py:591] (5/6) 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 18:35:13,674 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 18:37:33,178 INFO [train.py:527] (5/6) Epoch 415, batch 114, global_batch_idx: 51450, batch size: 68, loss[discriminator_loss=2.718, discriminator_real_loss=1.378, discriminator_fake_loss=1.341, generator_loss=27.12, generator_mel_loss=17.62, generator_kl_loss=1.308, generator_dur_loss=1.833, generator_adv_loss=1.827, generator_feat_match_loss=4.536, over 68.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.373, discriminator_fake_loss=1.336, generator_loss=27.95, generator_mel_loss=18.09, generator_kl_loss=1.415, generator_dur_loss=1.764, generator_adv_loss=1.944, generator_feat_match_loss=4.733, over 6593.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:38:00,412 INFO [train.py:919] (5/6) Start epoch 416 +2024-03-13 18:40:16,423 INFO [train.py:527] (5/6) Epoch 416, batch 40, global_batch_idx: 51500, batch size: 88, loss[discriminator_loss=2.758, discriminator_real_loss=1.28, discriminator_fake_loss=1.478, generator_loss=28.44, generator_mel_loss=18.2, generator_kl_loss=1.362, generator_dur_loss=1.845, generator_adv_loss=2.22, generator_feat_match_loss=4.811, over 88.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.371, discriminator_fake_loss=1.341, generator_loss=28.11, generator_mel_loss=18.16, generator_kl_loss=1.427, generator_dur_loss=1.762, generator_adv_loss=1.954, generator_feat_match_loss=4.805, over 2354.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:42:34,117 INFO [train.py:527] (5/6) Epoch 416, batch 90, global_batch_idx: 51550, batch size: 50, loss[discriminator_loss=2.617, discriminator_real_loss=1.271, discriminator_fake_loss=1.345, generator_loss=28.48, generator_mel_loss=18.11, generator_kl_loss=1.493, generator_dur_loss=1.654, generator_adv_loss=1.983, generator_feat_match_loss=5.24, over 50.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.377, discriminator_fake_loss=1.336, generator_loss=27.95, generator_mel_loss=18.1, generator_kl_loss=1.405, generator_dur_loss=1.767, generator_adv_loss=1.945, generator_feat_match_loss=4.725, over 5425.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:44:03,686 INFO [train.py:919] (5/6) Start epoch 417 +2024-03-13 18:45:10,649 INFO [train.py:527] (5/6) Epoch 417, batch 16, global_batch_idx: 51600, batch size: 25, loss[discriminator_loss=2.721, discriminator_real_loss=1.41, discriminator_fake_loss=1.311, generator_loss=28.13, generator_mel_loss=18.08, generator_kl_loss=1.632, generator_dur_loss=1.606, generator_adv_loss=1.952, generator_feat_match_loss=4.857, over 25.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.384, discriminator_fake_loss=1.353, generator_loss=27.99, generator_mel_loss=18.22, generator_kl_loss=1.392, generator_dur_loss=1.743, generator_adv_loss=1.902, generator_feat_match_loss=4.732, over 902.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:45:10,651 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 18:45:18,452 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 18:47:36,679 INFO [train.py:527] (5/6) Epoch 417, batch 66, global_batch_idx: 51650, batch size: 39, loss[discriminator_loss=2.721, discriminator_real_loss=1.295, discriminator_fake_loss=1.426, generator_loss=27.58, generator_mel_loss=17.89, generator_kl_loss=1.398, generator_dur_loss=1.668, generator_adv_loss=1.903, generator_feat_match_loss=4.72, over 39.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.377, discriminator_fake_loss=1.335, generator_loss=27.94, generator_mel_loss=18.07, generator_kl_loss=1.421, generator_dur_loss=1.75, generator_adv_loss=1.944, generator_feat_match_loss=4.755, over 3717.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:49:56,184 INFO [train.py:527] (5/6) Epoch 417, batch 116, global_batch_idx: 51700, batch size: 62, loss[discriminator_loss=2.754, discriminator_real_loss=1.398, discriminator_fake_loss=1.356, generator_loss=28.07, generator_mel_loss=18.15, generator_kl_loss=1.422, generator_dur_loss=1.75, generator_adv_loss=1.964, generator_feat_match_loss=4.782, over 62.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.373, discriminator_fake_loss=1.336, generator_loss=27.9, generator_mel_loss=18.05, generator_kl_loss=1.414, generator_dur_loss=1.759, generator_adv_loss=1.945, generator_feat_match_loss=4.739, over 6635.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:50:18,873 INFO [train.py:919] (5/6) Start epoch 418 +2024-03-13 18:52:38,470 INFO [train.py:527] (5/6) Epoch 418, batch 42, global_batch_idx: 51750, batch size: 80, loss[discriminator_loss=2.664, discriminator_real_loss=1.353, discriminator_fake_loss=1.311, generator_loss=27.59, generator_mel_loss=17.81, generator_kl_loss=1.358, generator_dur_loss=1.787, generator_adv_loss=2.007, generator_feat_match_loss=4.624, over 80.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.34, generator_loss=28.03, generator_mel_loss=18.12, generator_kl_loss=1.426, generator_dur_loss=1.772, generator_adv_loss=1.955, generator_feat_match_loss=4.758, over 2530.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:54:54,695 INFO [train.py:527] (5/6) Epoch 418, batch 92, global_batch_idx: 51800, batch size: 31, loss[discriminator_loss=2.699, discriminator_real_loss=1.34, discriminator_fake_loss=1.359, generator_loss=28.53, generator_mel_loss=18.2, generator_kl_loss=1.663, generator_dur_loss=1.651, generator_adv_loss=2.155, generator_feat_match_loss=4.857, over 31.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=28.03, generator_mel_loss=18.14, generator_kl_loss=1.408, generator_dur_loss=1.762, generator_adv_loss=1.96, generator_feat_match_loss=4.753, over 5392.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 18:54:54,696 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 18:55:03,511 INFO [train.py:591] (5/6) 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 18:55:03,512 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 18:56:31,662 INFO [train.py:919] (5/6) Start epoch 419 +2024-03-13 18:57:48,378 INFO [train.py:527] (5/6) Epoch 419, batch 18, global_batch_idx: 51850, batch size: 55, loss[discriminator_loss=2.717, discriminator_real_loss=1.402, discriminator_fake_loss=1.315, generator_loss=27.98, generator_mel_loss=18.4, generator_kl_loss=1.406, generator_dur_loss=1.753, generator_adv_loss=1.906, generator_feat_match_loss=4.511, over 55.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.385, discriminator_fake_loss=1.353, generator_loss=27.85, generator_mel_loss=18.15, generator_kl_loss=1.417, generator_dur_loss=1.757, generator_adv_loss=1.942, generator_feat_match_loss=4.585, over 1007.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:00:10,322 INFO [train.py:527] (5/6) Epoch 419, batch 68, global_batch_idx: 51900, batch size: 95, loss[discriminator_loss=2.725, discriminator_real_loss=1.399, discriminator_fake_loss=1.326, generator_loss=27.34, generator_mel_loss=17.6, generator_kl_loss=1.302, generator_dur_loss=1.843, generator_adv_loss=2.065, generator_feat_match_loss=4.529, over 95.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.38, discriminator_fake_loss=1.347, generator_loss=27.94, generator_mel_loss=18.06, generator_kl_loss=1.405, generator_dur_loss=1.771, generator_adv_loss=1.948, generator_feat_match_loss=4.757, over 4083.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:02:22,549 INFO [train.py:527] (5/6) Epoch 419, batch 118, global_batch_idx: 51950, batch size: 14, loss[discriminator_loss=2.694, discriminator_real_loss=1.322, discriminator_fake_loss=1.372, generator_loss=29.25, generator_mel_loss=18.94, generator_kl_loss=1.66, generator_dur_loss=1.651, generator_adv_loss=1.982, generator_feat_match_loss=5.011, over 14.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.38, discriminator_fake_loss=1.342, generator_loss=27.95, generator_mel_loss=18.09, generator_kl_loss=1.42, generator_dur_loss=1.755, generator_adv_loss=1.95, generator_feat_match_loss=4.738, over 6576.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:02:38,326 INFO [train.py:919] (5/6) Start epoch 420 +2024-03-13 19:05:05,345 INFO [train.py:527] (5/6) Epoch 420, batch 44, global_batch_idx: 52000, batch size: 72, loss[discriminator_loss=2.681, discriminator_real_loss=1.331, discriminator_fake_loss=1.35, generator_loss=28.71, generator_mel_loss=18.16, generator_kl_loss=1.447, generator_dur_loss=1.791, generator_adv_loss=1.963, generator_feat_match_loss=5.347, over 72.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.37, discriminator_fake_loss=1.34, generator_loss=28.06, generator_mel_loss=18.14, generator_kl_loss=1.423, generator_dur_loss=1.749, generator_adv_loss=1.962, generator_feat_match_loss=4.789, over 2520.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:05:05,346 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 19:05:13,470 INFO [train.py:591] (5/6) 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 19:05:13,471 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 19:07:32,963 INFO [train.py:527] (5/6) Epoch 420, batch 94, global_batch_idx: 52050, batch size: 15, loss[discriminator_loss=2.676, discriminator_real_loss=1.397, discriminator_fake_loss=1.28, generator_loss=30.01, generator_mel_loss=18.82, generator_kl_loss=1.955, generator_dur_loss=1.567, generator_adv_loss=2.013, generator_feat_match_loss=5.657, over 15.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.369, discriminator_fake_loss=1.336, generator_loss=28, generator_mel_loss=18.08, generator_kl_loss=1.425, generator_dur_loss=1.751, generator_adv_loss=1.956, generator_feat_match_loss=4.794, over 5382.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:08:53,888 INFO [train.py:919] (5/6) Start epoch 421 +2024-03-13 19:10:15,602 INFO [train.py:527] (5/6) Epoch 421, batch 20, global_batch_idx: 52100, batch size: 52, loss[discriminator_loss=2.722, discriminator_real_loss=1.349, discriminator_fake_loss=1.372, generator_loss=27.95, generator_mel_loss=17.91, generator_kl_loss=1.507, generator_dur_loss=1.686, generator_adv_loss=2.046, generator_feat_match_loss=4.802, over 52.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.369, discriminator_fake_loss=1.337, generator_loss=27.88, generator_mel_loss=18.01, generator_kl_loss=1.371, generator_dur_loss=1.785, generator_adv_loss=1.949, generator_feat_match_loss=4.769, over 1343.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:12:34,693 INFO [train.py:527] (5/6) Epoch 421, batch 70, global_batch_idx: 52150, batch size: 88, loss[discriminator_loss=2.713, discriminator_real_loss=1.392, discriminator_fake_loss=1.321, generator_loss=27.48, generator_mel_loss=17.69, generator_kl_loss=1.3, generator_dur_loss=1.82, generator_adv_loss=1.876, generator_feat_match_loss=4.792, over 88.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.374, discriminator_fake_loss=1.338, generator_loss=27.85, generator_mel_loss=18.02, generator_kl_loss=1.372, generator_dur_loss=1.786, generator_adv_loss=1.95, generator_feat_match_loss=4.719, over 4412.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:14:48,367 INFO [train.py:527] (5/6) Epoch 421, batch 120, global_batch_idx: 52200, batch size: 70, loss[discriminator_loss=2.731, discriminator_real_loss=1.334, discriminator_fake_loss=1.397, generator_loss=28.08, generator_mel_loss=18.22, generator_kl_loss=1.331, generator_dur_loss=1.832, generator_adv_loss=2.11, generator_feat_match_loss=4.583, over 70.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=27.92, generator_mel_loss=18.07, generator_kl_loss=1.391, generator_dur_loss=1.776, generator_adv_loss=1.95, generator_feat_match_loss=4.728, over 7113.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:14:48,369 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 19:14:57,112 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 19:15:06,506 INFO [train.py:919] (5/6) Start epoch 422 +2024-03-13 19:17:39,943 INFO [train.py:527] (5/6) Epoch 422, batch 46, global_batch_idx: 52250, batch size: 55, loss[discriminator_loss=2.696, discriminator_real_loss=1.356, discriminator_fake_loss=1.34, generator_loss=28.11, generator_mel_loss=18.19, generator_kl_loss=1.544, generator_dur_loss=1.758, generator_adv_loss=1.894, generator_feat_match_loss=4.717, over 55.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.366, discriminator_fake_loss=1.353, generator_loss=28.05, generator_mel_loss=18.15, generator_kl_loss=1.437, generator_dur_loss=1.742, generator_adv_loss=1.947, generator_feat_match_loss=4.773, over 2574.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:19:59,208 INFO [train.py:527] (5/6) Epoch 422, batch 96, global_batch_idx: 52300, batch size: 39, loss[discriminator_loss=2.695, discriminator_real_loss=1.305, discriminator_fake_loss=1.39, generator_loss=28.11, generator_mel_loss=18.32, generator_kl_loss=1.461, generator_dur_loss=1.732, generator_adv_loss=1.991, generator_feat_match_loss=4.607, over 39.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.369, discriminator_fake_loss=1.346, generator_loss=28.02, generator_mel_loss=18.11, generator_kl_loss=1.438, generator_dur_loss=1.749, generator_adv_loss=1.947, generator_feat_match_loss=4.775, over 5271.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:21:13,109 INFO [train.py:919] (5/6) Start epoch 423 +2024-03-13 19:22:39,212 INFO [train.py:527] (5/6) Epoch 423, batch 22, global_batch_idx: 52350, batch size: 96, loss[discriminator_loss=2.707, discriminator_real_loss=1.398, discriminator_fake_loss=1.309, generator_loss=27.39, generator_mel_loss=17.55, generator_kl_loss=1.343, generator_dur_loss=1.861, generator_adv_loss=1.849, generator_feat_match_loss=4.784, over 96.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.359, discriminator_fake_loss=1.345, generator_loss=28.07, generator_mel_loss=18.08, generator_kl_loss=1.411, generator_dur_loss=1.756, generator_adv_loss=1.952, generator_feat_match_loss=4.876, over 1372.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:24:57,182 INFO [train.py:527] (5/6) Epoch 423, batch 72, global_batch_idx: 52400, batch size: 96, loss[discriminator_loss=2.782, discriminator_real_loss=1.285, discriminator_fake_loss=1.497, generator_loss=27.97, generator_mel_loss=17.94, generator_kl_loss=1.341, generator_dur_loss=1.822, generator_adv_loss=2.287, generator_feat_match_loss=4.583, over 96.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.338, generator_loss=27.97, generator_mel_loss=18.05, generator_kl_loss=1.408, generator_dur_loss=1.756, generator_adv_loss=1.97, generator_feat_match_loss=4.789, over 4156.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:24:57,183 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 19:25:05,341 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 19:27:23,341 INFO [train.py:527] (5/6) Epoch 423, batch 122, global_batch_idx: 52450, batch size: 15, loss[discriminator_loss=2.569, discriminator_real_loss=1.289, discriminator_fake_loss=1.279, generator_loss=29.99, generator_mel_loss=18.92, generator_kl_loss=1.668, generator_dur_loss=1.66, generator_adv_loss=1.971, generator_feat_match_loss=5.764, over 15.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.365, discriminator_fake_loss=1.338, generator_loss=28.02, generator_mel_loss=18.05, generator_kl_loss=1.393, generator_dur_loss=1.761, generator_adv_loss=1.967, generator_feat_match_loss=4.85, over 7204.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:27:27,919 INFO [train.py:919] (5/6) Start epoch 424 +2024-03-13 19:30:06,234 INFO [train.py:527] (5/6) Epoch 424, batch 48, global_batch_idx: 52500, batch size: 50, loss[discriminator_loss=2.761, discriminator_real_loss=1.37, discriminator_fake_loss=1.39, generator_loss=26.98, generator_mel_loss=17.31, generator_kl_loss=1.268, generator_dur_loss=1.714, generator_adv_loss=1.989, generator_feat_match_loss=4.698, over 50.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.367, discriminator_fake_loss=1.347, generator_loss=27.94, generator_mel_loss=18.04, generator_kl_loss=1.397, generator_dur_loss=1.748, generator_adv_loss=1.948, generator_feat_match_loss=4.804, over 2854.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:32:24,351 INFO [train.py:527] (5/6) Epoch 424, batch 98, global_batch_idx: 52550, batch size: 74, loss[discriminator_loss=2.809, discriminator_real_loss=1.388, discriminator_fake_loss=1.421, generator_loss=27.44, generator_mel_loss=18.01, generator_kl_loss=1.168, generator_dur_loss=1.872, generator_adv_loss=1.927, generator_feat_match_loss=4.461, over 74.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.371, discriminator_fake_loss=1.347, generator_loss=27.99, generator_mel_loss=18.1, generator_kl_loss=1.407, generator_dur_loss=1.743, generator_adv_loss=1.946, generator_feat_match_loss=4.789, over 5679.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:33:32,970 INFO [train.py:919] (5/6) Start epoch 425 +2024-03-13 19:35:02,193 INFO [train.py:527] (5/6) Epoch 425, batch 24, global_batch_idx: 52600, batch size: 83, loss[discriminator_loss=2.694, discriminator_real_loss=1.329, discriminator_fake_loss=1.365, generator_loss=27.66, generator_mel_loss=17.68, generator_kl_loss=1.361, generator_dur_loss=1.794, generator_adv_loss=2.003, generator_feat_match_loss=4.821, over 83.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.359, discriminator_fake_loss=1.348, generator_loss=27.98, generator_mel_loss=18.11, generator_kl_loss=1.414, generator_dur_loss=1.765, generator_adv_loss=1.94, generator_feat_match_loss=4.749, over 1408.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:35:02,195 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 19:35:10,065 INFO [train.py:591] (5/6) 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 19:35:10,066 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 19:37:30,492 INFO [train.py:527] (5/6) Epoch 425, batch 74, global_batch_idx: 52650, batch size: 77, loss[discriminator_loss=2.672, discriminator_real_loss=1.439, discriminator_fake_loss=1.233, generator_loss=28.62, generator_mel_loss=18.06, generator_kl_loss=1.387, generator_dur_loss=1.795, generator_adv_loss=2.193, generator_feat_match_loss=5.18, over 77.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.369, discriminator_fake_loss=1.347, generator_loss=27.94, generator_mel_loss=18.05, generator_kl_loss=1.411, generator_dur_loss=1.773, generator_adv_loss=1.942, generator_feat_match_loss=4.771, over 4570.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:39:44,966 INFO [train.py:919] (5/6) Start epoch 426 +2024-03-13 19:40:10,300 INFO [train.py:527] (5/6) Epoch 426, batch 0, global_batch_idx: 52700, batch size: 64, loss[discriminator_loss=2.743, discriminator_real_loss=1.386, discriminator_fake_loss=1.357, generator_loss=28.1, generator_mel_loss=18.04, generator_kl_loss=1.386, generator_dur_loss=1.786, generator_adv_loss=1.839, generator_feat_match_loss=5.056, over 64.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.386, discriminator_fake_loss=1.357, generator_loss=28.1, generator_mel_loss=18.04, generator_kl_loss=1.386, generator_dur_loss=1.786, generator_adv_loss=1.839, generator_feat_match_loss=5.056, over 64.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:42:32,707 INFO [train.py:527] (5/6) Epoch 426, batch 50, global_batch_idx: 52750, batch size: 66, loss[discriminator_loss=2.746, discriminator_real_loss=1.467, discriminator_fake_loss=1.279, generator_loss=27.61, generator_mel_loss=17.7, generator_kl_loss=1.214, generator_dur_loss=1.781, generator_adv_loss=1.937, generator_feat_match_loss=4.978, over 66.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.38, discriminator_fake_loss=1.344, generator_loss=27.96, generator_mel_loss=18.1, generator_kl_loss=1.396, generator_dur_loss=1.765, generator_adv_loss=1.937, generator_feat_match_loss=4.764, over 2968.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:44:48,299 INFO [train.py:527] (5/6) Epoch 426, batch 100, global_batch_idx: 52800, batch size: 31, loss[discriminator_loss=2.753, discriminator_real_loss=1.333, discriminator_fake_loss=1.42, generator_loss=29.13, generator_mel_loss=18.37, generator_kl_loss=1.613, generator_dur_loss=1.667, generator_adv_loss=2.095, generator_feat_match_loss=5.38, over 31.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=27.95, generator_mel_loss=18.1, generator_kl_loss=1.392, generator_dur_loss=1.769, generator_adv_loss=1.94, generator_feat_match_loss=4.755, over 6002.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:44:48,301 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 19:44:57,102 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 19:46:03,133 INFO [train.py:919] (5/6) Start epoch 427 +2024-03-13 19:47:37,228 INFO [train.py:527] (5/6) Epoch 427, batch 26, global_batch_idx: 52850, batch size: 52, loss[discriminator_loss=2.7, discriminator_real_loss=1.332, discriminator_fake_loss=1.367, generator_loss=28.15, generator_mel_loss=18.41, generator_kl_loss=1.628, generator_dur_loss=1.676, generator_adv_loss=1.879, generator_feat_match_loss=4.56, over 52.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.366, discriminator_fake_loss=1.35, generator_loss=28.11, generator_mel_loss=18.18, generator_kl_loss=1.433, generator_dur_loss=1.753, generator_adv_loss=1.939, generator_feat_match_loss=4.805, over 1597.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:49:57,010 INFO [train.py:527] (5/6) Epoch 427, batch 76, global_batch_idx: 52900, batch size: 31, loss[discriminator_loss=2.657, discriminator_real_loss=1.323, discriminator_fake_loss=1.334, generator_loss=28.79, generator_mel_loss=18.52, generator_kl_loss=1.525, generator_dur_loss=1.56, generator_adv_loss=2.081, generator_feat_match_loss=5.099, over 31.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.364, discriminator_fake_loss=1.345, generator_loss=28.03, generator_mel_loss=18.09, generator_kl_loss=1.418, generator_dur_loss=1.748, generator_adv_loss=1.965, generator_feat_match_loss=4.809, over 4463.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:52:09,703 INFO [train.py:919] (5/6) Start epoch 428 +2024-03-13 19:52:39,502 INFO [train.py:527] (5/6) Epoch 428, batch 2, global_batch_idx: 52950, batch size: 31, loss[discriminator_loss=2.729, discriminator_real_loss=1.311, discriminator_fake_loss=1.418, generator_loss=28.02, generator_mel_loss=17.97, generator_kl_loss=1.583, generator_dur_loss=1.635, generator_adv_loss=2.062, generator_feat_match_loss=4.771, over 31.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.429, discriminator_fake_loss=1.326, generator_loss=27.79, generator_mel_loss=18.01, generator_kl_loss=1.466, generator_dur_loss=1.704, generator_adv_loss=1.89, generator_feat_match_loss=4.723, over 140.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:54:59,000 INFO [train.py:527] (5/6) Epoch 428, batch 52, global_batch_idx: 53000, batch size: 74, loss[discriminator_loss=2.726, discriminator_real_loss=1.28, discriminator_fake_loss=1.446, generator_loss=28.36, generator_mel_loss=18.09, generator_kl_loss=1.295, generator_dur_loss=1.809, generator_adv_loss=2.067, generator_feat_match_loss=5.098, over 74.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.96, generator_mel_loss=18.08, generator_kl_loss=1.425, generator_dur_loss=1.756, generator_adv_loss=1.935, generator_feat_match_loss=4.769, over 3086.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:54:59,002 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 19:55:07,117 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 19:57:24,476 INFO [train.py:527] (5/6) Epoch 428, batch 102, global_batch_idx: 53050, batch size: 39, loss[discriminator_loss=2.663, discriminator_real_loss=1.28, discriminator_fake_loss=1.383, generator_loss=27.84, generator_mel_loss=17.95, generator_kl_loss=1.591, generator_dur_loss=1.643, generator_adv_loss=1.952, generator_feat_match_loss=4.708, over 39.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=27.98, generator_mel_loss=18.08, generator_kl_loss=1.43, generator_dur_loss=1.745, generator_adv_loss=1.936, generator_feat_match_loss=4.789, over 5787.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 19:58:22,647 INFO [train.py:919] (5/6) Start epoch 429 +2024-03-13 20:00:04,653 INFO [train.py:527] (5/6) Epoch 429, batch 28, global_batch_idx: 53100, batch size: 31, loss[discriminator_loss=2.73, discriminator_real_loss=1.467, discriminator_fake_loss=1.262, generator_loss=28.14, generator_mel_loss=18.33, generator_kl_loss=1.599, generator_dur_loss=1.62, generator_adv_loss=2.09, generator_feat_match_loss=4.505, over 31.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.373, discriminator_fake_loss=1.333, generator_loss=27.96, generator_mel_loss=18.01, generator_kl_loss=1.417, generator_dur_loss=1.763, generator_adv_loss=1.951, generator_feat_match_loss=4.814, over 1649.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:02:26,052 INFO [train.py:527] (5/6) Epoch 429, batch 78, global_batch_idx: 53150, batch size: 56, loss[discriminator_loss=2.64, discriminator_real_loss=1.326, discriminator_fake_loss=1.314, generator_loss=28.88, generator_mel_loss=18.6, generator_kl_loss=1.372, generator_dur_loss=1.697, generator_adv_loss=1.96, generator_feat_match_loss=5.245, over 56.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.375, discriminator_fake_loss=1.337, generator_loss=27.91, generator_mel_loss=18.03, generator_kl_loss=1.399, generator_dur_loss=1.77, generator_adv_loss=1.943, generator_feat_match_loss=4.776, over 4727.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:04:26,669 INFO [train.py:919] (5/6) Start epoch 430 +2024-03-13 20:05:00,160 INFO [train.py:527] (5/6) Epoch 430, batch 4, global_batch_idx: 53200, batch size: 52, loss[discriminator_loss=2.72, discriminator_real_loss=1.369, discriminator_fake_loss=1.351, generator_loss=28.26, generator_mel_loss=18.23, generator_kl_loss=1.509, generator_dur_loss=1.698, generator_adv_loss=1.885, generator_feat_match_loss=4.938, over 52.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=28.38, generator_mel_loss=18.16, generator_kl_loss=1.522, generator_dur_loss=1.712, generator_adv_loss=1.971, generator_feat_match_loss=5.018, over 231.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:05:00,162 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 20:05:08,014 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 20:07:29,647 INFO [train.py:527] (5/6) Epoch 430, batch 54, global_batch_idx: 53250, batch size: 77, loss[discriminator_loss=2.711, discriminator_real_loss=1.321, discriminator_fake_loss=1.39, generator_loss=27.4, generator_mel_loss=17.92, generator_kl_loss=1.25, generator_dur_loss=1.836, generator_adv_loss=1.924, generator_feat_match_loss=4.467, over 77.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.381, discriminator_fake_loss=1.338, generator_loss=27.94, generator_mel_loss=18.07, generator_kl_loss=1.412, generator_dur_loss=1.75, generator_adv_loss=1.949, generator_feat_match_loss=4.76, over 3219.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:09:48,903 INFO [train.py:527] (5/6) Epoch 430, batch 104, global_batch_idx: 53300, batch size: 47, loss[discriminator_loss=2.629, discriminator_real_loss=1.386, discriminator_fake_loss=1.243, generator_loss=28.09, generator_mel_loss=17.98, generator_kl_loss=1.551, generator_dur_loss=1.693, generator_adv_loss=1.996, generator_feat_match_loss=4.873, over 47.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.07, generator_mel_loss=18.09, generator_kl_loss=1.423, generator_dur_loss=1.759, generator_adv_loss=1.958, generator_feat_match_loss=4.843, over 6175.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:10:42,179 INFO [train.py:919] (5/6) Start epoch 431 +2024-03-13 20:12:27,701 INFO [train.py:527] (5/6) Epoch 431, batch 30, global_batch_idx: 53350, batch size: 70, loss[discriminator_loss=2.743, discriminator_real_loss=1.361, discriminator_fake_loss=1.382, generator_loss=27.28, generator_mel_loss=17.82, generator_kl_loss=1.32, generator_dur_loss=1.867, generator_adv_loss=1.836, generator_feat_match_loss=4.434, over 70.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.378, discriminator_fake_loss=1.342, generator_loss=28.03, generator_mel_loss=18.13, generator_kl_loss=1.443, generator_dur_loss=1.736, generator_adv_loss=1.957, generator_feat_match_loss=4.765, over 1647.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:14:43,606 INFO [train.py:527] (5/6) Epoch 431, batch 80, global_batch_idx: 53400, batch size: 25, loss[discriminator_loss=2.607, discriminator_real_loss=1.254, discriminator_fake_loss=1.353, generator_loss=29, generator_mel_loss=18.48, generator_kl_loss=1.662, generator_dur_loss=1.589, generator_adv_loss=2.017, generator_feat_match_loss=5.246, over 25.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.378, discriminator_fake_loss=1.334, generator_loss=28.07, generator_mel_loss=18.12, generator_kl_loss=1.427, generator_dur_loss=1.742, generator_adv_loss=1.964, generator_feat_match_loss=4.824, over 4385.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:14:43,608 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 20:14:51,579 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 20:16:53,647 INFO [train.py:919] (5/6) Start epoch 432 +2024-03-13 20:17:34,696 INFO [train.py:527] (5/6) Epoch 432, batch 6, global_batch_idx: 53450, batch size: 88, loss[discriminator_loss=2.754, discriminator_real_loss=1.384, discriminator_fake_loss=1.37, generator_loss=27.47, generator_mel_loss=17.9, generator_kl_loss=1.451, generator_dur_loss=1.804, generator_adv_loss=1.904, generator_feat_match_loss=4.411, over 88.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.359, discriminator_fake_loss=1.366, generator_loss=27.87, generator_mel_loss=18.07, generator_kl_loss=1.431, generator_dur_loss=1.753, generator_adv_loss=1.94, generator_feat_match_loss=4.675, over 390.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:19:56,408 INFO [train.py:527] (5/6) Epoch 432, batch 56, global_batch_idx: 53500, batch size: 88, loss[discriminator_loss=2.707, discriminator_real_loss=1.498, discriminator_fake_loss=1.209, generator_loss=27.46, generator_mel_loss=17.66, generator_kl_loss=1.207, generator_dur_loss=1.809, generator_adv_loss=2.021, generator_feat_match_loss=4.767, over 88.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.374, discriminator_fake_loss=1.344, generator_loss=27.98, generator_mel_loss=18.04, generator_kl_loss=1.409, generator_dur_loss=1.773, generator_adv_loss=1.951, generator_feat_match_loss=4.802, over 3404.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:22:15,143 INFO [train.py:527] (5/6) Epoch 432, batch 106, global_batch_idx: 53550, batch size: 53, loss[discriminator_loss=2.812, discriminator_real_loss=1.546, discriminator_fake_loss=1.266, generator_loss=28.29, generator_mel_loss=18.06, generator_kl_loss=1.45, generator_dur_loss=1.709, generator_adv_loss=1.864, generator_feat_match_loss=5.2, over 53.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=28.02, generator_mel_loss=18.04, generator_kl_loss=1.405, generator_dur_loss=1.77, generator_adv_loss=1.959, generator_feat_match_loss=4.85, over 6247.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, +2024-03-13 20:23:01,424 INFO [train.py:919] (5/6) Start epoch 433 +2024-03-13 20:24:55,091 INFO [train.py:527] (5/6) Epoch 433, batch 32, global_batch_idx: 53600, batch size: 74, loss[discriminator_loss=2.733, discriminator_real_loss=1.403, discriminator_fake_loss=1.33, generator_loss=28.89, generator_mel_loss=18.19, generator_kl_loss=1.512, generator_dur_loss=1.806, generator_adv_loss=2.029, generator_feat_match_loss=5.352, over 74.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.375, discriminator_fake_loss=1.333, generator_loss=28.28, generator_mel_loss=18.2, generator_kl_loss=1.455, generator_dur_loss=1.747, generator_adv_loss=1.956, generator_feat_match_loss=4.924, over 1710.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:24:55,093 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 20:25:02,938 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 20:27:20,284 INFO [train.py:527] (5/6) Epoch 433, batch 82, global_batch_idx: 53650, batch size: 59, loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=27.68, generator_mel_loss=18.05, generator_kl_loss=1.339, generator_dur_loss=1.82, generator_adv_loss=2.059, generator_feat_match_loss=4.404, over 59.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.338, generator_loss=28.13, generator_mel_loss=18.12, generator_kl_loss=1.447, generator_dur_loss=1.753, generator_adv_loss=1.957, generator_feat_match_loss=4.848, over 4601.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:29:12,937 INFO [train.py:919] (5/6) Start epoch 434 +2024-03-13 20:29:56,696 INFO [train.py:527] (5/6) Epoch 434, batch 8, global_batch_idx: 53700, batch size: 66, loss[discriminator_loss=2.745, discriminator_real_loss=1.443, discriminator_fake_loss=1.303, generator_loss=27.39, generator_mel_loss=17.71, generator_kl_loss=1.406, generator_dur_loss=1.779, generator_adv_loss=1.834, generator_feat_match_loss=4.658, over 66.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.378, discriminator_fake_loss=1.338, generator_loss=28.38, generator_mel_loss=18.27, generator_kl_loss=1.408, generator_dur_loss=1.734, generator_adv_loss=1.946, generator_feat_match_loss=5.021, over 549.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:32:14,614 INFO [train.py:527] (5/6) Epoch 434, batch 58, global_batch_idx: 53750, batch size: 25, loss[discriminator_loss=2.698, discriminator_real_loss=1.351, discriminator_fake_loss=1.347, generator_loss=29.12, generator_mel_loss=19.19, generator_kl_loss=1.667, generator_dur_loss=1.589, generator_adv_loss=1.952, generator_feat_match_loss=4.721, over 25.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.372, discriminator_fake_loss=1.346, generator_loss=28.08, generator_mel_loss=18.13, generator_kl_loss=1.389, generator_dur_loss=1.763, generator_adv_loss=1.939, generator_feat_match_loss=4.858, over 3525.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:34:33,480 INFO [train.py:527] (5/6) Epoch 434, batch 108, global_batch_idx: 53800, batch size: 72, loss[discriminator_loss=2.769, discriminator_real_loss=1.452, discriminator_fake_loss=1.317, generator_loss=26.81, generator_mel_loss=17.71, generator_kl_loss=1.236, generator_dur_loss=1.793, generator_adv_loss=1.868, generator_feat_match_loss=4.209, over 72.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.367, discriminator_fake_loss=1.35, generator_loss=28.04, generator_mel_loss=18.08, generator_kl_loss=1.387, generator_dur_loss=1.763, generator_adv_loss=1.946, generator_feat_match_loss=4.859, over 6701.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:34:33,481 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 20:34:42,396 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 20:35:25,005 INFO [train.py:919] (5/6) Start epoch 435 +2024-03-13 20:37:25,025 INFO [train.py:527] (5/6) Epoch 435, batch 34, global_batch_idx: 53850, batch size: 47, loss[discriminator_loss=2.783, discriminator_real_loss=1.434, discriminator_fake_loss=1.349, generator_loss=27.75, generator_mel_loss=18.3, generator_kl_loss=1.558, generator_dur_loss=1.75, generator_adv_loss=1.851, generator_feat_match_loss=4.291, over 47.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.369, discriminator_fake_loss=1.355, generator_loss=27.78, generator_mel_loss=17.91, generator_kl_loss=1.391, generator_dur_loss=1.799, generator_adv_loss=1.938, generator_feat_match_loss=4.739, over 2356.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:39:41,116 INFO [train.py:527] (5/6) Epoch 435, batch 84, global_batch_idx: 53900, batch size: 42, loss[discriminator_loss=2.664, discriminator_real_loss=1.296, discriminator_fake_loss=1.368, generator_loss=29.56, generator_mel_loss=18.43, generator_kl_loss=1.719, generator_dur_loss=1.676, generator_adv_loss=1.936, generator_feat_match_loss=5.799, over 42.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.372, discriminator_fake_loss=1.35, generator_loss=28.01, generator_mel_loss=18.05, generator_kl_loss=1.424, generator_dur_loss=1.774, generator_adv_loss=1.944, generator_feat_match_loss=4.814, over 5029.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:41:31,983 INFO [train.py:919] (5/6) Start epoch 436 +2024-03-13 20:42:23,690 INFO [train.py:527] (5/6) Epoch 436, batch 10, global_batch_idx: 53950, batch size: 66, loss[discriminator_loss=2.71, discriminator_real_loss=1.382, discriminator_fake_loss=1.328, generator_loss=27.5, generator_mel_loss=17.74, generator_kl_loss=1.284, generator_dur_loss=1.767, generator_adv_loss=1.893, generator_feat_match_loss=4.815, over 66.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.388, discriminator_fake_loss=1.33, generator_loss=27.81, generator_mel_loss=17.93, generator_kl_loss=1.38, generator_dur_loss=1.769, generator_adv_loss=1.953, generator_feat_match_loss=4.771, over 675.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:44:40,875 INFO [train.py:527] (5/6) Epoch 436, batch 60, global_batch_idx: 54000, batch size: 61, loss[discriminator_loss=2.747, discriminator_real_loss=1.387, discriminator_fake_loss=1.359, generator_loss=27.65, generator_mel_loss=18.19, generator_kl_loss=1.517, generator_dur_loss=1.741, generator_adv_loss=1.982, generator_feat_match_loss=4.222, over 61.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.338, generator_loss=28, generator_mel_loss=18.07, generator_kl_loss=1.422, generator_dur_loss=1.76, generator_adv_loss=1.945, generator_feat_match_loss=4.803, over 3657.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:44:40,878 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 20:44:49,015 INFO [train.py:591] (5/6) 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 20:44:49,017 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 20:47:08,852 INFO [train.py:527] (5/6) Epoch 436, batch 110, global_batch_idx: 54050, batch size: 58, loss[discriminator_loss=2.749, discriminator_real_loss=1.325, discriminator_fake_loss=1.425, generator_loss=28.14, generator_mel_loss=18.29, generator_kl_loss=1.423, generator_dur_loss=1.776, generator_adv_loss=1.86, generator_feat_match_loss=4.793, over 58.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=27.95, generator_mel_loss=18.04, generator_kl_loss=1.418, generator_dur_loss=1.759, generator_adv_loss=1.946, generator_feat_match_loss=4.783, over 6654.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:47:45,388 INFO [train.py:919] (5/6) Start epoch 437 +2024-03-13 20:49:50,275 INFO [train.py:527] (5/6) Epoch 437, batch 36, global_batch_idx: 54100, batch size: 58, loss[discriminator_loss=2.775, discriminator_real_loss=1.413, discriminator_fake_loss=1.362, generator_loss=28.26, generator_mel_loss=18.36, generator_kl_loss=1.483, generator_dur_loss=1.736, generator_adv_loss=1.918, generator_feat_match_loss=4.759, over 58.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.375, discriminator_fake_loss=1.336, generator_loss=28.09, generator_mel_loss=18.02, generator_kl_loss=1.446, generator_dur_loss=1.748, generator_adv_loss=1.964, generator_feat_match_loss=4.908, over 2022.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:52:06,294 INFO [train.py:527] (5/6) Epoch 437, batch 86, global_batch_idx: 54150, batch size: 70, loss[discriminator_loss=2.581, discriminator_real_loss=1.323, discriminator_fake_loss=1.257, generator_loss=29, generator_mel_loss=18.02, generator_kl_loss=1.357, generator_dur_loss=1.776, generator_adv_loss=2.235, generator_feat_match_loss=5.62, over 70.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.367, discriminator_fake_loss=1.339, generator_loss=28.16, generator_mel_loss=18.1, generator_kl_loss=1.443, generator_dur_loss=1.739, generator_adv_loss=1.971, generator_feat_match_loss=4.909, over 4718.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:53:51,151 INFO [train.py:919] (5/6) Start epoch 438 +2024-03-13 20:54:48,974 INFO [train.py:527] (5/6) Epoch 438, batch 12, global_batch_idx: 54200, batch size: 45, loss[discriminator_loss=2.703, discriminator_real_loss=1.354, discriminator_fake_loss=1.349, generator_loss=27.9, generator_mel_loss=18.22, generator_kl_loss=1.31, generator_dur_loss=1.676, generator_adv_loss=1.94, generator_feat_match_loss=4.749, over 45.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.356, discriminator_fake_loss=1.351, generator_loss=28.19, generator_mel_loss=18.11, generator_kl_loss=1.44, generator_dur_loss=1.739, generator_adv_loss=1.957, generator_feat_match_loss=4.94, over 639.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:54:48,976 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 20:54:56,886 INFO [train.py:591] (5/6) 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 20:54:56,887 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 20:57:16,535 INFO [train.py:527] (5/6) Epoch 438, batch 62, global_batch_idx: 54250, batch size: 70, loss[discriminator_loss=2.724, discriminator_real_loss=1.402, discriminator_fake_loss=1.322, generator_loss=27.37, generator_mel_loss=17.39, generator_kl_loss=1.451, generator_dur_loss=1.836, generator_adv_loss=2.013, generator_feat_match_loss=4.686, over 70.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.376, discriminator_fake_loss=1.347, generator_loss=27.86, generator_mel_loss=18.01, generator_kl_loss=1.395, generator_dur_loss=1.771, generator_adv_loss=1.941, generator_feat_match_loss=4.748, over 3631.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 20:59:32,974 INFO [train.py:527] (5/6) Epoch 438, batch 112, global_batch_idx: 54300, batch size: 15, loss[discriminator_loss=2.647, discriminator_real_loss=1.315, discriminator_fake_loss=1.331, generator_loss=30.06, generator_mel_loss=19.12, generator_kl_loss=1.696, generator_dur_loss=1.593, generator_adv_loss=2.171, generator_feat_match_loss=5.486, over 15.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.93, generator_mel_loss=18.04, generator_kl_loss=1.389, generator_dur_loss=1.771, generator_adv_loss=1.94, generator_feat_match_loss=4.793, over 6620.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:00:05,379 INFO [train.py:919] (5/6) Start epoch 439 +2024-03-13 21:02:12,241 INFO [train.py:527] (5/6) Epoch 439, batch 38, global_batch_idx: 54350, batch size: 42, loss[discriminator_loss=2.671, discriminator_real_loss=1.364, discriminator_fake_loss=1.307, generator_loss=26.99, generator_mel_loss=17.67, generator_kl_loss=1.403, generator_dur_loss=1.715, generator_adv_loss=2.026, generator_feat_match_loss=4.175, over 42.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.337, generator_loss=27.99, generator_mel_loss=18.02, generator_kl_loss=1.414, generator_dur_loss=1.773, generator_adv_loss=1.953, generator_feat_match_loss=4.83, over 2249.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:04:32,717 INFO [train.py:527] (5/6) Epoch 439, batch 88, global_batch_idx: 54400, batch size: 70, loss[discriminator_loss=2.761, discriminator_real_loss=1.406, discriminator_fake_loss=1.355, generator_loss=27.87, generator_mel_loss=17.84, generator_kl_loss=1.487, generator_dur_loss=1.801, generator_adv_loss=2.016, generator_feat_match_loss=4.724, over 70.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=27.92, generator_mel_loss=18.02, generator_kl_loss=1.421, generator_dur_loss=1.772, generator_adv_loss=1.942, generator_feat_match_loss=4.764, over 5135.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:04:32,718 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 21:04:41,976 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 21:06:18,619 INFO [train.py:919] (5/6) Start epoch 440 +2024-03-13 21:07:20,422 INFO [train.py:527] (5/6) Epoch 440, batch 14, global_batch_idx: 54450, batch size: 68, loss[discriminator_loss=2.7, discriminator_real_loss=1.296, discriminator_fake_loss=1.404, generator_loss=28.62, generator_mel_loss=18.12, generator_kl_loss=1.489, generator_dur_loss=1.747, generator_adv_loss=2.102, generator_feat_match_loss=5.158, over 68.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.36, discriminator_fake_loss=1.349, generator_loss=27.99, generator_mel_loss=18.05, generator_kl_loss=1.436, generator_dur_loss=1.76, generator_adv_loss=1.951, generator_feat_match_loss=4.795, over 881.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:09:37,422 INFO [train.py:527] (5/6) Epoch 440, batch 64, global_batch_idx: 54500, batch size: 64, loss[discriminator_loss=2.784, discriminator_real_loss=1.479, discriminator_fake_loss=1.305, generator_loss=27.17, generator_mel_loss=17.77, generator_kl_loss=1.33, generator_dur_loss=1.724, generator_adv_loss=1.877, generator_feat_match_loss=4.47, over 64.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=28.08, generator_mel_loss=18.13, generator_kl_loss=1.432, generator_dur_loss=1.73, generator_adv_loss=1.946, generator_feat_match_loss=4.844, over 3483.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:11:56,643 INFO [train.py:527] (5/6) Epoch 440, batch 114, global_batch_idx: 54550, batch size: 68, loss[discriminator_loss=2.727, discriminator_real_loss=1.362, discriminator_fake_loss=1.365, generator_loss=28.44, generator_mel_loss=17.98, generator_kl_loss=1.441, generator_dur_loss=1.77, generator_adv_loss=1.965, generator_feat_match_loss=5.287, over 68.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.373, discriminator_fake_loss=1.347, generator_loss=28.1, generator_mel_loss=18.11, generator_kl_loss=1.426, generator_dur_loss=1.737, generator_adv_loss=1.954, generator_feat_match_loss=4.867, over 6554.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:12:24,337 INFO [train.py:919] (5/6) Start epoch 441 +2024-03-13 21:14:37,116 INFO [train.py:527] (5/6) Epoch 441, batch 40, global_batch_idx: 54600, batch size: 77, loss[discriminator_loss=2.776, discriminator_real_loss=1.431, discriminator_fake_loss=1.345, generator_loss=27.91, generator_mel_loss=18.5, generator_kl_loss=1.313, generator_dur_loss=1.849, generator_adv_loss=1.779, generator_feat_match_loss=4.469, over 77.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.382, discriminator_fake_loss=1.337, generator_loss=27.94, generator_mel_loss=18.08, generator_kl_loss=1.385, generator_dur_loss=1.775, generator_adv_loss=1.945, generator_feat_match_loss=4.75, over 2468.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:14:37,118 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 21:14:45,257 INFO [train.py:591] (5/6) 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 21:14:45,258 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 21:17:05,717 INFO [train.py:527] (5/6) Epoch 441, batch 90, global_batch_idx: 54650, batch size: 96, loss[discriminator_loss=2.689, discriminator_real_loss=1.288, discriminator_fake_loss=1.402, generator_loss=27.67, generator_mel_loss=17.55, generator_kl_loss=1.232, generator_dur_loss=1.857, generator_adv_loss=2.045, generator_feat_match_loss=4.986, over 96.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.02, generator_mel_loss=18.09, generator_kl_loss=1.388, generator_dur_loss=1.762, generator_adv_loss=1.956, generator_feat_match_loss=4.83, over 5405.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:18:39,531 INFO [train.py:919] (5/6) Start epoch 442 +2024-03-13 21:19:47,031 INFO [train.py:527] (5/6) Epoch 442, batch 16, global_batch_idx: 54700, batch size: 50, loss[discriminator_loss=2.73, discriminator_real_loss=1.439, discriminator_fake_loss=1.291, generator_loss=27.63, generator_mel_loss=17.87, generator_kl_loss=1.418, generator_dur_loss=1.703, generator_adv_loss=1.93, generator_feat_match_loss=4.708, over 50.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.368, discriminator_fake_loss=1.355, generator_loss=28.01, generator_mel_loss=18.09, generator_kl_loss=1.408, generator_dur_loss=1.753, generator_adv_loss=1.956, generator_feat_match_loss=4.809, over 975.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:22:08,429 INFO [train.py:527] (5/6) Epoch 442, batch 66, global_batch_idx: 54750, batch size: 47, loss[discriminator_loss=2.745, discriminator_real_loss=1.449, discriminator_fake_loss=1.296, generator_loss=28.29, generator_mel_loss=17.84, generator_kl_loss=1.506, generator_dur_loss=1.725, generator_adv_loss=2.018, generator_feat_match_loss=5.204, over 47.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.04, generator_mel_loss=18.11, generator_kl_loss=1.409, generator_dur_loss=1.75, generator_adv_loss=1.952, generator_feat_match_loss=4.817, over 3795.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:24:26,451 INFO [train.py:527] (5/6) Epoch 442, batch 116, global_batch_idx: 54800, batch size: 62, loss[discriminator_loss=2.745, discriminator_real_loss=1.438, discriminator_fake_loss=1.307, generator_loss=28.21, generator_mel_loss=18.43, generator_kl_loss=1.401, generator_dur_loss=1.746, generator_adv_loss=1.921, generator_feat_match_loss=4.717, over 62.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.374, discriminator_fake_loss=1.341, generator_loss=28.07, generator_mel_loss=18.1, generator_kl_loss=1.418, generator_dur_loss=1.752, generator_adv_loss=1.953, generator_feat_match_loss=4.839, over 6609.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:24:26,453 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 21:24:35,318 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 21:24:54,269 INFO [train.py:919] (5/6) Start epoch 443 +2024-03-13 21:27:15,637 INFO [train.py:527] (5/6) Epoch 443, batch 42, global_batch_idx: 54850, batch size: 68, loss[discriminator_loss=2.663, discriminator_real_loss=1.288, discriminator_fake_loss=1.375, generator_loss=28.63, generator_mel_loss=18.62, generator_kl_loss=1.333, generator_dur_loss=1.777, generator_adv_loss=2.043, generator_feat_match_loss=4.858, over 68.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.381, discriminator_fake_loss=1.35, generator_loss=28.04, generator_mel_loss=18.11, generator_kl_loss=1.384, generator_dur_loss=1.78, generator_adv_loss=1.937, generator_feat_match_loss=4.823, over 2634.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:29:35,338 INFO [train.py:527] (5/6) Epoch 443, batch 92, global_batch_idx: 54900, batch size: 55, loss[discriminator_loss=2.645, discriminator_real_loss=1.37, discriminator_fake_loss=1.275, generator_loss=28.79, generator_mel_loss=18.4, generator_kl_loss=1.482, generator_dur_loss=1.723, generator_adv_loss=2.178, generator_feat_match_loss=5.006, over 55.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.368, discriminator_fake_loss=1.343, generator_loss=28.17, generator_mel_loss=18.08, generator_kl_loss=1.42, generator_dur_loss=1.766, generator_adv_loss=1.988, generator_feat_match_loss=4.912, over 5325.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:30:59,696 INFO [train.py:919] (5/6) Start epoch 444 +2024-03-13 21:32:11,055 INFO [train.py:527] (5/6) Epoch 444, batch 18, global_batch_idx: 54950, batch size: 58, loss[discriminator_loss=2.764, discriminator_real_loss=1.414, discriminator_fake_loss=1.35, generator_loss=28.1, generator_mel_loss=18.05, generator_kl_loss=1.6, generator_dur_loss=1.752, generator_adv_loss=1.923, generator_feat_match_loss=4.772, over 58.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.38, discriminator_fake_loss=1.342, generator_loss=28.02, generator_mel_loss=18.14, generator_kl_loss=1.424, generator_dur_loss=1.764, generator_adv_loss=1.951, generator_feat_match_loss=4.742, over 1102.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:34:31,957 INFO [train.py:527] (5/6) Epoch 444, batch 68, global_batch_idx: 55000, batch size: 77, loss[discriminator_loss=2.747, discriminator_real_loss=1.351, discriminator_fake_loss=1.396, generator_loss=28.11, generator_mel_loss=17.99, generator_kl_loss=1.46, generator_dur_loss=1.85, generator_adv_loss=1.799, generator_feat_match_loss=5.02, over 77.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.377, discriminator_fake_loss=1.347, generator_loss=28, generator_mel_loss=18.1, generator_kl_loss=1.415, generator_dur_loss=1.757, generator_adv_loss=1.938, generator_feat_match_loss=4.785, over 3912.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:34:31,958 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 21:34:40,031 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 21:36:56,030 INFO [train.py:527] (5/6) Epoch 444, batch 118, global_batch_idx: 55050, batch size: 72, loss[discriminator_loss=2.758, discriminator_real_loss=1.44, discriminator_fake_loss=1.318, generator_loss=27.55, generator_mel_loss=18.11, generator_kl_loss=1.343, generator_dur_loss=1.831, generator_adv_loss=1.892, generator_feat_match_loss=4.373, over 72.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.377, discriminator_fake_loss=1.347, generator_loss=28, generator_mel_loss=18.1, generator_kl_loss=1.416, generator_dur_loss=1.762, generator_adv_loss=1.938, generator_feat_match_loss=4.778, over 6679.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:37:10,944 INFO [train.py:919] (5/6) Start epoch 445 +2024-03-13 21:39:41,555 INFO [train.py:527] (5/6) Epoch 445, batch 44, global_batch_idx: 55100, batch size: 48, loss[discriminator_loss=2.67, discriminator_real_loss=1.32, discriminator_fake_loss=1.35, generator_loss=28.64, generator_mel_loss=18.36, generator_kl_loss=1.461, generator_dur_loss=1.711, generator_adv_loss=2.115, generator_feat_match_loss=4.987, over 48.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.353, discriminator_fake_loss=1.344, generator_loss=28.17, generator_mel_loss=18.12, generator_kl_loss=1.4, generator_dur_loss=1.778, generator_adv_loss=1.954, generator_feat_match_loss=4.921, over 2717.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:42:00,780 INFO [train.py:527] (5/6) Epoch 445, batch 94, global_batch_idx: 55150, batch size: 52, loss[discriminator_loss=2.739, discriminator_real_loss=1.368, discriminator_fake_loss=1.37, generator_loss=28.64, generator_mel_loss=18.56, generator_kl_loss=1.354, generator_dur_loss=1.701, generator_adv_loss=1.884, generator_feat_match_loss=5.142, over 52.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.364, discriminator_fake_loss=1.339, generator_loss=28.12, generator_mel_loss=18.13, generator_kl_loss=1.415, generator_dur_loss=1.767, generator_adv_loss=1.952, generator_feat_match_loss=4.864, over 5375.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:43:17,096 INFO [train.py:919] (5/6) Start epoch 446 +2024-03-13 21:44:33,590 INFO [train.py:527] (5/6) Epoch 446, batch 20, global_batch_idx: 55200, batch size: 83, loss[discriminator_loss=2.697, discriminator_real_loss=1.427, discriminator_fake_loss=1.27, generator_loss=28.01, generator_mel_loss=18.21, generator_kl_loss=1.377, generator_dur_loss=1.839, generator_adv_loss=1.836, generator_feat_match_loss=4.748, over 83.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.39, discriminator_fake_loss=1.329, generator_loss=28, generator_mel_loss=18.02, generator_kl_loss=1.413, generator_dur_loss=1.779, generator_adv_loss=1.952, generator_feat_match_loss=4.838, over 1235.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:44:33,591 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 21:44:41,674 INFO [train.py:591] (5/6) 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 21:44:41,675 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 21:46:59,740 INFO [train.py:527] (5/6) Epoch 446, batch 70, global_batch_idx: 55250, batch size: 68, loss[discriminator_loss=2.775, discriminator_real_loss=1.487, discriminator_fake_loss=1.288, generator_loss=27.36, generator_mel_loss=17.92, generator_kl_loss=1.441, generator_dur_loss=1.796, generator_adv_loss=1.841, generator_feat_match_loss=4.358, over 68.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.338, generator_loss=28.04, generator_mel_loss=18.08, generator_kl_loss=1.421, generator_dur_loss=1.774, generator_adv_loss=1.952, generator_feat_match_loss=4.814, over 4236.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:49:19,405 INFO [train.py:527] (5/6) Epoch 446, batch 120, global_batch_idx: 55300, batch size: 50, loss[discriminator_loss=2.7, discriminator_real_loss=1.368, discriminator_fake_loss=1.332, generator_loss=27.11, generator_mel_loss=17.8, generator_kl_loss=1.471, generator_dur_loss=1.722, generator_adv_loss=1.985, generator_feat_match_loss=4.135, over 50.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=28.09, generator_mel_loss=18.1, generator_kl_loss=1.422, generator_dur_loss=1.769, generator_adv_loss=1.953, generator_feat_match_loss=4.845, over 7156.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:49:29,369 INFO [train.py:919] (5/6) Start epoch 447 +2024-03-13 21:52:03,005 INFO [train.py:527] (5/6) Epoch 447, batch 46, global_batch_idx: 55350, batch size: 44, loss[discriminator_loss=2.637, discriminator_real_loss=1.313, discriminator_fake_loss=1.324, generator_loss=29.55, generator_mel_loss=18.4, generator_kl_loss=1.66, generator_dur_loss=1.704, generator_adv_loss=2.086, generator_feat_match_loss=5.697, over 44.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.376, discriminator_fake_loss=1.342, generator_loss=28.19, generator_mel_loss=18.13, generator_kl_loss=1.458, generator_dur_loss=1.755, generator_adv_loss=1.968, generator_feat_match_loss=4.874, over 2589.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:54:22,860 INFO [train.py:527] (5/6) Epoch 447, batch 96, global_batch_idx: 55400, batch size: 42, loss[discriminator_loss=2.757, discriminator_real_loss=1.399, discriminator_fake_loss=1.358, generator_loss=27.45, generator_mel_loss=18.1, generator_kl_loss=1.461, generator_dur_loss=1.692, generator_adv_loss=1.986, generator_feat_match_loss=4.22, over 42.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=28.1, generator_mel_loss=18.12, generator_kl_loss=1.435, generator_dur_loss=1.763, generator_adv_loss=1.965, generator_feat_match_loss=4.815, over 5469.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:54:22,862 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 21:54:31,585 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 21:55:44,875 INFO [train.py:919] (5/6) Start epoch 448 +2024-03-13 21:57:09,232 INFO [train.py:527] (5/6) Epoch 448, batch 22, global_batch_idx: 55450, batch size: 42, loss[discriminator_loss=2.697, discriminator_real_loss=1.422, discriminator_fake_loss=1.275, generator_loss=28.67, generator_mel_loss=18.06, generator_kl_loss=1.726, generator_dur_loss=1.712, generator_adv_loss=1.917, generator_feat_match_loss=5.257, over 42.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.356, discriminator_fake_loss=1.346, generator_loss=28.3, generator_mel_loss=18.19, generator_kl_loss=1.428, generator_dur_loss=1.746, generator_adv_loss=1.966, generator_feat_match_loss=4.969, over 1212.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 21:59:27,772 INFO [train.py:527] (5/6) Epoch 448, batch 72, global_batch_idx: 55500, batch size: 45, loss[discriminator_loss=2.719, discriminator_real_loss=1.445, discriminator_fake_loss=1.274, generator_loss=27.89, generator_mel_loss=18.16, generator_kl_loss=1.298, generator_dur_loss=1.76, generator_adv_loss=1.987, generator_feat_match_loss=4.686, over 45.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.366, discriminator_fake_loss=1.34, generator_loss=28.18, generator_mel_loss=18.12, generator_kl_loss=1.415, generator_dur_loss=1.773, generator_adv_loss=1.963, generator_feat_match_loss=4.909, over 4104.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:01:46,007 INFO [train.py:527] (5/6) Epoch 448, batch 122, global_batch_idx: 55550, batch size: 56, loss[discriminator_loss=2.658, discriminator_real_loss=1.338, discriminator_fake_loss=1.319, generator_loss=28.97, generator_mel_loss=18.68, generator_kl_loss=1.443, generator_dur_loss=1.751, generator_adv_loss=1.921, generator_feat_match_loss=5.174, over 56.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.368, discriminator_fake_loss=1.344, generator_loss=28.15, generator_mel_loss=18.11, generator_kl_loss=1.406, generator_dur_loss=1.771, generator_adv_loss=1.957, generator_feat_match_loss=4.911, over 7067.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:01:50,690 INFO [train.py:919] (5/6) Start epoch 449 +2024-03-13 22:04:26,666 INFO [train.py:527] (5/6) Epoch 449, batch 48, global_batch_idx: 55600, batch size: 64, loss[discriminator_loss=2.736, discriminator_real_loss=1.371, discriminator_fake_loss=1.364, generator_loss=28.38, generator_mel_loss=18.35, generator_kl_loss=1.336, generator_dur_loss=1.775, generator_adv_loss=1.86, generator_feat_match_loss=5.065, over 64.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.381, discriminator_fake_loss=1.341, generator_loss=27.94, generator_mel_loss=18.06, generator_kl_loss=1.402, generator_dur_loss=1.757, generator_adv_loss=1.946, generator_feat_match_loss=4.784, over 2800.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:04:26,667 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 22:04:34,678 INFO [train.py:591] (5/6) 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 22:04:34,678 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 22:06:50,822 INFO [train.py:527] (5/6) Epoch 449, batch 98, global_batch_idx: 55650, batch size: 66, loss[discriminator_loss=2.673, discriminator_real_loss=1.419, discriminator_fake_loss=1.254, generator_loss=27.5, generator_mel_loss=17.79, generator_kl_loss=1.323, generator_dur_loss=1.769, generator_adv_loss=2.023, generator_feat_match_loss=4.598, over 66.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.379, discriminator_fake_loss=1.341, generator_loss=27.94, generator_mel_loss=18.05, generator_kl_loss=1.392, generator_dur_loss=1.762, generator_adv_loss=1.945, generator_feat_match_loss=4.794, over 5871.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:08:02,586 INFO [train.py:919] (5/6) Start epoch 450 +2024-03-13 22:09:34,088 INFO [train.py:527] (5/6) Epoch 450, batch 24, global_batch_idx: 55700, batch size: 61, loss[discriminator_loss=2.766, discriminator_real_loss=1.456, discriminator_fake_loss=1.31, generator_loss=28.06, generator_mel_loss=18.43, generator_kl_loss=1.36, generator_dur_loss=1.758, generator_adv_loss=1.919, generator_feat_match_loss=4.594, over 61.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.366, discriminator_fake_loss=1.35, generator_loss=28, generator_mel_loss=18.07, generator_kl_loss=1.416, generator_dur_loss=1.749, generator_adv_loss=1.963, generator_feat_match_loss=4.805, over 1504.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:11:52,363 INFO [train.py:527] (5/6) Epoch 450, batch 74, global_batch_idx: 55750, batch size: 31, loss[discriminator_loss=2.65, discriminator_real_loss=1.274, discriminator_fake_loss=1.376, generator_loss=28.33, generator_mel_loss=17.78, generator_kl_loss=1.645, generator_dur_loss=1.601, generator_adv_loss=1.889, generator_feat_match_loss=5.423, over 31.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.365, discriminator_fake_loss=1.348, generator_loss=28.03, generator_mel_loss=18.02, generator_kl_loss=1.41, generator_dur_loss=1.761, generator_adv_loss=1.971, generator_feat_match_loss=4.867, over 4535.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:14:07,667 INFO [train.py:919] (5/6) Start epoch 451 +2024-03-13 22:14:31,913 INFO [train.py:527] (5/6) Epoch 451, batch 0, global_batch_idx: 55800, batch size: 74, loss[discriminator_loss=2.716, discriminator_real_loss=1.342, discriminator_fake_loss=1.374, generator_loss=27.61, generator_mel_loss=17.83, generator_kl_loss=1.197, generator_dur_loss=1.803, generator_adv_loss=2.026, generator_feat_match_loss=4.755, over 74.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.342, discriminator_fake_loss=1.374, generator_loss=27.61, generator_mel_loss=17.83, generator_kl_loss=1.197, generator_dur_loss=1.803, generator_adv_loss=2.026, generator_feat_match_loss=4.755, over 74.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:14:31,916 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 22:14:39,768 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 22:16:57,332 INFO [train.py:527] (5/6) Epoch 451, batch 50, global_batch_idx: 55850, batch size: 56, loss[discriminator_loss=2.735, discriminator_real_loss=1.319, discriminator_fake_loss=1.416, generator_loss=27.27, generator_mel_loss=17.79, generator_kl_loss=1.339, generator_dur_loss=1.787, generator_adv_loss=1.988, generator_feat_match_loss=4.372, over 56.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.368, discriminator_fake_loss=1.351, generator_loss=28.08, generator_mel_loss=18.07, generator_kl_loss=1.41, generator_dur_loss=1.769, generator_adv_loss=1.935, generator_feat_match_loss=4.896, over 2951.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:19:14,530 INFO [train.py:527] (5/6) Epoch 451, batch 100, global_batch_idx: 55900, batch size: 25, loss[discriminator_loss=2.762, discriminator_real_loss=1.427, discriminator_fake_loss=1.336, generator_loss=29.28, generator_mel_loss=18.26, generator_kl_loss=1.75, generator_dur_loss=1.572, generator_adv_loss=2.071, generator_feat_match_loss=5.623, over 25.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.37, discriminator_fake_loss=1.345, generator_loss=28.03, generator_mel_loss=18.04, generator_kl_loss=1.407, generator_dur_loss=1.761, generator_adv_loss=1.938, generator_feat_match_loss=4.884, over 5753.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:20:21,080 INFO [train.py:919] (5/6) Start epoch 452 +2024-03-13 22:21:57,312 INFO [train.py:527] (5/6) Epoch 452, batch 26, global_batch_idx: 55950, batch size: 64, loss[discriminator_loss=2.731, discriminator_real_loss=1.367, discriminator_fake_loss=1.364, generator_loss=28.3, generator_mel_loss=18.29, generator_kl_loss=1.28, generator_dur_loss=1.797, generator_adv_loss=1.935, generator_feat_match_loss=4.999, over 64.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=28.08, generator_mel_loss=18.08, generator_kl_loss=1.416, generator_dur_loss=1.765, generator_adv_loss=1.969, generator_feat_match_loss=4.857, over 1509.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:24:17,354 INFO [train.py:527] (5/6) Epoch 452, batch 76, global_batch_idx: 56000, batch size: 70, loss[discriminator_loss=2.753, discriminator_real_loss=1.46, discriminator_fake_loss=1.293, generator_loss=28.13, generator_mel_loss=18.03, generator_kl_loss=1.401, generator_dur_loss=1.818, generator_adv_loss=2.036, generator_feat_match_loss=4.848, over 70.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.34, generator_loss=27.99, generator_mel_loss=18, generator_kl_loss=1.424, generator_dur_loss=1.759, generator_adv_loss=1.958, generator_feat_match_loss=4.844, over 4400.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:24:17,355 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 22:24:25,386 INFO [train.py:591] (5/6) 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 22:24:25,387 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 22:26:33,476 INFO [train.py:919] (5/6) Start epoch 453 +2024-03-13 22:27:01,562 INFO [train.py:527] (5/6) Epoch 453, batch 2, global_batch_idx: 56050, batch size: 39, loss[discriminator_loss=2.64, discriminator_real_loss=1.304, discriminator_fake_loss=1.336, generator_loss=29.23, generator_mel_loss=18.84, generator_kl_loss=1.469, generator_dur_loss=1.707, generator_adv_loss=2.016, generator_feat_match_loss=5.191, over 39.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.348, discriminator_fake_loss=1.338, generator_loss=28.01, generator_mel_loss=18.05, generator_kl_loss=1.418, generator_dur_loss=1.791, generator_adv_loss=1.955, generator_feat_match_loss=4.8, over 196.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:29:16,626 INFO [train.py:527] (5/6) Epoch 453, batch 52, global_batch_idx: 56100, batch size: 53, loss[discriminator_loss=2.732, discriminator_real_loss=1.37, discriminator_fake_loss=1.361, generator_loss=28.08, generator_mel_loss=18.38, generator_kl_loss=1.501, generator_dur_loss=1.728, generator_adv_loss=2.065, generator_feat_match_loss=4.404, over 53.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.369, discriminator_fake_loss=1.345, generator_loss=28.07, generator_mel_loss=18.14, generator_kl_loss=1.407, generator_dur_loss=1.761, generator_adv_loss=1.945, generator_feat_match_loss=4.815, over 2902.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:31:35,316 INFO [train.py:527] (5/6) Epoch 453, batch 102, global_batch_idx: 56150, batch size: 53, loss[discriminator_loss=2.72, discriminator_real_loss=1.367, discriminator_fake_loss=1.353, generator_loss=27.89, generator_mel_loss=18.1, generator_kl_loss=1.328, generator_dur_loss=1.728, generator_adv_loss=1.962, generator_feat_match_loss=4.766, over 53.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=28.02, generator_mel_loss=18.08, generator_kl_loss=1.417, generator_dur_loss=1.755, generator_adv_loss=1.949, generator_feat_match_loss=4.819, over 5684.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:32:35,628 INFO [train.py:919] (5/6) Start epoch 454 +2024-03-13 22:34:17,174 INFO [train.py:527] (5/6) Epoch 454, batch 28, global_batch_idx: 56200, batch size: 60, loss[discriminator_loss=2.728, discriminator_real_loss=1.365, discriminator_fake_loss=1.362, generator_loss=28.68, generator_mel_loss=18.25, generator_kl_loss=1.512, generator_dur_loss=1.72, generator_adv_loss=2.09, generator_feat_match_loss=5.108, over 60.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.353, discriminator_fake_loss=1.354, generator_loss=28.49, generator_mel_loss=18.24, generator_kl_loss=1.434, generator_dur_loss=1.745, generator_adv_loss=2.036, generator_feat_match_loss=5.034, over 1501.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:34:17,176 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 22:34:25,073 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 22:36:45,225 INFO [train.py:527] (5/6) Epoch 454, batch 78, global_batch_idx: 56250, batch size: 53, loss[discriminator_loss=2.698, discriminator_real_loss=1.384, discriminator_fake_loss=1.314, generator_loss=28.05, generator_mel_loss=17.98, generator_kl_loss=1.394, generator_dur_loss=1.664, generator_adv_loss=2.038, generator_feat_match_loss=4.974, over 53.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.376, discriminator_fake_loss=1.341, generator_loss=28.14, generator_mel_loss=18.1, generator_kl_loss=1.417, generator_dur_loss=1.752, generator_adv_loss=1.987, generator_feat_match_loss=4.887, over 4284.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:38:49,911 INFO [train.py:919] (5/6) Start epoch 455 +2024-03-13 22:39:24,141 INFO [train.py:527] (5/6) Epoch 455, batch 4, global_batch_idx: 56300, batch size: 25, loss[discriminator_loss=2.769, discriminator_real_loss=1.372, discriminator_fake_loss=1.397, generator_loss=30.67, generator_mel_loss=18.99, generator_kl_loss=1.782, generator_dur_loss=1.501, generator_adv_loss=2.038, generator_feat_match_loss=6.359, over 25.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.391, discriminator_fake_loss=1.323, generator_loss=28.58, generator_mel_loss=18.25, generator_kl_loss=1.46, generator_dur_loss=1.71, generator_adv_loss=1.993, generator_feat_match_loss=5.168, over 257.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:41:43,649 INFO [train.py:527] (5/6) Epoch 455, batch 54, global_batch_idx: 56350, batch size: 45, loss[discriminator_loss=2.686, discriminator_real_loss=1.274, discriminator_fake_loss=1.411, generator_loss=28.5, generator_mel_loss=17.86, generator_kl_loss=1.489, generator_dur_loss=1.654, generator_adv_loss=1.939, generator_feat_match_loss=5.558, over 45.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.384, discriminator_fake_loss=1.345, generator_loss=27.96, generator_mel_loss=17.98, generator_kl_loss=1.427, generator_dur_loss=1.75, generator_adv_loss=1.945, generator_feat_match_loss=4.857, over 3186.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:44:02,026 INFO [train.py:527] (5/6) Epoch 455, batch 104, global_batch_idx: 56400, batch size: 80, loss[discriminator_loss=2.732, discriminator_real_loss=1.332, discriminator_fake_loss=1.4, generator_loss=27.9, generator_mel_loss=18.14, generator_kl_loss=1.34, generator_dur_loss=1.803, generator_adv_loss=2.036, generator_feat_match_loss=4.583, over 80.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.347, generator_loss=27.99, generator_mel_loss=18.02, generator_kl_loss=1.413, generator_dur_loss=1.742, generator_adv_loss=1.95, generator_feat_match_loss=4.868, over 6032.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:44:02,027 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 22:44:11,040 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 22:45:05,141 INFO [train.py:919] (5/6) Start epoch 456 +2024-03-13 22:46:51,751 INFO [train.py:527] (5/6) Epoch 456, batch 30, global_batch_idx: 56450, batch size: 14, loss[discriminator_loss=2.747, discriminator_real_loss=1.357, discriminator_fake_loss=1.39, generator_loss=28.97, generator_mel_loss=18.56, generator_kl_loss=1.7, generator_dur_loss=1.574, generator_adv_loss=2.139, generator_feat_match_loss=5, over 14.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=27.91, generator_mel_loss=18.02, generator_kl_loss=1.404, generator_dur_loss=1.765, generator_adv_loss=1.939, generator_feat_match_loss=4.791, over 1795.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:49:11,320 INFO [train.py:527] (5/6) Epoch 456, batch 80, global_batch_idx: 56500, batch size: 31, loss[discriminator_loss=2.715, discriminator_real_loss=1.467, discriminator_fake_loss=1.248, generator_loss=26.38, generator_mel_loss=17.31, generator_kl_loss=1.401, generator_dur_loss=1.667, generator_adv_loss=1.977, generator_feat_match_loss=4.018, over 31.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.343, generator_loss=28.1, generator_mel_loss=18.06, generator_kl_loss=1.435, generator_dur_loss=1.746, generator_adv_loss=1.946, generator_feat_match_loss=4.911, over 4469.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:51:12,135 INFO [train.py:919] (5/6) Start epoch 457 +2024-03-13 22:51:51,404 INFO [train.py:527] (5/6) Epoch 457, batch 6, global_batch_idx: 56550, batch size: 31, loss[discriminator_loss=2.731, discriminator_real_loss=1.363, discriminator_fake_loss=1.369, generator_loss=27.13, generator_mel_loss=17.76, generator_kl_loss=1.687, generator_dur_loss=1.675, generator_adv_loss=1.963, generator_feat_match_loss=4.047, over 31.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.376, discriminator_fake_loss=1.383, generator_loss=27.92, generator_mel_loss=17.99, generator_kl_loss=1.514, generator_dur_loss=1.724, generator_adv_loss=1.928, generator_feat_match_loss=4.767, over 325.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:54:13,889 INFO [train.py:527] (5/6) Epoch 457, batch 56, global_batch_idx: 56600, batch size: 62, loss[discriminator_loss=2.655, discriminator_real_loss=1.365, discriminator_fake_loss=1.29, generator_loss=28.06, generator_mel_loss=18.12, generator_kl_loss=1.366, generator_dur_loss=1.726, generator_adv_loss=2.024, generator_feat_match_loss=4.824, over 62.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.378, discriminator_fake_loss=1.347, generator_loss=27.95, generator_mel_loss=18.01, generator_kl_loss=1.423, generator_dur_loss=1.76, generator_adv_loss=1.954, generator_feat_match_loss=4.807, over 3196.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:54:13,890 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 22:54:21,934 INFO [train.py:591] (5/6) 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 22:54:21,935 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 22:56:41,979 INFO [train.py:527] (5/6) Epoch 457, batch 106, global_batch_idx: 56650, batch size: 56, loss[discriminator_loss=2.697, discriminator_real_loss=1.315, discriminator_fake_loss=1.382, generator_loss=28.33, generator_mel_loss=18.12, generator_kl_loss=1.37, generator_dur_loss=1.721, generator_adv_loss=2.056, generator_feat_match_loss=5.061, over 56.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.379, discriminator_fake_loss=1.346, generator_loss=28.03, generator_mel_loss=18.07, generator_kl_loss=1.413, generator_dur_loss=1.768, generator_adv_loss=1.95, generator_feat_match_loss=4.835, over 6212.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 22:57:26,579 INFO [train.py:919] (5/6) Start epoch 458 +2024-03-13 22:59:21,158 INFO [train.py:527] (5/6) Epoch 458, batch 32, global_batch_idx: 56700, batch size: 66, loss[discriminator_loss=2.778, discriminator_real_loss=1.448, discriminator_fake_loss=1.331, generator_loss=27.38, generator_mel_loss=18.13, generator_kl_loss=1.396, generator_dur_loss=1.837, generator_adv_loss=1.791, generator_feat_match_loss=4.224, over 66.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.385, discriminator_fake_loss=1.327, generator_loss=27.92, generator_mel_loss=18.07, generator_kl_loss=1.445, generator_dur_loss=1.772, generator_adv_loss=1.933, generator_feat_match_loss=4.699, over 1848.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:01:40,200 INFO [train.py:527] (5/6) Epoch 458, batch 82, global_batch_idx: 56750, batch size: 59, loss[discriminator_loss=2.724, discriminator_real_loss=1.437, discriminator_fake_loss=1.286, generator_loss=26.93, generator_mel_loss=17.57, generator_kl_loss=1.433, generator_dur_loss=1.733, generator_adv_loss=1.98, generator_feat_match_loss=4.211, over 59.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.378, discriminator_fake_loss=1.333, generator_loss=28, generator_mel_loss=18.03, generator_kl_loss=1.438, generator_dur_loss=1.753, generator_adv_loss=1.947, generator_feat_match_loss=4.827, over 4610.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:03:34,101 INFO [train.py:919] (5/6) Start epoch 459 +2024-03-13 23:04:20,636 INFO [train.py:527] (5/6) Epoch 459, batch 8, global_batch_idx: 56800, batch size: 52, loss[discriminator_loss=2.871, discriminator_real_loss=1.621, discriminator_fake_loss=1.249, generator_loss=27.12, generator_mel_loss=17.7, generator_kl_loss=1.458, generator_dur_loss=1.635, generator_adv_loss=1.847, generator_feat_match_loss=4.476, over 52.00 samples.], tot_loss[discriminator_loss=2.776, discriminator_real_loss=1.421, discriminator_fake_loss=1.355, generator_loss=27.65, generator_mel_loss=18.03, generator_kl_loss=1.365, generator_dur_loss=1.72, generator_adv_loss=1.936, generator_feat_match_loss=4.605, over 542.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:04:20,639 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 23:04:28,784 INFO [train.py:591] (5/6) 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 23:04:28,786 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 23:06:47,994 INFO [train.py:527] (5/6) Epoch 459, batch 58, global_batch_idx: 56850, batch size: 15, loss[discriminator_loss=2.745, discriminator_real_loss=1.275, discriminator_fake_loss=1.469, generator_loss=29.25, generator_mel_loss=18.94, generator_kl_loss=1.764, generator_dur_loss=1.534, generator_adv_loss=2.099, generator_feat_match_loss=4.914, over 15.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.344, generator_loss=27.98, generator_mel_loss=18.03, generator_kl_loss=1.402, generator_dur_loss=1.735, generator_adv_loss=1.937, generator_feat_match_loss=4.875, over 3251.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:09:07,424 INFO [train.py:527] (5/6) Epoch 459, batch 108, global_batch_idx: 56900, batch size: 74, loss[discriminator_loss=2.723, discriminator_real_loss=1.365, discriminator_fake_loss=1.358, generator_loss=28.03, generator_mel_loss=18.08, generator_kl_loss=1.442, generator_dur_loss=1.775, generator_adv_loss=2.015, generator_feat_match_loss=4.717, over 74.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.372, discriminator_fake_loss=1.337, generator_loss=28.01, generator_mel_loss=18.02, generator_kl_loss=1.411, generator_dur_loss=1.744, generator_adv_loss=1.961, generator_feat_match_loss=4.874, over 6114.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:09:50,060 INFO [train.py:919] (5/6) Start epoch 460 +2024-03-13 23:11:48,827 INFO [train.py:527] (5/6) Epoch 460, batch 34, global_batch_idx: 56950, batch size: 31, loss[discriminator_loss=2.743, discriminator_real_loss=1.472, discriminator_fake_loss=1.271, generator_loss=26.72, generator_mel_loss=17.97, generator_kl_loss=1.43, generator_dur_loss=1.659, generator_adv_loss=1.87, generator_feat_match_loss=3.793, over 31.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.385, discriminator_fake_loss=1.347, generator_loss=28.03, generator_mel_loss=18.09, generator_kl_loss=1.399, generator_dur_loss=1.766, generator_adv_loss=1.941, generator_feat_match_loss=4.835, over 1897.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:14:09,531 INFO [train.py:527] (5/6) Epoch 460, batch 84, global_batch_idx: 57000, batch size: 58, loss[discriminator_loss=2.722, discriminator_real_loss=1.365, discriminator_fake_loss=1.357, generator_loss=28.2, generator_mel_loss=18.12, generator_kl_loss=1.29, generator_dur_loss=1.736, generator_adv_loss=2.054, generator_feat_match_loss=5.006, over 58.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.377, discriminator_fake_loss=1.34, generator_loss=28.1, generator_mel_loss=18.06, generator_kl_loss=1.41, generator_dur_loss=1.763, generator_adv_loss=1.949, generator_feat_match_loss=4.919, over 4797.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:14:09,532 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 23:14:18,391 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 23:16:10,324 INFO [train.py:919] (5/6) Start epoch 461 +2024-03-13 23:17:01,951 INFO [train.py:527] (5/6) Epoch 461, batch 10, global_batch_idx: 57050, batch size: 61, loss[discriminator_loss=2.727, discriminator_real_loss=1.424, discriminator_fake_loss=1.302, generator_loss=27.76, generator_mel_loss=18.14, generator_kl_loss=1.297, generator_dur_loss=1.72, generator_adv_loss=1.843, generator_feat_match_loss=4.76, over 61.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.37, discriminator_fake_loss=1.334, generator_loss=27.87, generator_mel_loss=17.94, generator_kl_loss=1.436, generator_dur_loss=1.738, generator_adv_loss=1.907, generator_feat_match_loss=4.843, over 583.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:19:22,937 INFO [train.py:527] (5/6) Epoch 461, batch 60, global_batch_idx: 57100, batch size: 74, loss[discriminator_loss=2.703, discriminator_real_loss=1.316, discriminator_fake_loss=1.387, generator_loss=28.13, generator_mel_loss=18.16, generator_kl_loss=1.305, generator_dur_loss=1.796, generator_adv_loss=1.981, generator_feat_match_loss=4.888, over 74.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.375, discriminator_fake_loss=1.342, generator_loss=27.91, generator_mel_loss=17.98, generator_kl_loss=1.409, generator_dur_loss=1.751, generator_adv_loss=1.938, generator_feat_match_loss=4.835, over 3479.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:21:41,925 INFO [train.py:527] (5/6) Epoch 461, batch 110, global_batch_idx: 57150, batch size: 52, loss[discriminator_loss=2.641, discriminator_real_loss=1.315, discriminator_fake_loss=1.326, generator_loss=28.91, generator_mel_loss=18.08, generator_kl_loss=1.569, generator_dur_loss=1.655, generator_adv_loss=1.993, generator_feat_match_loss=5.617, over 52.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.373, discriminator_fake_loss=1.344, generator_loss=27.93, generator_mel_loss=18, generator_kl_loss=1.402, generator_dur_loss=1.746, generator_adv_loss=1.94, generator_feat_match_loss=4.844, over 6487.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:22:19,156 INFO [train.py:919] (5/6) Start epoch 462 +2024-03-13 23:24:25,921 INFO [train.py:527] (5/6) Epoch 462, batch 36, global_batch_idx: 57200, batch size: 25, loss[discriminator_loss=2.71, discriminator_real_loss=1.261, discriminator_fake_loss=1.449, generator_loss=29, generator_mel_loss=18.52, generator_kl_loss=1.716, generator_dur_loss=1.587, generator_adv_loss=2.059, generator_feat_match_loss=5.116, over 25.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.392, discriminator_fake_loss=1.337, generator_loss=28.04, generator_mel_loss=18.08, generator_kl_loss=1.427, generator_dur_loss=1.744, generator_adv_loss=1.951, generator_feat_match_loss=4.84, over 2236.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:24:25,923 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 23:24:33,977 INFO [train.py:591] (5/6) 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 23:24:33,978 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 23:26:50,841 INFO [train.py:527] (5/6) Epoch 462, batch 86, global_batch_idx: 57250, batch size: 47, loss[discriminator_loss=2.743, discriminator_real_loss=1.455, discriminator_fake_loss=1.289, generator_loss=27.38, generator_mel_loss=17.92, generator_kl_loss=1.386, generator_dur_loss=1.716, generator_adv_loss=1.823, generator_feat_match_loss=4.536, over 47.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.382, discriminator_fake_loss=1.343, generator_loss=28.06, generator_mel_loss=18.1, generator_kl_loss=1.441, generator_dur_loss=1.746, generator_adv_loss=1.94, generator_feat_match_loss=4.828, over 5031.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:28:36,554 INFO [train.py:919] (5/6) Start epoch 463 +2024-03-13 23:29:31,933 INFO [train.py:527] (5/6) Epoch 463, batch 12, global_batch_idx: 57300, batch size: 80, loss[discriminator_loss=2.672, discriminator_real_loss=1.356, discriminator_fake_loss=1.316, generator_loss=27.95, generator_mel_loss=17.69, generator_kl_loss=1.417, generator_dur_loss=1.812, generator_adv_loss=1.91, generator_feat_match_loss=5.122, over 80.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.376, discriminator_fake_loss=1.353, generator_loss=27.9, generator_mel_loss=17.99, generator_kl_loss=1.389, generator_dur_loss=1.766, generator_adv_loss=1.929, generator_feat_match_loss=4.827, over 819.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:31:50,174 INFO [train.py:527] (5/6) Epoch 463, batch 62, global_batch_idx: 57350, batch size: 66, loss[discriminator_loss=2.691, discriminator_real_loss=1.31, discriminator_fake_loss=1.381, generator_loss=29.12, generator_mel_loss=18.46, generator_kl_loss=1.321, generator_dur_loss=1.763, generator_adv_loss=2.161, generator_feat_match_loss=5.416, over 66.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.16, generator_mel_loss=18.06, generator_kl_loss=1.391, generator_dur_loss=1.753, generator_adv_loss=1.964, generator_feat_match_loss=4.998, over 3745.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:34:12,350 INFO [train.py:527] (5/6) Epoch 463, batch 112, global_batch_idx: 57400, batch size: 45, loss[discriminator_loss=2.74, discriminator_real_loss=1.323, discriminator_fake_loss=1.417, generator_loss=27.96, generator_mel_loss=18.24, generator_kl_loss=1.504, generator_dur_loss=1.663, generator_adv_loss=1.877, generator_feat_match_loss=4.683, over 45.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.371, discriminator_fake_loss=1.349, generator_loss=28.09, generator_mel_loss=18.06, generator_kl_loss=1.395, generator_dur_loss=1.752, generator_adv_loss=1.95, generator_feat_match_loss=4.929, over 6634.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:34:12,352 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 23:34:21,113 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 23:34:54,959 INFO [train.py:919] (5/6) Start epoch 464 +2024-03-13 23:37:06,048 INFO [train.py:527] (5/6) Epoch 464, batch 38, global_batch_idx: 57450, batch size: 50, loss[discriminator_loss=2.704, discriminator_real_loss=1.414, discriminator_fake_loss=1.289, generator_loss=27.93, generator_mel_loss=18.19, generator_kl_loss=1.344, generator_dur_loss=1.715, generator_adv_loss=1.831, generator_feat_match_loss=4.854, over 50.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.379, discriminator_fake_loss=1.346, generator_loss=27.9, generator_mel_loss=17.94, generator_kl_loss=1.414, generator_dur_loss=1.76, generator_adv_loss=1.95, generator_feat_match_loss=4.839, over 2224.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:39:24,516 INFO [train.py:527] (5/6) Epoch 464, batch 88, global_batch_idx: 57500, batch size: 42, loss[discriminator_loss=2.713, discriminator_real_loss=1.387, discriminator_fake_loss=1.326, generator_loss=28.76, generator_mel_loss=18.3, generator_kl_loss=1.568, generator_dur_loss=1.677, generator_adv_loss=1.862, generator_feat_match_loss=5.357, over 42.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.378, discriminator_fake_loss=1.34, generator_loss=27.94, generator_mel_loss=17.98, generator_kl_loss=1.41, generator_dur_loss=1.775, generator_adv_loss=1.951, generator_feat_match_loss=4.822, over 5107.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:41:04,289 INFO [train.py:919] (5/6) Start epoch 465 +2024-03-13 23:42:06,785 INFO [train.py:527] (5/6) Epoch 465, batch 14, global_batch_idx: 57550, batch size: 72, loss[discriminator_loss=2.711, discriminator_real_loss=1.252, discriminator_fake_loss=1.459, generator_loss=28.62, generator_mel_loss=18.52, generator_kl_loss=1.341, generator_dur_loss=1.763, generator_adv_loss=2.003, generator_feat_match_loss=4.992, over 72.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.385, discriminator_fake_loss=1.334, generator_loss=27.94, generator_mel_loss=18.05, generator_kl_loss=1.416, generator_dur_loss=1.756, generator_adv_loss=1.951, generator_feat_match_loss=4.759, over 888.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:44:25,182 INFO [train.py:527] (5/6) Epoch 465, batch 64, global_batch_idx: 57600, batch size: 77, loss[discriminator_loss=2.692, discriminator_real_loss=1.284, discriminator_fake_loss=1.408, generator_loss=28.41, generator_mel_loss=18.07, generator_kl_loss=1.348, generator_dur_loss=1.782, generator_adv_loss=1.994, generator_feat_match_loss=5.22, over 77.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=27.99, generator_mel_loss=18, generator_kl_loss=1.409, generator_dur_loss=1.738, generator_adv_loss=1.947, generator_feat_match_loss=4.9, over 3780.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:44:25,184 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 23:44:33,229 INFO [train.py:591] (5/6) 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 23:44:33,230 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 23:46:49,290 INFO [train.py:527] (5/6) Epoch 465, batch 114, global_batch_idx: 57650, batch size: 48, loss[discriminator_loss=2.713, discriminator_real_loss=1.463, discriminator_fake_loss=1.25, generator_loss=27.67, generator_mel_loss=18.33, generator_kl_loss=1.386, generator_dur_loss=1.709, generator_adv_loss=1.807, generator_feat_match_loss=4.436, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=28.04, generator_mel_loss=18.02, generator_kl_loss=1.407, generator_dur_loss=1.745, generator_adv_loss=1.946, generator_feat_match_loss=4.925, over 6585.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:47:16,138 INFO [train.py:919] (5/6) Start epoch 466 +2024-03-13 23:49:34,368 INFO [train.py:527] (5/6) Epoch 466, batch 40, global_batch_idx: 57700, batch size: 56, loss[discriminator_loss=2.7, discriminator_real_loss=1.421, discriminator_fake_loss=1.279, generator_loss=26.34, generator_mel_loss=17.25, generator_kl_loss=1.378, generator_dur_loss=1.758, generator_adv_loss=1.854, generator_feat_match_loss=4.099, over 56.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.366, discriminator_fake_loss=1.349, generator_loss=28.1, generator_mel_loss=18.03, generator_kl_loss=1.428, generator_dur_loss=1.762, generator_adv_loss=1.941, generator_feat_match_loss=4.937, over 2466.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:51:52,847 INFO [train.py:527] (5/6) Epoch 466, batch 90, global_batch_idx: 57750, batch size: 45, loss[discriminator_loss=2.683, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=27.66, generator_mel_loss=17.71, generator_kl_loss=1.345, generator_dur_loss=1.682, generator_adv_loss=2.048, generator_feat_match_loss=4.882, over 45.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.373, discriminator_fake_loss=1.343, generator_loss=28.05, generator_mel_loss=18.03, generator_kl_loss=1.412, generator_dur_loss=1.769, generator_adv_loss=1.947, generator_feat_match_loss=4.892, over 5356.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:53:25,782 INFO [train.py:919] (5/6) Start epoch 467 +2024-03-13 23:54:34,106 INFO [train.py:527] (5/6) Epoch 467, batch 16, global_batch_idx: 57800, batch size: 39, loss[discriminator_loss=2.741, discriminator_real_loss=1.407, discriminator_fake_loss=1.335, generator_loss=28.23, generator_mel_loss=18.37, generator_kl_loss=1.413, generator_dur_loss=1.74, generator_adv_loss=2.007, generator_feat_match_loss=4.696, over 39.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.37, discriminator_fake_loss=1.355, generator_loss=28.21, generator_mel_loss=18.2, generator_kl_loss=1.447, generator_dur_loss=1.738, generator_adv_loss=1.941, generator_feat_match_loss=4.884, over 871.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:54:34,107 INFO [train.py:581] (5/6) Computing validation loss +2024-03-13 23:54:42,160 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-13 23:57:00,064 INFO [train.py:527] (5/6) Epoch 467, batch 66, global_batch_idx: 57850, batch size: 83, loss[discriminator_loss=2.779, discriminator_real_loss=1.384, discriminator_fake_loss=1.395, generator_loss=28.87, generator_mel_loss=18.56, generator_kl_loss=1.222, generator_dur_loss=1.843, generator_adv_loss=1.911, generator_feat_match_loss=5.337, over 83.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=28.14, generator_mel_loss=18.08, generator_kl_loss=1.413, generator_dur_loss=1.759, generator_adv_loss=1.955, generator_feat_match_loss=4.928, over 3759.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:59:19,441 INFO [train.py:527] (5/6) Epoch 467, batch 116, global_batch_idx: 57900, batch size: 66, loss[discriminator_loss=2.744, discriminator_real_loss=1.407, discriminator_fake_loss=1.338, generator_loss=27.54, generator_mel_loss=17.96, generator_kl_loss=1.446, generator_dur_loss=1.809, generator_adv_loss=1.778, generator_feat_match_loss=4.551, over 66.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.376, discriminator_fake_loss=1.341, generator_loss=28.18, generator_mel_loss=18.11, generator_kl_loss=1.42, generator_dur_loss=1.755, generator_adv_loss=1.951, generator_feat_match_loss=4.946, over 6383.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-13 23:59:41,762 INFO [train.py:919] (5/6) Start epoch 468 +2024-03-14 00:02:04,392 INFO [train.py:527] (5/6) Epoch 468, batch 42, global_batch_idx: 57950, batch size: 66, loss[discriminator_loss=2.716, discriminator_real_loss=1.483, discriminator_fake_loss=1.233, generator_loss=26.69, generator_mel_loss=17.49, generator_kl_loss=1.289, generator_dur_loss=1.808, generator_adv_loss=1.836, generator_feat_match_loss=4.268, over 66.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=28.22, generator_mel_loss=18, generator_kl_loss=1.427, generator_dur_loss=1.749, generator_adv_loss=1.96, generator_feat_match_loss=5.083, over 2338.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:04:25,005 INFO [train.py:527] (5/6) Epoch 468, batch 92, global_batch_idx: 58000, batch size: 44, loss[discriminator_loss=2.683, discriminator_real_loss=1.359, discriminator_fake_loss=1.324, generator_loss=29.37, generator_mel_loss=18.28, generator_kl_loss=1.496, generator_dur_loss=1.735, generator_adv_loss=2.05, generator_feat_match_loss=5.807, over 44.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.364, discriminator_fake_loss=1.34, generator_loss=28.13, generator_mel_loss=17.99, generator_kl_loss=1.42, generator_dur_loss=1.751, generator_adv_loss=1.954, generator_feat_match_loss=5.009, over 5097.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:04:25,006 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 00:04:34,033 INFO [train.py:591] (5/6) 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 00:04:34,034 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 00:05:57,103 INFO [train.py:919] (5/6) Start epoch 469 +2024-03-14 00:07:12,627 INFO [train.py:527] (5/6) Epoch 469, batch 18, global_batch_idx: 58050, batch size: 80, loss[discriminator_loss=2.794, discriminator_real_loss=1.329, discriminator_fake_loss=1.465, generator_loss=29, generator_mel_loss=18.56, generator_kl_loss=1.263, generator_dur_loss=1.814, generator_adv_loss=2.096, generator_feat_match_loss=5.263, over 80.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.372, discriminator_fake_loss=1.363, generator_loss=28.1, generator_mel_loss=18.09, generator_kl_loss=1.397, generator_dur_loss=1.771, generator_adv_loss=1.957, generator_feat_match_loss=4.89, over 1113.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:09:35,689 INFO [train.py:527] (5/6) Epoch 469, batch 68, global_batch_idx: 58100, batch size: 66, loss[discriminator_loss=2.664, discriminator_real_loss=1.366, discriminator_fake_loss=1.298, generator_loss=28.07, generator_mel_loss=18.01, generator_kl_loss=1.265, generator_dur_loss=1.763, generator_adv_loss=1.994, generator_feat_match_loss=5.036, over 66.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.351, generator_loss=28.08, generator_mel_loss=18.04, generator_kl_loss=1.408, generator_dur_loss=1.769, generator_adv_loss=1.939, generator_feat_match_loss=4.926, over 4099.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:11:56,275 INFO [train.py:527] (5/6) Epoch 469, batch 118, global_batch_idx: 58150, batch size: 39, loss[discriminator_loss=2.751, discriminator_real_loss=1.367, discriminator_fake_loss=1.384, generator_loss=27.84, generator_mel_loss=18.06, generator_kl_loss=1.515, generator_dur_loss=1.706, generator_adv_loss=2.046, generator_feat_match_loss=4.513, over 39.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=28.07, generator_mel_loss=18.03, generator_kl_loss=1.41, generator_dur_loss=1.767, generator_adv_loss=1.942, generator_feat_match_loss=4.919, over 6791.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:12:10,082 INFO [train.py:919] (5/6) Start epoch 470 +2024-03-14 00:14:36,276 INFO [train.py:527] (5/6) Epoch 470, batch 44, global_batch_idx: 58200, batch size: 42, loss[discriminator_loss=2.624, discriminator_real_loss=1.341, discriminator_fake_loss=1.283, generator_loss=29.31, generator_mel_loss=18.4, generator_kl_loss=1.612, generator_dur_loss=1.641, generator_adv_loss=2.038, generator_feat_match_loss=5.618, over 42.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.383, discriminator_fake_loss=1.333, generator_loss=27.95, generator_mel_loss=18, generator_kl_loss=1.451, generator_dur_loss=1.747, generator_adv_loss=1.943, generator_feat_match_loss=4.805, over 2336.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:14:36,277 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 00:14:44,172 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 00:17:05,757 INFO [train.py:527] (5/6) Epoch 470, batch 94, global_batch_idx: 58250, batch size: 52, loss[discriminator_loss=2.758, discriminator_real_loss=1.527, discriminator_fake_loss=1.231, generator_loss=27.06, generator_mel_loss=18.1, generator_kl_loss=1.419, generator_dur_loss=1.659, generator_adv_loss=1.77, generator_feat_match_loss=4.108, over 52.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.377, discriminator_fake_loss=1.335, generator_loss=28.05, generator_mel_loss=18.01, generator_kl_loss=1.428, generator_dur_loss=1.753, generator_adv_loss=1.944, generator_feat_match_loss=4.908, over 5074.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:18:32,070 INFO [train.py:919] (5/6) Start epoch 471 +2024-03-14 00:19:56,163 INFO [train.py:527] (5/6) Epoch 471, batch 20, global_batch_idx: 58300, batch size: 25, loss[discriminator_loss=2.672, discriminator_real_loss=1.31, discriminator_fake_loss=1.362, generator_loss=29.81, generator_mel_loss=18.82, generator_kl_loss=1.92, generator_dur_loss=1.593, generator_adv_loss=1.937, generator_feat_match_loss=5.54, over 25.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.379, discriminator_fake_loss=1.342, generator_loss=28.2, generator_mel_loss=17.98, generator_kl_loss=1.414, generator_dur_loss=1.754, generator_adv_loss=1.981, generator_feat_match_loss=5.067, over 1189.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:22:19,663 INFO [train.py:527] (5/6) Epoch 471, batch 70, global_batch_idx: 58350, batch size: 88, loss[discriminator_loss=2.72, discriminator_real_loss=1.392, discriminator_fake_loss=1.328, generator_loss=28.43, generator_mel_loss=18.01, generator_kl_loss=1.319, generator_dur_loss=1.802, generator_adv_loss=1.997, generator_feat_match_loss=5.307, over 88.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.369, discriminator_fake_loss=1.347, generator_loss=28.22, generator_mel_loss=18.05, generator_kl_loss=1.408, generator_dur_loss=1.754, generator_adv_loss=1.966, generator_feat_match_loss=5.048, over 4079.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:24:37,656 INFO [train.py:527] (5/6) Epoch 471, batch 120, global_batch_idx: 58400, batch size: 48, loss[discriminator_loss=2.736, discriminator_real_loss=1.454, discriminator_fake_loss=1.282, generator_loss=27.26, generator_mel_loss=17.66, generator_kl_loss=1.451, generator_dur_loss=1.679, generator_adv_loss=1.842, generator_feat_match_loss=4.631, over 48.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.367, discriminator_fake_loss=1.349, generator_loss=28.12, generator_mel_loss=18.03, generator_kl_loss=1.409, generator_dur_loss=1.759, generator_adv_loss=1.952, generator_feat_match_loss=4.979, over 6932.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:24:37,657 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 00:24:46,222 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 00:24:55,374 INFO [train.py:919] (5/6) Start epoch 472 +2024-03-14 00:27:26,470 INFO [train.py:527] (5/6) Epoch 472, batch 46, global_batch_idx: 58450, batch size: 97, loss[discriminator_loss=2.726, discriminator_real_loss=1.322, discriminator_fake_loss=1.404, generator_loss=26.85, generator_mel_loss=17.45, generator_kl_loss=1.144, generator_dur_loss=1.853, generator_adv_loss=2.012, generator_feat_match_loss=4.39, over 97.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.37, discriminator_fake_loss=1.345, generator_loss=28.04, generator_mel_loss=18.07, generator_kl_loss=1.402, generator_dur_loss=1.75, generator_adv_loss=1.943, generator_feat_match_loss=4.866, over 2641.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:29:48,864 INFO [train.py:527] (5/6) Epoch 472, batch 96, global_batch_idx: 58500, batch size: 83, loss[discriminator_loss=2.764, discriminator_real_loss=1.233, discriminator_fake_loss=1.531, generator_loss=28.22, generator_mel_loss=18.03, generator_kl_loss=1.379, generator_dur_loss=1.801, generator_adv_loss=2.062, generator_feat_match_loss=4.945, over 83.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.375, discriminator_fake_loss=1.344, generator_loss=28.07, generator_mel_loss=18.04, generator_kl_loss=1.412, generator_dur_loss=1.751, generator_adv_loss=1.949, generator_feat_match_loss=4.917, over 5528.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:31:07,053 INFO [train.py:919] (5/6) Start epoch 473 +2024-03-14 00:32:34,912 INFO [train.py:527] (5/6) Epoch 473, batch 22, global_batch_idx: 58550, batch size: 96, loss[discriminator_loss=2.688, discriminator_real_loss=1.366, discriminator_fake_loss=1.321, generator_loss=28.52, generator_mel_loss=17.87, generator_kl_loss=1.433, generator_dur_loss=1.863, generator_adv_loss=1.87, generator_feat_match_loss=5.483, over 96.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=28.1, generator_mel_loss=18.01, generator_kl_loss=1.376, generator_dur_loss=1.773, generator_adv_loss=1.957, generator_feat_match_loss=4.981, over 1451.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:34:57,170 INFO [train.py:527] (5/6) Epoch 473, batch 72, global_batch_idx: 58600, batch size: 31, loss[discriminator_loss=2.715, discriminator_real_loss=1.543, discriminator_fake_loss=1.172, generator_loss=27.59, generator_mel_loss=17.87, generator_kl_loss=1.397, generator_dur_loss=1.634, generator_adv_loss=1.95, generator_feat_match_loss=4.734, over 31.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.377, discriminator_fake_loss=1.35, generator_loss=28.14, generator_mel_loss=18.06, generator_kl_loss=1.406, generator_dur_loss=1.759, generator_adv_loss=1.97, generator_feat_match_loss=4.943, over 4346.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:34:57,171 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 00:35:05,376 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 00:37:25,756 INFO [train.py:527] (5/6) Epoch 473, batch 122, global_batch_idx: 58650, batch size: 42, loss[discriminator_loss=2.697, discriminator_real_loss=1.315, discriminator_fake_loss=1.382, generator_loss=28.26, generator_mel_loss=18.03, generator_kl_loss=1.509, generator_dur_loss=1.68, generator_adv_loss=1.979, generator_feat_match_loss=5.064, over 42.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=28.08, generator_mel_loss=18.01, generator_kl_loss=1.416, generator_dur_loss=1.756, generator_adv_loss=1.96, generator_feat_match_loss=4.932, over 7162.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:37:30,178 INFO [train.py:919] (5/6) Start epoch 474 +2024-03-14 00:40:13,526 INFO [train.py:527] (5/6) Epoch 474, batch 48, global_batch_idx: 58700, batch size: 74, loss[discriminator_loss=2.711, discriminator_real_loss=1.295, discriminator_fake_loss=1.416, generator_loss=28.25, generator_mel_loss=18.28, generator_kl_loss=1.398, generator_dur_loss=1.764, generator_adv_loss=2.068, generator_feat_match_loss=4.744, over 74.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.38, discriminator_fake_loss=1.332, generator_loss=28.08, generator_mel_loss=18.04, generator_kl_loss=1.45, generator_dur_loss=1.741, generator_adv_loss=1.945, generator_feat_match_loss=4.907, over 2540.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:42:34,267 INFO [train.py:527] (5/6) Epoch 474, batch 98, global_batch_idx: 58750, batch size: 80, loss[discriminator_loss=2.712, discriminator_real_loss=1.326, discriminator_fake_loss=1.386, generator_loss=27.95, generator_mel_loss=18, generator_kl_loss=1.316, generator_dur_loss=1.807, generator_adv_loss=1.99, generator_feat_match_loss=4.836, over 80.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=28.06, generator_mel_loss=18.03, generator_kl_loss=1.416, generator_dur_loss=1.752, generator_adv_loss=1.935, generator_feat_match_loss=4.929, over 5510.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, +2024-03-14 00:43:48,036 INFO [train.py:919] (5/6) Start epoch 475 +2024-03-14 00:45:23,444 INFO [train.py:527] (5/6) Epoch 475, batch 24, global_batch_idx: 58800, batch size: 80, loss[discriminator_loss=2.685, discriminator_real_loss=1.365, discriminator_fake_loss=1.32, generator_loss=27.3, generator_mel_loss=17.65, generator_kl_loss=1.3, generator_dur_loss=1.796, generator_adv_loss=1.917, generator_feat_match_loss=4.639, over 80.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.389, discriminator_fake_loss=1.336, generator_loss=28, generator_mel_loss=18.06, generator_kl_loss=1.455, generator_dur_loss=1.727, generator_adv_loss=1.944, generator_feat_match_loss=4.815, over 1265.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:45:23,445 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 00:45:31,208 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 00:47:52,080 INFO [train.py:527] (5/6) Epoch 475, batch 74, global_batch_idx: 58850, batch size: 74, loss[discriminator_loss=2.7, discriminator_real_loss=1.403, discriminator_fake_loss=1.297, generator_loss=27.46, generator_mel_loss=17.62, generator_kl_loss=1.382, generator_dur_loss=1.804, generator_adv_loss=1.976, generator_feat_match_loss=4.674, over 74.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.345, generator_loss=27.98, generator_mel_loss=18.02, generator_kl_loss=1.416, generator_dur_loss=1.752, generator_adv_loss=1.94, generator_feat_match_loss=4.862, over 4279.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:50:09,420 INFO [train.py:919] (5/6) Start epoch 476 +2024-03-14 00:50:34,905 INFO [train.py:527] (5/6) Epoch 476, batch 0, global_batch_idx: 58900, batch size: 64, loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=28.16, generator_mel_loss=18.1, generator_kl_loss=1.463, generator_dur_loss=1.844, generator_adv_loss=2.007, generator_feat_match_loss=4.749, over 64.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=28.16, generator_mel_loss=18.1, generator_kl_loss=1.463, generator_dur_loss=1.844, generator_adv_loss=2.007, generator_feat_match_loss=4.749, over 64.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:52:59,146 INFO [train.py:527] (5/6) Epoch 476, batch 50, global_batch_idx: 58950, batch size: 72, loss[discriminator_loss=2.711, discriminator_real_loss=1.42, discriminator_fake_loss=1.291, generator_loss=27.07, generator_mel_loss=17.77, generator_kl_loss=1.16, generator_dur_loss=1.848, generator_adv_loss=1.833, generator_feat_match_loss=4.456, over 72.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.361, discriminator_fake_loss=1.338, generator_loss=28.2, generator_mel_loss=18.02, generator_kl_loss=1.392, generator_dur_loss=1.777, generator_adv_loss=1.95, generator_feat_match_loss=5.056, over 3115.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:55:19,574 INFO [train.py:527] (5/6) Epoch 476, batch 100, global_batch_idx: 59000, batch size: 52, loss[discriminator_loss=2.72, discriminator_real_loss=1.472, discriminator_fake_loss=1.249, generator_loss=27.4, generator_mel_loss=18, generator_kl_loss=1.347, generator_dur_loss=1.661, generator_adv_loss=1.901, generator_feat_match_loss=4.495, over 52.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.364, discriminator_fake_loss=1.34, generator_loss=28.11, generator_mel_loss=17.99, generator_kl_loss=1.399, generator_dur_loss=1.77, generator_adv_loss=1.97, generator_feat_match_loss=4.982, over 6334.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 00:55:19,575 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 00:55:28,426 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 00:56:30,801 INFO [train.py:919] (5/6) Start epoch 477 +2024-03-14 00:58:09,117 INFO [train.py:527] (5/6) Epoch 477, batch 26, global_batch_idx: 59050, batch size: 77, loss[discriminator_loss=2.705, discriminator_real_loss=1.409, discriminator_fake_loss=1.296, generator_loss=27.89, generator_mel_loss=17.88, generator_kl_loss=1.398, generator_dur_loss=1.794, generator_adv_loss=1.879, generator_feat_match_loss=4.936, over 77.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.385, discriminator_fake_loss=1.339, generator_loss=28, generator_mel_loss=17.96, generator_kl_loss=1.378, generator_dur_loss=1.769, generator_adv_loss=1.94, generator_feat_match_loss=4.949, over 1830.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:00:33,351 INFO [train.py:527] (5/6) Epoch 477, batch 76, global_batch_idx: 59100, batch size: 42, loss[discriminator_loss=2.747, discriminator_real_loss=1.358, discriminator_fake_loss=1.389, generator_loss=28.29, generator_mel_loss=18.18, generator_kl_loss=1.642, generator_dur_loss=1.703, generator_adv_loss=2.009, generator_feat_match_loss=4.765, over 42.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.99, generator_mel_loss=17.97, generator_kl_loss=1.408, generator_dur_loss=1.757, generator_adv_loss=1.942, generator_feat_match_loss=4.912, over 4619.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:02:41,810 INFO [train.py:919] (5/6) Start epoch 478 +2024-03-14 01:03:10,913 INFO [train.py:527] (5/6) Epoch 478, batch 2, global_batch_idx: 59150, batch size: 47, loss[discriminator_loss=2.77, discriminator_real_loss=1.547, discriminator_fake_loss=1.223, generator_loss=28.75, generator_mel_loss=18.69, generator_kl_loss=1.499, generator_dur_loss=1.66, generator_adv_loss=1.939, generator_feat_match_loss=4.958, over 47.00 samples.], tot_loss[discriminator_loss=2.816, discriminator_real_loss=1.459, discriminator_fake_loss=1.357, generator_loss=28.53, generator_mel_loss=18.47, generator_kl_loss=1.482, generator_dur_loss=1.672, generator_adv_loss=1.932, generator_feat_match_loss=4.978, over 148.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:05:34,122 INFO [train.py:527] (5/6) Epoch 478, batch 52, global_batch_idx: 59200, batch size: 95, loss[discriminator_loss=2.67, discriminator_real_loss=1.356, discriminator_fake_loss=1.314, generator_loss=27.6, generator_mel_loss=17.98, generator_kl_loss=1.217, generator_dur_loss=1.891, generator_adv_loss=1.921, generator_feat_match_loss=4.586, over 95.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=28.21, generator_mel_loss=18.15, generator_kl_loss=1.421, generator_dur_loss=1.747, generator_adv_loss=1.939, generator_feat_match_loss=4.95, over 3042.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:05:34,124 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 01:05:41,916 INFO [train.py:591] (5/6) 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 01:05:41,917 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 01:08:05,603 INFO [train.py:527] (5/6) Epoch 478, batch 102, global_batch_idx: 59250, batch size: 58, loss[discriminator_loss=2.743, discriminator_real_loss=1.271, discriminator_fake_loss=1.472, generator_loss=28.72, generator_mel_loss=17.92, generator_kl_loss=1.559, generator_dur_loss=1.706, generator_adv_loss=1.936, generator_feat_match_loss=5.595, over 58.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=28.1, generator_mel_loss=18.04, generator_kl_loss=1.4, generator_dur_loss=1.761, generator_adv_loss=1.943, generator_feat_match_loss=4.95, over 5977.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:09:05,017 INFO [train.py:919] (5/6) Start epoch 479 +2024-03-14 01:10:51,250 INFO [train.py:527] (5/6) Epoch 479, batch 28, global_batch_idx: 59300, batch size: 48, loss[discriminator_loss=2.709, discriminator_real_loss=1.338, discriminator_fake_loss=1.371, generator_loss=27.83, generator_mel_loss=17.91, generator_kl_loss=1.611, generator_dur_loss=1.737, generator_adv_loss=2.019, generator_feat_match_loss=4.556, over 48.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.365, discriminator_fake_loss=1.341, generator_loss=28.12, generator_mel_loss=17.99, generator_kl_loss=1.435, generator_dur_loss=1.769, generator_adv_loss=1.943, generator_feat_match_loss=4.98, over 1633.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:13:13,857 INFO [train.py:527] (5/6) Epoch 479, batch 78, global_batch_idx: 59350, batch size: 77, loss[discriminator_loss=2.729, discriminator_real_loss=1.374, discriminator_fake_loss=1.354, generator_loss=28.33, generator_mel_loss=17.88, generator_kl_loss=1.29, generator_dur_loss=1.816, generator_adv_loss=1.93, generator_feat_match_loss=5.416, over 77.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.36, discriminator_fake_loss=1.346, generator_loss=28.17, generator_mel_loss=18.01, generator_kl_loss=1.423, generator_dur_loss=1.767, generator_adv_loss=1.941, generator_feat_match_loss=5.021, over 4618.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:15:20,248 INFO [train.py:919] (5/6) Start epoch 480 +2024-03-14 01:15:56,064 INFO [train.py:527] (5/6) Epoch 480, batch 4, global_batch_idx: 59400, batch size: 56, loss[discriminator_loss=2.696, discriminator_real_loss=1.332, discriminator_fake_loss=1.364, generator_loss=28.78, generator_mel_loss=17.84, generator_kl_loss=1.354, generator_dur_loss=1.767, generator_adv_loss=2.064, generator_feat_match_loss=5.758, over 56.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.351, discriminator_fake_loss=1.344, generator_loss=28.32, generator_mel_loss=17.93, generator_kl_loss=1.379, generator_dur_loss=1.782, generator_adv_loss=1.978, generator_feat_match_loss=5.252, over 315.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:15:56,066 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 01:16:03,714 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 01:18:25,420 INFO [train.py:527] (5/6) Epoch 480, batch 54, global_batch_idx: 59450, batch size: 74, loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=28.18, generator_mel_loss=17.93, generator_kl_loss=1.436, generator_dur_loss=1.791, generator_adv_loss=2.104, generator_feat_match_loss=4.924, over 74.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=28.19, generator_mel_loss=18.04, generator_kl_loss=1.401, generator_dur_loss=1.758, generator_adv_loss=1.978, generator_feat_match_loss=5.012, over 3249.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:20:46,635 INFO [train.py:527] (5/6) Epoch 480, batch 104, global_batch_idx: 59500, batch size: 52, loss[discriminator_loss=2.75, discriminator_real_loss=1.374, discriminator_fake_loss=1.377, generator_loss=28.07, generator_mel_loss=18.39, generator_kl_loss=1.413, generator_dur_loss=1.68, generator_adv_loss=1.875, generator_feat_match_loss=4.714, over 52.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=28.14, generator_mel_loss=18.03, generator_kl_loss=1.401, generator_dur_loss=1.762, generator_adv_loss=1.967, generator_feat_match_loss=4.979, over 6207.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:21:42,447 INFO [train.py:919] (5/6) Start epoch 481 +2024-03-14 01:23:35,268 INFO [train.py:527] (5/6) Epoch 481, batch 30, global_batch_idx: 59550, batch size: 74, loss[discriminator_loss=2.706, discriminator_real_loss=1.344, discriminator_fake_loss=1.362, generator_loss=27.74, generator_mel_loss=17.87, generator_kl_loss=1.279, generator_dur_loss=1.783, generator_adv_loss=2.067, generator_feat_match_loss=4.744, over 74.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.371, discriminator_fake_loss=1.341, generator_loss=28.24, generator_mel_loss=18.1, generator_kl_loss=1.425, generator_dur_loss=1.74, generator_adv_loss=1.944, generator_feat_match_loss=5.026, over 1671.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:25:59,993 INFO [train.py:527] (5/6) Epoch 481, batch 80, global_batch_idx: 59600, batch size: 56, loss[discriminator_loss=2.785, discriminator_real_loss=1.354, discriminator_fake_loss=1.431, generator_loss=28.41, generator_mel_loss=18.21, generator_kl_loss=1.434, generator_dur_loss=1.786, generator_adv_loss=1.866, generator_feat_match_loss=5.11, over 56.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.369, discriminator_fake_loss=1.345, generator_loss=28.11, generator_mel_loss=18.03, generator_kl_loss=1.386, generator_dur_loss=1.757, generator_adv_loss=1.945, generator_feat_match_loss=4.996, over 4800.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:25:59,994 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 01:26:08,544 INFO [train.py:591] (5/6) 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 01:26:08,545 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 01:28:08,644 INFO [train.py:919] (5/6) Start epoch 482 +2024-03-14 01:28:50,651 INFO [train.py:527] (5/6) Epoch 482, batch 6, global_batch_idx: 59650, batch size: 62, loss[discriminator_loss=2.689, discriminator_real_loss=1.375, discriminator_fake_loss=1.314, generator_loss=28.53, generator_mel_loss=18.27, generator_kl_loss=1.458, generator_dur_loss=1.743, generator_adv_loss=1.853, generator_feat_match_loss=5.205, over 62.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.394, discriminator_fake_loss=1.336, generator_loss=28.22, generator_mel_loss=18.06, generator_kl_loss=1.464, generator_dur_loss=1.707, generator_adv_loss=1.982, generator_feat_match_loss=5.001, over 356.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:31:11,727 INFO [train.py:527] (5/6) Epoch 482, batch 56, global_batch_idx: 59700, batch size: 77, loss[discriminator_loss=2.737, discriminator_real_loss=1.382, discriminator_fake_loss=1.355, generator_loss=27.93, generator_mel_loss=17.89, generator_kl_loss=1.269, generator_dur_loss=1.807, generator_adv_loss=1.95, generator_feat_match_loss=5.013, over 77.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.38, discriminator_fake_loss=1.336, generator_loss=28.06, generator_mel_loss=17.99, generator_kl_loss=1.436, generator_dur_loss=1.752, generator_adv_loss=1.956, generator_feat_match_loss=4.933, over 3265.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:33:31,789 INFO [train.py:527] (5/6) Epoch 482, batch 106, global_batch_idx: 59750, batch size: 62, loss[discriminator_loss=2.709, discriminator_real_loss=1.455, discriminator_fake_loss=1.254, generator_loss=28.11, generator_mel_loss=18.05, generator_kl_loss=1.428, generator_dur_loss=1.733, generator_adv_loss=1.984, generator_feat_match_loss=4.925, over 62.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.379, discriminator_fake_loss=1.339, generator_loss=28.07, generator_mel_loss=17.97, generator_kl_loss=1.424, generator_dur_loss=1.755, generator_adv_loss=1.954, generator_feat_match_loss=4.96, over 6181.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:34:23,285 INFO [train.py:919] (5/6) Start epoch 483 +2024-03-14 01:36:18,584 INFO [train.py:527] (5/6) Epoch 483, batch 32, global_batch_idx: 59800, batch size: 58, loss[discriminator_loss=2.668, discriminator_real_loss=1.26, discriminator_fake_loss=1.408, generator_loss=28.92, generator_mel_loss=18.22, generator_kl_loss=1.522, generator_dur_loss=1.719, generator_adv_loss=2.133, generator_feat_match_loss=5.322, over 58.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.357, discriminator_fake_loss=1.337, generator_loss=28.26, generator_mel_loss=18.09, generator_kl_loss=1.402, generator_dur_loss=1.767, generator_adv_loss=1.971, generator_feat_match_loss=5.034, over 1840.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:36:18,586 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 01:36:26,621 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 01:38:50,012 INFO [train.py:527] (5/6) Epoch 483, batch 82, global_batch_idx: 59850, batch size: 64, loss[discriminator_loss=2.736, discriminator_real_loss=1.405, discriminator_fake_loss=1.331, generator_loss=27.5, generator_mel_loss=18, generator_kl_loss=1.372, generator_dur_loss=1.769, generator_adv_loss=1.909, generator_feat_match_loss=4.453, over 64.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.339, generator_loss=28.14, generator_mel_loss=18.03, generator_kl_loss=1.404, generator_dur_loss=1.77, generator_adv_loss=1.953, generator_feat_match_loss=4.982, over 4900.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:40:45,498 INFO [train.py:919] (5/6) Start epoch 484 +2024-03-14 01:41:33,009 INFO [train.py:527] (5/6) Epoch 484, batch 8, global_batch_idx: 59900, batch size: 64, loss[discriminator_loss=2.659, discriminator_real_loss=1.367, discriminator_fake_loss=1.292, generator_loss=28.47, generator_mel_loss=18.1, generator_kl_loss=1.401, generator_dur_loss=1.818, generator_adv_loss=1.975, generator_feat_match_loss=5.17, over 64.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.368, discriminator_fake_loss=1.326, generator_loss=28.56, generator_mel_loss=18.11, generator_kl_loss=1.435, generator_dur_loss=1.777, generator_adv_loss=1.982, generator_feat_match_loss=5.261, over 521.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:43:54,223 INFO [train.py:527] (5/6) Epoch 484, batch 58, global_batch_idx: 59950, batch size: 72, loss[discriminator_loss=2.712, discriminator_real_loss=1.371, discriminator_fake_loss=1.341, generator_loss=27.68, generator_mel_loss=17.74, generator_kl_loss=1.369, generator_dur_loss=1.824, generator_adv_loss=2.225, generator_feat_match_loss=4.518, over 72.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.37, discriminator_fake_loss=1.337, generator_loss=28.13, generator_mel_loss=17.99, generator_kl_loss=1.407, generator_dur_loss=1.778, generator_adv_loss=1.97, generator_feat_match_loss=4.99, over 3470.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:46:14,255 INFO [train.py:527] (5/6) Epoch 484, batch 108, global_batch_idx: 60000, batch size: 17, loss[discriminator_loss=2.742, discriminator_real_loss=1.458, discriminator_fake_loss=1.284, generator_loss=26.09, generator_mel_loss=17.3, generator_kl_loss=1.422, generator_dur_loss=1.629, generator_adv_loss=1.853, generator_feat_match_loss=3.893, over 17.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=28.12, generator_mel_loss=18.01, generator_kl_loss=1.425, generator_dur_loss=1.762, generator_adv_loss=1.959, generator_feat_match_loss=4.965, over 6074.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:46:14,256 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 01:46:22,957 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 01:47:06,333 INFO [train.py:919] (5/6) Start epoch 485 +2024-03-14 01:49:07,752 INFO [train.py:527] (5/6) Epoch 485, batch 34, global_batch_idx: 60050, batch size: 14, loss[discriminator_loss=2.667, discriminator_real_loss=1.284, discriminator_fake_loss=1.384, generator_loss=29.56, generator_mel_loss=18.71, generator_kl_loss=1.927, generator_dur_loss=1.657, generator_adv_loss=2.032, generator_feat_match_loss=5.236, over 14.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.382, discriminator_fake_loss=1.334, generator_loss=28.03, generator_mel_loss=17.9, generator_kl_loss=1.399, generator_dur_loss=1.777, generator_adv_loss=1.961, generator_feat_match_loss=4.996, over 2135.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:51:25,469 INFO [train.py:527] (5/6) Epoch 485, batch 84, global_batch_idx: 60100, batch size: 58, loss[discriminator_loss=2.703, discriminator_real_loss=1.375, discriminator_fake_loss=1.328, generator_loss=28.16, generator_mel_loss=18.26, generator_kl_loss=1.363, generator_dur_loss=1.766, generator_adv_loss=1.897, generator_feat_match_loss=4.877, over 58.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.373, discriminator_fake_loss=1.343, generator_loss=28.06, generator_mel_loss=17.99, generator_kl_loss=1.391, generator_dur_loss=1.772, generator_adv_loss=1.944, generator_feat_match_loss=4.957, over 5000.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:53:14,015 INFO [train.py:919] (5/6) Start epoch 486 +2024-03-14 01:54:02,940 INFO [train.py:527] (5/6) Epoch 486, batch 10, global_batch_idx: 60150, batch size: 25, loss[discriminator_loss=2.796, discriminator_real_loss=1.483, discriminator_fake_loss=1.312, generator_loss=28.22, generator_mel_loss=18.38, generator_kl_loss=1.603, generator_dur_loss=1.608, generator_adv_loss=1.91, generator_feat_match_loss=4.719, over 25.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.387, discriminator_fake_loss=1.341, generator_loss=28.32, generator_mel_loss=18.25, generator_kl_loss=1.392, generator_dur_loss=1.753, generator_adv_loss=1.955, generator_feat_match_loss=4.966, over 620.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:56:22,063 INFO [train.py:527] (5/6) Epoch 486, batch 60, global_batch_idx: 60200, batch size: 42, loss[discriminator_loss=2.712, discriminator_real_loss=1.359, discriminator_fake_loss=1.353, generator_loss=28.75, generator_mel_loss=18.26, generator_kl_loss=1.488, generator_dur_loss=1.667, generator_adv_loss=1.85, generator_feat_match_loss=5.487, over 42.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.372, discriminator_fake_loss=1.34, generator_loss=28.3, generator_mel_loss=18.12, generator_kl_loss=1.422, generator_dur_loss=1.749, generator_adv_loss=1.954, generator_feat_match_loss=5.052, over 3589.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:56:22,064 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 01:56:30,162 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 01:58:51,787 INFO [train.py:527] (5/6) Epoch 486, batch 110, global_batch_idx: 60250, batch size: 17, loss[discriminator_loss=2.782, discriminator_real_loss=1.354, discriminator_fake_loss=1.429, generator_loss=29.57, generator_mel_loss=19.07, generator_kl_loss=1.828, generator_dur_loss=1.566, generator_adv_loss=1.954, generator_feat_match_loss=5.143, over 17.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.342, generator_loss=28.22, generator_mel_loss=18.08, generator_kl_loss=1.404, generator_dur_loss=1.755, generator_adv_loss=1.954, generator_feat_match_loss=5.025, over 6586.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 01:59:27,040 INFO [train.py:919] (5/6) Start epoch 487 +2024-03-14 02:01:30,022 INFO [train.py:527] (5/6) Epoch 487, batch 36, global_batch_idx: 60300, batch size: 61, loss[discriminator_loss=2.708, discriminator_real_loss=1.452, discriminator_fake_loss=1.256, generator_loss=28.47, generator_mel_loss=18.14, generator_kl_loss=1.49, generator_dur_loss=1.725, generator_adv_loss=1.967, generator_feat_match_loss=5.148, over 61.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.07, generator_mel_loss=18.02, generator_kl_loss=1.439, generator_dur_loss=1.728, generator_adv_loss=1.944, generator_feat_match_loss=4.932, over 2161.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:03:49,127 INFO [train.py:527] (5/6) Epoch 487, batch 86, global_batch_idx: 60350, batch size: 53, loss[discriminator_loss=2.713, discriminator_real_loss=1.342, discriminator_fake_loss=1.371, generator_loss=27.53, generator_mel_loss=17.47, generator_kl_loss=1.56, generator_dur_loss=1.677, generator_adv_loss=2.062, generator_feat_match_loss=4.766, over 53.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=28.08, generator_mel_loss=17.98, generator_kl_loss=1.416, generator_dur_loss=1.748, generator_adv_loss=1.953, generator_feat_match_loss=4.978, over 5225.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:05:35,532 INFO [train.py:919] (5/6) Start epoch 488 +2024-03-14 02:06:34,154 INFO [train.py:527] (5/6) Epoch 488, batch 12, global_batch_idx: 60400, batch size: 61, loss[discriminator_loss=2.752, discriminator_real_loss=1.488, discriminator_fake_loss=1.265, generator_loss=27.52, generator_mel_loss=17.77, generator_kl_loss=1.437, generator_dur_loss=1.713, generator_adv_loss=1.843, generator_feat_match_loss=4.755, over 61.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.389, discriminator_fake_loss=1.335, generator_loss=28.03, generator_mel_loss=17.96, generator_kl_loss=1.37, generator_dur_loss=1.78, generator_adv_loss=1.976, generator_feat_match_loss=4.942, over 756.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:06:34,157 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 02:06:42,166 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 02:09:02,871 INFO [train.py:527] (5/6) Epoch 488, batch 62, global_batch_idx: 60450, batch size: 53, loss[discriminator_loss=2.758, discriminator_real_loss=1.413, discriminator_fake_loss=1.345, generator_loss=28.05, generator_mel_loss=18.31, generator_kl_loss=1.312, generator_dur_loss=1.686, generator_adv_loss=1.942, generator_feat_match_loss=4.796, over 53.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.381, discriminator_fake_loss=1.344, generator_loss=28.12, generator_mel_loss=18.02, generator_kl_loss=1.393, generator_dur_loss=1.757, generator_adv_loss=1.949, generator_feat_match_loss=5.006, over 3533.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:11:20,143 INFO [train.py:527] (5/6) Epoch 488, batch 112, global_batch_idx: 60500, batch size: 31, loss[discriminator_loss=2.669, discriminator_real_loss=1.284, discriminator_fake_loss=1.386, generator_loss=28.95, generator_mel_loss=18.27, generator_kl_loss=1.596, generator_dur_loss=1.612, generator_adv_loss=2.063, generator_feat_match_loss=5.409, over 31.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.377, discriminator_fake_loss=1.34, generator_loss=28.08, generator_mel_loss=17.97, generator_kl_loss=1.399, generator_dur_loss=1.756, generator_adv_loss=1.952, generator_feat_match_loss=5.002, over 6365.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:11:51,099 INFO [train.py:919] (5/6) Start epoch 489 +2024-03-14 02:14:00,768 INFO [train.py:527] (5/6) Epoch 489, batch 38, global_batch_idx: 60550, batch size: 31, loss[discriminator_loss=2.599, discriminator_real_loss=1.334, discriminator_fake_loss=1.265, generator_loss=28.31, generator_mel_loss=18.1, generator_kl_loss=1.452, generator_dur_loss=1.629, generator_adv_loss=2.065, generator_feat_match_loss=5.072, over 31.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.372, discriminator_fake_loss=1.352, generator_loss=27.96, generator_mel_loss=17.92, generator_kl_loss=1.405, generator_dur_loss=1.75, generator_adv_loss=1.966, generator_feat_match_loss=4.925, over 2250.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:16:20,625 INFO [train.py:527] (5/6) Epoch 489, batch 88, global_batch_idx: 60600, batch size: 74, loss[discriminator_loss=2.767, discriminator_real_loss=1.396, discriminator_fake_loss=1.371, generator_loss=27.67, generator_mel_loss=17.73, generator_kl_loss=1.43, generator_dur_loss=1.757, generator_adv_loss=1.955, generator_feat_match_loss=4.8, over 74.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.377, discriminator_fake_loss=1.339, generator_loss=28, generator_mel_loss=17.95, generator_kl_loss=1.415, generator_dur_loss=1.745, generator_adv_loss=1.963, generator_feat_match_loss=4.927, over 5039.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:16:20,626 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 02:16:29,497 INFO [train.py:591] (5/6) 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 02:16:29,498 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 02:18:06,635 INFO [train.py:919] (5/6) Start epoch 490 +2024-03-14 02:19:10,393 INFO [train.py:527] (5/6) Epoch 490, batch 14, global_batch_idx: 60650, batch size: 15, loss[discriminator_loss=2.65, discriminator_real_loss=1.325, discriminator_fake_loss=1.324, generator_loss=29.96, generator_mel_loss=18.25, generator_kl_loss=1.666, generator_dur_loss=1.544, generator_adv_loss=2.173, generator_feat_match_loss=6.327, over 15.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.347, discriminator_fake_loss=1.362, generator_loss=27.94, generator_mel_loss=17.93, generator_kl_loss=1.387, generator_dur_loss=1.746, generator_adv_loss=1.929, generator_feat_match_loss=4.953, over 835.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:21:27,305 INFO [train.py:527] (5/6) Epoch 490, batch 64, global_batch_idx: 60700, batch size: 52, loss[discriminator_loss=2.675, discriminator_real_loss=1.357, discriminator_fake_loss=1.318, generator_loss=30.2, generator_mel_loss=18.63, generator_kl_loss=1.668, generator_dur_loss=1.698, generator_adv_loss=2.072, generator_feat_match_loss=6.132, over 52.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.15, generator_mel_loss=18, generator_kl_loss=1.426, generator_dur_loss=1.736, generator_adv_loss=1.988, generator_feat_match_loss=5.005, over 3688.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:23:47,037 INFO [train.py:527] (5/6) Epoch 490, batch 114, global_batch_idx: 60750, batch size: 25, loss[discriminator_loss=2.698, discriminator_real_loss=1.409, discriminator_fake_loss=1.289, generator_loss=29.69, generator_mel_loss=18.9, generator_kl_loss=1.63, generator_dur_loss=1.598, generator_adv_loss=1.846, generator_feat_match_loss=5.716, over 25.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.372, discriminator_fake_loss=1.34, generator_loss=28.1, generator_mel_loss=18.01, generator_kl_loss=1.424, generator_dur_loss=1.736, generator_adv_loss=1.964, generator_feat_match_loss=4.965, over 6678.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:24:12,635 INFO [train.py:919] (5/6) Start epoch 491 +2024-03-14 02:26:29,232 INFO [train.py:527] (5/6) Epoch 491, batch 40, global_batch_idx: 60800, batch size: 55, loss[discriminator_loss=2.659, discriminator_real_loss=1.307, discriminator_fake_loss=1.351, generator_loss=28.51, generator_mel_loss=18.41, generator_kl_loss=1.325, generator_dur_loss=1.731, generator_adv_loss=1.994, generator_feat_match_loss=5.05, over 55.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.367, discriminator_fake_loss=1.343, generator_loss=28.08, generator_mel_loss=18.05, generator_kl_loss=1.394, generator_dur_loss=1.756, generator_adv_loss=1.944, generator_feat_match_loss=4.938, over 2307.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:26:29,234 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 02:26:37,143 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 02:28:55,260 INFO [train.py:527] (5/6) Epoch 491, batch 90, global_batch_idx: 60850, batch size: 66, loss[discriminator_loss=2.719, discriminator_real_loss=1.46, discriminator_fake_loss=1.259, generator_loss=28.03, generator_mel_loss=17.99, generator_kl_loss=1.266, generator_dur_loss=1.754, generator_adv_loss=1.936, generator_feat_match_loss=5.08, over 66.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.368, discriminator_fake_loss=1.343, generator_loss=28.11, generator_mel_loss=18.01, generator_kl_loss=1.401, generator_dur_loss=1.759, generator_adv_loss=1.955, generator_feat_match_loss=4.985, over 5210.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:30:28,102 INFO [train.py:919] (5/6) Start epoch 492 +2024-03-14 02:31:38,294 INFO [train.py:527] (5/6) Epoch 492, batch 16, global_batch_idx: 60900, batch size: 77, loss[discriminator_loss=2.769, discriminator_real_loss=1.453, discriminator_fake_loss=1.316, generator_loss=27.42, generator_mel_loss=17.71, generator_kl_loss=1.372, generator_dur_loss=1.816, generator_adv_loss=1.838, generator_feat_match_loss=4.68, over 77.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=27.93, generator_mel_loss=17.95, generator_kl_loss=1.421, generator_dur_loss=1.767, generator_adv_loss=1.919, generator_feat_match_loss=4.872, over 1023.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:33:57,954 INFO [train.py:527] (5/6) Epoch 492, batch 66, global_batch_idx: 60950, batch size: 48, loss[discriminator_loss=2.723, discriminator_real_loss=1.458, discriminator_fake_loss=1.264, generator_loss=27.91, generator_mel_loss=17.59, generator_kl_loss=1.525, generator_dur_loss=1.7, generator_adv_loss=1.878, generator_feat_match_loss=5.209, over 48.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.381, discriminator_fake_loss=1.343, generator_loss=28.09, generator_mel_loss=18, generator_kl_loss=1.389, generator_dur_loss=1.776, generator_adv_loss=1.944, generator_feat_match_loss=4.977, over 3990.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:36:17,221 INFO [train.py:527] (5/6) Epoch 492, batch 116, global_batch_idx: 61000, batch size: 56, loss[discriminator_loss=2.753, discriminator_real_loss=1.349, discriminator_fake_loss=1.404, generator_loss=28.15, generator_mel_loss=18.25, generator_kl_loss=1.474, generator_dur_loss=1.761, generator_adv_loss=2.054, generator_feat_match_loss=4.613, over 56.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=28.17, generator_mel_loss=18.04, generator_kl_loss=1.399, generator_dur_loss=1.778, generator_adv_loss=1.952, generator_feat_match_loss=5.005, over 7018.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:36:17,223 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 02:36:26,123 INFO [train.py:591] (5/6) 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 02:36:26,123 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 02:36:47,790 INFO [train.py:919] (5/6) Start epoch 493 +2024-03-14 02:39:06,977 INFO [train.py:527] (5/6) Epoch 493, batch 42, global_batch_idx: 61050, batch size: 47, loss[discriminator_loss=2.708, discriminator_real_loss=1.31, discriminator_fake_loss=1.397, generator_loss=29, generator_mel_loss=18.28, generator_kl_loss=1.474, generator_dur_loss=1.667, generator_adv_loss=1.989, generator_feat_match_loss=5.587, over 47.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.37, discriminator_fake_loss=1.345, generator_loss=27.96, generator_mel_loss=17.95, generator_kl_loss=1.393, generator_dur_loss=1.745, generator_adv_loss=1.943, generator_feat_match_loss=4.933, over 2550.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:41:25,991 INFO [train.py:527] (5/6) Epoch 493, batch 92, global_batch_idx: 61100, batch size: 55, loss[discriminator_loss=2.669, discriminator_real_loss=1.353, discriminator_fake_loss=1.316, generator_loss=28.93, generator_mel_loss=18.37, generator_kl_loss=1.42, generator_dur_loss=1.707, generator_adv_loss=2.056, generator_feat_match_loss=5.376, over 55.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.07, generator_mel_loss=17.99, generator_kl_loss=1.402, generator_dur_loss=1.743, generator_adv_loss=1.951, generator_feat_match_loss=4.981, over 5586.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:42:51,951 INFO [train.py:919] (5/6) Start epoch 494 +2024-03-14 02:44:05,479 INFO [train.py:527] (5/6) Epoch 494, batch 18, global_batch_idx: 61150, batch size: 31, loss[discriminator_loss=2.657, discriminator_real_loss=1.36, discriminator_fake_loss=1.297, generator_loss=28.62, generator_mel_loss=18.26, generator_kl_loss=1.546, generator_dur_loss=1.656, generator_adv_loss=1.962, generator_feat_match_loss=5.191, over 31.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.373, discriminator_fake_loss=1.337, generator_loss=28.11, generator_mel_loss=18.03, generator_kl_loss=1.46, generator_dur_loss=1.714, generator_adv_loss=1.936, generator_feat_match_loss=4.975, over 984.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:46:26,250 INFO [train.py:527] (5/6) Epoch 494, batch 68, global_batch_idx: 61200, batch size: 88, loss[discriminator_loss=2.706, discriminator_real_loss=1.328, discriminator_fake_loss=1.377, generator_loss=28.65, generator_mel_loss=18.15, generator_kl_loss=1.451, generator_dur_loss=1.816, generator_adv_loss=1.957, generator_feat_match_loss=5.275, over 88.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.372, discriminator_fake_loss=1.335, generator_loss=28.08, generator_mel_loss=17.97, generator_kl_loss=1.437, generator_dur_loss=1.72, generator_adv_loss=1.947, generator_feat_match_loss=5.003, over 3851.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:46:26,251 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 02:46:34,378 INFO [train.py:591] (5/6) 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 02:46:34,379 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 02:48:51,411 INFO [train.py:527] (5/6) Epoch 494, batch 118, global_batch_idx: 61250, batch size: 62, loss[discriminator_loss=2.784, discriminator_real_loss=1.31, discriminator_fake_loss=1.474, generator_loss=28.77, generator_mel_loss=18.15, generator_kl_loss=1.58, generator_dur_loss=1.719, generator_adv_loss=1.864, generator_feat_match_loss=5.454, over 62.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.37, discriminator_fake_loss=1.34, generator_loss=28.16, generator_mel_loss=18.04, generator_kl_loss=1.435, generator_dur_loss=1.729, generator_adv_loss=1.949, generator_feat_match_loss=5.016, over 6588.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:49:06,901 INFO [train.py:919] (5/6) Start epoch 495 +2024-03-14 02:51:31,741 INFO [train.py:527] (5/6) Epoch 495, batch 44, global_batch_idx: 61300, batch size: 70, loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.322, generator_loss=28.55, generator_mel_loss=18.05, generator_kl_loss=1.419, generator_dur_loss=1.796, generator_adv_loss=1.959, generator_feat_match_loss=5.321, over 70.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.37, discriminator_fake_loss=1.336, generator_loss=28.16, generator_mel_loss=17.96, generator_kl_loss=1.424, generator_dur_loss=1.762, generator_adv_loss=1.961, generator_feat_match_loss=5.053, over 2644.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:53:52,261 INFO [train.py:527] (5/6) Epoch 495, batch 94, global_batch_idx: 61350, batch size: 61, loss[discriminator_loss=2.714, discriminator_real_loss=1.387, discriminator_fake_loss=1.327, generator_loss=28.49, generator_mel_loss=18.19, generator_kl_loss=1.461, generator_dur_loss=1.73, generator_adv_loss=1.954, generator_feat_match_loss=5.158, over 61.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=28.16, generator_mel_loss=17.98, generator_kl_loss=1.421, generator_dur_loss=1.766, generator_adv_loss=1.958, generator_feat_match_loss=5.033, over 5773.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:55:13,009 INFO [train.py:919] (5/6) Start epoch 496 +2024-03-14 02:56:33,639 INFO [train.py:527] (5/6) Epoch 496, batch 20, global_batch_idx: 61400, batch size: 58, loss[discriminator_loss=2.69, discriminator_real_loss=1.321, discriminator_fake_loss=1.369, generator_loss=28.38, generator_mel_loss=18.34, generator_kl_loss=1.373, generator_dur_loss=1.773, generator_adv_loss=2.014, generator_feat_match_loss=4.881, over 58.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.378, discriminator_fake_loss=1.336, generator_loss=28.18, generator_mel_loss=18.03, generator_kl_loss=1.432, generator_dur_loss=1.736, generator_adv_loss=1.949, generator_feat_match_loss=5.033, over 1167.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 02:56:33,640 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 02:56:41,777 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 02:59:00,516 INFO [train.py:527] (5/6) Epoch 496, batch 70, global_batch_idx: 61450, batch size: 77, loss[discriminator_loss=2.718, discriminator_real_loss=1.327, discriminator_fake_loss=1.391, generator_loss=27.59, generator_mel_loss=17.83, generator_kl_loss=1.352, generator_dur_loss=1.772, generator_adv_loss=1.804, generator_feat_match_loss=4.829, over 77.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.368, discriminator_fake_loss=1.344, generator_loss=28.09, generator_mel_loss=17.96, generator_kl_loss=1.411, generator_dur_loss=1.752, generator_adv_loss=1.938, generator_feat_match_loss=5.024, over 4107.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:01:19,069 INFO [train.py:527] (5/6) Epoch 496, batch 120, global_batch_idx: 61500, batch size: 70, loss[discriminator_loss=2.73, discriminator_real_loss=1.413, discriminator_fake_loss=1.317, generator_loss=27.52, generator_mel_loss=17.93, generator_kl_loss=1.295, generator_dur_loss=1.785, generator_adv_loss=1.871, generator_feat_match_loss=4.641, over 70.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=28.1, generator_mel_loss=17.97, generator_kl_loss=1.415, generator_dur_loss=1.757, generator_adv_loss=1.943, generator_feat_match_loss=5.017, over 7159.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:01:29,094 INFO [train.py:919] (5/6) Start epoch 497 +2024-03-14 03:04:01,910 INFO [train.py:527] (5/6) Epoch 497, batch 46, global_batch_idx: 61550, batch size: 25, loss[discriminator_loss=2.71, discriminator_real_loss=1.363, discriminator_fake_loss=1.346, generator_loss=29.18, generator_mel_loss=18.55, generator_kl_loss=1.735, generator_dur_loss=1.556, generator_adv_loss=2.154, generator_feat_match_loss=5.18, over 25.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.375, discriminator_fake_loss=1.336, generator_loss=28.04, generator_mel_loss=17.97, generator_kl_loss=1.444, generator_dur_loss=1.742, generator_adv_loss=1.963, generator_feat_match_loss=4.923, over 2473.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:06:17,968 INFO [train.py:527] (5/6) Epoch 497, batch 96, global_batch_idx: 61600, batch size: 66, loss[discriminator_loss=2.731, discriminator_real_loss=1.336, discriminator_fake_loss=1.395, generator_loss=28.46, generator_mel_loss=18.14, generator_kl_loss=1.5, generator_dur_loss=1.758, generator_adv_loss=1.943, generator_feat_match_loss=5.115, over 66.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.27, generator_mel_loss=18.05, generator_kl_loss=1.447, generator_dur_loss=1.746, generator_adv_loss=1.965, generator_feat_match_loss=5.06, over 5104.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:06:17,969 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 03:06:26,498 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 03:07:42,226 INFO [train.py:919] (5/6) Start epoch 498 +2024-03-14 03:09:08,303 INFO [train.py:527] (5/6) Epoch 498, batch 22, global_batch_idx: 61650, batch size: 48, loss[discriminator_loss=2.691, discriminator_real_loss=1.336, discriminator_fake_loss=1.355, generator_loss=28.3, generator_mel_loss=17.96, generator_kl_loss=1.514, generator_dur_loss=1.664, generator_adv_loss=1.996, generator_feat_match_loss=5.167, over 48.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.38, discriminator_fake_loss=1.336, generator_loss=28.26, generator_mel_loss=17.96, generator_kl_loss=1.389, generator_dur_loss=1.759, generator_adv_loss=1.964, generator_feat_match_loss=5.188, over 1426.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:11:26,820 INFO [train.py:527] (5/6) Epoch 498, batch 72, global_batch_idx: 61700, batch size: 68, loss[discriminator_loss=2.736, discriminator_real_loss=1.417, discriminator_fake_loss=1.32, generator_loss=28.06, generator_mel_loss=18.08, generator_kl_loss=1.324, generator_dur_loss=1.775, generator_adv_loss=1.861, generator_feat_match_loss=5.024, over 68.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.11, generator_mel_loss=17.94, generator_kl_loss=1.401, generator_dur_loss=1.762, generator_adv_loss=1.951, generator_feat_match_loss=5.057, over 4465.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:13:45,223 INFO [train.py:527] (5/6) Epoch 498, batch 122, global_batch_idx: 61750, batch size: 31, loss[discriminator_loss=2.705, discriminator_real_loss=1.26, discriminator_fake_loss=1.444, generator_loss=28.43, generator_mel_loss=18.13, generator_kl_loss=1.533, generator_dur_loss=1.675, generator_adv_loss=1.958, generator_feat_match_loss=5.135, over 31.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=28.2, generator_mel_loss=18, generator_kl_loss=1.416, generator_dur_loss=1.761, generator_adv_loss=1.954, generator_feat_match_loss=5.078, over 7106.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:13:50,064 INFO [train.py:919] (5/6) Start epoch 499 +2024-03-14 03:16:23,908 INFO [train.py:527] (5/6) Epoch 499, batch 48, global_batch_idx: 61800, batch size: 36, loss[discriminator_loss=2.756, discriminator_real_loss=1.464, discriminator_fake_loss=1.293, generator_loss=27.84, generator_mel_loss=17.97, generator_kl_loss=1.586, generator_dur_loss=1.717, generator_adv_loss=1.908, generator_feat_match_loss=4.658, over 36.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.378, discriminator_fake_loss=1.341, generator_loss=28.14, generator_mel_loss=17.97, generator_kl_loss=1.435, generator_dur_loss=1.755, generator_adv_loss=1.958, generator_feat_match_loss=5.025, over 2695.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:16:23,909 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 03:16:31,894 INFO [train.py:591] (5/6) 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 03:16:31,896 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 03:18:47,730 INFO [train.py:527] (5/6) Epoch 499, batch 98, global_batch_idx: 61850, batch size: 31, loss[discriminator_loss=2.665, discriminator_real_loss=1.42, discriminator_fake_loss=1.244, generator_loss=28.37, generator_mel_loss=18.12, generator_kl_loss=1.433, generator_dur_loss=1.608, generator_adv_loss=2.171, generator_feat_match_loss=5.032, over 31.00 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, generator_kl_loss=1.44, generator_dur_loss=1.757, generator_adv_loss=1.967, generator_feat_match_loss=5.06, over 5324.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:20:01,249 INFO [train.py:919] (5/6) Start epoch 500 +2024-03-14 03:21:33,367 INFO [train.py:527] (5/6) Epoch 500, batch 24, global_batch_idx: 61900, batch size: 16, loss[discriminator_loss=2.638, discriminator_real_loss=1.358, discriminator_fake_loss=1.28, generator_loss=30.49, generator_mel_loss=18.32, generator_kl_loss=1.888, generator_dur_loss=1.537, generator_adv_loss=2.028, generator_feat_match_loss=6.726, over 16.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.369, discriminator_fake_loss=1.351, generator_loss=28.22, generator_mel_loss=17.99, generator_kl_loss=1.414, generator_dur_loss=1.763, generator_adv_loss=1.943, generator_feat_match_loss=5.107, over 1525.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:23:53,856 INFO [train.py:527] (5/6) Epoch 500, batch 74, global_batch_idx: 61950, batch size: 80, loss[discriminator_loss=2.725, discriminator_real_loss=1.436, discriminator_fake_loss=1.289, generator_loss=28, generator_mel_loss=17.87, generator_kl_loss=1.432, generator_dur_loss=1.828, generator_adv_loss=1.847, generator_feat_match_loss=5.019, over 80.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.368, discriminator_fake_loss=1.347, generator_loss=28.17, generator_mel_loss=17.97, generator_kl_loss=1.429, generator_dur_loss=1.758, generator_adv_loss=1.95, generator_feat_match_loss=5.056, over 4206.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:26:07,837 INFO [train.py:919] (5/6) Start epoch 501 +2024-03-14 03:26:30,844 INFO [train.py:527] (5/6) Epoch 501, batch 0, global_batch_idx: 62000, batch size: 31, loss[discriminator_loss=2.752, discriminator_real_loss=1.421, discriminator_fake_loss=1.331, generator_loss=28.63, generator_mel_loss=18.42, generator_kl_loss=1.719, generator_dur_loss=1.678, generator_adv_loss=1.966, generator_feat_match_loss=4.849, over 31.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.421, discriminator_fake_loss=1.331, generator_loss=28.63, generator_mel_loss=18.42, generator_kl_loss=1.719, generator_dur_loss=1.678, generator_adv_loss=1.966, generator_feat_match_loss=4.849, over 31.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:26:30,847 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 03:26:38,670 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 03:29:01,888 INFO [train.py:527] (5/6) Epoch 501, batch 50, global_batch_idx: 62050, batch size: 59, loss[discriminator_loss=2.764, discriminator_real_loss=1.391, discriminator_fake_loss=1.373, generator_loss=28.74, generator_mel_loss=18.28, generator_kl_loss=1.373, generator_dur_loss=1.736, generator_adv_loss=1.935, generator_feat_match_loss=5.419, over 59.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.38, discriminator_fake_loss=1.343, generator_loss=28.15, generator_mel_loss=18.07, generator_kl_loss=1.41, generator_dur_loss=1.751, generator_adv_loss=1.937, generator_feat_match_loss=4.982, over 2982.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:31:20,011 INFO [train.py:527] (5/6) Epoch 501, batch 100, global_batch_idx: 62100, batch size: 48, loss[discriminator_loss=2.706, discriminator_real_loss=1.446, discriminator_fake_loss=1.261, generator_loss=27.95, generator_mel_loss=18.05, generator_kl_loss=1.566, generator_dur_loss=1.651, generator_adv_loss=1.884, generator_feat_match_loss=4.799, over 48.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.13, generator_mel_loss=18.01, generator_kl_loss=1.411, generator_dur_loss=1.757, generator_adv_loss=1.948, generator_feat_match_loss=5.008, over 5847.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:32:19,419 INFO [train.py:919] (5/6) Start epoch 502 +2024-03-14 03:33:55,536 INFO [train.py:527] (5/6) Epoch 502, batch 26, global_batch_idx: 62150, batch size: 47, loss[discriminator_loss=2.702, discriminator_real_loss=1.248, discriminator_fake_loss=1.454, generator_loss=29.79, generator_mel_loss=18.49, generator_kl_loss=1.622, generator_dur_loss=1.702, generator_adv_loss=2.167, generator_feat_match_loss=5.815, over 47.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.359, discriminator_fake_loss=1.343, generator_loss=28.29, generator_mel_loss=17.98, generator_kl_loss=1.439, generator_dur_loss=1.768, generator_adv_loss=1.972, generator_feat_match_loss=5.125, over 1654.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:36:15,006 INFO [train.py:527] (5/6) Epoch 502, batch 76, global_batch_idx: 62200, batch size: 77, loss[discriminator_loss=2.739, discriminator_real_loss=1.42, discriminator_fake_loss=1.318, generator_loss=27.88, generator_mel_loss=17.99, generator_kl_loss=1.383, generator_dur_loss=1.796, generator_adv_loss=2.064, generator_feat_match_loss=4.647, over 77.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.367, discriminator_fake_loss=1.339, generator_loss=28.21, generator_mel_loss=17.98, generator_kl_loss=1.435, generator_dur_loss=1.757, generator_adv_loss=1.956, generator_feat_match_loss=5.073, over 4427.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:36:15,007 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 03:36:23,146 INFO [train.py:591] (5/6) 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 03:36:23,147 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 03:38:34,223 INFO [train.py:919] (5/6) Start epoch 503 +2024-03-14 03:39:03,018 INFO [train.py:527] (5/6) Epoch 503, batch 2, global_batch_idx: 62250, batch size: 52, loss[discriminator_loss=2.779, discriminator_real_loss=1.384, discriminator_fake_loss=1.395, generator_loss=29.1, generator_mel_loss=18.42, generator_kl_loss=1.374, generator_dur_loss=1.71, generator_adv_loss=1.935, generator_feat_match_loss=5.655, over 52.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.413, discriminator_fake_loss=1.336, generator_loss=28.52, generator_mel_loss=17.73, generator_kl_loss=1.486, generator_dur_loss=1.701, generator_adv_loss=2, generator_feat_match_loss=5.61, over 139.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:41:21,208 INFO [train.py:527] (5/6) Epoch 503, batch 52, global_batch_idx: 62300, batch size: 36, loss[discriminator_loss=2.702, discriminator_real_loss=1.325, discriminator_fake_loss=1.376, generator_loss=28.99, generator_mel_loss=18.63, generator_kl_loss=1.468, generator_dur_loss=1.677, generator_adv_loss=1.989, generator_feat_match_loss=5.228, over 36.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=28.1, generator_mel_loss=17.96, generator_kl_loss=1.424, generator_dur_loss=1.743, generator_adv_loss=1.95, generator_feat_match_loss=5.02, over 2735.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:43:38,675 INFO [train.py:527] (5/6) Epoch 503, batch 102, global_batch_idx: 62350, batch size: 80, loss[discriminator_loss=2.68, discriminator_real_loss=1.4, discriminator_fake_loss=1.28, generator_loss=29.98, generator_mel_loss=18.54, generator_kl_loss=1.421, generator_dur_loss=1.784, generator_adv_loss=2.031, generator_feat_match_loss=6.211, over 80.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=28.11, generator_mel_loss=17.96, generator_kl_loss=1.417, generator_dur_loss=1.748, generator_adv_loss=1.951, generator_feat_match_loss=5.031, over 5607.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:44:39,774 INFO [train.py:919] (5/6) Start epoch 504 +2024-03-14 03:46:22,688 INFO [train.py:527] (5/6) Epoch 504, batch 28, global_batch_idx: 62400, batch size: 25, loss[discriminator_loss=2.714, discriminator_real_loss=1.386, discriminator_fake_loss=1.328, generator_loss=28.74, generator_mel_loss=18.12, generator_kl_loss=1.751, generator_dur_loss=1.56, generator_adv_loss=2.054, generator_feat_match_loss=5.258, over 25.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.366, discriminator_fake_loss=1.334, generator_loss=28.2, generator_mel_loss=17.99, generator_kl_loss=1.429, generator_dur_loss=1.746, generator_adv_loss=1.97, generator_feat_match_loss=5.068, over 1538.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:46:22,689 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 03:46:30,661 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 03:48:48,904 INFO [train.py:527] (5/6) Epoch 504, batch 78, global_batch_idx: 62450, batch size: 74, loss[discriminator_loss=2.718, discriminator_real_loss=1.463, discriminator_fake_loss=1.255, generator_loss=28.39, generator_mel_loss=18.15, generator_kl_loss=1.347, generator_dur_loss=1.828, generator_adv_loss=1.898, generator_feat_match_loss=5.17, over 74.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.373, discriminator_fake_loss=1.337, generator_loss=28.14, generator_mel_loss=17.97, generator_kl_loss=1.41, generator_dur_loss=1.753, generator_adv_loss=1.959, generator_feat_match_loss=5.045, over 4322.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:50:53,689 INFO [train.py:919] (5/6) Start epoch 505 +2024-03-14 03:51:28,730 INFO [train.py:527] (5/6) Epoch 505, batch 4, global_batch_idx: 62500, batch size: 56, loss[discriminator_loss=2.708, discriminator_real_loss=1.278, discriminator_fake_loss=1.43, generator_loss=28.56, generator_mel_loss=18.26, generator_kl_loss=1.458, generator_dur_loss=1.693, generator_adv_loss=1.921, generator_feat_match_loss=5.23, over 56.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.385, discriminator_fake_loss=1.322, generator_loss=28.07, generator_mel_loss=18.09, generator_kl_loss=1.38, generator_dur_loss=1.766, generator_adv_loss=1.965, generator_feat_match_loss=4.875, over 318.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:53:49,000 INFO [train.py:527] (5/6) Epoch 505, batch 54, global_batch_idx: 62550, batch size: 80, loss[discriminator_loss=2.708, discriminator_real_loss=1.354, discriminator_fake_loss=1.353, generator_loss=28.04, generator_mel_loss=17.96, generator_kl_loss=1.495, generator_dur_loss=1.825, generator_adv_loss=1.897, generator_feat_match_loss=4.867, over 80.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=28.14, generator_mel_loss=18.03, generator_kl_loss=1.407, generator_dur_loss=1.764, generator_adv_loss=1.951, generator_feat_match_loss=4.993, over 3107.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:56:08,508 INFO [train.py:527] (5/6) Epoch 505, batch 104, global_batch_idx: 62600, batch size: 48, loss[discriminator_loss=2.722, discriminator_real_loss=1.422, discriminator_fake_loss=1.299, generator_loss=29.88, generator_mel_loss=18.64, generator_kl_loss=1.378, generator_dur_loss=1.754, generator_adv_loss=1.971, generator_feat_match_loss=6.136, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.374, discriminator_fake_loss=1.339, generator_loss=28.14, generator_mel_loss=18.01, generator_kl_loss=1.4, generator_dur_loss=1.765, generator_adv_loss=1.948, generator_feat_match_loss=5.014, over 6025.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 03:56:08,509 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 03:56:17,700 INFO [train.py:591] (5/6) 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 03:56:17,701 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 03:57:09,452 INFO [train.py:919] (5/6) Start epoch 506 +2024-03-14 03:58:56,621 INFO [train.py:527] (5/6) Epoch 506, batch 30, global_batch_idx: 62650, batch size: 48, loss[discriminator_loss=2.713, discriminator_real_loss=1.334, discriminator_fake_loss=1.379, generator_loss=28.22, generator_mel_loss=17.8, generator_kl_loss=1.494, generator_dur_loss=1.654, generator_adv_loss=2.079, generator_feat_match_loss=5.193, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.375, discriminator_fake_loss=1.337, generator_loss=28.31, generator_mel_loss=17.95, generator_kl_loss=1.426, generator_dur_loss=1.751, generator_adv_loss=1.977, generator_feat_match_loss=5.203, over 1750.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:01:18,709 INFO [train.py:527] (5/6) Epoch 506, batch 80, global_batch_idx: 62700, batch size: 61, loss[discriminator_loss=2.731, discriminator_real_loss=1.387, discriminator_fake_loss=1.344, generator_loss=27.81, generator_mel_loss=17.82, generator_kl_loss=1.414, generator_dur_loss=1.751, generator_adv_loss=1.946, generator_feat_match_loss=4.877, over 61.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.374, discriminator_fake_loss=1.334, generator_loss=28.18, generator_mel_loss=17.97, generator_kl_loss=1.412, generator_dur_loss=1.766, generator_adv_loss=1.968, generator_feat_match_loss=5.064, over 4745.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:03:16,368 INFO [train.py:919] (5/6) Start epoch 507 +2024-03-14 04:03:55,926 INFO [train.py:527] (5/6) Epoch 507, batch 6, global_batch_idx: 62750, batch size: 70, loss[discriminator_loss=2.682, discriminator_real_loss=1.323, discriminator_fake_loss=1.359, generator_loss=28.65, generator_mel_loss=17.99, generator_kl_loss=1.402, generator_dur_loss=1.806, generator_adv_loss=1.993, generator_feat_match_loss=5.461, over 70.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.354, discriminator_fake_loss=1.35, generator_loss=28.44, generator_mel_loss=18.06, generator_kl_loss=1.41, generator_dur_loss=1.777, generator_adv_loss=1.96, generator_feat_match_loss=5.234, over 424.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:06:15,471 INFO [train.py:527] (5/6) Epoch 507, batch 56, global_batch_idx: 62800, batch size: 68, loss[discriminator_loss=2.724, discriminator_real_loss=1.347, discriminator_fake_loss=1.377, generator_loss=27.96, generator_mel_loss=18.05, generator_kl_loss=1.348, generator_dur_loss=1.82, generator_adv_loss=1.939, generator_feat_match_loss=4.812, over 68.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.19, generator_mel_loss=18.01, generator_kl_loss=1.381, generator_dur_loss=1.764, generator_adv_loss=1.954, generator_feat_match_loss=5.085, over 3297.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:06:15,473 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 04:06:23,517 INFO [train.py:591] (5/6) 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 04:06:23,518 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 04:08:41,908 INFO [train.py:527] (5/6) Epoch 507, batch 106, global_batch_idx: 62850, batch size: 80, loss[discriminator_loss=2.767, discriminator_real_loss=1.427, discriminator_fake_loss=1.339, generator_loss=27.33, generator_mel_loss=17.62, generator_kl_loss=1.459, generator_dur_loss=1.864, generator_adv_loss=1.951, generator_feat_match_loss=4.439, over 80.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.1, generator_mel_loss=17.98, generator_kl_loss=1.382, generator_dur_loss=1.768, generator_adv_loss=1.955, generator_feat_match_loss=5.017, over 6423.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:09:30,144 INFO [train.py:919] (5/6) Start epoch 508 +2024-03-14 04:11:20,134 INFO [train.py:527] (5/6) Epoch 508, batch 32, global_batch_idx: 62900, batch size: 53, loss[discriminator_loss=2.673, discriminator_real_loss=1.402, discriminator_fake_loss=1.271, generator_loss=27.54, generator_mel_loss=17.93, generator_kl_loss=1.267, generator_dur_loss=1.684, generator_adv_loss=1.85, generator_feat_match_loss=4.808, over 53.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.17, generator_mel_loss=17.97, generator_kl_loss=1.39, generator_dur_loss=1.75, generator_adv_loss=1.968, generator_feat_match_loss=5.09, over 1936.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:13:41,995 INFO [train.py:527] (5/6) Epoch 508, batch 82, global_batch_idx: 62950, batch size: 47, loss[discriminator_loss=2.676, discriminator_real_loss=1.347, discriminator_fake_loss=1.329, generator_loss=29.68, generator_mel_loss=18.77, generator_kl_loss=1.549, generator_dur_loss=1.701, generator_adv_loss=1.925, generator_feat_match_loss=5.74, over 47.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.368, discriminator_fake_loss=1.342, generator_loss=28.22, generator_mel_loss=18, generator_kl_loss=1.397, generator_dur_loss=1.751, generator_adv_loss=1.959, generator_feat_match_loss=5.113, over 4905.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:15:36,472 INFO [train.py:919] (5/6) Start epoch 509 +2024-03-14 04:16:22,902 INFO [train.py:527] (5/6) Epoch 509, batch 8, global_batch_idx: 63000, batch size: 66, loss[discriminator_loss=2.76, discriminator_real_loss=1.449, discriminator_fake_loss=1.311, generator_loss=26.89, generator_mel_loss=17.15, generator_kl_loss=1.371, generator_dur_loss=1.743, generator_adv_loss=1.831, generator_feat_match_loss=4.796, over 66.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.382, discriminator_fake_loss=1.303, generator_loss=28.23, generator_mel_loss=17.83, generator_kl_loss=1.424, generator_dur_loss=1.735, generator_adv_loss=1.976, generator_feat_match_loss=5.27, over 520.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:16:22,944 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 04:16:30,826 INFO [train.py:591] (5/6) 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 04:16:30,828 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 04:18:51,719 INFO [train.py:527] (5/6) Epoch 509, batch 58, global_batch_idx: 63050, batch size: 39, loss[discriminator_loss=2.73, discriminator_real_loss=1.295, discriminator_fake_loss=1.436, generator_loss=27.97, generator_mel_loss=18.13, generator_kl_loss=1.412, generator_dur_loss=1.704, generator_adv_loss=2.009, generator_feat_match_loss=4.723, over 39.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=28.07, generator_mel_loss=17.97, generator_kl_loss=1.411, generator_dur_loss=1.742, generator_adv_loss=1.943, generator_feat_match_loss=5, over 3431.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:21:11,253 INFO [train.py:527] (5/6) Epoch 509, batch 108, global_batch_idx: 63100, batch size: 48, loss[discriminator_loss=2.728, discriminator_real_loss=1.377, discriminator_fake_loss=1.351, generator_loss=29.16, generator_mel_loss=18.59, generator_kl_loss=1.601, generator_dur_loss=1.609, generator_adv_loss=1.813, generator_feat_match_loss=5.555, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.17, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.734, generator_adv_loss=1.952, generator_feat_match_loss=5.057, over 6108.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:21:55,685 INFO [train.py:919] (5/6) Start epoch 510 +2024-03-14 04:23:52,001 INFO [train.py:527] (5/6) Epoch 510, batch 34, global_batch_idx: 63150, batch size: 56, loss[discriminator_loss=2.82, discriminator_real_loss=1.354, discriminator_fake_loss=1.466, generator_loss=28.25, generator_mel_loss=18.11, generator_kl_loss=1.372, generator_dur_loss=1.663, generator_adv_loss=2.1, generator_feat_match_loss=5.009, over 56.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.369, discriminator_fake_loss=1.35, generator_loss=28.2, generator_mel_loss=17.97, generator_kl_loss=1.425, generator_dur_loss=1.72, generator_adv_loss=1.948, generator_feat_match_loss=5.142, over 1904.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:26:11,962 INFO [train.py:527] (5/6) Epoch 510, batch 84, global_batch_idx: 63200, batch size: 44, loss[discriminator_loss=2.729, discriminator_real_loss=1.291, discriminator_fake_loss=1.438, generator_loss=27.93, generator_mel_loss=17.79, generator_kl_loss=1.391, generator_dur_loss=1.666, generator_adv_loss=1.941, generator_feat_match_loss=5.141, over 44.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=28.14, generator_mel_loss=18, generator_kl_loss=1.407, generator_dur_loss=1.738, generator_adv_loss=1.945, generator_feat_match_loss=5.054, over 4597.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:26:11,964 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 04:26:21,110 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 04:28:09,686 INFO [train.py:919] (5/6) Start epoch 511 +2024-03-14 04:29:03,943 INFO [train.py:527] (5/6) Epoch 511, batch 10, global_batch_idx: 63250, batch size: 47, loss[discriminator_loss=2.683, discriminator_real_loss=1.279, discriminator_fake_loss=1.404, generator_loss=28.82, generator_mel_loss=18.12, generator_kl_loss=1.553, generator_dur_loss=1.645, generator_adv_loss=2.032, generator_feat_match_loss=5.465, over 47.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.326, generator_loss=28.27, generator_mel_loss=17.92, generator_kl_loss=1.405, generator_dur_loss=1.78, generator_adv_loss=1.991, generator_feat_match_loss=5.172, over 692.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:31:24,677 INFO [train.py:527] (5/6) Epoch 511, batch 60, global_batch_idx: 63300, batch size: 83, loss[discriminator_loss=2.66, discriminator_real_loss=1.372, discriminator_fake_loss=1.288, generator_loss=27.82, generator_mel_loss=17.99, generator_kl_loss=1.405, generator_dur_loss=1.837, generator_adv_loss=1.917, generator_feat_match_loss=4.671, over 83.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.365, discriminator_fake_loss=1.345, generator_loss=28.16, generator_mel_loss=17.98, generator_kl_loss=1.406, generator_dur_loss=1.764, generator_adv_loss=1.952, generator_feat_match_loss=5.057, over 3536.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:33:43,490 INFO [train.py:527] (5/6) Epoch 511, batch 110, global_batch_idx: 63350, batch size: 62, loss[discriminator_loss=2.725, discriminator_real_loss=1.376, discriminator_fake_loss=1.349, generator_loss=27.73, generator_mel_loss=17.74, generator_kl_loss=1.465, generator_dur_loss=1.751, generator_adv_loss=2.025, generator_feat_match_loss=4.752, over 62.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.18, generator_mel_loss=18, generator_kl_loss=1.401, generator_dur_loss=1.758, generator_adv_loss=1.958, generator_feat_match_loss=5.062, over 6238.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:34:19,406 INFO [train.py:919] (5/6) Start epoch 512 +2024-03-14 04:36:22,519 INFO [train.py:527] (5/6) Epoch 512, batch 36, global_batch_idx: 63400, batch size: 25, loss[discriminator_loss=2.748, discriminator_real_loss=1.334, discriminator_fake_loss=1.414, generator_loss=29.25, generator_mel_loss=18.53, generator_kl_loss=1.62, generator_dur_loss=1.54, generator_adv_loss=1.891, generator_feat_match_loss=5.665, over 25.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.38, discriminator_fake_loss=1.335, generator_loss=28.33, generator_mel_loss=18.05, generator_kl_loss=1.423, generator_dur_loss=1.756, generator_adv_loss=1.964, generator_feat_match_loss=5.132, over 2119.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:36:22,520 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 04:36:30,269 INFO [train.py:591] (5/6) 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 04:36:30,270 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 04:38:46,879 INFO [train.py:527] (5/6) Epoch 512, batch 86, global_batch_idx: 63450, batch size: 62, loss[discriminator_loss=2.809, discriminator_real_loss=1.492, discriminator_fake_loss=1.317, generator_loss=28.04, generator_mel_loss=18.05, generator_kl_loss=1.432, generator_dur_loss=1.75, generator_adv_loss=1.906, generator_feat_match_loss=4.903, over 62.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.378, discriminator_fake_loss=1.343, generator_loss=28.27, generator_mel_loss=18.06, generator_kl_loss=1.431, generator_dur_loss=1.755, generator_adv_loss=1.955, generator_feat_match_loss=5.069, over 4843.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:40:33,510 INFO [train.py:919] (5/6) Start epoch 513 +2024-03-14 04:41:33,125 INFO [train.py:527] (5/6) Epoch 513, batch 12, global_batch_idx: 63500, batch size: 25, loss[discriminator_loss=2.676, discriminator_real_loss=1.392, discriminator_fake_loss=1.284, generator_loss=29.67, generator_mel_loss=18.96, generator_kl_loss=1.711, generator_dur_loss=1.562, generator_adv_loss=1.869, generator_feat_match_loss=5.564, over 25.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.356, discriminator_fake_loss=1.349, generator_loss=28.18, generator_mel_loss=18.02, generator_kl_loss=1.391, generator_dur_loss=1.772, generator_adv_loss=1.948, generator_feat_match_loss=5.045, over 777.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:43:53,092 INFO [train.py:527] (5/6) Epoch 513, batch 62, global_batch_idx: 63550, batch size: 25, loss[discriminator_loss=2.641, discriminator_real_loss=1.382, discriminator_fake_loss=1.259, generator_loss=29.39, generator_mel_loss=18.58, generator_kl_loss=1.707, generator_dur_loss=1.628, generator_adv_loss=2.005, generator_feat_match_loss=5.472, over 25.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.369, discriminator_fake_loss=1.349, generator_loss=28.2, generator_mel_loss=18.04, generator_kl_loss=1.395, generator_dur_loss=1.766, generator_adv_loss=1.937, generator_feat_match_loss=5.056, over 3504.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:46:10,269 INFO [train.py:527] (5/6) Epoch 513, batch 112, global_batch_idx: 63600, batch size: 72, loss[discriminator_loss=2.667, discriminator_real_loss=1.378, discriminator_fake_loss=1.289, generator_loss=28.09, generator_mel_loss=18, generator_kl_loss=1.206, generator_dur_loss=1.82, generator_adv_loss=2, generator_feat_match_loss=5.071, over 72.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.368, discriminator_fake_loss=1.343, generator_loss=28.25, generator_mel_loss=18, generator_kl_loss=1.387, generator_dur_loss=1.77, generator_adv_loss=1.979, generator_feat_match_loss=5.107, over 6529.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:46:10,271 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 04:46:19,190 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 04:46:48,269 INFO [train.py:919] (5/6) Start epoch 514 +2024-03-14 04:48:59,901 INFO [train.py:527] (5/6) Epoch 514, batch 38, global_batch_idx: 63650, batch size: 80, loss[discriminator_loss=2.69, discriminator_real_loss=1.33, discriminator_fake_loss=1.36, generator_loss=28.02, generator_mel_loss=17.93, generator_kl_loss=1.492, generator_dur_loss=1.784, generator_adv_loss=1.972, generator_feat_match_loss=4.847, over 80.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.372, discriminator_fake_loss=1.333, generator_loss=28.18, generator_mel_loss=17.98, generator_kl_loss=1.396, generator_dur_loss=1.761, generator_adv_loss=1.968, generator_feat_match_loss=5.08, over 2199.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:51:21,632 INFO [train.py:527] (5/6) Epoch 514, batch 88, global_batch_idx: 63700, batch size: 68, loss[discriminator_loss=2.757, discriminator_real_loss=1.361, discriminator_fake_loss=1.396, generator_loss=27.36, generator_mel_loss=18, generator_kl_loss=1.345, generator_dur_loss=1.767, generator_adv_loss=1.782, generator_feat_match_loss=4.461, over 68.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.375, discriminator_fake_loss=1.332, generator_loss=28.17, generator_mel_loss=17.97, generator_kl_loss=1.412, generator_dur_loss=1.758, generator_adv_loss=1.96, generator_feat_match_loss=5.066, over 4995.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:52:58,761 INFO [train.py:919] (5/6) Start epoch 515 +2024-03-14 04:54:00,987 INFO [train.py:527] (5/6) Epoch 515, batch 14, global_batch_idx: 63750, batch size: 31, loss[discriminator_loss=2.693, discriminator_real_loss=1.454, discriminator_fake_loss=1.239, generator_loss=28.67, generator_mel_loss=18.22, generator_kl_loss=1.516, generator_dur_loss=1.672, generator_adv_loss=2.081, generator_feat_match_loss=5.181, over 31.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.4, discriminator_fake_loss=1.332, generator_loss=27.82, generator_mel_loss=18.01, generator_kl_loss=1.37, generator_dur_loss=1.741, generator_adv_loss=1.968, generator_feat_match_loss=4.729, over 792.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:56:20,915 INFO [train.py:527] (5/6) Epoch 515, batch 64, global_batch_idx: 63800, batch size: 44, loss[discriminator_loss=2.772, discriminator_real_loss=1.467, discriminator_fake_loss=1.305, generator_loss=27.23, generator_mel_loss=17.32, generator_kl_loss=1.462, generator_dur_loss=1.708, generator_adv_loss=1.992, generator_feat_match_loss=4.749, over 44.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.341, generator_loss=28.2, generator_mel_loss=18.04, generator_kl_loss=1.421, generator_dur_loss=1.751, generator_adv_loss=1.957, generator_feat_match_loss=5.029, over 3625.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:56:20,916 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 04:56:28,857 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 04:58:48,084 INFO [train.py:527] (5/6) Epoch 515, batch 114, global_batch_idx: 63850, batch size: 31, loss[discriminator_loss=2.755, discriminator_real_loss=1.458, discriminator_fake_loss=1.298, generator_loss=27.1, generator_mel_loss=17.69, generator_kl_loss=1.43, generator_dur_loss=1.639, generator_adv_loss=1.849, generator_feat_match_loss=4.498, over 31.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=28.24, generator_mel_loss=18.03, generator_kl_loss=1.42, generator_dur_loss=1.753, generator_adv_loss=1.958, generator_feat_match_loss=5.082, over 6456.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 04:59:15,571 INFO [train.py:919] (5/6) Start epoch 516 +2024-03-14 05:01:29,139 INFO [train.py:527] (5/6) Epoch 516, batch 40, global_batch_idx: 63900, batch size: 80, loss[discriminator_loss=2.704, discriminator_real_loss=1.41, discriminator_fake_loss=1.293, generator_loss=28.11, generator_mel_loss=17.82, generator_kl_loss=1.396, generator_dur_loss=1.804, generator_adv_loss=1.864, generator_feat_match_loss=5.228, over 80.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.371, discriminator_fake_loss=1.335, generator_loss=28.13, generator_mel_loss=17.94, generator_kl_loss=1.397, generator_dur_loss=1.774, generator_adv_loss=1.961, generator_feat_match_loss=5.053, over 2519.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:03:47,516 INFO [train.py:527] (5/6) Epoch 516, batch 90, global_batch_idx: 63950, batch size: 12, loss[discriminator_loss=2.707, discriminator_real_loss=1.403, discriminator_fake_loss=1.304, generator_loss=29.98, generator_mel_loss=17.64, generator_kl_loss=1.9, generator_dur_loss=1.626, generator_adv_loss=2.095, generator_feat_match_loss=6.723, over 12.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.16, generator_mel_loss=17.96, generator_kl_loss=1.409, generator_dur_loss=1.763, generator_adv_loss=1.953, generator_feat_match_loss=5.074, over 5291.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:05:19,751 INFO [train.py:919] (5/6) Start epoch 517 +2024-03-14 05:06:28,760 INFO [train.py:527] (5/6) Epoch 517, batch 16, global_batch_idx: 64000, batch size: 74, loss[discriminator_loss=2.702, discriminator_real_loss=1.33, discriminator_fake_loss=1.372, generator_loss=29.37, generator_mel_loss=18.46, generator_kl_loss=1.589, generator_dur_loss=1.798, generator_adv_loss=1.935, generator_feat_match_loss=5.584, over 74.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.359, discriminator_fake_loss=1.339, generator_loss=28.27, generator_mel_loss=17.99, generator_kl_loss=1.415, generator_dur_loss=1.75, generator_adv_loss=1.963, generator_feat_match_loss=5.153, over 1000.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:06:28,761 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 05:06:36,606 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 05:08:59,589 INFO [train.py:527] (5/6) Epoch 517, batch 66, global_batch_idx: 64050, batch size: 59, loss[discriminator_loss=2.722, discriminator_real_loss=1.317, discriminator_fake_loss=1.405, generator_loss=28.2, generator_mel_loss=17.83, generator_kl_loss=1.375, generator_dur_loss=1.759, generator_adv_loss=1.952, generator_feat_match_loss=5.279, over 59.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.367, discriminator_fake_loss=1.338, generator_loss=28.2, generator_mel_loss=17.98, generator_kl_loss=1.392, generator_dur_loss=1.751, generator_adv_loss=1.961, generator_feat_match_loss=5.109, over 3845.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:11:15,854 INFO [train.py:527] (5/6) Epoch 517, batch 116, global_batch_idx: 64100, batch size: 55, loss[discriminator_loss=2.714, discriminator_real_loss=1.447, discriminator_fake_loss=1.268, generator_loss=28.42, generator_mel_loss=18.27, generator_kl_loss=1.468, generator_dur_loss=1.732, generator_adv_loss=1.964, generator_feat_match_loss=4.986, over 55.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.371, discriminator_fake_loss=1.34, generator_loss=28.18, generator_mel_loss=17.98, generator_kl_loss=1.398, generator_dur_loss=1.746, generator_adv_loss=1.961, generator_feat_match_loss=5.1, over 6540.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, +2024-03-14 05:11:37,480 INFO [train.py:919] (5/6) Start epoch 518 +2024-03-14 05:13:59,012 INFO [train.py:527] (5/6) Epoch 518, batch 42, global_batch_idx: 64150, batch size: 83, loss[discriminator_loss=2.688, discriminator_real_loss=1.44, discriminator_fake_loss=1.248, generator_loss=27.95, generator_mel_loss=17.83, generator_kl_loss=1.396, generator_dur_loss=1.878, generator_adv_loss=1.872, generator_feat_match_loss=4.979, over 83.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.382, discriminator_fake_loss=1.329, generator_loss=28.34, generator_mel_loss=18.04, generator_kl_loss=1.403, generator_dur_loss=1.754, generator_adv_loss=1.948, generator_feat_match_loss=5.196, over 2342.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:16:18,583 INFO [train.py:527] (5/6) Epoch 518, batch 92, global_batch_idx: 64200, batch size: 56, loss[discriminator_loss=2.706, discriminator_real_loss=1.424, discriminator_fake_loss=1.282, generator_loss=28.23, generator_mel_loss=17.99, generator_kl_loss=1.494, generator_dur_loss=1.713, generator_adv_loss=1.851, generator_feat_match_loss=5.187, over 56.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.377, discriminator_fake_loss=1.334, generator_loss=28.23, generator_mel_loss=18, generator_kl_loss=1.41, generator_dur_loss=1.757, generator_adv_loss=1.949, generator_feat_match_loss=5.114, over 5205.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:16:18,584 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 05:16:27,416 INFO [train.py:591] (5/6) 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 05:16:27,417 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 05:17:52,849 INFO [train.py:919] (5/6) Start epoch 519 +2024-03-14 05:19:05,906 INFO [train.py:527] (5/6) Epoch 519, batch 18, global_batch_idx: 64250, batch size: 56, loss[discriminator_loss=2.713, discriminator_real_loss=1.367, discriminator_fake_loss=1.347, generator_loss=28.3, generator_mel_loss=17.91, generator_kl_loss=1.419, generator_dur_loss=1.722, generator_adv_loss=2.021, generator_feat_match_loss=5.23, over 56.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.361, discriminator_fake_loss=1.328, generator_loss=28.17, generator_mel_loss=17.94, generator_kl_loss=1.409, generator_dur_loss=1.754, generator_adv_loss=1.968, generator_feat_match_loss=5.101, over 1105.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:21:24,477 INFO [train.py:527] (5/6) Epoch 519, batch 68, global_batch_idx: 64300, batch size: 47, loss[discriminator_loss=2.74, discriminator_real_loss=1.342, discriminator_fake_loss=1.398, generator_loss=29.12, generator_mel_loss=18.32, generator_kl_loss=1.815, generator_dur_loss=1.74, generator_adv_loss=1.958, generator_feat_match_loss=5.289, over 47.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.361, discriminator_fake_loss=1.348, generator_loss=28.19, generator_mel_loss=18, generator_kl_loss=1.408, generator_dur_loss=1.766, generator_adv_loss=1.96, generator_feat_match_loss=5.058, over 4007.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:23:42,043 INFO [train.py:527] (5/6) Epoch 519, batch 118, global_batch_idx: 64350, batch size: 42, loss[discriminator_loss=2.624, discriminator_real_loss=1.38, discriminator_fake_loss=1.244, generator_loss=29.36, generator_mel_loss=18.21, generator_kl_loss=1.699, generator_dur_loss=1.724, generator_adv_loss=2.271, generator_feat_match_loss=5.451, over 42.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.364, discriminator_fake_loss=1.346, generator_loss=28.31, generator_mel_loss=18.01, generator_kl_loss=1.428, generator_dur_loss=1.761, generator_adv_loss=1.979, generator_feat_match_loss=5.13, over 6657.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:23:57,949 INFO [train.py:919] (5/6) Start epoch 520 +2024-03-14 05:26:22,356 INFO [train.py:527] (5/6) Epoch 520, batch 44, global_batch_idx: 64400, batch size: 39, loss[discriminator_loss=2.6, discriminator_real_loss=1.291, discriminator_fake_loss=1.309, generator_loss=27.74, generator_mel_loss=17.84, generator_kl_loss=1.469, generator_dur_loss=1.755, generator_adv_loss=2.031, generator_feat_match_loss=4.649, over 39.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=28, generator_mel_loss=17.92, generator_kl_loss=1.424, generator_dur_loss=1.752, generator_adv_loss=1.952, generator_feat_match_loss=4.959, over 2483.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:26:22,360 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 05:26:30,251 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 05:28:48,681 INFO [train.py:527] (5/6) Epoch 520, batch 94, global_batch_idx: 64450, batch size: 36, loss[discriminator_loss=2.732, discriminator_real_loss=1.41, discriminator_fake_loss=1.321, generator_loss=27.84, generator_mel_loss=18.13, generator_kl_loss=1.561, generator_dur_loss=1.642, generator_adv_loss=1.813, generator_feat_match_loss=4.696, over 36.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.06, generator_mel_loss=17.97, generator_kl_loss=1.426, generator_dur_loss=1.748, generator_adv_loss=1.951, generator_feat_match_loss=4.971, over 5227.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:30:10,315 INFO [train.py:919] (5/6) Start epoch 521 +2024-03-14 05:31:30,955 INFO [train.py:527] (5/6) Epoch 521, batch 20, global_batch_idx: 64500, batch size: 80, loss[discriminator_loss=2.716, discriminator_real_loss=1.353, discriminator_fake_loss=1.363, generator_loss=28.01, generator_mel_loss=17.96, generator_kl_loss=1.416, generator_dur_loss=1.805, generator_adv_loss=2.059, generator_feat_match_loss=4.778, over 80.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.381, discriminator_fake_loss=1.342, generator_loss=28.11, generator_mel_loss=17.93, generator_kl_loss=1.452, generator_dur_loss=1.74, generator_adv_loss=1.96, generator_feat_match_loss=5.031, over 1263.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:33:50,004 INFO [train.py:527] (5/6) Epoch 521, batch 70, global_batch_idx: 64550, batch size: 44, loss[discriminator_loss=2.735, discriminator_real_loss=1.345, discriminator_fake_loss=1.39, generator_loss=28.7, generator_mel_loss=18.16, generator_kl_loss=1.526, generator_dur_loss=1.653, generator_adv_loss=2.002, generator_feat_match_loss=5.357, over 44.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.375, discriminator_fake_loss=1.342, generator_loss=28.2, generator_mel_loss=17.99, generator_kl_loss=1.432, generator_dur_loss=1.748, generator_adv_loss=1.95, generator_feat_match_loss=5.08, over 3976.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:36:07,809 INFO [train.py:527] (5/6) Epoch 521, batch 120, global_batch_idx: 64600, batch size: 62, loss[discriminator_loss=2.714, discriminator_real_loss=1.376, discriminator_fake_loss=1.337, generator_loss=28.33, generator_mel_loss=18.41, generator_kl_loss=1.34, generator_dur_loss=1.766, generator_adv_loss=2.066, generator_feat_match_loss=4.743, over 62.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.38, discriminator_fake_loss=1.34, generator_loss=28.18, generator_mel_loss=17.98, generator_kl_loss=1.43, generator_dur_loss=1.746, generator_adv_loss=1.955, generator_feat_match_loss=5.064, over 6697.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:36:07,811 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 05:36:16,582 INFO [train.py:591] (5/6) 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 05:36:16,583 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 05:36:25,564 INFO [train.py:919] (5/6) Start epoch 522 +2024-03-14 05:38:57,102 INFO [train.py:527] (5/6) Epoch 522, batch 46, global_batch_idx: 64650, batch size: 31, loss[discriminator_loss=2.791, discriminator_real_loss=1.436, discriminator_fake_loss=1.356, generator_loss=27.79, generator_mel_loss=18.32, generator_kl_loss=1.546, generator_dur_loss=1.629, generator_adv_loss=1.83, generator_feat_match_loss=4.462, over 31.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=28.29, generator_mel_loss=18.01, generator_kl_loss=1.434, generator_dur_loss=1.747, generator_adv_loss=1.96, generator_feat_match_loss=5.136, over 2618.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:41:15,713 INFO [train.py:527] (5/6) Epoch 522, batch 96, global_batch_idx: 64700, batch size: 96, loss[discriminator_loss=2.703, discriminator_real_loss=1.412, discriminator_fake_loss=1.291, generator_loss=27.78, generator_mel_loss=18.01, generator_kl_loss=1.291, generator_dur_loss=1.842, generator_adv_loss=1.802, generator_feat_match_loss=4.833, over 96.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=28.23, generator_mel_loss=17.99, generator_kl_loss=1.415, generator_dur_loss=1.763, generator_adv_loss=1.957, generator_feat_match_loss=5.101, over 5721.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:42:30,392 INFO [train.py:919] (5/6) Start epoch 523 +2024-03-14 05:43:53,435 INFO [train.py:527] (5/6) Epoch 523, batch 22, global_batch_idx: 64750, batch size: 56, loss[discriminator_loss=2.757, discriminator_real_loss=1.45, discriminator_fake_loss=1.307, generator_loss=27.65, generator_mel_loss=17.91, generator_kl_loss=1.433, generator_dur_loss=1.722, generator_adv_loss=1.873, generator_feat_match_loss=4.708, over 56.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.387, discriminator_fake_loss=1.351, generator_loss=27.82, generator_mel_loss=17.96, generator_kl_loss=1.384, generator_dur_loss=1.743, generator_adv_loss=1.919, generator_feat_match_loss=4.821, over 1274.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:46:13,668 INFO [train.py:527] (5/6) Epoch 523, batch 72, global_batch_idx: 64800, batch size: 45, loss[discriminator_loss=2.765, discriminator_real_loss=1.464, discriminator_fake_loss=1.3, generator_loss=26.46, generator_mel_loss=17.44, generator_kl_loss=1.471, generator_dur_loss=1.769, generator_adv_loss=1.899, generator_feat_match_loss=3.888, over 45.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.377, discriminator_fake_loss=1.347, generator_loss=28.08, generator_mel_loss=17.97, generator_kl_loss=1.421, generator_dur_loss=1.748, generator_adv_loss=1.947, generator_feat_match_loss=4.997, over 4105.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:46:13,669 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 05:46:21,896 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 05:48:40,688 INFO [train.py:527] (5/6) Epoch 523, batch 122, global_batch_idx: 64850, batch size: 16, loss[discriminator_loss=2.695, discriminator_real_loss=1.239, discriminator_fake_loss=1.456, generator_loss=31.54, generator_mel_loss=19.53, generator_kl_loss=1.81, generator_dur_loss=1.608, generator_adv_loss=2.047, generator_feat_match_loss=6.544, over 16.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.372, discriminator_fake_loss=1.348, generator_loss=28.07, generator_mel_loss=17.96, generator_kl_loss=1.405, generator_dur_loss=1.76, generator_adv_loss=1.947, generator_feat_match_loss=5.001, over 7192.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:48:45,748 INFO [train.py:919] (5/6) Start epoch 524 +2024-03-14 05:51:24,144 INFO [train.py:527] (5/6) Epoch 524, batch 48, global_batch_idx: 64900, batch size: 50, loss[discriminator_loss=2.669, discriminator_real_loss=1.347, discriminator_fake_loss=1.322, generator_loss=28.77, generator_mel_loss=18.14, generator_kl_loss=1.416, generator_dur_loss=1.713, generator_adv_loss=2.035, generator_feat_match_loss=5.466, over 50.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.367, discriminator_fake_loss=1.344, generator_loss=28.28, generator_mel_loss=17.95, generator_kl_loss=1.397, generator_dur_loss=1.75, generator_adv_loss=2.002, generator_feat_match_loss=5.187, over 2932.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:53:42,546 INFO [train.py:527] (5/6) Epoch 524, batch 98, global_batch_idx: 64950, batch size: 12, loss[discriminator_loss=2.687, discriminator_real_loss=1.261, discriminator_fake_loss=1.426, generator_loss=30.15, generator_mel_loss=18.39, generator_kl_loss=1.775, generator_dur_loss=1.647, generator_adv_loss=2.127, generator_feat_match_loss=6.21, over 12.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.34, generator_loss=28.25, generator_mel_loss=17.97, generator_kl_loss=1.414, generator_dur_loss=1.744, generator_adv_loss=1.977, generator_feat_match_loss=5.148, over 5762.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:54:51,499 INFO [train.py:919] (5/6) Start epoch 525 +2024-03-14 05:56:19,698 INFO [train.py:527] (5/6) Epoch 525, batch 24, global_batch_idx: 65000, batch size: 72, loss[discriminator_loss=2.713, discriminator_real_loss=1.357, discriminator_fake_loss=1.356, generator_loss=28.55, generator_mel_loss=18.17, generator_kl_loss=1.348, generator_dur_loss=1.784, generator_adv_loss=1.954, generator_feat_match_loss=5.298, over 72.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.379, discriminator_fake_loss=1.333, generator_loss=28.26, generator_mel_loss=17.99, generator_kl_loss=1.417, generator_dur_loss=1.745, generator_adv_loss=1.956, generator_feat_match_loss=5.153, over 1356.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 05:56:19,699 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 05:56:27,761 INFO [train.py:591] (5/6) 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 05:56:27,763 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 05:58:47,338 INFO [train.py:527] (5/6) Epoch 525, batch 74, global_batch_idx: 65050, batch size: 66, loss[discriminator_loss=2.73, discriminator_real_loss=1.269, discriminator_fake_loss=1.46, generator_loss=28.34, generator_mel_loss=17.85, generator_kl_loss=1.313, generator_dur_loss=1.78, generator_adv_loss=2.025, generator_feat_match_loss=5.372, over 66.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.338, generator_loss=28.19, generator_mel_loss=17.92, generator_kl_loss=1.419, generator_dur_loss=1.753, generator_adv_loss=1.96, generator_feat_match_loss=5.132, over 4282.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:01:06,665 INFO [train.py:919] (5/6) Start epoch 526 +2024-03-14 06:01:28,256 INFO [train.py:527] (5/6) Epoch 526, batch 0, global_batch_idx: 65100, batch size: 88, loss[discriminator_loss=2.745, discriminator_real_loss=1.403, discriminator_fake_loss=1.341, generator_loss=27.85, generator_mel_loss=17.94, generator_kl_loss=1.378, generator_dur_loss=1.868, generator_adv_loss=1.923, generator_feat_match_loss=4.738, over 88.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.403, discriminator_fake_loss=1.341, generator_loss=27.85, generator_mel_loss=17.94, generator_kl_loss=1.378, generator_dur_loss=1.868, generator_adv_loss=1.923, generator_feat_match_loss=4.738, over 88.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:03:46,199 INFO [train.py:527] (5/6) Epoch 526, batch 50, global_batch_idx: 65150, batch size: 44, loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=27, generator_mel_loss=17.6, generator_kl_loss=1.336, generator_dur_loss=1.707, generator_adv_loss=1.867, generator_feat_match_loss=4.496, over 44.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.367, discriminator_fake_loss=1.345, generator_loss=28.25, generator_mel_loss=18.01, generator_kl_loss=1.409, generator_dur_loss=1.758, generator_adv_loss=1.962, generator_feat_match_loss=5.116, over 2940.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:06:02,915 INFO [train.py:527] (5/6) Epoch 526, batch 100, global_batch_idx: 65200, batch size: 61, loss[discriminator_loss=2.654, discriminator_real_loss=1.339, discriminator_fake_loss=1.314, generator_loss=27.8, generator_mel_loss=17.52, generator_kl_loss=1.358, generator_dur_loss=1.726, generator_adv_loss=1.975, generator_feat_match_loss=5.224, over 61.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.368, discriminator_fake_loss=1.345, generator_loss=28.34, generator_mel_loss=18.05, generator_kl_loss=1.405, generator_dur_loss=1.756, generator_adv_loss=1.958, generator_feat_match_loss=5.164, over 5683.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:06:02,916 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 06:06:11,751 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 06:07:16,579 INFO [train.py:919] (5/6) Start epoch 527 +2024-03-14 06:08:53,190 INFO [train.py:527] (5/6) Epoch 527, batch 26, global_batch_idx: 65250, batch size: 96, loss[discriminator_loss=2.686, discriminator_real_loss=1.325, discriminator_fake_loss=1.361, generator_loss=27.63, generator_mel_loss=17.48, generator_kl_loss=1.361, generator_dur_loss=1.874, generator_adv_loss=1.911, generator_feat_match_loss=5.006, over 96.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.372, discriminator_fake_loss=1.326, generator_loss=28.3, generator_mel_loss=17.98, generator_kl_loss=1.444, generator_dur_loss=1.758, generator_adv_loss=1.996, generator_feat_match_loss=5.121, over 1352.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:11:14,294 INFO [train.py:527] (5/6) Epoch 527, batch 76, global_batch_idx: 65300, batch size: 64, loss[discriminator_loss=2.693, discriminator_real_loss=1.316, discriminator_fake_loss=1.376, generator_loss=28.61, generator_mel_loss=18.07, generator_kl_loss=1.604, generator_dur_loss=1.722, generator_adv_loss=1.941, generator_feat_match_loss=5.271, over 64.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.372, discriminator_fake_loss=1.332, generator_loss=28.18, generator_mel_loss=17.95, generator_kl_loss=1.442, generator_dur_loss=1.76, generator_adv_loss=1.973, generator_feat_match_loss=5.051, over 4119.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:13:24,766 INFO [train.py:919] (5/6) Start epoch 528 +2024-03-14 06:13:54,179 INFO [train.py:527] (5/6) Epoch 528, batch 2, global_batch_idx: 65350, batch size: 58, loss[discriminator_loss=2.755, discriminator_real_loss=1.414, discriminator_fake_loss=1.341, generator_loss=28.7, generator_mel_loss=18.25, generator_kl_loss=1.343, generator_dur_loss=1.734, generator_adv_loss=1.896, generator_feat_match_loss=5.48, over 58.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.418, discriminator_fake_loss=1.312, generator_loss=28.32, generator_mel_loss=18.18, generator_kl_loss=1.415, generator_dur_loss=1.736, generator_adv_loss=1.971, generator_feat_match_loss=5.021, over 177.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:16:12,100 INFO [train.py:527] (5/6) Epoch 528, batch 52, global_batch_idx: 65400, batch size: 88, loss[discriminator_loss=2.691, discriminator_real_loss=1.321, discriminator_fake_loss=1.371, generator_loss=28.28, generator_mel_loss=17.7, generator_kl_loss=1.528, generator_dur_loss=1.797, generator_adv_loss=1.93, generator_feat_match_loss=5.321, over 88.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.17, generator_mel_loss=17.97, generator_kl_loss=1.401, generator_dur_loss=1.756, generator_adv_loss=1.958, generator_feat_match_loss=5.081, over 3085.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:16:12,102 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 06:16:20,250 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 06:18:39,607 INFO [train.py:527] (5/6) Epoch 528, batch 102, global_batch_idx: 65450, batch size: 50, loss[discriminator_loss=2.706, discriminator_real_loss=1.313, discriminator_fake_loss=1.393, generator_loss=27.95, generator_mel_loss=17.72, generator_kl_loss=1.33, generator_dur_loss=1.751, generator_adv_loss=2.02, generator_feat_match_loss=5.124, over 50.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=28.12, generator_mel_loss=17.95, generator_kl_loss=1.403, generator_dur_loss=1.75, generator_adv_loss=1.958, generator_feat_match_loss=5.062, over 5613.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:19:39,561 INFO [train.py:919] (5/6) Start epoch 529 +2024-03-14 06:21:21,290 INFO [train.py:527] (5/6) Epoch 529, batch 28, global_batch_idx: 65500, batch size: 44, loss[discriminator_loss=2.741, discriminator_real_loss=1.396, discriminator_fake_loss=1.344, generator_loss=27.93, generator_mel_loss=17.73, generator_kl_loss=1.598, generator_dur_loss=1.718, generator_adv_loss=2.05, generator_feat_match_loss=4.837, over 44.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.361, discriminator_fake_loss=1.334, generator_loss=28.38, generator_mel_loss=18.03, generator_kl_loss=1.432, generator_dur_loss=1.756, generator_adv_loss=1.972, generator_feat_match_loss=5.198, over 1663.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:23:39,348 INFO [train.py:527] (5/6) Epoch 529, batch 78, global_batch_idx: 65550, batch size: 58, loss[discriminator_loss=2.743, discriminator_real_loss=1.475, discriminator_fake_loss=1.268, generator_loss=27.78, generator_mel_loss=17.94, generator_kl_loss=1.49, generator_dur_loss=1.697, generator_adv_loss=1.859, generator_feat_match_loss=4.794, over 58.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.369, discriminator_fake_loss=1.333, generator_loss=28.2, generator_mel_loss=17.98, generator_kl_loss=1.423, generator_dur_loss=1.761, generator_adv_loss=1.96, generator_feat_match_loss=5.081, over 4683.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:25:45,196 INFO [train.py:919] (5/6) Start epoch 530 +2024-03-14 06:26:19,691 INFO [train.py:527] (5/6) Epoch 530, batch 4, global_batch_idx: 65600, batch size: 25, loss[discriminator_loss=2.708, discriminator_real_loss=1.358, discriminator_fake_loss=1.351, generator_loss=28.02, generator_mel_loss=17.96, generator_kl_loss=1.677, generator_dur_loss=1.521, generator_adv_loss=1.952, generator_feat_match_loss=4.907, over 25.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.342, discriminator_fake_loss=1.37, generator_loss=28.2, generator_mel_loss=17.95, generator_kl_loss=1.43, generator_dur_loss=1.737, generator_adv_loss=1.921, generator_feat_match_loss=5.161, over 268.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:26:19,694 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 06:26:27,499 INFO [train.py:591] (5/6) 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 06:26:27,501 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 06:28:44,993 INFO [train.py:527] (5/6) Epoch 530, batch 54, global_batch_idx: 65650, batch size: 56, loss[discriminator_loss=2.67, discriminator_real_loss=1.418, discriminator_fake_loss=1.253, generator_loss=27.76, generator_mel_loss=17.81, generator_kl_loss=1.426, generator_dur_loss=1.701, generator_adv_loss=1.99, generator_feat_match_loss=4.827, over 56.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.19, generator_mel_loss=17.97, generator_kl_loss=1.431, generator_dur_loss=1.749, generator_adv_loss=1.958, generator_feat_match_loss=5.082, over 3138.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:31:02,559 INFO [train.py:527] (5/6) Epoch 530, batch 104, global_batch_idx: 65700, batch size: 48, loss[discriminator_loss=2.758, discriminator_real_loss=1.318, discriminator_fake_loss=1.44, generator_loss=29.64, generator_mel_loss=18.21, generator_kl_loss=1.482, generator_dur_loss=1.676, generator_adv_loss=1.94, generator_feat_match_loss=6.327, over 48.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.365, discriminator_fake_loss=1.34, generator_loss=28.22, generator_mel_loss=17.96, generator_kl_loss=1.425, generator_dur_loss=1.752, generator_adv_loss=1.969, generator_feat_match_loss=5.113, over 5972.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:31:58,402 INFO [train.py:919] (5/6) Start epoch 531 +2024-03-14 06:33:43,951 INFO [train.py:527] (5/6) Epoch 531, batch 30, global_batch_idx: 65750, batch size: 66, loss[discriminator_loss=2.709, discriminator_real_loss=1.408, discriminator_fake_loss=1.301, generator_loss=28.99, generator_mel_loss=18, generator_kl_loss=1.436, generator_dur_loss=1.821, generator_adv_loss=1.92, generator_feat_match_loss=5.809, over 66.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.374, discriminator_fake_loss=1.332, generator_loss=28.41, generator_mel_loss=18.09, generator_kl_loss=1.415, generator_dur_loss=1.733, generator_adv_loss=1.956, generator_feat_match_loss=5.218, over 1654.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:36:02,460 INFO [train.py:527] (5/6) Epoch 531, batch 80, global_batch_idx: 65800, batch size: 53, loss[discriminator_loss=2.667, discriminator_real_loss=1.371, discriminator_fake_loss=1.297, generator_loss=29.1, generator_mel_loss=18.16, generator_kl_loss=1.479, generator_dur_loss=1.675, generator_adv_loss=2.07, generator_feat_match_loss=5.719, over 53.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.339, generator_loss=28.33, generator_mel_loss=18.07, generator_kl_loss=1.41, generator_dur_loss=1.748, generator_adv_loss=1.958, generator_feat_match_loss=5.144, over 4509.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:36:02,461 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 06:36:10,371 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 06:38:10,709 INFO [train.py:919] (5/6) Start epoch 532 +2024-03-14 06:38:51,760 INFO [train.py:527] (5/6) Epoch 532, batch 6, global_batch_idx: 65850, batch size: 56, loss[discriminator_loss=2.767, discriminator_real_loss=1.53, discriminator_fake_loss=1.237, generator_loss=27.82, generator_mel_loss=18.15, generator_kl_loss=1.414, generator_dur_loss=1.714, generator_adv_loss=1.784, generator_feat_match_loss=4.762, over 56.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.393, discriminator_fake_loss=1.326, generator_loss=28.11, generator_mel_loss=17.96, generator_kl_loss=1.401, generator_dur_loss=1.713, generator_adv_loss=1.993, generator_feat_match_loss=5.035, over 392.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:41:13,475 INFO [train.py:527] (5/6) Epoch 532, batch 56, global_batch_idx: 65900, batch size: 48, loss[discriminator_loss=2.723, discriminator_real_loss=1.386, discriminator_fake_loss=1.337, generator_loss=28.16, generator_mel_loss=18.32, generator_kl_loss=1.642, generator_dur_loss=1.716, generator_adv_loss=1.92, generator_feat_match_loss=4.557, over 48.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.381, discriminator_fake_loss=1.334, generator_loss=28.47, generator_mel_loss=18.08, generator_kl_loss=1.439, generator_dur_loss=1.727, generator_adv_loss=1.98, generator_feat_match_loss=5.238, over 3208.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:43:36,969 INFO [train.py:527] (5/6) Epoch 532, batch 106, global_batch_idx: 65950, batch size: 50, loss[discriminator_loss=2.736, discriminator_real_loss=1.419, discriminator_fake_loss=1.316, generator_loss=28.61, generator_mel_loss=18.09, generator_kl_loss=1.37, generator_dur_loss=1.662, generator_adv_loss=1.891, generator_feat_match_loss=5.598, over 50.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.379, discriminator_fake_loss=1.338, generator_loss=28.35, generator_mel_loss=18.01, generator_kl_loss=1.44, generator_dur_loss=1.733, generator_adv_loss=1.964, generator_feat_match_loss=5.202, over 6088.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:44:25,980 INFO [train.py:919] (5/6) Start epoch 533 +2024-03-14 06:46:21,459 INFO [train.py:527] (5/6) Epoch 533, batch 32, global_batch_idx: 66000, batch size: 88, loss[discriminator_loss=2.672, discriminator_real_loss=1.291, discriminator_fake_loss=1.381, generator_loss=28.17, generator_mel_loss=17.85, generator_kl_loss=1.513, generator_dur_loss=1.841, generator_adv_loss=2.041, generator_feat_match_loss=4.924, over 88.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.41, generator_mel_loss=18.04, generator_kl_loss=1.47, generator_dur_loss=1.728, generator_adv_loss=1.986, generator_feat_match_loss=5.183, over 1824.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:46:21,460 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 06:46:29,365 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 06:48:50,813 INFO [train.py:527] (5/6) Epoch 533, batch 82, global_batch_idx: 66050, batch size: 66, loss[discriminator_loss=2.745, discriminator_real_loss=1.387, discriminator_fake_loss=1.359, generator_loss=27.39, generator_mel_loss=17.78, generator_kl_loss=1.309, generator_dur_loss=1.768, generator_adv_loss=1.884, generator_feat_match_loss=4.648, over 66.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=28.28, generator_mel_loss=18, generator_kl_loss=1.422, generator_dur_loss=1.748, generator_adv_loss=1.967, generator_feat_match_loss=5.143, over 4830.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:50:46,586 INFO [train.py:919] (5/6) Start epoch 534 +2024-03-14 06:51:32,847 INFO [train.py:527] (5/6) Epoch 534, batch 8, global_batch_idx: 66100, batch size: 77, loss[discriminator_loss=2.717, discriminator_real_loss=1.369, discriminator_fake_loss=1.349, generator_loss=28.29, generator_mel_loss=18.09, generator_kl_loss=1.224, generator_dur_loss=1.828, generator_adv_loss=2.011, generator_feat_match_loss=5.131, over 77.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.4, discriminator_fake_loss=1.319, generator_loss=28.2, generator_mel_loss=17.98, generator_kl_loss=1.362, generator_dur_loss=1.774, generator_adv_loss=1.999, generator_feat_match_loss=5.091, over 602.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:53:53,995 INFO [train.py:527] (5/6) Epoch 534, batch 58, global_batch_idx: 66150, batch size: 13, loss[discriminator_loss=2.704, discriminator_real_loss=1.42, discriminator_fake_loss=1.284, generator_loss=29.08, generator_mel_loss=18.67, generator_kl_loss=1.982, generator_dur_loss=1.63, generator_adv_loss=1.835, generator_feat_match_loss=4.968, over 13.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.374, discriminator_fake_loss=1.336, generator_loss=28.29, generator_mel_loss=18, generator_kl_loss=1.429, generator_dur_loss=1.745, generator_adv_loss=1.97, generator_feat_match_loss=5.147, over 3258.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:56:16,842 INFO [train.py:527] (5/6) Epoch 534, batch 108, global_batch_idx: 66200, batch size: 80, loss[discriminator_loss=2.677, discriminator_real_loss=1.336, discriminator_fake_loss=1.341, generator_loss=28.43, generator_mel_loss=17.88, generator_kl_loss=1.394, generator_dur_loss=1.789, generator_adv_loss=2.037, generator_feat_match_loss=5.332, over 80.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=28.26, generator_mel_loss=18, generator_kl_loss=1.423, generator_dur_loss=1.752, generator_adv_loss=1.961, generator_feat_match_loss=5.125, over 6171.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 06:56:16,843 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 06:56:25,596 INFO [train.py:591] (5/6) 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 06:56:25,597 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 06:57:07,744 INFO [train.py:919] (5/6) Start epoch 535 +2024-03-14 06:59:12,981 INFO [train.py:527] (5/6) Epoch 535, batch 34, global_batch_idx: 66250, batch size: 58, loss[discriminator_loss=2.724, discriminator_real_loss=1.291, discriminator_fake_loss=1.433, generator_loss=27.76, generator_mel_loss=17.73, generator_kl_loss=1.275, generator_dur_loss=1.751, generator_adv_loss=2.032, generator_feat_match_loss=4.973, over 58.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.374, discriminator_fake_loss=1.346, generator_loss=28.16, generator_mel_loss=17.92, generator_kl_loss=1.434, generator_dur_loss=1.752, generator_adv_loss=1.945, generator_feat_match_loss=5.106, over 2024.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:01:34,790 INFO [train.py:527] (5/6) Epoch 535, batch 84, global_batch_idx: 66300, batch size: 13, loss[discriminator_loss=2.656, discriminator_real_loss=1.329, discriminator_fake_loss=1.327, generator_loss=28.35, generator_mel_loss=18.07, generator_kl_loss=1.428, generator_dur_loss=1.589, generator_adv_loss=2.17, generator_feat_match_loss=5.085, over 13.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.367, discriminator_fake_loss=1.346, generator_loss=28.2, generator_mel_loss=17.99, generator_kl_loss=1.427, generator_dur_loss=1.746, generator_adv_loss=1.964, generator_feat_match_loss=5.073, over 4750.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:03:22,337 INFO [train.py:919] (5/6) Start epoch 536 +2024-03-14 07:04:14,333 INFO [train.py:527] (5/6) Epoch 536, batch 10, global_batch_idx: 66350, batch size: 31, loss[discriminator_loss=2.767, discriminator_real_loss=1.533, discriminator_fake_loss=1.234, generator_loss=26.72, generator_mel_loss=17.44, generator_kl_loss=1.725, generator_dur_loss=1.631, generator_adv_loss=1.853, generator_feat_match_loss=4.065, over 31.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.409, discriminator_fake_loss=1.332, generator_loss=28.38, generator_mel_loss=18.17, generator_kl_loss=1.439, generator_dur_loss=1.707, generator_adv_loss=1.959, generator_feat_match_loss=5.101, over 579.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:06:35,671 INFO [train.py:527] (5/6) Epoch 536, batch 60, global_batch_idx: 66400, batch size: 53, loss[discriminator_loss=2.695, discriminator_real_loss=1.31, discriminator_fake_loss=1.384, generator_loss=28.66, generator_mel_loss=18.04, generator_kl_loss=1.589, generator_dur_loss=1.66, generator_adv_loss=1.998, generator_feat_match_loss=5.371, over 53.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=28.07, generator_mel_loss=17.91, generator_kl_loss=1.418, generator_dur_loss=1.721, generator_adv_loss=1.94, generator_feat_match_loss=5.084, over 3455.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:06:35,672 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 07:06:43,888 INFO [train.py:591] (5/6) 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 07:06:43,889 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 07:09:04,942 INFO [train.py:527] (5/6) Epoch 536, batch 110, global_batch_idx: 66450, batch size: 36, loss[discriminator_loss=2.742, discriminator_real_loss=1.406, discriminator_fake_loss=1.335, generator_loss=27.37, generator_mel_loss=18.03, generator_kl_loss=1.587, generator_dur_loss=1.639, generator_adv_loss=1.801, generator_feat_match_loss=4.313, over 36.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.369, discriminator_fake_loss=1.339, generator_loss=28.22, generator_mel_loss=17.96, generator_kl_loss=1.423, generator_dur_loss=1.732, generator_adv_loss=1.948, generator_feat_match_loss=5.155, over 6475.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:09:43,008 INFO [train.py:919] (5/6) Start epoch 537 +2024-03-14 07:11:48,570 INFO [train.py:527] (5/6) Epoch 537, batch 36, global_batch_idx: 66500, batch size: 48, loss[discriminator_loss=2.794, discriminator_real_loss=1.529, discriminator_fake_loss=1.265, generator_loss=26.45, generator_mel_loss=17.35, generator_kl_loss=1.329, generator_dur_loss=1.715, generator_adv_loss=1.8, generator_feat_match_loss=4.252, over 48.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.368, discriminator_fake_loss=1.341, generator_loss=28.21, generator_mel_loss=17.96, generator_kl_loss=1.433, generator_dur_loss=1.742, generator_adv_loss=1.95, generator_feat_match_loss=5.124, over 2101.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:14:06,628 INFO [train.py:527] (5/6) Epoch 537, batch 86, global_batch_idx: 66550, batch size: 42, loss[discriminator_loss=2.785, discriminator_real_loss=1.287, discriminator_fake_loss=1.497, generator_loss=28.39, generator_mel_loss=17.46, generator_kl_loss=1.583, generator_dur_loss=1.69, generator_adv_loss=2.044, generator_feat_match_loss=5.612, over 42.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.368, discriminator_fake_loss=1.346, generator_loss=28.26, generator_mel_loss=17.99, generator_kl_loss=1.427, generator_dur_loss=1.755, generator_adv_loss=1.951, generator_feat_match_loss=5.132, over 4964.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:15:54,125 INFO [train.py:919] (5/6) Start epoch 538 +2024-03-14 07:16:52,468 INFO [train.py:527] (5/6) Epoch 538, batch 12, global_batch_idx: 66600, batch size: 42, loss[discriminator_loss=2.7, discriminator_real_loss=1.404, discriminator_fake_loss=1.296, generator_loss=27.83, generator_mel_loss=18.17, generator_kl_loss=1.502, generator_dur_loss=1.684, generator_adv_loss=1.854, generator_feat_match_loss=4.611, over 42.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.364, discriminator_fake_loss=1.338, generator_loss=28.36, generator_mel_loss=18.03, generator_kl_loss=1.434, generator_dur_loss=1.728, generator_adv_loss=1.977, generator_feat_match_loss=5.191, over 752.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:16:52,470 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 07:17:00,242 INFO [train.py:591] (5/6) 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 07:17:00,243 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 07:19:21,585 INFO [train.py:527] (5/6) Epoch 538, batch 62, global_batch_idx: 66650, batch size: 61, loss[discriminator_loss=2.671, discriminator_real_loss=1.275, discriminator_fake_loss=1.396, generator_loss=28.71, generator_mel_loss=18.18, generator_kl_loss=1.431, generator_dur_loss=1.713, generator_adv_loss=2.005, generator_feat_match_loss=5.381, over 61.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=28.21, generator_mel_loss=17.99, generator_kl_loss=1.441, generator_dur_loss=1.725, generator_adv_loss=1.959, generator_feat_match_loss=5.1, over 3565.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:21:42,538 INFO [train.py:527] (5/6) Epoch 538, batch 112, global_batch_idx: 66700, batch size: 45, loss[discriminator_loss=2.722, discriminator_real_loss=1.341, discriminator_fake_loss=1.381, generator_loss=27.7, generator_mel_loss=18.02, generator_kl_loss=1.377, generator_dur_loss=1.688, generator_adv_loss=1.991, generator_feat_match_loss=4.625, over 45.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.24, generator_mel_loss=18.01, generator_kl_loss=1.433, generator_dur_loss=1.721, generator_adv_loss=1.96, generator_feat_match_loss=5.116, over 6236.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:22:15,621 INFO [train.py:919] (5/6) Start epoch 539 +2024-03-14 07:24:30,604 INFO [train.py:527] (5/6) Epoch 539, batch 38, global_batch_idx: 66750, batch size: 42, loss[discriminator_loss=2.671, discriminator_real_loss=1.366, discriminator_fake_loss=1.306, generator_loss=29.11, generator_mel_loss=18.64, generator_kl_loss=1.634, generator_dur_loss=1.662, generator_adv_loss=1.92, generator_feat_match_loss=5.261, over 42.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.51, generator_mel_loss=18.11, generator_kl_loss=1.429, generator_dur_loss=1.749, generator_adv_loss=1.982, generator_feat_match_loss=5.244, over 2182.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:26:51,896 INFO [train.py:527] (5/6) Epoch 539, batch 88, global_batch_idx: 66800, batch size: 15, loss[discriminator_loss=2.672, discriminator_real_loss=1.409, discriminator_fake_loss=1.263, generator_loss=28.7, generator_mel_loss=18.17, generator_kl_loss=1.723, generator_dur_loss=1.62, generator_adv_loss=2.142, generator_feat_match_loss=5.047, over 15.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.361, discriminator_fake_loss=1.339, generator_loss=28.4, generator_mel_loss=18.05, generator_kl_loss=1.422, generator_dur_loss=1.745, generator_adv_loss=1.973, generator_feat_match_loss=5.203, over 4962.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:26:51,897 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 07:27:00,804 INFO [train.py:591] (5/6) 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 07:27:00,805 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 07:28:39,296 INFO [train.py:919] (5/6) Start epoch 540 +2024-03-14 07:29:44,209 INFO [train.py:527] (5/6) Epoch 540, batch 14, global_batch_idx: 66850, batch size: 96, loss[discriminator_loss=2.701, discriminator_real_loss=1.357, discriminator_fake_loss=1.344, generator_loss=28.69, generator_mel_loss=18.05, generator_kl_loss=1.529, generator_dur_loss=1.825, generator_adv_loss=1.783, generator_feat_match_loss=5.504, 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=18.03, generator_kl_loss=1.445, generator_dur_loss=1.754, generator_adv_loss=1.944, generator_feat_match_loss=5.238, over 920.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:32:07,904 INFO [train.py:527] (5/6) Epoch 540, batch 64, global_batch_idx: 66900, batch size: 80, loss[discriminator_loss=2.731, discriminator_real_loss=1.399, discriminator_fake_loss=1.331, generator_loss=27.94, generator_mel_loss=18.18, generator_kl_loss=1.235, generator_dur_loss=1.829, generator_adv_loss=1.99, generator_feat_match_loss=4.706, over 80.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.364, discriminator_fake_loss=1.347, generator_loss=28.3, generator_mel_loss=18.06, generator_kl_loss=1.426, generator_dur_loss=1.757, generator_adv_loss=1.944, generator_feat_match_loss=5.116, over 3623.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:34:28,301 INFO [train.py:527] (5/6) Epoch 540, batch 114, global_batch_idx: 66950, batch size: 31, loss[discriminator_loss=2.767, discriminator_real_loss=1.437, discriminator_fake_loss=1.331, generator_loss=28.78, generator_mel_loss=18.4, generator_kl_loss=1.456, generator_dur_loss=1.667, generator_adv_loss=1.894, generator_feat_match_loss=5.362, over 31.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.364, discriminator_fake_loss=1.339, generator_loss=28.35, generator_mel_loss=18.04, generator_kl_loss=1.409, generator_dur_loss=1.764, generator_adv_loss=1.956, generator_feat_match_loss=5.178, over 6614.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:34:52,724 INFO [train.py:919] (5/6) Start epoch 541 +2024-03-14 07:37:09,275 INFO [train.py:527] (5/6) Epoch 541, batch 40, global_batch_idx: 67000, batch size: 70, loss[discriminator_loss=2.654, discriminator_real_loss=1.334, discriminator_fake_loss=1.32, generator_loss=29.17, generator_mel_loss=18, generator_kl_loss=1.384, generator_dur_loss=1.769, generator_adv_loss=1.99, generator_feat_match_loss=6.029, over 70.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.23, generator_mel_loss=17.96, generator_kl_loss=1.412, generator_dur_loss=1.734, generator_adv_loss=1.956, generator_feat_match_loss=5.17, over 2361.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:37:09,277 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 07:37:17,459 INFO [train.py:591] (5/6) 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 07:37:17,460 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 07:39:39,261 INFO [train.py:527] (5/6) Epoch 541, batch 90, global_batch_idx: 67050, batch size: 64, loss[discriminator_loss=2.749, discriminator_real_loss=1.421, discriminator_fake_loss=1.328, generator_loss=27.68, generator_mel_loss=17.95, generator_kl_loss=1.42, generator_dur_loss=1.748, generator_adv_loss=1.904, generator_feat_match_loss=4.658, over 64.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.24, generator_mel_loss=17.97, generator_kl_loss=1.404, generator_dur_loss=1.733, generator_adv_loss=1.954, generator_feat_match_loss=5.175, over 5081.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:41:13,161 INFO [train.py:919] (5/6) Start epoch 542 +2024-03-14 07:42:24,484 INFO [train.py:527] (5/6) Epoch 542, batch 16, global_batch_idx: 67100, batch size: 45, loss[discriminator_loss=2.692, discriminator_real_loss=1.291, discriminator_fake_loss=1.401, generator_loss=28.9, generator_mel_loss=18.37, generator_kl_loss=1.489, generator_dur_loss=1.674, generator_adv_loss=2.062, generator_feat_match_loss=5.309, over 45.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.386, discriminator_fake_loss=1.327, generator_loss=28.21, generator_mel_loss=18.01, generator_kl_loss=1.451, generator_dur_loss=1.729, generator_adv_loss=1.972, generator_feat_match_loss=5.048, over 908.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:44:45,976 INFO [train.py:527] (5/6) Epoch 542, batch 66, global_batch_idx: 67150, batch size: 88, loss[discriminator_loss=2.733, discriminator_real_loss=1.335, discriminator_fake_loss=1.399, generator_loss=28.28, generator_mel_loss=17.96, generator_kl_loss=1.334, generator_dur_loss=1.827, generator_adv_loss=2.023, generator_feat_match_loss=5.141, over 88.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=28.19, generator_mel_loss=18, generator_kl_loss=1.407, generator_dur_loss=1.74, generator_adv_loss=1.951, generator_feat_match_loss=5.09, over 3931.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:47:07,883 INFO [train.py:527] (5/6) Epoch 542, batch 116, global_batch_idx: 67200, batch size: 97, loss[discriminator_loss=2.695, discriminator_real_loss=1.3, discriminator_fake_loss=1.396, generator_loss=28.53, generator_mel_loss=18.08, generator_kl_loss=1.408, generator_dur_loss=1.782, generator_adv_loss=1.954, generator_feat_match_loss=5.305, over 97.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=28.22, generator_mel_loss=17.97, generator_kl_loss=1.424, generator_dur_loss=1.731, generator_adv_loss=1.967, generator_feat_match_loss=5.126, over 6708.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:47:07,885 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 07:47:16,874 INFO [train.py:591] (5/6) 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 07:47:16,875 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 07:47:38,437 INFO [train.py:919] (5/6) Start epoch 543 +2024-03-14 07:50:02,701 INFO [train.py:527] (5/6) Epoch 543, batch 42, global_batch_idx: 67250, batch size: 70, loss[discriminator_loss=2.676, discriminator_real_loss=1.351, discriminator_fake_loss=1.325, generator_loss=27.63, generator_mel_loss=17.98, generator_kl_loss=1.395, generator_dur_loss=1.772, generator_adv_loss=1.993, generator_feat_match_loss=4.484, over 70.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.346, generator_loss=28.09, generator_mel_loss=17.92, generator_kl_loss=1.415, generator_dur_loss=1.755, generator_adv_loss=1.947, generator_feat_match_loss=5.057, over 2472.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:52:24,849 INFO [train.py:527] (5/6) Epoch 543, batch 92, global_batch_idx: 67300, batch size: 72, loss[discriminator_loss=2.777, discriminator_real_loss=1.393, discriminator_fake_loss=1.384, generator_loss=28.33, generator_mel_loss=18.05, generator_kl_loss=1.327, generator_dur_loss=1.755, generator_adv_loss=1.937, generator_feat_match_loss=5.258, over 72.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.369, discriminator_fake_loss=1.345, generator_loss=28.19, generator_mel_loss=17.96, generator_kl_loss=1.406, generator_dur_loss=1.758, generator_adv_loss=1.95, generator_feat_match_loss=5.122, over 5288.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:53:50,976 INFO [train.py:919] (5/6) Start epoch 544 +2024-03-14 07:55:06,226 INFO [train.py:527] (5/6) Epoch 544, batch 18, global_batch_idx: 67350, batch size: 47, loss[discriminator_loss=2.68, discriminator_real_loss=1.289, discriminator_fake_loss=1.392, generator_loss=29.25, generator_mel_loss=18.27, generator_kl_loss=1.47, generator_dur_loss=1.72, generator_adv_loss=2.069, generator_feat_match_loss=5.729, over 47.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.343, generator_loss=28.57, generator_mel_loss=18.12, generator_kl_loss=1.431, generator_dur_loss=1.712, generator_adv_loss=1.996, generator_feat_match_loss=5.315, over 951.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:57:29,426 INFO [train.py:527] (5/6) Epoch 544, batch 68, global_batch_idx: 67400, batch size: 68, loss[discriminator_loss=2.665, discriminator_real_loss=1.264, discriminator_fake_loss=1.401, generator_loss=26.99, generator_mel_loss=17.52, generator_kl_loss=1.467, generator_dur_loss=1.792, generator_adv_loss=2.012, generator_feat_match_loss=4.199, over 68.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.33, generator_mel_loss=17.95, generator_kl_loss=1.408, generator_dur_loss=1.755, generator_adv_loss=1.999, generator_feat_match_loss=5.215, over 3989.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 07:57:29,428 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 07:57:37,698 INFO [train.py:591] (5/6) 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 07:57:37,699 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 07:59:57,097 INFO [train.py:527] (5/6) Epoch 544, batch 118, global_batch_idx: 67450, batch size: 59, loss[discriminator_loss=2.704, discriminator_real_loss=1.388, discriminator_fake_loss=1.317, generator_loss=28.71, generator_mel_loss=18.05, generator_kl_loss=1.308, generator_dur_loss=1.749, generator_adv_loss=1.984, generator_feat_match_loss=5.619, over 59.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.367, discriminator_fake_loss=1.333, generator_loss=28.38, generator_mel_loss=17.98, generator_kl_loss=1.411, generator_dur_loss=1.757, generator_adv_loss=1.989, generator_feat_match_loss=5.24, over 6838.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:00:12,067 INFO [train.py:919] (5/6) Start epoch 545 +2024-03-14 08:02:42,053 INFO [train.py:527] (5/6) Epoch 545, batch 44, global_batch_idx: 67500, batch size: 96, loss[discriminator_loss=2.679, discriminator_real_loss=1.353, discriminator_fake_loss=1.326, generator_loss=27.31, generator_mel_loss=17.54, generator_kl_loss=1.366, generator_dur_loss=1.854, generator_adv_loss=1.826, generator_feat_match_loss=4.73, over 96.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.36, generator_mel_loss=18.01, generator_kl_loss=1.411, generator_dur_loss=1.768, generator_adv_loss=1.948, generator_feat_match_loss=5.215, over 2766.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:05:03,502 INFO [train.py:527] (5/6) Epoch 545, batch 94, global_batch_idx: 67550, batch size: 68, loss[discriminator_loss=2.706, discriminator_real_loss=1.371, discriminator_fake_loss=1.335, generator_loss=28.19, generator_mel_loss=17.95, generator_kl_loss=1.45, generator_dur_loss=1.738, generator_adv_loss=2.01, generator_feat_match_loss=5.049, over 68.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=28.27, generator_mel_loss=17.98, generator_kl_loss=1.4, generator_dur_loss=1.769, generator_adv_loss=1.952, generator_feat_match_loss=5.168, over 5998.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:06:25,467 INFO [train.py:919] (5/6) Start epoch 546 +2024-03-14 08:07:45,710 INFO [train.py:527] (5/6) Epoch 546, batch 20, global_batch_idx: 67600, batch size: 70, loss[discriminator_loss=2.721, discriminator_real_loss=1.388, discriminator_fake_loss=1.333, generator_loss=27.35, generator_mel_loss=17.68, generator_kl_loss=1.356, generator_dur_loss=1.789, generator_adv_loss=1.965, generator_feat_match_loss=4.56, over 70.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.39, discriminator_fake_loss=1.334, generator_loss=28.11, generator_mel_loss=17.94, generator_kl_loss=1.405, generator_dur_loss=1.732, generator_adv_loss=1.971, generator_feat_match_loss=5.061, over 1199.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:07:45,711 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 08:07:53,537 INFO [train.py:591] (5/6) 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 08:07:53,538 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 08:10:17,382 INFO [train.py:527] (5/6) Epoch 546, batch 70, global_batch_idx: 67650, batch size: 52, loss[discriminator_loss=2.691, discriminator_real_loss=1.311, discriminator_fake_loss=1.38, generator_loss=28.38, generator_mel_loss=17.85, generator_kl_loss=1.486, generator_dur_loss=1.694, generator_adv_loss=2.1, generator_feat_match_loss=5.243, over 52.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.374, discriminator_fake_loss=1.349, generator_loss=28.16, generator_mel_loss=17.95, generator_kl_loss=1.405, generator_dur_loss=1.761, generator_adv_loss=1.949, generator_feat_match_loss=5.092, over 4266.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:12:33,666 INFO [train.py:527] (5/6) Epoch 546, batch 120, global_batch_idx: 67700, batch size: 31, loss[discriminator_loss=2.754, discriminator_real_loss=1.438, discriminator_fake_loss=1.316, generator_loss=29.56, generator_mel_loss=19.05, generator_kl_loss=1.856, generator_dur_loss=1.621, generator_adv_loss=1.908, generator_feat_match_loss=5.128, over 31.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=28.19, generator_mel_loss=17.96, generator_kl_loss=1.4, generator_dur_loss=1.758, generator_adv_loss=1.957, generator_feat_match_loss=5.118, over 7141.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:12:44,261 INFO [train.py:919] (5/6) Start epoch 547 +2024-03-14 08:15:20,853 INFO [train.py:527] (5/6) Epoch 547, batch 46, global_batch_idx: 67750, batch size: 56, loss[discriminator_loss=2.725, discriminator_real_loss=1.372, discriminator_fake_loss=1.353, generator_loss=26.65, generator_mel_loss=17.46, generator_kl_loss=1.354, generator_dur_loss=1.639, generator_adv_loss=1.994, generator_feat_match_loss=4.199, over 56.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=28.25, generator_mel_loss=17.93, generator_kl_loss=1.444, generator_dur_loss=1.737, generator_adv_loss=1.978, generator_feat_match_loss=5.162, over 2567.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:17:43,960 INFO [train.py:527] (5/6) Epoch 547, batch 96, global_batch_idx: 67800, batch size: 66, loss[discriminator_loss=2.731, discriminator_real_loss=1.358, discriminator_fake_loss=1.374, generator_loss=27.41, generator_mel_loss=17.73, generator_kl_loss=1.432, generator_dur_loss=1.743, generator_adv_loss=1.951, generator_feat_match_loss=4.556, over 66.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.367, discriminator_fake_loss=1.339, generator_loss=28.24, generator_mel_loss=17.96, generator_kl_loss=1.443, generator_dur_loss=1.729, generator_adv_loss=1.964, generator_feat_match_loss=5.151, over 5210.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:17:43,962 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 08:17:52,787 INFO [train.py:591] (5/6) 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 08:17:52,788 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 08:19:07,190 INFO [train.py:919] (5/6) Start epoch 548 +2024-03-14 08:20:34,362 INFO [train.py:527] (5/6) Epoch 548, batch 22, global_batch_idx: 67850, batch size: 53, loss[discriminator_loss=2.675, discriminator_real_loss=1.311, discriminator_fake_loss=1.364, generator_loss=28.69, generator_mel_loss=18.08, generator_kl_loss=1.444, generator_dur_loss=1.651, generator_adv_loss=1.939, generator_feat_match_loss=5.582, over 53.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.365, discriminator_fake_loss=1.339, generator_loss=28.44, generator_mel_loss=18.02, generator_kl_loss=1.464, generator_dur_loss=1.706, generator_adv_loss=1.979, generator_feat_match_loss=5.268, over 1167.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:22:55,284 INFO [train.py:527] (5/6) Epoch 548, batch 72, global_batch_idx: 67900, batch size: 66, loss[discriminator_loss=2.706, discriminator_real_loss=1.305, discriminator_fake_loss=1.401, generator_loss=28.3, generator_mel_loss=18.08, generator_kl_loss=1.491, generator_dur_loss=1.709, generator_adv_loss=1.984, generator_feat_match_loss=5.03, over 66.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.376, discriminator_fake_loss=1.337, generator_loss=28.21, generator_mel_loss=17.99, generator_kl_loss=1.455, generator_dur_loss=1.714, generator_adv_loss=1.963, generator_feat_match_loss=5.085, over 4012.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:25:18,876 INFO [train.py:527] (5/6) Epoch 548, batch 122, global_batch_idx: 67950, batch size: 25, loss[discriminator_loss=2.785, discriminator_real_loss=1.399, discriminator_fake_loss=1.386, generator_loss=28.25, generator_mel_loss=18.61, generator_kl_loss=1.638, generator_dur_loss=1.571, generator_adv_loss=1.904, generator_feat_match_loss=4.535, over 25.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.374, discriminator_fake_loss=1.338, generator_loss=28.17, generator_mel_loss=18, generator_kl_loss=1.447, generator_dur_loss=1.716, generator_adv_loss=1.959, generator_feat_match_loss=5.044, over 6839.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:25:23,545 INFO [train.py:919] (5/6) Start epoch 549 +2024-03-14 08:28:01,936 INFO [train.py:527] (5/6) Epoch 549, batch 48, global_batch_idx: 68000, batch size: 68, loss[discriminator_loss=2.678, discriminator_real_loss=1.42, discriminator_fake_loss=1.258, generator_loss=27.87, generator_mel_loss=18.02, generator_kl_loss=1.365, generator_dur_loss=1.788, generator_adv_loss=1.937, generator_feat_match_loss=4.768, over 68.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=28.22, generator_mel_loss=18.02, generator_kl_loss=1.408, generator_dur_loss=1.748, generator_adv_loss=1.959, generator_feat_match_loss=5.086, over 2814.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:28:01,937 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 08:28:10,806 INFO [train.py:591] (5/6) 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 08:28:10,807 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 08:30:31,611 INFO [train.py:527] (5/6) Epoch 549, batch 98, global_batch_idx: 68050, batch size: 74, loss[discriminator_loss=2.75, discriminator_real_loss=1.32, discriminator_fake_loss=1.43, generator_loss=29.61, generator_mel_loss=18.76, generator_kl_loss=1.365, generator_dur_loss=1.787, generator_adv_loss=2.038, generator_feat_match_loss=5.655, over 74.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.34, generator_mel_loss=18.04, generator_kl_loss=1.407, generator_dur_loss=1.753, generator_adv_loss=1.974, generator_feat_match_loss=5.163, over 5716.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:31:43,702 INFO [train.py:919] (5/6) Start epoch 550 +2024-03-14 08:33:15,659 INFO [train.py:527] (5/6) Epoch 550, batch 24, global_batch_idx: 68100, batch size: 45, loss[discriminator_loss=2.651, discriminator_real_loss=1.292, discriminator_fake_loss=1.359, generator_loss=28.94, generator_mel_loss=18.28, generator_kl_loss=1.618, generator_dur_loss=1.714, generator_adv_loss=1.973, generator_feat_match_loss=5.357, over 45.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.356, discriminator_fake_loss=1.334, generator_loss=28.29, generator_mel_loss=18, generator_kl_loss=1.395, generator_dur_loss=1.743, generator_adv_loss=1.957, generator_feat_match_loss=5.196, over 1399.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:35:33,966 INFO [train.py:527] (5/6) Epoch 550, batch 74, global_batch_idx: 68150, batch size: 70, loss[discriminator_loss=2.702, discriminator_real_loss=1.399, discriminator_fake_loss=1.303, generator_loss=28.18, generator_mel_loss=17.74, generator_kl_loss=1.46, generator_dur_loss=1.807, generator_adv_loss=2.046, generator_feat_match_loss=5.13, over 70.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.375, discriminator_fake_loss=1.335, generator_loss=28.2, generator_mel_loss=17.96, generator_kl_loss=1.392, generator_dur_loss=1.754, generator_adv_loss=1.96, generator_feat_match_loss=5.136, over 4349.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:37:55,124 INFO [train.py:919] (5/6) Start epoch 551 +2024-03-14 08:38:18,892 INFO [train.py:527] (5/6) Epoch 551, batch 0, global_batch_idx: 68200, batch size: 53, loss[discriminator_loss=2.781, discriminator_real_loss=1.42, discriminator_fake_loss=1.361, generator_loss=28.1, generator_mel_loss=17.96, generator_kl_loss=1.382, generator_dur_loss=1.713, generator_adv_loss=1.929, generator_feat_match_loss=5.118, over 53.00 samples.], tot_loss[discriminator_loss=2.781, discriminator_real_loss=1.42, discriminator_fake_loss=1.361, generator_loss=28.1, generator_mel_loss=17.96, generator_kl_loss=1.382, generator_dur_loss=1.713, generator_adv_loss=1.929, generator_feat_match_loss=5.118, over 53.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:38:18,895 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 08:38:26,771 INFO [train.py:591] (5/6) 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 08:38:26,774 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 08:40:50,232 INFO [train.py:527] (5/6) Epoch 551, batch 50, global_batch_idx: 68250, batch size: 74, loss[discriminator_loss=2.705, discriminator_real_loss=1.355, discriminator_fake_loss=1.35, generator_loss=28.12, generator_mel_loss=18.11, generator_kl_loss=1.252, generator_dur_loss=1.827, generator_adv_loss=1.98, generator_feat_match_loss=4.949, over 74.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.339, generator_loss=28.28, generator_mel_loss=18, generator_kl_loss=1.404, generator_dur_loss=1.76, generator_adv_loss=1.953, generator_feat_match_loss=5.162, over 2939.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:43:11,311 INFO [train.py:527] (5/6) Epoch 551, batch 100, global_batch_idx: 68300, batch size: 14, loss[discriminator_loss=2.688, discriminator_real_loss=1.473, discriminator_fake_loss=1.215, generator_loss=29.46, generator_mel_loss=18.73, generator_kl_loss=1.871, generator_dur_loss=1.691, generator_adv_loss=1.87, generator_feat_match_loss=5.299, over 14.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.362, discriminator_fake_loss=1.342, generator_loss=28.36, generator_mel_loss=18.01, generator_kl_loss=1.421, generator_dur_loss=1.753, generator_adv_loss=1.962, generator_feat_match_loss=5.206, over 5540.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:44:14,658 INFO [train.py:919] (5/6) Start epoch 552 +2024-03-14 08:45:53,473 INFO [train.py:527] (5/6) Epoch 552, batch 26, global_batch_idx: 68350, batch size: 44, loss[discriminator_loss=2.71, discriminator_real_loss=1.366, discriminator_fake_loss=1.344, generator_loss=27.98, generator_mel_loss=18.2, generator_kl_loss=1.521, generator_dur_loss=1.725, generator_adv_loss=1.953, generator_feat_match_loss=4.586, over 44.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.36, discriminator_fake_loss=1.34, generator_loss=28.36, generator_mel_loss=17.96, generator_kl_loss=1.43, generator_dur_loss=1.747, generator_adv_loss=1.977, generator_feat_match_loss=5.248, over 1476.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:48:15,083 INFO [train.py:527] (5/6) Epoch 552, batch 76, global_batch_idx: 68400, batch size: 61, loss[discriminator_loss=2.675, discriminator_real_loss=1.364, discriminator_fake_loss=1.311, generator_loss=28.02, generator_mel_loss=17.73, generator_kl_loss=1.276, generator_dur_loss=1.752, generator_adv_loss=1.928, generator_feat_match_loss=5.34, over 61.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.31, generator_mel_loss=17.99, generator_kl_loss=1.402, generator_dur_loss=1.755, generator_adv_loss=1.962, generator_feat_match_loss=5.203, over 4494.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:48:15,084 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 08:48:23,802 INFO [train.py:591] (5/6) 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 08:48:23,803 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 08:50:33,534 INFO [train.py:919] (5/6) Start epoch 553 +2024-03-14 08:51:04,211 INFO [train.py:527] (5/6) Epoch 553, batch 2, global_batch_idx: 68450, batch size: 83, loss[discriminator_loss=2.673, discriminator_real_loss=1.373, discriminator_fake_loss=1.3, generator_loss=28.08, generator_mel_loss=17.89, generator_kl_loss=1.279, generator_dur_loss=1.841, generator_adv_loss=2.038, generator_feat_match_loss=5.031, over 83.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.334, discriminator_fake_loss=1.392, generator_loss=28.26, generator_mel_loss=17.92, generator_kl_loss=1.358, generator_dur_loss=1.801, generator_adv_loss=2.003, generator_feat_match_loss=5.177, over 210.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:53:26,775 INFO [train.py:527] (5/6) Epoch 553, batch 52, global_batch_idx: 68500, batch size: 56, loss[discriminator_loss=2.759, discriminator_real_loss=1.392, discriminator_fake_loss=1.367, generator_loss=27.68, generator_mel_loss=17.86, generator_kl_loss=1.408, generator_dur_loss=1.719, generator_adv_loss=1.921, generator_feat_match_loss=4.772, over 56.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.364, discriminator_fake_loss=1.338, generator_loss=28.26, generator_mel_loss=17.88, generator_kl_loss=1.41, generator_dur_loss=1.757, generator_adv_loss=1.977, generator_feat_match_loss=5.233, over 2945.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:55:46,638 INFO [train.py:527] (5/6) Epoch 553, batch 102, global_batch_idx: 68550, batch size: 64, loss[discriminator_loss=2.765, discriminator_real_loss=1.444, discriminator_fake_loss=1.321, generator_loss=28, generator_mel_loss=17.71, generator_kl_loss=1.414, generator_dur_loss=1.729, generator_adv_loss=1.925, generator_feat_match_loss=5.224, over 64.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.34, generator_loss=28.28, generator_mel_loss=17.95, generator_kl_loss=1.414, generator_dur_loss=1.756, generator_adv_loss=1.961, generator_feat_match_loss=5.194, over 5824.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:56:48,271 INFO [train.py:919] (5/6) Start epoch 554 +2024-03-14 08:58:31,795 INFO [train.py:527] (5/6) Epoch 554, batch 28, global_batch_idx: 68600, batch size: 61, loss[discriminator_loss=2.708, discriminator_real_loss=1.399, discriminator_fake_loss=1.31, generator_loss=27.63, generator_mel_loss=17.63, generator_kl_loss=1.429, generator_dur_loss=1.768, generator_adv_loss=1.806, generator_feat_match_loss=4.994, over 61.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.378, discriminator_fake_loss=1.336, generator_loss=28.18, generator_mel_loss=17.94, generator_kl_loss=1.409, generator_dur_loss=1.754, generator_adv_loss=1.977, generator_feat_match_loss=5.103, over 1647.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 08:58:31,796 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 08:58:40,004 INFO [train.py:591] (5/6) 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 08:58:40,005 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 09:00:58,277 INFO [train.py:527] (5/6) Epoch 554, batch 78, global_batch_idx: 68650, batch size: 16, loss[discriminator_loss=2.674, discriminator_real_loss=1.35, discriminator_fake_loss=1.324, generator_loss=30.65, generator_mel_loss=19.03, generator_kl_loss=1.858, generator_dur_loss=1.659, generator_adv_loss=1.993, generator_feat_match_loss=6.108, over 16.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.336, generator_loss=28.36, generator_mel_loss=17.98, generator_kl_loss=1.414, generator_dur_loss=1.756, generator_adv_loss=1.968, generator_feat_match_loss=5.241, over 4572.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:03:08,417 INFO [train.py:919] (5/6) Start epoch 555 +2024-03-14 09:03:43,831 INFO [train.py:527] (5/6) Epoch 555, batch 4, global_batch_idx: 68700, batch size: 66, loss[discriminator_loss=2.688, discriminator_real_loss=1.32, discriminator_fake_loss=1.368, generator_loss=28.7, generator_mel_loss=18.08, generator_kl_loss=1.587, generator_dur_loss=1.689, generator_adv_loss=2.025, generator_feat_match_loss=5.322, over 66.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.363, discriminator_fake_loss=1.34, generator_loss=28.22, generator_mel_loss=18, generator_kl_loss=1.449, generator_dur_loss=1.716, generator_adv_loss=1.961, generator_feat_match_loss=5.095, over 296.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:06:07,427 INFO [train.py:527] (5/6) Epoch 555, batch 54, global_batch_idx: 68750, batch size: 52, loss[discriminator_loss=2.763, discriminator_real_loss=1.423, discriminator_fake_loss=1.339, generator_loss=28.71, generator_mel_loss=17.85, generator_kl_loss=1.536, generator_dur_loss=1.623, generator_adv_loss=1.99, generator_feat_match_loss=5.712, over 52.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.368, discriminator_fake_loss=1.346, generator_loss=28.26, generator_mel_loss=17.94, generator_kl_loss=1.404, generator_dur_loss=1.747, generator_adv_loss=1.963, generator_feat_match_loss=5.205, over 3394.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:08:29,662 INFO [train.py:527] (5/6) Epoch 555, batch 104, global_batch_idx: 68800, batch size: 50, loss[discriminator_loss=2.713, discriminator_real_loss=1.374, discriminator_fake_loss=1.339, generator_loss=28.13, generator_mel_loss=18.14, generator_kl_loss=1.413, generator_dur_loss=1.74, generator_adv_loss=1.852, generator_feat_match_loss=4.987, over 50.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.368, discriminator_fake_loss=1.349, generator_loss=28.28, generator_mel_loss=17.97, generator_kl_loss=1.4, generator_dur_loss=1.752, generator_adv_loss=1.958, generator_feat_match_loss=5.199, over 6318.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:08:29,663 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 09:08:38,551 INFO [train.py:591] (5/6) 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 09:08:38,552 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 09:09:32,820 INFO [train.py:919] (5/6) Start epoch 556 +2024-03-14 09:11:22,828 INFO [train.py:527] (5/6) Epoch 556, batch 30, global_batch_idx: 68850, batch size: 25, loss[discriminator_loss=2.669, discriminator_real_loss=1.31, discriminator_fake_loss=1.358, generator_loss=30.34, generator_mel_loss=18.7, generator_kl_loss=1.684, generator_dur_loss=1.607, generator_adv_loss=2.057, generator_feat_match_loss=6.288, over 25.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.371, discriminator_fake_loss=1.328, generator_loss=28.39, generator_mel_loss=17.99, generator_kl_loss=1.418, generator_dur_loss=1.754, generator_adv_loss=2.018, generator_feat_match_loss=5.213, over 1738.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:13:42,350 INFO [train.py:527] (5/6) Epoch 556, batch 80, global_batch_idx: 68900, batch size: 31, loss[discriminator_loss=2.596, discriminator_real_loss=1.273, discriminator_fake_loss=1.323, generator_loss=29.16, generator_mel_loss=18.07, generator_kl_loss=1.696, generator_dur_loss=1.595, generator_adv_loss=2.05, generator_feat_match_loss=5.752, over 31.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.371, discriminator_fake_loss=1.335, generator_loss=28.39, generator_mel_loss=17.99, generator_kl_loss=1.426, generator_dur_loss=1.754, generator_adv_loss=1.984, generator_feat_match_loss=5.231, over 4484.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:15:44,074 INFO [train.py:919] (5/6) Start epoch 557 +2024-03-14 09:16:26,676 INFO [train.py:527] (5/6) Epoch 557, batch 6, global_batch_idx: 68950, batch size: 70, loss[discriminator_loss=2.747, discriminator_real_loss=1.485, discriminator_fake_loss=1.262, generator_loss=27.61, generator_mel_loss=17.82, generator_kl_loss=1.334, generator_dur_loss=1.828, generator_adv_loss=1.848, generator_feat_match_loss=4.78, over 70.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.34, generator_loss=27.87, generator_mel_loss=17.77, generator_kl_loss=1.377, generator_dur_loss=1.803, generator_adv_loss=1.965, generator_feat_match_loss=4.953, over 495.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:18:48,121 INFO [train.py:527] (5/6) Epoch 557, batch 56, global_batch_idx: 69000, batch size: 53, loss[discriminator_loss=2.742, discriminator_real_loss=1.388, discriminator_fake_loss=1.354, generator_loss=27.6, generator_mel_loss=17.72, generator_kl_loss=1.365, generator_dur_loss=1.684, generator_adv_loss=1.896, generator_feat_match_loss=4.936, over 53.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.373, discriminator_fake_loss=1.345, generator_loss=28.22, generator_mel_loss=17.95, generator_kl_loss=1.402, generator_dur_loss=1.763, generator_adv_loss=1.955, generator_feat_match_loss=5.145, over 3471.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:18:48,122 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 09:18:56,042 INFO [train.py:591] (5/6) 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 09:18:56,042 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 09:21:15,776 INFO [train.py:527] (5/6) Epoch 557, batch 106, global_batch_idx: 69050, batch size: 25, loss[discriminator_loss=2.644, discriminator_real_loss=1.421, discriminator_fake_loss=1.222, generator_loss=29.6, generator_mel_loss=18.78, generator_kl_loss=1.821, generator_dur_loss=1.586, generator_adv_loss=2.028, generator_feat_match_loss=5.381, over 25.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=28.29, generator_mel_loss=17.99, generator_kl_loss=1.42, generator_dur_loss=1.752, generator_adv_loss=1.965, generator_feat_match_loss=5.162, over 6175.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:22:05,194 INFO [train.py:919] (5/6) Start epoch 558 +2024-03-14 09:24:03,431 INFO [train.py:527] (5/6) Epoch 558, batch 32, global_batch_idx: 69100, batch size: 50, loss[discriminator_loss=2.724, discriminator_real_loss=1.458, discriminator_fake_loss=1.265, generator_loss=27.94, generator_mel_loss=18.06, generator_kl_loss=1.317, generator_dur_loss=1.667, generator_adv_loss=1.914, generator_feat_match_loss=4.98, over 50.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.361, discriminator_fake_loss=1.341, generator_loss=28.25, generator_mel_loss=17.95, generator_kl_loss=1.414, generator_dur_loss=1.741, generator_adv_loss=1.963, generator_feat_match_loss=5.178, over 1870.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:26:24,031 INFO [train.py:527] (5/6) Epoch 558, batch 82, global_batch_idx: 69150, batch size: 74, loss[discriminator_loss=2.68, discriminator_real_loss=1.358, discriminator_fake_loss=1.322, generator_loss=28.07, generator_mel_loss=17.71, generator_kl_loss=1.425, generator_dur_loss=1.761, generator_adv_loss=1.929, generator_feat_match_loss=5.247, over 74.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.363, discriminator_fake_loss=1.339, generator_loss=28.34, generator_mel_loss=17.98, generator_kl_loss=1.426, generator_dur_loss=1.744, generator_adv_loss=1.961, generator_feat_match_loss=5.226, over 4806.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:28:20,888 INFO [train.py:919] (5/6) Start epoch 559 +2024-03-14 09:29:07,760 INFO [train.py:527] (5/6) Epoch 559, batch 8, global_batch_idx: 69200, batch size: 55, loss[discriminator_loss=2.742, discriminator_real_loss=1.398, discriminator_fake_loss=1.344, generator_loss=28.53, generator_mel_loss=17.76, generator_kl_loss=1.488, generator_dur_loss=1.733, generator_adv_loss=1.874, generator_feat_match_loss=5.671, over 55.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=28.38, generator_mel_loss=18.02, generator_kl_loss=1.433, generator_dur_loss=1.73, generator_adv_loss=1.939, generator_feat_match_loss=5.26, over 514.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:29:07,762 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 09:29:15,985 INFO [train.py:591] (5/6) 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 09:29:15,988 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 09:31:36,815 INFO [train.py:527] (5/6) Epoch 559, batch 58, global_batch_idx: 69250, batch size: 77, loss[discriminator_loss=2.721, discriminator_real_loss=1.461, discriminator_fake_loss=1.26, generator_loss=27.54, generator_mel_loss=17.79, generator_kl_loss=1.34, generator_dur_loss=1.828, generator_adv_loss=1.919, generator_feat_match_loss=4.664, over 77.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.43, generator_mel_loss=18, generator_kl_loss=1.433, generator_dur_loss=1.747, generator_adv_loss=1.963, generator_feat_match_loss=5.286, over 3361.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:33:58,935 INFO [train.py:527] (5/6) Epoch 559, batch 108, global_batch_idx: 69300, batch size: 53, loss[discriminator_loss=2.713, discriminator_real_loss=1.424, discriminator_fake_loss=1.289, generator_loss=27.76, generator_mel_loss=18.07, generator_kl_loss=1.378, generator_dur_loss=1.717, generator_adv_loss=1.88, generator_feat_match_loss=4.713, over 53.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.341, generator_loss=28.41, generator_mel_loss=18, generator_kl_loss=1.412, generator_dur_loss=1.755, generator_adv_loss=1.959, generator_feat_match_loss=5.285, over 6344.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:34:40,791 INFO [train.py:919] (5/6) Start epoch 560 +2024-03-14 09:36:42,361 INFO [train.py:527] (5/6) Epoch 560, batch 34, global_batch_idx: 69350, batch size: 83, loss[discriminator_loss=2.671, discriminator_real_loss=1.339, discriminator_fake_loss=1.332, generator_loss=28.59, generator_mel_loss=18.16, generator_kl_loss=1.341, generator_dur_loss=1.861, generator_adv_loss=1.865, generator_feat_match_loss=5.356, over 83.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.23, generator_mel_loss=17.88, generator_kl_loss=1.408, generator_dur_loss=1.774, generator_adv_loss=1.954, generator_feat_match_loss=5.216, over 2172.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:39:03,952 INFO [train.py:527] (5/6) Epoch 560, batch 84, global_batch_idx: 69400, batch size: 83, loss[discriminator_loss=2.705, discriminator_real_loss=1.371, discriminator_fake_loss=1.334, generator_loss=27.66, generator_mel_loss=18.01, generator_kl_loss=1.372, generator_dur_loss=1.876, generator_adv_loss=1.891, generator_feat_match_loss=4.513, over 83.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.23, generator_mel_loss=17.9, generator_kl_loss=1.393, generator_dur_loss=1.781, generator_adv_loss=1.956, generator_feat_match_loss=5.196, over 5386.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, +2024-03-14 09:39:03,953 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 09:39:13,161 INFO [train.py:591] (5/6) 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 09:39:13,162 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 09:41:00,248 INFO [train.py:919] (5/6) Start epoch 561 +2024-03-14 09:41:52,130 INFO [train.py:527] (5/6) Epoch 561, batch 10, global_batch_idx: 69450, batch size: 96, loss[discriminator_loss=2.707, discriminator_real_loss=1.344, discriminator_fake_loss=1.362, generator_loss=28.22, generator_mel_loss=18.09, generator_kl_loss=1.251, generator_dur_loss=1.885, generator_adv_loss=1.822, generator_feat_match_loss=5.171, over 96.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.376, discriminator_fake_loss=1.34, generator_loss=28.37, generator_mel_loss=18, generator_kl_loss=1.398, generator_dur_loss=1.781, generator_adv_loss=1.956, generator_feat_match_loss=5.235, over 738.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:44:11,575 INFO [train.py:527] (5/6) Epoch 561, batch 60, global_batch_idx: 69500, batch size: 62, loss[discriminator_loss=2.659, discriminator_real_loss=1.335, discriminator_fake_loss=1.324, generator_loss=28.13, generator_mel_loss=17.78, generator_kl_loss=1.34, generator_dur_loss=1.719, generator_adv_loss=2.202, generator_feat_match_loss=5.094, over 62.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.36, discriminator_fake_loss=1.336, generator_loss=28.37, generator_mel_loss=18.02, generator_kl_loss=1.384, generator_dur_loss=1.763, generator_adv_loss=1.971, generator_feat_match_loss=5.235, over 3599.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:46:29,752 INFO [train.py:527] (5/6) Epoch 561, batch 110, global_batch_idx: 69550, batch size: 56, loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=29.12, generator_mel_loss=18.11, generator_kl_loss=1.454, generator_dur_loss=1.723, generator_adv_loss=2.09, generator_feat_match_loss=5.742, over 56.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.363, discriminator_fake_loss=1.334, generator_loss=28.41, generator_mel_loss=18.01, generator_kl_loss=1.409, generator_dur_loss=1.749, generator_adv_loss=1.973, generator_feat_match_loss=5.265, over 6378.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:47:07,471 INFO [train.py:919] (5/6) Start epoch 562 +2024-03-14 09:49:14,030 INFO [train.py:527] (5/6) Epoch 562, batch 36, global_batch_idx: 69600, batch size: 53, loss[discriminator_loss=2.694, discriminator_real_loss=1.345, discriminator_fake_loss=1.349, generator_loss=28.33, generator_mel_loss=17.79, generator_kl_loss=1.466, generator_dur_loss=1.634, generator_adv_loss=2.052, generator_feat_match_loss=5.384, over 53.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=28.29, generator_mel_loss=17.94, generator_kl_loss=1.392, generator_dur_loss=1.762, generator_adv_loss=1.966, generator_feat_match_loss=5.228, over 2189.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:49:14,031 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 09:49:21,868 INFO [train.py:591] (5/6) 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 09:49:21,868 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 09:51:41,003 INFO [train.py:527] (5/6) Epoch 562, batch 86, global_batch_idx: 69650, batch size: 47, loss[discriminator_loss=2.721, discriminator_real_loss=1.349, discriminator_fake_loss=1.372, generator_loss=28.49, generator_mel_loss=18.08, generator_kl_loss=1.686, generator_dur_loss=1.686, generator_adv_loss=1.967, generator_feat_match_loss=5.074, over 47.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.367, discriminator_fake_loss=1.344, generator_loss=28.33, generator_mel_loss=17.99, generator_kl_loss=1.406, generator_dur_loss=1.757, generator_adv_loss=1.962, generator_feat_match_loss=5.219, over 5099.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:53:22,701 INFO [train.py:919] (5/6) Start epoch 563 +2024-03-14 09:54:19,264 INFO [train.py:527] (5/6) Epoch 563, batch 12, global_batch_idx: 69700, batch size: 56, loss[discriminator_loss=2.639, discriminator_real_loss=1.315, discriminator_fake_loss=1.324, generator_loss=28.66, generator_mel_loss=17.8, generator_kl_loss=1.434, generator_dur_loss=1.686, generator_adv_loss=2.062, generator_feat_match_loss=5.675, over 56.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.358, discriminator_fake_loss=1.337, generator_loss=28.5, generator_mel_loss=17.95, generator_kl_loss=1.433, generator_dur_loss=1.76, generator_adv_loss=1.97, generator_feat_match_loss=5.386, over 784.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:56:37,472 INFO [train.py:527] (5/6) Epoch 563, batch 62, global_batch_idx: 69750, batch size: 61, loss[discriminator_loss=2.72, discriminator_real_loss=1.478, discriminator_fake_loss=1.242, generator_loss=28.56, generator_mel_loss=17.99, generator_kl_loss=1.284, generator_dur_loss=1.737, generator_adv_loss=2.054, generator_feat_match_loss=5.501, over 61.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.363, discriminator_fake_loss=1.344, generator_loss=28.48, generator_mel_loss=18.01, generator_kl_loss=1.422, generator_dur_loss=1.765, generator_adv_loss=1.971, generator_feat_match_loss=5.313, over 3795.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:59:00,137 INFO [train.py:527] (5/6) Epoch 563, batch 112, global_batch_idx: 69800, batch size: 68, loss[discriminator_loss=2.658, discriminator_real_loss=1.345, discriminator_fake_loss=1.313, generator_loss=28.53, generator_mel_loss=17.8, generator_kl_loss=1.396, generator_dur_loss=1.736, generator_adv_loss=1.907, generator_feat_match_loss=5.695, over 68.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.365, discriminator_fake_loss=1.337, generator_loss=28.4, generator_mel_loss=17.97, generator_kl_loss=1.416, generator_dur_loss=1.763, generator_adv_loss=1.969, generator_feat_match_loss=5.286, over 6548.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 09:59:00,138 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 09:59:08,734 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 09:59:37,582 INFO [train.py:919] (5/6) Start epoch 564 +2024-03-14 10:01:47,464 INFO [train.py:527] (5/6) Epoch 564, batch 38, global_batch_idx: 69850, batch size: 80, loss[discriminator_loss=2.705, discriminator_real_loss=1.463, discriminator_fake_loss=1.242, generator_loss=27.9, generator_mel_loss=17.82, generator_kl_loss=1.329, generator_dur_loss=1.785, generator_adv_loss=1.807, generator_feat_match_loss=5.163, over 80.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.376, discriminator_fake_loss=1.334, generator_loss=28.41, generator_mel_loss=17.97, generator_kl_loss=1.432, generator_dur_loss=1.769, generator_adv_loss=1.965, generator_feat_match_loss=5.274, over 2218.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:04:04,306 INFO [train.py:527] (5/6) Epoch 564, batch 88, global_batch_idx: 69900, batch size: 56, loss[discriminator_loss=2.767, discriminator_real_loss=1.417, discriminator_fake_loss=1.35, generator_loss=27.95, generator_mel_loss=18.21, generator_kl_loss=1.338, generator_dur_loss=1.709, generator_adv_loss=1.8, generator_feat_match_loss=4.888, over 56.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.371, discriminator_fake_loss=1.337, generator_loss=28.42, generator_mel_loss=18, generator_kl_loss=1.433, generator_dur_loss=1.755, generator_adv_loss=1.968, generator_feat_match_loss=5.264, over 4877.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:05:43,552 INFO [train.py:919] (5/6) Start epoch 565 +2024-03-14 10:06:48,914 INFO [train.py:527] (5/6) Epoch 565, batch 14, global_batch_idx: 69950, batch size: 42, loss[discriminator_loss=2.625, discriminator_real_loss=1.224, discriminator_fake_loss=1.401, generator_loss=28.8, generator_mel_loss=18.13, generator_kl_loss=1.51, generator_dur_loss=1.693, generator_adv_loss=2.004, generator_feat_match_loss=5.463, over 42.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.349, discriminator_fake_loss=1.358, generator_loss=28.25, generator_mel_loss=17.9, generator_kl_loss=1.415, generator_dur_loss=1.749, generator_adv_loss=1.95, generator_feat_match_loss=5.236, over 897.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:09:07,115 INFO [train.py:527] (5/6) Epoch 565, batch 64, global_batch_idx: 70000, batch size: 48, loss[discriminator_loss=2.718, discriminator_real_loss=1.396, discriminator_fake_loss=1.322, generator_loss=26.32, generator_mel_loss=16.98, generator_kl_loss=1.529, generator_dur_loss=1.649, generator_adv_loss=1.947, generator_feat_match_loss=4.218, over 48.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.366, discriminator_fake_loss=1.342, generator_loss=28.25, generator_mel_loss=17.93, generator_kl_loss=1.425, generator_dur_loss=1.729, generator_adv_loss=1.961, generator_feat_match_loss=5.207, over 3598.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:09:07,116 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 10:09:15,109 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 10:11:33,396 INFO [train.py:527] (5/6) Epoch 565, batch 114, global_batch_idx: 70050, batch size: 53, loss[discriminator_loss=2.715, discriminator_real_loss=1.265, discriminator_fake_loss=1.45, generator_loss=29.46, generator_mel_loss=18.52, generator_kl_loss=1.587, generator_dur_loss=1.697, generator_adv_loss=2.025, generator_feat_match_loss=5.631, over 53.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.365, discriminator_fake_loss=1.342, generator_loss=28.35, generator_mel_loss=17.96, generator_kl_loss=1.434, generator_dur_loss=1.74, generator_adv_loss=1.966, generator_feat_match_loss=5.245, over 6401.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:11:58,953 INFO [train.py:919] (5/6) Start epoch 566 +2024-03-14 10:14:15,780 INFO [train.py:527] (5/6) Epoch 566, batch 40, global_batch_idx: 70100, batch size: 59, loss[discriminator_loss=2.687, discriminator_real_loss=1.272, discriminator_fake_loss=1.416, generator_loss=29.29, generator_mel_loss=18.54, generator_kl_loss=1.385, generator_dur_loss=1.726, generator_adv_loss=1.97, generator_feat_match_loss=5.669, over 59.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.376, discriminator_fake_loss=1.338, generator_loss=28.42, generator_mel_loss=18.03, generator_kl_loss=1.415, generator_dur_loss=1.762, generator_adv_loss=1.956, generator_feat_match_loss=5.26, over 2331.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:16:36,055 INFO [train.py:527] (5/6) Epoch 566, batch 90, global_batch_idx: 70150, batch size: 48, loss[discriminator_loss=2.712, discriminator_real_loss=1.38, discriminator_fake_loss=1.332, generator_loss=28.41, generator_mel_loss=18.03, generator_kl_loss=1.593, generator_dur_loss=1.652, generator_adv_loss=2.015, generator_feat_match_loss=5.12, over 48.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.368, discriminator_fake_loss=1.338, generator_loss=28.39, generator_mel_loss=17.99, generator_kl_loss=1.414, generator_dur_loss=1.761, generator_adv_loss=1.96, generator_feat_match_loss=5.262, over 5274.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:18:08,231 INFO [train.py:919] (5/6) Start epoch 567 +2024-03-14 10:19:16,572 INFO [train.py:527] (5/6) Epoch 567, batch 16, global_batch_idx: 70200, batch size: 12, loss[discriminator_loss=2.551, discriminator_real_loss=1.301, discriminator_fake_loss=1.25, generator_loss=28.14, generator_mel_loss=17.43, generator_kl_loss=1.82, generator_dur_loss=1.607, generator_adv_loss=2.098, generator_feat_match_loss=5.186, over 12.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.355, discriminator_fake_loss=1.351, generator_loss=28.8, generator_mel_loss=18.23, generator_kl_loss=1.498, generator_dur_loss=1.692, generator_adv_loss=1.962, generator_feat_match_loss=5.421, over 778.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:19:16,574 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 10:19:24,538 INFO [train.py:591] (5/6) 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 10:19:24,539 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 10:21:43,335 INFO [train.py:527] (5/6) Epoch 567, batch 66, global_batch_idx: 70250, batch size: 64, loss[discriminator_loss=2.677, discriminator_real_loss=1.403, discriminator_fake_loss=1.274, generator_loss=28.27, generator_mel_loss=17.95, generator_kl_loss=1.485, generator_dur_loss=1.745, generator_adv_loss=1.881, generator_feat_match_loss=5.21, over 64.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.38, generator_mel_loss=18.02, generator_kl_loss=1.444, generator_dur_loss=1.732, generator_adv_loss=1.976, generator_feat_match_loss=5.206, over 3731.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:24:02,243 INFO [train.py:527] (5/6) Epoch 567, batch 116, global_batch_idx: 70300, batch size: 59, loss[discriminator_loss=2.717, discriminator_real_loss=1.361, discriminator_fake_loss=1.356, generator_loss=28.76, generator_mel_loss=18.04, generator_kl_loss=1.439, generator_dur_loss=1.687, generator_adv_loss=2.064, generator_feat_match_loss=5.539, over 59.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.371, discriminator_fake_loss=1.337, generator_loss=28.38, generator_mel_loss=18.03, generator_kl_loss=1.447, generator_dur_loss=1.727, generator_adv_loss=1.975, generator_feat_match_loss=5.196, over 6470.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:24:23,518 INFO [train.py:919] (5/6) Start epoch 568 +2024-03-14 10:26:47,014 INFO [train.py:527] (5/6) Epoch 568, batch 42, global_batch_idx: 70350, batch size: 53, loss[discriminator_loss=2.733, discriminator_real_loss=1.314, discriminator_fake_loss=1.419, generator_loss=28.75, generator_mel_loss=18.31, generator_kl_loss=1.443, generator_dur_loss=1.631, generator_adv_loss=2.052, generator_feat_match_loss=5.313, over 53.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.379, discriminator_fake_loss=1.333, generator_loss=28.18, generator_mel_loss=17.9, generator_kl_loss=1.417, generator_dur_loss=1.747, generator_adv_loss=1.964, generator_feat_match_loss=5.156, over 2464.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:29:06,545 INFO [train.py:527] (5/6) Epoch 568, batch 92, global_batch_idx: 70400, batch size: 55, loss[discriminator_loss=2.628, discriminator_real_loss=1.303, discriminator_fake_loss=1.325, generator_loss=28.82, generator_mel_loss=18.19, generator_kl_loss=1.467, generator_dur_loss=1.71, generator_adv_loss=2.014, generator_feat_match_loss=5.441, over 55.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.367, discriminator_fake_loss=1.34, generator_loss=28.27, generator_mel_loss=17.91, generator_kl_loss=1.419, generator_dur_loss=1.752, generator_adv_loss=1.96, generator_feat_match_loss=5.235, over 5380.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:29:06,547 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 10:29:15,278 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 10:30:42,452 INFO [train.py:919] (5/6) Start epoch 569 +2024-03-14 10:31:56,615 INFO [train.py:527] (5/6) Epoch 569, batch 18, global_batch_idx: 70450, batch size: 55, loss[discriminator_loss=2.688, discriminator_real_loss=1.391, discriminator_fake_loss=1.297, generator_loss=27.79, generator_mel_loss=17.84, generator_kl_loss=1.496, generator_dur_loss=1.673, generator_adv_loss=1.918, generator_feat_match_loss=4.858, over 55.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.358, discriminator_fake_loss=1.34, generator_loss=28.3, generator_mel_loss=17.96, generator_kl_loss=1.468, generator_dur_loss=1.722, generator_adv_loss=1.967, generator_feat_match_loss=5.176, over 924.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:34:16,897 INFO [train.py:527] (5/6) Epoch 569, batch 68, global_batch_idx: 70500, batch size: 62, loss[discriminator_loss=2.749, discriminator_real_loss=1.3, discriminator_fake_loss=1.449, generator_loss=27.51, generator_mel_loss=17.58, generator_kl_loss=1.3, generator_dur_loss=1.785, generator_adv_loss=2.107, generator_feat_match_loss=4.736, over 62.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.364, discriminator_fake_loss=1.343, generator_loss=28.39, generator_mel_loss=18.06, generator_kl_loss=1.414, generator_dur_loss=1.739, generator_adv_loss=1.963, generator_feat_match_loss=5.215, over 3667.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:36:35,856 INFO [train.py:527] (5/6) Epoch 569, batch 118, global_batch_idx: 70550, batch size: 25, loss[discriminator_loss=2.715, discriminator_real_loss=1.339, discriminator_fake_loss=1.376, generator_loss=30.37, generator_mel_loss=19.03, generator_kl_loss=1.761, generator_dur_loss=1.609, generator_adv_loss=2.064, generator_feat_match_loss=5.906, over 25.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.363, discriminator_fake_loss=1.339, generator_loss=28.45, generator_mel_loss=18.06, generator_kl_loss=1.418, generator_dur_loss=1.748, generator_adv_loss=1.974, generator_feat_match_loss=5.252, over 6615.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:36:51,870 INFO [train.py:919] (5/6) Start epoch 570 +2024-03-14 10:39:16,032 INFO [train.py:527] (5/6) Epoch 570, batch 44, global_batch_idx: 70600, batch size: 13, loss[discriminator_loss=2.692, discriminator_real_loss=1.418, discriminator_fake_loss=1.274, generator_loss=29.15, generator_mel_loss=19.5, generator_kl_loss=1.793, generator_dur_loss=1.577, generator_adv_loss=1.857, generator_feat_match_loss=4.417, over 13.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.31, generator_mel_loss=17.96, generator_kl_loss=1.418, generator_dur_loss=1.742, generator_adv_loss=1.976, generator_feat_match_loss=5.217, over 2547.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:39:16,033 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 10:39:24,036 INFO [train.py:591] (5/6) 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 10:39:24,037 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 10:41:42,110 INFO [train.py:527] (5/6) Epoch 570, batch 94, global_batch_idx: 70650, batch size: 48, loss[discriminator_loss=2.74, discriminator_real_loss=1.391, discriminator_fake_loss=1.349, generator_loss=29.07, generator_mel_loss=18.39, generator_kl_loss=1.507, generator_dur_loss=1.68, generator_adv_loss=2.011, generator_feat_match_loss=5.484, over 48.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.362, discriminator_fake_loss=1.336, generator_loss=28.43, generator_mel_loss=18.02, generator_kl_loss=1.411, generator_dur_loss=1.738, generator_adv_loss=1.971, generator_feat_match_loss=5.283, over 5266.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:43:04,736 INFO [train.py:919] (5/6) Start epoch 571 +2024-03-14 10:44:25,901 INFO [train.py:527] (5/6) Epoch 571, batch 20, global_batch_idx: 70700, batch size: 66, loss[discriminator_loss=2.753, discriminator_real_loss=1.465, discriminator_fake_loss=1.288, generator_loss=28.25, generator_mel_loss=18.11, generator_kl_loss=1.5, generator_dur_loss=1.76, generator_adv_loss=1.9, generator_feat_match_loss=4.977, over 66.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.371, discriminator_fake_loss=1.33, generator_loss=28.16, generator_mel_loss=17.86, generator_kl_loss=1.422, generator_dur_loss=1.751, generator_adv_loss=1.983, generator_feat_match_loss=5.148, over 1192.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:46:43,866 INFO [train.py:527] (5/6) Epoch 571, batch 70, global_batch_idx: 70750, batch size: 74, loss[discriminator_loss=2.687, discriminator_real_loss=1.413, discriminator_fake_loss=1.273, generator_loss=28.41, generator_mel_loss=17.87, generator_kl_loss=1.345, generator_dur_loss=1.794, generator_adv_loss=1.929, generator_feat_match_loss=5.471, over 74.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.372, discriminator_fake_loss=1.331, generator_loss=28.33, generator_mel_loss=17.97, generator_kl_loss=1.416, generator_dur_loss=1.74, generator_adv_loss=1.969, generator_feat_match_loss=5.232, over 3906.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:49:02,188 INFO [train.py:527] (5/6) Epoch 571, batch 120, global_batch_idx: 70800, batch size: 70, loss[discriminator_loss=2.77, discriminator_real_loss=1.474, discriminator_fake_loss=1.296, generator_loss=27.06, generator_mel_loss=17.52, generator_kl_loss=1.257, generator_dur_loss=1.784, generator_adv_loss=1.837, generator_feat_match_loss=4.657, over 70.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.37, discriminator_fake_loss=1.334, generator_loss=28.37, generator_mel_loss=17.98, generator_kl_loss=1.418, generator_dur_loss=1.736, generator_adv_loss=1.975, generator_feat_match_loss=5.255, over 6606.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:49:02,189 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 10:49:10,965 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 10:49:20,398 INFO [train.py:919] (5/6) Start epoch 572 +2024-03-14 10:51:52,261 INFO [train.py:527] (5/6) Epoch 572, batch 46, global_batch_idx: 70850, batch size: 56, loss[discriminator_loss=2.695, discriminator_real_loss=1.392, discriminator_fake_loss=1.302, generator_loss=27.82, generator_mel_loss=18.03, generator_kl_loss=1.449, generator_dur_loss=1.662, generator_adv_loss=1.884, generator_feat_match_loss=4.787, over 56.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.377, discriminator_fake_loss=1.338, generator_loss=28.32, generator_mel_loss=17.99, generator_kl_loss=1.392, generator_dur_loss=1.74, generator_adv_loss=1.961, generator_feat_match_loss=5.231, over 2586.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:54:11,170 INFO [train.py:527] (5/6) Epoch 572, batch 96, global_batch_idx: 70900, batch size: 62, loss[discriminator_loss=2.703, discriminator_real_loss=1.369, discriminator_fake_loss=1.333, generator_loss=28.5, generator_mel_loss=17.94, generator_kl_loss=1.464, generator_dur_loss=1.777, generator_adv_loss=1.917, generator_feat_match_loss=5.395, over 62.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=28.32, generator_mel_loss=17.95, generator_kl_loss=1.419, generator_dur_loss=1.742, generator_adv_loss=1.964, generator_feat_match_loss=5.245, over 5432.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:55:27,789 INFO [train.py:919] (5/6) Start epoch 573 +2024-03-14 10:56:53,840 INFO [train.py:527] (5/6) Epoch 573, batch 22, global_batch_idx: 70950, batch size: 61, loss[discriminator_loss=2.629, discriminator_real_loss=1.353, discriminator_fake_loss=1.276, generator_loss=28.71, generator_mel_loss=18.05, generator_kl_loss=1.299, generator_dur_loss=1.773, generator_adv_loss=2.062, generator_feat_match_loss=5.526, over 61.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.34, generator_loss=28.19, generator_mel_loss=17.83, generator_kl_loss=1.405, generator_dur_loss=1.757, generator_adv_loss=1.961, generator_feat_match_loss=5.23, over 1347.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:59:14,979 INFO [train.py:527] (5/6) Epoch 573, batch 72, global_batch_idx: 71000, batch size: 48, loss[discriminator_loss=2.785, discriminator_real_loss=1.449, discriminator_fake_loss=1.336, generator_loss=27.91, generator_mel_loss=18.07, generator_kl_loss=1.415, generator_dur_loss=1.662, generator_adv_loss=1.966, generator_feat_match_loss=4.797, over 48.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.37, generator_mel_loss=17.94, generator_kl_loss=1.427, generator_dur_loss=1.75, generator_adv_loss=1.966, generator_feat_match_loss=5.285, over 4048.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 10:59:14,980 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 10:59:23,685 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 11:01:39,163 INFO [train.py:527] (5/6) Epoch 573, batch 122, global_batch_idx: 71050, batch size: 58, loss[discriminator_loss=2.656, discriminator_real_loss=1.283, discriminator_fake_loss=1.373, generator_loss=28.26, generator_mel_loss=17.83, generator_kl_loss=1.508, generator_dur_loss=1.716, generator_adv_loss=1.884, generator_feat_match_loss=5.327, over 58.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.368, discriminator_fake_loss=1.336, generator_loss=28.42, generator_mel_loss=17.96, generator_kl_loss=1.431, generator_dur_loss=1.744, generator_adv_loss=1.971, generator_feat_match_loss=5.307, over 6623.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:01:44,586 INFO [train.py:919] (5/6) Start epoch 574 +2024-03-14 11:04:18,815 INFO [train.py:527] (5/6) Epoch 574, batch 48, global_batch_idx: 71100, batch size: 62, loss[discriminator_loss=2.768, discriminator_real_loss=1.441, discriminator_fake_loss=1.326, generator_loss=28.7, generator_mel_loss=17.89, generator_kl_loss=1.492, generator_dur_loss=1.735, generator_adv_loss=1.892, generator_feat_match_loss=5.69, over 62.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.37, discriminator_fake_loss=1.34, generator_loss=28.42, generator_mel_loss=18.01, generator_kl_loss=1.412, generator_dur_loss=1.756, generator_adv_loss=1.959, generator_feat_match_loss=5.281, over 2799.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:06:39,790 INFO [train.py:527] (5/6) Epoch 574, batch 98, global_batch_idx: 71150, batch size: 59, loss[discriminator_loss=2.737, discriminator_real_loss=1.373, discriminator_fake_loss=1.364, generator_loss=26.89, generator_mel_loss=17.47, generator_kl_loss=1.398, generator_dur_loss=1.709, generator_adv_loss=1.829, generator_feat_match_loss=4.486, over 59.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.37, generator_mel_loss=17.99, generator_kl_loss=1.407, generator_dur_loss=1.757, generator_adv_loss=1.967, generator_feat_match_loss=5.249, over 5782.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:07:51,501 INFO [train.py:919] (5/6) Start epoch 575 +2024-03-14 11:09:22,401 INFO [train.py:527] (5/6) Epoch 575, batch 24, global_batch_idx: 71200, batch size: 80, loss[discriminator_loss=2.721, discriminator_real_loss=1.341, discriminator_fake_loss=1.38, generator_loss=27.93, generator_mel_loss=17.83, generator_kl_loss=1.353, generator_dur_loss=1.825, generator_adv_loss=2.045, generator_feat_match_loss=4.873, over 80.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.346, discriminator_fake_loss=1.351, generator_loss=28.32, generator_mel_loss=17.98, generator_kl_loss=1.392, generator_dur_loss=1.756, generator_adv_loss=1.95, generator_feat_match_loss=5.247, over 1424.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:09:22,403 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 11:09:30,378 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 11:11:48,590 INFO [train.py:527] (5/6) Epoch 575, batch 74, global_batch_idx: 71250, batch size: 45, loss[discriminator_loss=2.753, discriminator_real_loss=1.331, discriminator_fake_loss=1.422, generator_loss=29.24, generator_mel_loss=18.14, generator_kl_loss=1.669, generator_dur_loss=1.618, generator_adv_loss=1.953, generator_feat_match_loss=5.861, over 45.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.367, discriminator_fake_loss=1.338, generator_loss=28.46, generator_mel_loss=18.02, generator_kl_loss=1.407, generator_dur_loss=1.746, generator_adv_loss=1.968, generator_feat_match_loss=5.321, over 4369.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:14:03,330 INFO [train.py:919] (5/6) Start epoch 576 +2024-03-14 11:14:27,750 INFO [train.py:527] (5/6) Epoch 576, batch 0, global_batch_idx: 71300, batch size: 61, loss[discriminator_loss=2.744, discriminator_real_loss=1.366, discriminator_fake_loss=1.379, generator_loss=28.35, generator_mel_loss=18.18, generator_kl_loss=1.433, generator_dur_loss=1.722, generator_adv_loss=2.065, generator_feat_match_loss=4.949, over 61.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.366, discriminator_fake_loss=1.379, generator_loss=28.35, generator_mel_loss=18.18, generator_kl_loss=1.433, generator_dur_loss=1.722, generator_adv_loss=2.065, generator_feat_match_loss=4.949, over 61.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:16:46,082 INFO [train.py:527] (5/6) Epoch 576, batch 50, global_batch_idx: 71350, batch size: 44, loss[discriminator_loss=2.718, discriminator_real_loss=1.466, discriminator_fake_loss=1.252, generator_loss=27.93, generator_mel_loss=17.77, generator_kl_loss=1.628, generator_dur_loss=1.679, generator_adv_loss=1.812, generator_feat_match_loss=5.048, over 44.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.379, discriminator_fake_loss=1.336, generator_loss=28.42, generator_mel_loss=18.07, generator_kl_loss=1.427, generator_dur_loss=1.745, generator_adv_loss=1.977, generator_feat_match_loss=5.208, over 2973.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:19:06,117 INFO [train.py:527] (5/6) Epoch 576, batch 100, global_batch_idx: 71400, batch size: 53, loss[discriminator_loss=2.728, discriminator_real_loss=1.322, discriminator_fake_loss=1.407, generator_loss=28.31, generator_mel_loss=17.77, generator_kl_loss=1.473, generator_dur_loss=1.721, generator_adv_loss=2.035, generator_feat_match_loss=5.309, over 53.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.368, discriminator_fake_loss=1.341, generator_loss=28.43, generator_mel_loss=18.04, generator_kl_loss=1.414, generator_dur_loss=1.751, generator_adv_loss=1.973, generator_feat_match_loss=5.25, over 5957.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:19:06,118 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 11:19:15,118 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 11:20:15,960 INFO [train.py:919] (5/6) Start epoch 577 +2024-03-14 11:21:54,974 INFO [train.py:527] (5/6) Epoch 577, batch 26, global_batch_idx: 71450, batch size: 60, loss[discriminator_loss=2.712, discriminator_real_loss=1.415, discriminator_fake_loss=1.297, generator_loss=27.83, generator_mel_loss=18.11, generator_kl_loss=1.392, generator_dur_loss=1.727, generator_adv_loss=1.868, generator_feat_match_loss=4.728, over 60.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.369, discriminator_fake_loss=1.336, generator_loss=28.77, generator_mel_loss=18.09, generator_kl_loss=1.436, generator_dur_loss=1.776, generator_adv_loss=1.999, generator_feat_match_loss=5.473, over 1544.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:24:14,064 INFO [train.py:527] (5/6) Epoch 577, batch 76, global_batch_idx: 71500, batch size: 74, loss[discriminator_loss=2.71, discriminator_real_loss=1.368, discriminator_fake_loss=1.342, generator_loss=28.71, generator_mel_loss=18.07, generator_kl_loss=1.342, generator_dur_loss=1.83, generator_adv_loss=1.779, generator_feat_match_loss=5.691, over 74.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.377, discriminator_fake_loss=1.333, generator_loss=28.48, generator_mel_loss=18.02, generator_kl_loss=1.429, generator_dur_loss=1.767, generator_adv_loss=1.975, generator_feat_match_loss=5.291, over 4423.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:26:20,659 INFO [train.py:919] (5/6) Start epoch 578 +2024-03-14 11:26:50,740 INFO [train.py:527] (5/6) Epoch 578, batch 2, global_batch_idx: 71550, batch size: 48, loss[discriminator_loss=2.653, discriminator_real_loss=1.374, discriminator_fake_loss=1.278, generator_loss=27.95, generator_mel_loss=17.68, generator_kl_loss=1.373, generator_dur_loss=1.659, generator_adv_loss=2.045, generator_feat_match_loss=5.186, over 48.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.372, discriminator_fake_loss=1.346, generator_loss=27.53, generator_mel_loss=17.52, generator_kl_loss=1.255, generator_dur_loss=1.765, generator_adv_loss=1.948, generator_feat_match_loss=5.042, over 191.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:29:09,123 INFO [train.py:527] (5/6) Epoch 578, batch 52, global_batch_idx: 71600, batch size: 68, loss[discriminator_loss=2.659, discriminator_real_loss=1.326, discriminator_fake_loss=1.333, generator_loss=29.16, generator_mel_loss=18.36, generator_kl_loss=1.374, generator_dur_loss=1.771, generator_adv_loss=2.087, generator_feat_match_loss=5.568, over 68.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.36, discriminator_fake_loss=1.339, generator_loss=28.31, generator_mel_loss=17.92, generator_kl_loss=1.379, generator_dur_loss=1.77, generator_adv_loss=1.971, generator_feat_match_loss=5.276, over 3323.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:29:09,124 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 11:29:17,345 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 11:31:36,487 INFO [train.py:527] (5/6) Epoch 578, batch 102, global_batch_idx: 71650, batch size: 62, loss[discriminator_loss=2.725, discriminator_real_loss=1.315, discriminator_fake_loss=1.41, generator_loss=28.03, generator_mel_loss=17.73, generator_kl_loss=1.5, generator_dur_loss=1.699, generator_adv_loss=2.049, generator_feat_match_loss=5.047, over 62.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.365, discriminator_fake_loss=1.333, generator_loss=28.34, generator_mel_loss=17.93, generator_kl_loss=1.39, generator_dur_loss=1.76, generator_adv_loss=1.971, generator_feat_match_loss=5.281, over 6329.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:32:34,189 INFO [train.py:919] (5/6) Start epoch 579 +2024-03-14 11:34:17,665 INFO [train.py:527] (5/6) Epoch 579, batch 28, global_batch_idx: 71700, batch size: 56, loss[discriminator_loss=2.681, discriminator_real_loss=1.374, discriminator_fake_loss=1.307, generator_loss=28.69, generator_mel_loss=18.04, generator_kl_loss=1.396, generator_dur_loss=1.718, generator_adv_loss=1.94, generator_feat_match_loss=5.594, over 56.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.374, discriminator_fake_loss=1.336, generator_loss=28.6, generator_mel_loss=18.13, generator_kl_loss=1.417, generator_dur_loss=1.738, generator_adv_loss=1.965, generator_feat_match_loss=5.355, over 1514.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:36:37,132 INFO [train.py:527] (5/6) Epoch 579, batch 78, global_batch_idx: 71750, batch size: 53, loss[discriminator_loss=2.626, discriminator_real_loss=1.315, discriminator_fake_loss=1.311, generator_loss=29.63, generator_mel_loss=18.35, generator_kl_loss=1.561, generator_dur_loss=1.6, generator_adv_loss=2.066, generator_feat_match_loss=6.046, over 53.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.369, discriminator_fake_loss=1.332, generator_loss=28.55, generator_mel_loss=18.01, generator_kl_loss=1.412, generator_dur_loss=1.745, generator_adv_loss=1.997, generator_feat_match_loss=5.388, over 4399.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:38:42,235 INFO [train.py:919] (5/6) Start epoch 580 +2024-03-14 11:39:18,869 INFO [train.py:527] (5/6) Epoch 580, batch 4, global_batch_idx: 71800, batch size: 77, loss[discriminator_loss=2.719, discriminator_real_loss=1.278, discriminator_fake_loss=1.441, generator_loss=29.03, generator_mel_loss=17.9, generator_kl_loss=1.389, generator_dur_loss=1.798, generator_adv_loss=2.113, generator_feat_match_loss=5.827, over 77.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.324, discriminator_fake_loss=1.365, generator_loss=28.53, generator_mel_loss=18.06, generator_kl_loss=1.307, generator_dur_loss=1.832, generator_adv_loss=1.965, generator_feat_match_loss=5.373, over 364.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:39:18,872 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 11:39:26,755 INFO [train.py:591] (5/6) 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 11:39:26,758 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 11:41:44,296 INFO [train.py:527] (5/6) Epoch 580, batch 54, global_batch_idx: 71850, batch size: 48, loss[discriminator_loss=2.701, discriminator_real_loss=1.456, discriminator_fake_loss=1.245, generator_loss=28.02, generator_mel_loss=17.61, generator_kl_loss=1.567, generator_dur_loss=1.719, generator_adv_loss=1.833, generator_feat_match_loss=5.291, over 48.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.372, discriminator_fake_loss=1.334, generator_loss=28.55, generator_mel_loss=18.09, generator_kl_loss=1.402, generator_dur_loss=1.767, generator_adv_loss=1.968, generator_feat_match_loss=5.317, over 3228.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:44:03,550 INFO [train.py:527] (5/6) Epoch 580, batch 104, global_batch_idx: 71900, batch size: 31, loss[discriminator_loss=2.718, discriminator_real_loss=1.528, discriminator_fake_loss=1.19, generator_loss=27.43, generator_mel_loss=17.48, generator_kl_loss=1.577, generator_dur_loss=1.656, generator_adv_loss=1.948, generator_feat_match_loss=4.77, over 31.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.373, discriminator_fake_loss=1.337, generator_loss=28.42, generator_mel_loss=18.04, generator_kl_loss=1.416, generator_dur_loss=1.767, generator_adv_loss=1.965, generator_feat_match_loss=5.232, over 6002.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:44:57,013 INFO [train.py:919] (5/6) Start epoch 581 +2024-03-14 11:46:43,690 INFO [train.py:527] (5/6) Epoch 581, batch 30, global_batch_idx: 71950, batch size: 70, loss[discriminator_loss=2.738, discriminator_real_loss=1.341, discriminator_fake_loss=1.397, generator_loss=27.78, generator_mel_loss=17.98, generator_kl_loss=1.334, generator_dur_loss=1.808, generator_adv_loss=1.986, generator_feat_match_loss=4.674, over 70.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.54, generator_mel_loss=18.1, generator_kl_loss=1.399, generator_dur_loss=1.761, generator_adv_loss=1.987, generator_feat_match_loss=5.287, over 1865.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:49:02,158 INFO [train.py:527] (5/6) Epoch 581, batch 80, global_batch_idx: 72000, batch size: 66, loss[discriminator_loss=2.7, discriminator_real_loss=1.423, discriminator_fake_loss=1.277, generator_loss=27.83, generator_mel_loss=17.83, generator_kl_loss=1.35, generator_dur_loss=1.789, generator_adv_loss=1.94, generator_feat_match_loss=4.925, over 66.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.368, discriminator_fake_loss=1.334, generator_loss=28.45, generator_mel_loss=18.02, generator_kl_loss=1.417, generator_dur_loss=1.753, generator_adv_loss=1.981, generator_feat_match_loss=5.285, over 4598.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:49:02,159 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 11:49:11,003 INFO [train.py:591] (5/6) 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 11:49:11,004 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 11:51:11,391 INFO [train.py:919] (5/6) Start epoch 582 +2024-03-14 11:51:50,764 INFO [train.py:527] (5/6) Epoch 582, batch 6, global_batch_idx: 72050, batch size: 53, loss[discriminator_loss=2.663, discriminator_real_loss=1.341, discriminator_fake_loss=1.322, generator_loss=28.77, generator_mel_loss=18.39, generator_kl_loss=1.484, generator_dur_loss=1.691, generator_adv_loss=2.012, generator_feat_match_loss=5.189, over 53.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.363, discriminator_fake_loss=1.336, generator_loss=28.21, generator_mel_loss=17.98, generator_kl_loss=1.407, generator_dur_loss=1.747, generator_adv_loss=1.953, generator_feat_match_loss=5.123, over 412.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:54:09,068 INFO [train.py:527] (5/6) Epoch 582, batch 56, global_batch_idx: 72100, batch size: 25, loss[discriminator_loss=2.703, discriminator_real_loss=1.456, discriminator_fake_loss=1.247, generator_loss=29.4, generator_mel_loss=18.22, generator_kl_loss=1.666, generator_dur_loss=1.546, generator_adv_loss=1.913, generator_feat_match_loss=6.059, over 25.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.368, discriminator_fake_loss=1.329, generator_loss=28.46, generator_mel_loss=17.97, generator_kl_loss=1.438, generator_dur_loss=1.742, generator_adv_loss=1.965, generator_feat_match_loss=5.344, over 3179.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:56:29,810 INFO [train.py:527] (5/6) Epoch 582, batch 106, global_batch_idx: 72150, batch size: 62, loss[discriminator_loss=2.714, discriminator_real_loss=1.459, discriminator_fake_loss=1.254, generator_loss=28.41, generator_mel_loss=17.9, generator_kl_loss=1.401, generator_dur_loss=1.703, generator_adv_loss=1.775, generator_feat_match_loss=5.627, over 62.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.46, generator_mel_loss=17.97, generator_kl_loss=1.404, generator_dur_loss=1.752, generator_adv_loss=1.961, generator_feat_match_loss=5.364, over 6151.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:57:17,863 INFO [train.py:919] (5/6) Start epoch 583 +2024-03-14 11:59:10,495 INFO [train.py:527] (5/6) Epoch 583, batch 32, global_batch_idx: 72200, batch size: 36, loss[discriminator_loss=2.756, discriminator_real_loss=1.269, discriminator_fake_loss=1.487, generator_loss=28.05, generator_mel_loss=17.76, generator_kl_loss=1.53, generator_dur_loss=1.648, generator_adv_loss=1.856, generator_feat_match_loss=5.255, over 36.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.328, generator_loss=28.6, generator_mel_loss=18.02, generator_kl_loss=1.413, generator_dur_loss=1.756, generator_adv_loss=1.971, generator_feat_match_loss=5.434, over 1901.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 11:59:10,497 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 11:59:18,464 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 12:01:36,789 INFO [train.py:527] (5/6) Epoch 583, batch 82, global_batch_idx: 72250, batch size: 68, loss[discriminator_loss=2.769, discriminator_real_loss=1.366, discriminator_fake_loss=1.403, generator_loss=28.72, generator_mel_loss=18.33, generator_kl_loss=1.262, generator_dur_loss=1.794, generator_adv_loss=2.001, generator_feat_match_loss=5.335, over 68.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.53, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.746, generator_adv_loss=1.963, generator_feat_match_loss=5.389, over 4746.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:03:30,718 INFO [train.py:919] (5/6) Start epoch 584 +2024-03-14 12:04:18,592 INFO [train.py:527] (5/6) Epoch 584, batch 8, global_batch_idx: 72300, batch size: 68, loss[discriminator_loss=2.726, discriminator_real_loss=1.353, discriminator_fake_loss=1.372, generator_loss=29.41, generator_mel_loss=18.31, generator_kl_loss=1.437, generator_dur_loss=1.804, generator_adv_loss=2.098, generator_feat_match_loss=5.756, over 68.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.344, discriminator_fake_loss=1.34, generator_loss=28.56, generator_mel_loss=17.98, generator_kl_loss=1.462, generator_dur_loss=1.775, generator_adv_loss=1.994, generator_feat_match_loss=5.354, over 569.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:06:33,581 INFO [train.py:527] (5/6) Epoch 584, batch 58, global_batch_idx: 72350, batch size: 83, loss[discriminator_loss=2.678, discriminator_real_loss=1.326, discriminator_fake_loss=1.352, generator_loss=28.87, generator_mel_loss=17.98, generator_kl_loss=1.271, generator_dur_loss=1.848, generator_adv_loss=1.94, generator_feat_match_loss=5.835, over 83.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.359, discriminator_fake_loss=1.342, generator_loss=28.47, generator_mel_loss=18, generator_kl_loss=1.426, generator_dur_loss=1.75, generator_adv_loss=1.968, generator_feat_match_loss=5.323, over 3363.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:08:53,084 INFO [train.py:527] (5/6) Epoch 584, batch 108, global_batch_idx: 72400, batch size: 61, loss[discriminator_loss=2.722, discriminator_real_loss=1.378, discriminator_fake_loss=1.344, generator_loss=28.23, generator_mel_loss=17.77, generator_kl_loss=1.529, generator_dur_loss=1.724, generator_adv_loss=2.017, generator_feat_match_loss=5.194, over 61.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.366, discriminator_fake_loss=1.344, generator_loss=28.46, generator_mel_loss=18.01, generator_kl_loss=1.429, generator_dur_loss=1.741, generator_adv_loss=1.978, generator_feat_match_loss=5.305, over 5985.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:08:53,085 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 12:09:02,014 INFO [train.py:591] (5/6) 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 12:09:02,015 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 12:09:45,845 INFO [train.py:919] (5/6) Start epoch 585 +2024-03-14 12:11:44,640 INFO [train.py:527] (5/6) Epoch 585, batch 34, global_batch_idx: 72450, batch size: 42, loss[discriminator_loss=2.722, discriminator_real_loss=1.389, discriminator_fake_loss=1.333, generator_loss=28.65, generator_mel_loss=18.35, generator_kl_loss=1.56, generator_dur_loss=1.648, generator_adv_loss=2.027, generator_feat_match_loss=5.072, over 42.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.386, discriminator_fake_loss=1.325, generator_loss=28.46, generator_mel_loss=18.07, generator_kl_loss=1.37, generator_dur_loss=1.752, generator_adv_loss=1.984, generator_feat_match_loss=5.279, over 1901.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:14:02,755 INFO [train.py:527] (5/6) Epoch 585, batch 84, global_batch_idx: 72500, batch size: 48, loss[discriminator_loss=2.696, discriminator_real_loss=1.419, discriminator_fake_loss=1.277, generator_loss=28.25, generator_mel_loss=17.86, generator_kl_loss=1.492, generator_dur_loss=1.663, generator_adv_loss=1.99, generator_feat_match_loss=5.248, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.52, generator_mel_loss=18.05, generator_kl_loss=1.414, generator_dur_loss=1.749, generator_adv_loss=1.978, generator_feat_match_loss=5.329, over 4688.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:15:54,151 INFO [train.py:919] (5/6) Start epoch 586 +2024-03-14 12:16:44,989 INFO [train.py:527] (5/6) Epoch 586, batch 10, global_batch_idx: 72550, batch size: 39, loss[discriminator_loss=2.756, discriminator_real_loss=1.321, discriminator_fake_loss=1.434, generator_loss=27.48, generator_mel_loss=17.88, generator_kl_loss=1.505, generator_dur_loss=1.633, generator_adv_loss=1.973, generator_feat_match_loss=4.485, over 39.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.371, discriminator_fake_loss=1.332, generator_loss=28.06, generator_mel_loss=17.86, generator_kl_loss=1.43, generator_dur_loss=1.729, generator_adv_loss=1.966, generator_feat_match_loss=5.07, over 632.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:19:05,854 INFO [train.py:527] (5/6) Epoch 586, batch 60, global_batch_idx: 72600, batch size: 42, loss[discriminator_loss=2.78, discriminator_real_loss=1.506, discriminator_fake_loss=1.274, generator_loss=27.53, generator_mel_loss=17.89, generator_kl_loss=1.598, generator_dur_loss=1.681, generator_adv_loss=1.941, generator_feat_match_loss=4.42, over 42.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.368, discriminator_fake_loss=1.334, generator_loss=28.31, generator_mel_loss=17.95, generator_kl_loss=1.43, generator_dur_loss=1.734, generator_adv_loss=1.975, generator_feat_match_loss=5.215, over 3464.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:19:05,856 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 12:19:13,986 INFO [train.py:591] (5/6) 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 12:19:13,987 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 12:21:36,537 INFO [train.py:527] (5/6) Epoch 586, batch 110, global_batch_idx: 72650, batch size: 80, loss[discriminator_loss=2.664, discriminator_real_loss=1.344, discriminator_fake_loss=1.321, generator_loss=28.63, generator_mel_loss=18.11, generator_kl_loss=1.295, generator_dur_loss=1.786, generator_adv_loss=1.97, generator_feat_match_loss=5.468, over 80.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.364, discriminator_fake_loss=1.337, generator_loss=28.37, generator_mel_loss=17.99, generator_kl_loss=1.421, generator_dur_loss=1.741, generator_adv_loss=1.967, generator_feat_match_loss=5.246, over 6256.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:22:13,354 INFO [train.py:919] (5/6) Start epoch 587 +2024-03-14 12:24:15,687 INFO [train.py:527] (5/6) Epoch 587, batch 36, global_batch_idx: 72700, batch size: 68, loss[discriminator_loss=2.716, discriminator_real_loss=1.419, discriminator_fake_loss=1.297, generator_loss=28.16, generator_mel_loss=18.2, generator_kl_loss=1.424, generator_dur_loss=1.794, generator_adv_loss=1.874, generator_feat_match_loss=4.869, over 68.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.362, discriminator_fake_loss=1.341, generator_loss=28.52, generator_mel_loss=18.11, generator_kl_loss=1.416, generator_dur_loss=1.727, generator_adv_loss=1.959, generator_feat_match_loss=5.307, over 1927.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:26:32,061 INFO [train.py:527] (5/6) Epoch 587, batch 86, global_batch_idx: 72750, batch size: 42, loss[discriminator_loss=2.748, discriminator_real_loss=1.35, discriminator_fake_loss=1.397, generator_loss=28.44, generator_mel_loss=18.36, generator_kl_loss=1.52, generator_dur_loss=1.703, generator_adv_loss=1.787, generator_feat_match_loss=5.074, over 42.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.364, discriminator_fake_loss=1.336, generator_loss=28.56, generator_mel_loss=18.07, generator_kl_loss=1.422, generator_dur_loss=1.727, generator_adv_loss=1.975, generator_feat_match_loss=5.366, over 4617.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:28:18,268 INFO [train.py:919] (5/6) Start epoch 588 +2024-03-14 12:29:15,008 INFO [train.py:527] (5/6) Epoch 588, batch 12, global_batch_idx: 72800, batch size: 80, loss[discriminator_loss=2.735, discriminator_real_loss=1.387, discriminator_fake_loss=1.348, generator_loss=28.1, generator_mel_loss=18.14, generator_kl_loss=1.316, generator_dur_loss=1.85, generator_adv_loss=1.908, generator_feat_match_loss=4.883, over 80.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.375, discriminator_fake_loss=1.321, generator_loss=28.35, generator_mel_loss=17.92, generator_kl_loss=1.403, generator_dur_loss=1.741, generator_adv_loss=1.98, generator_feat_match_loss=5.306, over 705.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:29:15,010 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 12:29:22,918 INFO [train.py:591] (5/6) 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 12:29:22,919 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 12:31:42,099 INFO [train.py:527] (5/6) Epoch 588, batch 62, global_batch_idx: 72850, batch size: 52, loss[discriminator_loss=2.7, discriminator_real_loss=1.388, discriminator_fake_loss=1.312, generator_loss=29.12, generator_mel_loss=18.36, generator_kl_loss=1.512, generator_dur_loss=1.699, generator_adv_loss=1.932, generator_feat_match_loss=5.62, over 52.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.362, discriminator_fake_loss=1.335, generator_loss=28.52, generator_mel_loss=17.98, generator_kl_loss=1.424, generator_dur_loss=1.757, generator_adv_loss=1.967, generator_feat_match_loss=5.394, over 3676.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:34:01,578 INFO [train.py:527] (5/6) Epoch 588, batch 112, global_batch_idx: 72900, batch size: 64, loss[discriminator_loss=2.704, discriminator_real_loss=1.307, discriminator_fake_loss=1.397, generator_loss=28.83, generator_mel_loss=17.94, generator_kl_loss=1.374, generator_dur_loss=1.715, generator_adv_loss=1.982, generator_feat_match_loss=5.819, over 64.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.363, discriminator_fake_loss=1.335, generator_loss=28.58, generator_mel_loss=18.01, generator_kl_loss=1.43, generator_dur_loss=1.743, generator_adv_loss=1.977, generator_feat_match_loss=5.423, over 6382.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:34:33,372 INFO [train.py:919] (5/6) Start epoch 589 +2024-03-14 12:36:39,665 INFO [train.py:527] (5/6) Epoch 589, batch 38, global_batch_idx: 72950, batch size: 31, loss[discriminator_loss=2.744, discriminator_real_loss=1.318, discriminator_fake_loss=1.426, generator_loss=27.85, generator_mel_loss=17.82, generator_kl_loss=1.578, generator_dur_loss=1.621, generator_adv_loss=1.876, generator_feat_match_loss=4.948, over 31.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.369, discriminator_fake_loss=1.334, generator_loss=28.49, generator_mel_loss=18.05, generator_kl_loss=1.468, generator_dur_loss=1.707, generator_adv_loss=1.968, generator_feat_match_loss=5.306, over 1927.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:38:59,583 INFO [train.py:527] (5/6) Epoch 589, batch 88, global_batch_idx: 73000, batch size: 48, loss[discriminator_loss=2.719, discriminator_real_loss=1.412, discriminator_fake_loss=1.308, generator_loss=28.71, generator_mel_loss=18.61, generator_kl_loss=1.434, generator_dur_loss=1.679, generator_adv_loss=1.947, generator_feat_match_loss=5.034, over 48.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.37, discriminator_fake_loss=1.337, generator_loss=28.44, generator_mel_loss=18.03, generator_kl_loss=1.423, generator_dur_loss=1.73, generator_adv_loss=1.97, generator_feat_match_loss=5.285, over 4810.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:38:59,584 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 12:39:08,252 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 12:40:46,021 INFO [train.py:919] (5/6) Start epoch 590 +2024-03-14 12:41:47,464 INFO [train.py:527] (5/6) Epoch 590, batch 14, global_batch_idx: 73050, batch size: 56, loss[discriminator_loss=2.795, discriminator_real_loss=1.428, discriminator_fake_loss=1.367, generator_loss=27.59, generator_mel_loss=17.63, generator_kl_loss=1.526, generator_dur_loss=1.732, generator_adv_loss=1.952, generator_feat_match_loss=4.752, over 56.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.379, discriminator_fake_loss=1.34, generator_loss=28.24, generator_mel_loss=17.97, generator_kl_loss=1.379, generator_dur_loss=1.807, generator_adv_loss=1.94, generator_feat_match_loss=5.141, over 1026.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:44:08,865 INFO [train.py:527] (5/6) Epoch 590, batch 64, global_batch_idx: 73100, batch size: 96, loss[discriminator_loss=2.671, discriminator_real_loss=1.24, discriminator_fake_loss=1.431, generator_loss=28.97, generator_mel_loss=17.91, generator_kl_loss=1.431, generator_dur_loss=1.864, generator_adv_loss=2.081, generator_feat_match_loss=5.68, over 96.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.37, discriminator_fake_loss=1.336, generator_loss=28.41, generator_mel_loss=18.03, generator_kl_loss=1.42, generator_dur_loss=1.757, generator_adv_loss=1.961, generator_feat_match_loss=5.236, over 3717.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:46:26,557 INFO [train.py:527] (5/6) Epoch 590, batch 114, global_batch_idx: 73150, batch size: 25, loss[discriminator_loss=2.669, discriminator_real_loss=1.389, discriminator_fake_loss=1.28, generator_loss=30.78, generator_mel_loss=18.95, generator_kl_loss=1.889, generator_dur_loss=1.544, generator_adv_loss=2.122, generator_feat_match_loss=6.278, over 25.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.37, discriminator_fake_loss=1.341, generator_loss=28.51, generator_mel_loss=18.04, generator_kl_loss=1.431, generator_dur_loss=1.753, generator_adv_loss=1.977, generator_feat_match_loss=5.317, over 6543.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:46:53,324 INFO [train.py:919] (5/6) Start epoch 591 +2024-03-14 12:49:10,692 INFO [train.py:527] (5/6) Epoch 591, batch 40, global_batch_idx: 73200, batch size: 47, loss[discriminator_loss=2.724, discriminator_real_loss=1.396, discriminator_fake_loss=1.329, generator_loss=29.12, generator_mel_loss=18.22, generator_kl_loss=1.538, generator_dur_loss=1.673, generator_adv_loss=1.863, generator_feat_match_loss=5.827, over 47.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=28.49, generator_mel_loss=18.06, generator_kl_loss=1.374, generator_dur_loss=1.766, generator_adv_loss=1.96, generator_feat_match_loss=5.334, over 2524.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:49:10,693 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 12:49:18,754 INFO [train.py:591] (5/6) 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 12:49:18,760 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 12:51:37,312 INFO [train.py:527] (5/6) Epoch 591, batch 90, global_batch_idx: 73250, batch size: 56, loss[discriminator_loss=2.736, discriminator_real_loss=1.29, discriminator_fake_loss=1.446, generator_loss=28.3, generator_mel_loss=17.82, generator_kl_loss=1.354, generator_dur_loss=1.716, generator_adv_loss=1.959, generator_feat_match_loss=5.45, over 56.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.372, discriminator_fake_loss=1.337, generator_loss=28.51, generator_mel_loss=18.06, generator_kl_loss=1.405, generator_dur_loss=1.747, generator_adv_loss=1.965, generator_feat_match_loss=5.332, over 5122.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:53:08,697 INFO [train.py:919] (5/6) Start epoch 592 +2024-03-14 12:54:18,445 INFO [train.py:527] (5/6) Epoch 592, batch 16, global_batch_idx: 73300, batch size: 58, loss[discriminator_loss=2.66, discriminator_real_loss=1.356, discriminator_fake_loss=1.305, generator_loss=28.71, generator_mel_loss=17.69, generator_kl_loss=1.423, generator_dur_loss=1.758, generator_adv_loss=2.119, generator_feat_match_loss=5.727, over 58.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.366, discriminator_fake_loss=1.319, generator_loss=28.53, generator_mel_loss=17.95, generator_kl_loss=1.425, generator_dur_loss=1.742, generator_adv_loss=1.99, generator_feat_match_loss=5.425, over 961.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:56:37,688 INFO [train.py:527] (5/6) Epoch 592, batch 66, global_batch_idx: 73350, batch size: 89, loss[discriminator_loss=2.729, discriminator_real_loss=1.374, discriminator_fake_loss=1.356, generator_loss=28, generator_mel_loss=17.89, generator_kl_loss=1.309, generator_dur_loss=1.883, generator_adv_loss=1.909, generator_feat_match_loss=5.005, over 89.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.367, discriminator_fake_loss=1.328, generator_loss=28.7, generator_mel_loss=18.05, generator_kl_loss=1.438, generator_dur_loss=1.754, generator_adv_loss=1.985, generator_feat_match_loss=5.473, over 3651.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:58:58,853 INFO [train.py:527] (5/6) Epoch 592, batch 116, global_batch_idx: 73400, batch size: 64, loss[discriminator_loss=2.766, discriminator_real_loss=1.31, discriminator_fake_loss=1.456, generator_loss=28.64, generator_mel_loss=17.86, generator_kl_loss=1.341, generator_dur_loss=1.755, generator_adv_loss=2.084, generator_feat_match_loss=5.601, over 64.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.37, discriminator_fake_loss=1.336, generator_loss=28.57, generator_mel_loss=18.02, generator_kl_loss=1.421, generator_dur_loss=1.761, generator_adv_loss=1.969, generator_feat_match_loss=5.399, over 6642.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 12:58:58,855 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 12:59:07,932 INFO [train.py:591] (5/6) 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 12:59:07,933 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 12:59:29,827 INFO [train.py:919] (5/6) Start epoch 593 +2024-03-14 13:01:48,747 INFO [train.py:527] (5/6) Epoch 593, batch 42, global_batch_idx: 73450, batch size: 31, loss[discriminator_loss=2.707, discriminator_real_loss=1.403, discriminator_fake_loss=1.304, generator_loss=28.45, generator_mel_loss=17.83, generator_kl_loss=1.339, generator_dur_loss=1.689, generator_adv_loss=1.986, generator_feat_match_loss=5.611, over 31.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.364, discriminator_fake_loss=1.339, generator_loss=28.23, generator_mel_loss=17.92, generator_kl_loss=1.426, generator_dur_loss=1.759, generator_adv_loss=1.96, generator_feat_match_loss=5.171, over 2371.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:04:07,609 INFO [train.py:527] (5/6) Epoch 593, batch 92, global_batch_idx: 73500, batch size: 45, loss[discriminator_loss=2.648, discriminator_real_loss=1.377, discriminator_fake_loss=1.271, generator_loss=29.12, generator_mel_loss=18.21, generator_kl_loss=1.585, generator_dur_loss=1.682, generator_adv_loss=1.962, generator_feat_match_loss=5.68, over 45.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.368, discriminator_fake_loss=1.332, generator_loss=28.35, generator_mel_loss=17.97, generator_kl_loss=1.425, generator_dur_loss=1.754, generator_adv_loss=1.967, generator_feat_match_loss=5.233, over 5299.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:05:33,828 INFO [train.py:919] (5/6) Start epoch 594 +2024-03-14 13:06:50,888 INFO [train.py:527] (5/6) Epoch 594, batch 18, global_batch_idx: 73550, batch size: 42, loss[discriminator_loss=2.734, discriminator_real_loss=1.401, discriminator_fake_loss=1.333, generator_loss=28.29, generator_mel_loss=18.11, generator_kl_loss=1.542, generator_dur_loss=1.696, generator_adv_loss=2.085, generator_feat_match_loss=4.849, over 42.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.364, discriminator_fake_loss=1.321, generator_loss=28.57, generator_mel_loss=18, generator_kl_loss=1.43, generator_dur_loss=1.753, generator_adv_loss=1.999, generator_feat_match_loss=5.395, over 1053.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:09:08,316 INFO [train.py:527] (5/6) Epoch 594, batch 68, global_batch_idx: 73600, batch size: 15, loss[discriminator_loss=2.678, discriminator_real_loss=1.318, discriminator_fake_loss=1.36, generator_loss=28.56, generator_mel_loss=18.36, generator_kl_loss=1.625, generator_dur_loss=1.526, generator_adv_loss=1.996, generator_feat_match_loss=5.049, over 15.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.365, discriminator_fake_loss=1.334, generator_loss=28.47, generator_mel_loss=18.04, generator_kl_loss=1.409, generator_dur_loss=1.751, generator_adv_loss=1.978, generator_feat_match_loss=5.292, over 3782.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:09:08,317 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 13:09:17,139 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 13:11:34,955 INFO [train.py:527] (5/6) Epoch 594, batch 118, global_batch_idx: 73650, batch size: 31, loss[discriminator_loss=2.776, discriminator_real_loss=1.429, discriminator_fake_loss=1.347, generator_loss=28.75, generator_mel_loss=17.87, generator_kl_loss=1.609, generator_dur_loss=1.662, generator_adv_loss=2.009, generator_feat_match_loss=5.595, over 31.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.369, discriminator_fake_loss=1.331, generator_loss=28.5, generator_mel_loss=18.04, generator_kl_loss=1.417, generator_dur_loss=1.748, generator_adv_loss=1.978, generator_feat_match_loss=5.315, over 6582.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:11:50,094 INFO [train.py:919] (5/6) Start epoch 595 +2024-03-14 13:14:15,281 INFO [train.py:527] (5/6) Epoch 595, batch 44, global_batch_idx: 73700, batch size: 62, loss[discriminator_loss=2.682, discriminator_real_loss=1.419, discriminator_fake_loss=1.263, generator_loss=27.87, generator_mel_loss=17.56, generator_kl_loss=1.429, generator_dur_loss=1.727, generator_adv_loss=1.876, generator_feat_match_loss=5.281, over 62.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.361, discriminator_fake_loss=1.333, generator_loss=28.39, generator_mel_loss=17.97, generator_kl_loss=1.422, generator_dur_loss=1.738, generator_adv_loss=1.966, generator_feat_match_loss=5.295, over 2607.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:16:36,060 INFO [train.py:527] (5/6) Epoch 595, batch 94, global_batch_idx: 73750, batch size: 42, loss[discriminator_loss=2.711, discriminator_real_loss=1.375, discriminator_fake_loss=1.337, generator_loss=28.05, generator_mel_loss=17.63, generator_kl_loss=1.618, generator_dur_loss=1.689, generator_adv_loss=2.013, generator_feat_match_loss=5.1, over 42.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.52, generator_mel_loss=18.02, generator_kl_loss=1.416, generator_dur_loss=1.75, generator_adv_loss=1.977, generator_feat_match_loss=5.361, over 5637.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:17:55,419 INFO [train.py:919] (5/6) Start epoch 596 +2024-03-14 13:19:12,893 INFO [train.py:527] (5/6) Epoch 596, batch 20, global_batch_idx: 73800, batch size: 14, loss[discriminator_loss=2.682, discriminator_real_loss=1.426, discriminator_fake_loss=1.256, generator_loss=30.15, generator_mel_loss=18.43, generator_kl_loss=1.815, generator_dur_loss=1.61, generator_adv_loss=1.988, generator_feat_match_loss=6.309, over 14.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.369, discriminator_fake_loss=1.342, generator_loss=28.71, generator_mel_loss=18.1, generator_kl_loss=1.432, generator_dur_loss=1.74, generator_adv_loss=1.965, generator_feat_match_loss=5.465, over 1169.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:19:12,895 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 13:19:20,980 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 13:21:39,518 INFO [train.py:527] (5/6) Epoch 596, batch 70, global_batch_idx: 73850, batch size: 70, loss[discriminator_loss=2.7, discriminator_real_loss=1.414, discriminator_fake_loss=1.286, generator_loss=28.55, generator_mel_loss=17.94, generator_kl_loss=1.382, generator_dur_loss=1.77, generator_adv_loss=2.064, generator_feat_match_loss=5.385, over 70.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.366, discriminator_fake_loss=1.336, generator_loss=28.43, generator_mel_loss=18, generator_kl_loss=1.396, generator_dur_loss=1.757, generator_adv_loss=1.971, generator_feat_match_loss=5.303, over 4231.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:23:57,246 INFO [train.py:527] (5/6) Epoch 596, batch 120, global_batch_idx: 73900, batch size: 36, loss[discriminator_loss=2.837, discriminator_real_loss=1.36, discriminator_fake_loss=1.476, generator_loss=28.65, generator_mel_loss=18.44, generator_kl_loss=1.432, generator_dur_loss=1.709, generator_adv_loss=1.908, generator_feat_match_loss=5.166, over 36.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.43, generator_mel_loss=18, generator_kl_loss=1.397, generator_dur_loss=1.752, generator_adv_loss=1.977, generator_feat_match_loss=5.305, over 7100.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:24:08,420 INFO [train.py:919] (5/6) Start epoch 597 +2024-03-14 13:26:40,752 INFO [train.py:527] (5/6) Epoch 597, batch 46, global_batch_idx: 73950, batch size: 45, loss[discriminator_loss=2.643, discriminator_real_loss=1.334, discriminator_fake_loss=1.309, generator_loss=29.53, generator_mel_loss=18.12, generator_kl_loss=1.39, generator_dur_loss=1.694, generator_adv_loss=2.07, generator_feat_match_loss=6.262, over 45.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.362, discriminator_fake_loss=1.333, generator_loss=28.52, generator_mel_loss=18.02, generator_kl_loss=1.399, generator_dur_loss=1.765, generator_adv_loss=1.979, generator_feat_match_loss=5.358, over 2769.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:29:01,402 INFO [train.py:527] (5/6) Epoch 597, batch 96, global_batch_idx: 74000, batch size: 80, loss[discriminator_loss=2.726, discriminator_real_loss=1.286, discriminator_fake_loss=1.44, generator_loss=28.46, generator_mel_loss=18.19, generator_kl_loss=1.205, generator_dur_loss=1.822, generator_adv_loss=1.961, generator_feat_match_loss=5.286, over 80.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=28.52, generator_mel_loss=18.06, generator_kl_loss=1.406, generator_dur_loss=1.766, generator_adv_loss=1.965, generator_feat_match_loss=5.317, over 5853.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:29:01,404 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 13:29:10,117 INFO [train.py:591] (5/6) 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 13:29:10,118 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 13:30:22,268 INFO [train.py:919] (5/6) Start epoch 598 +2024-03-14 13:31:45,917 INFO [train.py:527] (5/6) Epoch 598, batch 22, global_batch_idx: 74050, batch size: 15, loss[discriminator_loss=2.592, discriminator_real_loss=1.277, discriminator_fake_loss=1.316, generator_loss=28.91, generator_mel_loss=18.32, generator_kl_loss=1.484, generator_dur_loss=1.563, generator_adv_loss=1.963, generator_feat_match_loss=5.58, over 15.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.358, discriminator_fake_loss=1.338, generator_loss=28.61, generator_mel_loss=18.07, generator_kl_loss=1.403, generator_dur_loss=1.753, generator_adv_loss=1.95, generator_feat_match_loss=5.435, over 1331.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:34:05,445 INFO [train.py:527] (5/6) Epoch 598, batch 72, global_batch_idx: 74100, batch size: 61, loss[discriminator_loss=2.641, discriminator_real_loss=1.403, discriminator_fake_loss=1.238, generator_loss=28.14, generator_mel_loss=18.1, generator_kl_loss=1.294, generator_dur_loss=1.779, generator_adv_loss=1.985, generator_feat_match_loss=4.976, over 61.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.64, generator_mel_loss=18.07, generator_kl_loss=1.398, generator_dur_loss=1.755, generator_adv_loss=1.963, generator_feat_match_loss=5.453, over 4240.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:36:21,489 INFO [train.py:527] (5/6) Epoch 598, batch 122, global_batch_idx: 74150, batch size: 25, loss[discriminator_loss=2.672, discriminator_real_loss=1.342, discriminator_fake_loss=1.33, generator_loss=29.79, generator_mel_loss=19.13, generator_kl_loss=1.848, generator_dur_loss=1.602, generator_adv_loss=2, generator_feat_match_loss=5.217, over 25.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.366, discriminator_fake_loss=1.333, generator_loss=28.56, generator_mel_loss=18.07, generator_kl_loss=1.394, generator_dur_loss=1.757, generator_adv_loss=1.965, generator_feat_match_loss=5.374, over 7007.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:36:26,558 INFO [train.py:919] (5/6) Start epoch 599 +2024-03-14 13:39:04,778 INFO [train.py:527] (5/6) Epoch 599, batch 48, global_batch_idx: 74200, batch size: 88, loss[discriminator_loss=2.748, discriminator_real_loss=1.29, discriminator_fake_loss=1.458, generator_loss=28.28, generator_mel_loss=17.66, generator_kl_loss=1.382, generator_dur_loss=1.82, generator_adv_loss=1.999, generator_feat_match_loss=5.414, over 88.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.365, discriminator_fake_loss=1.345, generator_loss=28.48, generator_mel_loss=18.02, generator_kl_loss=1.411, generator_dur_loss=1.756, generator_adv_loss=1.965, generator_feat_match_loss=5.323, over 2810.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:39:04,780 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 13:39:12,920 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 13:41:35,655 INFO [train.py:527] (5/6) Epoch 599, batch 98, global_batch_idx: 74250, batch size: 80, loss[discriminator_loss=2.676, discriminator_real_loss=1.33, discriminator_fake_loss=1.346, generator_loss=28.57, generator_mel_loss=18.26, generator_kl_loss=1.333, generator_dur_loss=1.776, generator_adv_loss=1.877, generator_feat_match_loss=5.317, over 80.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.365, discriminator_fake_loss=1.341, generator_loss=28.48, generator_mel_loss=18.02, generator_kl_loss=1.417, generator_dur_loss=1.749, generator_adv_loss=1.963, generator_feat_match_loss=5.339, over 5633.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:42:43,052 INFO [train.py:919] (5/6) Start epoch 600 +2024-03-14 13:44:13,828 INFO [train.py:527] (5/6) Epoch 600, batch 24, global_batch_idx: 74300, batch size: 31, loss[discriminator_loss=2.68, discriminator_real_loss=1.411, discriminator_fake_loss=1.269, generator_loss=27.83, generator_mel_loss=17.65, generator_kl_loss=1.65, generator_dur_loss=1.618, generator_adv_loss=2.001, generator_feat_match_loss=4.912, over 31.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.35, discriminator_fake_loss=1.344, generator_loss=28.4, generator_mel_loss=18.02, generator_kl_loss=1.422, generator_dur_loss=1.749, generator_adv_loss=1.948, generator_feat_match_loss=5.257, over 1447.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:46:29,163 INFO [train.py:527] (5/6) Epoch 600, batch 74, global_batch_idx: 74350, batch size: 58, loss[discriminator_loss=2.766, discriminator_real_loss=1.429, discriminator_fake_loss=1.337, generator_loss=27.81, generator_mel_loss=17.93, generator_kl_loss=1.349, generator_dur_loss=1.788, generator_adv_loss=1.98, generator_feat_match_loss=4.765, over 58.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=28.48, generator_mel_loss=17.97, generator_kl_loss=1.398, generator_dur_loss=1.759, generator_adv_loss=1.991, generator_feat_match_loss=5.363, over 4343.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:48:48,261 INFO [train.py:919] (5/6) Start epoch 601 +2024-03-14 13:49:13,074 INFO [train.py:527] (5/6) Epoch 601, batch 0, global_batch_idx: 74400, batch size: 97, loss[discriminator_loss=2.692, discriminator_real_loss=1.362, discriminator_fake_loss=1.331, generator_loss=28.83, generator_mel_loss=18.14, generator_kl_loss=1.254, generator_dur_loss=1.885, generator_adv_loss=1.92, generator_feat_match_loss=5.635, over 97.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.362, discriminator_fake_loss=1.331, generator_loss=28.83, generator_mel_loss=18.14, generator_kl_loss=1.254, generator_dur_loss=1.885, generator_adv_loss=1.92, generator_feat_match_loss=5.635, over 97.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:49:13,077 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 13:49:21,058 INFO [train.py:591] (5/6) 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 13:49:21,060 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 13:51:41,511 INFO [train.py:527] (5/6) Epoch 601, batch 50, global_batch_idx: 74450, batch size: 42, loss[discriminator_loss=2.673, discriminator_real_loss=1.334, discriminator_fake_loss=1.339, generator_loss=28.87, generator_mel_loss=18.59, generator_kl_loss=1.509, generator_dur_loss=1.668, generator_adv_loss=2.071, generator_feat_match_loss=5.036, over 42.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.37, discriminator_fake_loss=1.34, generator_loss=28.31, generator_mel_loss=17.95, generator_kl_loss=1.373, generator_dur_loss=1.76, generator_adv_loss=1.949, generator_feat_match_loss=5.273, over 2964.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:53:59,507 INFO [train.py:527] (5/6) Epoch 601, batch 100, global_batch_idx: 74500, batch size: 56, loss[discriminator_loss=2.631, discriminator_real_loss=1.257, discriminator_fake_loss=1.374, generator_loss=29.29, generator_mel_loss=18.27, generator_kl_loss=1.397, generator_dur_loss=1.732, generator_adv_loss=2.148, generator_feat_match_loss=5.744, over 56.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.37, discriminator_fake_loss=1.339, generator_loss=28.47, generator_mel_loss=17.99, generator_kl_loss=1.398, generator_dur_loss=1.753, generator_adv_loss=1.958, generator_feat_match_loss=5.363, over 5769.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:55:06,942 INFO [train.py:919] (5/6) Start epoch 602 +2024-03-14 13:56:40,402 INFO [train.py:527] (5/6) Epoch 602, batch 26, global_batch_idx: 74550, batch size: 45, loss[discriminator_loss=2.689, discriminator_real_loss=1.29, discriminator_fake_loss=1.399, generator_loss=27.91, generator_mel_loss=17.71, generator_kl_loss=1.33, generator_dur_loss=1.676, generator_adv_loss=1.981, generator_feat_match_loss=5.213, over 45.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.374, discriminator_fake_loss=1.339, generator_loss=28.54, generator_mel_loss=18, generator_kl_loss=1.478, generator_dur_loss=1.718, generator_adv_loss=1.978, generator_feat_match_loss=5.37, over 1398.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:58:59,320 INFO [train.py:527] (5/6) Epoch 602, batch 76, global_batch_idx: 74600, batch size: 53, loss[discriminator_loss=2.663, discriminator_real_loss=1.409, discriminator_fake_loss=1.254, generator_loss=28.17, generator_mel_loss=17.69, generator_kl_loss=1.498, generator_dur_loss=1.652, generator_adv_loss=1.794, generator_feat_match_loss=5.531, over 53.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.375, discriminator_fake_loss=1.338, generator_loss=28.49, generator_mel_loss=18.04, generator_kl_loss=1.45, generator_dur_loss=1.731, generator_adv_loss=1.961, generator_feat_match_loss=5.305, over 4148.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 13:58:59,321 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 13:59:08,242 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 14:01:20,576 INFO [train.py:919] (5/6) Start epoch 603 +2024-03-14 14:01:46,498 INFO [train.py:527] (5/6) Epoch 603, batch 2, global_batch_idx: 74650, batch size: 74, loss[discriminator_loss=2.691, discriminator_real_loss=1.408, discriminator_fake_loss=1.283, generator_loss=28.5, generator_mel_loss=17.75, generator_kl_loss=1.443, generator_dur_loss=1.804, generator_adv_loss=1.986, generator_feat_match_loss=5.521, over 74.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.379, discriminator_fake_loss=1.326, generator_loss=28.3, generator_mel_loss=17.77, generator_kl_loss=1.432, generator_dur_loss=1.741, generator_adv_loss=1.973, generator_feat_match_loss=5.384, over 197.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 14:04:04,830 INFO [train.py:527] (5/6) Epoch 603, batch 52, global_batch_idx: 74700, batch size: 48, loss[discriminator_loss=2.673, discriminator_real_loss=1.381, discriminator_fake_loss=1.292, generator_loss=28.38, generator_mel_loss=17.87, generator_kl_loss=1.626, generator_dur_loss=1.68, generator_adv_loss=2.011, generator_feat_match_loss=5.196, over 48.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.339, generator_loss=28.43, generator_mel_loss=18.06, generator_kl_loss=1.423, generator_dur_loss=1.728, generator_adv_loss=1.957, generator_feat_match_loss=5.264, over 2827.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 14:06:22,682 INFO [train.py:527] (5/6) Epoch 603, batch 102, global_batch_idx: 74750, batch size: 25, loss[discriminator_loss=2.723, discriminator_real_loss=1.39, discriminator_fake_loss=1.333, generator_loss=29.66, generator_mel_loss=18.47, generator_kl_loss=1.702, generator_dur_loss=1.559, generator_adv_loss=1.937, generator_feat_match_loss=5.993, over 25.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.338, generator_loss=28.42, generator_mel_loss=18.05, generator_kl_loss=1.442, generator_dur_loss=1.722, generator_adv_loss=1.964, generator_feat_match_loss=5.247, over 5280.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, +2024-03-14 14:07:25,595 INFO [train.py:919] (5/6) Start epoch 604 +2024-03-14 14:09:09,321 INFO [train.py:527] (5/6) Epoch 604, batch 28, global_batch_idx: 74800, batch size: 70, loss[discriminator_loss=2.659, discriminator_real_loss=1.436, discriminator_fake_loss=1.223, generator_loss=28.61, generator_mel_loss=17.96, generator_kl_loss=1.491, generator_dur_loss=1.767, generator_adv_loss=1.966, generator_feat_match_loss=5.428, over 70.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.355, discriminator_fake_loss=1.337, generator_loss=28.44, generator_mel_loss=17.97, generator_kl_loss=1.398, generator_dur_loss=1.742, generator_adv_loss=1.981, generator_feat_match_loss=5.352, over 1775.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:09:09,322 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 14:09:17,229 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 14:11:36,593 INFO [train.py:527] (5/6) Epoch 604, batch 78, global_batch_idx: 74850, batch size: 72, loss[discriminator_loss=2.729, discriminator_real_loss=1.362, discriminator_fake_loss=1.367, generator_loss=28.07, generator_mel_loss=17.61, generator_kl_loss=1.213, generator_dur_loss=1.794, generator_adv_loss=1.887, generator_feat_match_loss=5.569, over 72.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.369, discriminator_fake_loss=1.342, generator_loss=28.42, generator_mel_loss=17.96, generator_kl_loss=1.396, generator_dur_loss=1.743, generator_adv_loss=1.967, generator_feat_match_loss=5.35, over 4807.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:13:39,849 INFO [train.py:919] (5/6) Start epoch 605 +2024-03-14 14:14:16,496 INFO [train.py:527] (5/6) Epoch 605, batch 4, global_batch_idx: 74900, batch size: 96, loss[discriminator_loss=2.704, discriminator_real_loss=1.356, discriminator_fake_loss=1.348, generator_loss=28.34, generator_mel_loss=17.96, generator_kl_loss=1.39, generator_dur_loss=1.841, generator_adv_loss=1.868, generator_feat_match_loss=5.276, over 96.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.389, discriminator_fake_loss=1.355, generator_loss=28.3, generator_mel_loss=18.02, generator_kl_loss=1.37, generator_dur_loss=1.752, generator_adv_loss=1.922, generator_feat_match_loss=5.241, over 313.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:16:33,115 INFO [train.py:527] (5/6) Epoch 605, batch 54, global_batch_idx: 74950, batch size: 50, loss[discriminator_loss=2.72, discriminator_real_loss=1.279, discriminator_fake_loss=1.441, generator_loss=29.54, generator_mel_loss=17.92, generator_kl_loss=1.604, generator_dur_loss=1.699, generator_adv_loss=2.076, generator_feat_match_loss=6.235, over 50.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.344, generator_loss=28.45, generator_mel_loss=18.04, generator_kl_loss=1.43, generator_dur_loss=1.747, generator_adv_loss=1.953, generator_feat_match_loss=5.287, over 3064.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:18:54,464 INFO [train.py:527] (5/6) Epoch 605, batch 104, global_batch_idx: 75000, batch size: 64, loss[discriminator_loss=2.745, discriminator_real_loss=1.475, discriminator_fake_loss=1.27, generator_loss=27.46, generator_mel_loss=18.01, generator_kl_loss=1.209, generator_dur_loss=1.81, generator_adv_loss=1.848, generator_feat_match_loss=4.583, over 64.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.343, generator_loss=28.45, generator_mel_loss=18.04, generator_kl_loss=1.416, generator_dur_loss=1.746, generator_adv_loss=1.953, generator_feat_match_loss=5.298, over 5986.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:18:54,465 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 14:19:03,050 INFO [train.py:591] (5/6) 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 14:19:03,051 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 14:19:54,928 INFO [train.py:919] (5/6) Start epoch 606 +2024-03-14 14:21:42,338 INFO [train.py:527] (5/6) Epoch 606, batch 30, global_batch_idx: 75050, batch size: 72, loss[discriminator_loss=2.745, discriminator_real_loss=1.374, discriminator_fake_loss=1.371, generator_loss=28.81, generator_mel_loss=18.14, generator_kl_loss=1.336, generator_dur_loss=1.794, generator_adv_loss=1.959, generator_feat_match_loss=5.582, over 72.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.358, discriminator_fake_loss=1.331, generator_loss=28.49, generator_mel_loss=18.03, generator_kl_loss=1.413, generator_dur_loss=1.741, generator_adv_loss=1.964, generator_feat_match_loss=5.343, over 1818.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:24:04,828 INFO [train.py:527] (5/6) Epoch 606, batch 80, global_batch_idx: 75100, batch size: 50, loss[discriminator_loss=2.701, discriminator_real_loss=1.397, discriminator_fake_loss=1.304, generator_loss=28.94, generator_mel_loss=17.83, generator_kl_loss=1.649, generator_dur_loss=1.671, generator_adv_loss=2.017, generator_feat_match_loss=5.77, over 50.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.361, discriminator_fake_loss=1.34, generator_loss=28.38, generator_mel_loss=17.95, generator_kl_loss=1.416, generator_dur_loss=1.748, generator_adv_loss=1.956, generator_feat_match_loss=5.308, over 4698.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:26:04,891 INFO [train.py:919] (5/6) Start epoch 607 +2024-03-14 14:26:45,411 INFO [train.py:527] (5/6) Epoch 607, batch 6, global_batch_idx: 75150, batch size: 50, loss[discriminator_loss=2.718, discriminator_real_loss=1.353, discriminator_fake_loss=1.366, generator_loss=28.37, generator_mel_loss=18.01, generator_kl_loss=1.422, generator_dur_loss=1.673, generator_adv_loss=1.943, generator_feat_match_loss=5.32, over 50.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.364, discriminator_fake_loss=1.347, generator_loss=28.74, generator_mel_loss=18.31, generator_kl_loss=1.444, generator_dur_loss=1.714, generator_adv_loss=1.972, generator_feat_match_loss=5.304, over 347.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:29:00,950 INFO [train.py:527] (5/6) Epoch 607, batch 56, global_batch_idx: 75200, batch size: 68, loss[discriminator_loss=2.724, discriminator_real_loss=1.35, discriminator_fake_loss=1.374, generator_loss=28.1, generator_mel_loss=17.79, generator_kl_loss=1.429, generator_dur_loss=1.771, generator_adv_loss=1.942, generator_feat_match_loss=5.171, over 68.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.37, discriminator_fake_loss=1.34, generator_loss=28.54, generator_mel_loss=18.08, generator_kl_loss=1.413, generator_dur_loss=1.74, generator_adv_loss=1.97, generator_feat_match_loss=5.344, over 3270.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:29:00,952 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 14:29:09,186 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 14:31:28,905 INFO [train.py:527] (5/6) Epoch 607, batch 106, global_batch_idx: 75250, batch size: 55, loss[discriminator_loss=2.818, discriminator_real_loss=1.257, discriminator_fake_loss=1.562, generator_loss=28.13, generator_mel_loss=17.76, generator_kl_loss=1.396, generator_dur_loss=1.729, generator_adv_loss=2.324, generator_feat_match_loss=4.924, over 55.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.34, generator_loss=28.56, generator_mel_loss=18.06, generator_kl_loss=1.408, generator_dur_loss=1.74, generator_adv_loss=1.977, generator_feat_match_loss=5.38, over 6118.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:32:17,861 INFO [train.py:919] (5/6) Start epoch 608 +2024-03-14 14:34:09,983 INFO [train.py:527] (5/6) Epoch 608, batch 32, global_batch_idx: 75300, batch size: 70, loss[discriminator_loss=2.699, discriminator_real_loss=1.346, discriminator_fake_loss=1.354, generator_loss=28.42, generator_mel_loss=17.76, generator_kl_loss=1.383, generator_dur_loss=1.778, generator_adv_loss=1.997, generator_feat_match_loss=5.5, over 70.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.378, discriminator_fake_loss=1.315, generator_loss=28.37, generator_mel_loss=17.87, generator_kl_loss=1.396, generator_dur_loss=1.737, generator_adv_loss=1.991, generator_feat_match_loss=5.375, over 1859.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:36:29,333 INFO [train.py:527] (5/6) Epoch 608, batch 82, global_batch_idx: 75350, batch size: 55, loss[discriminator_loss=2.729, discriminator_real_loss=1.287, discriminator_fake_loss=1.442, generator_loss=28.48, generator_mel_loss=18.15, generator_kl_loss=1.435, generator_dur_loss=1.638, generator_adv_loss=2.005, generator_feat_match_loss=5.246, over 55.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.372, discriminator_fake_loss=1.329, generator_loss=28.43, generator_mel_loss=17.94, generator_kl_loss=1.415, generator_dur_loss=1.743, generator_adv_loss=1.974, generator_feat_match_loss=5.352, over 4834.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:38:21,304 INFO [train.py:919] (5/6) Start epoch 609 +2024-03-14 14:39:08,789 INFO [train.py:527] (5/6) Epoch 609, batch 8, global_batch_idx: 75400, batch size: 77, loss[discriminator_loss=2.686, discriminator_real_loss=1.336, discriminator_fake_loss=1.35, generator_loss=28.07, generator_mel_loss=17.77, generator_kl_loss=1.198, generator_dur_loss=1.831, generator_adv_loss=2.061, generator_feat_match_loss=5.211, over 77.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.37, discriminator_fake_loss=1.326, generator_loss=28.11, generator_mel_loss=17.87, generator_kl_loss=1.371, generator_dur_loss=1.771, generator_adv_loss=1.937, generator_feat_match_loss=5.163, over 515.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:39:08,792 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 14:39:16,785 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 14:41:33,442 INFO [train.py:527] (5/6) Epoch 609, batch 58, global_batch_idx: 75450, batch size: 61, loss[discriminator_loss=2.756, discriminator_real_loss=1.393, discriminator_fake_loss=1.363, generator_loss=27.54, generator_mel_loss=17.8, generator_kl_loss=1.316, generator_dur_loss=1.714, generator_adv_loss=1.943, generator_feat_match_loss=4.768, over 61.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.367, discriminator_fake_loss=1.331, generator_loss=28.44, generator_mel_loss=17.98, generator_kl_loss=1.427, generator_dur_loss=1.732, generator_adv_loss=1.958, generator_feat_match_loss=5.336, over 3308.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:43:52,363 INFO [train.py:527] (5/6) Epoch 609, batch 108, global_batch_idx: 75500, batch size: 36, loss[discriminator_loss=2.712, discriminator_real_loss=1.37, discriminator_fake_loss=1.341, generator_loss=30.02, generator_mel_loss=18.33, generator_kl_loss=1.587, generator_dur_loss=1.658, generator_adv_loss=2.064, generator_feat_match_loss=6.376, over 36.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.366, discriminator_fake_loss=1.335, generator_loss=28.42, generator_mel_loss=17.95, generator_kl_loss=1.424, generator_dur_loss=1.741, generator_adv_loss=1.961, generator_feat_match_loss=5.346, over 6187.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:44:36,169 INFO [train.py:919] (5/6) Start epoch 610 +2024-03-14 14:46:32,239 INFO [train.py:527] (5/6) Epoch 610, batch 34, global_batch_idx: 75550, batch size: 52, loss[discriminator_loss=2.771, discriminator_real_loss=1.427, discriminator_fake_loss=1.344, generator_loss=26.99, generator_mel_loss=17.33, generator_kl_loss=1.233, generator_dur_loss=1.761, generator_adv_loss=2.015, generator_feat_match_loss=4.65, over 52.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.382, discriminator_fake_loss=1.331, generator_loss=28.42, generator_mel_loss=17.93, generator_kl_loss=1.413, generator_dur_loss=1.753, generator_adv_loss=1.987, generator_feat_match_loss=5.335, over 2016.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:48:50,518 INFO [train.py:527] (5/6) Epoch 610, batch 84, global_batch_idx: 75600, batch size: 39, loss[discriminator_loss=2.708, discriminator_real_loss=1.425, discriminator_fake_loss=1.283, generator_loss=27.94, generator_mel_loss=18.24, generator_kl_loss=1.414, generator_dur_loss=1.713, generator_adv_loss=1.926, generator_feat_match_loss=4.647, over 39.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.379, discriminator_fake_loss=1.33, generator_loss=28.37, generator_mel_loss=17.93, generator_kl_loss=1.407, generator_dur_loss=1.756, generator_adv_loss=1.978, generator_feat_match_loss=5.3, over 4955.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:48:50,520 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 14:48:59,261 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 14:50:50,411 INFO [train.py:919] (5/6) Start epoch 611 +2024-03-14 14:51:41,479 INFO [train.py:527] (5/6) Epoch 611, batch 10, global_batch_idx: 75650, batch size: 80, loss[discriminator_loss=2.741, discriminator_real_loss=1.38, discriminator_fake_loss=1.361, generator_loss=27.84, generator_mel_loss=17.48, generator_kl_loss=1.45, generator_dur_loss=1.83, generator_adv_loss=1.98, generator_feat_match_loss=5.099, over 80.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.36, discriminator_fake_loss=1.362, generator_loss=28.6, generator_mel_loss=17.94, generator_kl_loss=1.408, generator_dur_loss=1.773, generator_adv_loss=1.977, generator_feat_match_loss=5.501, over 606.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:53:58,048 INFO [train.py:527] (5/6) Epoch 611, batch 60, global_batch_idx: 75700, batch size: 14, loss[discriminator_loss=2.659, discriminator_real_loss=1.283, discriminator_fake_loss=1.376, generator_loss=32.05, generator_mel_loss=19.06, generator_kl_loss=1.974, generator_dur_loss=1.486, generator_adv_loss=2.046, generator_feat_match_loss=7.486, over 14.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.372, discriminator_fake_loss=1.337, generator_loss=28.54, generator_mel_loss=18, generator_kl_loss=1.431, generator_dur_loss=1.75, generator_adv_loss=1.989, generator_feat_match_loss=5.371, over 3240.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:56:18,241 INFO [train.py:527] (5/6) Epoch 611, batch 110, global_batch_idx: 75750, batch size: 66, loss[discriminator_loss=2.687, discriminator_real_loss=1.306, discriminator_fake_loss=1.381, generator_loss=28.31, generator_mel_loss=18.09, generator_kl_loss=1.42, generator_dur_loss=1.772, generator_adv_loss=1.909, generator_feat_match_loss=5.125, over 66.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.49, generator_mel_loss=17.99, generator_kl_loss=1.423, generator_dur_loss=1.751, generator_adv_loss=1.978, generator_feat_match_loss=5.346, over 6089.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:56:56,351 INFO [train.py:919] (5/6) Start epoch 612 +2024-03-14 14:59:01,024 INFO [train.py:527] (5/6) Epoch 612, batch 36, global_batch_idx: 75800, batch size: 59, loss[discriminator_loss=2.714, discriminator_real_loss=1.357, discriminator_fake_loss=1.356, generator_loss=28.49, generator_mel_loss=18.08, generator_kl_loss=1.427, generator_dur_loss=1.709, generator_adv_loss=2.039, generator_feat_match_loss=5.243, over 59.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.359, discriminator_fake_loss=1.332, generator_loss=28.65, generator_mel_loss=18.03, generator_kl_loss=1.439, generator_dur_loss=1.755, generator_adv_loss=1.968, generator_feat_match_loss=5.454, over 2070.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 14:59:01,025 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 14:59:08,881 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 15:01:27,499 INFO [train.py:527] (5/6) Epoch 612, batch 86, global_batch_idx: 75850, batch size: 74, loss[discriminator_loss=2.681, discriminator_real_loss=1.32, discriminator_fake_loss=1.361, generator_loss=28.82, generator_mel_loss=18.24, generator_kl_loss=1.379, generator_dur_loss=1.804, generator_adv_loss=2.076, generator_feat_match_loss=5.317, over 74.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.65, generator_mel_loss=18.05, generator_kl_loss=1.44, generator_dur_loss=1.744, generator_adv_loss=1.975, generator_feat_match_loss=5.444, over 4661.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:03:12,935 INFO [train.py:919] (5/6) Start epoch 613 +2024-03-14 15:04:09,347 INFO [train.py:527] (5/6) Epoch 613, batch 12, global_batch_idx: 75900, batch size: 14, loss[discriminator_loss=2.615, discriminator_real_loss=1.307, discriminator_fake_loss=1.308, generator_loss=31.05, generator_mel_loss=18.96, generator_kl_loss=1.982, generator_dur_loss=1.543, generator_adv_loss=1.865, generator_feat_match_loss=6.708, over 14.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.33, generator_loss=28.42, generator_mel_loss=17.84, generator_kl_loss=1.429, generator_dur_loss=1.719, generator_adv_loss=1.973, generator_feat_match_loss=5.461, over 666.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:06:27,939 INFO [train.py:527] (5/6) Epoch 613, batch 62, global_batch_idx: 75950, batch size: 74, loss[discriminator_loss=2.699, discriminator_real_loss=1.377, discriminator_fake_loss=1.323, generator_loss=28.93, generator_mel_loss=17.98, generator_kl_loss=1.495, generator_dur_loss=1.799, generator_adv_loss=1.999, generator_feat_match_loss=5.658, over 74.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.37, discriminator_fake_loss=1.334, generator_loss=28.45, generator_mel_loss=17.91, generator_kl_loss=1.438, generator_dur_loss=1.731, generator_adv_loss=1.978, generator_feat_match_loss=5.395, over 3296.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:08:46,722 INFO [train.py:527] (5/6) Epoch 613, batch 112, global_batch_idx: 76000, batch size: 42, loss[discriminator_loss=2.628, discriminator_real_loss=1.356, discriminator_fake_loss=1.272, generator_loss=30.53, generator_mel_loss=18.49, generator_kl_loss=1.616, generator_dur_loss=1.611, generator_adv_loss=1.907, generator_feat_match_loss=6.907, over 42.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.371, discriminator_fake_loss=1.329, generator_loss=28.49, generator_mel_loss=17.95, generator_kl_loss=1.434, generator_dur_loss=1.737, generator_adv_loss=1.979, generator_feat_match_loss=5.391, over 6070.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:08:46,723 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 15:08:55,768 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 15:09:29,493 INFO [train.py:919] (5/6) Start epoch 614 +2024-03-14 15:11:40,187 INFO [train.py:527] (5/6) Epoch 614, batch 38, global_batch_idx: 76050, batch size: 74, loss[discriminator_loss=2.734, discriminator_real_loss=1.355, discriminator_fake_loss=1.379, generator_loss=28.5, generator_mel_loss=17.93, generator_kl_loss=1.409, generator_dur_loss=1.818, generator_adv_loss=2.017, generator_feat_match_loss=5.325, over 74.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.36, discriminator_fake_loss=1.335, generator_loss=28.67, generator_mel_loss=18.04, generator_kl_loss=1.44, generator_dur_loss=1.762, generator_adv_loss=1.981, generator_feat_match_loss=5.454, over 2243.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:14:00,567 INFO [train.py:527] (5/6) Epoch 614, batch 88, global_batch_idx: 76100, batch size: 39, loss[discriminator_loss=2.755, discriminator_real_loss=1.339, discriminator_fake_loss=1.416, generator_loss=28.65, generator_mel_loss=18.04, generator_kl_loss=1.566, generator_dur_loss=1.668, generator_adv_loss=1.926, generator_feat_match_loss=5.453, over 39.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.61, generator_mel_loss=18, generator_kl_loss=1.449, generator_dur_loss=1.753, generator_adv_loss=1.976, generator_feat_match_loss=5.435, over 5056.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:15:37,099 INFO [train.py:919] (5/6) Start epoch 615 +2024-03-14 15:16:41,691 INFO [train.py:527] (5/6) Epoch 615, batch 14, global_batch_idx: 76150, batch size: 42, loss[discriminator_loss=2.745, discriminator_real_loss=1.31, discriminator_fake_loss=1.435, generator_loss=30.06, generator_mel_loss=18.27, generator_kl_loss=1.576, generator_dur_loss=1.669, generator_adv_loss=1.989, generator_feat_match_loss=6.557, over 42.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.23, generator_mel_loss=17.86, generator_kl_loss=1.372, generator_dur_loss=1.763, generator_adv_loss=1.955, generator_feat_match_loss=5.284, over 944.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:18:59,528 INFO [train.py:527] (5/6) Epoch 615, batch 64, global_batch_idx: 76200, batch size: 68, loss[discriminator_loss=2.674, discriminator_real_loss=1.351, discriminator_fake_loss=1.323, generator_loss=29.05, generator_mel_loss=18.35, generator_kl_loss=1.423, generator_dur_loss=1.766, generator_adv_loss=1.841, generator_feat_match_loss=5.669, over 68.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.366, discriminator_fake_loss=1.336, generator_loss=28.54, generator_mel_loss=17.96, generator_kl_loss=1.416, generator_dur_loss=1.752, generator_adv_loss=1.981, generator_feat_match_loss=5.427, over 3765.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:18:59,529 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 15:19:08,427 INFO [train.py:591] (5/6) 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 15:19:08,428 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 15:21:28,241 INFO [train.py:527] (5/6) Epoch 615, batch 114, global_batch_idx: 76250, batch size: 72, loss[discriminator_loss=2.709, discriminator_real_loss=1.392, discriminator_fake_loss=1.317, generator_loss=28.64, generator_mel_loss=18.08, generator_kl_loss=1.29, generator_dur_loss=1.764, generator_adv_loss=2.05, generator_feat_match_loss=5.456, over 72.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.53, generator_mel_loss=17.97, generator_kl_loss=1.402, generator_dur_loss=1.752, generator_adv_loss=1.974, generator_feat_match_loss=5.43, over 6572.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:21:54,297 INFO [train.py:919] (5/6) Start epoch 616 +2024-03-14 15:24:09,250 INFO [train.py:527] (5/6) Epoch 616, batch 40, global_batch_idx: 76300, batch size: 39, loss[discriminator_loss=2.738, discriminator_real_loss=1.375, discriminator_fake_loss=1.363, generator_loss=27.13, generator_mel_loss=17.49, generator_kl_loss=1.376, generator_dur_loss=1.703, generator_adv_loss=2.092, generator_feat_match_loss=4.473, over 39.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.37, discriminator_fake_loss=1.342, generator_loss=28.45, generator_mel_loss=17.94, generator_kl_loss=1.415, generator_dur_loss=1.74, generator_adv_loss=1.972, generator_feat_match_loss=5.383, over 2296.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:26:26,328 INFO [train.py:527] (5/6) Epoch 616, batch 90, global_batch_idx: 76350, batch size: 48, loss[discriminator_loss=2.665, discriminator_real_loss=1.407, discriminator_fake_loss=1.259, generator_loss=29.47, generator_mel_loss=18.7, generator_kl_loss=1.452, generator_dur_loss=1.633, generator_adv_loss=2.051, generator_feat_match_loss=5.631, over 48.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.367, discriminator_fake_loss=1.336, generator_loss=28.6, generator_mel_loss=18.02, generator_kl_loss=1.421, generator_dur_loss=1.74, generator_adv_loss=1.977, generator_feat_match_loss=5.447, over 5083.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:27:59,898 INFO [train.py:919] (5/6) Start epoch 617 +2024-03-14 15:29:07,876 INFO [train.py:527] (5/6) Epoch 617, batch 16, global_batch_idx: 76400, batch size: 47, loss[discriminator_loss=2.685, discriminator_real_loss=1.44, discriminator_fake_loss=1.244, generator_loss=28.59, generator_mel_loss=17.88, generator_kl_loss=1.623, generator_dur_loss=1.692, generator_adv_loss=2, generator_feat_match_loss=5.395, over 47.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.359, discriminator_fake_loss=1.307, generator_loss=28.91, generator_mel_loss=18.1, generator_kl_loss=1.457, generator_dur_loss=1.724, generator_adv_loss=1.995, generator_feat_match_loss=5.631, over 927.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:29:07,877 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 15:29:16,009 INFO [train.py:591] (5/6) 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 15:29:16,010 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 15:31:36,492 INFO [train.py:527] (5/6) Epoch 617, batch 66, global_batch_idx: 76450, batch size: 48, loss[discriminator_loss=2.688, discriminator_real_loss=1.384, discriminator_fake_loss=1.305, generator_loss=27.42, generator_mel_loss=17.79, generator_kl_loss=1.496, generator_dur_loss=1.717, generator_adv_loss=1.959, generator_feat_match_loss=4.456, over 48.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.367, discriminator_fake_loss=1.328, generator_loss=28.69, generator_mel_loss=18.05, generator_kl_loss=1.452, generator_dur_loss=1.721, generator_adv_loss=1.981, generator_feat_match_loss=5.488, over 3521.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:33:53,738 INFO [train.py:527] (5/6) Epoch 617, batch 116, global_batch_idx: 76500, batch size: 83, loss[discriminator_loss=2.645, discriminator_real_loss=1.279, discriminator_fake_loss=1.366, generator_loss=29.41, generator_mel_loss=18.4, generator_kl_loss=1.5, generator_dur_loss=1.797, generator_adv_loss=2.07, generator_feat_match_loss=5.641, over 83.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.366, discriminator_fake_loss=1.331, generator_loss=28.64, generator_mel_loss=18.02, generator_kl_loss=1.447, generator_dur_loss=1.728, generator_adv_loss=1.98, generator_feat_match_loss=5.462, over 6382.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:34:13,851 INFO [train.py:919] (5/6) Start epoch 618 +2024-03-14 15:36:33,335 INFO [train.py:527] (5/6) Epoch 618, batch 42, global_batch_idx: 76550, batch size: 74, loss[discriminator_loss=2.668, discriminator_real_loss=1.377, discriminator_fake_loss=1.292, generator_loss=27.97, generator_mel_loss=17.65, generator_kl_loss=1.428, generator_dur_loss=1.789, generator_adv_loss=1.891, generator_feat_match_loss=5.213, over 74.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.38, discriminator_fake_loss=1.333, generator_loss=28.45, generator_mel_loss=17.96, generator_kl_loss=1.428, generator_dur_loss=1.747, generator_adv_loss=1.969, generator_feat_match_loss=5.355, over 2570.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:38:53,468 INFO [train.py:527] (5/6) Epoch 618, batch 92, global_batch_idx: 76600, batch size: 31, loss[discriminator_loss=2.666, discriminator_real_loss=1.341, discriminator_fake_loss=1.325, generator_loss=28.55, generator_mel_loss=17.84, generator_kl_loss=1.648, generator_dur_loss=1.663, generator_adv_loss=2.053, generator_feat_match_loss=5.338, over 31.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.375, discriminator_fake_loss=1.333, generator_loss=28.47, generator_mel_loss=17.95, generator_kl_loss=1.439, generator_dur_loss=1.74, generator_adv_loss=1.972, generator_feat_match_loss=5.374, over 5268.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:38:53,469 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 15:39:02,304 INFO [train.py:591] (5/6) 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 15:39:02,305 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 15:40:29,606 INFO [train.py:919] (5/6) Start epoch 619 +2024-03-14 15:41:44,082 INFO [train.py:527] (5/6) Epoch 619, batch 18, global_batch_idx: 76650, batch size: 39, loss[discriminator_loss=2.72, discriminator_real_loss=1.324, discriminator_fake_loss=1.397, generator_loss=29.6, generator_mel_loss=18.37, generator_kl_loss=1.473, generator_dur_loss=1.646, generator_adv_loss=2.022, generator_feat_match_loss=6.084, over 39.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.365, discriminator_fake_loss=1.32, generator_loss=28.66, generator_mel_loss=18.06, generator_kl_loss=1.413, generator_dur_loss=1.706, generator_adv_loss=1.985, generator_feat_match_loss=5.496, over 1105.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:44:04,012 INFO [train.py:527] (5/6) Epoch 619, batch 68, global_batch_idx: 76700, batch size: 61, loss[discriminator_loss=2.753, discriminator_real_loss=1.389, discriminator_fake_loss=1.364, generator_loss=28.62, generator_mel_loss=18.3, generator_kl_loss=1.448, generator_dur_loss=1.744, generator_adv_loss=1.914, generator_feat_match_loss=5.22, over 61.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.65, generator_mel_loss=18.03, generator_kl_loss=1.443, generator_dur_loss=1.709, generator_adv_loss=1.963, generator_feat_match_loss=5.503, over 3761.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:46:21,559 INFO [train.py:527] (5/6) Epoch 619, batch 118, global_batch_idx: 76750, batch size: 64, loss[discriminator_loss=2.725, discriminator_real_loss=1.294, discriminator_fake_loss=1.431, generator_loss=28.67, generator_mel_loss=18.02, generator_kl_loss=1.305, generator_dur_loss=1.768, generator_adv_loss=2.06, generator_feat_match_loss=5.52, over 64.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.366, discriminator_fake_loss=1.336, generator_loss=28.6, generator_mel_loss=18.01, generator_kl_loss=1.438, generator_dur_loss=1.717, generator_adv_loss=1.968, generator_feat_match_loss=5.46, over 6626.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:46:37,256 INFO [train.py:919] (5/6) Start epoch 620 +2024-03-14 15:49:01,658 INFO [train.py:527] (5/6) Epoch 620, batch 44, global_batch_idx: 76800, batch size: 42, loss[discriminator_loss=2.743, discriminator_real_loss=1.48, discriminator_fake_loss=1.263, generator_loss=28.96, generator_mel_loss=18.27, generator_kl_loss=1.403, generator_dur_loss=1.672, generator_adv_loss=1.847, generator_feat_match_loss=5.768, over 42.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.351, discriminator_fake_loss=1.341, generator_loss=28.73, generator_mel_loss=18.05, generator_kl_loss=1.476, generator_dur_loss=1.708, generator_adv_loss=1.976, generator_feat_match_loss=5.514, over 2342.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:49:01,660 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 15:49:09,771 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 15:51:27,851 INFO [train.py:527] (5/6) Epoch 620, batch 94, global_batch_idx: 76850, batch size: 50, loss[discriminator_loss=2.692, discriminator_real_loss=1.343, discriminator_fake_loss=1.349, generator_loss=28.72, generator_mel_loss=18.14, generator_kl_loss=1.546, generator_dur_loss=1.635, generator_adv_loss=2.018, generator_feat_match_loss=5.378, over 50.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.357, discriminator_fake_loss=1.337, generator_loss=28.57, generator_mel_loss=17.96, generator_kl_loss=1.463, generator_dur_loss=1.724, generator_adv_loss=1.978, generator_feat_match_loss=5.452, over 5223.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:52:52,156 INFO [train.py:919] (5/6) Start epoch 621 +2024-03-14 15:54:12,781 INFO [train.py:527] (5/6) Epoch 621, batch 20, global_batch_idx: 76900, batch size: 52, loss[discriminator_loss=2.676, discriminator_real_loss=1.317, discriminator_fake_loss=1.359, generator_loss=28.43, generator_mel_loss=17.96, generator_kl_loss=1.308, generator_dur_loss=1.675, generator_adv_loss=2.099, generator_feat_match_loss=5.384, over 52.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.358, discriminator_fake_loss=1.33, generator_loss=28.78, generator_mel_loss=18.04, generator_kl_loss=1.45, generator_dur_loss=1.716, generator_adv_loss=1.996, generator_feat_match_loss=5.583, over 1188.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:56:29,060 INFO [train.py:527] (5/6) Epoch 621, batch 70, global_batch_idx: 76950, batch size: 52, loss[discriminator_loss=2.674, discriminator_real_loss=1.429, discriminator_fake_loss=1.244, generator_loss=29.27, generator_mel_loss=18.39, generator_kl_loss=1.418, generator_dur_loss=1.694, generator_adv_loss=1.952, generator_feat_match_loss=5.82, over 52.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.366, discriminator_fake_loss=1.331, generator_loss=28.72, generator_mel_loss=18.04, generator_kl_loss=1.456, generator_dur_loss=1.72, generator_adv_loss=1.986, generator_feat_match_loss=5.516, over 3892.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:58:46,182 INFO [train.py:527] (5/6) Epoch 621, batch 120, global_batch_idx: 77000, batch size: 77, loss[discriminator_loss=2.743, discriminator_real_loss=1.448, discriminator_fake_loss=1.295, generator_loss=28.25, generator_mel_loss=17.9, generator_kl_loss=1.286, generator_dur_loss=1.824, generator_adv_loss=1.804, generator_feat_match_loss=5.433, over 77.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.363, discriminator_fake_loss=1.336, generator_loss=28.7, generator_mel_loss=18.02, generator_kl_loss=1.432, generator_dur_loss=1.74, generator_adv_loss=1.997, generator_feat_match_loss=5.514, over 6648.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 15:58:46,183 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 15:58:54,767 INFO [train.py:591] (5/6) 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 15:58:54,768 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 15:59:04,523 INFO [train.py:919] (5/6) Start epoch 622 +2024-03-14 16:01:37,721 INFO [train.py:527] (5/6) Epoch 622, batch 46, global_batch_idx: 77050, batch size: 64, loss[discriminator_loss=2.681, discriminator_real_loss=1.351, discriminator_fake_loss=1.33, generator_loss=28.93, generator_mel_loss=17.87, generator_kl_loss=1.418, generator_dur_loss=1.819, generator_adv_loss=2.092, generator_feat_match_loss=5.725, over 64.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.368, discriminator_fake_loss=1.322, generator_loss=28.52, generator_mel_loss=18.01, generator_kl_loss=1.417, generator_dur_loss=1.75, generator_adv_loss=1.996, generator_feat_match_loss=5.351, over 2612.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:03:59,760 INFO [train.py:527] (5/6) Epoch 622, batch 96, global_batch_idx: 77100, batch size: 15, loss[discriminator_loss=2.726, discriminator_real_loss=1.369, discriminator_fake_loss=1.357, generator_loss=29.09, generator_mel_loss=18.3, generator_kl_loss=1.917, generator_dur_loss=1.574, generator_adv_loss=1.847, generator_feat_match_loss=5.453, over 15.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.37, discriminator_fake_loss=1.328, generator_loss=28.57, generator_mel_loss=18.05, generator_kl_loss=1.426, generator_dur_loss=1.739, generator_adv_loss=1.984, generator_feat_match_loss=5.362, over 5199.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:05:15,448 INFO [train.py:919] (5/6) Start epoch 623 +2024-03-14 16:06:39,150 INFO [train.py:527] (5/6) Epoch 623, batch 22, global_batch_idx: 77150, batch size: 96, loss[discriminator_loss=2.673, discriminator_real_loss=1.408, discriminator_fake_loss=1.265, generator_loss=28.4, generator_mel_loss=18.06, generator_kl_loss=1.284, generator_dur_loss=1.872, generator_adv_loss=1.904, generator_feat_match_loss=5.275, over 96.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.371, discriminator_fake_loss=1.33, generator_loss=28.61, generator_mel_loss=18.03, generator_kl_loss=1.402, generator_dur_loss=1.762, generator_adv_loss=1.975, generator_feat_match_loss=5.448, over 1484.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:08:58,388 INFO [train.py:527] (5/6) Epoch 623, batch 72, global_batch_idx: 77200, batch size: 44, loss[discriminator_loss=2.63, discriminator_real_loss=1.283, discriminator_fake_loss=1.346, generator_loss=29.06, generator_mel_loss=17.71, generator_kl_loss=1.547, generator_dur_loss=1.682, generator_adv_loss=1.919, generator_feat_match_loss=6.196, over 44.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.364, discriminator_fake_loss=1.337, generator_loss=28.5, generator_mel_loss=17.98, generator_kl_loss=1.413, generator_dur_loss=1.743, generator_adv_loss=1.969, generator_feat_match_loss=5.394, over 4284.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:08:58,390 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 16:09:07,223 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 16:11:25,820 INFO [train.py:527] (5/6) Epoch 623, batch 122, global_batch_idx: 77250, batch size: 25, loss[discriminator_loss=2.704, discriminator_real_loss=1.365, discriminator_fake_loss=1.34, generator_loss=30.35, generator_mel_loss=18.7, generator_kl_loss=1.733, generator_dur_loss=1.547, generator_adv_loss=2.251, generator_feat_match_loss=6.115, over 25.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.363, discriminator_fake_loss=1.337, generator_loss=28.56, generator_mel_loss=18, generator_kl_loss=1.421, generator_dur_loss=1.74, generator_adv_loss=1.97, generator_feat_match_loss=5.425, over 7147.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:11:31,012 INFO [train.py:919] (5/6) Start epoch 624 +2024-03-14 16:14:06,404 INFO [train.py:527] (5/6) Epoch 624, batch 48, global_batch_idx: 77300, batch size: 66, loss[discriminator_loss=2.679, discriminator_real_loss=1.294, discriminator_fake_loss=1.385, generator_loss=28.45, generator_mel_loss=17.84, generator_kl_loss=1.467, generator_dur_loss=1.722, generator_adv_loss=2.014, generator_feat_match_loss=5.405, over 66.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.361, discriminator_fake_loss=1.337, generator_loss=28.34, generator_mel_loss=17.82, generator_kl_loss=1.411, generator_dur_loss=1.746, generator_adv_loss=1.977, generator_feat_match_loss=5.379, over 2998.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:16:24,652 INFO [train.py:527] (5/6) Epoch 624, batch 98, global_batch_idx: 77350, batch size: 96, loss[discriminator_loss=2.736, discriminator_real_loss=1.443, discriminator_fake_loss=1.293, generator_loss=27.82, generator_mel_loss=17.57, generator_kl_loss=1.35, generator_dur_loss=1.827, generator_adv_loss=1.894, generator_feat_match_loss=5.179, over 96.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.367, discriminator_fake_loss=1.336, generator_loss=28.4, generator_mel_loss=17.87, generator_kl_loss=1.414, generator_dur_loss=1.739, generator_adv_loss=1.977, generator_feat_match_loss=5.397, over 5942.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:17:35,468 INFO [train.py:919] (5/6) Start epoch 625 +2024-03-14 16:19:06,983 INFO [train.py:527] (5/6) Epoch 625, batch 24, global_batch_idx: 77400, batch size: 96, loss[discriminator_loss=2.658, discriminator_real_loss=1.305, discriminator_fake_loss=1.352, generator_loss=27.54, generator_mel_loss=17.71, generator_kl_loss=1.263, generator_dur_loss=1.839, generator_adv_loss=1.884, generator_feat_match_loss=4.837, over 96.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.361, discriminator_fake_loss=1.34, generator_loss=28.66, generator_mel_loss=17.97, generator_kl_loss=1.431, generator_dur_loss=1.746, generator_adv_loss=1.999, generator_feat_match_loss=5.519, over 1486.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:19:06,986 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 16:19:15,011 INFO [train.py:591] (5/6) 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 16:19:15,012 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 16:21:33,743 INFO [train.py:527] (5/6) Epoch 625, batch 74, global_batch_idx: 77450, batch size: 56, loss[discriminator_loss=2.735, discriminator_real_loss=1.371, discriminator_fake_loss=1.364, generator_loss=29.89, generator_mel_loss=17.99, generator_kl_loss=1.544, generator_dur_loss=1.736, generator_adv_loss=2.083, generator_feat_match_loss=6.535, over 56.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.368, discriminator_fake_loss=1.333, generator_loss=28.6, generator_mel_loss=17.96, generator_kl_loss=1.44, generator_dur_loss=1.742, generator_adv_loss=1.988, generator_feat_match_loss=5.465, over 4354.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:23:51,470 INFO [train.py:919] (5/6) Start epoch 626 +2024-03-14 16:24:13,164 INFO [train.py:527] (5/6) Epoch 626, batch 0, global_batch_idx: 77500, batch size: 83, loss[discriminator_loss=2.698, discriminator_real_loss=1.371, discriminator_fake_loss=1.327, generator_loss=29.09, generator_mel_loss=18.24, generator_kl_loss=1.492, generator_dur_loss=1.847, generator_adv_loss=2.108, generator_feat_match_loss=5.407, over 83.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.371, discriminator_fake_loss=1.327, generator_loss=29.09, generator_mel_loss=18.24, generator_kl_loss=1.492, generator_dur_loss=1.847, generator_adv_loss=2.108, generator_feat_match_loss=5.407, over 83.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:26:30,722 INFO [train.py:527] (5/6) Epoch 626, batch 50, global_batch_idx: 77550, batch size: 47, loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=29.16, generator_mel_loss=18.35, generator_kl_loss=1.591, generator_dur_loss=1.736, generator_adv_loss=2.046, generator_feat_match_loss=5.439, over 47.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.371, discriminator_fake_loss=1.332, generator_loss=28.56, generator_mel_loss=18.01, generator_kl_loss=1.436, generator_dur_loss=1.759, generator_adv_loss=1.973, generator_feat_match_loss=5.385, over 3014.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:28:48,478 INFO [train.py:527] (5/6) Epoch 626, batch 100, global_batch_idx: 77600, batch size: 31, loss[discriminator_loss=2.732, discriminator_real_loss=1.412, discriminator_fake_loss=1.32, generator_loss=28.99, generator_mel_loss=18.19, generator_kl_loss=1.731, generator_dur_loss=1.588, generator_adv_loss=2.036, generator_feat_match_loss=5.444, over 31.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.365, discriminator_fake_loss=1.328, generator_loss=28.52, generator_mel_loss=17.97, generator_kl_loss=1.427, generator_dur_loss=1.759, generator_adv_loss=1.973, generator_feat_match_loss=5.392, over 5843.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:28:48,480 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 16:28:57,308 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 16:30:03,219 INFO [train.py:919] (5/6) Start epoch 627 +2024-03-14 16:31:38,199 INFO [train.py:527] (5/6) Epoch 627, batch 26, global_batch_idx: 77650, batch size: 16, loss[discriminator_loss=2.788, discriminator_real_loss=1.369, discriminator_fake_loss=1.419, generator_loss=28.54, generator_mel_loss=17.91, generator_kl_loss=1.889, generator_dur_loss=1.598, generator_adv_loss=1.772, generator_feat_match_loss=5.366, over 16.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.377, discriminator_fake_loss=1.319, generator_loss=28.45, generator_mel_loss=17.92, generator_kl_loss=1.435, generator_dur_loss=1.724, generator_adv_loss=1.98, generator_feat_match_loss=5.382, over 1419.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:33:58,473 INFO [train.py:527] (5/6) Epoch 627, batch 76, global_batch_idx: 77700, batch size: 48, loss[discriminator_loss=2.661, discriminator_real_loss=1.379, discriminator_fake_loss=1.282, generator_loss=29.3, generator_mel_loss=18.1, generator_kl_loss=1.379, generator_dur_loss=1.704, generator_adv_loss=2.089, generator_feat_match_loss=6.036, over 48.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.369, discriminator_fake_loss=1.322, generator_loss=28.46, generator_mel_loss=17.9, generator_kl_loss=1.437, generator_dur_loss=1.732, generator_adv_loss=1.982, generator_feat_match_loss=5.412, over 4207.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:36:09,608 INFO [train.py:919] (5/6) Start epoch 628 +2024-03-14 16:36:40,104 INFO [train.py:527] (5/6) Epoch 628, batch 2, global_batch_idx: 77750, batch size: 80, loss[discriminator_loss=2.642, discriminator_real_loss=1.344, discriminator_fake_loss=1.298, generator_loss=29.15, generator_mel_loss=18.14, generator_kl_loss=1.391, generator_dur_loss=1.833, generator_adv_loss=2.119, generator_feat_match_loss=5.661, over 80.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.36, discriminator_fake_loss=1.311, generator_loss=28.82, generator_mel_loss=18.09, generator_kl_loss=1.38, generator_dur_loss=1.771, generator_adv_loss=1.967, generator_feat_match_loss=5.606, over 188.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:39:00,309 INFO [train.py:527] (5/6) Epoch 628, batch 52, global_batch_idx: 77800, batch size: 48, loss[discriminator_loss=2.709, discriminator_real_loss=1.423, discriminator_fake_loss=1.286, generator_loss=28.78, generator_mel_loss=18.03, generator_kl_loss=1.47, generator_dur_loss=1.681, generator_adv_loss=1.913, generator_feat_match_loss=5.686, over 48.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.378, discriminator_fake_loss=1.333, generator_loss=28.53, generator_mel_loss=17.99, generator_kl_loss=1.416, generator_dur_loss=1.751, generator_adv_loss=1.955, generator_feat_match_loss=5.419, over 2981.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:39:00,310 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 16:39:08,520 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 16:41:26,677 INFO [train.py:527] (5/6) Epoch 628, batch 102, global_batch_idx: 77850, batch size: 25, loss[discriminator_loss=2.689, discriminator_real_loss=1.379, discriminator_fake_loss=1.31, generator_loss=29.26, generator_mel_loss=18.91, generator_kl_loss=1.574, generator_dur_loss=1.618, generator_adv_loss=2.019, generator_feat_match_loss=5.141, over 25.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.374, discriminator_fake_loss=1.335, generator_loss=28.51, generator_mel_loss=17.95, generator_kl_loss=1.431, generator_dur_loss=1.741, generator_adv_loss=1.96, generator_feat_match_loss=5.426, over 5674.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:42:25,151 INFO [train.py:919] (5/6) Start epoch 629 +2024-03-14 16:44:06,966 INFO [train.py:527] (5/6) Epoch 629, batch 28, global_batch_idx: 77900, batch size: 62, loss[discriminator_loss=2.7, discriminator_real_loss=1.38, discriminator_fake_loss=1.32, generator_loss=28.25, generator_mel_loss=17.8, generator_kl_loss=1.373, generator_dur_loss=1.722, generator_adv_loss=1.987, generator_feat_match_loss=5.364, over 62.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.354, discriminator_fake_loss=1.35, generator_loss=28.86, generator_mel_loss=18.13, generator_kl_loss=1.424, generator_dur_loss=1.746, generator_adv_loss=1.961, generator_feat_match_loss=5.599, over 1706.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:46:27,965 INFO [train.py:527] (5/6) Epoch 629, batch 78, global_batch_idx: 77950, batch size: 88, loss[discriminator_loss=2.652, discriminator_real_loss=1.363, discriminator_fake_loss=1.289, generator_loss=29.28, generator_mel_loss=18.21, generator_kl_loss=1.439, generator_dur_loss=1.866, generator_adv_loss=1.985, generator_feat_match_loss=5.785, over 88.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.62, generator_mel_loss=17.98, generator_kl_loss=1.414, generator_dur_loss=1.776, generator_adv_loss=1.967, generator_feat_match_loss=5.48, over 4883.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:48:29,032 INFO [train.py:919] (5/6) Start epoch 630 +2024-03-14 16:49:03,350 INFO [train.py:527] (5/6) Epoch 630, batch 4, global_batch_idx: 78000, batch size: 55, loss[discriminator_loss=2.661, discriminator_real_loss=1.355, discriminator_fake_loss=1.305, generator_loss=28.79, generator_mel_loss=18, generator_kl_loss=1.463, generator_dur_loss=1.697, generator_adv_loss=2.032, generator_feat_match_loss=5.598, over 55.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.342, discriminator_fake_loss=1.345, generator_loss=28.66, generator_mel_loss=18.16, generator_kl_loss=1.426, generator_dur_loss=1.706, generator_adv_loss=1.969, generator_feat_match_loss=5.393, over 259.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:49:03,352 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 16:49:11,212 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 16:51:32,501 INFO [train.py:527] (5/6) Epoch 630, batch 54, global_batch_idx: 78050, batch size: 48, loss[discriminator_loss=2.742, discriminator_real_loss=1.432, discriminator_fake_loss=1.31, generator_loss=28.83, generator_mel_loss=18.21, generator_kl_loss=1.39, generator_dur_loss=1.687, generator_adv_loss=1.907, generator_feat_match_loss=5.635, over 48.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.377, discriminator_fake_loss=1.335, generator_loss=28.45, generator_mel_loss=17.98, generator_kl_loss=1.409, generator_dur_loss=1.752, generator_adv_loss=1.975, generator_feat_match_loss=5.335, over 3167.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:53:47,791 INFO [train.py:527] (5/6) Epoch 630, batch 104, global_batch_idx: 78100, batch size: 88, loss[discriminator_loss=2.712, discriminator_real_loss=1.416, discriminator_fake_loss=1.296, generator_loss=28.47, generator_mel_loss=17.99, generator_kl_loss=1.32, generator_dur_loss=1.838, generator_adv_loss=2.047, generator_feat_match_loss=5.274, over 88.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.376, discriminator_fake_loss=1.336, generator_loss=28.4, generator_mel_loss=17.96, generator_kl_loss=1.409, generator_dur_loss=1.752, generator_adv_loss=1.967, generator_feat_match_loss=5.307, over 6172.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:54:41,037 INFO [train.py:919] (5/6) Start epoch 631 +2024-03-14 16:56:27,402 INFO [train.py:527] (5/6) Epoch 631, batch 30, global_batch_idx: 78150, batch size: 66, loss[discriminator_loss=2.776, discriminator_real_loss=1.412, discriminator_fake_loss=1.365, generator_loss=27.63, generator_mel_loss=17.47, generator_kl_loss=1.422, generator_dur_loss=1.805, generator_adv_loss=1.995, generator_feat_match_loss=4.942, over 66.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.363, discriminator_fake_loss=1.353, generator_loss=28.52, generator_mel_loss=17.98, generator_kl_loss=1.443, generator_dur_loss=1.75, generator_adv_loss=1.957, generator_feat_match_loss=5.386, over 1770.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:58:46,208 INFO [train.py:527] (5/6) Epoch 631, batch 80, global_batch_idx: 78200, batch size: 62, loss[discriminator_loss=2.72, discriminator_real_loss=1.37, discriminator_fake_loss=1.35, generator_loss=29.23, generator_mel_loss=18.15, generator_kl_loss=1.5, generator_dur_loss=1.741, generator_adv_loss=1.919, generator_feat_match_loss=5.917, over 62.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=28.5, generator_mel_loss=17.96, generator_kl_loss=1.444, generator_dur_loss=1.743, generator_adv_loss=1.97, generator_feat_match_loss=5.382, over 4502.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 16:58:46,209 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 16:58:55,114 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 17:00:57,070 INFO [train.py:919] (5/6) Start epoch 632 +2024-03-14 17:01:38,830 INFO [train.py:527] (5/6) Epoch 632, batch 6, global_batch_idx: 78250, batch size: 61, loss[discriminator_loss=2.634, discriminator_real_loss=1.349, discriminator_fake_loss=1.285, generator_loss=28.51, generator_mel_loss=17.65, generator_kl_loss=1.421, generator_dur_loss=1.753, generator_adv_loss=2.014, generator_feat_match_loss=5.671, over 61.00 samples.], tot_loss[discriminator_loss=2.652, discriminator_real_loss=1.365, discriminator_fake_loss=1.288, generator_loss=28.6, generator_mel_loss=17.85, generator_kl_loss=1.432, generator_dur_loss=1.728, generator_adv_loss=2, generator_feat_match_loss=5.582, over 373.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:03:55,739 INFO [train.py:527] (5/6) Epoch 632, batch 56, global_batch_idx: 78300, batch size: 48, loss[discriminator_loss=2.779, discriminator_real_loss=1.417, discriminator_fake_loss=1.362, generator_loss=27.84, generator_mel_loss=17.58, generator_kl_loss=1.33, generator_dur_loss=1.673, generator_adv_loss=1.922, generator_feat_match_loss=5.335, over 48.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.366, discriminator_fake_loss=1.32, generator_loss=28.62, generator_mel_loss=17.93, generator_kl_loss=1.426, generator_dur_loss=1.742, generator_adv_loss=1.985, generator_feat_match_loss=5.53, over 3167.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:06:17,374 INFO [train.py:527] (5/6) Epoch 632, batch 106, global_batch_idx: 78350, batch size: 52, loss[discriminator_loss=2.708, discriminator_real_loss=1.4, discriminator_fake_loss=1.307, generator_loss=27.88, generator_mel_loss=18.02, generator_kl_loss=1.44, generator_dur_loss=1.635, generator_adv_loss=1.797, generator_feat_match_loss=4.986, over 52.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.368, discriminator_fake_loss=1.328, generator_loss=28.59, generator_mel_loss=17.95, generator_kl_loss=1.421, generator_dur_loss=1.74, generator_adv_loss=1.978, generator_feat_match_loss=5.497, over 6008.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:07:03,569 INFO [train.py:919] (5/6) Start epoch 633 +2024-03-14 17:08:58,822 INFO [train.py:527] (5/6) Epoch 633, batch 32, global_batch_idx: 78400, batch size: 72, loss[discriminator_loss=2.74, discriminator_real_loss=1.325, discriminator_fake_loss=1.415, generator_loss=27.81, generator_mel_loss=17.83, generator_kl_loss=1.314, generator_dur_loss=1.755, generator_adv_loss=2.008, generator_feat_match_loss=4.904, over 72.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.367, discriminator_fake_loss=1.332, generator_loss=28.56, generator_mel_loss=18.06, generator_kl_loss=1.367, generator_dur_loss=1.755, generator_adv_loss=1.978, generator_feat_match_loss=5.408, over 1979.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:08:58,823 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 17:09:07,180 INFO [train.py:591] (5/6) 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 17:09:07,181 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 17:11:28,202 INFO [train.py:527] (5/6) Epoch 633, batch 82, global_batch_idx: 78450, batch size: 64, loss[discriminator_loss=2.699, discriminator_real_loss=1.35, discriminator_fake_loss=1.348, generator_loss=28.96, generator_mel_loss=17.76, generator_kl_loss=1.434, generator_dur_loss=1.724, generator_adv_loss=2.055, generator_feat_match_loss=5.984, over 64.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.365, discriminator_fake_loss=1.334, generator_loss=28.54, generator_mel_loss=18, generator_kl_loss=1.38, generator_dur_loss=1.752, generator_adv_loss=1.97, generator_feat_match_loss=5.435, over 5043.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:13:21,034 INFO [train.py:919] (5/6) Start epoch 634 +2024-03-14 17:14:04,087 INFO [train.py:527] (5/6) Epoch 634, batch 8, global_batch_idx: 78500, batch size: 68, loss[discriminator_loss=2.728, discriminator_real_loss=1.395, discriminator_fake_loss=1.333, generator_loss=28.03, generator_mel_loss=17.76, generator_kl_loss=1.357, generator_dur_loss=1.74, generator_adv_loss=1.989, generator_feat_match_loss=5.181, over 68.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.381, discriminator_fake_loss=1.351, generator_loss=28.48, generator_mel_loss=18.05, generator_kl_loss=1.425, generator_dur_loss=1.739, generator_adv_loss=1.954, generator_feat_match_loss=5.314, over 519.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:16:22,927 INFO [train.py:527] (5/6) Epoch 634, batch 58, global_batch_idx: 78550, batch size: 96, loss[discriminator_loss=2.733, discriminator_real_loss=1.384, discriminator_fake_loss=1.349, generator_loss=27.31, generator_mel_loss=17.31, generator_kl_loss=1.226, generator_dur_loss=1.857, generator_adv_loss=1.937, generator_feat_match_loss=4.983, over 96.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.47, generator_mel_loss=17.94, generator_kl_loss=1.397, generator_dur_loss=1.755, generator_adv_loss=1.975, generator_feat_match_loss=5.407, over 3802.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:18:42,645 INFO [train.py:527] (5/6) Epoch 634, batch 108, global_batch_idx: 78600, batch size: 96, loss[discriminator_loss=2.684, discriminator_real_loss=1.331, discriminator_fake_loss=1.354, generator_loss=27.94, generator_mel_loss=17.83, generator_kl_loss=1.364, generator_dur_loss=1.864, generator_adv_loss=1.847, generator_feat_match_loss=5.035, over 96.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.37, discriminator_fake_loss=1.335, generator_loss=28.44, generator_mel_loss=17.94, generator_kl_loss=1.399, generator_dur_loss=1.748, generator_adv_loss=1.971, generator_feat_match_loss=5.384, over 6638.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:18:42,647 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 17:18:51,363 INFO [train.py:591] (5/6) 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 17:18:51,364 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 17:19:33,523 INFO [train.py:919] (5/6) Start epoch 635 +2024-03-14 17:21:33,216 INFO [train.py:527] (5/6) Epoch 635, batch 34, global_batch_idx: 78650, batch size: 80, loss[discriminator_loss=2.698, discriminator_real_loss=1.39, discriminator_fake_loss=1.308, generator_loss=28.37, generator_mel_loss=18.05, generator_kl_loss=1.42, generator_dur_loss=1.854, generator_adv_loss=1.898, generator_feat_match_loss=5.147, over 80.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.375, discriminator_fake_loss=1.332, generator_loss=28.43, generator_mel_loss=17.93, generator_kl_loss=1.414, generator_dur_loss=1.765, generator_adv_loss=1.972, generator_feat_match_loss=5.352, over 2087.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:23:53,386 INFO [train.py:527] (5/6) Epoch 635, batch 84, global_batch_idx: 78700, batch size: 61, loss[discriminator_loss=2.683, discriminator_real_loss=1.413, discriminator_fake_loss=1.271, generator_loss=27.87, generator_mel_loss=17.71, generator_kl_loss=1.241, generator_dur_loss=1.742, generator_adv_loss=2.027, generator_feat_match_loss=5.158, over 61.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.372, discriminator_fake_loss=1.329, generator_loss=28.6, generator_mel_loss=17.98, generator_kl_loss=1.434, generator_dur_loss=1.745, generator_adv_loss=1.988, generator_feat_match_loss=5.448, over 4717.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:25:40,817 INFO [train.py:919] (5/6) Start epoch 636 +2024-03-14 17:26:32,557 INFO [train.py:527] (5/6) Epoch 636, batch 10, global_batch_idx: 78750, batch size: 68, loss[discriminator_loss=2.721, discriminator_real_loss=1.388, discriminator_fake_loss=1.333, generator_loss=27.83, generator_mel_loss=17.9, generator_kl_loss=1.275, generator_dur_loss=1.782, generator_adv_loss=1.946, generator_feat_match_loss=4.93, over 68.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.362, discriminator_fake_loss=1.338, generator_loss=28.78, generator_mel_loss=18.02, generator_kl_loss=1.4, generator_dur_loss=1.742, generator_adv_loss=1.989, generator_feat_match_loss=5.632, over 602.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:28:51,955 INFO [train.py:527] (5/6) Epoch 636, batch 60, global_batch_idx: 78800, batch size: 44, loss[discriminator_loss=2.74, discriminator_real_loss=1.39, discriminator_fake_loss=1.35, generator_loss=28.56, generator_mel_loss=18.17, generator_kl_loss=1.515, generator_dur_loss=1.656, generator_adv_loss=1.876, generator_feat_match_loss=5.346, over 44.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.367, discriminator_fake_loss=1.333, generator_loss=28.57, generator_mel_loss=17.97, generator_kl_loss=1.381, generator_dur_loss=1.754, generator_adv_loss=1.991, generator_feat_match_loss=5.477, over 3502.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:28:51,956 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 17:29:00,091 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 17:31:19,371 INFO [train.py:527] (5/6) Epoch 636, batch 110, global_batch_idx: 78850, batch size: 96, loss[discriminator_loss=2.68, discriminator_real_loss=1.251, discriminator_fake_loss=1.429, generator_loss=28.68, generator_mel_loss=18.02, generator_kl_loss=1.306, generator_dur_loss=1.814, generator_adv_loss=1.944, generator_feat_match_loss=5.598, over 96.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.366, discriminator_fake_loss=1.333, generator_loss=28.54, generator_mel_loss=17.96, generator_kl_loss=1.38, generator_dur_loss=1.752, generator_adv_loss=1.984, generator_feat_match_loss=5.46, over 6264.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:31:55,380 INFO [train.py:919] (5/6) Start epoch 637 +2024-03-14 17:34:01,480 INFO [train.py:527] (5/6) Epoch 637, batch 36, global_batch_idx: 78900, batch size: 58, loss[discriminator_loss=2.645, discriminator_real_loss=1.261, discriminator_fake_loss=1.384, generator_loss=28.52, generator_mel_loss=18, generator_kl_loss=1.339, generator_dur_loss=1.714, generator_adv_loss=1.97, generator_feat_match_loss=5.502, over 58.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.361, discriminator_fake_loss=1.319, generator_loss=28.52, generator_mel_loss=17.89, generator_kl_loss=1.407, generator_dur_loss=1.72, generator_adv_loss=2.004, generator_feat_match_loss=5.504, over 2148.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:36:21,644 INFO [train.py:527] (5/6) Epoch 637, batch 86, global_batch_idx: 78950, batch size: 61, loss[discriminator_loss=2.704, discriminator_real_loss=1.411, discriminator_fake_loss=1.293, generator_loss=28.15, generator_mel_loss=17.84, generator_kl_loss=1.288, generator_dur_loss=1.758, generator_adv_loss=1.872, generator_feat_match_loss=5.395, over 61.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.367, discriminator_fake_loss=1.323, generator_loss=28.54, generator_mel_loss=17.93, generator_kl_loss=1.413, generator_dur_loss=1.723, generator_adv_loss=1.992, generator_feat_match_loss=5.487, over 4873.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:38:05,450 INFO [train.py:919] (5/6) Start epoch 638 +2024-03-14 17:39:02,849 INFO [train.py:527] (5/6) Epoch 638, batch 12, global_batch_idx: 79000, batch size: 64, loss[discriminator_loss=2.687, discriminator_real_loss=1.288, discriminator_fake_loss=1.399, generator_loss=29.44, generator_mel_loss=18.37, generator_kl_loss=1.298, generator_dur_loss=1.767, generator_adv_loss=2.128, generator_feat_match_loss=5.874, over 64.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.35, discriminator_fake_loss=1.334, generator_loss=28.92, generator_mel_loss=18.05, generator_kl_loss=1.46, generator_dur_loss=1.711, generator_adv_loss=2.003, generator_feat_match_loss=5.691, over 702.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:39:02,864 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 17:39:10,863 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 17:41:29,722 INFO [train.py:527] (5/6) Epoch 638, batch 62, global_batch_idx: 79050, batch size: 59, loss[discriminator_loss=2.734, discriminator_real_loss=1.425, discriminator_fake_loss=1.308, generator_loss=28.38, generator_mel_loss=17.63, generator_kl_loss=1.419, generator_dur_loss=1.735, generator_adv_loss=2, generator_feat_match_loss=5.601, over 59.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.371, discriminator_fake_loss=1.33, generator_loss=28.55, generator_mel_loss=17.94, generator_kl_loss=1.432, generator_dur_loss=1.729, generator_adv_loss=1.978, generator_feat_match_loss=5.467, over 3884.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:43:48,992 INFO [train.py:527] (5/6) Epoch 638, batch 112, global_batch_idx: 79100, batch size: 55, loss[discriminator_loss=2.765, discriminator_real_loss=1.585, discriminator_fake_loss=1.18, generator_loss=27.6, generator_mel_loss=17.84, generator_kl_loss=1.348, generator_dur_loss=1.687, generator_adv_loss=1.893, generator_feat_match_loss=4.832, over 55.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.37, discriminator_fake_loss=1.331, generator_loss=28.58, generator_mel_loss=17.96, generator_kl_loss=1.425, generator_dur_loss=1.739, generator_adv_loss=1.978, generator_feat_match_loss=5.483, over 6691.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:44:18,835 INFO [train.py:919] (5/6) Start epoch 639 +2024-03-14 17:46:29,987 INFO [train.py:527] (5/6) Epoch 639, batch 38, global_batch_idx: 79150, batch size: 61, loss[discriminator_loss=2.713, discriminator_real_loss=1.397, discriminator_fake_loss=1.316, generator_loss=28.39, generator_mel_loss=18.02, generator_kl_loss=1.358, generator_dur_loss=1.729, generator_adv_loss=2.048, generator_feat_match_loss=5.24, over 61.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.33, generator_loss=28.76, generator_mel_loss=18.02, generator_kl_loss=1.435, generator_dur_loss=1.763, generator_adv_loss=1.983, generator_feat_match_loss=5.553, over 2371.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:48:47,234 INFO [train.py:527] (5/6) Epoch 639, batch 88, global_batch_idx: 79200, batch size: 66, loss[discriminator_loss=2.693, discriminator_real_loss=1.382, discriminator_fake_loss=1.311, generator_loss=29.49, generator_mel_loss=17.96, generator_kl_loss=1.38, generator_dur_loss=1.754, generator_adv_loss=2.08, generator_feat_match_loss=6.315, over 66.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.363, discriminator_fake_loss=1.33, generator_loss=28.72, generator_mel_loss=17.99, generator_kl_loss=1.425, generator_dur_loss=1.758, generator_adv_loss=2.008, generator_feat_match_loss=5.547, over 5248.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:48:47,235 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 17:48:56,150 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 17:50:32,599 INFO [train.py:919] (5/6) Start epoch 640 +2024-03-14 17:51:37,841 INFO [train.py:527] (5/6) Epoch 640, batch 14, global_batch_idx: 79250, batch size: 70, loss[discriminator_loss=2.665, discriminator_real_loss=1.337, discriminator_fake_loss=1.328, generator_loss=28.8, generator_mel_loss=18.12, generator_kl_loss=1.39, generator_dur_loss=1.728, generator_adv_loss=1.927, generator_feat_match_loss=5.64, over 70.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=28.37, generator_mel_loss=17.82, generator_kl_loss=1.4, generator_dur_loss=1.745, generator_adv_loss=1.955, generator_feat_match_loss=5.45, over 897.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:53:54,760 INFO [train.py:527] (5/6) Epoch 640, batch 64, global_batch_idx: 79300, batch size: 52, loss[discriminator_loss=2.749, discriminator_real_loss=1.436, discriminator_fake_loss=1.312, generator_loss=28.79, generator_mel_loss=18.11, generator_kl_loss=1.498, generator_dur_loss=1.71, generator_adv_loss=1.994, generator_feat_match_loss=5.48, over 52.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=28.58, generator_mel_loss=17.92, generator_kl_loss=1.415, generator_dur_loss=1.747, generator_adv_loss=1.983, generator_feat_match_loss=5.517, over 3769.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:56:14,330 INFO [train.py:527] (5/6) Epoch 640, batch 114, global_batch_idx: 79350, batch size: 48, loss[discriminator_loss=2.682, discriminator_real_loss=1.344, discriminator_fake_loss=1.337, generator_loss=29.06, generator_mel_loss=18.22, generator_kl_loss=1.495, generator_dur_loss=1.694, generator_adv_loss=2.001, generator_feat_match_loss=5.644, over 48.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.331, generator_loss=28.56, generator_mel_loss=17.93, generator_kl_loss=1.422, generator_dur_loss=1.749, generator_adv_loss=1.977, generator_feat_match_loss=5.483, over 6762.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:56:41,728 INFO [train.py:919] (5/6) Start epoch 641 +2024-03-14 17:58:56,455 INFO [train.py:527] (5/6) Epoch 641, batch 40, global_batch_idx: 79400, batch size: 16, loss[discriminator_loss=2.729, discriminator_real_loss=1.457, discriminator_fake_loss=1.273, generator_loss=28.37, generator_mel_loss=18.25, generator_kl_loss=1.676, generator_dur_loss=1.624, generator_adv_loss=1.889, generator_feat_match_loss=4.93, over 16.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.367, discriminator_fake_loss=1.332, generator_loss=28.44, generator_mel_loss=17.89, generator_kl_loss=1.423, generator_dur_loss=1.752, generator_adv_loss=1.977, generator_feat_match_loss=5.396, over 2268.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 17:58:56,456 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 17:59:04,409 INFO [train.py:591] (5/6) 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 17:59:04,410 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 18:01:24,949 INFO [train.py:527] (5/6) Epoch 641, batch 90, global_batch_idx: 79450, batch size: 88, loss[discriminator_loss=2.699, discriminator_real_loss=1.328, discriminator_fake_loss=1.371, generator_loss=28.87, generator_mel_loss=18.02, generator_kl_loss=1.353, generator_dur_loss=1.82, generator_adv_loss=1.937, generator_feat_match_loss=5.741, over 88.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.359, discriminator_fake_loss=1.336, generator_loss=28.6, generator_mel_loss=17.95, generator_kl_loss=1.418, generator_dur_loss=1.758, generator_adv_loss=1.981, generator_feat_match_loss=5.495, over 5180.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:02:55,932 INFO [train.py:919] (5/6) Start epoch 642 +2024-03-14 18:04:05,217 INFO [train.py:527] (5/6) Epoch 642, batch 16, global_batch_idx: 79500, batch size: 47, loss[discriminator_loss=2.721, discriminator_real_loss=1.462, discriminator_fake_loss=1.259, generator_loss=28.67, generator_mel_loss=18.01, generator_kl_loss=1.595, generator_dur_loss=1.698, generator_adv_loss=1.869, generator_feat_match_loss=5.495, over 47.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=28.89, generator_mel_loss=18.08, generator_kl_loss=1.479, generator_dur_loss=1.729, generator_adv_loss=1.98, generator_feat_match_loss=5.617, over 877.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:06:24,275 INFO [train.py:527] (5/6) Epoch 642, batch 66, global_batch_idx: 79550, batch size: 15, loss[discriminator_loss=2.792, discriminator_real_loss=1.41, discriminator_fake_loss=1.382, generator_loss=29.57, generator_mel_loss=18.25, generator_kl_loss=1.805, generator_dur_loss=1.552, generator_adv_loss=2.254, generator_feat_match_loss=5.711, over 15.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.365, discriminator_fake_loss=1.333, generator_loss=28.63, generator_mel_loss=17.98, generator_kl_loss=1.449, generator_dur_loss=1.738, generator_adv_loss=1.987, generator_feat_match_loss=5.478, over 3416.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:08:44,238 INFO [train.py:527] (5/6) Epoch 642, batch 116, global_batch_idx: 79600, batch size: 48, loss[discriminator_loss=2.691, discriminator_real_loss=1.363, discriminator_fake_loss=1.328, generator_loss=28.92, generator_mel_loss=17.63, generator_kl_loss=1.627, generator_dur_loss=1.682, generator_adv_loss=1.934, generator_feat_match_loss=6.042, over 48.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.67, generator_mel_loss=17.98, generator_kl_loss=1.432, generator_dur_loss=1.746, generator_adv_loss=1.999, generator_feat_match_loss=5.514, over 6378.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:08:44,239 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 18:08:52,919 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 18:09:15,029 INFO [train.py:919] (5/6) Start epoch 643 +2024-03-14 18:11:35,688 INFO [train.py:527] (5/6) Epoch 643, batch 42, global_batch_idx: 79650, batch size: 62, loss[discriminator_loss=2.707, discriminator_real_loss=1.393, discriminator_fake_loss=1.314, generator_loss=27.91, generator_mel_loss=17.53, generator_kl_loss=1.579, generator_dur_loss=1.708, generator_adv_loss=1.926, generator_feat_match_loss=5.163, over 62.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.364, discriminator_fake_loss=1.326, generator_loss=28.61, generator_mel_loss=17.97, generator_kl_loss=1.458, generator_dur_loss=1.743, generator_adv_loss=1.976, generator_feat_match_loss=5.461, over 2367.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:13:54,430 INFO [train.py:527] (5/6) Epoch 643, batch 92, global_batch_idx: 79700, batch size: 77, loss[discriminator_loss=2.718, discriminator_real_loss=1.355, discriminator_fake_loss=1.362, generator_loss=28.71, generator_mel_loss=17.88, generator_kl_loss=1.462, generator_dur_loss=1.781, generator_adv_loss=1.887, generator_feat_match_loss=5.697, over 77.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.365, discriminator_fake_loss=1.331, generator_loss=28.5, generator_mel_loss=17.94, generator_kl_loss=1.441, generator_dur_loss=1.741, generator_adv_loss=1.964, generator_feat_match_loss=5.411, over 5130.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:15:22,810 INFO [train.py:919] (5/6) Start epoch 644 +2024-03-14 18:16:39,278 INFO [train.py:527] (5/6) Epoch 644, batch 18, global_batch_idx: 79750, batch size: 77, loss[discriminator_loss=2.689, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.07, generator_mel_loss=17.74, generator_kl_loss=1.507, generator_dur_loss=1.834, generator_adv_loss=1.968, generator_feat_match_loss=5.014, over 77.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.367, discriminator_fake_loss=1.331, generator_loss=28.84, generator_mel_loss=18.13, generator_kl_loss=1.441, generator_dur_loss=1.751, generator_adv_loss=1.977, generator_feat_match_loss=5.543, over 1098.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:18:58,804 INFO [train.py:527] (5/6) Epoch 644, batch 68, global_batch_idx: 79800, batch size: 88, loss[discriminator_loss=2.614, discriminator_real_loss=1.38, discriminator_fake_loss=1.234, generator_loss=29.09, generator_mel_loss=17.79, generator_kl_loss=1.384, generator_dur_loss=1.85, generator_adv_loss=2.015, generator_feat_match_loss=6.047, over 88.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.366, discriminator_fake_loss=1.325, generator_loss=28.72, generator_mel_loss=18.05, generator_kl_loss=1.426, generator_dur_loss=1.753, generator_adv_loss=1.98, generator_feat_match_loss=5.516, over 3922.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:18:58,806 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 18:19:07,778 INFO [train.py:591] (5/6) 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 18:19:07,778 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 18:21:27,610 INFO [train.py:527] (5/6) Epoch 644, batch 118, global_batch_idx: 79850, batch size: 56, loss[discriminator_loss=2.686, discriminator_real_loss=1.322, discriminator_fake_loss=1.363, generator_loss=28.74, generator_mel_loss=17.59, generator_kl_loss=1.326, generator_dur_loss=1.712, generator_adv_loss=2.007, generator_feat_match_loss=6.103, over 56.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.368, discriminator_fake_loss=1.332, generator_loss=28.65, generator_mel_loss=18.03, generator_kl_loss=1.417, generator_dur_loss=1.757, generator_adv_loss=1.971, generator_feat_match_loss=5.483, over 6801.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:21:43,614 INFO [train.py:919] (5/6) Start epoch 645 +2024-03-14 18:24:13,381 INFO [train.py:527] (5/6) Epoch 645, batch 44, global_batch_idx: 79900, batch size: 50, loss[discriminator_loss=2.671, discriminator_real_loss=1.419, discriminator_fake_loss=1.252, generator_loss=28.27, generator_mel_loss=17.9, generator_kl_loss=1.381, generator_dur_loss=1.682, generator_adv_loss=1.999, generator_feat_match_loss=5.308, over 50.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=28.7, generator_mel_loss=18, generator_kl_loss=1.395, generator_dur_loss=1.76, generator_adv_loss=1.981, generator_feat_match_loss=5.568, over 2642.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:26:34,760 INFO [train.py:527] (5/6) Epoch 645, batch 94, global_batch_idx: 79950, batch size: 44, loss[discriminator_loss=2.729, discriminator_real_loss=1.283, discriminator_fake_loss=1.445, generator_loss=28.8, generator_mel_loss=18.45, generator_kl_loss=1.437, generator_dur_loss=1.704, generator_adv_loss=2.094, generator_feat_match_loss=5.115, over 44.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.357, discriminator_fake_loss=1.33, generator_loss=28.75, generator_mel_loss=18, generator_kl_loss=1.405, generator_dur_loss=1.758, generator_adv_loss=1.985, generator_feat_match_loss=5.603, over 5540.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:27:53,628 INFO [train.py:919] (5/6) Start epoch 646 +2024-03-14 18:29:16,717 INFO [train.py:527] (5/6) Epoch 646, batch 20, global_batch_idx: 80000, batch size: 53, loss[discriminator_loss=2.63, discriminator_real_loss=1.413, discriminator_fake_loss=1.217, generator_loss=29.93, generator_mel_loss=18.77, generator_kl_loss=1.444, generator_dur_loss=1.663, generator_adv_loss=1.991, generator_feat_match_loss=6.062, over 53.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.364, discriminator_fake_loss=1.337, generator_loss=28.63, generator_mel_loss=18.06, generator_kl_loss=1.417, generator_dur_loss=1.771, generator_adv_loss=1.958, generator_feat_match_loss=5.423, over 1269.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:29:16,719 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 18:29:24,871 INFO [train.py:591] (5/6) 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 18:29:24,872 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 18:31:44,364 INFO [train.py:527] (5/6) Epoch 646, batch 70, global_batch_idx: 80050, batch size: 58, loss[discriminator_loss=2.649, discriminator_real_loss=1.347, discriminator_fake_loss=1.302, generator_loss=29.75, generator_mel_loss=18.14, generator_kl_loss=1.618, generator_dur_loss=1.748, generator_adv_loss=2.033, generator_feat_match_loss=6.212, over 58.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.62, generator_mel_loss=17.98, generator_kl_loss=1.406, generator_dur_loss=1.768, generator_adv_loss=1.971, generator_feat_match_loss=5.493, over 4209.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:34:03,568 INFO [train.py:527] (5/6) Epoch 646, batch 120, global_batch_idx: 80100, batch size: 16, loss[discriminator_loss=2.771, discriminator_real_loss=1.37, discriminator_fake_loss=1.401, generator_loss=29.2, generator_mel_loss=18.79, generator_kl_loss=1.689, generator_dur_loss=1.566, generator_adv_loss=2.273, generator_feat_match_loss=4.882, over 16.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.362, discriminator_fake_loss=1.332, generator_loss=28.63, generator_mel_loss=17.99, generator_kl_loss=1.415, generator_dur_loss=1.763, generator_adv_loss=1.971, generator_feat_match_loss=5.491, over 6984.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, +2024-03-14 18:34:12,828 INFO [train.py:919] (5/6) Start epoch 647 +2024-03-14 18:36:49,242 INFO [train.py:527] (5/6) Epoch 647, batch 46, global_batch_idx: 80150, batch size: 55, loss[discriminator_loss=2.623, discriminator_real_loss=1.366, discriminator_fake_loss=1.257, generator_loss=29.86, generator_mel_loss=18.33, generator_kl_loss=1.56, generator_dur_loss=1.705, generator_adv_loss=2.161, generator_feat_match_loss=6.099, over 55.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=28.73, generator_mel_loss=17.99, generator_kl_loss=1.472, generator_dur_loss=1.739, generator_adv_loss=1.973, generator_feat_match_loss=5.556, over 2545.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:39:08,779 INFO [train.py:527] (5/6) Epoch 647, batch 96, global_batch_idx: 80200, batch size: 47, loss[discriminator_loss=2.681, discriminator_real_loss=1.27, discriminator_fake_loss=1.411, generator_loss=30.22, generator_mel_loss=18.08, generator_kl_loss=1.65, generator_dur_loss=1.645, generator_adv_loss=2.07, generator_feat_match_loss=6.777, over 47.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.66, generator_mel_loss=17.98, generator_kl_loss=1.44, generator_dur_loss=1.748, generator_adv_loss=1.97, generator_feat_match_loss=5.515, over 5422.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:39:08,781 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 18:39:17,848 INFO [train.py:591] (5/6) 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 18:39:17,849 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 18:40:35,281 INFO [train.py:919] (5/6) Start epoch 648 +2024-03-14 18:42:03,870 INFO [train.py:527] (5/6) Epoch 648, batch 22, global_batch_idx: 80250, batch size: 53, loss[discriminator_loss=2.637, discriminator_real_loss=1.297, discriminator_fake_loss=1.34, generator_loss=29.16, generator_mel_loss=18.12, generator_kl_loss=1.295, generator_dur_loss=1.688, generator_adv_loss=2.066, generator_feat_match_loss=5.995, over 53.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.358, discriminator_fake_loss=1.338, generator_loss=28.43, generator_mel_loss=17.95, generator_kl_loss=1.404, generator_dur_loss=1.736, generator_adv_loss=1.967, generator_feat_match_loss=5.373, over 1231.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:44:22,139 INFO [train.py:527] (5/6) Epoch 648, batch 72, global_batch_idx: 80300, batch size: 39, loss[discriminator_loss=2.734, discriminator_real_loss=1.368, discriminator_fake_loss=1.366, generator_loss=29.29, generator_mel_loss=18.25, generator_kl_loss=1.517, generator_dur_loss=1.731, generator_adv_loss=1.91, generator_feat_match_loss=5.886, over 39.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=28.51, generator_mel_loss=17.97, generator_kl_loss=1.419, generator_dur_loss=1.73, generator_adv_loss=1.979, generator_feat_match_loss=5.411, over 3743.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:46:42,934 INFO [train.py:527] (5/6) Epoch 648, batch 122, global_batch_idx: 80350, batch size: 31, loss[discriminator_loss=2.729, discriminator_real_loss=1.464, discriminator_fake_loss=1.265, generator_loss=28.65, generator_mel_loss=17.87, generator_kl_loss=1.635, generator_dur_loss=1.589, generator_adv_loss=1.91, generator_feat_match_loss=5.647, over 31.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.371, discriminator_fake_loss=1.337, generator_loss=28.54, generator_mel_loss=17.95, generator_kl_loss=1.417, generator_dur_loss=1.744, generator_adv_loss=1.983, generator_feat_match_loss=5.444, over 6651.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:46:48,470 INFO [train.py:919] (5/6) Start epoch 649 +2024-03-14 18:49:23,133 INFO [train.py:527] (5/6) Epoch 649, batch 48, global_batch_idx: 80400, batch size: 44, loss[discriminator_loss=2.695, discriminator_real_loss=1.402, discriminator_fake_loss=1.293, generator_loss=28.36, generator_mel_loss=18.02, generator_kl_loss=1.443, generator_dur_loss=1.637, generator_adv_loss=1.989, generator_feat_match_loss=5.274, over 44.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.356, discriminator_fake_loss=1.343, generator_loss=28.7, generator_mel_loss=17.97, generator_kl_loss=1.404, generator_dur_loss=1.759, generator_adv_loss=1.973, generator_feat_match_loss=5.596, over 2863.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:49:23,135 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 18:49:31,230 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 18:51:51,891 INFO [train.py:527] (5/6) Epoch 649, batch 98, global_batch_idx: 80450, batch size: 80, loss[discriminator_loss=2.702, discriminator_real_loss=1.383, discriminator_fake_loss=1.319, generator_loss=28.28, generator_mel_loss=17.86, generator_kl_loss=1.345, generator_dur_loss=1.788, generator_adv_loss=1.951, generator_feat_match_loss=5.338, over 80.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.353, discriminator_fake_loss=1.337, generator_loss=28.67, generator_mel_loss=17.94, generator_kl_loss=1.41, generator_dur_loss=1.762, generator_adv_loss=1.991, generator_feat_match_loss=5.571, over 5911.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:53:00,393 INFO [train.py:919] (5/6) Start epoch 650 +2024-03-14 18:54:29,804 INFO [train.py:527] (5/6) Epoch 650, batch 24, global_batch_idx: 80500, batch size: 36, loss[discriminator_loss=2.68, discriminator_real_loss=1.267, discriminator_fake_loss=1.414, generator_loss=29.06, generator_mel_loss=17.69, generator_kl_loss=1.489, generator_dur_loss=1.691, generator_adv_loss=1.925, generator_feat_match_loss=6.266, over 36.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.357, discriminator_fake_loss=1.338, generator_loss=28.71, generator_mel_loss=18.01, generator_kl_loss=1.433, generator_dur_loss=1.744, generator_adv_loss=1.97, generator_feat_match_loss=5.544, over 1327.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:56:49,017 INFO [train.py:527] (5/6) Epoch 650, batch 74, global_batch_idx: 80550, batch size: 53, loss[discriminator_loss=2.745, discriminator_real_loss=1.487, discriminator_fake_loss=1.258, generator_loss=28.74, generator_mel_loss=18.15, generator_kl_loss=1.518, generator_dur_loss=1.693, generator_adv_loss=1.954, generator_feat_match_loss=5.432, over 53.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.361, discriminator_fake_loss=1.325, generator_loss=28.69, generator_mel_loss=18, generator_kl_loss=1.434, generator_dur_loss=1.742, generator_adv_loss=1.978, generator_feat_match_loss=5.533, over 4030.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:59:05,770 INFO [train.py:919] (5/6) Start epoch 651 +2024-03-14 18:59:29,152 INFO [train.py:527] (5/6) Epoch 651, batch 0, global_batch_idx: 80600, batch size: 61, loss[discriminator_loss=2.668, discriminator_real_loss=1.46, discriminator_fake_loss=1.208, generator_loss=28.85, generator_mel_loss=18.19, generator_kl_loss=1.395, generator_dur_loss=1.768, generator_adv_loss=2.013, generator_feat_match_loss=5.478, over 61.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.46, discriminator_fake_loss=1.208, generator_loss=28.85, generator_mel_loss=18.19, generator_kl_loss=1.395, generator_dur_loss=1.768, generator_adv_loss=2.013, generator_feat_match_loss=5.478, over 61.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 18:59:29,154 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 18:59:36,909 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 19:01:55,833 INFO [train.py:527] (5/6) Epoch 651, batch 50, global_batch_idx: 80650, batch size: 55, loss[discriminator_loss=2.686, discriminator_real_loss=1.315, discriminator_fake_loss=1.371, generator_loss=29.28, generator_mel_loss=17.92, generator_kl_loss=1.421, generator_dur_loss=1.751, generator_adv_loss=1.972, generator_feat_match_loss=6.215, over 55.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.353, discriminator_fake_loss=1.343, generator_loss=28.77, generator_mel_loss=18.01, generator_kl_loss=1.417, generator_dur_loss=1.744, generator_adv_loss=1.976, generator_feat_match_loss=5.616, over 2783.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:04:14,993 INFO [train.py:527] (5/6) Epoch 651, batch 100, global_batch_idx: 80700, batch size: 64, loss[discriminator_loss=2.714, discriminator_real_loss=1.385, discriminator_fake_loss=1.328, generator_loss=29.06, generator_mel_loss=18.09, generator_kl_loss=1.312, generator_dur_loss=1.766, generator_adv_loss=2.039, generator_feat_match_loss=5.849, over 64.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.357, discriminator_fake_loss=1.334, generator_loss=28.76, generator_mel_loss=17.98, generator_kl_loss=1.408, generator_dur_loss=1.751, generator_adv_loss=1.984, generator_feat_match_loss=5.629, over 5711.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:05:19,545 INFO [train.py:919] (5/6) Start epoch 652 +2024-03-14 19:06:57,752 INFO [train.py:527] (5/6) Epoch 652, batch 26, global_batch_idx: 80750, batch size: 55, loss[discriminator_loss=2.659, discriminator_real_loss=1.327, discriminator_fake_loss=1.332, generator_loss=29.19, generator_mel_loss=18.16, generator_kl_loss=1.573, generator_dur_loss=1.755, generator_adv_loss=1.996, generator_feat_match_loss=5.706, over 55.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.359, discriminator_fake_loss=1.321, generator_loss=28.89, generator_mel_loss=18.06, generator_kl_loss=1.452, generator_dur_loss=1.722, generator_adv_loss=1.993, generator_feat_match_loss=5.667, over 1328.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:09:15,663 INFO [train.py:527] (5/6) Epoch 652, batch 76, global_batch_idx: 80800, batch size: 52, loss[discriminator_loss=2.732, discriminator_real_loss=1.432, discriminator_fake_loss=1.301, generator_loss=29.11, generator_mel_loss=18.46, generator_kl_loss=1.51, generator_dur_loss=1.69, generator_adv_loss=1.893, generator_feat_match_loss=5.55, over 52.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.362, discriminator_fake_loss=1.329, generator_loss=28.71, generator_mel_loss=18.01, generator_kl_loss=1.428, generator_dur_loss=1.748, generator_adv_loss=1.993, generator_feat_match_loss=5.536, over 4135.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:09:15,664 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 19:09:24,373 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 19:11:35,418 INFO [train.py:919] (5/6) Start epoch 653 +2024-03-14 19:12:04,919 INFO [train.py:527] (5/6) Epoch 653, batch 2, global_batch_idx: 80850, batch size: 83, loss[discriminator_loss=2.675, discriminator_real_loss=1.317, discriminator_fake_loss=1.358, generator_loss=28.47, generator_mel_loss=18.03, generator_kl_loss=1.305, generator_dur_loss=1.852, generator_adv_loss=2.004, generator_feat_match_loss=5.276, over 83.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.336, discriminator_fake_loss=1.369, generator_loss=28.64, generator_mel_loss=18.1, generator_kl_loss=1.351, generator_dur_loss=1.79, generator_adv_loss=1.956, generator_feat_match_loss=5.448, over 192.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:14:23,231 INFO [train.py:527] (5/6) Epoch 653, batch 52, global_batch_idx: 80900, batch size: 39, loss[discriminator_loss=2.665, discriminator_real_loss=1.381, discriminator_fake_loss=1.284, generator_loss=27.69, generator_mel_loss=17.28, generator_kl_loss=1.509, generator_dur_loss=1.705, generator_adv_loss=2.033, generator_feat_match_loss=5.159, over 39.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.358, discriminator_fake_loss=1.335, generator_loss=28.85, generator_mel_loss=18.02, generator_kl_loss=1.407, generator_dur_loss=1.766, generator_adv_loss=1.988, generator_feat_match_loss=5.672, over 3224.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:16:43,662 INFO [train.py:527] (5/6) Epoch 653, batch 102, global_batch_idx: 80950, batch size: 31, loss[discriminator_loss=2.66, discriminator_real_loss=1.26, discriminator_fake_loss=1.4, generator_loss=29.7, generator_mel_loss=18.64, generator_kl_loss=1.526, generator_dur_loss=1.637, generator_adv_loss=2.116, generator_feat_match_loss=5.777, over 31.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.78, generator_mel_loss=17.98, generator_kl_loss=1.416, generator_dur_loss=1.761, generator_adv_loss=1.996, generator_feat_match_loss=5.625, over 6022.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:17:43,554 INFO [train.py:919] (5/6) Start epoch 654 +2024-03-14 19:19:27,168 INFO [train.py:527] (5/6) Epoch 654, batch 28, global_batch_idx: 81000, batch size: 56, loss[discriminator_loss=2.674, discriminator_real_loss=1.316, discriminator_fake_loss=1.358, generator_loss=29.24, generator_mel_loss=18.09, generator_kl_loss=1.409, generator_dur_loss=1.712, generator_adv_loss=2.055, generator_feat_match_loss=5.975, over 56.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.355, discriminator_fake_loss=1.332, generator_loss=29.02, generator_mel_loss=18, generator_kl_loss=1.491, generator_dur_loss=1.744, generator_adv_loss=1.99, generator_feat_match_loss=5.788, over 1518.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:19:27,169 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 19:19:35,256 INFO [train.py:591] (5/6) 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 19:19:35,257 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 19:21:51,344 INFO [train.py:527] (5/6) Epoch 654, batch 78, global_batch_idx: 81050, batch size: 61, loss[discriminator_loss=2.675, discriminator_real_loss=1.362, discriminator_fake_loss=1.313, generator_loss=28.97, generator_mel_loss=18.17, generator_kl_loss=1.584, generator_dur_loss=1.757, generator_adv_loss=1.916, generator_feat_match_loss=5.551, over 61.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.358, discriminator_fake_loss=1.33, generator_loss=28.76, generator_mel_loss=17.95, generator_kl_loss=1.443, generator_dur_loss=1.74, generator_adv_loss=1.992, generator_feat_match_loss=5.638, over 4221.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:24:00,159 INFO [train.py:919] (5/6) Start epoch 655 +2024-03-14 19:24:35,460 INFO [train.py:527] (5/6) Epoch 655, batch 4, global_batch_idx: 81100, batch size: 74, loss[discriminator_loss=2.726, discriminator_real_loss=1.255, discriminator_fake_loss=1.472, generator_loss=30.53, generator_mel_loss=18.45, generator_kl_loss=1.493, generator_dur_loss=1.789, generator_adv_loss=2.139, generator_feat_match_loss=6.666, over 74.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.36, discriminator_fake_loss=1.359, generator_loss=29.39, generator_mel_loss=18.15, generator_kl_loss=1.48, generator_dur_loss=1.752, generator_adv_loss=2.039, generator_feat_match_loss=5.971, over 285.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:26:53,571 INFO [train.py:527] (5/6) Epoch 655, batch 54, global_batch_idx: 81150, batch size: 52, loss[discriminator_loss=2.663, discriminator_real_loss=1.342, discriminator_fake_loss=1.32, generator_loss=29.15, generator_mel_loss=18.43, generator_kl_loss=1.417, generator_dur_loss=1.675, generator_adv_loss=2.003, generator_feat_match_loss=5.627, over 52.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=28.81, generator_mel_loss=17.99, generator_kl_loss=1.43, generator_dur_loss=1.723, generator_adv_loss=2.026, generator_feat_match_loss=5.64, over 2878.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:29:13,869 INFO [train.py:527] (5/6) Epoch 655, batch 104, global_batch_idx: 81200, batch size: 16, loss[discriminator_loss=2.658, discriminator_real_loss=1.325, discriminator_fake_loss=1.332, generator_loss=30.32, generator_mel_loss=18.41, generator_kl_loss=1.75, generator_dur_loss=1.596, generator_adv_loss=2.141, generator_feat_match_loss=6.425, over 16.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=28.81, generator_mel_loss=18.01, generator_kl_loss=1.426, generator_dur_loss=1.728, generator_adv_loss=2.004, generator_feat_match_loss=5.642, over 5665.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:29:13,870 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 19:29:22,650 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 19:30:14,630 INFO [train.py:919] (5/6) Start epoch 656 +2024-03-14 19:32:03,279 INFO [train.py:527] (5/6) Epoch 656, batch 30, global_batch_idx: 81250, batch size: 66, loss[discriminator_loss=2.675, discriminator_real_loss=1.383, discriminator_fake_loss=1.293, generator_loss=28.85, generator_mel_loss=18.06, generator_kl_loss=1.339, generator_dur_loss=1.759, generator_adv_loss=1.938, generator_feat_match_loss=5.756, over 66.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.373, discriminator_fake_loss=1.317, generator_loss=28.63, generator_mel_loss=17.97, generator_kl_loss=1.398, generator_dur_loss=1.747, generator_adv_loss=1.972, generator_feat_match_loss=5.548, over 1738.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:34:21,052 INFO [train.py:527] (5/6) Epoch 656, batch 80, global_batch_idx: 81300, batch size: 64, loss[discriminator_loss=2.71, discriminator_real_loss=1.366, discriminator_fake_loss=1.344, generator_loss=27.57, generator_mel_loss=17.43, generator_kl_loss=1.357, generator_dur_loss=1.781, generator_adv_loss=2.074, generator_feat_match_loss=4.921, over 64.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.365, discriminator_fake_loss=1.327, generator_loss=28.71, generator_mel_loss=17.98, generator_kl_loss=1.416, generator_dur_loss=1.752, generator_adv_loss=1.989, generator_feat_match_loss=5.573, over 4717.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:36:16,190 INFO [train.py:919] (5/6) Start epoch 657 +2024-03-14 19:36:56,976 INFO [train.py:527] (5/6) Epoch 657, batch 6, global_batch_idx: 81350, batch size: 52, loss[discriminator_loss=2.7, discriminator_real_loss=1.361, discriminator_fake_loss=1.339, generator_loss=28.13, generator_mel_loss=17.7, generator_kl_loss=1.279, generator_dur_loss=1.723, generator_adv_loss=1.917, generator_feat_match_loss=5.513, over 52.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.35, discriminator_fake_loss=1.347, generator_loss=28.56, generator_mel_loss=17.93, generator_kl_loss=1.421, generator_dur_loss=1.747, generator_adv_loss=1.952, generator_feat_match_loss=5.512, over 366.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:39:13,472 INFO [train.py:527] (5/6) Epoch 657, batch 56, global_batch_idx: 81400, batch size: 80, loss[discriminator_loss=2.701, discriminator_real_loss=1.292, discriminator_fake_loss=1.409, generator_loss=27.99, generator_mel_loss=17.5, generator_kl_loss=1.375, generator_dur_loss=1.8, generator_adv_loss=2.073, generator_feat_match_loss=5.248, over 80.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=28.6, generator_mel_loss=17.92, generator_kl_loss=1.406, generator_dur_loss=1.739, generator_adv_loss=1.974, generator_feat_match_loss=5.555, over 3128.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:39:13,473 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 19:39:21,547 INFO [train.py:591] (5/6) 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 19:39:21,548 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 19:41:38,991 INFO [train.py:527] (5/6) Epoch 657, batch 106, global_batch_idx: 81450, batch size: 64, loss[discriminator_loss=2.638, discriminator_real_loss=1.312, discriminator_fake_loss=1.326, generator_loss=29.38, generator_mel_loss=18.01, generator_kl_loss=1.223, generator_dur_loss=1.777, generator_adv_loss=1.991, generator_feat_match_loss=6.374, over 64.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.361, discriminator_fake_loss=1.331, generator_loss=28.68, generator_mel_loss=17.98, generator_kl_loss=1.411, generator_dur_loss=1.741, generator_adv_loss=1.976, generator_feat_match_loss=5.567, over 5925.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:42:29,916 INFO [train.py:919] (5/6) Start epoch 658 +2024-03-14 19:44:21,189 INFO [train.py:527] (5/6) Epoch 658, batch 32, global_batch_idx: 81500, batch size: 61, loss[discriminator_loss=2.616, discriminator_real_loss=1.332, discriminator_fake_loss=1.284, generator_loss=29.24, generator_mel_loss=18.06, generator_kl_loss=1.364, generator_dur_loss=1.705, generator_adv_loss=2.003, generator_feat_match_loss=6.111, over 61.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.354, discriminator_fake_loss=1.331, generator_loss=28.67, generator_mel_loss=17.94, generator_kl_loss=1.41, generator_dur_loss=1.748, generator_adv_loss=1.974, generator_feat_match_loss=5.59, over 1868.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:46:41,868 INFO [train.py:527] (5/6) Epoch 658, batch 82, global_batch_idx: 81550, batch size: 61, loss[discriminator_loss=2.707, discriminator_real_loss=1.391, discriminator_fake_loss=1.316, generator_loss=29.14, generator_mel_loss=18.35, generator_kl_loss=1.367, generator_dur_loss=1.78, generator_adv_loss=2.118, generator_feat_match_loss=5.523, over 61.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.361, discriminator_fake_loss=1.339, generator_loss=28.67, generator_mel_loss=17.96, generator_kl_loss=1.384, generator_dur_loss=1.757, generator_adv_loss=1.99, generator_feat_match_loss=5.57, over 4920.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:48:37,706 INFO [train.py:919] (5/6) Start epoch 659 +2024-03-14 19:49:23,443 INFO [train.py:527] (5/6) Epoch 659, batch 8, global_batch_idx: 81600, batch size: 50, loss[discriminator_loss=2.729, discriminator_real_loss=1.33, discriminator_fake_loss=1.399, generator_loss=27.2, generator_mel_loss=17.2, generator_kl_loss=1.465, generator_dur_loss=1.663, generator_adv_loss=2.029, generator_feat_match_loss=4.841, over 50.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.345, discriminator_fake_loss=1.333, generator_loss=28.51, generator_mel_loss=17.94, generator_kl_loss=1.425, generator_dur_loss=1.734, generator_adv_loss=1.988, generator_feat_match_loss=5.426, over 499.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:49:23,446 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 19:49:31,216 INFO [train.py:591] (5/6) 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 19:49:31,218 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 19:51:49,913 INFO [train.py:527] (5/6) Epoch 659, batch 58, global_batch_idx: 81650, batch size: 56, loss[discriminator_loss=2.682, discriminator_real_loss=1.418, discriminator_fake_loss=1.265, generator_loss=28.72, generator_mel_loss=17.85, generator_kl_loss=1.475, generator_dur_loss=1.684, generator_adv_loss=1.857, generator_feat_match_loss=5.856, over 56.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.361, discriminator_fake_loss=1.325, generator_loss=28.66, generator_mel_loss=17.92, generator_kl_loss=1.392, generator_dur_loss=1.761, generator_adv_loss=1.982, generator_feat_match_loss=5.605, over 3760.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:54:08,830 INFO [train.py:527] (5/6) Epoch 659, batch 108, global_batch_idx: 81700, batch size: 66, loss[discriminator_loss=2.647, discriminator_real_loss=1.349, discriminator_fake_loss=1.297, generator_loss=28.75, generator_mel_loss=17.81, generator_kl_loss=1.298, generator_dur_loss=1.79, generator_adv_loss=2.098, generator_feat_match_loss=5.763, over 66.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.365, discriminator_fake_loss=1.325, generator_loss=28.66, generator_mel_loss=17.94, generator_kl_loss=1.417, generator_dur_loss=1.746, generator_adv_loss=1.987, generator_feat_match_loss=5.572, over 6324.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:54:51,530 INFO [train.py:919] (5/6) Start epoch 660 +2024-03-14 19:56:52,243 INFO [train.py:527] (5/6) Epoch 660, batch 34, global_batch_idx: 81750, batch size: 72, loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.89, generator_mel_loss=17.7, generator_kl_loss=1.317, generator_dur_loss=1.793, generator_adv_loss=1.962, generator_feat_match_loss=6.125, over 72.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.363, discriminator_fake_loss=1.339, generator_loss=28.66, generator_mel_loss=17.98, generator_kl_loss=1.459, generator_dur_loss=1.753, generator_adv_loss=1.97, generator_feat_match_loss=5.501, over 1977.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:59:10,382 INFO [train.py:527] (5/6) Epoch 660, batch 84, global_batch_idx: 81800, batch size: 36, loss[discriminator_loss=2.721, discriminator_real_loss=1.403, discriminator_fake_loss=1.319, generator_loss=27.97, generator_mel_loss=18.08, generator_kl_loss=1.466, generator_dur_loss=1.633, generator_adv_loss=2.001, generator_feat_match_loss=4.79, over 36.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.369, discriminator_fake_loss=1.333, generator_loss=28.6, generator_mel_loss=17.97, generator_kl_loss=1.421, generator_dur_loss=1.748, generator_adv_loss=1.976, generator_feat_match_loss=5.481, over 4871.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 19:59:10,383 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 19:59:19,066 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 20:01:07,860 INFO [train.py:919] (5/6) Start epoch 661 +2024-03-14 20:02:02,455 INFO [train.py:527] (5/6) Epoch 661, batch 10, global_batch_idx: 81850, batch size: 72, loss[discriminator_loss=2.734, discriminator_real_loss=1.348, discriminator_fake_loss=1.385, generator_loss=28.87, generator_mel_loss=17.82, generator_kl_loss=1.336, generator_dur_loss=1.809, generator_adv_loss=1.983, generator_feat_match_loss=5.918, over 72.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.348, discriminator_fake_loss=1.346, generator_loss=28.82, generator_mel_loss=18.05, generator_kl_loss=1.431, generator_dur_loss=1.745, generator_adv_loss=1.966, generator_feat_match_loss=5.624, over 570.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:04:22,925 INFO [train.py:527] (5/6) Epoch 661, batch 60, global_batch_idx: 81900, batch size: 44, loss[discriminator_loss=2.703, discriminator_real_loss=1.32, discriminator_fake_loss=1.383, generator_loss=28.76, generator_mel_loss=18.27, generator_kl_loss=1.493, generator_dur_loss=1.635, generator_adv_loss=2.016, generator_feat_match_loss=5.34, over 44.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.363, discriminator_fake_loss=1.332, generator_loss=28.64, generator_mel_loss=17.97, generator_kl_loss=1.45, generator_dur_loss=1.73, generator_adv_loss=1.989, generator_feat_match_loss=5.505, over 3286.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:06:41,334 INFO [train.py:527] (5/6) Epoch 661, batch 110, global_batch_idx: 81950, batch size: 53, loss[discriminator_loss=2.672, discriminator_real_loss=1.315, discriminator_fake_loss=1.357, generator_loss=29.14, generator_mel_loss=18.32, generator_kl_loss=1.434, generator_dur_loss=1.676, generator_adv_loss=1.99, generator_feat_match_loss=5.714, over 53.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.36, discriminator_fake_loss=1.334, generator_loss=28.67, generator_mel_loss=17.99, generator_kl_loss=1.428, generator_dur_loss=1.729, generator_adv_loss=1.984, generator_feat_match_loss=5.545, over 6213.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:07:18,105 INFO [train.py:919] (5/6) Start epoch 662 +2024-03-14 20:09:23,454 INFO [train.py:527] (5/6) Epoch 662, batch 36, global_batch_idx: 82000, batch size: 74, loss[discriminator_loss=2.674, discriminator_real_loss=1.346, discriminator_fake_loss=1.329, generator_loss=27.39, generator_mel_loss=17.6, generator_kl_loss=1.317, generator_dur_loss=1.799, generator_adv_loss=1.863, generator_feat_match_loss=4.814, over 74.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.365, discriminator_fake_loss=1.337, generator_loss=28.62, generator_mel_loss=17.93, generator_kl_loss=1.41, generator_dur_loss=1.742, generator_adv_loss=2.007, generator_feat_match_loss=5.54, over 2179.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:09:23,456 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 20:09:31,284 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 20:11:49,876 INFO [train.py:527] (5/6) Epoch 662, batch 86, global_batch_idx: 82050, batch size: 68, loss[discriminator_loss=2.703, discriminator_real_loss=1.326, discriminator_fake_loss=1.377, generator_loss=29.08, generator_mel_loss=18.21, generator_kl_loss=1.491, generator_dur_loss=1.841, generator_adv_loss=1.989, generator_feat_match_loss=5.549, over 68.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.355, discriminator_fake_loss=1.344, generator_loss=28.7, generator_mel_loss=17.95, generator_kl_loss=1.416, generator_dur_loss=1.758, generator_adv_loss=1.989, generator_feat_match_loss=5.587, over 5246.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:13:30,427 INFO [train.py:919] (5/6) Start epoch 663 +2024-03-14 20:14:30,104 INFO [train.py:527] (5/6) Epoch 663, batch 12, global_batch_idx: 82100, batch size: 14, loss[discriminator_loss=2.738, discriminator_real_loss=1.406, discriminator_fake_loss=1.332, generator_loss=29.58, generator_mel_loss=18.17, generator_kl_loss=1.756, generator_dur_loss=1.603, generator_adv_loss=2.078, generator_feat_match_loss=5.973, over 14.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.37, discriminator_fake_loss=1.338, generator_loss=28.66, generator_mel_loss=18.05, generator_kl_loss=1.506, generator_dur_loss=1.715, generator_adv_loss=1.987, generator_feat_match_loss=5.405, over 610.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:16:47,574 INFO [train.py:527] (5/6) Epoch 663, batch 62, global_batch_idx: 82150, batch size: 48, loss[discriminator_loss=2.751, discriminator_real_loss=1.351, discriminator_fake_loss=1.401, generator_loss=29.31, generator_mel_loss=18.59, generator_kl_loss=1.566, generator_dur_loss=1.691, generator_adv_loss=1.915, generator_feat_match_loss=5.547, over 48.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=28.63, generator_mel_loss=17.98, generator_kl_loss=1.425, generator_dur_loss=1.757, generator_adv_loss=1.975, generator_feat_match_loss=5.497, over 3607.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:19:06,829 INFO [train.py:527] (5/6) Epoch 663, batch 112, global_batch_idx: 82200, batch size: 31, loss[discriminator_loss=2.588, discriminator_real_loss=1.368, discriminator_fake_loss=1.22, generator_loss=29.2, generator_mel_loss=18.64, generator_kl_loss=1.61, generator_dur_loss=1.61, generator_adv_loss=2.003, generator_feat_match_loss=5.338, over 31.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.363, discriminator_fake_loss=1.335, generator_loss=28.63, generator_mel_loss=17.96, generator_kl_loss=1.41, generator_dur_loss=1.754, generator_adv_loss=1.979, generator_feat_match_loss=5.521, over 6505.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:19:06,831 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 20:19:15,746 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 20:19:44,638 INFO [train.py:919] (5/6) Start epoch 664 +2024-03-14 20:21:53,614 INFO [train.py:527] (5/6) Epoch 664, batch 38, global_batch_idx: 82250, batch size: 66, loss[discriminator_loss=2.734, discriminator_real_loss=1.373, discriminator_fake_loss=1.361, generator_loss=28.8, generator_mel_loss=17.93, generator_kl_loss=1.477, generator_dur_loss=1.769, generator_adv_loss=1.993, generator_feat_match_loss=5.628, over 66.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.366, discriminator_fake_loss=1.331, generator_loss=28.59, generator_mel_loss=17.99, generator_kl_loss=1.426, generator_dur_loss=1.745, generator_adv_loss=1.974, generator_feat_match_loss=5.457, over 2218.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:24:12,415 INFO [train.py:527] (5/6) Epoch 664, batch 88, global_batch_idx: 82300, batch size: 53, loss[discriminator_loss=2.716, discriminator_real_loss=1.345, discriminator_fake_loss=1.371, generator_loss=28.63, generator_mel_loss=17.62, generator_kl_loss=1.603, generator_dur_loss=1.61, generator_adv_loss=1.872, generator_feat_match_loss=5.918, over 53.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.6, generator_mel_loss=17.94, generator_kl_loss=1.428, generator_dur_loss=1.755, generator_adv_loss=1.976, generator_feat_match_loss=5.498, over 5136.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:25:51,244 INFO [train.py:919] (5/6) Start epoch 665 +2024-03-14 20:26:54,132 INFO [train.py:527] (5/6) Epoch 665, batch 14, global_batch_idx: 82350, batch size: 68, loss[discriminator_loss=2.786, discriminator_real_loss=1.342, discriminator_fake_loss=1.445, generator_loss=29.78, generator_mel_loss=18.37, generator_kl_loss=1.525, generator_dur_loss=1.774, generator_adv_loss=2.249, generator_feat_match_loss=5.869, over 68.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.346, discriminator_fake_loss=1.355, generator_loss=28.35, generator_mel_loss=17.81, generator_kl_loss=1.432, generator_dur_loss=1.76, generator_adv_loss=1.994, generator_feat_match_loss=5.359, over 852.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:29:13,266 INFO [train.py:527] (5/6) Epoch 665, batch 64, global_batch_idx: 82400, batch size: 70, loss[discriminator_loss=2.719, discriminator_real_loss=1.414, discriminator_fake_loss=1.305, generator_loss=28.94, generator_mel_loss=17.95, generator_kl_loss=1.453, generator_dur_loss=1.776, generator_adv_loss=1.829, generator_feat_match_loss=5.928, over 70.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.367, discriminator_fake_loss=1.329, generator_loss=28.59, generator_mel_loss=17.88, generator_kl_loss=1.447, generator_dur_loss=1.74, generator_adv_loss=1.995, generator_feat_match_loss=5.523, over 3658.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:29:13,268 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 20:29:21,240 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 20:31:40,178 INFO [train.py:527] (5/6) Epoch 665, batch 114, global_batch_idx: 82450, batch size: 42, loss[discriminator_loss=2.631, discriminator_real_loss=1.385, discriminator_fake_loss=1.246, generator_loss=29.19, generator_mel_loss=18.17, generator_kl_loss=1.524, generator_dur_loss=1.642, generator_adv_loss=2.029, generator_feat_match_loss=5.824, over 42.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.366, discriminator_fake_loss=1.327, generator_loss=28.63, generator_mel_loss=17.9, generator_kl_loss=1.452, generator_dur_loss=1.742, generator_adv_loss=1.993, generator_feat_match_loss=5.549, over 6475.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:32:03,324 INFO [train.py:919] (5/6) Start epoch 666 +2024-03-14 20:34:16,531 INFO [train.py:527] (5/6) Epoch 666, batch 40, global_batch_idx: 82500, batch size: 96, loss[discriminator_loss=2.706, discriminator_real_loss=1.405, discriminator_fake_loss=1.301, generator_loss=28.24, generator_mel_loss=17.62, generator_kl_loss=1.296, generator_dur_loss=1.841, generator_adv_loss=1.914, generator_feat_match_loss=5.574, over 96.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.372, discriminator_fake_loss=1.332, generator_loss=28.52, generator_mel_loss=17.9, generator_kl_loss=1.435, generator_dur_loss=1.74, generator_adv_loss=1.979, generator_feat_match_loss=5.469, over 2265.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:36:37,910 INFO [train.py:527] (5/6) Epoch 666, batch 90, global_batch_idx: 82550, batch size: 31, loss[discriminator_loss=2.764, discriminator_real_loss=1.418, discriminator_fake_loss=1.346, generator_loss=28.78, generator_mel_loss=18.67, generator_kl_loss=1.599, generator_dur_loss=1.61, generator_adv_loss=1.956, generator_feat_match_loss=4.95, over 31.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.368, discriminator_fake_loss=1.336, generator_loss=28.56, generator_mel_loss=17.95, generator_kl_loss=1.416, generator_dur_loss=1.741, generator_adv_loss=1.974, generator_feat_match_loss=5.48, over 5083.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:38:13,040 INFO [train.py:919] (5/6) Start epoch 667 +2024-03-14 20:39:20,966 INFO [train.py:527] (5/6) Epoch 667, batch 16, global_batch_idx: 82600, batch size: 31, loss[discriminator_loss=2.723, discriminator_real_loss=1.362, discriminator_fake_loss=1.36, generator_loss=28.9, generator_mel_loss=18.2, generator_kl_loss=1.699, generator_dur_loss=1.651, generator_adv_loss=2.082, generator_feat_match_loss=5.275, over 31.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.361, discriminator_fake_loss=1.341, generator_loss=28.88, generator_mel_loss=18.04, generator_kl_loss=1.448, generator_dur_loss=1.753, generator_adv_loss=1.968, generator_feat_match_loss=5.667, over 900.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:39:20,967 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 20:39:28,899 INFO [train.py:591] (5/6) 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 20:39:28,900 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 20:41:48,964 INFO [train.py:527] (5/6) Epoch 667, batch 66, global_batch_idx: 82650, batch size: 80, loss[discriminator_loss=2.663, discriminator_real_loss=1.391, discriminator_fake_loss=1.272, generator_loss=28.93, generator_mel_loss=17.82, generator_kl_loss=1.306, generator_dur_loss=1.836, generator_adv_loss=1.955, generator_feat_match_loss=6.016, over 80.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=28.76, generator_mel_loss=17.94, generator_kl_loss=1.414, generator_dur_loss=1.751, generator_adv_loss=1.972, generator_feat_match_loss=5.679, over 3776.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:44:05,809 INFO [train.py:527] (5/6) Epoch 667, batch 116, global_batch_idx: 82700, batch size: 52, loss[discriminator_loss=2.669, discriminator_real_loss=1.287, discriminator_fake_loss=1.382, generator_loss=29.73, generator_mel_loss=18, generator_kl_loss=1.455, generator_dur_loss=1.7, generator_adv_loss=2.213, generator_feat_match_loss=6.364, over 52.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.359, discriminator_fake_loss=1.331, generator_loss=28.81, generator_mel_loss=17.98, generator_kl_loss=1.409, generator_dur_loss=1.755, generator_adv_loss=2.001, generator_feat_match_loss=5.669, over 6558.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:44:27,474 INFO [train.py:919] (5/6) Start epoch 668 +2024-03-14 20:46:49,155 INFO [train.py:527] (5/6) Epoch 668, batch 42, global_batch_idx: 82750, batch size: 66, loss[discriminator_loss=2.722, discriminator_real_loss=1.391, discriminator_fake_loss=1.332, generator_loss=29.03, generator_mel_loss=18.32, generator_kl_loss=1.314, generator_dur_loss=1.792, generator_adv_loss=2.026, generator_feat_match_loss=5.582, over 66.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.37, discriminator_fake_loss=1.316, generator_loss=28.48, generator_mel_loss=17.84, generator_kl_loss=1.385, generator_dur_loss=1.749, generator_adv_loss=1.984, generator_feat_match_loss=5.529, over 2581.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:49:06,913 INFO [train.py:527] (5/6) Epoch 668, batch 92, global_batch_idx: 82800, batch size: 83, loss[discriminator_loss=2.711, discriminator_real_loss=1.396, discriminator_fake_loss=1.315, generator_loss=28.32, generator_mel_loss=17.86, generator_kl_loss=1.437, generator_dur_loss=1.833, generator_adv_loss=1.891, generator_feat_match_loss=5.294, over 83.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.365, discriminator_fake_loss=1.324, generator_loss=28.53, generator_mel_loss=17.86, generator_kl_loss=1.402, generator_dur_loss=1.751, generator_adv_loss=1.979, generator_feat_match_loss=5.531, over 5584.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:49:06,915 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 20:49:15,917 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 20:50:40,367 INFO [train.py:919] (5/6) Start epoch 669 +2024-03-14 20:51:53,024 INFO [train.py:527] (5/6) Epoch 669, batch 18, global_batch_idx: 82850, batch size: 83, loss[discriminator_loss=2.671, discriminator_real_loss=1.304, discriminator_fake_loss=1.367, generator_loss=29.14, generator_mel_loss=18.09, generator_kl_loss=1.354, generator_dur_loss=1.837, generator_adv_loss=2.156, generator_feat_match_loss=5.701, over 83.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.366, discriminator_fake_loss=1.323, generator_loss=28.96, generator_mel_loss=18.04, generator_kl_loss=1.42, generator_dur_loss=1.766, generator_adv_loss=1.992, generator_feat_match_loss=5.748, over 1158.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:54:14,719 INFO [train.py:527] (5/6) Epoch 669, batch 68, global_batch_idx: 82900, batch size: 68, loss[discriminator_loss=2.687, discriminator_real_loss=1.399, discriminator_fake_loss=1.288, generator_loss=27.73, generator_mel_loss=17.47, generator_kl_loss=1.386, generator_dur_loss=1.791, generator_adv_loss=1.832, generator_feat_match_loss=5.254, over 68.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.362, discriminator_fake_loss=1.332, generator_loss=28.65, generator_mel_loss=17.92, generator_kl_loss=1.416, generator_dur_loss=1.759, generator_adv_loss=1.975, generator_feat_match_loss=5.578, over 4155.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:56:30,682 INFO [train.py:527] (5/6) Epoch 669, batch 118, global_batch_idx: 82950, batch size: 52, loss[discriminator_loss=2.647, discriminator_real_loss=1.327, discriminator_fake_loss=1.321, generator_loss=29.38, generator_mel_loss=18.28, generator_kl_loss=1.449, generator_dur_loss=1.659, generator_adv_loss=1.979, generator_feat_match_loss=6.01, over 52.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.364, discriminator_fake_loss=1.335, generator_loss=28.63, generator_mel_loss=17.93, generator_kl_loss=1.4, generator_dur_loss=1.762, generator_adv_loss=1.973, generator_feat_match_loss=5.565, over 7167.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:56:44,670 INFO [train.py:919] (5/6) Start epoch 670 +2024-03-14 20:59:08,301 INFO [train.py:527] (5/6) Epoch 670, batch 44, global_batch_idx: 83000, batch size: 72, loss[discriminator_loss=2.718, discriminator_real_loss=1.389, discriminator_fake_loss=1.329, generator_loss=27.87, generator_mel_loss=17.28, generator_kl_loss=1.302, generator_dur_loss=1.776, generator_adv_loss=1.984, generator_feat_match_loss=5.527, over 72.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.367, discriminator_fake_loss=1.325, generator_loss=28.78, generator_mel_loss=18.03, generator_kl_loss=1.433, generator_dur_loss=1.753, generator_adv_loss=1.994, generator_feat_match_loss=5.572, over 2415.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 20:59:08,303 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 20:59:16,319 INFO [train.py:591] (5/6) 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 20:59:16,320 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 21:01:37,693 INFO [train.py:527] (5/6) Epoch 670, batch 94, global_batch_idx: 83050, batch size: 80, loss[discriminator_loss=2.681, discriminator_real_loss=1.33, discriminator_fake_loss=1.351, generator_loss=28.31, generator_mel_loss=17.63, generator_kl_loss=1.247, generator_dur_loss=1.798, generator_adv_loss=2.043, generator_feat_match_loss=5.591, over 80.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.37, discriminator_fake_loss=1.325, generator_loss=28.66, generator_mel_loss=17.99, generator_kl_loss=1.41, generator_dur_loss=1.753, generator_adv_loss=1.994, generator_feat_match_loss=5.521, over 5242.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:02:57,006 INFO [train.py:919] (5/6) Start epoch 671 +2024-03-14 21:04:15,668 INFO [train.py:527] (5/6) Epoch 671, batch 20, global_batch_idx: 83100, batch size: 31, loss[discriminator_loss=2.745, discriminator_real_loss=1.48, discriminator_fake_loss=1.265, generator_loss=27.94, generator_mel_loss=17.75, generator_kl_loss=1.571, generator_dur_loss=1.648, generator_adv_loss=1.845, generator_feat_match_loss=5.12, over 31.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.35, discriminator_fake_loss=1.338, generator_loss=28.98, generator_mel_loss=18.09, generator_kl_loss=1.402, generator_dur_loss=1.735, generator_adv_loss=1.978, generator_feat_match_loss=5.773, over 1152.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:06:31,274 INFO [train.py:527] (5/6) Epoch 671, batch 70, global_batch_idx: 83150, batch size: 62, loss[discriminator_loss=2.688, discriminator_real_loss=1.328, discriminator_fake_loss=1.36, generator_loss=28.79, generator_mel_loss=17.99, generator_kl_loss=1.296, generator_dur_loss=1.731, generator_adv_loss=2.08, generator_feat_match_loss=5.701, over 62.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.357, discriminator_fake_loss=1.338, generator_loss=28.89, generator_mel_loss=18.03, generator_kl_loss=1.432, generator_dur_loss=1.72, generator_adv_loss=1.983, generator_feat_match_loss=5.727, over 3730.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:08:49,660 INFO [train.py:527] (5/6) Epoch 671, batch 120, global_batch_idx: 83200, batch size: 31, loss[discriminator_loss=2.7, discriminator_real_loss=1.337, discriminator_fake_loss=1.363, generator_loss=28.76, generator_mel_loss=17.71, generator_kl_loss=1.533, generator_dur_loss=1.609, generator_adv_loss=2.16, generator_feat_match_loss=5.743, over 31.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.358, discriminator_fake_loss=1.334, generator_loss=28.81, generator_mel_loss=18, generator_kl_loss=1.421, generator_dur_loss=1.734, generator_adv_loss=1.98, generator_feat_match_loss=5.681, over 6751.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:08:49,661 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 21:08:58,668 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 21:09:09,393 INFO [train.py:919] (5/6) Start epoch 672 +2024-03-14 21:11:38,115 INFO [train.py:527] (5/6) Epoch 672, batch 46, global_batch_idx: 83250, batch size: 80, loss[discriminator_loss=2.721, discriminator_real_loss=1.306, discriminator_fake_loss=1.414, generator_loss=29.27, generator_mel_loss=17.96, generator_kl_loss=1.298, generator_dur_loss=1.777, generator_adv_loss=1.964, generator_feat_match_loss=6.27, over 80.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.366, discriminator_fake_loss=1.334, generator_loss=28.8, generator_mel_loss=18, generator_kl_loss=1.426, generator_dur_loss=1.735, generator_adv_loss=1.98, generator_feat_match_loss=5.652, over 2527.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:13:55,317 INFO [train.py:527] (5/6) Epoch 672, batch 96, global_batch_idx: 83300, batch size: 59, loss[discriminator_loss=2.711, discriminator_real_loss=1.377, discriminator_fake_loss=1.334, generator_loss=28.29, generator_mel_loss=17.75, generator_kl_loss=1.332, generator_dur_loss=1.693, generator_adv_loss=1.907, generator_feat_match_loss=5.605, over 59.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.369, discriminator_fake_loss=1.331, generator_loss=28.78, generator_mel_loss=18.01, generator_kl_loss=1.432, generator_dur_loss=1.731, generator_adv_loss=1.981, generator_feat_match_loss=5.623, over 5136.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:15:13,836 INFO [train.py:919] (5/6) Start epoch 673 +2024-03-14 21:16:38,386 INFO [train.py:527] (5/6) Epoch 673, batch 22, global_batch_idx: 83350, batch size: 88, loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=28.36, generator_mel_loss=17.84, generator_kl_loss=1.252, generator_dur_loss=1.844, generator_adv_loss=1.985, generator_feat_match_loss=5.432, over 88.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.363, discriminator_fake_loss=1.325, generator_loss=28.63, generator_mel_loss=17.92, generator_kl_loss=1.376, generator_dur_loss=1.759, generator_adv_loss=2.03, generator_feat_match_loss=5.553, over 1388.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:18:57,239 INFO [train.py:527] (5/6) Epoch 673, batch 72, global_batch_idx: 83400, batch size: 59, loss[discriminator_loss=2.791, discriminator_real_loss=1.316, discriminator_fake_loss=1.474, generator_loss=28.48, generator_mel_loss=17.63, generator_kl_loss=1.484, generator_dur_loss=1.671, generator_adv_loss=2.053, generator_feat_match_loss=5.643, over 59.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.368, discriminator_fake_loss=1.325, generator_loss=28.59, generator_mel_loss=17.91, generator_kl_loss=1.401, generator_dur_loss=1.746, generator_adv_loss=1.992, generator_feat_match_loss=5.547, over 4267.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:18:57,241 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 21:19:06,066 INFO [train.py:591] (5/6) 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 21:19:06,068 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 21:21:22,857 INFO [train.py:527] (5/6) Epoch 673, batch 122, global_batch_idx: 83450, batch size: 83, loss[discriminator_loss=2.644, discriminator_real_loss=1.336, discriminator_fake_loss=1.308, generator_loss=28.56, generator_mel_loss=17.88, generator_kl_loss=1.493, generator_dur_loss=1.773, generator_adv_loss=1.911, generator_feat_match_loss=5.507, over 83.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.37, discriminator_fake_loss=1.328, generator_loss=28.55, generator_mel_loss=17.93, generator_kl_loss=1.398, generator_dur_loss=1.758, generator_adv_loss=1.981, generator_feat_match_loss=5.487, over 7437.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:21:27,730 INFO [train.py:919] (5/6) Start epoch 674 +2024-03-14 21:24:02,509 INFO [train.py:527] (5/6) Epoch 674, batch 48, global_batch_idx: 83500, batch size: 52, loss[discriminator_loss=2.672, discriminator_real_loss=1.38, discriminator_fake_loss=1.293, generator_loss=29.72, generator_mel_loss=18.24, generator_kl_loss=1.516, generator_dur_loss=1.65, generator_adv_loss=1.986, generator_feat_match_loss=6.332, over 52.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.371, discriminator_fake_loss=1.327, generator_loss=28.7, generator_mel_loss=18.01, generator_kl_loss=1.45, generator_dur_loss=1.732, generator_adv_loss=1.975, generator_feat_match_loss=5.529, over 2690.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:26:21,393 INFO [train.py:527] (5/6) Epoch 674, batch 98, global_batch_idx: 83550, batch size: 88, loss[discriminator_loss=2.741, discriminator_real_loss=1.338, discriminator_fake_loss=1.404, generator_loss=28.78, generator_mel_loss=18.03, generator_kl_loss=1.393, generator_dur_loss=1.807, generator_adv_loss=2.055, generator_feat_match_loss=5.494, over 88.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.37, discriminator_fake_loss=1.327, generator_loss=28.73, generator_mel_loss=18.01, generator_kl_loss=1.43, generator_dur_loss=1.731, generator_adv_loss=1.981, generator_feat_match_loss=5.578, over 5475.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:27:34,276 INFO [train.py:919] (5/6) Start epoch 675 +2024-03-14 21:29:05,992 INFO [train.py:527] (5/6) Epoch 675, batch 24, global_batch_idx: 83600, batch size: 52, loss[discriminator_loss=2.697, discriminator_real_loss=1.404, discriminator_fake_loss=1.293, generator_loss=29.85, generator_mel_loss=18.22, generator_kl_loss=1.483, generator_dur_loss=1.681, generator_adv_loss=1.958, generator_feat_match_loss=6.512, over 52.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.359, discriminator_fake_loss=1.324, generator_loss=28.65, generator_mel_loss=17.95, generator_kl_loss=1.445, generator_dur_loss=1.728, generator_adv_loss=1.993, generator_feat_match_loss=5.53, over 1340.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:29:05,994 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 21:29:14,044 INFO [train.py:591] (5/6) 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 21:29:14,045 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 21:31:34,597 INFO [train.py:527] (5/6) Epoch 675, batch 74, global_batch_idx: 83650, batch size: 77, loss[discriminator_loss=2.706, discriminator_real_loss=1.341, discriminator_fake_loss=1.365, generator_loss=28.28, generator_mel_loss=17.8, generator_kl_loss=1.285, generator_dur_loss=1.815, generator_adv_loss=2.037, generator_feat_match_loss=5.347, over 77.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.361, discriminator_fake_loss=1.326, generator_loss=28.59, generator_mel_loss=17.9, generator_kl_loss=1.417, generator_dur_loss=1.741, generator_adv_loss=1.982, generator_feat_match_loss=5.546, over 4165.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:33:50,925 INFO [train.py:919] (5/6) Start epoch 676 +2024-03-14 21:34:15,358 INFO [train.py:527] (5/6) Epoch 676, batch 0, global_batch_idx: 83700, batch size: 39, loss[discriminator_loss=2.708, discriminator_real_loss=1.387, discriminator_fake_loss=1.321, generator_loss=28.97, generator_mel_loss=18.23, generator_kl_loss=1.412, generator_dur_loss=1.716, generator_adv_loss=1.921, generator_feat_match_loss=5.684, over 39.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.387, discriminator_fake_loss=1.321, generator_loss=28.97, generator_mel_loss=18.23, generator_kl_loss=1.412, generator_dur_loss=1.716, generator_adv_loss=1.921, generator_feat_match_loss=5.684, over 39.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:36:34,358 INFO [train.py:527] (5/6) Epoch 676, batch 50, global_batch_idx: 83750, batch size: 47, loss[discriminator_loss=2.672, discriminator_real_loss=1.35, discriminator_fake_loss=1.323, generator_loss=28.87, generator_mel_loss=17.86, generator_kl_loss=1.601, generator_dur_loss=1.71, generator_adv_loss=2.094, generator_feat_match_loss=5.599, over 47.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.338, discriminator_fake_loss=1.335, generator_loss=28.86, generator_mel_loss=17.96, generator_kl_loss=1.424, generator_dur_loss=1.735, generator_adv_loss=2.049, generator_feat_match_loss=5.698, over 2887.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:38:52,518 INFO [train.py:527] (5/6) Epoch 676, batch 100, global_batch_idx: 83800, batch size: 70, loss[discriminator_loss=2.724, discriminator_real_loss=1.355, discriminator_fake_loss=1.369, generator_loss=27.9, generator_mel_loss=17.87, generator_kl_loss=1.389, generator_dur_loss=1.715, generator_adv_loss=1.933, generator_feat_match_loss=4.997, over 70.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.351, discriminator_fake_loss=1.327, generator_loss=28.77, generator_mel_loss=17.94, generator_kl_loss=1.421, generator_dur_loss=1.737, generator_adv_loss=2.025, generator_feat_match_loss=5.651, over 5723.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:38:52,519 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 21:39:01,204 INFO [train.py:591] (5/6) 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 21:39:01,205 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 21:40:06,079 INFO [train.py:919] (5/6) Start epoch 677 +2024-03-14 21:41:40,149 INFO [train.py:527] (5/6) Epoch 677, batch 26, global_batch_idx: 83850, batch size: 68, loss[discriminator_loss=2.718, discriminator_real_loss=1.396, discriminator_fake_loss=1.322, generator_loss=28.52, generator_mel_loss=17.73, generator_kl_loss=1.534, generator_dur_loss=1.82, generator_adv_loss=1.933, generator_feat_match_loss=5.501, over 68.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.372, discriminator_fake_loss=1.332, generator_loss=28.48, generator_mel_loss=17.87, generator_kl_loss=1.388, generator_dur_loss=1.744, generator_adv_loss=1.975, generator_feat_match_loss=5.506, over 1604.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:43:57,736 INFO [train.py:527] (5/6) Epoch 677, batch 76, global_batch_idx: 83900, batch size: 50, loss[discriminator_loss=2.731, discriminator_real_loss=1.419, discriminator_fake_loss=1.312, generator_loss=29.7, generator_mel_loss=18.71, generator_kl_loss=1.369, generator_dur_loss=1.66, generator_adv_loss=2.018, generator_feat_match_loss=5.948, over 50.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.362, discriminator_fake_loss=1.324, generator_loss=28.65, generator_mel_loss=17.95, generator_kl_loss=1.414, generator_dur_loss=1.739, generator_adv_loss=1.989, generator_feat_match_loss=5.56, over 4325.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:46:09,433 INFO [train.py:919] (5/6) Start epoch 678 +2024-03-14 21:46:39,038 INFO [train.py:527] (5/6) Epoch 678, batch 2, global_batch_idx: 83950, batch size: 50, loss[discriminator_loss=2.769, discriminator_real_loss=1.295, discriminator_fake_loss=1.474, generator_loss=27.42, generator_mel_loss=18.06, generator_kl_loss=1.391, generator_dur_loss=1.717, generator_adv_loss=1.92, generator_feat_match_loss=4.329, over 50.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.4, discriminator_fake_loss=1.354, generator_loss=28.4, generator_mel_loss=18.07, generator_kl_loss=1.476, generator_dur_loss=1.712, generator_adv_loss=1.955, generator_feat_match_loss=5.19, over 173.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:48:57,996 INFO [train.py:527] (5/6) Epoch 678, batch 52, global_batch_idx: 84000, batch size: 42, loss[discriminator_loss=2.611, discriminator_real_loss=1.242, discriminator_fake_loss=1.369, generator_loss=29.54, generator_mel_loss=18.1, generator_kl_loss=1.615, generator_dur_loss=1.659, generator_adv_loss=2.205, generator_feat_match_loss=5.957, over 42.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.367, discriminator_fake_loss=1.331, generator_loss=28.68, generator_mel_loss=18.01, generator_kl_loss=1.42, generator_dur_loss=1.776, generator_adv_loss=1.974, generator_feat_match_loss=5.495, over 3167.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:48:57,998 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 21:49:05,962 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 21:51:21,575 INFO [train.py:527] (5/6) Epoch 678, batch 102, global_batch_idx: 84050, batch size: 56, loss[discriminator_loss=2.714, discriminator_real_loss=1.363, discriminator_fake_loss=1.351, generator_loss=29.25, generator_mel_loss=17.73, generator_kl_loss=1.498, generator_dur_loss=1.687, generator_adv_loss=2.027, generator_feat_match_loss=6.305, over 56.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.365, discriminator_fake_loss=1.332, generator_loss=28.61, generator_mel_loss=17.93, generator_kl_loss=1.416, generator_dur_loss=1.767, generator_adv_loss=1.975, generator_feat_match_loss=5.523, over 6111.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:52:21,772 INFO [train.py:919] (5/6) Start epoch 679 +2024-03-14 21:54:00,104 INFO [train.py:527] (5/6) Epoch 679, batch 28, global_batch_idx: 84100, batch size: 12, loss[discriminator_loss=2.658, discriminator_real_loss=1.326, discriminator_fake_loss=1.332, generator_loss=30.02, generator_mel_loss=19.02, generator_kl_loss=1.8, generator_dur_loss=1.593, generator_adv_loss=1.915, generator_feat_match_loss=5.692, over 12.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.369, discriminator_fake_loss=1.332, generator_loss=28.63, generator_mel_loss=17.95, generator_kl_loss=1.39, generator_dur_loss=1.738, generator_adv_loss=1.976, generator_feat_match_loss=5.571, over 1627.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:56:16,800 INFO [train.py:527] (5/6) Epoch 679, batch 78, global_batch_idx: 84150, batch size: 61, loss[discriminator_loss=2.614, discriminator_real_loss=1.328, discriminator_fake_loss=1.286, generator_loss=29.03, generator_mel_loss=18.08, generator_kl_loss=1.378, generator_dur_loss=1.788, generator_adv_loss=2.069, generator_feat_match_loss=5.712, over 61.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.364, discriminator_fake_loss=1.33, generator_loss=28.61, generator_mel_loss=17.95, generator_kl_loss=1.406, generator_dur_loss=1.733, generator_adv_loss=1.974, generator_feat_match_loss=5.548, over 4302.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:58:24,391 INFO [train.py:919] (5/6) Start epoch 680 +2024-03-14 21:58:59,281 INFO [train.py:527] (5/6) Epoch 680, batch 4, global_batch_idx: 84200, batch size: 39, loss[discriminator_loss=2.665, discriminator_real_loss=1.335, discriminator_fake_loss=1.329, generator_loss=28.22, generator_mel_loss=17.94, generator_kl_loss=1.606, generator_dur_loss=1.658, generator_adv_loss=1.934, generator_feat_match_loss=5.084, over 39.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.352, discriminator_fake_loss=1.337, generator_loss=28.87, generator_mel_loss=18.12, generator_kl_loss=1.444, generator_dur_loss=1.767, generator_adv_loss=1.972, generator_feat_match_loss=5.564, over 282.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 21:58:59,284 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 21:59:07,140 INFO [train.py:591] (5/6) 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 21:59:07,143 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 22:01:24,645 INFO [train.py:527] (5/6) Epoch 680, batch 54, global_batch_idx: 84250, batch size: 55, loss[discriminator_loss=2.628, discriminator_real_loss=1.295, discriminator_fake_loss=1.332, generator_loss=30.13, generator_mel_loss=18.48, generator_kl_loss=1.583, generator_dur_loss=1.689, generator_adv_loss=1.936, generator_feat_match_loss=6.441, over 55.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.357, discriminator_fake_loss=1.336, generator_loss=28.77, generator_mel_loss=17.95, generator_kl_loss=1.398, generator_dur_loss=1.771, generator_adv_loss=1.977, generator_feat_match_loss=5.677, over 3321.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:03:44,731 INFO [train.py:527] (5/6) Epoch 680, batch 104, global_batch_idx: 84300, batch size: 56, loss[discriminator_loss=2.773, discriminator_real_loss=1.407, discriminator_fake_loss=1.366, generator_loss=27.64, generator_mel_loss=17.57, generator_kl_loss=1.423, generator_dur_loss=1.71, generator_adv_loss=1.87, generator_feat_match_loss=5.065, over 56.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.357, discriminator_fake_loss=1.334, generator_loss=28.82, generator_mel_loss=17.96, generator_kl_loss=1.411, generator_dur_loss=1.763, generator_adv_loss=1.999, generator_feat_match_loss=5.686, over 6209.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:04:39,567 INFO [train.py:919] (5/6) Start epoch 681 +2024-03-14 22:06:29,061 INFO [train.py:527] (5/6) Epoch 681, batch 30, global_batch_idx: 84350, batch size: 83, loss[discriminator_loss=2.659, discriminator_real_loss=1.409, discriminator_fake_loss=1.249, generator_loss=28.64, generator_mel_loss=17.84, generator_kl_loss=1.288, generator_dur_loss=1.843, generator_adv_loss=2.008, generator_feat_match_loss=5.664, over 83.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.366, discriminator_fake_loss=1.325, generator_loss=28.63, generator_mel_loss=17.84, generator_kl_loss=1.407, generator_dur_loss=1.757, generator_adv_loss=1.986, generator_feat_match_loss=5.634, over 1859.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:08:49,591 INFO [train.py:527] (5/6) Epoch 681, batch 80, global_batch_idx: 84400, batch size: 48, loss[discriminator_loss=2.623, discriminator_real_loss=1.324, discriminator_fake_loss=1.299, generator_loss=28.35, generator_mel_loss=17.9, generator_kl_loss=1.604, generator_dur_loss=1.624, generator_adv_loss=2.004, generator_feat_match_loss=5.222, over 48.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.364, discriminator_fake_loss=1.326, generator_loss=28.55, generator_mel_loss=17.85, generator_kl_loss=1.431, generator_dur_loss=1.743, generator_adv_loss=1.977, generator_feat_match_loss=5.553, over 4564.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:08:49,593 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 22:08:58,437 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 22:10:54,366 INFO [train.py:919] (5/6) Start epoch 682 +2024-03-14 22:11:36,740 INFO [train.py:527] (5/6) Epoch 682, batch 6, global_batch_idx: 84450, batch size: 59, loss[discriminator_loss=2.671, discriminator_real_loss=1.397, discriminator_fake_loss=1.274, generator_loss=29.07, generator_mel_loss=18.28, generator_kl_loss=1.53, generator_dur_loss=1.725, generator_adv_loss=2.012, generator_feat_match_loss=5.523, over 59.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.379, discriminator_fake_loss=1.296, generator_loss=28.93, generator_mel_loss=18.04, generator_kl_loss=1.539, generator_dur_loss=1.692, generator_adv_loss=1.998, generator_feat_match_loss=5.663, over 284.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:13:52,685 INFO [train.py:527] (5/6) Epoch 682, batch 56, global_batch_idx: 84500, batch size: 31, loss[discriminator_loss=2.636, discriminator_real_loss=1.368, discriminator_fake_loss=1.268, generator_loss=29.43, generator_mel_loss=18.62, generator_kl_loss=1.582, generator_dur_loss=1.606, generator_adv_loss=1.874, generator_feat_match_loss=5.753, over 31.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=28.74, generator_mel_loss=18, generator_kl_loss=1.43, generator_dur_loss=1.734, generator_adv_loss=1.976, generator_feat_match_loss=5.599, over 3035.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:16:13,608 INFO [train.py:527] (5/6) Epoch 682, batch 106, global_batch_idx: 84550, batch size: 52, loss[discriminator_loss=2.686, discriminator_real_loss=1.435, discriminator_fake_loss=1.25, generator_loss=28.66, generator_mel_loss=17.67, generator_kl_loss=1.596, generator_dur_loss=1.728, generator_adv_loss=1.962, generator_feat_match_loss=5.703, over 52.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.36, discriminator_fake_loss=1.334, generator_loss=28.75, generator_mel_loss=17.99, generator_kl_loss=1.432, generator_dur_loss=1.735, generator_adv_loss=1.981, generator_feat_match_loss=5.613, over 5834.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:17:04,805 INFO [train.py:919] (5/6) Start epoch 683 +2024-03-14 22:18:57,675 INFO [train.py:527] (5/6) Epoch 683, batch 32, global_batch_idx: 84600, batch size: 59, loss[discriminator_loss=2.701, discriminator_real_loss=1.307, discriminator_fake_loss=1.394, generator_loss=27.38, generator_mel_loss=17.64, generator_kl_loss=1.269, generator_dur_loss=1.753, generator_adv_loss=2.053, generator_feat_match_loss=4.657, over 59.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.369, discriminator_fake_loss=1.336, generator_loss=28.48, generator_mel_loss=17.91, generator_kl_loss=1.351, generator_dur_loss=1.771, generator_adv_loss=1.974, generator_feat_match_loss=5.478, over 2072.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:18:57,676 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 22:19:05,696 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 22:21:28,062 INFO [train.py:527] (5/6) Epoch 683, batch 82, global_batch_idx: 84650, batch size: 45, loss[discriminator_loss=2.761, discriminator_real_loss=1.444, discriminator_fake_loss=1.318, generator_loss=27.51, generator_mel_loss=17.88, generator_kl_loss=1.452, generator_dur_loss=1.647, generator_adv_loss=1.974, generator_feat_match_loss=4.549, over 45.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.368, discriminator_fake_loss=1.33, generator_loss=28.55, generator_mel_loss=17.96, generator_kl_loss=1.384, generator_dur_loss=1.75, generator_adv_loss=1.974, generator_feat_match_loss=5.478, over 4871.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:23:20,415 INFO [train.py:919] (5/6) Start epoch 684 +2024-03-14 22:24:06,500 INFO [train.py:527] (5/6) Epoch 684, batch 8, global_batch_idx: 84700, batch size: 95, loss[discriminator_loss=2.711, discriminator_real_loss=1.289, discriminator_fake_loss=1.422, generator_loss=28.96, generator_mel_loss=18.04, generator_kl_loss=1.353, generator_dur_loss=1.838, generator_adv_loss=1.882, generator_feat_match_loss=5.846, over 95.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.344, discriminator_fake_loss=1.353, generator_loss=28.95, generator_mel_loss=17.95, generator_kl_loss=1.431, generator_dur_loss=1.752, generator_adv_loss=1.988, generator_feat_match_loss=5.824, over 505.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:26:26,004 INFO [train.py:527] (5/6) Epoch 684, batch 58, global_batch_idx: 84750, batch size: 47, loss[discriminator_loss=2.736, discriminator_real_loss=1.456, discriminator_fake_loss=1.28, generator_loss=28.38, generator_mel_loss=17.67, generator_kl_loss=1.453, generator_dur_loss=1.671, generator_adv_loss=2.025, generator_feat_match_loss=5.557, over 47.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.358, discriminator_fake_loss=1.333, generator_loss=28.73, generator_mel_loss=17.93, generator_kl_loss=1.425, generator_dur_loss=1.74, generator_adv_loss=1.992, generator_feat_match_loss=5.635, over 3333.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:28:47,646 INFO [train.py:527] (5/6) Epoch 684, batch 108, global_batch_idx: 84800, batch size: 45, loss[discriminator_loss=2.626, discriminator_real_loss=1.237, discriminator_fake_loss=1.389, generator_loss=29.69, generator_mel_loss=18.31, generator_kl_loss=1.396, generator_dur_loss=1.692, generator_adv_loss=2.079, generator_feat_match_loss=6.219, over 45.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.358, discriminator_fake_loss=1.332, generator_loss=28.68, generator_mel_loss=17.92, generator_kl_loss=1.407, generator_dur_loss=1.751, generator_adv_loss=1.987, generator_feat_match_loss=5.618, over 6299.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:28:47,648 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 22:28:56,610 INFO [train.py:591] (5/6) 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 22:28:56,610 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 22:29:39,024 INFO [train.py:919] (5/6) Start epoch 685 +2024-03-14 22:31:37,884 INFO [train.py:527] (5/6) Epoch 685, batch 34, global_batch_idx: 84850, batch size: 83, loss[discriminator_loss=2.699, discriminator_real_loss=1.322, discriminator_fake_loss=1.377, generator_loss=28.72, generator_mel_loss=18.02, generator_kl_loss=1.312, generator_dur_loss=1.817, generator_adv_loss=1.914, generator_feat_match_loss=5.659, over 83.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.35, discriminator_fake_loss=1.34, generator_loss=28.71, generator_mel_loss=17.98, generator_kl_loss=1.395, generator_dur_loss=1.76, generator_adv_loss=1.964, generator_feat_match_loss=5.612, over 2053.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:33:56,806 INFO [train.py:527] (5/6) Epoch 685, batch 84, global_batch_idx: 84900, batch size: 59, loss[discriminator_loss=2.756, discriminator_real_loss=1.385, discriminator_fake_loss=1.371, generator_loss=27.95, generator_mel_loss=17.61, generator_kl_loss=1.318, generator_dur_loss=1.764, generator_adv_loss=2.045, generator_feat_match_loss=5.213, over 59.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.361, discriminator_fake_loss=1.333, generator_loss=28.7, generator_mel_loss=17.95, generator_kl_loss=1.399, generator_dur_loss=1.755, generator_adv_loss=1.977, generator_feat_match_loss=5.615, over 5053.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:35:45,921 INFO [train.py:919] (5/6) Start epoch 686 +2024-03-14 22:36:40,425 INFO [train.py:527] (5/6) Epoch 686, batch 10, global_batch_idx: 84950, batch size: 47, loss[discriminator_loss=2.668, discriminator_real_loss=1.371, discriminator_fake_loss=1.298, generator_loss=28.77, generator_mel_loss=17.98, generator_kl_loss=1.302, generator_dur_loss=1.693, generator_adv_loss=1.996, generator_feat_match_loss=5.798, over 47.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.354, discriminator_fake_loss=1.332, generator_loss=28.86, generator_mel_loss=17.98, generator_kl_loss=1.37, generator_dur_loss=1.746, generator_adv_loss=1.982, generator_feat_match_loss=5.781, over 683.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:38:59,289 INFO [train.py:527] (5/6) Epoch 686, batch 60, global_batch_idx: 85000, batch size: 39, loss[discriminator_loss=2.682, discriminator_real_loss=1.315, discriminator_fake_loss=1.367, generator_loss=29.77, generator_mel_loss=18.57, generator_kl_loss=1.567, generator_dur_loss=1.679, generator_adv_loss=2.045, generator_feat_match_loss=5.91, over 39.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.361, discriminator_fake_loss=1.332, generator_loss=28.67, generator_mel_loss=17.95, generator_kl_loss=1.412, generator_dur_loss=1.743, generator_adv_loss=1.971, generator_feat_match_loss=5.6, over 3590.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:38:59,290 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 22:39:07,402 INFO [train.py:591] (5/6) 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 22:39:07,403 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 22:41:26,957 INFO [train.py:527] (5/6) Epoch 686, batch 110, global_batch_idx: 85050, batch size: 31, loss[discriminator_loss=2.76, discriminator_real_loss=1.415, discriminator_fake_loss=1.344, generator_loss=28.39, generator_mel_loss=18.13, generator_kl_loss=1.634, generator_dur_loss=1.595, generator_adv_loss=1.935, generator_feat_match_loss=5.093, over 31.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.357, discriminator_fake_loss=1.333, generator_loss=28.69, generator_mel_loss=17.94, generator_kl_loss=1.408, generator_dur_loss=1.746, generator_adv_loss=1.976, generator_feat_match_loss=5.619, over 6471.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:42:04,772 INFO [train.py:919] (5/6) Start epoch 687 +2024-03-14 22:44:09,395 INFO [train.py:527] (5/6) Epoch 687, batch 36, global_batch_idx: 85100, batch size: 70, loss[discriminator_loss=2.668, discriminator_real_loss=1.424, discriminator_fake_loss=1.244, generator_loss=28.29, generator_mel_loss=17.67, generator_kl_loss=1.203, generator_dur_loss=1.807, generator_adv_loss=1.944, generator_feat_match_loss=5.666, over 70.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.371, discriminator_fake_loss=1.328, generator_loss=28.64, generator_mel_loss=17.94, generator_kl_loss=1.406, generator_dur_loss=1.767, generator_adv_loss=1.983, generator_feat_match_loss=5.543, over 2258.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:46:28,423 INFO [train.py:527] (5/6) Epoch 687, batch 86, global_batch_idx: 85150, batch size: 42, loss[discriminator_loss=2.687, discriminator_real_loss=1.475, discriminator_fake_loss=1.212, generator_loss=26.97, generator_mel_loss=17.17, generator_kl_loss=1.475, generator_dur_loss=1.684, generator_adv_loss=1.984, generator_feat_match_loss=4.66, over 42.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.368, discriminator_fake_loss=1.331, generator_loss=28.72, generator_mel_loss=17.93, generator_kl_loss=1.427, generator_dur_loss=1.743, generator_adv_loss=2.024, generator_feat_match_loss=5.603, over 4901.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:48:12,670 INFO [train.py:919] (5/6) Start epoch 688 +2024-03-14 22:49:10,814 INFO [train.py:527] (5/6) Epoch 688, batch 12, global_batch_idx: 85200, batch size: 59, loss[discriminator_loss=2.74, discriminator_real_loss=1.411, discriminator_fake_loss=1.328, generator_loss=28.69, generator_mel_loss=18.05, generator_kl_loss=1.332, generator_dur_loss=1.769, generator_adv_loss=1.922, generator_feat_match_loss=5.617, over 59.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.37, discriminator_fake_loss=1.32, generator_loss=28.35, generator_mel_loss=17.83, generator_kl_loss=1.428, generator_dur_loss=1.768, generator_adv_loss=1.979, generator_feat_match_loss=5.342, over 805.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:49:10,817 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 22:49:18,930 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 22:51:34,912 INFO [train.py:527] (5/6) Epoch 688, batch 62, global_batch_idx: 85250, batch size: 39, loss[discriminator_loss=2.688, discriminator_real_loss=1.345, discriminator_fake_loss=1.342, generator_loss=28.18, generator_mel_loss=17.73, generator_kl_loss=1.389, generator_dur_loss=1.724, generator_adv_loss=1.909, generator_feat_match_loss=5.428, over 39.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.364, discriminator_fake_loss=1.328, generator_loss=28.72, generator_mel_loss=17.96, generator_kl_loss=1.432, generator_dur_loss=1.734, generator_adv_loss=1.981, generator_feat_match_loss=5.612, over 3452.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:53:54,570 INFO [train.py:527] (5/6) Epoch 688, batch 112, global_batch_idx: 85300, batch size: 52, loss[discriminator_loss=2.709, discriminator_real_loss=1.313, discriminator_fake_loss=1.396, generator_loss=29.01, generator_mel_loss=18.28, generator_kl_loss=1.549, generator_dur_loss=1.717, generator_adv_loss=1.94, generator_feat_match_loss=5.523, over 52.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.364, discriminator_fake_loss=1.328, generator_loss=28.71, generator_mel_loss=17.95, generator_kl_loss=1.434, generator_dur_loss=1.738, generator_adv_loss=1.979, generator_feat_match_loss=5.615, over 6403.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:54:26,564 INFO [train.py:919] (5/6) Start epoch 689 +2024-03-14 22:56:34,989 INFO [train.py:527] (5/6) Epoch 689, batch 38, global_batch_idx: 85350, batch size: 56, loss[discriminator_loss=2.625, discriminator_real_loss=1.281, discriminator_fake_loss=1.344, generator_loss=29.26, generator_mel_loss=18.2, generator_kl_loss=1.368, generator_dur_loss=1.742, generator_adv_loss=2.13, generator_feat_match_loss=5.813, over 56.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.363, discriminator_fake_loss=1.326, generator_loss=28.77, generator_mel_loss=18, generator_kl_loss=1.412, generator_dur_loss=1.735, generator_adv_loss=1.978, generator_feat_match_loss=5.646, over 2120.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:58:51,873 INFO [train.py:527] (5/6) Epoch 689, batch 88, global_batch_idx: 85400, batch size: 45, loss[discriminator_loss=2.722, discriminator_real_loss=1.388, discriminator_fake_loss=1.334, generator_loss=28.39, generator_mel_loss=17.63, generator_kl_loss=1.545, generator_dur_loss=1.697, generator_adv_loss=2, generator_feat_match_loss=5.527, over 45.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.329, generator_loss=28.76, generator_mel_loss=18.02, generator_kl_loss=1.4, generator_dur_loss=1.745, generator_adv_loss=1.974, generator_feat_match_loss=5.627, over 5095.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, +2024-03-14 22:58:51,875 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 22:59:00,798 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 23:00:41,891 INFO [train.py:919] (5/6) Start epoch 690 +2024-03-14 23:01:46,940 INFO [train.py:527] (5/6) Epoch 690, batch 14, global_batch_idx: 85450, batch size: 96, loss[discriminator_loss=2.681, discriminator_real_loss=1.356, discriminator_fake_loss=1.325, generator_loss=29.24, generator_mel_loss=17.85, generator_kl_loss=1.255, generator_dur_loss=1.884, generator_adv_loss=2.279, generator_feat_match_loss=5.975, over 96.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.363, discriminator_fake_loss=1.338, generator_loss=28.65, generator_mel_loss=17.9, generator_kl_loss=1.348, generator_dur_loss=1.773, generator_adv_loss=2.047, generator_feat_match_loss=5.584, over 959.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:04:07,189 INFO [train.py:527] (5/6) Epoch 690, batch 64, global_batch_idx: 85500, batch size: 53, loss[discriminator_loss=2.622, discriminator_real_loss=1.307, discriminator_fake_loss=1.315, generator_loss=29.07, generator_mel_loss=17.6, generator_kl_loss=1.579, generator_dur_loss=1.657, generator_adv_loss=2.034, generator_feat_match_loss=6.197, over 53.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.364, discriminator_fake_loss=1.328, generator_loss=28.7, generator_mel_loss=17.89, generator_kl_loss=1.398, generator_dur_loss=1.753, generator_adv_loss=2.016, generator_feat_match_loss=5.636, over 3950.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:06:22,284 INFO [train.py:527] (5/6) Epoch 690, batch 114, global_batch_idx: 85550, batch size: 36, loss[discriminator_loss=2.73, discriminator_real_loss=1.436, discriminator_fake_loss=1.294, generator_loss=28.96, generator_mel_loss=18.26, generator_kl_loss=1.548, generator_dur_loss=1.623, generator_adv_loss=1.944, generator_feat_match_loss=5.586, over 36.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.36, discriminator_fake_loss=1.331, generator_loss=28.73, generator_mel_loss=17.94, generator_kl_loss=1.416, generator_dur_loss=1.741, generator_adv_loss=2.002, generator_feat_match_loss=5.637, over 6604.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:06:47,597 INFO [train.py:919] (5/6) Start epoch 691 +2024-03-14 23:09:01,332 INFO [train.py:527] (5/6) Epoch 691, batch 40, global_batch_idx: 85600, batch size: 61, loss[discriminator_loss=2.621, discriminator_real_loss=1.3, discriminator_fake_loss=1.322, generator_loss=28.69, generator_mel_loss=17.8, generator_kl_loss=1.471, generator_dur_loss=1.701, generator_adv_loss=1.98, generator_feat_match_loss=5.738, over 61.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.362, discriminator_fake_loss=1.341, generator_loss=28.57, generator_mel_loss=17.97, generator_kl_loss=1.407, generator_dur_loss=1.732, generator_adv_loss=1.976, generator_feat_match_loss=5.483, over 2328.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:09:01,333 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 23:09:09,449 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 23:11:27,702 INFO [train.py:527] (5/6) Epoch 691, batch 90, global_batch_idx: 85650, batch size: 72, loss[discriminator_loss=2.688, discriminator_real_loss=1.393, discriminator_fake_loss=1.294, generator_loss=29.18, generator_mel_loss=17.87, generator_kl_loss=1.331, generator_dur_loss=1.765, generator_adv_loss=2.022, generator_feat_match_loss=6.197, over 72.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.36, discriminator_fake_loss=1.333, generator_loss=28.53, generator_mel_loss=17.91, generator_kl_loss=1.418, generator_dur_loss=1.727, generator_adv_loss=1.977, generator_feat_match_loss=5.498, over 5374.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:13:01,030 INFO [train.py:919] (5/6) Start epoch 692 +2024-03-14 23:14:07,498 INFO [train.py:527] (5/6) Epoch 692, batch 16, global_batch_idx: 85700, batch size: 52, loss[discriminator_loss=2.697, discriminator_real_loss=1.25, discriminator_fake_loss=1.447, generator_loss=28.86, generator_mel_loss=18.31, generator_kl_loss=1.362, generator_dur_loss=1.711, generator_adv_loss=2.037, generator_feat_match_loss=5.44, over 52.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.352, discriminator_fake_loss=1.347, generator_loss=28.74, generator_mel_loss=17.93, generator_kl_loss=1.375, generator_dur_loss=1.765, generator_adv_loss=1.996, generator_feat_match_loss=5.676, over 1031.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:16:26,985 INFO [train.py:527] (5/6) Epoch 692, batch 66, global_batch_idx: 85750, batch size: 55, loss[discriminator_loss=2.697, discriminator_real_loss=1.384, discriminator_fake_loss=1.313, generator_loss=28.9, generator_mel_loss=18.14, generator_kl_loss=1.465, generator_dur_loss=1.638, generator_adv_loss=2.008, generator_feat_match_loss=5.651, over 55.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.36, discriminator_fake_loss=1.337, generator_loss=28.59, generator_mel_loss=17.89, generator_kl_loss=1.396, generator_dur_loss=1.755, generator_adv_loss=1.982, generator_feat_match_loss=5.569, over 4012.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:18:45,419 INFO [train.py:527] (5/6) Epoch 692, batch 116, global_batch_idx: 85800, batch size: 83, loss[discriminator_loss=2.707, discriminator_real_loss=1.233, discriminator_fake_loss=1.474, generator_loss=29.3, generator_mel_loss=17.81, generator_kl_loss=1.315, generator_dur_loss=1.876, generator_adv_loss=2.096, generator_feat_match_loss=6.197, over 83.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.36, discriminator_fake_loss=1.335, generator_loss=28.56, generator_mel_loss=17.88, generator_kl_loss=1.393, generator_dur_loss=1.753, generator_adv_loss=1.984, generator_feat_match_loss=5.549, over 6989.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:18:45,420 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 23:18:54,407 INFO [train.py:591] (5/6) 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 23:18:54,408 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 23:19:15,463 INFO [train.py:919] (5/6) Start epoch 693 +2024-03-14 23:21:35,675 INFO [train.py:527] (5/6) Epoch 693, batch 42, global_batch_idx: 85850, batch size: 53, loss[discriminator_loss=2.783, discriminator_real_loss=1.363, discriminator_fake_loss=1.42, generator_loss=26.76, generator_mel_loss=17.13, generator_kl_loss=1.463, generator_dur_loss=1.725, generator_adv_loss=1.865, generator_feat_match_loss=4.575, over 53.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.361, discriminator_fake_loss=1.338, generator_loss=28.75, generator_mel_loss=17.92, generator_kl_loss=1.441, generator_dur_loss=1.742, generator_adv_loss=1.982, generator_feat_match_loss=5.673, over 2467.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:23:55,397 INFO [train.py:527] (5/6) Epoch 693, batch 92, global_batch_idx: 85900, batch size: 45, loss[discriminator_loss=2.699, discriminator_real_loss=1.408, discriminator_fake_loss=1.29, generator_loss=29.35, generator_mel_loss=17.85, generator_kl_loss=1.553, generator_dur_loss=1.657, generator_adv_loss=2.091, generator_feat_match_loss=6.196, over 45.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.359, discriminator_fake_loss=1.337, generator_loss=28.72, generator_mel_loss=17.91, generator_kl_loss=1.416, generator_dur_loss=1.75, generator_adv_loss=1.981, generator_feat_match_loss=5.66, over 5491.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:25:20,082 INFO [train.py:919] (5/6) Start epoch 694 +2024-03-14 23:26:34,748 INFO [train.py:527] (5/6) Epoch 694, batch 18, global_batch_idx: 85950, batch size: 36, loss[discriminator_loss=2.642, discriminator_real_loss=1.33, discriminator_fake_loss=1.312, generator_loss=29.2, generator_mel_loss=17.74, generator_kl_loss=1.515, generator_dur_loss=1.589, generator_adv_loss=2.083, generator_feat_match_loss=6.281, over 36.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.364, discriminator_fake_loss=1.335, generator_loss=28.64, generator_mel_loss=17.88, generator_kl_loss=1.374, generator_dur_loss=1.747, generator_adv_loss=1.988, generator_feat_match_loss=5.65, over 1126.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:28:56,365 INFO [train.py:527] (5/6) Epoch 694, batch 68, global_batch_idx: 86000, batch size: 64, loss[discriminator_loss=2.658, discriminator_real_loss=1.403, discriminator_fake_loss=1.255, generator_loss=29.03, generator_mel_loss=17.71, generator_kl_loss=1.449, generator_dur_loss=1.725, generator_adv_loss=2.109, generator_feat_match_loss=6.035, over 64.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=28.68, generator_mel_loss=17.91, generator_kl_loss=1.382, generator_dur_loss=1.748, generator_adv_loss=1.983, generator_feat_match_loss=5.658, over 3964.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:28:56,366 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 23:29:05,263 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 23:31:20,794 INFO [train.py:527] (5/6) Epoch 694, batch 118, global_batch_idx: 86050, batch size: 42, loss[discriminator_loss=2.666, discriminator_real_loss=1.338, discriminator_fake_loss=1.327, generator_loss=28.72, generator_mel_loss=17.64, generator_kl_loss=1.527, generator_dur_loss=1.652, generator_adv_loss=1.911, generator_feat_match_loss=5.996, over 42.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.326, generator_loss=28.7, generator_mel_loss=17.9, generator_kl_loss=1.394, generator_dur_loss=1.741, generator_adv_loss=1.988, generator_feat_match_loss=5.677, over 6639.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:31:36,713 INFO [train.py:919] (5/6) Start epoch 695 +2024-03-14 23:34:03,429 INFO [train.py:527] (5/6) Epoch 695, batch 44, global_batch_idx: 86100, batch size: 44, loss[discriminator_loss=2.701, discriminator_real_loss=1.437, discriminator_fake_loss=1.264, generator_loss=27.39, generator_mel_loss=17.52, generator_kl_loss=1.495, generator_dur_loss=1.649, generator_adv_loss=2.052, generator_feat_match_loss=4.68, over 44.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.365, discriminator_fake_loss=1.337, generator_loss=28.66, generator_mel_loss=17.95, generator_kl_loss=1.417, generator_dur_loss=1.754, generator_adv_loss=1.98, generator_feat_match_loss=5.555, over 2684.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:36:21,417 INFO [train.py:527] (5/6) Epoch 695, batch 94, global_batch_idx: 86150, batch size: 66, loss[discriminator_loss=2.729, discriminator_real_loss=1.367, discriminator_fake_loss=1.361, generator_loss=29.08, generator_mel_loss=17.7, generator_kl_loss=1.33, generator_dur_loss=1.76, generator_adv_loss=2.044, generator_feat_match_loss=6.24, over 66.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.361, discriminator_fake_loss=1.336, generator_loss=28.77, generator_mel_loss=17.96, generator_kl_loss=1.405, generator_dur_loss=1.742, generator_adv_loss=1.99, generator_feat_match_loss=5.671, over 5549.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:37:40,956 INFO [train.py:919] (5/6) Start epoch 696 +2024-03-14 23:39:00,752 INFO [train.py:527] (5/6) Epoch 696, batch 20, global_batch_idx: 86200, batch size: 61, loss[discriminator_loss=2.719, discriminator_real_loss=1.378, discriminator_fake_loss=1.341, generator_loss=28.11, generator_mel_loss=17.64, generator_kl_loss=1.441, generator_dur_loss=1.745, generator_adv_loss=2.014, generator_feat_match_loss=5.269, over 61.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.375, discriminator_fake_loss=1.327, generator_loss=28.56, generator_mel_loss=17.88, generator_kl_loss=1.413, generator_dur_loss=1.762, generator_adv_loss=1.984, generator_feat_match_loss=5.523, over 1325.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:39:00,753 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 23:39:09,014 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 23:41:26,965 INFO [train.py:527] (5/6) Epoch 696, batch 70, global_batch_idx: 86250, batch size: 66, loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=29.04, generator_mel_loss=17.85, generator_kl_loss=1.577, generator_dur_loss=1.713, generator_adv_loss=2.005, generator_feat_match_loss=5.889, over 66.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.373, discriminator_fake_loss=1.337, generator_loss=28.55, generator_mel_loss=17.91, generator_kl_loss=1.406, generator_dur_loss=1.76, generator_adv_loss=1.978, generator_feat_match_loss=5.497, over 4303.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:43:47,167 INFO [train.py:527] (5/6) Epoch 696, batch 120, global_batch_idx: 86300, batch size: 36, loss[discriminator_loss=2.746, discriminator_real_loss=1.413, discriminator_fake_loss=1.333, generator_loss=27.49, generator_mel_loss=17.68, generator_kl_loss=1.475, generator_dur_loss=1.686, generator_adv_loss=2.02, generator_feat_match_loss=4.628, over 36.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=28.54, generator_mel_loss=17.9, generator_kl_loss=1.399, generator_dur_loss=1.759, generator_adv_loss=1.975, generator_feat_match_loss=5.51, over 7061.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:43:56,779 INFO [train.py:919] (5/6) Start epoch 697 +2024-03-14 23:46:28,272 INFO [train.py:527] (5/6) Epoch 697, batch 46, global_batch_idx: 86350, batch size: 25, loss[discriminator_loss=2.688, discriminator_real_loss=1.313, discriminator_fake_loss=1.375, generator_loss=29.85, generator_mel_loss=18.05, generator_kl_loss=1.666, generator_dur_loss=1.54, generator_adv_loss=2.088, generator_feat_match_loss=6.497, over 25.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.75, generator_mel_loss=17.99, generator_kl_loss=1.404, generator_dur_loss=1.732, generator_adv_loss=1.995, generator_feat_match_loss=5.626, over 2837.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:48:45,594 INFO [train.py:527] (5/6) Epoch 697, batch 96, global_batch_idx: 86400, batch size: 17, loss[discriminator_loss=2.595, discriminator_real_loss=1.326, discriminator_fake_loss=1.269, generator_loss=29.98, generator_mel_loss=18.39, generator_kl_loss=1.922, generator_dur_loss=1.591, generator_adv_loss=2.162, generator_feat_match_loss=5.92, over 17.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.71, generator_mel_loss=17.96, generator_kl_loss=1.419, generator_dur_loss=1.729, generator_adv_loss=1.986, generator_feat_match_loss=5.618, over 5632.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:48:45,595 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 23:48:54,259 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-14 23:50:11,241 INFO [train.py:919] (5/6) Start epoch 698 +2024-03-14 23:51:37,728 INFO [train.py:527] (5/6) Epoch 698, batch 22, global_batch_idx: 86450, batch size: 55, loss[discriminator_loss=2.715, discriminator_real_loss=1.436, discriminator_fake_loss=1.28, generator_loss=28.62, generator_mel_loss=17.72, generator_kl_loss=1.6, generator_dur_loss=1.701, generator_adv_loss=1.922, generator_feat_match_loss=5.685, over 55.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.37, discriminator_fake_loss=1.34, generator_loss=28.65, generator_mel_loss=17.9, generator_kl_loss=1.379, generator_dur_loss=1.752, generator_adv_loss=1.96, generator_feat_match_loss=5.661, over 1427.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:53:59,534 INFO [train.py:527] (5/6) Epoch 698, batch 72, global_batch_idx: 86500, batch size: 50, loss[discriminator_loss=2.706, discriminator_real_loss=1.341, discriminator_fake_loss=1.365, generator_loss=27.95, generator_mel_loss=17.55, generator_kl_loss=1.407, generator_dur_loss=1.694, generator_adv_loss=2.041, generator_feat_match_loss=5.26, over 50.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.338, generator_loss=28.57, generator_mel_loss=17.89, generator_kl_loss=1.407, generator_dur_loss=1.734, generator_adv_loss=1.971, generator_feat_match_loss=5.569, over 4189.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:56:14,833 INFO [train.py:527] (5/6) Epoch 698, batch 122, global_batch_idx: 86550, batch size: 55, loss[discriminator_loss=2.651, discriminator_real_loss=1.359, discriminator_fake_loss=1.292, generator_loss=29.55, generator_mel_loss=18.44, generator_kl_loss=1.349, generator_dur_loss=1.733, generator_adv_loss=1.911, generator_feat_match_loss=6.121, over 55.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.366, discriminator_fake_loss=1.332, generator_loss=28.62, generator_mel_loss=17.91, generator_kl_loss=1.409, generator_dur_loss=1.735, generator_adv_loss=1.974, generator_feat_match_loss=5.602, over 6957.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:56:19,479 INFO [train.py:919] (5/6) Start epoch 699 +2024-03-14 23:58:57,580 INFO [train.py:527] (5/6) Epoch 699, batch 48, global_batch_idx: 86600, batch size: 50, loss[discriminator_loss=2.697, discriminator_real_loss=1.428, discriminator_fake_loss=1.268, generator_loss=29.28, generator_mel_loss=18, generator_kl_loss=1.671, generator_dur_loss=1.634, generator_adv_loss=2.135, generator_feat_match_loss=5.843, over 50.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.359, discriminator_fake_loss=1.321, generator_loss=28.79, generator_mel_loss=17.92, generator_kl_loss=1.396, generator_dur_loss=1.746, generator_adv_loss=2.006, generator_feat_match_loss=5.726, over 2762.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-14 23:58:57,581 INFO [train.py:581] (5/6) Computing validation loss +2024-03-14 23:59:05,346 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 00:01:23,425 INFO [train.py:527] (5/6) Epoch 699, batch 98, global_batch_idx: 86650, batch size: 68, loss[discriminator_loss=2.68, discriminator_real_loss=1.435, discriminator_fake_loss=1.245, generator_loss=29.17, generator_mel_loss=17.86, generator_kl_loss=1.6, generator_dur_loss=1.788, generator_adv_loss=2.014, generator_feat_match_loss=5.905, over 68.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=28.77, generator_mel_loss=17.94, generator_kl_loss=1.404, generator_dur_loss=1.737, generator_adv_loss=1.999, generator_feat_match_loss=5.696, over 5552.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:02:31,478 INFO [train.py:919] (5/6) Start epoch 700 +2024-03-15 00:04:01,473 INFO [train.py:527] (5/6) Epoch 700, batch 24, global_batch_idx: 86700, batch size: 96, loss[discriminator_loss=2.659, discriminator_real_loss=1.332, discriminator_fake_loss=1.326, generator_loss=28.9, generator_mel_loss=18.06, generator_kl_loss=1.415, generator_dur_loss=1.859, generator_adv_loss=2.073, generator_feat_match_loss=5.486, over 96.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.347, discriminator_fake_loss=1.334, generator_loss=28.76, generator_mel_loss=18, generator_kl_loss=1.413, generator_dur_loss=1.729, generator_adv_loss=1.983, generator_feat_match_loss=5.635, over 1471.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:06:21,328 INFO [train.py:527] (5/6) Epoch 700, batch 74, global_batch_idx: 86750, batch size: 25, loss[discriminator_loss=2.652, discriminator_real_loss=1.319, discriminator_fake_loss=1.333, generator_loss=30.05, generator_mel_loss=18.71, generator_kl_loss=1.782, generator_dur_loss=1.586, generator_adv_loss=1.927, generator_feat_match_loss=6.046, over 25.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.36, discriminator_fake_loss=1.331, generator_loss=28.76, generator_mel_loss=17.98, generator_kl_loss=1.419, generator_dur_loss=1.737, generator_adv_loss=1.982, generator_feat_match_loss=5.644, over 4309.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:08:36,987 INFO [train.py:919] (5/6) Start epoch 701 +2024-03-15 00:09:01,132 INFO [train.py:527] (5/6) Epoch 701, batch 0, global_batch_idx: 86800, batch size: 64, loss[discriminator_loss=2.752, discriminator_real_loss=1.501, discriminator_fake_loss=1.251, generator_loss=29.05, generator_mel_loss=18.11, generator_kl_loss=1.375, generator_dur_loss=1.748, generator_adv_loss=1.92, generator_feat_match_loss=5.896, over 64.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.501, discriminator_fake_loss=1.251, generator_loss=29.05, generator_mel_loss=18.11, generator_kl_loss=1.375, generator_dur_loss=1.748, generator_adv_loss=1.92, generator_feat_match_loss=5.896, over 64.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:09:01,134 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 00:09:08,770 INFO [train.py:591] (5/6) 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 00:09:08,772 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 00:11:26,788 INFO [train.py:527] (5/6) Epoch 701, batch 50, global_batch_idx: 86850, batch size: 56, loss[discriminator_loss=2.645, discriminator_real_loss=1.395, discriminator_fake_loss=1.25, generator_loss=29.38, generator_mel_loss=17.97, generator_kl_loss=1.587, generator_dur_loss=1.708, generator_adv_loss=2.03, generator_feat_match_loss=6.091, over 56.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.368, discriminator_fake_loss=1.331, generator_loss=28.76, generator_mel_loss=17.91, generator_kl_loss=1.434, generator_dur_loss=1.734, generator_adv_loss=2.016, generator_feat_match_loss=5.666, over 2757.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:13:42,905 INFO [train.py:527] (5/6) Epoch 701, batch 100, global_batch_idx: 86900, batch size: 47, loss[discriminator_loss=2.735, discriminator_real_loss=1.377, discriminator_fake_loss=1.358, generator_loss=28.88, generator_mel_loss=18.24, generator_kl_loss=1.39, generator_dur_loss=1.656, generator_adv_loss=1.838, generator_feat_match_loss=5.759, over 47.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.366, discriminator_fake_loss=1.332, generator_loss=28.74, generator_mel_loss=17.95, generator_kl_loss=1.429, generator_dur_loss=1.721, generator_adv_loss=1.993, generator_feat_match_loss=5.651, over 5419.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:14:51,745 INFO [train.py:919] (5/6) Start epoch 702 +2024-03-15 00:16:27,423 INFO [train.py:527] (5/6) Epoch 702, batch 26, global_batch_idx: 86950, batch size: 68, loss[discriminator_loss=2.704, discriminator_real_loss=1.342, discriminator_fake_loss=1.362, generator_loss=28.68, generator_mel_loss=17.71, generator_kl_loss=1.43, generator_dur_loss=1.758, generator_adv_loss=1.889, generator_feat_match_loss=5.892, over 68.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.374, discriminator_fake_loss=1.328, generator_loss=28.92, generator_mel_loss=17.95, generator_kl_loss=1.484, generator_dur_loss=1.728, generator_adv_loss=1.978, generator_feat_match_loss=5.779, over 1530.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:18:46,822 INFO [train.py:527] (5/6) Epoch 702, batch 76, global_batch_idx: 87000, batch size: 95, loss[discriminator_loss=2.686, discriminator_real_loss=1.325, discriminator_fake_loss=1.361, generator_loss=28.73, generator_mel_loss=17.89, generator_kl_loss=1.306, generator_dur_loss=1.799, generator_adv_loss=2.032, generator_feat_match_loss=5.695, over 95.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.366, discriminator_fake_loss=1.33, generator_loss=28.82, generator_mel_loss=17.99, generator_kl_loss=1.435, generator_dur_loss=1.736, generator_adv_loss=1.98, generator_feat_match_loss=5.675, over 4377.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:18:46,825 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 00:18:55,634 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 00:21:07,544 INFO [train.py:919] (5/6) Start epoch 703 +2024-03-15 00:21:37,875 INFO [train.py:527] (5/6) Epoch 703, batch 2, global_batch_idx: 87050, batch size: 80, loss[discriminator_loss=2.781, discriminator_real_loss=1.377, discriminator_fake_loss=1.405, generator_loss=27.34, generator_mel_loss=17.39, generator_kl_loss=1.291, generator_dur_loss=1.83, generator_adv_loss=2.036, generator_feat_match_loss=4.794, over 80.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.376, discriminator_fake_loss=1.368, generator_loss=28.09, generator_mel_loss=17.75, generator_kl_loss=1.439, generator_dur_loss=1.744, generator_adv_loss=1.995, generator_feat_match_loss=5.167, over 147.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:23:57,367 INFO [train.py:527] (5/6) Epoch 703, batch 52, global_batch_idx: 87100, batch size: 83, loss[discriminator_loss=2.683, discriminator_real_loss=1.342, discriminator_fake_loss=1.341, generator_loss=28.95, generator_mel_loss=18.04, generator_kl_loss=1.276, generator_dur_loss=1.814, generator_adv_loss=1.939, generator_feat_match_loss=5.882, over 83.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.357, discriminator_fake_loss=1.345, generator_loss=28.71, generator_mel_loss=17.94, generator_kl_loss=1.426, generator_dur_loss=1.74, generator_adv_loss=1.977, generator_feat_match_loss=5.618, over 3105.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:26:16,250 INFO [train.py:527] (5/6) Epoch 703, batch 102, global_batch_idx: 87150, batch size: 61, loss[discriminator_loss=2.705, discriminator_real_loss=1.312, discriminator_fake_loss=1.393, generator_loss=29.11, generator_mel_loss=18.3, generator_kl_loss=1.428, generator_dur_loss=1.794, generator_adv_loss=1.902, generator_feat_match_loss=5.694, over 61.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.363, discriminator_fake_loss=1.341, generator_loss=28.7, generator_mel_loss=17.93, generator_kl_loss=1.432, generator_dur_loss=1.736, generator_adv_loss=1.977, generator_feat_match_loss=5.628, over 5793.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:27:15,214 INFO [train.py:919] (5/6) Start epoch 704 +2024-03-15 00:28:54,996 INFO [train.py:527] (5/6) Epoch 704, batch 28, global_batch_idx: 87200, batch size: 58, loss[discriminator_loss=2.634, discriminator_real_loss=1.317, discriminator_fake_loss=1.317, generator_loss=29.81, generator_mel_loss=18.48, generator_kl_loss=1.583, generator_dur_loss=1.715, generator_adv_loss=1.831, generator_feat_match_loss=6.201, over 58.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.371, discriminator_fake_loss=1.325, generator_loss=28.81, generator_mel_loss=18.01, generator_kl_loss=1.483, generator_dur_loss=1.711, generator_adv_loss=1.981, generator_feat_match_loss=5.627, over 1498.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:28:54,997 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 00:29:02,879 INFO [train.py:591] (5/6) 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 00:29:02,880 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 00:31:24,753 INFO [train.py:527] (5/6) Epoch 704, batch 78, global_batch_idx: 87250, batch size: 48, loss[discriminator_loss=2.705, discriminator_real_loss=1.308, discriminator_fake_loss=1.397, generator_loss=28.35, generator_mel_loss=17.78, generator_kl_loss=1.588, generator_dur_loss=1.688, generator_adv_loss=2.235, generator_feat_match_loss=5.058, over 48.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.364, discriminator_fake_loss=1.326, generator_loss=28.64, generator_mel_loss=17.9, generator_kl_loss=1.451, generator_dur_loss=1.736, generator_adv_loss=1.984, generator_feat_match_loss=5.575, over 4378.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:33:32,785 INFO [train.py:919] (5/6) Start epoch 705 +2024-03-15 00:34:08,435 INFO [train.py:527] (5/6) Epoch 705, batch 4, global_batch_idx: 87300, batch size: 47, loss[discriminator_loss=2.685, discriminator_real_loss=1.349, discriminator_fake_loss=1.336, generator_loss=28.06, generator_mel_loss=17.62, generator_kl_loss=1.475, generator_dur_loss=1.649, generator_adv_loss=2.008, generator_feat_match_loss=5.311, over 47.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.388, discriminator_fake_loss=1.3, generator_loss=28.36, generator_mel_loss=17.8, generator_kl_loss=1.361, generator_dur_loss=1.752, generator_adv_loss=2.006, generator_feat_match_loss=5.447, over 295.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:36:28,281 INFO [train.py:527] (5/6) Epoch 705, batch 54, global_batch_idx: 87350, batch size: 66, loss[discriminator_loss=2.72, discriminator_real_loss=1.442, discriminator_fake_loss=1.279, generator_loss=29.4, generator_mel_loss=18.5, generator_kl_loss=1.314, generator_dur_loss=1.693, generator_adv_loss=1.904, generator_feat_match_loss=5.989, over 66.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.363, discriminator_fake_loss=1.324, generator_loss=28.87, generator_mel_loss=17.97, generator_kl_loss=1.423, generator_dur_loss=1.725, generator_adv_loss=2.005, generator_feat_match_loss=5.75, over 3247.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:38:47,703 INFO [train.py:527] (5/6) Epoch 705, batch 104, global_batch_idx: 87400, batch size: 42, loss[discriminator_loss=2.672, discriminator_real_loss=1.319, discriminator_fake_loss=1.354, generator_loss=27.9, generator_mel_loss=17.85, generator_kl_loss=1.497, generator_dur_loss=1.641, generator_adv_loss=1.841, generator_feat_match_loss=5.076, over 42.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.82, generator_mel_loss=17.96, generator_kl_loss=1.414, generator_dur_loss=1.723, generator_adv_loss=1.995, generator_feat_match_loss=5.724, over 6322.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:38:47,704 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 00:38:56,480 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 00:39:49,208 INFO [train.py:919] (5/6) Start epoch 706 +2024-03-15 00:41:36,838 INFO [train.py:527] (5/6) Epoch 706, batch 30, global_batch_idx: 87450, batch size: 31, loss[discriminator_loss=2.653, discriminator_real_loss=1.371, discriminator_fake_loss=1.282, generator_loss=27.39, generator_mel_loss=17.55, generator_kl_loss=1.587, generator_dur_loss=1.656, generator_adv_loss=1.913, generator_feat_match_loss=4.686, over 31.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.348, discriminator_fake_loss=1.337, generator_loss=28.77, generator_mel_loss=17.94, generator_kl_loss=1.44, generator_dur_loss=1.713, generator_adv_loss=1.982, generator_feat_match_loss=5.703, over 1783.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:43:57,088 INFO [train.py:527] (5/6) Epoch 706, batch 80, global_batch_idx: 87500, batch size: 77, loss[discriminator_loss=2.693, discriminator_real_loss=1.258, discriminator_fake_loss=1.435, generator_loss=29.23, generator_mel_loss=17.97, generator_kl_loss=1.37, generator_dur_loss=1.785, generator_adv_loss=2.093, generator_feat_match_loss=6.011, over 77.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.354, discriminator_fake_loss=1.336, generator_loss=28.7, generator_mel_loss=17.92, generator_kl_loss=1.424, generator_dur_loss=1.719, generator_adv_loss=1.988, generator_feat_match_loss=5.657, over 4554.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:45:56,554 INFO [train.py:919] (5/6) Start epoch 707 +2024-03-15 00:46:35,730 INFO [train.py:527] (5/6) Epoch 707, batch 6, global_batch_idx: 87550, batch size: 62, loss[discriminator_loss=2.652, discriminator_real_loss=1.375, discriminator_fake_loss=1.276, generator_loss=28.81, generator_mel_loss=17.87, generator_kl_loss=1.468, generator_dur_loss=1.72, generator_adv_loss=1.95, generator_feat_match_loss=5.807, over 62.00 samples.], tot_loss[discriminator_loss=2.653, discriminator_real_loss=1.327, discriminator_fake_loss=1.326, generator_loss=28.99, generator_mel_loss=17.97, generator_kl_loss=1.497, generator_dur_loss=1.692, generator_adv_loss=2.016, generator_feat_match_loss=5.817, over 386.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:48:55,421 INFO [train.py:527] (5/6) Epoch 707, batch 56, global_batch_idx: 87600, batch size: 68, loss[discriminator_loss=2.662, discriminator_real_loss=1.376, discriminator_fake_loss=1.286, generator_loss=28.58, generator_mel_loss=17.56, generator_kl_loss=1.344, generator_dur_loss=1.78, generator_adv_loss=2.037, generator_feat_match_loss=5.863, over 68.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.36, discriminator_fake_loss=1.321, generator_loss=28.8, generator_mel_loss=17.92, generator_kl_loss=1.425, generator_dur_loss=1.732, generator_adv_loss=1.99, generator_feat_match_loss=5.726, over 3288.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:48:55,422 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 00:49:03,328 INFO [train.py:591] (5/6) 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 00:49:03,329 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 00:51:23,041 INFO [train.py:527] (5/6) Epoch 707, batch 106, global_batch_idx: 87650, batch size: 88, loss[discriminator_loss=2.68, discriminator_real_loss=1.377, discriminator_fake_loss=1.303, generator_loss=28.67, generator_mel_loss=17.74, generator_kl_loss=1.462, generator_dur_loss=1.815, generator_adv_loss=1.869, generator_feat_match_loss=5.78, over 88.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.357, discriminator_fake_loss=1.326, generator_loss=28.74, generator_mel_loss=17.89, generator_kl_loss=1.425, generator_dur_loss=1.74, generator_adv_loss=1.986, generator_feat_match_loss=5.695, over 6222.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:52:08,771 INFO [train.py:919] (5/6) Start epoch 708 +2024-03-15 00:54:01,607 INFO [train.py:527] (5/6) Epoch 708, batch 32, global_batch_idx: 87700, batch size: 88, loss[discriminator_loss=2.736, discriminator_real_loss=1.431, discriminator_fake_loss=1.305, generator_loss=27.15, generator_mel_loss=17.37, generator_kl_loss=1.259, generator_dur_loss=1.816, generator_adv_loss=1.937, generator_feat_match_loss=4.775, over 88.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.328, generator_loss=28.62, generator_mel_loss=17.94, generator_kl_loss=1.414, generator_dur_loss=1.746, generator_adv_loss=1.976, generator_feat_match_loss=5.551, over 1795.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:56:16,886 INFO [train.py:527] (5/6) Epoch 708, batch 82, global_batch_idx: 87750, batch size: 72, loss[discriminator_loss=2.683, discriminator_real_loss=1.4, discriminator_fake_loss=1.284, generator_loss=28.08, generator_mel_loss=17.48, generator_kl_loss=1.413, generator_dur_loss=1.823, generator_adv_loss=1.979, generator_feat_match_loss=5.393, over 72.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.363, discriminator_fake_loss=1.328, generator_loss=28.68, generator_mel_loss=17.9, generator_kl_loss=1.405, generator_dur_loss=1.738, generator_adv_loss=1.986, generator_feat_match_loss=5.65, over 4628.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:58:15,515 INFO [train.py:919] (5/6) Start epoch 709 +2024-03-15 00:59:02,277 INFO [train.py:527] (5/6) Epoch 709, batch 8, global_batch_idx: 87800, batch size: 12, loss[discriminator_loss=2.657, discriminator_real_loss=1.365, discriminator_fake_loss=1.292, generator_loss=29.04, generator_mel_loss=18.17, generator_kl_loss=1.867, generator_dur_loss=1.624, generator_adv_loss=1.969, generator_feat_match_loss=5.413, over 12.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.349, discriminator_fake_loss=1.348, generator_loss=28.74, generator_mel_loss=17.96, generator_kl_loss=1.404, generator_dur_loss=1.735, generator_adv_loss=1.99, generator_feat_match_loss=5.654, over 462.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 00:59:02,280 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 00:59:10,125 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 01:01:30,072 INFO [train.py:527] (5/6) Epoch 709, batch 58, global_batch_idx: 87850, batch size: 80, loss[discriminator_loss=2.632, discriminator_real_loss=1.328, discriminator_fake_loss=1.304, generator_loss=29.46, generator_mel_loss=17.98, generator_kl_loss=1.299, generator_dur_loss=1.765, generator_adv_loss=2.003, generator_feat_match_loss=6.42, over 80.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.354, discriminator_fake_loss=1.34, generator_loss=28.89, generator_mel_loss=18, generator_kl_loss=1.428, generator_dur_loss=1.729, generator_adv_loss=1.981, generator_feat_match_loss=5.751, over 3316.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:03:45,842 INFO [train.py:527] (5/6) Epoch 709, batch 108, global_batch_idx: 87900, batch size: 53, loss[discriminator_loss=2.623, discriminator_real_loss=1.368, discriminator_fake_loss=1.256, generator_loss=29.92, generator_mel_loss=18.54, generator_kl_loss=1.484, generator_dur_loss=1.699, generator_adv_loss=2.02, generator_feat_match_loss=6.17, over 53.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.355, discriminator_fake_loss=1.337, generator_loss=28.86, generator_mel_loss=17.97, generator_kl_loss=1.423, generator_dur_loss=1.725, generator_adv_loss=1.981, generator_feat_match_loss=5.76, over 6087.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:04:30,147 INFO [train.py:919] (5/6) Start epoch 710 +2024-03-15 01:06:28,103 INFO [train.py:527] (5/6) Epoch 710, batch 34, global_batch_idx: 87950, batch size: 58, loss[discriminator_loss=2.673, discriminator_real_loss=1.395, discriminator_fake_loss=1.278, generator_loss=29.18, generator_mel_loss=18.23, generator_kl_loss=1.376, generator_dur_loss=1.742, generator_adv_loss=1.981, generator_feat_match_loss=5.856, over 58.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.357, discriminator_fake_loss=1.332, generator_loss=28.78, generator_mel_loss=17.89, generator_kl_loss=1.458, generator_dur_loss=1.727, generator_adv_loss=1.988, generator_feat_match_loss=5.712, over 1911.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:08:49,103 INFO [train.py:527] (5/6) Epoch 710, batch 84, global_batch_idx: 88000, batch size: 39, loss[discriminator_loss=2.629, discriminator_real_loss=1.331, discriminator_fake_loss=1.298, generator_loss=29.79, generator_mel_loss=18.1, generator_kl_loss=1.7, generator_dur_loss=1.635, generator_adv_loss=1.996, generator_feat_match_loss=6.358, over 39.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.356, discriminator_fake_loss=1.332, generator_loss=28.88, generator_mel_loss=17.93, generator_kl_loss=1.433, generator_dur_loss=1.73, generator_adv_loss=1.986, generator_feat_match_loss=5.8, over 4880.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:08:49,104 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 01:08:57,851 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 01:10:45,187 INFO [train.py:919] (5/6) Start epoch 711 +2024-03-15 01:11:36,983 INFO [train.py:527] (5/6) Epoch 711, batch 10, global_batch_idx: 88050, batch size: 52, loss[discriminator_loss=2.76, discriminator_real_loss=1.295, discriminator_fake_loss=1.465, generator_loss=28.45, generator_mel_loss=17.74, generator_kl_loss=1.359, generator_dur_loss=1.747, generator_adv_loss=2.163, generator_feat_match_loss=5.44, over 52.00 samples.], tot_loss[discriminator_loss=2.617, discriminator_real_loss=1.308, discriminator_fake_loss=1.31, generator_loss=29.28, generator_mel_loss=17.91, generator_kl_loss=1.411, generator_dur_loss=1.721, generator_adv_loss=2.235, generator_feat_match_loss=6, over 532.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:13:57,357 INFO [train.py:527] (5/6) Epoch 711, batch 60, global_batch_idx: 88100, batch size: 66, loss[discriminator_loss=2.659, discriminator_real_loss=1.277, discriminator_fake_loss=1.381, generator_loss=28.18, generator_mel_loss=17.5, generator_kl_loss=1.365, generator_dur_loss=1.74, generator_adv_loss=1.988, generator_feat_match_loss=5.588, over 66.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.324, generator_loss=28.81, generator_mel_loss=17.88, generator_kl_loss=1.408, generator_dur_loss=1.745, generator_adv_loss=2.051, generator_feat_match_loss=5.725, over 3434.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:16:14,965 INFO [train.py:527] (5/6) Epoch 711, batch 110, global_batch_idx: 88150, batch size: 48, loss[discriminator_loss=2.73, discriminator_real_loss=1.364, discriminator_fake_loss=1.366, generator_loss=29.08, generator_mel_loss=17.88, generator_kl_loss=1.551, generator_dur_loss=1.62, generator_adv_loss=2.092, generator_feat_match_loss=5.935, over 48.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=28.69, generator_mel_loss=17.86, generator_kl_loss=1.398, generator_dur_loss=1.751, generator_adv_loss=2.022, generator_feat_match_loss=5.662, over 6380.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:16:50,998 INFO [train.py:919] (5/6) Start epoch 712 +2024-03-15 01:18:56,793 INFO [train.py:527] (5/6) Epoch 712, batch 36, global_batch_idx: 88200, batch size: 64, loss[discriminator_loss=2.681, discriminator_real_loss=1.362, discriminator_fake_loss=1.319, generator_loss=29.41, generator_mel_loss=18, generator_kl_loss=1.361, generator_dur_loss=1.762, generator_adv_loss=2.008, generator_feat_match_loss=6.28, over 64.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.363, discriminator_fake_loss=1.317, generator_loss=28.82, generator_mel_loss=17.94, generator_kl_loss=1.405, generator_dur_loss=1.741, generator_adv_loss=2.003, generator_feat_match_loss=5.738, over 2155.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:18:56,794 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 01:19:04,760 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 01:21:21,723 INFO [train.py:527] (5/6) Epoch 712, batch 86, global_batch_idx: 88250, batch size: 48, loss[discriminator_loss=2.669, discriminator_real_loss=1.275, discriminator_fake_loss=1.394, generator_loss=28.95, generator_mel_loss=17.99, generator_kl_loss=1.496, generator_dur_loss=1.69, generator_adv_loss=2.084, generator_feat_match_loss=5.697, over 48.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.354, discriminator_fake_loss=1.329, generator_loss=28.74, generator_mel_loss=17.95, generator_kl_loss=1.42, generator_dur_loss=1.741, generator_adv_loss=1.986, generator_feat_match_loss=5.65, over 4935.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:23:08,193 INFO [train.py:919] (5/6) Start epoch 713 +2024-03-15 01:24:05,810 INFO [train.py:527] (5/6) Epoch 713, batch 12, global_batch_idx: 88300, batch size: 80, loss[discriminator_loss=2.774, discriminator_real_loss=1.366, discriminator_fake_loss=1.407, generator_loss=28.58, generator_mel_loss=18.16, generator_kl_loss=1.374, generator_dur_loss=1.827, generator_adv_loss=1.903, generator_feat_match_loss=5.313, over 80.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.38, discriminator_fake_loss=1.317, generator_loss=28.93, generator_mel_loss=17.97, generator_kl_loss=1.479, generator_dur_loss=1.73, generator_adv_loss=2, generator_feat_match_loss=5.75, over 736.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:26:24,684 INFO [train.py:527] (5/6) Epoch 713, batch 62, global_batch_idx: 88350, batch size: 56, loss[discriminator_loss=2.705, discriminator_real_loss=1.343, discriminator_fake_loss=1.362, generator_loss=28.16, generator_mel_loss=17.86, generator_kl_loss=1.314, generator_dur_loss=1.724, generator_adv_loss=2.162, generator_feat_match_loss=5.107, over 56.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.363, discriminator_fake_loss=1.328, generator_loss=28.9, generator_mel_loss=17.96, generator_kl_loss=1.456, generator_dur_loss=1.735, generator_adv_loss=1.996, generator_feat_match_loss=5.744, over 3502.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:28:42,500 INFO [train.py:527] (5/6) Epoch 713, batch 112, global_batch_idx: 88400, batch size: 25, loss[discriminator_loss=2.592, discriminator_real_loss=1.269, discriminator_fake_loss=1.324, generator_loss=30.29, generator_mel_loss=18.34, generator_kl_loss=1.726, generator_dur_loss=1.585, generator_adv_loss=2.026, generator_feat_match_loss=6.613, over 25.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.361, discriminator_fake_loss=1.332, generator_loss=28.78, generator_mel_loss=17.92, generator_kl_loss=1.43, generator_dur_loss=1.744, generator_adv_loss=1.991, generator_feat_match_loss=5.696, over 6398.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:28:42,502 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 01:28:51,050 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 01:29:23,175 INFO [train.py:919] (5/6) Start epoch 714 +2024-03-15 01:31:29,685 INFO [train.py:527] (5/6) Epoch 714, batch 38, global_batch_idx: 88450, batch size: 96, loss[discriminator_loss=2.724, discriminator_real_loss=1.368, discriminator_fake_loss=1.356, generator_loss=28.17, generator_mel_loss=17.58, generator_kl_loss=1.36, generator_dur_loss=1.847, generator_adv_loss=1.937, generator_feat_match_loss=5.439, over 96.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.363, discriminator_fake_loss=1.334, generator_loss=28.68, generator_mel_loss=17.92, generator_kl_loss=1.388, generator_dur_loss=1.748, generator_adv_loss=1.976, generator_feat_match_loss=5.653, over 2266.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:33:51,464 INFO [train.py:527] (5/6) Epoch 714, batch 88, global_batch_idx: 88500, batch size: 44, loss[discriminator_loss=2.627, discriminator_real_loss=1.258, discriminator_fake_loss=1.37, generator_loss=29.13, generator_mel_loss=17.73, generator_kl_loss=1.641, generator_dur_loss=1.646, generator_adv_loss=1.922, generator_feat_match_loss=6.192, over 44.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.328, generator_loss=28.78, generator_mel_loss=17.89, generator_kl_loss=1.403, generator_dur_loss=1.742, generator_adv_loss=2.027, generator_feat_match_loss=5.713, over 4975.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:35:30,159 INFO [train.py:919] (5/6) Start epoch 715 +2024-03-15 01:36:33,325 INFO [train.py:527] (5/6) Epoch 715, batch 14, global_batch_idx: 88550, batch size: 36, loss[discriminator_loss=2.749, discriminator_real_loss=1.437, discriminator_fake_loss=1.312, generator_loss=28.42, generator_mel_loss=17.95, generator_kl_loss=1.454, generator_dur_loss=1.706, generator_adv_loss=2.023, generator_feat_match_loss=5.279, over 36.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.376, discriminator_fake_loss=1.31, generator_loss=28.7, generator_mel_loss=17.88, generator_kl_loss=1.433, generator_dur_loss=1.749, generator_adv_loss=1.996, generator_feat_match_loss=5.646, over 854.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:38:51,319 INFO [train.py:527] (5/6) Epoch 715, batch 64, global_batch_idx: 88600, batch size: 56, loss[discriminator_loss=2.619, discriminator_real_loss=1.29, discriminator_fake_loss=1.33, generator_loss=29.44, generator_mel_loss=18.07, generator_kl_loss=1.37, generator_dur_loss=1.767, generator_adv_loss=1.94, generator_feat_match_loss=6.291, over 56.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.359, discriminator_fake_loss=1.332, generator_loss=28.55, generator_mel_loss=17.85, generator_kl_loss=1.399, generator_dur_loss=1.761, generator_adv_loss=1.968, generator_feat_match_loss=5.568, over 3887.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:38:51,320 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 01:38:59,980 INFO [train.py:591] (5/6) 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 01:38:59,981 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 01:41:17,096 INFO [train.py:527] (5/6) Epoch 715, batch 114, global_batch_idx: 88650, batch size: 50, loss[discriminator_loss=2.677, discriminator_real_loss=1.304, discriminator_fake_loss=1.374, generator_loss=29.59, generator_mel_loss=18.39, generator_kl_loss=1.404, generator_dur_loss=1.681, generator_adv_loss=2.119, generator_feat_match_loss=5.995, over 50.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.355, discriminator_fake_loss=1.329, generator_loss=28.71, generator_mel_loss=17.88, generator_kl_loss=1.414, generator_dur_loss=1.751, generator_adv_loss=1.973, generator_feat_match_loss=5.689, over 6763.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:41:44,840 INFO [train.py:919] (5/6) Start epoch 716 +2024-03-15 01:44:01,676 INFO [train.py:527] (5/6) Epoch 716, batch 40, global_batch_idx: 88700, batch size: 56, loss[discriminator_loss=2.694, discriminator_real_loss=1.415, discriminator_fake_loss=1.279, generator_loss=28.6, generator_mel_loss=18.25, generator_kl_loss=1.442, generator_dur_loss=1.765, generator_adv_loss=1.88, generator_feat_match_loss=5.258, over 56.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.365, discriminator_fake_loss=1.322, generator_loss=28.59, generator_mel_loss=17.86, generator_kl_loss=1.358, generator_dur_loss=1.781, generator_adv_loss=1.991, generator_feat_match_loss=5.598, over 2581.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:46:19,795 INFO [train.py:527] (5/6) Epoch 716, batch 90, global_batch_idx: 88750, batch size: 39, loss[discriminator_loss=2.745, discriminator_real_loss=1.399, discriminator_fake_loss=1.346, generator_loss=29.55, generator_mel_loss=18.29, generator_kl_loss=1.472, generator_dur_loss=1.693, generator_adv_loss=2.091, generator_feat_match_loss=6.003, over 39.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.7, generator_mel_loss=17.88, generator_kl_loss=1.382, generator_dur_loss=1.764, generator_adv_loss=1.989, generator_feat_match_loss=5.682, over 5420.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:47:50,726 INFO [train.py:919] (5/6) Start epoch 717 +2024-03-15 01:48:57,840 INFO [train.py:527] (5/6) Epoch 717, batch 16, global_batch_idx: 88800, batch size: 74, loss[discriminator_loss=2.666, discriminator_real_loss=1.298, discriminator_fake_loss=1.368, generator_loss=28.02, generator_mel_loss=17.66, generator_kl_loss=1.342, generator_dur_loss=1.778, generator_adv_loss=2.146, generator_feat_match_loss=5.093, over 74.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.361, discriminator_fake_loss=1.333, generator_loss=28.55, generator_mel_loss=17.93, generator_kl_loss=1.38, generator_dur_loss=1.76, generator_adv_loss=1.993, generator_feat_match_loss=5.491, over 1014.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:48:57,842 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 01:49:05,800 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 01:51:22,264 INFO [train.py:527] (5/6) Epoch 717, batch 66, global_batch_idx: 88850, batch size: 74, loss[discriminator_loss=2.727, discriminator_real_loss=1.305, discriminator_fake_loss=1.422, generator_loss=28.15, generator_mel_loss=17.9, generator_kl_loss=1.36, generator_dur_loss=1.794, generator_adv_loss=1.952, generator_feat_match_loss=5.14, over 74.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.367, discriminator_fake_loss=1.331, generator_loss=28.6, generator_mel_loss=17.88, generator_kl_loss=1.399, generator_dur_loss=1.748, generator_adv_loss=1.983, generator_feat_match_loss=5.586, over 3775.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:53:43,573 INFO [train.py:527] (5/6) Epoch 717, batch 116, global_batch_idx: 88900, batch size: 55, loss[discriminator_loss=2.693, discriminator_real_loss=1.378, discriminator_fake_loss=1.315, generator_loss=29.47, generator_mel_loss=18.33, generator_kl_loss=1.41, generator_dur_loss=1.662, generator_adv_loss=1.84, generator_feat_match_loss=6.233, over 55.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.365, discriminator_fake_loss=1.332, generator_loss=28.71, generator_mel_loss=17.88, generator_kl_loss=1.402, generator_dur_loss=1.747, generator_adv_loss=1.984, generator_feat_match_loss=5.691, over 6711.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:54:04,304 INFO [train.py:919] (5/6) Start epoch 718 +2024-03-15 01:56:26,071 INFO [train.py:527] (5/6) Epoch 718, batch 42, global_batch_idx: 88950, batch size: 53, loss[discriminator_loss=2.749, discriminator_real_loss=1.442, discriminator_fake_loss=1.307, generator_loss=29.47, generator_mel_loss=17.81, generator_kl_loss=1.541, generator_dur_loss=1.654, generator_adv_loss=2.214, generator_feat_match_loss=6.247, over 53.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.357, discriminator_fake_loss=1.337, generator_loss=29, generator_mel_loss=17.95, generator_kl_loss=1.424, generator_dur_loss=1.724, generator_adv_loss=2.024, generator_feat_match_loss=5.876, over 2151.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:58:45,649 INFO [train.py:527] (5/6) Epoch 718, batch 92, global_batch_idx: 89000, batch size: 66, loss[discriminator_loss=2.691, discriminator_real_loss=1.447, discriminator_fake_loss=1.244, generator_loss=27.75, generator_mel_loss=17.51, generator_kl_loss=1.411, generator_dur_loss=1.756, generator_adv_loss=1.941, generator_feat_match_loss=5.138, over 66.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.365, discriminator_fake_loss=1.33, generator_loss=28.79, generator_mel_loss=17.88, generator_kl_loss=1.409, generator_dur_loss=1.745, generator_adv_loss=2.014, generator_feat_match_loss=5.738, over 5239.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 01:58:45,650 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 01:58:54,588 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 02:00:21,352 INFO [train.py:919] (5/6) Start epoch 719 +2024-03-15 02:01:33,168 INFO [train.py:527] (5/6) Epoch 719, batch 18, global_batch_idx: 89050, batch size: 66, loss[discriminator_loss=2.71, discriminator_real_loss=1.364, discriminator_fake_loss=1.346, generator_loss=28.36, generator_mel_loss=18.01, generator_kl_loss=1.273, generator_dur_loss=1.749, generator_adv_loss=1.91, generator_feat_match_loss=5.416, over 66.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.373, discriminator_fake_loss=1.322, generator_loss=28.65, generator_mel_loss=17.96, generator_kl_loss=1.456, generator_dur_loss=1.726, generator_adv_loss=1.979, generator_feat_match_loss=5.528, over 1039.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:03:52,423 INFO [train.py:527] (5/6) Epoch 719, batch 68, global_batch_idx: 89100, batch size: 48, loss[discriminator_loss=2.694, discriminator_real_loss=1.326, discriminator_fake_loss=1.368, generator_loss=29.26, generator_mel_loss=17.89, generator_kl_loss=1.36, generator_dur_loss=1.678, generator_adv_loss=1.895, generator_feat_match_loss=6.436, over 48.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.366, discriminator_fake_loss=1.321, generator_loss=28.64, generator_mel_loss=17.87, generator_kl_loss=1.437, generator_dur_loss=1.73, generator_adv_loss=1.985, generator_feat_match_loss=5.615, over 3825.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:06:13,108 INFO [train.py:527] (5/6) Epoch 719, batch 118, global_batch_idx: 89150, batch size: 56, loss[discriminator_loss=2.742, discriminator_real_loss=1.471, discriminator_fake_loss=1.271, generator_loss=28.68, generator_mel_loss=18.21, generator_kl_loss=1.376, generator_dur_loss=1.721, generator_adv_loss=1.834, generator_feat_match_loss=5.535, over 56.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.364, discriminator_fake_loss=1.327, generator_loss=28.66, generator_mel_loss=17.88, generator_kl_loss=1.421, generator_dur_loss=1.739, generator_adv_loss=1.983, generator_feat_match_loss=5.645, over 6791.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:06:30,649 INFO [train.py:919] (5/6) Start epoch 720 +2024-03-15 02:09:00,205 INFO [train.py:527] (5/6) Epoch 720, batch 44, global_batch_idx: 89200, batch size: 25, loss[discriminator_loss=2.666, discriminator_real_loss=1.389, discriminator_fake_loss=1.276, generator_loss=30.6, generator_mel_loss=18.78, generator_kl_loss=1.647, generator_dur_loss=1.524, generator_adv_loss=2.032, generator_feat_match_loss=6.616, over 25.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.353, discriminator_fake_loss=1.333, generator_loss=28.72, generator_mel_loss=17.88, generator_kl_loss=1.438, generator_dur_loss=1.725, generator_adv_loss=1.989, generator_feat_match_loss=5.687, over 2577.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:09:00,207 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 02:09:08,034 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 02:11:30,243 INFO [train.py:527] (5/6) Epoch 720, batch 94, global_batch_idx: 89250, batch size: 74, loss[discriminator_loss=2.558, discriminator_real_loss=1.324, discriminator_fake_loss=1.234, generator_loss=28.97, generator_mel_loss=17.54, generator_kl_loss=1.272, generator_dur_loss=1.795, generator_adv_loss=2.199, generator_feat_match_loss=6.159, over 74.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.354, discriminator_fake_loss=1.337, generator_loss=28.84, generator_mel_loss=17.93, generator_kl_loss=1.439, generator_dur_loss=1.728, generator_adv_loss=1.996, generator_feat_match_loss=5.746, over 5290.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:12:55,080 INFO [train.py:919] (5/6) Start epoch 721 +2024-03-15 02:14:20,712 INFO [train.py:527] (5/6) Epoch 721, batch 20, global_batch_idx: 89300, batch size: 96, loss[discriminator_loss=2.676, discriminator_real_loss=1.346, discriminator_fake_loss=1.33, generator_loss=28.74, generator_mel_loss=17.75, generator_kl_loss=1.274, generator_dur_loss=1.836, generator_adv_loss=1.961, generator_feat_match_loss=5.924, over 96.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.364, discriminator_fake_loss=1.335, generator_loss=28.66, generator_mel_loss=17.89, generator_kl_loss=1.377, generator_dur_loss=1.767, generator_adv_loss=1.981, generator_feat_match_loss=5.647, over 1401.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:16:42,991 INFO [train.py:527] (5/6) Epoch 721, batch 70, global_batch_idx: 89350, batch size: 96, loss[discriminator_loss=2.684, discriminator_real_loss=1.323, discriminator_fake_loss=1.361, generator_loss=28.4, generator_mel_loss=17.53, generator_kl_loss=1.206, generator_dur_loss=1.851, generator_adv_loss=2.071, generator_feat_match_loss=5.747, over 96.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.363, discriminator_fake_loss=1.325, generator_loss=28.74, generator_mel_loss=17.91, generator_kl_loss=1.383, generator_dur_loss=1.753, generator_adv_loss=1.994, generator_feat_match_loss=5.701, over 4463.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:18:59,372 INFO [train.py:527] (5/6) Epoch 721, batch 120, global_batch_idx: 89400, batch size: 68, loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=29.32, generator_mel_loss=17.85, generator_kl_loss=1.561, generator_dur_loss=1.753, generator_adv_loss=1.945, generator_feat_match_loss=6.21, over 68.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.365, discriminator_fake_loss=1.325, generator_loss=28.7, generator_mel_loss=17.89, generator_kl_loss=1.395, generator_dur_loss=1.744, generator_adv_loss=1.992, generator_feat_match_loss=5.679, over 7145.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:18:59,373 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 02:19:08,218 INFO [train.py:591] (5/6) 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 02:19:08,218 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 02:19:17,043 INFO [train.py:919] (5/6) Start epoch 722 +2024-03-15 02:21:54,338 INFO [train.py:527] (5/6) Epoch 722, batch 46, global_batch_idx: 89450, batch size: 31, loss[discriminator_loss=2.637, discriminator_real_loss=1.295, discriminator_fake_loss=1.342, generator_loss=29.46, generator_mel_loss=17.73, generator_kl_loss=1.451, generator_dur_loss=1.626, generator_adv_loss=2.028, generator_feat_match_loss=6.625, over 31.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.362, discriminator_fake_loss=1.331, generator_loss=28.72, generator_mel_loss=17.91, generator_kl_loss=1.411, generator_dur_loss=1.76, generator_adv_loss=1.977, generator_feat_match_loss=5.662, over 2607.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:24:16,891 INFO [train.py:527] (5/6) Epoch 722, batch 96, global_batch_idx: 89500, batch size: 66, loss[discriminator_loss=2.599, discriminator_real_loss=1.311, discriminator_fake_loss=1.288, generator_loss=29.56, generator_mel_loss=18.13, generator_kl_loss=1.433, generator_dur_loss=1.696, generator_adv_loss=2.102, generator_feat_match_loss=6.198, over 66.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.328, generator_loss=28.76, generator_mel_loss=17.94, generator_kl_loss=1.407, generator_dur_loss=1.758, generator_adv_loss=1.989, generator_feat_match_loss=5.659, over 5436.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:25:33,095 INFO [train.py:919] (5/6) Start epoch 723 +2024-03-15 02:26:58,524 INFO [train.py:527] (5/6) Epoch 723, batch 22, global_batch_idx: 89550, batch size: 48, loss[discriminator_loss=2.735, discriminator_real_loss=1.266, discriminator_fake_loss=1.469, generator_loss=29.89, generator_mel_loss=18.22, generator_kl_loss=1.541, generator_dur_loss=1.629, generator_adv_loss=2.005, generator_feat_match_loss=6.498, over 48.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.352, discriminator_fake_loss=1.347, generator_loss=28.81, generator_mel_loss=17.88, generator_kl_loss=1.434, generator_dur_loss=1.726, generator_adv_loss=1.988, generator_feat_match_loss=5.783, over 1223.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:29:24,544 INFO [train.py:527] (5/6) Epoch 723, batch 72, global_batch_idx: 89600, batch size: 97, loss[discriminator_loss=2.646, discriminator_real_loss=1.291, discriminator_fake_loss=1.355, generator_loss=29.16, generator_mel_loss=17.93, generator_kl_loss=1.317, generator_dur_loss=1.813, generator_adv_loss=2.052, generator_feat_match_loss=6.05, over 97.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.33, generator_loss=28.71, generator_mel_loss=17.85, generator_kl_loss=1.415, generator_dur_loss=1.742, generator_adv_loss=1.994, generator_feat_match_loss=5.705, over 4164.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:29:24,545 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 02:29:32,617 INFO [train.py:591] (5/6) 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 02:29:32,617 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 02:31:49,137 INFO [train.py:527] (5/6) Epoch 723, batch 122, global_batch_idx: 89650, batch size: 31, loss[discriminator_loss=2.64, discriminator_real_loss=1.435, discriminator_fake_loss=1.205, generator_loss=29.92, generator_mel_loss=18.42, generator_kl_loss=1.65, generator_dur_loss=1.63, generator_adv_loss=2.081, generator_feat_match_loss=6.142, over 31.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=28.76, generator_mel_loss=17.89, generator_kl_loss=1.422, generator_dur_loss=1.733, generator_adv_loss=1.989, generator_feat_match_loss=5.73, over 6737.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:31:54,078 INFO [train.py:919] (5/6) Start epoch 724 +2024-03-15 02:34:30,890 INFO [train.py:527] (5/6) Epoch 724, batch 48, global_batch_idx: 89700, batch size: 77, loss[discriminator_loss=2.679, discriminator_real_loss=1.265, discriminator_fake_loss=1.414, generator_loss=27.75, generator_mel_loss=17.63, generator_kl_loss=1.216, generator_dur_loss=1.835, generator_adv_loss=2.031, generator_feat_match_loss=5.044, over 77.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.353, discriminator_fake_loss=1.334, generator_loss=28.82, generator_mel_loss=17.98, generator_kl_loss=1.455, generator_dur_loss=1.728, generator_adv_loss=1.981, generator_feat_match_loss=5.673, over 2652.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:36:53,173 INFO [train.py:527] (5/6) Epoch 724, batch 98, global_batch_idx: 89750, batch size: 88, loss[discriminator_loss=2.664, discriminator_real_loss=1.301, discriminator_fake_loss=1.363, generator_loss=28.63, generator_mel_loss=17.61, generator_kl_loss=1.453, generator_dur_loss=1.779, generator_adv_loss=2.006, generator_feat_match_loss=5.787, over 88.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.85, generator_mel_loss=17.96, generator_kl_loss=1.446, generator_dur_loss=1.728, generator_adv_loss=1.997, generator_feat_match_loss=5.717, over 5359.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:38:04,238 INFO [train.py:919] (5/6) Start epoch 725 +2024-03-15 02:39:31,940 INFO [train.py:527] (5/6) Epoch 725, batch 24, global_batch_idx: 89800, batch size: 64, loss[discriminator_loss=2.726, discriminator_real_loss=1.359, discriminator_fake_loss=1.366, generator_loss=28.53, generator_mel_loss=18.03, generator_kl_loss=1.478, generator_dur_loss=1.734, generator_adv_loss=1.844, generator_feat_match_loss=5.443, over 64.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.355, discriminator_fake_loss=1.336, generator_loss=28.73, generator_mel_loss=17.92, generator_kl_loss=1.422, generator_dur_loss=1.728, generator_adv_loss=1.972, generator_feat_match_loss=5.689, over 1414.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:39:31,941 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 02:39:39,701 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 02:41:56,853 INFO [train.py:527] (5/6) Epoch 725, batch 74, global_batch_idx: 89850, batch size: 48, loss[discriminator_loss=2.682, discriminator_real_loss=1.409, discriminator_fake_loss=1.274, generator_loss=28.99, generator_mel_loss=18.03, generator_kl_loss=1.48, generator_dur_loss=1.691, generator_adv_loss=1.941, generator_feat_match_loss=5.845, over 48.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.354, discriminator_fake_loss=1.335, generator_loss=28.7, generator_mel_loss=17.88, generator_kl_loss=1.409, generator_dur_loss=1.745, generator_adv_loss=1.98, generator_feat_match_loss=5.687, over 4298.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:44:14,737 INFO [train.py:919] (5/6) Start epoch 726 +2024-03-15 02:44:38,432 INFO [train.py:527] (5/6) Epoch 726, batch 0, global_batch_idx: 89900, batch size: 56, loss[discriminator_loss=2.67, discriminator_real_loss=1.429, discriminator_fake_loss=1.241, generator_loss=29.48, generator_mel_loss=18.21, generator_kl_loss=1.572, generator_dur_loss=1.728, generator_adv_loss=1.995, generator_feat_match_loss=5.975, over 56.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.429, discriminator_fake_loss=1.241, generator_loss=29.48, generator_mel_loss=18.21, generator_kl_loss=1.572, generator_dur_loss=1.728, generator_adv_loss=1.995, generator_feat_match_loss=5.975, over 56.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:46:55,459 INFO [train.py:527] (5/6) Epoch 726, batch 50, global_batch_idx: 89950, batch size: 96, loss[discriminator_loss=2.725, discriminator_real_loss=1.357, discriminator_fake_loss=1.368, generator_loss=28.28, generator_mel_loss=17.62, generator_kl_loss=1.323, generator_dur_loss=1.859, generator_adv_loss=2.011, generator_feat_match_loss=5.468, over 96.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.36, discriminator_fake_loss=1.329, generator_loss=28.85, generator_mel_loss=17.88, generator_kl_loss=1.445, generator_dur_loss=1.734, generator_adv_loss=1.999, generator_feat_match_loss=5.793, over 2951.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:49:12,529 INFO [train.py:527] (5/6) Epoch 726, batch 100, global_batch_idx: 90000, batch size: 55, loss[discriminator_loss=2.688, discriminator_real_loss=1.35, discriminator_fake_loss=1.338, generator_loss=29.31, generator_mel_loss=18.2, generator_kl_loss=1.36, generator_dur_loss=1.737, generator_adv_loss=1.969, generator_feat_match_loss=6.049, over 55.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.357, discriminator_fake_loss=1.333, generator_loss=28.72, generator_mel_loss=17.85, generator_kl_loss=1.432, generator_dur_loss=1.738, generator_adv_loss=1.988, generator_feat_match_loss=5.713, over 5779.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:49:12,530 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 02:49:21,319 INFO [train.py:591] (5/6) 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 02:49:21,320 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 02:50:25,952 INFO [train.py:919] (5/6) Start epoch 727 +2024-03-15 02:52:03,334 INFO [train.py:527] (5/6) Epoch 727, batch 26, global_batch_idx: 90050, batch size: 45, loss[discriminator_loss=2.627, discriminator_real_loss=1.285, discriminator_fake_loss=1.342, generator_loss=30.76, generator_mel_loss=17.85, generator_kl_loss=1.713, generator_dur_loss=1.625, generator_adv_loss=1.92, generator_feat_match_loss=7.653, over 45.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.81, generator_mel_loss=17.81, generator_kl_loss=1.41, generator_dur_loss=1.748, generator_adv_loss=2, generator_feat_match_loss=5.848, over 1647.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:54:22,678 INFO [train.py:527] (5/6) Epoch 727, batch 76, global_batch_idx: 90100, batch size: 96, loss[discriminator_loss=2.71, discriminator_real_loss=1.368, discriminator_fake_loss=1.342, generator_loss=28.34, generator_mel_loss=17.62, generator_kl_loss=1.42, generator_dur_loss=1.849, generator_adv_loss=1.992, generator_feat_match_loss=5.466, over 96.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.36, discriminator_fake_loss=1.332, generator_loss=28.76, generator_mel_loss=17.86, generator_kl_loss=1.425, generator_dur_loss=1.732, generator_adv_loss=1.999, generator_feat_match_loss=5.745, over 4329.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:56:30,544 INFO [train.py:919] (5/6) Start epoch 728 +2024-03-15 02:56:59,582 INFO [train.py:527] (5/6) Epoch 728, batch 2, global_batch_idx: 90150, batch size: 42, loss[discriminator_loss=2.743, discriminator_real_loss=1.439, discriminator_fake_loss=1.304, generator_loss=28.88, generator_mel_loss=17.55, generator_kl_loss=1.608, generator_dur_loss=1.695, generator_adv_loss=1.997, generator_feat_match_loss=6.031, over 42.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.339, discriminator_fake_loss=1.364, generator_loss=29.08, generator_mel_loss=17.89, generator_kl_loss=1.551, generator_dur_loss=1.74, generator_adv_loss=1.984, generator_feat_match_loss=5.909, over 165.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:59:17,555 INFO [train.py:527] (5/6) Epoch 728, batch 52, global_batch_idx: 90200, batch size: 55, loss[discriminator_loss=2.689, discriminator_real_loss=1.341, discriminator_fake_loss=1.348, generator_loss=28.45, generator_mel_loss=18.34, generator_kl_loss=1.537, generator_dur_loss=1.68, generator_adv_loss=2.084, generator_feat_match_loss=4.814, over 55.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.76, generator_mel_loss=17.97, generator_kl_loss=1.425, generator_dur_loss=1.741, generator_adv_loss=1.98, generator_feat_match_loss=5.647, over 2965.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 02:59:17,556 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 02:59:25,701 INFO [train.py:591] (5/6) 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 02:59:25,702 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 03:01:42,911 INFO [train.py:527] (5/6) Epoch 728, batch 102, global_batch_idx: 90250, batch size: 62, loss[discriminator_loss=2.641, discriminator_real_loss=1.336, discriminator_fake_loss=1.305, generator_loss=28.49, generator_mel_loss=17.61, generator_kl_loss=1.433, generator_dur_loss=1.785, generator_adv_loss=2.016, generator_feat_match_loss=5.643, over 62.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.36, discriminator_fake_loss=1.331, generator_loss=28.71, generator_mel_loss=17.93, generator_kl_loss=1.414, generator_dur_loss=1.743, generator_adv_loss=1.988, generator_feat_match_loss=5.628, over 5847.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:02:44,129 INFO [train.py:919] (5/6) Start epoch 729 +2024-03-15 03:04:24,196 INFO [train.py:527] (5/6) Epoch 729, batch 28, global_batch_idx: 90300, batch size: 44, loss[discriminator_loss=2.673, discriminator_real_loss=1.362, discriminator_fake_loss=1.311, generator_loss=28.79, generator_mel_loss=17.53, generator_kl_loss=1.519, generator_dur_loss=1.678, generator_adv_loss=2.087, generator_feat_match_loss=5.974, over 44.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.356, discriminator_fake_loss=1.329, generator_loss=28.82, generator_mel_loss=17.88, generator_kl_loss=1.447, generator_dur_loss=1.746, generator_adv_loss=2.002, generator_feat_match_loss=5.74, over 1666.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:06:43,515 INFO [train.py:527] (5/6) Epoch 729, batch 78, global_batch_idx: 90350, batch size: 56, loss[discriminator_loss=2.733, discriminator_real_loss=1.367, discriminator_fake_loss=1.365, generator_loss=28.43, generator_mel_loss=17.94, generator_kl_loss=1.525, generator_dur_loss=1.748, generator_adv_loss=1.921, generator_feat_match_loss=5.297, over 56.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.79, generator_mel_loss=17.92, generator_kl_loss=1.433, generator_dur_loss=1.752, generator_adv_loss=1.988, generator_feat_match_loss=5.695, over 4687.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:08:48,053 INFO [train.py:919] (5/6) Start epoch 730 +2024-03-15 03:09:24,036 INFO [train.py:527] (5/6) Epoch 730, batch 4, global_batch_idx: 90400, batch size: 95, loss[discriminator_loss=2.689, discriminator_real_loss=1.346, discriminator_fake_loss=1.343, generator_loss=28.74, generator_mel_loss=18, generator_kl_loss=1.204, generator_dur_loss=1.882, generator_adv_loss=1.976, generator_feat_match_loss=5.678, over 95.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.357, discriminator_fake_loss=1.337, generator_loss=28.92, generator_mel_loss=18, generator_kl_loss=1.369, generator_dur_loss=1.793, generator_adv_loss=1.956, generator_feat_match_loss=5.811, over 341.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:09:24,039 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 03:09:31,910 INFO [train.py:591] (5/6) 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 03:09:31,912 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 03:11:51,525 INFO [train.py:527] (5/6) Epoch 730, batch 54, global_batch_idx: 90450, batch size: 47, loss[discriminator_loss=2.62, discriminator_real_loss=1.37, discriminator_fake_loss=1.25, generator_loss=29.65, generator_mel_loss=18.35, generator_kl_loss=1.521, generator_dur_loss=1.687, generator_adv_loss=2.069, generator_feat_match_loss=6.024, over 47.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.352, discriminator_fake_loss=1.327, generator_loss=28.84, generator_mel_loss=17.89, generator_kl_loss=1.412, generator_dur_loss=1.738, generator_adv_loss=2.018, generator_feat_match_loss=5.786, over 3134.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:14:08,342 INFO [train.py:527] (5/6) Epoch 730, batch 104, global_batch_idx: 90500, batch size: 25, loss[discriminator_loss=2.667, discriminator_real_loss=1.346, discriminator_fake_loss=1.321, generator_loss=29.3, generator_mel_loss=18.43, generator_kl_loss=1.471, generator_dur_loss=1.578, generator_adv_loss=1.905, generator_feat_match_loss=5.912, over 25.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.354, discriminator_fake_loss=1.33, generator_loss=28.81, generator_mel_loss=17.91, generator_kl_loss=1.409, generator_dur_loss=1.736, generator_adv_loss=2.007, generator_feat_match_loss=5.752, over 5897.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:14:59,001 INFO [train.py:919] (5/6) Start epoch 731 +2024-03-15 03:16:42,912 INFO [train.py:527] (5/6) Epoch 731, batch 30, global_batch_idx: 90550, batch size: 48, loss[discriminator_loss=2.674, discriminator_real_loss=1.314, discriminator_fake_loss=1.36, generator_loss=29.37, generator_mel_loss=17.83, generator_kl_loss=1.424, generator_dur_loss=1.655, generator_adv_loss=2.108, generator_feat_match_loss=6.348, over 48.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.373, discriminator_fake_loss=1.327, generator_loss=28.69, generator_mel_loss=17.89, generator_kl_loss=1.418, generator_dur_loss=1.746, generator_adv_loss=1.976, generator_feat_match_loss=5.659, over 1708.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:18:59,938 INFO [train.py:527] (5/6) Epoch 731, batch 80, global_batch_idx: 90600, batch size: 55, loss[discriminator_loss=2.704, discriminator_real_loss=1.46, discriminator_fake_loss=1.244, generator_loss=28.63, generator_mel_loss=17.94, generator_kl_loss=1.515, generator_dur_loss=1.653, generator_adv_loss=1.8, generator_feat_match_loss=5.727, over 55.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.366, discriminator_fake_loss=1.328, generator_loss=28.76, generator_mel_loss=17.9, generator_kl_loss=1.413, generator_dur_loss=1.755, generator_adv_loss=1.98, generator_feat_match_loss=5.713, over 4800.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:18:59,940 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 03:19:08,867 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 03:21:09,952 INFO [train.py:919] (5/6) Start epoch 732 +2024-03-15 03:21:47,914 INFO [train.py:527] (5/6) Epoch 732, batch 6, global_batch_idx: 90650, batch size: 36, loss[discriminator_loss=2.727, discriminator_real_loss=1.358, discriminator_fake_loss=1.368, generator_loss=28.91, generator_mel_loss=17.91, generator_kl_loss=1.579, generator_dur_loss=1.718, generator_adv_loss=1.907, generator_feat_match_loss=5.795, over 36.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.326, generator_loss=28.59, generator_mel_loss=17.81, generator_kl_loss=1.39, generator_dur_loss=1.748, generator_adv_loss=2.002, generator_feat_match_loss=5.633, over 423.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:24:06,773 INFO [train.py:527] (5/6) Epoch 732, batch 56, global_batch_idx: 90700, batch size: 61, loss[discriminator_loss=2.688, discriminator_real_loss=1.42, discriminator_fake_loss=1.269, generator_loss=29.55, generator_mel_loss=18.03, generator_kl_loss=1.385, generator_dur_loss=1.753, generator_adv_loss=2.017, generator_feat_match_loss=6.363, over 61.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.326, generator_loss=28.86, generator_mel_loss=17.86, generator_kl_loss=1.386, generator_dur_loss=1.759, generator_adv_loss=1.995, generator_feat_match_loss=5.866, over 3511.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:26:26,701 INFO [train.py:527] (5/6) Epoch 732, batch 106, global_batch_idx: 90750, batch size: 96, loss[discriminator_loss=2.656, discriminator_real_loss=1.325, discriminator_fake_loss=1.331, generator_loss=29.98, generator_mel_loss=18.28, generator_kl_loss=1.386, generator_dur_loss=1.828, generator_adv_loss=1.988, generator_feat_match_loss=6.49, over 96.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.358, discriminator_fake_loss=1.33, generator_loss=28.82, generator_mel_loss=17.86, generator_kl_loss=1.402, generator_dur_loss=1.759, generator_adv_loss=1.991, generator_feat_match_loss=5.807, over 6406.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:27:13,804 INFO [train.py:919] (5/6) Start epoch 733 +2024-03-15 03:29:06,703 INFO [train.py:527] (5/6) Epoch 733, batch 32, global_batch_idx: 90800, batch size: 66, loss[discriminator_loss=2.68, discriminator_real_loss=1.377, discriminator_fake_loss=1.303, generator_loss=27.67, generator_mel_loss=17.54, generator_kl_loss=1.292, generator_dur_loss=1.77, generator_adv_loss=1.89, generator_feat_match_loss=5.179, over 66.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.357, discriminator_fake_loss=1.326, generator_loss=28.88, generator_mel_loss=17.94, generator_kl_loss=1.403, generator_dur_loss=1.766, generator_adv_loss=1.991, generator_feat_match_loss=5.77, over 2015.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:29:06,705 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 03:29:14,669 INFO [train.py:591] (5/6) 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 03:29:14,670 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 03:31:35,928 INFO [train.py:527] (5/6) Epoch 733, batch 82, global_batch_idx: 90850, batch size: 39, loss[discriminator_loss=2.749, discriminator_real_loss=1.465, discriminator_fake_loss=1.283, generator_loss=28.94, generator_mel_loss=18.13, generator_kl_loss=1.642, generator_dur_loss=1.651, generator_adv_loss=1.905, generator_feat_match_loss=5.612, over 39.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.355, discriminator_fake_loss=1.329, generator_loss=28.85, generator_mel_loss=17.9, generator_kl_loss=1.405, generator_dur_loss=1.747, generator_adv_loss=1.991, generator_feat_match_loss=5.802, over 4833.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, +2024-03-15 03:33:28,117 INFO [train.py:919] (5/6) Start epoch 734 +2024-03-15 03:34:14,585 INFO [train.py:527] (5/6) Epoch 734, batch 8, global_batch_idx: 90900, batch size: 47, loss[discriminator_loss=2.684, discriminator_real_loss=1.37, discriminator_fake_loss=1.315, generator_loss=28.09, generator_mel_loss=17.68, generator_kl_loss=1.463, generator_dur_loss=1.733, generator_adv_loss=1.933, generator_feat_match_loss=5.284, over 47.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.327, discriminator_fake_loss=1.348, generator_loss=28.77, generator_mel_loss=17.95, generator_kl_loss=1.411, generator_dur_loss=1.744, generator_adv_loss=1.978, generator_feat_match_loss=5.691, over 519.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:36:32,441 INFO [train.py:527] (5/6) Epoch 734, batch 58, global_batch_idx: 90950, batch size: 56, loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.339, generator_loss=28.72, generator_mel_loss=17.94, generator_kl_loss=1.454, generator_dur_loss=1.677, generator_adv_loss=2.132, generator_feat_match_loss=5.521, over 56.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.355, discriminator_fake_loss=1.335, generator_loss=28.84, generator_mel_loss=17.93, generator_kl_loss=1.403, generator_dur_loss=1.744, generator_adv_loss=1.994, generator_feat_match_loss=5.766, over 3281.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:38:52,269 INFO [train.py:527] (5/6) Epoch 734, batch 108, global_batch_idx: 91000, batch size: 77, loss[discriminator_loss=2.656, discriminator_real_loss=1.357, discriminator_fake_loss=1.299, generator_loss=28.49, generator_mel_loss=17.91, generator_kl_loss=1.203, generator_dur_loss=1.82, generator_adv_loss=1.992, generator_feat_match_loss=5.566, over 77.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=28.86, generator_mel_loss=17.9, generator_kl_loss=1.394, generator_dur_loss=1.757, generator_adv_loss=1.997, generator_feat_match_loss=5.815, over 6406.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:38:52,271 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 03:39:01,388 INFO [train.py:591] (5/6) 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 03:39:01,389 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 03:39:44,485 INFO [train.py:919] (5/6) Start epoch 735 +2024-03-15 03:41:43,584 INFO [train.py:527] (5/6) Epoch 735, batch 34, global_batch_idx: 91050, batch size: 48, loss[discriminator_loss=2.76, discriminator_real_loss=1.308, discriminator_fake_loss=1.451, generator_loss=28.37, generator_mel_loss=17.68, generator_kl_loss=1.42, generator_dur_loss=1.724, generator_adv_loss=2.069, generator_feat_match_loss=5.473, over 48.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.9, generator_mel_loss=17.93, generator_kl_loss=1.426, generator_dur_loss=1.758, generator_adv_loss=1.985, generator_feat_match_loss=5.797, over 2058.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:44:01,060 INFO [train.py:527] (5/6) Epoch 735, batch 84, global_batch_idx: 91100, batch size: 64, loss[discriminator_loss=2.7, discriminator_real_loss=1.377, discriminator_fake_loss=1.323, generator_loss=29.06, generator_mel_loss=18.01, generator_kl_loss=1.397, generator_dur_loss=1.751, generator_adv_loss=2.188, generator_feat_match_loss=5.714, over 64.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.361, discriminator_fake_loss=1.333, generator_loss=28.99, generator_mel_loss=17.97, generator_kl_loss=1.428, generator_dur_loss=1.749, generator_adv_loss=1.994, generator_feat_match_loss=5.852, over 4915.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:45:51,349 INFO [train.py:919] (5/6) Start epoch 736 +2024-03-15 03:46:42,806 INFO [train.py:527] (5/6) Epoch 736, batch 10, global_batch_idx: 91150, batch size: 48, loss[discriminator_loss=2.707, discriminator_real_loss=1.41, discriminator_fake_loss=1.297, generator_loss=28.05, generator_mel_loss=17.85, generator_kl_loss=1.359, generator_dur_loss=1.693, generator_adv_loss=1.93, generator_feat_match_loss=5.215, over 48.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.359, discriminator_fake_loss=1.317, generator_loss=29.22, generator_mel_loss=18.11, generator_kl_loss=1.501, generator_dur_loss=1.685, generator_adv_loss=1.986, generator_feat_match_loss=5.945, over 490.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:49:01,674 INFO [train.py:527] (5/6) Epoch 736, batch 60, global_batch_idx: 91200, batch size: 47, loss[discriminator_loss=2.693, discriminator_real_loss=1.392, discriminator_fake_loss=1.302, generator_loss=28.7, generator_mel_loss=18.08, generator_kl_loss=1.482, generator_dur_loss=1.687, generator_adv_loss=2.16, generator_feat_match_loss=5.286, over 47.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.359, discriminator_fake_loss=1.32, generator_loss=28.99, generator_mel_loss=17.88, generator_kl_loss=1.444, generator_dur_loss=1.73, generator_adv_loss=2.017, generator_feat_match_loss=5.921, over 3286.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:49:01,676 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 03:49:09,685 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 03:51:27,964 INFO [train.py:527] (5/6) Epoch 736, batch 110, global_batch_idx: 91250, batch size: 14, loss[discriminator_loss=2.656, discriminator_real_loss=1.326, discriminator_fake_loss=1.33, generator_loss=29.94, generator_mel_loss=19.26, generator_kl_loss=1.625, generator_dur_loss=1.588, generator_adv_loss=2.12, generator_feat_match_loss=5.345, over 14.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.357, discriminator_fake_loss=1.324, generator_loss=28.92, generator_mel_loss=17.89, generator_kl_loss=1.436, generator_dur_loss=1.745, generator_adv_loss=1.997, generator_feat_match_loss=5.857, over 6171.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:52:04,262 INFO [train.py:919] (5/6) Start epoch 737 +2024-03-15 03:54:10,771 INFO [train.py:527] (5/6) Epoch 737, batch 36, global_batch_idx: 91300, batch size: 77, loss[discriminator_loss=2.642, discriminator_real_loss=1.347, discriminator_fake_loss=1.295, generator_loss=28.97, generator_mel_loss=17.7, generator_kl_loss=1.289, generator_dur_loss=1.834, generator_adv_loss=2.033, generator_feat_match_loss=6.111, over 77.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.354, discriminator_fake_loss=1.332, generator_loss=28.65, generator_mel_loss=17.93, generator_kl_loss=1.39, generator_dur_loss=1.753, generator_adv_loss=1.989, generator_feat_match_loss=5.581, over 2264.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:56:27,578 INFO [train.py:527] (5/6) Epoch 737, batch 86, global_batch_idx: 91350, batch size: 62, loss[discriminator_loss=2.778, discriminator_real_loss=1.518, discriminator_fake_loss=1.26, generator_loss=29, generator_mel_loss=18.27, generator_kl_loss=1.532, generator_dur_loss=1.717, generator_adv_loss=1.779, generator_feat_match_loss=5.707, over 62.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.329, generator_loss=28.78, generator_mel_loss=17.93, generator_kl_loss=1.413, generator_dur_loss=1.749, generator_adv_loss=1.995, generator_feat_match_loss=5.684, over 5057.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:58:12,148 INFO [train.py:919] (5/6) Start epoch 738 +2024-03-15 03:59:07,721 INFO [train.py:527] (5/6) Epoch 738, batch 12, global_batch_idx: 91400, batch size: 13, loss[discriminator_loss=2.804, discriminator_real_loss=1.485, discriminator_fake_loss=1.319, generator_loss=30.63, generator_mel_loss=18.79, generator_kl_loss=2.079, generator_dur_loss=1.561, generator_adv_loss=1.816, generator_feat_match_loss=6.386, over 13.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.359, discriminator_fake_loss=1.304, generator_loss=29.21, generator_mel_loss=18.06, generator_kl_loss=1.501, generator_dur_loss=1.728, generator_adv_loss=2.028, generator_feat_match_loss=5.894, over 605.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 03:59:07,723 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 03:59:15,743 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 04:01:31,902 INFO [train.py:527] (5/6) Epoch 738, batch 62, global_batch_idx: 91450, batch size: 64, loss[discriminator_loss=2.635, discriminator_real_loss=1.348, discriminator_fake_loss=1.287, generator_loss=29.61, generator_mel_loss=18.22, generator_kl_loss=1.424, generator_dur_loss=1.758, generator_adv_loss=1.979, generator_feat_match_loss=6.23, over 64.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.361, discriminator_fake_loss=1.333, generator_loss=28.93, generator_mel_loss=18.02, generator_kl_loss=1.45, generator_dur_loss=1.728, generator_adv_loss=1.995, generator_feat_match_loss=5.739, over 3392.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:03:50,070 INFO [train.py:527] (5/6) Epoch 738, batch 112, global_batch_idx: 91500, batch size: 68, loss[discriminator_loss=2.667, discriminator_real_loss=1.328, discriminator_fake_loss=1.339, generator_loss=28.85, generator_mel_loss=18.14, generator_kl_loss=1.383, generator_dur_loss=1.745, generator_adv_loss=1.874, generator_feat_match_loss=5.705, over 68.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.367, discriminator_fake_loss=1.328, generator_loss=28.85, generator_mel_loss=17.99, generator_kl_loss=1.435, generator_dur_loss=1.734, generator_adv_loss=1.991, generator_feat_match_loss=5.705, over 6204.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:04:25,343 INFO [train.py:919] (5/6) Start epoch 739 +2024-03-15 04:06:35,969 INFO [train.py:527] (5/6) Epoch 739, batch 38, global_batch_idx: 91550, batch size: 96, loss[discriminator_loss=2.678, discriminator_real_loss=1.342, discriminator_fake_loss=1.336, generator_loss=28.24, generator_mel_loss=17.69, generator_kl_loss=1.34, generator_dur_loss=1.855, generator_adv_loss=1.966, generator_feat_match_loss=5.391, over 96.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.36, discriminator_fake_loss=1.337, generator_loss=28.64, generator_mel_loss=17.89, generator_kl_loss=1.425, generator_dur_loss=1.731, generator_adv_loss=1.975, generator_feat_match_loss=5.626, over 2187.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:08:56,299 INFO [train.py:527] (5/6) Epoch 739, batch 88, global_batch_idx: 91600, batch size: 53, loss[discriminator_loss=2.686, discriminator_real_loss=1.207, discriminator_fake_loss=1.479, generator_loss=28.51, generator_mel_loss=17.82, generator_kl_loss=1.498, generator_dur_loss=1.736, generator_adv_loss=2.017, generator_feat_match_loss=5.437, over 53.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=28.83, generator_mel_loss=17.94, generator_kl_loss=1.441, generator_dur_loss=1.725, generator_adv_loss=1.991, generator_feat_match_loss=5.734, over 4925.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:08:56,300 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 04:09:05,236 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 04:10:43,454 INFO [train.py:919] (5/6) Start epoch 740 +2024-03-15 04:11:45,681 INFO [train.py:527] (5/6) Epoch 740, batch 14, global_batch_idx: 91650, batch size: 39, loss[discriminator_loss=2.687, discriminator_real_loss=1.376, discriminator_fake_loss=1.312, generator_loss=29.54, generator_mel_loss=18.07, generator_kl_loss=1.677, generator_dur_loss=1.628, generator_adv_loss=2.012, generator_feat_match_loss=6.16, over 39.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.358, discriminator_fake_loss=1.351, generator_loss=28.74, generator_mel_loss=17.83, generator_kl_loss=1.474, generator_dur_loss=1.734, generator_adv_loss=1.991, generator_feat_match_loss=5.71, over 813.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:14:05,745 INFO [train.py:527] (5/6) Epoch 740, batch 64, global_batch_idx: 91700, batch size: 52, loss[discriminator_loss=2.724, discriminator_real_loss=1.347, discriminator_fake_loss=1.377, generator_loss=28.26, generator_mel_loss=17.74, generator_kl_loss=1.358, generator_dur_loss=1.677, generator_adv_loss=1.915, generator_feat_match_loss=5.572, over 52.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.69, generator_mel_loss=17.83, generator_kl_loss=1.411, generator_dur_loss=1.746, generator_adv_loss=2.007, generator_feat_match_loss=5.701, over 3793.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:16:23,866 INFO [train.py:527] (5/6) Epoch 740, batch 114, global_batch_idx: 91750, batch size: 62, loss[discriminator_loss=2.7, discriminator_real_loss=1.344, discriminator_fake_loss=1.356, generator_loss=28.48, generator_mel_loss=17.79, generator_kl_loss=1.496, generator_dur_loss=1.729, generator_adv_loss=1.928, generator_feat_match_loss=5.539, over 62.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=28.76, generator_mel_loss=17.84, generator_kl_loss=1.416, generator_dur_loss=1.749, generator_adv_loss=1.999, generator_feat_match_loss=5.759, over 6743.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:16:51,212 INFO [train.py:919] (5/6) Start epoch 741 +2024-03-15 04:19:12,573 INFO [train.py:527] (5/6) Epoch 741, batch 40, global_batch_idx: 91800, batch size: 50, loss[discriminator_loss=2.725, discriminator_real_loss=1.311, discriminator_fake_loss=1.414, generator_loss=29.44, generator_mel_loss=18.03, generator_kl_loss=1.493, generator_dur_loss=1.667, generator_adv_loss=2.176, generator_feat_match_loss=6.078, over 50.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.358, discriminator_fake_loss=1.336, generator_loss=28.85, generator_mel_loss=17.88, generator_kl_loss=1.396, generator_dur_loss=1.756, generator_adv_loss=1.98, generator_feat_match_loss=5.841, over 2477.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:19:12,575 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 04:19:20,740 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 04:21:40,812 INFO [train.py:527] (5/6) Epoch 741, batch 90, global_batch_idx: 91850, batch size: 72, loss[discriminator_loss=2.712, discriminator_real_loss=1.377, discriminator_fake_loss=1.336, generator_loss=28.31, generator_mel_loss=17.54, generator_kl_loss=1.419, generator_dur_loss=1.793, generator_adv_loss=1.839, generator_feat_match_loss=5.717, over 72.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.361, discriminator_fake_loss=1.332, generator_loss=28.74, generator_mel_loss=17.85, generator_kl_loss=1.411, generator_dur_loss=1.756, generator_adv_loss=1.98, generator_feat_match_loss=5.742, over 5547.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:23:14,189 INFO [train.py:919] (5/6) Start epoch 742 +2024-03-15 04:24:23,314 INFO [train.py:527] (5/6) Epoch 742, batch 16, global_batch_idx: 91900, batch size: 45, loss[discriminator_loss=2.667, discriminator_real_loss=1.318, discriminator_fake_loss=1.349, generator_loss=29.78, generator_mel_loss=18.16, generator_kl_loss=1.587, generator_dur_loss=1.718, generator_adv_loss=2.067, generator_feat_match_loss=6.253, over 45.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.346, discriminator_fake_loss=1.318, generator_loss=28.99, generator_mel_loss=17.89, generator_kl_loss=1.368, generator_dur_loss=1.767, generator_adv_loss=2.021, generator_feat_match_loss=5.937, over 1034.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:26:43,227 INFO [train.py:527] (5/6) Epoch 742, batch 66, global_batch_idx: 91950, batch size: 42, loss[discriminator_loss=2.694, discriminator_real_loss=1.344, discriminator_fake_loss=1.35, generator_loss=29.21, generator_mel_loss=17.85, generator_kl_loss=1.434, generator_dur_loss=1.681, generator_adv_loss=1.862, generator_feat_match_loss=6.388, over 42.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=28.85, generator_mel_loss=17.95, generator_kl_loss=1.396, generator_dur_loss=1.752, generator_adv_loss=1.992, generator_feat_match_loss=5.761, over 3891.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:29:01,576 INFO [train.py:527] (5/6) Epoch 742, batch 116, global_batch_idx: 92000, batch size: 70, loss[discriminator_loss=2.746, discriminator_real_loss=1.34, discriminator_fake_loss=1.406, generator_loss=28.65, generator_mel_loss=18.22, generator_kl_loss=1.216, generator_dur_loss=1.82, generator_adv_loss=1.913, generator_feat_match_loss=5.485, over 70.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.355, discriminator_fake_loss=1.33, generator_loss=28.85, generator_mel_loss=17.95, generator_kl_loss=1.403, generator_dur_loss=1.752, generator_adv_loss=1.988, generator_feat_match_loss=5.759, over 6858.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:29:01,579 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 04:29:10,225 INFO [train.py:591] (5/6) 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 04:29:10,226 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 04:29:30,276 INFO [train.py:919] (5/6) Start epoch 743 +2024-03-15 04:31:51,630 INFO [train.py:527] (5/6) Epoch 743, batch 42, global_batch_idx: 92050, batch size: 74, loss[discriminator_loss=2.646, discriminator_real_loss=1.31, discriminator_fake_loss=1.335, generator_loss=29.69, generator_mel_loss=18, generator_kl_loss=1.476, generator_dur_loss=1.819, generator_adv_loss=1.989, generator_feat_match_loss=6.4, over 74.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.37, discriminator_fake_loss=1.324, generator_loss=28.78, generator_mel_loss=17.85, generator_kl_loss=1.4, generator_dur_loss=1.748, generator_adv_loss=1.979, generator_feat_match_loss=5.807, over 2529.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:34:13,603 INFO [train.py:527] (5/6) Epoch 743, batch 92, global_batch_idx: 92100, batch size: 77, loss[discriminator_loss=2.728, discriminator_real_loss=1.325, discriminator_fake_loss=1.403, generator_loss=29.46, generator_mel_loss=17.97, generator_kl_loss=1.447, generator_dur_loss=1.748, generator_adv_loss=1.954, generator_feat_match_loss=6.342, over 77.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.366, discriminator_fake_loss=1.325, generator_loss=28.89, generator_mel_loss=17.9, generator_kl_loss=1.408, generator_dur_loss=1.742, generator_adv_loss=1.993, generator_feat_match_loss=5.843, over 5354.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:35:39,786 INFO [train.py:919] (5/6) Start epoch 744 +2024-03-15 04:36:54,248 INFO [train.py:527] (5/6) Epoch 744, batch 18, global_batch_idx: 92150, batch size: 72, loss[discriminator_loss=2.674, discriminator_real_loss=1.334, discriminator_fake_loss=1.34, generator_loss=28.52, generator_mel_loss=17.68, generator_kl_loss=1.313, generator_dur_loss=1.833, generator_adv_loss=2.06, generator_feat_match_loss=5.63, over 72.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.346, discriminator_fake_loss=1.323, generator_loss=28.74, generator_mel_loss=17.85, generator_kl_loss=1.405, generator_dur_loss=1.751, generator_adv_loss=2.011, generator_feat_match_loss=5.718, over 1168.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:39:13,581 INFO [train.py:527] (5/6) Epoch 744, batch 68, global_batch_idx: 92200, batch size: 60, loss[discriminator_loss=2.675, discriminator_real_loss=1.364, discriminator_fake_loss=1.312, generator_loss=29.17, generator_mel_loss=17.76, generator_kl_loss=1.53, generator_dur_loss=1.705, generator_adv_loss=2.058, generator_feat_match_loss=6.119, over 60.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.352, discriminator_fake_loss=1.331, generator_loss=28.75, generator_mel_loss=17.83, generator_kl_loss=1.402, generator_dur_loss=1.748, generator_adv_loss=1.986, generator_feat_match_loss=5.78, over 4040.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:39:13,582 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 04:39:22,314 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 04:41:40,535 INFO [train.py:527] (5/6) Epoch 744, batch 118, global_batch_idx: 92250, batch size: 66, loss[discriminator_loss=2.78, discriminator_real_loss=1.474, discriminator_fake_loss=1.307, generator_loss=28.58, generator_mel_loss=17.84, generator_kl_loss=1.423, generator_dur_loss=1.795, generator_adv_loss=1.959, generator_feat_match_loss=5.573, over 66.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.355, discriminator_fake_loss=1.33, generator_loss=28.82, generator_mel_loss=17.86, generator_kl_loss=1.41, generator_dur_loss=1.751, generator_adv_loss=1.992, generator_feat_match_loss=5.799, over 6985.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:41:54,455 INFO [train.py:919] (5/6) Start epoch 745 +2024-03-15 04:44:22,442 INFO [train.py:527] (5/6) Epoch 745, batch 44, global_batch_idx: 92300, batch size: 53, loss[discriminator_loss=2.718, discriminator_real_loss=1.383, discriminator_fake_loss=1.336, generator_loss=28.14, generator_mel_loss=17.93, generator_kl_loss=1.381, generator_dur_loss=1.689, generator_adv_loss=2.048, generator_feat_match_loss=5.096, over 53.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.373, discriminator_fake_loss=1.328, generator_loss=28.69, generator_mel_loss=17.86, generator_kl_loss=1.447, generator_dur_loss=1.732, generator_adv_loss=1.985, generator_feat_match_loss=5.669, over 2339.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:46:40,584 INFO [train.py:527] (5/6) Epoch 745, batch 94, global_batch_idx: 92350, batch size: 45, loss[discriminator_loss=2.833, discriminator_real_loss=1.653, discriminator_fake_loss=1.179, generator_loss=27.25, generator_mel_loss=17.43, generator_kl_loss=1.431, generator_dur_loss=1.634, generator_adv_loss=1.795, generator_feat_match_loss=4.96, over 45.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.362, discriminator_fake_loss=1.33, generator_loss=28.79, generator_mel_loss=17.86, generator_kl_loss=1.428, generator_dur_loss=1.744, generator_adv_loss=1.989, generator_feat_match_loss=5.776, over 5172.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:48:01,537 INFO [train.py:919] (5/6) Start epoch 746 +2024-03-15 04:49:24,290 INFO [train.py:527] (5/6) Epoch 746, batch 20, global_batch_idx: 92400, batch size: 88, loss[discriminator_loss=2.533, discriminator_real_loss=1.19, discriminator_fake_loss=1.344, generator_loss=30.07, generator_mel_loss=18.02, generator_kl_loss=1.47, generator_dur_loss=1.843, generator_adv_loss=2.112, generator_feat_match_loss=6.625, over 88.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.332, discriminator_fake_loss=1.333, generator_loss=28.94, generator_mel_loss=17.84, generator_kl_loss=1.451, generator_dur_loss=1.762, generator_adv_loss=1.995, generator_feat_match_loss=5.89, over 1187.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:49:24,291 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 04:49:32,191 INFO [train.py:591] (5/6) 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 04:49:32,192 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 04:51:51,732 INFO [train.py:527] (5/6) Epoch 746, batch 70, global_batch_idx: 92450, batch size: 80, loss[discriminator_loss=2.695, discriminator_real_loss=1.341, discriminator_fake_loss=1.354, generator_loss=29.26, generator_mel_loss=17.98, generator_kl_loss=1.34, generator_dur_loss=1.788, generator_adv_loss=2.069, generator_feat_match_loss=6.089, over 80.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.353, discriminator_fake_loss=1.329, generator_loss=28.91, generator_mel_loss=17.84, generator_kl_loss=1.428, generator_dur_loss=1.746, generator_adv_loss=2.02, generator_feat_match_loss=5.872, over 4087.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:54:05,688 INFO [train.py:527] (5/6) Epoch 746, batch 120, global_batch_idx: 92500, batch size: 16, loss[discriminator_loss=2.689, discriminator_real_loss=1.353, discriminator_fake_loss=1.335, generator_loss=30.25, generator_mel_loss=18.67, generator_kl_loss=1.732, generator_dur_loss=1.557, generator_adv_loss=2.185, generator_feat_match_loss=6.111, over 16.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=28.89, generator_mel_loss=17.86, generator_kl_loss=1.435, generator_dur_loss=1.734, generator_adv_loss=2.012, generator_feat_match_loss=5.845, over 6753.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:54:16,377 INFO [train.py:919] (5/6) Start epoch 747 +2024-03-15 04:56:48,790 INFO [train.py:527] (5/6) Epoch 747, batch 46, global_batch_idx: 92550, batch size: 48, loss[discriminator_loss=2.605, discriminator_real_loss=1.33, discriminator_fake_loss=1.275, generator_loss=30.52, generator_mel_loss=18.11, generator_kl_loss=1.559, generator_dur_loss=1.661, generator_adv_loss=2.113, generator_feat_match_loss=7.076, over 48.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.359, discriminator_fake_loss=1.338, generator_loss=28.78, generator_mel_loss=17.9, generator_kl_loss=1.396, generator_dur_loss=1.756, generator_adv_loss=1.966, generator_feat_match_loss=5.758, over 2947.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:59:03,900 INFO [train.py:527] (5/6) Epoch 747, batch 96, global_batch_idx: 92600, batch size: 53, loss[discriminator_loss=2.694, discriminator_real_loss=1.285, discriminator_fake_loss=1.409, generator_loss=29.19, generator_mel_loss=17.86, generator_kl_loss=1.46, generator_dur_loss=1.707, generator_adv_loss=2.144, generator_feat_match_loss=6.016, over 53.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.359, discriminator_fake_loss=1.335, generator_loss=28.8, generator_mel_loss=17.88, generator_kl_loss=1.406, generator_dur_loss=1.749, generator_adv_loss=1.979, generator_feat_match_loss=5.785, over 5822.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 04:59:03,901 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 04:59:13,109 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 05:00:27,450 INFO [train.py:919] (5/6) Start epoch 748 +2024-03-15 05:01:51,830 INFO [train.py:527] (5/6) Epoch 748, batch 22, global_batch_idx: 92650, batch size: 61, loss[discriminator_loss=2.688, discriminator_real_loss=1.373, discriminator_fake_loss=1.315, generator_loss=28.86, generator_mel_loss=17.64, generator_kl_loss=1.415, generator_dur_loss=1.711, generator_adv_loss=2.157, generator_feat_match_loss=5.939, over 61.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.346, discriminator_fake_loss=1.335, generator_loss=29.02, generator_mel_loss=17.97, generator_kl_loss=1.416, generator_dur_loss=1.73, generator_adv_loss=2.015, generator_feat_match_loss=5.889, over 1133.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:04:13,335 INFO [train.py:527] (5/6) Epoch 748, batch 72, global_batch_idx: 92700, batch size: 77, loss[discriminator_loss=2.702, discriminator_real_loss=1.352, discriminator_fake_loss=1.35, generator_loss=28.5, generator_mel_loss=17.59, generator_kl_loss=1.251, generator_dur_loss=1.832, generator_adv_loss=2, generator_feat_match_loss=5.829, over 77.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=28.82, generator_mel_loss=17.89, generator_kl_loss=1.416, generator_dur_loss=1.729, generator_adv_loss=1.995, generator_feat_match_loss=5.781, over 3856.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:06:30,485 INFO [train.py:527] (5/6) Epoch 748, batch 122, global_batch_idx: 92750, batch size: 70, loss[discriminator_loss=2.643, discriminator_real_loss=1.371, discriminator_fake_loss=1.272, generator_loss=28.44, generator_mel_loss=18.1, generator_kl_loss=1.321, generator_dur_loss=1.783, generator_adv_loss=1.974, generator_feat_match_loss=5.265, over 70.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.8, generator_mel_loss=17.89, generator_kl_loss=1.403, generator_dur_loss=1.737, generator_adv_loss=1.986, generator_feat_match_loss=5.785, over 6757.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:06:35,329 INFO [train.py:919] (5/6) Start epoch 749 +2024-03-15 05:09:10,683 INFO [train.py:527] (5/6) Epoch 749, batch 48, global_batch_idx: 92800, batch size: 53, loss[discriminator_loss=2.713, discriminator_real_loss=1.335, discriminator_fake_loss=1.378, generator_loss=28.63, generator_mel_loss=17.7, generator_kl_loss=1.513, generator_dur_loss=1.69, generator_adv_loss=1.954, generator_feat_match_loss=5.775, over 53.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=28.83, generator_mel_loss=17.84, generator_kl_loss=1.422, generator_dur_loss=1.739, generator_adv_loss=2.002, generator_feat_match_loss=5.827, over 2744.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:09:10,684 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 05:09:18,731 INFO [train.py:591] (5/6) 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 05:09:18,732 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 05:11:40,306 INFO [train.py:527] (5/6) Epoch 749, batch 98, global_batch_idx: 92850, batch size: 68, loss[discriminator_loss=2.697, discriminator_real_loss=1.359, discriminator_fake_loss=1.338, generator_loss=28.72, generator_mel_loss=18.26, generator_kl_loss=1.299, generator_dur_loss=1.818, generator_adv_loss=2.011, generator_feat_match_loss=5.329, over 68.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=28.81, generator_mel_loss=17.85, generator_kl_loss=1.408, generator_dur_loss=1.751, generator_adv_loss=2, generator_feat_match_loss=5.806, over 5716.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:12:52,620 INFO [train.py:919] (5/6) Start epoch 750 +2024-03-15 05:14:21,265 INFO [train.py:527] (5/6) Epoch 750, batch 24, global_batch_idx: 92900, batch size: 53, loss[discriminator_loss=2.653, discriminator_real_loss=1.315, discriminator_fake_loss=1.337, generator_loss=29.2, generator_mel_loss=18.1, generator_kl_loss=1.275, generator_dur_loss=1.711, generator_adv_loss=2.103, generator_feat_match_loss=6.007, over 53.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.355, discriminator_fake_loss=1.33, generator_loss=28.88, generator_mel_loss=17.91, generator_kl_loss=1.407, generator_dur_loss=1.744, generator_adv_loss=1.997, generator_feat_match_loss=5.819, over 1422.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:16:41,117 INFO [train.py:527] (5/6) Epoch 750, batch 74, global_batch_idx: 92950, batch size: 74, loss[discriminator_loss=2.678, discriminator_real_loss=1.382, discriminator_fake_loss=1.297, generator_loss=27.73, generator_mel_loss=17.56, generator_kl_loss=1.143, generator_dur_loss=1.829, generator_adv_loss=1.951, generator_feat_match_loss=5.251, over 74.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=28.75, generator_mel_loss=17.83, generator_kl_loss=1.397, generator_dur_loss=1.749, generator_adv_loss=1.994, generator_feat_match_loss=5.773, over 4442.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:18:58,636 INFO [train.py:919] (5/6) Start epoch 751 +2024-03-15 05:19:22,996 INFO [train.py:527] (5/6) Epoch 751, batch 0, global_batch_idx: 93000, batch size: 96, loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=27.9, generator_mel_loss=17.86, generator_kl_loss=1.35, generator_dur_loss=1.765, generator_adv_loss=1.896, generator_feat_match_loss=5.027, over 96.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=27.9, generator_mel_loss=17.86, generator_kl_loss=1.35, generator_dur_loss=1.765, generator_adv_loss=1.896, generator_feat_match_loss=5.027, over 96.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:19:22,999 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 05:19:30,713 INFO [train.py:591] (5/6) 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 05:19:30,715 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 05:21:47,053 INFO [train.py:527] (5/6) Epoch 751, batch 50, global_batch_idx: 93050, batch size: 50, loss[discriminator_loss=2.718, discriminator_real_loss=1.487, discriminator_fake_loss=1.232, generator_loss=27.71, generator_mel_loss=17.5, generator_kl_loss=1.371, generator_dur_loss=1.728, generator_adv_loss=1.849, generator_feat_match_loss=5.256, over 50.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=28.83, generator_mel_loss=17.88, generator_kl_loss=1.408, generator_dur_loss=1.747, generator_adv_loss=2.006, generator_feat_match_loss=5.787, over 2923.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:24:07,023 INFO [train.py:527] (5/6) Epoch 751, batch 100, global_batch_idx: 93100, batch size: 68, loss[discriminator_loss=2.687, discriminator_real_loss=1.369, discriminator_fake_loss=1.317, generator_loss=28.78, generator_mel_loss=17.77, generator_kl_loss=1.426, generator_dur_loss=1.742, generator_adv_loss=2.009, generator_feat_match_loss=5.83, over 68.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.357, discriminator_fake_loss=1.329, generator_loss=28.82, generator_mel_loss=17.88, generator_kl_loss=1.396, generator_dur_loss=1.749, generator_adv_loss=1.996, generator_feat_match_loss=5.8, over 5911.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:25:11,378 INFO [train.py:919] (5/6) Start epoch 752 +2024-03-15 05:26:49,714 INFO [train.py:527] (5/6) Epoch 752, batch 26, global_batch_idx: 93150, batch size: 64, loss[discriminator_loss=2.73, discriminator_real_loss=1.325, discriminator_fake_loss=1.406, generator_loss=29.41, generator_mel_loss=17.92, generator_kl_loss=1.429, generator_dur_loss=1.777, generator_adv_loss=2.041, generator_feat_match_loss=6.239, over 64.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.36, discriminator_fake_loss=1.334, generator_loss=28.64, generator_mel_loss=17.76, generator_kl_loss=1.374, generator_dur_loss=1.761, generator_adv_loss=1.997, generator_feat_match_loss=5.744, over 1721.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:29:10,378 INFO [train.py:527] (5/6) Epoch 752, batch 76, global_batch_idx: 93200, batch size: 47, loss[discriminator_loss=2.682, discriminator_real_loss=1.349, discriminator_fake_loss=1.333, generator_loss=28.63, generator_mel_loss=17.49, generator_kl_loss=1.524, generator_dur_loss=1.581, generator_adv_loss=2.009, generator_feat_match_loss=6.028, over 47.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.361, discriminator_fake_loss=1.333, generator_loss=28.77, generator_mel_loss=17.83, generator_kl_loss=1.398, generator_dur_loss=1.75, generator_adv_loss=1.99, generator_feat_match_loss=5.803, over 4840.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:29:10,379 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 05:29:19,323 INFO [train.py:591] (5/6) 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 05:29:19,323 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 05:31:27,946 INFO [train.py:919] (5/6) Start epoch 753 +2024-03-15 05:31:58,307 INFO [train.py:527] (5/6) Epoch 753, batch 2, global_batch_idx: 93250, batch size: 96, loss[discriminator_loss=2.727, discriminator_real_loss=1.311, discriminator_fake_loss=1.415, generator_loss=28.72, generator_mel_loss=17.91, generator_kl_loss=1.388, generator_dur_loss=1.813, generator_adv_loss=2.021, generator_feat_match_loss=5.585, over 96.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.32, discriminator_fake_loss=1.388, generator_loss=28.98, generator_mel_loss=18.08, generator_kl_loss=1.427, generator_dur_loss=1.745, generator_adv_loss=1.988, generator_feat_match_loss=5.744, over 155.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:34:17,857 INFO [train.py:527] (5/6) Epoch 753, batch 52, global_batch_idx: 93300, batch size: 25, loss[discriminator_loss=2.717, discriminator_real_loss=1.353, discriminator_fake_loss=1.365, generator_loss=30.62, generator_mel_loss=18.98, generator_kl_loss=1.854, generator_dur_loss=1.548, generator_adv_loss=1.864, generator_feat_match_loss=6.379, over 25.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.321, generator_loss=28.79, generator_mel_loss=17.89, generator_kl_loss=1.426, generator_dur_loss=1.707, generator_adv_loss=1.994, generator_feat_match_loss=5.774, over 2897.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:36:40,904 INFO [train.py:527] (5/6) Epoch 753, batch 102, global_batch_idx: 93350, batch size: 68, loss[discriminator_loss=2.644, discriminator_real_loss=1.343, discriminator_fake_loss=1.301, generator_loss=28.71, generator_mel_loss=17.66, generator_kl_loss=1.329, generator_dur_loss=1.729, generator_adv_loss=2.079, generator_feat_match_loss=5.913, over 68.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.324, generator_loss=28.78, generator_mel_loss=17.87, generator_kl_loss=1.43, generator_dur_loss=1.724, generator_adv_loss=1.99, generator_feat_match_loss=5.773, over 5772.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:37:38,624 INFO [train.py:919] (5/6) Start epoch 754 +2024-03-15 05:39:17,150 INFO [train.py:527] (5/6) Epoch 754, batch 28, global_batch_idx: 93400, batch size: 55, loss[discriminator_loss=2.661, discriminator_real_loss=1.401, discriminator_fake_loss=1.26, generator_loss=28.75, generator_mel_loss=17.92, generator_kl_loss=1.47, generator_dur_loss=1.676, generator_adv_loss=1.997, generator_feat_match_loss=5.683, over 55.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.353, discriminator_fake_loss=1.332, generator_loss=28.95, generator_mel_loss=17.95, generator_kl_loss=1.435, generator_dur_loss=1.723, generator_adv_loss=1.988, generator_feat_match_loss=5.854, over 1636.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:39:17,151 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 05:39:25,179 INFO [train.py:591] (5/6) 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 05:39:25,180 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 05:41:46,177 INFO [train.py:527] (5/6) Epoch 754, batch 78, global_batch_idx: 93450, batch size: 56, loss[discriminator_loss=2.647, discriminator_real_loss=1.318, discriminator_fake_loss=1.33, generator_loss=30.17, generator_mel_loss=18.5, generator_kl_loss=1.582, generator_dur_loss=1.668, generator_adv_loss=1.974, generator_feat_match_loss=6.449, over 56.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=28.89, generator_mel_loss=17.87, generator_kl_loss=1.423, generator_dur_loss=1.734, generator_adv_loss=2.028, generator_feat_match_loss=5.834, over 4677.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:43:50,432 INFO [train.py:919] (5/6) Start epoch 755 +2024-03-15 05:44:26,500 INFO [train.py:527] (5/6) Epoch 755, batch 4, global_batch_idx: 93500, batch size: 88, loss[discriminator_loss=2.619, discriminator_real_loss=1.315, discriminator_fake_loss=1.304, generator_loss=29.13, generator_mel_loss=17.99, generator_kl_loss=1.31, generator_dur_loss=1.756, generator_adv_loss=1.859, generator_feat_match_loss=6.217, over 88.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.341, discriminator_fake_loss=1.34, generator_loss=29.24, generator_mel_loss=17.99, generator_kl_loss=1.379, generator_dur_loss=1.751, generator_adv_loss=1.994, generator_feat_match_loss=6.119, over 356.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:46:47,407 INFO [train.py:527] (5/6) Epoch 755, batch 54, global_batch_idx: 93550, batch size: 39, loss[discriminator_loss=2.678, discriminator_real_loss=1.4, discriminator_fake_loss=1.278, generator_loss=27.58, generator_mel_loss=17.66, generator_kl_loss=1.438, generator_dur_loss=1.658, generator_adv_loss=2.004, generator_feat_match_loss=4.818, over 39.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.82, generator_mel_loss=17.92, generator_kl_loss=1.397, generator_dur_loss=1.72, generator_adv_loss=1.984, generator_feat_match_loss=5.806, over 3240.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:49:04,321 INFO [train.py:527] (5/6) Epoch 755, batch 104, global_batch_idx: 93600, batch size: 74, loss[discriminator_loss=2.688, discriminator_real_loss=1.382, discriminator_fake_loss=1.306, generator_loss=28.54, generator_mel_loss=17.75, generator_kl_loss=1.429, generator_dur_loss=1.818, generator_adv_loss=1.982, generator_feat_match_loss=5.56, over 74.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=28.78, generator_mel_loss=17.88, generator_kl_loss=1.412, generator_dur_loss=1.735, generator_adv_loss=1.984, generator_feat_match_loss=5.767, over 6271.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:49:04,322 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 05:49:13,245 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 05:50:07,896 INFO [train.py:919] (5/6) Start epoch 756 +2024-03-15 05:51:56,052 INFO [train.py:527] (5/6) Epoch 756, batch 30, global_batch_idx: 93650, batch size: 74, loss[discriminator_loss=2.657, discriminator_real_loss=1.32, discriminator_fake_loss=1.337, generator_loss=29.58, generator_mel_loss=18.38, generator_kl_loss=1.33, generator_dur_loss=1.811, generator_adv_loss=2.047, generator_feat_match_loss=6.01, over 74.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.349, discriminator_fake_loss=1.322, generator_loss=28.84, generator_mel_loss=17.9, generator_kl_loss=1.417, generator_dur_loss=1.755, generator_adv_loss=1.994, generator_feat_match_loss=5.775, over 2020.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:54:15,414 INFO [train.py:527] (5/6) Epoch 756, batch 80, global_batch_idx: 93700, batch size: 58, loss[discriminator_loss=2.71, discriminator_real_loss=1.422, discriminator_fake_loss=1.288, generator_loss=29.09, generator_mel_loss=18.08, generator_kl_loss=1.32, generator_dur_loss=1.684, generator_adv_loss=1.974, generator_feat_match_loss=6.031, over 58.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=28.85, generator_mel_loss=17.9, generator_kl_loss=1.425, generator_dur_loss=1.731, generator_adv_loss=1.991, generator_feat_match_loss=5.806, over 4930.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:56:13,027 INFO [train.py:919] (5/6) Start epoch 757 +2024-03-15 05:56:54,335 INFO [train.py:527] (5/6) Epoch 757, batch 6, global_batch_idx: 93750, batch size: 32, loss[discriminator_loss=2.615, discriminator_real_loss=1.36, discriminator_fake_loss=1.254, generator_loss=29.45, generator_mel_loss=18.05, generator_kl_loss=1.59, generator_dur_loss=1.572, generator_adv_loss=2.025, generator_feat_match_loss=6.219, over 32.00 samples.], tot_loss[discriminator_loss=2.637, discriminator_real_loss=1.334, discriminator_fake_loss=1.303, generator_loss=29.43, generator_mel_loss=18, generator_kl_loss=1.39, generator_dur_loss=1.742, generator_adv_loss=2.016, generator_feat_match_loss=6.283, over 372.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:59:17,029 INFO [train.py:527] (5/6) Epoch 757, batch 56, global_batch_idx: 93800, batch size: 50, loss[discriminator_loss=2.672, discriminator_real_loss=1.423, discriminator_fake_loss=1.249, generator_loss=29.02, generator_mel_loss=18.05, generator_kl_loss=1.601, generator_dur_loss=1.608, generator_adv_loss=2.018, generator_feat_match_loss=5.747, over 50.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.323, generator_loss=29.06, generator_mel_loss=17.96, generator_kl_loss=1.389, generator_dur_loss=1.749, generator_adv_loss=1.999, generator_feat_match_loss=5.963, over 3365.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 05:59:17,031 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 05:59:25,148 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 06:01:45,136 INFO [train.py:527] (5/6) Epoch 757, batch 106, global_batch_idx: 93850, batch size: 47, loss[discriminator_loss=2.745, discriminator_real_loss=1.34, discriminator_fake_loss=1.405, generator_loss=29.2, generator_mel_loss=18.09, generator_kl_loss=1.424, generator_dur_loss=1.668, generator_adv_loss=1.96, generator_feat_match_loss=6.055, over 47.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=28.96, generator_mel_loss=17.92, generator_kl_loss=1.399, generator_dur_loss=1.744, generator_adv_loss=2.011, generator_feat_match_loss=5.883, over 6148.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:02:28,981 INFO [train.py:919] (5/6) Start epoch 758 +2024-03-15 06:04:21,199 INFO [train.py:527] (5/6) Epoch 758, batch 32, global_batch_idx: 93900, batch size: 53, loss[discriminator_loss=2.69, discriminator_real_loss=1.364, discriminator_fake_loss=1.327, generator_loss=28.98, generator_mel_loss=17.91, generator_kl_loss=1.445, generator_dur_loss=1.681, generator_adv_loss=1.987, generator_feat_match_loss=5.962, over 53.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.01, generator_mel_loss=17.92, generator_kl_loss=1.438, generator_dur_loss=1.696, generator_adv_loss=2.018, generator_feat_match_loss=5.941, over 1725.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:06:40,729 INFO [train.py:527] (5/6) Epoch 758, batch 82, global_batch_idx: 93950, batch size: 80, loss[discriminator_loss=2.67, discriminator_real_loss=1.369, discriminator_fake_loss=1.301, generator_loss=28.89, generator_mel_loss=17.91, generator_kl_loss=1.271, generator_dur_loss=1.792, generator_adv_loss=1.928, generator_feat_match_loss=5.996, over 80.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=28.93, generator_mel_loss=17.92, generator_kl_loss=1.415, generator_dur_loss=1.723, generator_adv_loss=1.999, generator_feat_match_loss=5.87, over 4669.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:08:35,721 INFO [train.py:919] (5/6) Start epoch 759 +2024-03-15 06:09:22,083 INFO [train.py:527] (5/6) Epoch 759, batch 8, global_batch_idx: 94000, batch size: 62, loss[discriminator_loss=2.711, discriminator_real_loss=1.326, discriminator_fake_loss=1.385, generator_loss=29.67, generator_mel_loss=17.85, generator_kl_loss=1.407, generator_dur_loss=1.689, generator_adv_loss=2.104, generator_feat_match_loss=6.624, over 62.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.331, discriminator_fake_loss=1.333, generator_loss=28.81, generator_mel_loss=17.7, generator_kl_loss=1.391, generator_dur_loss=1.727, generator_adv_loss=2.028, generator_feat_match_loss=5.965, over 562.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:09:22,100 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 06:09:29,729 INFO [train.py:591] (5/6) 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 06:09:29,730 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 06:11:48,959 INFO [train.py:527] (5/6) Epoch 759, batch 58, global_batch_idx: 94050, batch size: 74, loss[discriminator_loss=2.72, discriminator_real_loss=1.402, discriminator_fake_loss=1.318, generator_loss=28.93, generator_mel_loss=17.88, generator_kl_loss=1.341, generator_dur_loss=1.765, generator_adv_loss=2.057, generator_feat_match_loss=5.886, over 74.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.324, generator_loss=28.73, generator_mel_loss=17.83, generator_kl_loss=1.399, generator_dur_loss=1.717, generator_adv_loss=1.997, generator_feat_match_loss=5.782, over 3415.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:14:05,410 INFO [train.py:527] (5/6) Epoch 759, batch 108, global_batch_idx: 94100, batch size: 50, loss[discriminator_loss=2.665, discriminator_real_loss=1.434, discriminator_fake_loss=1.231, generator_loss=29.32, generator_mel_loss=18.19, generator_kl_loss=1.488, generator_dur_loss=1.64, generator_adv_loss=1.918, generator_feat_match_loss=6.084, over 50.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=28.76, generator_mel_loss=17.84, generator_kl_loss=1.416, generator_dur_loss=1.718, generator_adv_loss=1.995, generator_feat_match_loss=5.789, over 6273.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:14:47,143 INFO [train.py:919] (5/6) Start epoch 760 +2024-03-15 06:16:44,687 INFO [train.py:527] (5/6) Epoch 760, batch 34, global_batch_idx: 94150, batch size: 88, loss[discriminator_loss=2.67, discriminator_real_loss=1.337, discriminator_fake_loss=1.333, generator_loss=29.26, generator_mel_loss=17.98, generator_kl_loss=1.278, generator_dur_loss=1.857, generator_adv_loss=2.165, generator_feat_match_loss=5.978, over 88.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.365, discriminator_fake_loss=1.317, generator_loss=29.02, generator_mel_loss=17.9, generator_kl_loss=1.442, generator_dur_loss=1.723, generator_adv_loss=1.999, generator_feat_match_loss=5.955, over 1921.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:19:03,102 INFO [train.py:527] (5/6) Epoch 760, batch 84, global_batch_idx: 94200, batch size: 58, loss[discriminator_loss=2.621, discriminator_real_loss=1.313, discriminator_fake_loss=1.308, generator_loss=29.02, generator_mel_loss=17.73, generator_kl_loss=1.468, generator_dur_loss=1.737, generator_adv_loss=2.075, generator_feat_match_loss=6.005, over 58.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.36, discriminator_fake_loss=1.321, generator_loss=28.92, generator_mel_loss=17.86, generator_kl_loss=1.44, generator_dur_loss=1.735, generator_adv_loss=1.992, generator_feat_match_loss=5.894, over 4847.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:19:03,104 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 06:19:12,124 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 06:21:03,361 INFO [train.py:919] (5/6) Start epoch 761 +2024-03-15 06:22:00,600 INFO [train.py:527] (5/6) Epoch 761, batch 10, global_batch_idx: 94250, batch size: 42, loss[discriminator_loss=2.745, discriminator_real_loss=1.372, discriminator_fake_loss=1.373, generator_loss=28.89, generator_mel_loss=17.91, generator_kl_loss=1.542, generator_dur_loss=1.671, generator_adv_loss=1.866, generator_feat_match_loss=5.904, over 42.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.388, discriminator_fake_loss=1.333, generator_loss=28.75, generator_mel_loss=17.9, generator_kl_loss=1.45, generator_dur_loss=1.715, generator_adv_loss=1.973, generator_feat_match_loss=5.711, over 567.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:24:17,486 INFO [train.py:527] (5/6) Epoch 761, batch 60, global_batch_idx: 94300, batch size: 55, loss[discriminator_loss=2.634, discriminator_real_loss=1.326, discriminator_fake_loss=1.307, generator_loss=30.26, generator_mel_loss=18.91, generator_kl_loss=1.65, generator_dur_loss=1.701, generator_adv_loss=2.004, generator_feat_match_loss=5.992, over 55.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=28.69, generator_mel_loss=17.88, generator_kl_loss=1.412, generator_dur_loss=1.725, generator_adv_loss=1.99, generator_feat_match_loss=5.68, over 3280.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:26:33,090 INFO [train.py:527] (5/6) Epoch 761, batch 110, global_batch_idx: 94350, batch size: 55, loss[discriminator_loss=2.659, discriminator_real_loss=1.362, discriminator_fake_loss=1.297, generator_loss=28.67, generator_mel_loss=18.05, generator_kl_loss=1.513, generator_dur_loss=1.678, generator_adv_loss=2.04, generator_feat_match_loss=5.389, over 55.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.358, discriminator_fake_loss=1.33, generator_loss=28.77, generator_mel_loss=17.89, generator_kl_loss=1.436, generator_dur_loss=1.729, generator_adv_loss=1.99, generator_feat_match_loss=5.729, over 6006.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:27:11,969 INFO [train.py:919] (5/6) Start epoch 762 +2024-03-15 06:29:16,005 INFO [train.py:527] (5/6) Epoch 762, batch 36, global_batch_idx: 94400, batch size: 47, loss[discriminator_loss=2.665, discriminator_real_loss=1.32, discriminator_fake_loss=1.345, generator_loss=30.31, generator_mel_loss=18.61, generator_kl_loss=1.464, generator_dur_loss=1.678, generator_adv_loss=2.052, generator_feat_match_loss=6.503, over 47.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=28.96, generator_mel_loss=17.91, generator_kl_loss=1.433, generator_dur_loss=1.738, generator_adv_loss=1.997, generator_feat_match_loss=5.878, over 2117.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:29:16,006 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 06:29:24,052 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 06:31:41,076 INFO [train.py:527] (5/6) Epoch 762, batch 86, global_batch_idx: 94450, batch size: 68, loss[discriminator_loss=2.716, discriminator_real_loss=1.439, discriminator_fake_loss=1.277, generator_loss=28.69, generator_mel_loss=17.99, generator_kl_loss=1.386, generator_dur_loss=1.812, generator_adv_loss=1.869, generator_feat_match_loss=5.631, over 68.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.355, discriminator_fake_loss=1.331, generator_loss=28.88, generator_mel_loss=17.89, generator_kl_loss=1.409, generator_dur_loss=1.743, generator_adv_loss=2.005, generator_feat_match_loss=5.837, over 5084.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:33:29,530 INFO [train.py:919] (5/6) Start epoch 763 +2024-03-15 06:34:26,582 INFO [train.py:527] (5/6) Epoch 763, batch 12, global_batch_idx: 94500, batch size: 53, loss[discriminator_loss=2.673, discriminator_real_loss=1.332, discriminator_fake_loss=1.341, generator_loss=29.34, generator_mel_loss=18.01, generator_kl_loss=1.385, generator_dur_loss=1.672, generator_adv_loss=2.123, generator_feat_match_loss=6.145, over 53.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.357, discriminator_fake_loss=1.337, generator_loss=28.94, generator_mel_loss=17.99, generator_kl_loss=1.442, generator_dur_loss=1.724, generator_adv_loss=1.995, generator_feat_match_loss=5.788, over 609.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:36:48,793 INFO [train.py:527] (5/6) Epoch 763, batch 62, global_batch_idx: 94550, batch size: 48, loss[discriminator_loss=2.735, discriminator_real_loss=1.478, discriminator_fake_loss=1.257, generator_loss=28.04, generator_mel_loss=18.09, generator_kl_loss=1.467, generator_dur_loss=1.698, generator_adv_loss=1.887, generator_feat_match_loss=4.896, over 48.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.356, discriminator_fake_loss=1.331, generator_loss=29.04, generator_mel_loss=18, generator_kl_loss=1.421, generator_dur_loss=1.74, generator_adv_loss=1.991, generator_feat_match_loss=5.892, over 3362.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:39:08,375 INFO [train.py:527] (5/6) Epoch 763, batch 112, global_batch_idx: 94600, batch size: 80, loss[discriminator_loss=2.654, discriminator_real_loss=1.376, discriminator_fake_loss=1.279, generator_loss=29.26, generator_mel_loss=17.86, generator_kl_loss=1.324, generator_dur_loss=1.775, generator_adv_loss=2.001, generator_feat_match_loss=6.3, over 80.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=28.98, generator_mel_loss=17.94, generator_kl_loss=1.42, generator_dur_loss=1.746, generator_adv_loss=1.999, generator_feat_match_loss=5.876, over 6077.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:39:08,377 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 06:39:17,117 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 06:39:50,461 INFO [train.py:919] (5/6) Start epoch 764 +2024-03-15 06:42:01,014 INFO [train.py:527] (5/6) Epoch 764, batch 38, global_batch_idx: 94650, batch size: 48, loss[discriminator_loss=2.693, discriminator_real_loss=1.425, discriminator_fake_loss=1.268, generator_loss=28.69, generator_mel_loss=17.89, generator_kl_loss=1.506, generator_dur_loss=1.677, generator_adv_loss=2.082, generator_feat_match_loss=5.53, over 48.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=28.91, generator_mel_loss=17.85, generator_kl_loss=1.462, generator_dur_loss=1.737, generator_adv_loss=1.995, generator_feat_match_loss=5.865, over 2107.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:44:20,428 INFO [train.py:527] (5/6) Epoch 764, batch 88, global_batch_idx: 94700, batch size: 47, loss[discriminator_loss=2.671, discriminator_real_loss=1.406, discriminator_fake_loss=1.265, generator_loss=28.46, generator_mel_loss=17.95, generator_kl_loss=1.481, generator_dur_loss=1.707, generator_adv_loss=2.029, generator_feat_match_loss=5.292, over 47.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.365, discriminator_fake_loss=1.332, generator_loss=28.88, generator_mel_loss=17.87, generator_kl_loss=1.429, generator_dur_loss=1.754, generator_adv_loss=1.991, generator_feat_match_loss=5.834, over 5002.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:45:59,427 INFO [train.py:919] (5/6) Start epoch 765 +2024-03-15 06:47:02,926 INFO [train.py:527] (5/6) Epoch 765, batch 14, global_batch_idx: 94750, batch size: 44, loss[discriminator_loss=2.769, discriminator_real_loss=1.412, discriminator_fake_loss=1.357, generator_loss=29.17, generator_mel_loss=17.45, generator_kl_loss=1.574, generator_dur_loss=1.682, generator_adv_loss=2.022, generator_feat_match_loss=6.441, over 44.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.377, discriminator_fake_loss=1.314, generator_loss=29.06, generator_mel_loss=17.98, generator_kl_loss=1.41, generator_dur_loss=1.735, generator_adv_loss=1.992, generator_feat_match_loss=5.938, over 718.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:49:20,947 INFO [train.py:527] (5/6) Epoch 765, batch 64, global_batch_idx: 94800, batch size: 68, loss[discriminator_loss=2.698, discriminator_real_loss=1.31, discriminator_fake_loss=1.387, generator_loss=28.51, generator_mel_loss=17.57, generator_kl_loss=1.45, generator_dur_loss=1.805, generator_adv_loss=1.927, generator_feat_match_loss=5.755, over 68.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=28.99, generator_mel_loss=17.93, generator_kl_loss=1.433, generator_dur_loss=1.741, generator_adv_loss=1.989, generator_feat_match_loss=5.899, over 3483.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:49:20,949 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 06:49:28,784 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 06:51:46,884 INFO [train.py:527] (5/6) Epoch 765, batch 114, global_batch_idx: 94850, batch size: 59, loss[discriminator_loss=2.77, discriminator_real_loss=1.465, discriminator_fake_loss=1.304, generator_loss=28.67, generator_mel_loss=18.43, generator_kl_loss=1.513, generator_dur_loss=1.743, generator_adv_loss=1.785, generator_feat_match_loss=5.2, over 59.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=29.01, generator_mel_loss=17.94, generator_kl_loss=1.433, generator_dur_loss=1.746, generator_adv_loss=1.997, generator_feat_match_loss=5.893, over 6288.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:52:16,204 INFO [train.py:919] (5/6) Start epoch 766 +2024-03-15 06:54:37,675 INFO [train.py:527] (5/6) Epoch 766, batch 40, global_batch_idx: 94900, batch size: 88, loss[discriminator_loss=2.684, discriminator_real_loss=1.337, discriminator_fake_loss=1.347, generator_loss=28.56, generator_mel_loss=17.62, generator_kl_loss=1.369, generator_dur_loss=1.849, generator_adv_loss=1.972, generator_feat_match_loss=5.754, over 88.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=28.63, generator_mel_loss=17.73, generator_kl_loss=1.4, generator_dur_loss=1.761, generator_adv_loss=1.986, generator_feat_match_loss=5.75, over 2523.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:56:57,365 INFO [train.py:527] (5/6) Epoch 766, batch 90, global_batch_idx: 94950, batch size: 72, loss[discriminator_loss=2.735, discriminator_real_loss=1.418, discriminator_fake_loss=1.316, generator_loss=27.08, generator_mel_loss=17.46, generator_kl_loss=1.231, generator_dur_loss=1.831, generator_adv_loss=1.92, generator_feat_match_loss=4.638, over 72.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=28.82, generator_mel_loss=17.83, generator_kl_loss=1.408, generator_dur_loss=1.748, generator_adv_loss=1.989, generator_feat_match_loss=5.85, over 5302.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:58:29,006 INFO [train.py:919] (5/6) Start epoch 767 +2024-03-15 06:59:38,290 INFO [train.py:527] (5/6) Epoch 767, batch 16, global_batch_idx: 95000, batch size: 56, loss[discriminator_loss=2.691, discriminator_real_loss=1.389, discriminator_fake_loss=1.301, generator_loss=27.79, generator_mel_loss=17.67, generator_kl_loss=1.277, generator_dur_loss=1.74, generator_adv_loss=2.003, generator_feat_match_loss=5.101, over 56.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.367, discriminator_fake_loss=1.31, generator_loss=28.76, generator_mel_loss=17.92, generator_kl_loss=1.405, generator_dur_loss=1.74, generator_adv_loss=2, generator_feat_match_loss=5.689, over 944.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 06:59:38,292 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 06:59:46,427 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 07:02:10,555 INFO [train.py:527] (5/6) Epoch 767, batch 66, global_batch_idx: 95050, batch size: 25, loss[discriminator_loss=2.722, discriminator_real_loss=1.211, discriminator_fake_loss=1.511, generator_loss=30.43, generator_mel_loss=19.59, generator_kl_loss=1.888, generator_dur_loss=1.533, generator_adv_loss=1.902, generator_feat_match_loss=5.515, 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=17.84, generator_kl_loss=1.429, generator_dur_loss=1.751, generator_adv_loss=1.992, generator_feat_match_loss=5.794, over 3809.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:04:29,916 INFO [train.py:527] (5/6) Epoch 767, batch 116, global_batch_idx: 95100, batch size: 45, loss[discriminator_loss=2.722, discriminator_real_loss=1.281, discriminator_fake_loss=1.441, generator_loss=28.64, generator_mel_loss=17.76, generator_kl_loss=1.587, generator_dur_loss=1.661, generator_adv_loss=2.075, generator_feat_match_loss=5.562, over 45.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=28.78, generator_mel_loss=17.84, generator_kl_loss=1.417, generator_dur_loss=1.75, generator_adv_loss=1.99, generator_feat_match_loss=5.786, over 6684.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:04:50,202 INFO [train.py:919] (5/6) Start epoch 768 +2024-03-15 07:07:12,335 INFO [train.py:527] (5/6) Epoch 768, batch 42, global_batch_idx: 95150, batch size: 47, loss[discriminator_loss=2.642, discriminator_real_loss=1.269, discriminator_fake_loss=1.373, generator_loss=29.56, generator_mel_loss=18, generator_kl_loss=1.545, generator_dur_loss=1.687, generator_adv_loss=2.097, generator_feat_match_loss=6.222, over 47.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=29, generator_mel_loss=17.94, generator_kl_loss=1.447, generator_dur_loss=1.726, generator_adv_loss=1.998, generator_feat_match_loss=5.889, over 2327.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:09:33,682 INFO [train.py:527] (5/6) Epoch 768, batch 92, global_batch_idx: 95200, batch size: 66, loss[discriminator_loss=2.714, discriminator_real_loss=1.397, discriminator_fake_loss=1.317, generator_loss=28.67, generator_mel_loss=17.89, generator_kl_loss=1.366, generator_dur_loss=1.723, generator_adv_loss=2.123, generator_feat_match_loss=5.57, over 66.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.359, discriminator_fake_loss=1.332, generator_loss=28.98, generator_mel_loss=17.9, generator_kl_loss=1.426, generator_dur_loss=1.746, generator_adv_loss=2.013, generator_feat_match_loss=5.896, over 5336.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:09:33,683 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 07:09:42,415 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 07:11:12,041 INFO [train.py:919] (5/6) Start epoch 769 +2024-03-15 07:12:30,263 INFO [train.py:527] (5/6) Epoch 769, batch 18, global_batch_idx: 95250, batch size: 61, loss[discriminator_loss=2.695, discriminator_real_loss=1.312, discriminator_fake_loss=1.383, generator_loss=28.38, generator_mel_loss=17.7, generator_kl_loss=1.454, generator_dur_loss=1.671, generator_adv_loss=2.136, generator_feat_match_loss=5.417, over 61.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.362, discriminator_fake_loss=1.332, generator_loss=28.7, generator_mel_loss=17.78, generator_kl_loss=1.415, generator_dur_loss=1.751, generator_adv_loss=1.998, generator_feat_match_loss=5.758, over 1148.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:14:48,456 INFO [train.py:527] (5/6) Epoch 769, batch 68, global_batch_idx: 95300, batch size: 70, loss[discriminator_loss=2.711, discriminator_real_loss=1.404, discriminator_fake_loss=1.307, generator_loss=28.25, generator_mel_loss=17.93, generator_kl_loss=1.465, generator_dur_loss=1.788, generator_adv_loss=1.818, generator_feat_match_loss=5.252, over 70.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.358, discriminator_fake_loss=1.332, generator_loss=28.83, generator_mel_loss=17.91, generator_kl_loss=1.414, generator_dur_loss=1.751, generator_adv_loss=1.995, generator_feat_match_loss=5.76, over 3962.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:17:09,128 INFO [train.py:527] (5/6) Epoch 769, batch 118, global_batch_idx: 95350, batch size: 66, loss[discriminator_loss=2.715, discriminator_real_loss=1.253, discriminator_fake_loss=1.462, generator_loss=28.69, generator_mel_loss=17.98, generator_kl_loss=1.43, generator_dur_loss=1.789, generator_adv_loss=2.188, generator_feat_match_loss=5.307, over 66.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.357, discriminator_fake_loss=1.331, generator_loss=28.87, generator_mel_loss=17.88, generator_kl_loss=1.41, generator_dur_loss=1.753, generator_adv_loss=2.002, generator_feat_match_loss=5.827, over 6805.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:17:24,703 INFO [train.py:919] (5/6) Start epoch 770 +2024-03-15 07:19:54,101 INFO [train.py:527] (5/6) Epoch 770, batch 44, global_batch_idx: 95400, batch size: 31, loss[discriminator_loss=2.7, discriminator_real_loss=1.406, discriminator_fake_loss=1.294, generator_loss=28.98, generator_mel_loss=18.37, generator_kl_loss=1.605, generator_dur_loss=1.643, generator_adv_loss=1.789, generator_feat_match_loss=5.572, over 31.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.358, discriminator_fake_loss=1.334, generator_loss=28.87, generator_mel_loss=17.91, generator_kl_loss=1.375, generator_dur_loss=1.761, generator_adv_loss=1.981, generator_feat_match_loss=5.841, over 2742.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:19:54,103 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 07:20:02,102 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 07:22:21,240 INFO [train.py:527] (5/6) Epoch 770, batch 94, global_batch_idx: 95450, batch size: 58, loss[discriminator_loss=2.682, discriminator_real_loss=1.35, discriminator_fake_loss=1.332, generator_loss=28.57, generator_mel_loss=18.22, generator_kl_loss=1.271, generator_dur_loss=1.775, generator_adv_loss=2.012, generator_feat_match_loss=5.292, over 58.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=28.97, generator_mel_loss=17.9, generator_kl_loss=1.391, generator_dur_loss=1.763, generator_adv_loss=1.993, generator_feat_match_loss=5.92, over 5595.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:23:44,150 INFO [train.py:919] (5/6) Start epoch 771 +2024-03-15 07:25:07,407 INFO [train.py:527] (5/6) Epoch 771, batch 20, global_batch_idx: 95500, batch size: 66, loss[discriminator_loss=2.72, discriminator_real_loss=1.423, discriminator_fake_loss=1.297, generator_loss=27.91, generator_mel_loss=17.71, generator_kl_loss=1.339, generator_dur_loss=1.783, generator_adv_loss=1.888, generator_feat_match_loss=5.184, over 66.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.351, discriminator_fake_loss=1.323, generator_loss=29.02, generator_mel_loss=17.95, generator_kl_loss=1.404, generator_dur_loss=1.754, generator_adv_loss=1.999, generator_feat_match_loss=5.909, over 1219.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:27:27,454 INFO [train.py:527] (5/6) Epoch 771, batch 70, global_batch_idx: 95550, batch size: 36, loss[discriminator_loss=2.698, discriminator_real_loss=1.416, discriminator_fake_loss=1.282, generator_loss=28.46, generator_mel_loss=17.48, generator_kl_loss=1.469, generator_dur_loss=1.655, generator_adv_loss=2.052, generator_feat_match_loss=5.8, over 36.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.361, discriminator_fake_loss=1.326, generator_loss=28.88, generator_mel_loss=17.91, generator_kl_loss=1.398, generator_dur_loss=1.76, generator_adv_loss=1.991, generator_feat_match_loss=5.814, over 4186.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:29:46,218 INFO [train.py:527] (5/6) Epoch 771, batch 120, global_batch_idx: 95600, batch size: 48, loss[discriminator_loss=2.671, discriminator_real_loss=1.335, discriminator_fake_loss=1.336, generator_loss=28.75, generator_mel_loss=17.91, generator_kl_loss=1.589, generator_dur_loss=1.642, generator_adv_loss=1.883, generator_feat_match_loss=5.726, over 48.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=28.83, generator_mel_loss=17.88, generator_kl_loss=1.401, generator_dur_loss=1.756, generator_adv_loss=1.991, generator_feat_match_loss=5.807, over 7097.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:29:46,220 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 07:29:54,766 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 07:30:05,441 INFO [train.py:919] (5/6) Start epoch 772 +2024-03-15 07:32:36,066 INFO [train.py:527] (5/6) Epoch 772, batch 46, global_batch_idx: 95650, batch size: 53, loss[discriminator_loss=2.638, discriminator_real_loss=1.416, discriminator_fake_loss=1.222, generator_loss=29.26, generator_mel_loss=17.92, generator_kl_loss=1.479, generator_dur_loss=1.636, generator_adv_loss=2.047, generator_feat_match_loss=6.174, over 53.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.359, discriminator_fake_loss=1.32, generator_loss=28.85, generator_mel_loss=17.8, generator_kl_loss=1.399, generator_dur_loss=1.739, generator_adv_loss=2.032, generator_feat_match_loss=5.888, over 2740.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:34:53,245 INFO [train.py:527] (5/6) Epoch 772, batch 96, global_batch_idx: 95700, batch size: 50, loss[discriminator_loss=2.703, discriminator_real_loss=1.375, discriminator_fake_loss=1.328, generator_loss=28.39, generator_mel_loss=17.87, generator_kl_loss=1.524, generator_dur_loss=1.658, generator_adv_loss=2.061, generator_feat_match_loss=5.274, over 50.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.324, generator_loss=28.92, generator_mel_loss=17.85, generator_kl_loss=1.421, generator_dur_loss=1.738, generator_adv_loss=2.015, generator_feat_match_loss=5.905, over 5539.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:36:04,381 INFO [train.py:919] (5/6) Start epoch 773 +2024-03-15 07:37:29,987 INFO [train.py:527] (5/6) Epoch 773, batch 22, global_batch_idx: 95750, batch size: 64, loss[discriminator_loss=2.618, discriminator_real_loss=1.329, discriminator_fake_loss=1.289, generator_loss=29.24, generator_mel_loss=18.03, generator_kl_loss=1.389, generator_dur_loss=1.742, generator_adv_loss=2.087, generator_feat_match_loss=6.001, over 64.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.343, discriminator_fake_loss=1.336, generator_loss=28.81, generator_mel_loss=17.83, generator_kl_loss=1.439, generator_dur_loss=1.741, generator_adv_loss=1.99, generator_feat_match_loss=5.812, over 1278.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:39:47,975 INFO [train.py:527] (5/6) Epoch 773, batch 72, global_batch_idx: 95800, batch size: 72, loss[discriminator_loss=2.671, discriminator_real_loss=1.364, discriminator_fake_loss=1.307, generator_loss=28.6, generator_mel_loss=18.08, generator_kl_loss=1.413, generator_dur_loss=1.768, generator_adv_loss=2.015, generator_feat_match_loss=5.322, over 72.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=28.92, generator_mel_loss=17.85, generator_kl_loss=1.434, generator_dur_loss=1.736, generator_adv_loss=1.998, generator_feat_match_loss=5.904, over 4106.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:39:47,976 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 07:39:56,232 INFO [train.py:591] (5/6) 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 07:39:56,232 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 07:42:19,748 INFO [train.py:527] (5/6) Epoch 773, batch 122, global_batch_idx: 95850, batch size: 74, loss[discriminator_loss=2.693, discriminator_real_loss=1.438, discriminator_fake_loss=1.255, generator_loss=28.41, generator_mel_loss=17.58, generator_kl_loss=1.381, generator_dur_loss=1.766, generator_adv_loss=1.997, generator_feat_match_loss=5.682, over 74.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.323, generator_loss=28.87, generator_mel_loss=17.84, generator_kl_loss=1.435, generator_dur_loss=1.745, generator_adv_loss=1.999, generator_feat_match_loss=5.853, over 7027.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:42:24,678 INFO [train.py:919] (5/6) Start epoch 774 +2024-03-15 07:45:02,258 INFO [train.py:527] (5/6) Epoch 774, batch 48, global_batch_idx: 95900, batch size: 44, loss[discriminator_loss=2.695, discriminator_real_loss=1.449, discriminator_fake_loss=1.247, generator_loss=29.27, generator_mel_loss=17.65, generator_kl_loss=1.59, generator_dur_loss=1.662, generator_adv_loss=2.105, generator_feat_match_loss=6.26, over 44.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.352, discriminator_fake_loss=1.34, generator_loss=28.76, generator_mel_loss=17.79, generator_kl_loss=1.401, generator_dur_loss=1.752, generator_adv_loss=1.975, generator_feat_match_loss=5.837, over 2801.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:47:22,467 INFO [train.py:527] (5/6) Epoch 774, batch 98, global_batch_idx: 95950, batch size: 70, loss[discriminator_loss=2.785, discriminator_real_loss=1.252, discriminator_fake_loss=1.534, generator_loss=29.16, generator_mel_loss=17.96, generator_kl_loss=1.409, generator_dur_loss=1.741, generator_adv_loss=2.21, generator_feat_match_loss=5.843, over 70.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.356, discriminator_fake_loss=1.329, generator_loss=28.87, generator_mel_loss=17.82, generator_kl_loss=1.394, generator_dur_loss=1.747, generator_adv_loss=1.992, generator_feat_match_loss=5.915, over 5651.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:48:32,433 INFO [train.py:919] (5/6) Start epoch 775 +2024-03-15 07:50:03,356 INFO [train.py:527] (5/6) Epoch 775, batch 24, global_batch_idx: 96000, batch size: 58, loss[discriminator_loss=2.668, discriminator_real_loss=1.348, discriminator_fake_loss=1.32, generator_loss=28.31, generator_mel_loss=18.07, generator_kl_loss=1.364, generator_dur_loss=1.733, generator_adv_loss=2.021, generator_feat_match_loss=5.124, over 58.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.355, discriminator_fake_loss=1.335, generator_loss=28.83, generator_mel_loss=17.92, generator_kl_loss=1.394, generator_dur_loss=1.731, generator_adv_loss=1.992, generator_feat_match_loss=5.793, over 1516.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:50:03,358 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 07:50:11,377 INFO [train.py:591] (5/6) 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 07:50:11,378 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 07:52:32,674 INFO [train.py:527] (5/6) Epoch 775, batch 74, global_batch_idx: 96050, batch size: 64, loss[discriminator_loss=2.674, discriminator_real_loss=1.365, discriminator_fake_loss=1.309, generator_loss=28.12, generator_mel_loss=17.64, generator_kl_loss=1.441, generator_dur_loss=1.731, generator_adv_loss=1.886, generator_feat_match_loss=5.424, over 64.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.86, generator_mel_loss=17.9, generator_kl_loss=1.402, generator_dur_loss=1.735, generator_adv_loss=1.995, generator_feat_match_loss=5.837, over 4428.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:54:47,029 INFO [train.py:919] (5/6) Start epoch 776 +2024-03-15 07:55:09,660 INFO [train.py:527] (5/6) Epoch 776, batch 0, global_batch_idx: 96100, batch size: 61, loss[discriminator_loss=2.772, discriminator_real_loss=1.523, discriminator_fake_loss=1.249, generator_loss=28.87, generator_mel_loss=18.06, generator_kl_loss=1.556, generator_dur_loss=1.714, generator_adv_loss=1.947, generator_feat_match_loss=5.59, over 61.00 samples.], tot_loss[discriminator_loss=2.772, discriminator_real_loss=1.523, discriminator_fake_loss=1.249, generator_loss=28.87, generator_mel_loss=18.06, generator_kl_loss=1.556, generator_dur_loss=1.714, generator_adv_loss=1.947, generator_feat_match_loss=5.59, over 61.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:57:27,814 INFO [train.py:527] (5/6) Epoch 776, batch 50, global_batch_idx: 96150, batch size: 61, loss[discriminator_loss=2.716, discriminator_real_loss=1.392, discriminator_fake_loss=1.324, generator_loss=29.09, generator_mel_loss=17.95, generator_kl_loss=1.505, generator_dur_loss=1.709, generator_adv_loss=2.026, generator_feat_match_loss=5.902, over 61.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.378, discriminator_fake_loss=1.321, generator_loss=28.84, generator_mel_loss=17.84, generator_kl_loss=1.459, generator_dur_loss=1.711, generator_adv_loss=2.002, generator_feat_match_loss=5.834, over 2656.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:59:46,266 INFO [train.py:527] (5/6) Epoch 776, batch 100, global_batch_idx: 96200, batch size: 39, loss[discriminator_loss=2.721, discriminator_real_loss=1.289, discriminator_fake_loss=1.432, generator_loss=27.87, generator_mel_loss=17.56, generator_kl_loss=1.293, generator_dur_loss=1.674, generator_adv_loss=2.036, generator_feat_match_loss=5.305, over 39.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.364, discriminator_fake_loss=1.329, generator_loss=28.84, generator_mel_loss=17.86, generator_kl_loss=1.434, generator_dur_loss=1.728, generator_adv_loss=1.99, generator_feat_match_loss=5.826, over 5680.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 07:59:46,267 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 07:59:55,169 INFO [train.py:591] (5/6) 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 07:59:55,169 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 08:01:00,438 INFO [train.py:919] (5/6) Start epoch 777 +2024-03-15 08:02:38,173 INFO [train.py:527] (5/6) Epoch 777, batch 26, global_batch_idx: 96250, batch size: 36, loss[discriminator_loss=2.705, discriminator_real_loss=1.313, discriminator_fake_loss=1.392, generator_loss=29.67, generator_mel_loss=18.27, generator_kl_loss=1.519, generator_dur_loss=1.679, generator_adv_loss=2.032, generator_feat_match_loss=6.169, over 36.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.326, generator_loss=28.94, generator_mel_loss=17.88, generator_kl_loss=1.447, generator_dur_loss=1.728, generator_adv_loss=2.015, generator_feat_match_loss=5.87, over 1517.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 08:04:57,621 INFO [train.py:527] (5/6) Epoch 777, batch 76, global_batch_idx: 96300, batch size: 47, loss[discriminator_loss=2.678, discriminator_real_loss=1.414, discriminator_fake_loss=1.265, generator_loss=27.39, generator_mel_loss=17.37, generator_kl_loss=1.555, generator_dur_loss=1.624, generator_adv_loss=1.941, generator_feat_match_loss=4.905, over 47.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.357, discriminator_fake_loss=1.329, generator_loss=28.86, generator_mel_loss=17.86, generator_kl_loss=1.439, generator_dur_loss=1.729, generator_adv_loss=2.014, generator_feat_match_loss=5.816, over 4357.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, +2024-03-15 08:07:07,787 INFO [train.py:919] (5/6) Start epoch 778 +2024-03-15 08:07:37,267 INFO [train.py:527] (5/6) Epoch 778, batch 2, global_batch_idx: 96350, batch size: 59, loss[discriminator_loss=2.696, discriminator_real_loss=1.34, discriminator_fake_loss=1.356, generator_loss=28.24, generator_mel_loss=17.94, generator_kl_loss=1.348, generator_dur_loss=1.758, generator_adv_loss=2.029, generator_feat_match_loss=5.168, over 59.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.36, discriminator_fake_loss=1.334, generator_loss=29.12, generator_mel_loss=17.98, generator_kl_loss=1.264, generator_dur_loss=1.808, generator_adv_loss=2.002, generator_feat_match_loss=6.072, over 217.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:09:57,648 INFO [train.py:527] (5/6) Epoch 778, batch 52, global_batch_idx: 96400, batch size: 36, loss[discriminator_loss=2.787, discriminator_real_loss=1.493, discriminator_fake_loss=1.294, generator_loss=29.64, generator_mel_loss=18.05, generator_kl_loss=1.551, generator_dur_loss=1.686, generator_adv_loss=1.93, generator_feat_match_loss=6.429, over 36.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.95, generator_mel_loss=17.94, generator_kl_loss=1.404, generator_dur_loss=1.738, generator_adv_loss=1.991, generator_feat_match_loss=5.877, over 3052.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:09:57,649 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 08:10:05,729 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 08:12:23,820 INFO [train.py:527] (5/6) Epoch 778, batch 102, global_batch_idx: 96450, batch size: 64, loss[discriminator_loss=2.743, discriminator_real_loss=1.392, discriminator_fake_loss=1.351, generator_loss=28.63, generator_mel_loss=17.53, generator_kl_loss=1.584, generator_dur_loss=1.665, generator_adv_loss=1.936, generator_feat_match_loss=5.921, over 64.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.362, discriminator_fake_loss=1.331, generator_loss=28.89, generator_mel_loss=17.88, generator_kl_loss=1.403, generator_dur_loss=1.736, generator_adv_loss=1.997, generator_feat_match_loss=5.879, over 6042.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:13:21,448 INFO [train.py:919] (5/6) Start epoch 779 +2024-03-15 08:15:01,043 INFO [train.py:527] (5/6) Epoch 779, batch 28, global_batch_idx: 96500, batch size: 58, loss[discriminator_loss=2.703, discriminator_real_loss=1.485, discriminator_fake_loss=1.219, generator_loss=28.79, generator_mel_loss=17.77, generator_kl_loss=1.292, generator_dur_loss=1.734, generator_adv_loss=1.966, generator_feat_match_loss=6.03, over 58.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.36, discriminator_fake_loss=1.339, generator_loss=29.06, generator_mel_loss=18.1, generator_kl_loss=1.444, generator_dur_loss=1.704, generator_adv_loss=2, generator_feat_match_loss=5.814, over 1444.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:17:20,556 INFO [train.py:527] (5/6) Epoch 779, batch 78, global_batch_idx: 96550, batch size: 56, loss[discriminator_loss=2.647, discriminator_real_loss=1.346, discriminator_fake_loss=1.301, generator_loss=29.61, generator_mel_loss=18.08, generator_kl_loss=1.541, generator_dur_loss=1.73, generator_adv_loss=1.999, generator_feat_match_loss=6.26, over 56.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.328, generator_loss=28.94, generator_mel_loss=17.93, generator_kl_loss=1.441, generator_dur_loss=1.722, generator_adv_loss=2.007, generator_feat_match_loss=5.834, over 4331.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:19:30,491 INFO [train.py:919] (5/6) Start epoch 780 +2024-03-15 08:20:06,442 INFO [train.py:527] (5/6) Epoch 780, batch 4, global_batch_idx: 96600, batch size: 61, loss[discriminator_loss=2.711, discriminator_real_loss=1.408, discriminator_fake_loss=1.304, generator_loss=28, generator_mel_loss=17.8, generator_kl_loss=1.382, generator_dur_loss=1.764, generator_adv_loss=1.989, generator_feat_match_loss=5.064, over 61.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.386, discriminator_fake_loss=1.33, generator_loss=28.8, generator_mel_loss=17.89, generator_kl_loss=1.401, generator_dur_loss=1.75, generator_adv_loss=1.969, generator_feat_match_loss=5.782, over 300.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:20:06,444 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 08:20:14,317 INFO [train.py:591] (5/6) 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 08:20:14,319 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 08:22:32,334 INFO [train.py:527] (5/6) Epoch 780, batch 54, global_batch_idx: 96650, batch size: 66, loss[discriminator_loss=2.636, discriminator_real_loss=1.356, discriminator_fake_loss=1.279, generator_loss=29.33, generator_mel_loss=18.07, generator_kl_loss=1.435, generator_dur_loss=1.773, generator_adv_loss=1.978, generator_feat_match_loss=6.071, over 66.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=28.77, generator_mel_loss=17.81, generator_kl_loss=1.421, generator_dur_loss=1.739, generator_adv_loss=1.976, generator_feat_match_loss=5.824, over 3186.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:24:52,810 INFO [train.py:527] (5/6) Epoch 780, batch 104, global_batch_idx: 96700, batch size: 80, loss[discriminator_loss=2.69, discriminator_real_loss=1.341, discriminator_fake_loss=1.35, generator_loss=29.15, generator_mel_loss=17.6, generator_kl_loss=1.207, generator_dur_loss=1.778, generator_adv_loss=1.894, generator_feat_match_loss=6.664, over 80.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.351, discriminator_fake_loss=1.325, generator_loss=28.76, generator_mel_loss=17.81, generator_kl_loss=1.41, generator_dur_loss=1.744, generator_adv_loss=1.985, generator_feat_match_loss=5.807, over 6104.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:25:47,466 INFO [train.py:919] (5/6) Start epoch 781 +2024-03-15 08:27:38,890 INFO [train.py:527] (5/6) Epoch 781, batch 30, global_batch_idx: 96750, batch size: 56, loss[discriminator_loss=2.715, discriminator_real_loss=1.349, discriminator_fake_loss=1.366, generator_loss=27.76, generator_mel_loss=17.49, generator_kl_loss=1.438, generator_dur_loss=1.695, generator_adv_loss=1.997, generator_feat_match_loss=5.134, over 56.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=28.8, generator_mel_loss=17.82, generator_kl_loss=1.45, generator_dur_loss=1.73, generator_adv_loss=1.997, generator_feat_match_loss=5.805, over 1781.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:29:55,310 INFO [train.py:527] (5/6) Epoch 781, batch 80, global_batch_idx: 96800, batch size: 36, loss[discriminator_loss=2.636, discriminator_real_loss=1.229, discriminator_fake_loss=1.407, generator_loss=29.41, generator_mel_loss=17.86, generator_kl_loss=1.548, generator_dur_loss=1.655, generator_adv_loss=2.07, generator_feat_match_loss=6.286, over 36.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.354, discriminator_fake_loss=1.326, generator_loss=28.85, generator_mel_loss=17.81, generator_kl_loss=1.44, generator_dur_loss=1.723, generator_adv_loss=1.995, generator_feat_match_loss=5.878, over 4466.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:29:55,313 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 08:30:03,855 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 08:32:07,435 INFO [train.py:919] (5/6) Start epoch 782 +2024-03-15 08:32:49,270 INFO [train.py:527] (5/6) Epoch 782, batch 6, global_batch_idx: 96850, batch size: 55, loss[discriminator_loss=2.707, discriminator_real_loss=1.356, discriminator_fake_loss=1.351, generator_loss=29.41, generator_mel_loss=17.84, generator_kl_loss=1.498, generator_dur_loss=1.703, generator_adv_loss=2.088, generator_feat_match_loss=6.281, over 55.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.389, discriminator_fake_loss=1.3, generator_loss=28.99, generator_mel_loss=17.92, generator_kl_loss=1.438, generator_dur_loss=1.741, generator_adv_loss=2.017, generator_feat_match_loss=5.869, over 419.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:35:10,493 INFO [train.py:527] (5/6) Epoch 782, batch 56, global_batch_idx: 96900, batch size: 96, loss[discriminator_loss=2.653, discriminator_real_loss=1.323, discriminator_fake_loss=1.331, generator_loss=29.12, generator_mel_loss=17.65, generator_kl_loss=1.409, generator_dur_loss=1.816, generator_adv_loss=1.904, generator_feat_match_loss=6.345, over 96.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.363, discriminator_fake_loss=1.321, generator_loss=28.93, generator_mel_loss=17.86, generator_kl_loss=1.43, generator_dur_loss=1.751, generator_adv_loss=1.998, generator_feat_match_loss=5.896, over 3327.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:37:28,775 INFO [train.py:527] (5/6) Epoch 782, batch 106, global_batch_idx: 96950, batch size: 13, loss[discriminator_loss=2.712, discriminator_real_loss=1.271, discriminator_fake_loss=1.441, generator_loss=29.74, generator_mel_loss=18.45, generator_kl_loss=1.711, generator_dur_loss=1.622, generator_adv_loss=1.955, generator_feat_match_loss=6.006, over 13.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.326, generator_loss=28.93, generator_mel_loss=17.85, generator_kl_loss=1.426, generator_dur_loss=1.75, generator_adv_loss=1.994, generator_feat_match_loss=5.912, over 6148.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:38:15,330 INFO [train.py:919] (5/6) Start epoch 783 +2024-03-15 08:40:07,232 INFO [train.py:527] (5/6) Epoch 783, batch 32, global_batch_idx: 97000, batch size: 36, loss[discriminator_loss=2.706, discriminator_real_loss=1.353, discriminator_fake_loss=1.353, generator_loss=28.09, generator_mel_loss=17.79, generator_kl_loss=1.408, generator_dur_loss=1.685, generator_adv_loss=1.883, generator_feat_match_loss=5.332, over 36.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.93, generator_mel_loss=17.94, generator_kl_loss=1.455, generator_dur_loss=1.708, generator_adv_loss=1.983, generator_feat_match_loss=5.847, over 1759.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:40:07,234 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 08:40:15,060 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 08:42:36,396 INFO [train.py:527] (5/6) Epoch 783, batch 82, global_batch_idx: 97050, batch size: 59, loss[discriminator_loss=2.711, discriminator_real_loss=1.469, discriminator_fake_loss=1.242, generator_loss=28.41, generator_mel_loss=17.76, generator_kl_loss=1.297, generator_dur_loss=1.752, generator_adv_loss=1.969, generator_feat_match_loss=5.622, over 59.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.32, generator_loss=28.91, generator_mel_loss=17.84, generator_kl_loss=1.418, generator_dur_loss=1.727, generator_adv_loss=2.004, generator_feat_match_loss=5.915, over 4778.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:44:27,993 INFO [train.py:919] (5/6) Start epoch 784 +2024-03-15 08:45:12,142 INFO [train.py:527] (5/6) Epoch 784, batch 8, global_batch_idx: 97100, batch size: 64, loss[discriminator_loss=2.725, discriminator_real_loss=1.377, discriminator_fake_loss=1.349, generator_loss=28.09, generator_mel_loss=17.3, generator_kl_loss=1.561, generator_dur_loss=1.739, generator_adv_loss=1.989, generator_feat_match_loss=5.497, over 64.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.374, discriminator_fake_loss=1.319, generator_loss=28.88, generator_mel_loss=17.77, generator_kl_loss=1.498, generator_dur_loss=1.746, generator_adv_loss=2, generator_feat_match_loss=5.867, over 566.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:47:31,231 INFO [train.py:527] (5/6) Epoch 784, batch 58, global_batch_idx: 97150, batch size: 56, loss[discriminator_loss=2.634, discriminator_real_loss=1.351, discriminator_fake_loss=1.283, generator_loss=27.76, generator_mel_loss=17.36, generator_kl_loss=1.251, generator_dur_loss=1.724, generator_adv_loss=1.981, generator_feat_match_loss=5.445, over 56.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=28.85, generator_mel_loss=17.83, generator_kl_loss=1.433, generator_dur_loss=1.73, generator_adv_loss=1.995, generator_feat_match_loss=5.852, over 3436.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:49:49,908 INFO [train.py:527] (5/6) Epoch 784, batch 108, global_batch_idx: 97200, batch size: 48, loss[discriminator_loss=2.641, discriminator_real_loss=1.347, discriminator_fake_loss=1.294, generator_loss=29.81, generator_mel_loss=18.2, generator_kl_loss=1.614, generator_dur_loss=1.663, generator_adv_loss=1.991, generator_feat_match_loss=6.342, over 48.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=28.88, generator_mel_loss=17.84, generator_kl_loss=1.436, generator_dur_loss=1.734, generator_adv_loss=1.996, generator_feat_match_loss=5.869, over 6160.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:49:49,910 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 08:49:58,659 INFO [train.py:591] (5/6) 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 08:49:58,660 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 08:50:43,378 INFO [train.py:919] (5/6) Start epoch 785 +2024-03-15 08:52:39,187 INFO [train.py:527] (5/6) Epoch 785, batch 34, global_batch_idx: 97250, batch size: 25, loss[discriminator_loss=2.646, discriminator_real_loss=1.408, discriminator_fake_loss=1.238, generator_loss=29.8, generator_mel_loss=18.73, generator_kl_loss=1.682, generator_dur_loss=1.548, generator_adv_loss=2.046, generator_feat_match_loss=5.786, over 25.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.326, generator_loss=29.33, generator_mel_loss=17.95, generator_kl_loss=1.445, generator_dur_loss=1.738, generator_adv_loss=2.046, generator_feat_match_loss=6.151, over 1816.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:54:55,355 INFO [train.py:527] (5/6) Epoch 785, batch 84, global_batch_idx: 97300, batch size: 62, loss[discriminator_loss=2.709, discriminator_real_loss=1.35, discriminator_fake_loss=1.359, generator_loss=28.61, generator_mel_loss=18.05, generator_kl_loss=1.49, generator_dur_loss=1.761, generator_adv_loss=1.918, generator_feat_match_loss=5.382, over 62.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.356, discriminator_fake_loss=1.322, generator_loss=29.05, generator_mel_loss=17.9, generator_kl_loss=1.431, generator_dur_loss=1.735, generator_adv_loss=2.021, generator_feat_match_loss=5.967, over 4600.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:56:45,848 INFO [train.py:919] (5/6) Start epoch 786 +2024-03-15 08:57:37,882 INFO [train.py:527] (5/6) Epoch 786, batch 10, global_batch_idx: 97350, batch size: 68, loss[discriminator_loss=2.692, discriminator_real_loss=1.34, discriminator_fake_loss=1.352, generator_loss=28.22, generator_mel_loss=18.11, generator_kl_loss=1.428, generator_dur_loss=1.774, generator_adv_loss=1.979, generator_feat_match_loss=4.931, over 68.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.354, discriminator_fake_loss=1.352, generator_loss=28.74, generator_mel_loss=17.92, generator_kl_loss=1.417, generator_dur_loss=1.733, generator_adv_loss=1.967, generator_feat_match_loss=5.703, over 615.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:59:57,164 INFO [train.py:527] (5/6) Epoch 786, batch 60, global_batch_idx: 97400, batch size: 52, loss[discriminator_loss=2.751, discriminator_real_loss=1.319, discriminator_fake_loss=1.432, generator_loss=27.66, generator_mel_loss=17.61, generator_kl_loss=1.368, generator_dur_loss=1.667, generator_adv_loss=1.98, generator_feat_match_loss=5.038, over 52.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.363, discriminator_fake_loss=1.331, generator_loss=28.88, generator_mel_loss=17.87, generator_kl_loss=1.433, generator_dur_loss=1.744, generator_adv_loss=1.996, generator_feat_match_loss=5.837, over 3539.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 08:59:57,165 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 09:00:06,018 INFO [train.py:591] (5/6) 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 09:00:06,020 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 09:02:26,430 INFO [train.py:527] (5/6) Epoch 786, batch 110, global_batch_idx: 97450, batch size: 39, loss[discriminator_loss=2.693, discriminator_real_loss=1.405, discriminator_fake_loss=1.288, generator_loss=28.61, generator_mel_loss=17.88, generator_kl_loss=1.576, generator_dur_loss=1.659, generator_adv_loss=1.851, generator_feat_match_loss=5.638, over 39.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.327, generator_loss=28.85, generator_mel_loss=17.87, generator_kl_loss=1.416, generator_dur_loss=1.747, generator_adv_loss=1.996, generator_feat_match_loss=5.826, over 6613.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:03:03,803 INFO [train.py:919] (5/6) Start epoch 787 +2024-03-15 09:05:06,741 INFO [train.py:527] (5/6) Epoch 787, batch 36, global_batch_idx: 97500, batch size: 61, loss[discriminator_loss=2.728, discriminator_real_loss=1.367, discriminator_fake_loss=1.361, generator_loss=29.06, generator_mel_loss=18.36, generator_kl_loss=1.589, generator_dur_loss=1.739, generator_adv_loss=1.951, generator_feat_match_loss=5.415, over 61.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.365, discriminator_fake_loss=1.323, generator_loss=28.86, generator_mel_loss=17.92, generator_kl_loss=1.448, generator_dur_loss=1.74, generator_adv_loss=1.992, generator_feat_match_loss=5.754, over 2057.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:07:26,574 INFO [train.py:527] (5/6) Epoch 787, batch 86, global_batch_idx: 97550, batch size: 96, loss[discriminator_loss=2.718, discriminator_real_loss=1.399, discriminator_fake_loss=1.319, generator_loss=28.43, generator_mel_loss=17.49, generator_kl_loss=1.152, generator_dur_loss=1.862, generator_adv_loss=1.977, generator_feat_match_loss=5.953, over 96.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.357, discriminator_fake_loss=1.323, generator_loss=29.01, generator_mel_loss=17.93, generator_kl_loss=1.438, generator_dur_loss=1.733, generator_adv_loss=2.007, generator_feat_match_loss=5.894, over 4672.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:09:15,513 INFO [train.py:919] (5/6) Start epoch 788 +2024-03-15 09:10:14,332 INFO [train.py:527] (5/6) Epoch 788, batch 12, global_batch_idx: 97600, batch size: 50, loss[discriminator_loss=2.639, discriminator_real_loss=1.37, discriminator_fake_loss=1.269, generator_loss=29.53, generator_mel_loss=17.85, generator_kl_loss=1.563, generator_dur_loss=1.687, generator_adv_loss=2.012, generator_feat_match_loss=6.413, over 50.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.36, discriminator_fake_loss=1.331, generator_loss=29.16, generator_mel_loss=17.87, generator_kl_loss=1.442, generator_dur_loss=1.743, generator_adv_loss=1.98, generator_feat_match_loss=6.121, over 772.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:10:14,334 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 09:10:21,986 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 09:12:41,283 INFO [train.py:527] (5/6) Epoch 788, batch 62, global_batch_idx: 97650, batch size: 62, loss[discriminator_loss=2.695, discriminator_real_loss=1.341, discriminator_fake_loss=1.354, generator_loss=28.37, generator_mel_loss=17.68, generator_kl_loss=1.384, generator_dur_loss=1.702, generator_adv_loss=1.863, generator_feat_match_loss=5.742, over 62.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=29, generator_mel_loss=17.89, generator_kl_loss=1.428, generator_dur_loss=1.732, generator_adv_loss=1.99, generator_feat_match_loss=5.957, over 3605.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:14:58,420 INFO [train.py:527] (5/6) Epoch 788, batch 112, global_batch_idx: 97700, batch size: 83, loss[discriminator_loss=2.7, discriminator_real_loss=1.27, discriminator_fake_loss=1.43, generator_loss=28.64, generator_mel_loss=17.96, generator_kl_loss=1.341, generator_dur_loss=1.838, generator_adv_loss=2.046, generator_feat_match_loss=5.454, over 83.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=28.99, generator_mel_loss=17.88, generator_kl_loss=1.428, generator_dur_loss=1.732, generator_adv_loss=1.996, generator_feat_match_loss=5.953, over 6381.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:15:31,937 INFO [train.py:919] (5/6) Start epoch 789 +2024-03-15 09:17:42,788 INFO [train.py:527] (5/6) Epoch 789, batch 38, global_batch_idx: 97750, batch size: 45, loss[discriminator_loss=2.731, discriminator_real_loss=1.325, discriminator_fake_loss=1.406, generator_loss=28.97, generator_mel_loss=17.63, generator_kl_loss=1.495, generator_dur_loss=1.64, generator_adv_loss=2.072, generator_feat_match_loss=6.132, over 45.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=29, generator_mel_loss=17.88, generator_kl_loss=1.415, generator_dur_loss=1.758, generator_adv_loss=2.035, generator_feat_match_loss=5.914, over 2268.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:19:58,617 INFO [train.py:527] (5/6) Epoch 789, batch 88, global_batch_idx: 97800, batch size: 59, loss[discriminator_loss=2.656, discriminator_real_loss=1.309, discriminator_fake_loss=1.347, generator_loss=28.12, generator_mel_loss=17.6, generator_kl_loss=1.355, generator_dur_loss=1.72, generator_adv_loss=1.897, generator_feat_match_loss=5.55, over 59.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.359, discriminator_fake_loss=1.319, generator_loss=28.89, generator_mel_loss=17.83, generator_kl_loss=1.41, generator_dur_loss=1.746, generator_adv_loss=2.016, generator_feat_match_loss=5.884, over 5112.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:19:58,619 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 09:20:07,303 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 09:21:50,919 INFO [train.py:919] (5/6) Start epoch 790 +2024-03-15 09:22:54,275 INFO [train.py:527] (5/6) Epoch 790, batch 14, global_batch_idx: 97850, batch size: 15, loss[discriminator_loss=2.646, discriminator_real_loss=1.303, discriminator_fake_loss=1.343, generator_loss=29.74, generator_mel_loss=18.26, generator_kl_loss=1.824, generator_dur_loss=1.513, generator_adv_loss=2.033, generator_feat_match_loss=6.111, over 15.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.354, discriminator_fake_loss=1.316, generator_loss=28.81, generator_mel_loss=17.78, generator_kl_loss=1.441, generator_dur_loss=1.727, generator_adv_loss=2.017, generator_feat_match_loss=5.846, over 758.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:25:11,761 INFO [train.py:527] (5/6) Epoch 790, batch 64, global_batch_idx: 97900, batch size: 45, loss[discriminator_loss=2.668, discriminator_real_loss=1.302, discriminator_fake_loss=1.366, generator_loss=28.12, generator_mel_loss=17.7, generator_kl_loss=1.51, generator_dur_loss=1.697, generator_adv_loss=2.005, generator_feat_match_loss=5.208, over 45.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=28.79, generator_mel_loss=17.81, generator_kl_loss=1.412, generator_dur_loss=1.738, generator_adv_loss=1.994, generator_feat_match_loss=5.836, over 3636.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:27:31,656 INFO [train.py:527] (5/6) Epoch 790, batch 114, global_batch_idx: 97950, batch size: 59, loss[discriminator_loss=2.65, discriminator_real_loss=1.27, discriminator_fake_loss=1.379, generator_loss=28.95, generator_mel_loss=18.01, generator_kl_loss=1.44, generator_dur_loss=1.718, generator_adv_loss=1.88, generator_feat_match_loss=5.905, over 59.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=28.84, generator_mel_loss=17.85, generator_kl_loss=1.417, generator_dur_loss=1.732, generator_adv_loss=1.992, generator_feat_match_loss=5.85, over 6406.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:27:56,936 INFO [train.py:919] (5/6) Start epoch 791 +2024-03-15 09:30:13,459 INFO [train.py:527] (5/6) Epoch 791, batch 40, global_batch_idx: 98000, batch size: 83, loss[discriminator_loss=2.677, discriminator_real_loss=1.282, discriminator_fake_loss=1.394, generator_loss=28.76, generator_mel_loss=17.62, generator_kl_loss=1.34, generator_dur_loss=1.861, generator_adv_loss=1.954, generator_feat_match_loss=5.979, over 83.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.348, discriminator_fake_loss=1.332, generator_loss=28.91, generator_mel_loss=17.87, generator_kl_loss=1.417, generator_dur_loss=1.753, generator_adv_loss=1.985, generator_feat_match_loss=5.887, over 2533.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:30:13,460 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 09:30:21,549 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 09:32:39,909 INFO [train.py:527] (5/6) Epoch 791, batch 90, global_batch_idx: 98050, batch size: 58, loss[discriminator_loss=2.662, discriminator_real_loss=1.372, discriminator_fake_loss=1.29, generator_loss=28.56, generator_mel_loss=17.73, generator_kl_loss=1.41, generator_dur_loss=1.751, generator_adv_loss=2.018, generator_feat_match_loss=5.655, over 58.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.347, discriminator_fake_loss=1.325, generator_loss=28.98, generator_mel_loss=17.84, generator_kl_loss=1.431, generator_dur_loss=1.745, generator_adv_loss=1.996, generator_feat_match_loss=5.964, over 5424.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:34:13,753 INFO [train.py:919] (5/6) Start epoch 792 +2024-03-15 09:35:21,564 INFO [train.py:527] (5/6) Epoch 792, batch 16, global_batch_idx: 98100, batch size: 88, loss[discriminator_loss=2.746, discriminator_real_loss=1.343, discriminator_fake_loss=1.403, generator_loss=28.42, generator_mel_loss=17.7, generator_kl_loss=1.089, generator_dur_loss=1.847, generator_adv_loss=1.957, generator_feat_match_loss=5.829, over 88.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.372, discriminator_fake_loss=1.325, generator_loss=28.78, generator_mel_loss=17.86, generator_kl_loss=1.414, generator_dur_loss=1.755, generator_adv_loss=1.993, generator_feat_match_loss=5.753, over 959.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:37:41,103 INFO [train.py:527] (5/6) Epoch 792, batch 66, global_batch_idx: 98150, batch size: 53, loss[discriminator_loss=2.696, discriminator_real_loss=1.358, discriminator_fake_loss=1.338, generator_loss=27.99, generator_mel_loss=17.92, generator_kl_loss=1.424, generator_dur_loss=1.661, generator_adv_loss=1.955, generator_feat_match_loss=5.031, over 53.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.368, discriminator_fake_loss=1.321, generator_loss=28.91, generator_mel_loss=17.86, generator_kl_loss=1.447, generator_dur_loss=1.727, generator_adv_loss=2.004, generator_feat_match_loss=5.874, over 3760.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:39:58,423 INFO [train.py:527] (5/6) Epoch 792, batch 116, global_batch_idx: 98200, batch size: 58, loss[discriminator_loss=2.672, discriminator_real_loss=1.388, discriminator_fake_loss=1.283, generator_loss=30.14, generator_mel_loss=18.38, generator_kl_loss=1.432, generator_dur_loss=1.688, generator_adv_loss=2.005, generator_feat_match_loss=6.633, over 58.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.366, discriminator_fake_loss=1.324, generator_loss=28.98, generator_mel_loss=17.88, generator_kl_loss=1.44, generator_dur_loss=1.728, generator_adv_loss=2.004, generator_feat_match_loss=5.919, over 6566.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:39:58,424 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 09:40:07,209 INFO [train.py:591] (5/6) 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 09:40:07,210 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 09:40:28,936 INFO [train.py:919] (5/6) Start epoch 793 +2024-03-15 09:42:50,100 INFO [train.py:527] (5/6) Epoch 793, batch 42, global_batch_idx: 98250, batch size: 42, loss[discriminator_loss=2.664, discriminator_real_loss=1.295, discriminator_fake_loss=1.369, generator_loss=28.97, generator_mel_loss=17.87, generator_kl_loss=1.547, generator_dur_loss=1.659, generator_adv_loss=2.09, generator_feat_match_loss=5.803, over 42.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.361, discriminator_fake_loss=1.333, generator_loss=28.9, generator_mel_loss=17.87, generator_kl_loss=1.426, generator_dur_loss=1.733, generator_adv_loss=1.99, generator_feat_match_loss=5.876, over 2306.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:45:08,673 INFO [train.py:527] (5/6) Epoch 793, batch 92, global_batch_idx: 98300, batch size: 64, loss[discriminator_loss=2.717, discriminator_real_loss=1.385, discriminator_fake_loss=1.332, generator_loss=28.66, generator_mel_loss=17.77, generator_kl_loss=1.482, generator_dur_loss=1.744, generator_adv_loss=2.053, generator_feat_match_loss=5.61, over 64.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.369, discriminator_fake_loss=1.335, generator_loss=28.91, generator_mel_loss=17.86, generator_kl_loss=1.426, generator_dur_loss=1.736, generator_adv_loss=1.996, generator_feat_match_loss=5.897, over 5042.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:46:37,273 INFO [train.py:919] (5/6) Start epoch 794 +2024-03-15 09:47:52,186 INFO [train.py:527] (5/6) Epoch 794, batch 18, global_batch_idx: 98350, batch size: 64, loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=29.52, generator_mel_loss=17.94, generator_kl_loss=1.339, generator_dur_loss=1.782, generator_adv_loss=1.983, generator_feat_match_loss=6.476, over 64.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.354, discriminator_fake_loss=1.328, generator_loss=29.03, generator_mel_loss=18, generator_kl_loss=1.351, generator_dur_loss=1.755, generator_adv_loss=1.992, generator_feat_match_loss=5.934, over 1212.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:50:10,991 INFO [train.py:527] (5/6) Epoch 794, batch 68, global_batch_idx: 98400, batch size: 96, loss[discriminator_loss=2.639, discriminator_real_loss=1.357, discriminator_fake_loss=1.282, generator_loss=28.64, generator_mel_loss=17.37, generator_kl_loss=1.365, generator_dur_loss=1.851, generator_adv_loss=2.167, generator_feat_match_loss=5.888, over 96.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.355, discriminator_fake_loss=1.334, generator_loss=28.98, generator_mel_loss=17.91, generator_kl_loss=1.414, generator_dur_loss=1.743, generator_adv_loss=2.006, generator_feat_match_loss=5.907, over 3969.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:50:10,992 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 09:50:19,876 INFO [train.py:591] (5/6) 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 09:50:19,876 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 09:52:39,495 INFO [train.py:527] (5/6) Epoch 794, batch 118, global_batch_idx: 98450, batch size: 55, loss[discriminator_loss=2.748, discriminator_real_loss=1.388, discriminator_fake_loss=1.36, generator_loss=29.17, generator_mel_loss=17.84, generator_kl_loss=1.573, generator_dur_loss=1.672, generator_adv_loss=1.968, generator_feat_match_loss=6.121, over 55.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.358, discriminator_fake_loss=1.329, generator_loss=28.93, generator_mel_loss=17.85, generator_kl_loss=1.427, generator_dur_loss=1.738, generator_adv_loss=2.011, generator_feat_match_loss=5.901, over 6937.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:52:53,378 INFO [train.py:919] (5/6) Start epoch 795 +2024-03-15 09:55:22,351 INFO [train.py:527] (5/6) Epoch 795, batch 44, global_batch_idx: 98500, batch size: 47, loss[discriminator_loss=2.659, discriminator_real_loss=1.344, discriminator_fake_loss=1.315, generator_loss=28.31, generator_mel_loss=17.98, generator_kl_loss=1.489, generator_dur_loss=1.696, generator_adv_loss=1.916, generator_feat_match_loss=5.231, over 47.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.355, discriminator_fake_loss=1.33, generator_loss=28.88, generator_mel_loss=17.81, generator_kl_loss=1.435, generator_dur_loss=1.742, generator_adv_loss=1.994, generator_feat_match_loss=5.9, over 2681.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:57:44,666 INFO [train.py:527] (5/6) Epoch 795, batch 94, global_batch_idx: 98550, batch size: 62, loss[discriminator_loss=2.68, discriminator_real_loss=1.393, discriminator_fake_loss=1.287, generator_loss=28.78, generator_mel_loss=18.05, generator_kl_loss=1.42, generator_dur_loss=1.7, generator_adv_loss=1.912, generator_feat_match_loss=5.694, over 62.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.79, generator_mel_loss=17.81, generator_kl_loss=1.428, generator_dur_loss=1.741, generator_adv_loss=1.995, generator_feat_match_loss=5.812, over 5543.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 09:59:04,312 INFO [train.py:919] (5/6) Start epoch 796 +2024-03-15 10:00:25,431 INFO [train.py:527] (5/6) Epoch 796, batch 20, global_batch_idx: 98600, batch size: 58, loss[discriminator_loss=2.687, discriminator_real_loss=1.407, discriminator_fake_loss=1.28, generator_loss=29.34, generator_mel_loss=17.64, generator_kl_loss=1.372, generator_dur_loss=1.722, generator_adv_loss=2.033, generator_feat_match_loss=6.57, over 58.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.355, discriminator_fake_loss=1.318, generator_loss=28.91, generator_mel_loss=17.78, generator_kl_loss=1.418, generator_dur_loss=1.725, generator_adv_loss=2.009, generator_feat_match_loss=5.979, over 1120.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:00:25,433 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 10:00:33,304 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 10:02:53,554 INFO [train.py:527] (5/6) Epoch 796, batch 70, global_batch_idx: 98650, batch size: 59, loss[discriminator_loss=2.726, discriminator_real_loss=1.315, discriminator_fake_loss=1.411, generator_loss=28.43, generator_mel_loss=17.67, generator_kl_loss=1.219, generator_dur_loss=1.726, generator_adv_loss=2.186, generator_feat_match_loss=5.632, over 59.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.323, generator_loss=28.98, generator_mel_loss=17.85, generator_kl_loss=1.423, generator_dur_loss=1.748, generator_adv_loss=2.02, generator_feat_match_loss=5.945, over 4044.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:05:10,165 INFO [train.py:527] (5/6) Epoch 796, batch 120, global_batch_idx: 98700, batch size: 56, loss[discriminator_loss=2.635, discriminator_real_loss=1.355, discriminator_fake_loss=1.28, generator_loss=29.55, generator_mel_loss=17.87, generator_kl_loss=1.543, generator_dur_loss=1.722, generator_adv_loss=1.994, generator_feat_match_loss=6.428, over 56.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.359, discriminator_fake_loss=1.321, generator_loss=28.93, generator_mel_loss=17.84, generator_kl_loss=1.424, generator_dur_loss=1.746, generator_adv_loss=2.014, generator_feat_match_loss=5.909, over 6725.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:05:18,336 INFO [train.py:919] (5/6) Start epoch 797 +2024-03-15 10:07:52,333 INFO [train.py:527] (5/6) Epoch 797, batch 46, global_batch_idx: 98750, batch size: 55, loss[discriminator_loss=2.618, discriminator_real_loss=1.329, discriminator_fake_loss=1.289, generator_loss=29.6, generator_mel_loss=18.11, generator_kl_loss=1.502, generator_dur_loss=1.725, generator_adv_loss=2.071, generator_feat_match_loss=6.191, over 55.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.361, discriminator_fake_loss=1.316, generator_loss=28.91, generator_mel_loss=17.85, generator_kl_loss=1.417, generator_dur_loss=1.759, generator_adv_loss=2, generator_feat_match_loss=5.884, over 2867.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:10:12,913 INFO [train.py:527] (5/6) Epoch 797, batch 96, global_batch_idx: 98800, batch size: 48, loss[discriminator_loss=2.671, discriminator_real_loss=1.385, discriminator_fake_loss=1.286, generator_loss=30.31, generator_mel_loss=17.9, generator_kl_loss=1.58, generator_dur_loss=1.675, generator_adv_loss=1.968, generator_feat_match_loss=7.182, over 48.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.36, discriminator_fake_loss=1.321, generator_loss=28.89, generator_mel_loss=17.84, generator_kl_loss=1.426, generator_dur_loss=1.745, generator_adv_loss=1.997, generator_feat_match_loss=5.886, over 5609.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:10:12,914 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 10:10:21,698 INFO [train.py:591] (5/6) 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 10:10:21,698 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 10:11:35,783 INFO [train.py:919] (5/6) Start epoch 798 +2024-03-15 10:12:59,895 INFO [train.py:527] (5/6) Epoch 798, batch 22, global_batch_idx: 98850, batch size: 48, loss[discriminator_loss=2.694, discriminator_real_loss=1.42, discriminator_fake_loss=1.274, generator_loss=29.13, generator_mel_loss=18.28, generator_kl_loss=1.63, generator_dur_loss=1.705, generator_adv_loss=2.038, generator_feat_match_loss=5.471, over 48.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.359, discriminator_fake_loss=1.316, generator_loss=28.76, generator_mel_loss=17.88, generator_kl_loss=1.407, generator_dur_loss=1.74, generator_adv_loss=2.004, generator_feat_match_loss=5.734, over 1386.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:15:18,185 INFO [train.py:527] (5/6) Epoch 798, batch 72, global_batch_idx: 98900, batch size: 70, loss[discriminator_loss=2.734, discriminator_real_loss=1.33, discriminator_fake_loss=1.404, generator_loss=28.91, generator_mel_loss=18.08, generator_kl_loss=1.433, generator_dur_loss=1.789, generator_adv_loss=1.985, generator_feat_match_loss=5.629, over 70.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.363, discriminator_fake_loss=1.319, generator_loss=28.74, generator_mel_loss=17.83, generator_kl_loss=1.432, generator_dur_loss=1.727, generator_adv_loss=1.995, generator_feat_match_loss=5.751, over 4043.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:17:36,017 INFO [train.py:527] (5/6) Epoch 798, batch 122, global_batch_idx: 98950, batch size: 25, loss[discriminator_loss=2.586, discriminator_real_loss=1.312, discriminator_fake_loss=1.274, generator_loss=30.88, generator_mel_loss=18.6, generator_kl_loss=1.842, generator_dur_loss=1.532, generator_adv_loss=2.044, generator_feat_match_loss=6.865, over 25.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.323, generator_loss=28.8, generator_mel_loss=17.82, generator_kl_loss=1.431, generator_dur_loss=1.736, generator_adv_loss=2, generator_feat_match_loss=5.814, over 7053.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:17:40,883 INFO [train.py:919] (5/6) Start epoch 799 +2024-03-15 10:20:18,110 INFO [train.py:527] (5/6) Epoch 799, batch 48, global_batch_idx: 99000, batch size: 44, loss[discriminator_loss=2.669, discriminator_real_loss=1.304, discriminator_fake_loss=1.365, generator_loss=28.95, generator_mel_loss=17.67, generator_kl_loss=1.579, generator_dur_loss=1.657, generator_adv_loss=2.249, generator_feat_match_loss=5.794, over 44.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.353, discriminator_fake_loss=1.332, generator_loss=29.01, generator_mel_loss=17.91, generator_kl_loss=1.403, generator_dur_loss=1.762, generator_adv_loss=1.994, generator_feat_match_loss=5.942, over 3046.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:20:18,111 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 10:20:26,124 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 10:22:43,932 INFO [train.py:527] (5/6) Epoch 799, batch 98, global_batch_idx: 99050, batch size: 53, loss[discriminator_loss=2.668, discriminator_real_loss=1.281, discriminator_fake_loss=1.387, generator_loss=28.55, generator_mel_loss=17.15, generator_kl_loss=1.324, generator_dur_loss=1.685, generator_adv_loss=2.034, generator_feat_match_loss=6.363, over 53.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=28.9, generator_mel_loss=17.86, generator_kl_loss=1.397, generator_dur_loss=1.755, generator_adv_loss=2.003, generator_feat_match_loss=5.889, over 6065.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:23:55,068 INFO [train.py:919] (5/6) Start epoch 800 +2024-03-15 10:25:24,092 INFO [train.py:527] (5/6) Epoch 800, batch 24, global_batch_idx: 99100, batch size: 74, loss[discriminator_loss=2.694, discriminator_real_loss=1.411, discriminator_fake_loss=1.283, generator_loss=29.21, generator_mel_loss=17.8, generator_kl_loss=1.425, generator_dur_loss=1.794, generator_adv_loss=1.974, generator_feat_match_loss=6.218, over 74.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.354, discriminator_fake_loss=1.328, generator_loss=28.78, generator_mel_loss=17.77, generator_kl_loss=1.422, generator_dur_loss=1.736, generator_adv_loss=1.991, generator_feat_match_loss=5.859, over 1437.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:27:42,612 INFO [train.py:527] (5/6) Epoch 800, batch 74, global_batch_idx: 99150, batch size: 25, loss[discriminator_loss=2.634, discriminator_real_loss=1.316, discriminator_fake_loss=1.318, generator_loss=31.18, generator_mel_loss=18.5, generator_kl_loss=1.663, generator_dur_loss=1.52, generator_adv_loss=2.229, generator_feat_match_loss=7.267, over 25.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.362, discriminator_fake_loss=1.327, generator_loss=28.83, generator_mel_loss=17.79, generator_kl_loss=1.423, generator_dur_loss=1.731, generator_adv_loss=2.005, generator_feat_match_loss=5.886, over 4195.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:30:01,562 INFO [train.py:919] (5/6) Start epoch 801 +2024-03-15 10:30:25,673 INFO [train.py:527] (5/6) Epoch 801, batch 0, global_batch_idx: 99200, batch size: 52, loss[discriminator_loss=2.736, discriminator_real_loss=1.36, discriminator_fake_loss=1.375, generator_loss=27.53, generator_mel_loss=17.62, generator_kl_loss=1.53, generator_dur_loss=1.659, generator_adv_loss=1.976, generator_feat_match_loss=4.741, over 52.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.36, discriminator_fake_loss=1.375, generator_loss=27.53, generator_mel_loss=17.62, generator_kl_loss=1.53, generator_dur_loss=1.659, generator_adv_loss=1.976, generator_feat_match_loss=4.741, over 52.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:30:25,676 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 10:30:33,648 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 10:32:53,962 INFO [train.py:527] (5/6) Epoch 801, batch 50, global_batch_idx: 99250, batch size: 70, loss[discriminator_loss=2.695, discriminator_real_loss=1.347, discriminator_fake_loss=1.348, generator_loss=28.85, generator_mel_loss=17.52, generator_kl_loss=1.351, generator_dur_loss=1.782, generator_adv_loss=1.945, generator_feat_match_loss=6.245, over 70.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=28.79, generator_mel_loss=17.74, generator_kl_loss=1.436, generator_dur_loss=1.744, generator_adv_loss=2, generator_feat_match_loss=5.868, over 2941.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:35:13,701 INFO [train.py:527] (5/6) Epoch 801, batch 100, global_batch_idx: 99300, batch size: 42, loss[discriminator_loss=2.673, discriminator_real_loss=1.319, discriminator_fake_loss=1.354, generator_loss=27.71, generator_mel_loss=17.84, generator_kl_loss=1.394, generator_dur_loss=1.713, generator_adv_loss=2, generator_feat_match_loss=4.763, over 42.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=28.85, generator_mel_loss=17.78, generator_kl_loss=1.432, generator_dur_loss=1.752, generator_adv_loss=2.019, generator_feat_match_loss=5.869, over 5879.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:36:19,953 INFO [train.py:919] (5/6) Start epoch 802 +2024-03-15 10:37:54,680 INFO [train.py:527] (5/6) Epoch 802, batch 26, global_batch_idx: 99350, batch size: 74, loss[discriminator_loss=2.697, discriminator_real_loss=1.323, discriminator_fake_loss=1.375, generator_loss=29.04, generator_mel_loss=17.85, generator_kl_loss=1.474, generator_dur_loss=1.849, generator_adv_loss=2.128, generator_feat_match_loss=5.734, over 74.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.355, discriminator_fake_loss=1.318, generator_loss=29, generator_mel_loss=17.95, generator_kl_loss=1.443, generator_dur_loss=1.758, generator_adv_loss=2.012, generator_feat_match_loss=5.836, over 1598.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:40:14,172 INFO [train.py:527] (5/6) Epoch 802, batch 76, global_batch_idx: 99400, batch size: 70, loss[discriminator_loss=2.674, discriminator_real_loss=1.383, discriminator_fake_loss=1.291, generator_loss=28.7, generator_mel_loss=17.83, generator_kl_loss=1.372, generator_dur_loss=1.811, generator_adv_loss=1.932, generator_feat_match_loss=5.76, over 70.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.324, generator_loss=29.06, generator_mel_loss=17.93, generator_kl_loss=1.442, generator_dur_loss=1.763, generator_adv_loss=1.993, generator_feat_match_loss=5.93, over 4657.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:40:14,174 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 10:40:23,115 INFO [train.py:591] (5/6) 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 10:40:23,116 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 10:42:36,541 INFO [train.py:919] (5/6) Start epoch 803 +2024-03-15 10:43:07,052 INFO [train.py:527] (5/6) Epoch 803, batch 2, global_batch_idx: 99450, batch size: 47, loss[discriminator_loss=2.671, discriminator_real_loss=1.371, discriminator_fake_loss=1.299, generator_loss=28.91, generator_mel_loss=17.62, generator_kl_loss=1.519, generator_dur_loss=1.678, generator_adv_loss=1.965, generator_feat_match_loss=6.127, over 47.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.35, discriminator_fake_loss=1.364, generator_loss=28.4, generator_mel_loss=17.61, generator_kl_loss=1.394, generator_dur_loss=1.727, generator_adv_loss=1.941, generator_feat_match_loss=5.731, over 177.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:45:25,756 INFO [train.py:527] (5/6) Epoch 803, batch 52, global_batch_idx: 99500, batch size: 77, loss[discriminator_loss=2.678, discriminator_real_loss=1.367, discriminator_fake_loss=1.311, generator_loss=28.98, generator_mel_loss=18.06, generator_kl_loss=1.32, generator_dur_loss=1.799, generator_adv_loss=1.975, generator_feat_match_loss=5.82, over 77.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.32, generator_loss=29.01, generator_mel_loss=17.89, generator_kl_loss=1.437, generator_dur_loss=1.731, generator_adv_loss=2.006, generator_feat_match_loss=5.948, over 2970.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:47:46,029 INFO [train.py:527] (5/6) Epoch 803, batch 102, global_batch_idx: 99550, batch size: 53, loss[discriminator_loss=2.669, discriminator_real_loss=1.367, discriminator_fake_loss=1.302, generator_loss=29.28, generator_mel_loss=17.74, generator_kl_loss=1.451, generator_dur_loss=1.741, generator_adv_loss=1.953, generator_feat_match_loss=6.398, over 53.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=28.99, generator_mel_loss=17.86, generator_kl_loss=1.421, generator_dur_loss=1.74, generator_adv_loss=2.013, generator_feat_match_loss=5.949, over 5847.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:48:45,314 INFO [train.py:919] (5/6) Start epoch 804 +2024-03-15 10:50:28,484 INFO [train.py:527] (5/6) Epoch 804, batch 28, global_batch_idx: 99600, batch size: 70, loss[discriminator_loss=2.746, discriminator_real_loss=1.408, discriminator_fake_loss=1.338, generator_loss=28.64, generator_mel_loss=17.76, generator_kl_loss=1.468, generator_dur_loss=1.803, generator_adv_loss=1.91, generator_feat_match_loss=5.702, over 70.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.361, discriminator_fake_loss=1.31, generator_loss=29.09, generator_mel_loss=17.87, generator_kl_loss=1.445, generator_dur_loss=1.731, generator_adv_loss=2.027, generator_feat_match_loss=6.013, over 1588.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:50:28,485 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 10:50:36,563 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 10:52:54,926 INFO [train.py:527] (5/6) Epoch 804, batch 78, global_batch_idx: 99650, batch size: 96, loss[discriminator_loss=2.703, discriminator_real_loss=1.414, discriminator_fake_loss=1.289, generator_loss=28.98, generator_mel_loss=17.91, generator_kl_loss=1.408, generator_dur_loss=1.836, generator_adv_loss=1.912, generator_feat_match_loss=5.909, over 96.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.364, discriminator_fake_loss=1.32, generator_loss=29, generator_mel_loss=17.87, generator_kl_loss=1.417, generator_dur_loss=1.746, generator_adv_loss=2.006, generator_feat_match_loss=5.959, over 4418.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:54:58,507 INFO [train.py:919] (5/6) Start epoch 805 +2024-03-15 10:55:33,729 INFO [train.py:527] (5/6) Epoch 805, batch 4, global_batch_idx: 99700, batch size: 48, loss[discriminator_loss=2.698, discriminator_real_loss=1.369, discriminator_fake_loss=1.329, generator_loss=28.79, generator_mel_loss=17.79, generator_kl_loss=1.503, generator_dur_loss=1.661, generator_adv_loss=2.053, generator_feat_match_loss=5.785, over 48.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.384, discriminator_fake_loss=1.339, generator_loss=28.73, generator_mel_loss=17.66, generator_kl_loss=1.422, generator_dur_loss=1.708, generator_adv_loss=1.987, generator_feat_match_loss=5.946, over 262.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 10:57:51,562 INFO [train.py:527] (5/6) Epoch 805, batch 54, global_batch_idx: 99750, batch size: 68, loss[discriminator_loss=2.706, discriminator_real_loss=1.425, discriminator_fake_loss=1.28, generator_loss=28.62, generator_mel_loss=17.4, generator_kl_loss=1.347, generator_dur_loss=1.811, generator_adv_loss=1.965, generator_feat_match_loss=6.103, over 68.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.366, discriminator_fake_loss=1.321, generator_loss=28.79, generator_mel_loss=17.76, generator_kl_loss=1.423, generator_dur_loss=1.758, generator_adv_loss=2.03, generator_feat_match_loss=5.822, over 3212.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:00:12,948 INFO [train.py:527] (5/6) Epoch 805, batch 104, global_batch_idx: 99800, batch size: 83, loss[discriminator_loss=2.715, discriminator_real_loss=1.469, discriminator_fake_loss=1.246, generator_loss=28.61, generator_mel_loss=17.7, generator_kl_loss=1.576, generator_dur_loss=1.816, generator_adv_loss=1.821, generator_feat_match_loss=5.704, over 83.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.37, discriminator_fake_loss=1.323, generator_loss=28.85, generator_mel_loss=17.83, generator_kl_loss=1.435, generator_dur_loss=1.75, generator_adv_loss=2.013, generator_feat_match_loss=5.823, over 5959.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:00:12,950 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 11:00:21,934 INFO [train.py:591] (5/6) 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 11:00:21,936 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 11:01:14,786 INFO [train.py:919] (5/6) Start epoch 806 +2024-03-15 11:03:00,290 INFO [train.py:527] (5/6) Epoch 806, batch 30, global_batch_idx: 99850, batch size: 42, loss[discriminator_loss=2.706, discriminator_real_loss=1.286, discriminator_fake_loss=1.421, generator_loss=29.32, generator_mel_loss=18.02, generator_kl_loss=1.667, generator_dur_loss=1.66, generator_adv_loss=1.983, generator_feat_match_loss=5.987, over 42.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=28.94, generator_mel_loss=17.86, generator_kl_loss=1.456, generator_dur_loss=1.728, generator_adv_loss=1.998, generator_feat_match_loss=5.899, over 1647.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:05:18,913 INFO [train.py:527] (5/6) Epoch 806, batch 80, global_batch_idx: 99900, batch size: 77, loss[discriminator_loss=2.66, discriminator_real_loss=1.415, discriminator_fake_loss=1.245, generator_loss=29.74, generator_mel_loss=18.17, generator_kl_loss=1.31, generator_dur_loss=1.831, generator_adv_loss=1.821, generator_feat_match_loss=6.607, over 77.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.358, discriminator_fake_loss=1.316, generator_loss=28.92, generator_mel_loss=17.85, generator_kl_loss=1.439, generator_dur_loss=1.737, generator_adv_loss=1.998, generator_feat_match_loss=5.897, over 4522.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:07:20,160 INFO [train.py:919] (5/6) Start epoch 807 +2024-03-15 11:08:01,156 INFO [train.py:527] (5/6) Epoch 807, batch 6, global_batch_idx: 99950, batch size: 72, loss[discriminator_loss=2.672, discriminator_real_loss=1.346, discriminator_fake_loss=1.327, generator_loss=29.51, generator_mel_loss=17.83, generator_kl_loss=1.29, generator_dur_loss=1.836, generator_adv_loss=2.012, generator_feat_match_loss=6.543, over 72.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.342, discriminator_fake_loss=1.333, generator_loss=29.08, generator_mel_loss=17.83, generator_kl_loss=1.414, generator_dur_loss=1.763, generator_adv_loss=2.017, generator_feat_match_loss=6.057, over 421.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:10:20,844 INFO [train.py:527] (5/6) Epoch 807, batch 56, global_batch_idx: 100000, batch size: 42, loss[discriminator_loss=2.65, discriminator_real_loss=1.344, discriminator_fake_loss=1.306, generator_loss=29.01, generator_mel_loss=17.68, generator_kl_loss=1.359, generator_dur_loss=1.644, generator_adv_loss=2.004, generator_feat_match_loss=6.326, over 42.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.362, discriminator_fake_loss=1.327, generator_loss=28.8, generator_mel_loss=17.78, generator_kl_loss=1.426, generator_dur_loss=1.733, generator_adv_loss=2, generator_feat_match_loss=5.862, over 3161.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:10:20,845 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 11:10:28,930 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 11:12:46,892 INFO [train.py:527] (5/6) Epoch 807, batch 106, global_batch_idx: 100050, batch size: 77, loss[discriminator_loss=2.793, discriminator_real_loss=1.513, discriminator_fake_loss=1.28, generator_loss=29.15, generator_mel_loss=17.83, generator_kl_loss=1.533, generator_dur_loss=1.787, generator_adv_loss=1.834, generator_feat_match_loss=6.165, over 77.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.361, discriminator_fake_loss=1.328, generator_loss=28.83, generator_mel_loss=17.8, generator_kl_loss=1.439, generator_dur_loss=1.734, generator_adv_loss=1.994, generator_feat_match_loss=5.86, over 6124.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:13:36,214 INFO [train.py:919] (5/6) Start epoch 808 +2024-03-15 11:15:28,514 INFO [train.py:527] (5/6) Epoch 808, batch 32, global_batch_idx: 100100, batch size: 66, loss[discriminator_loss=2.652, discriminator_real_loss=1.357, discriminator_fake_loss=1.294, generator_loss=29.33, generator_mel_loss=17.82, generator_kl_loss=1.494, generator_dur_loss=1.724, generator_adv_loss=2.161, generator_feat_match_loss=6.129, over 66.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.373, discriminator_fake_loss=1.318, generator_loss=28.81, generator_mel_loss=17.85, generator_kl_loss=1.455, generator_dur_loss=1.741, generator_adv_loss=2.009, generator_feat_match_loss=5.756, over 1960.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:17:49,500 INFO [train.py:527] (5/6) Epoch 808, batch 82, global_batch_idx: 100150, batch size: 70, loss[discriminator_loss=2.626, discriminator_real_loss=1.4, discriminator_fake_loss=1.226, generator_loss=29.31, generator_mel_loss=18.18, generator_kl_loss=1.451, generator_dur_loss=1.757, generator_adv_loss=2.082, generator_feat_match_loss=5.835, over 70.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.361, discriminator_fake_loss=1.325, generator_loss=28.9, generator_mel_loss=17.83, generator_kl_loss=1.435, generator_dur_loss=1.74, generator_adv_loss=1.997, generator_feat_match_loss=5.9, over 4845.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:19:47,057 INFO [train.py:919] (5/6) Start epoch 809 +2024-03-15 11:20:33,747 INFO [train.py:527] (5/6) Epoch 809, batch 8, global_batch_idx: 100200, batch size: 72, loss[discriminator_loss=2.691, discriminator_real_loss=1.362, discriminator_fake_loss=1.329, generator_loss=27.91, generator_mel_loss=17.3, generator_kl_loss=1.283, generator_dur_loss=1.796, generator_adv_loss=2.106, generator_feat_match_loss=5.427, over 72.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.348, discriminator_fake_loss=1.335, generator_loss=28.92, generator_mel_loss=17.72, generator_kl_loss=1.44, generator_dur_loss=1.74, generator_adv_loss=2.025, generator_feat_match_loss=5.999, over 490.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:20:33,751 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 11:20:41,722 INFO [train.py:591] (5/6) 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 11:20:41,724 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 11:22:59,985 INFO [train.py:527] (5/6) Epoch 809, batch 58, global_batch_idx: 100250, batch size: 59, loss[discriminator_loss=2.624, discriminator_real_loss=1.359, discriminator_fake_loss=1.265, generator_loss=28.79, generator_mel_loss=17.9, generator_kl_loss=1.327, generator_dur_loss=1.788, generator_adv_loss=2.076, generator_feat_match_loss=5.694, over 59.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=29.05, generator_mel_loss=17.87, generator_kl_loss=1.406, generator_dur_loss=1.75, generator_adv_loss=2.015, generator_feat_match_loss=6.013, over 3417.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:25:18,736 INFO [train.py:527] (5/6) Epoch 809, batch 108, global_batch_idx: 100300, batch size: 58, loss[discriminator_loss=2.667, discriminator_real_loss=1.289, discriminator_fake_loss=1.379, generator_loss=29.88, generator_mel_loss=18.46, generator_kl_loss=1.407, generator_dur_loss=1.74, generator_adv_loss=2.036, generator_feat_match_loss=6.241, over 58.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=29.05, generator_mel_loss=17.88, generator_kl_loss=1.408, generator_dur_loss=1.75, generator_adv_loss=2.007, generator_feat_match_loss=6.012, over 6350.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:26:00,362 INFO [train.py:919] (5/6) Start epoch 810 +2024-03-15 11:27:58,482 INFO [train.py:527] (5/6) Epoch 810, batch 34, global_batch_idx: 100350, batch size: 80, loss[discriminator_loss=2.753, discriminator_real_loss=1.416, discriminator_fake_loss=1.337, generator_loss=28.38, generator_mel_loss=17.8, generator_kl_loss=1.331, generator_dur_loss=1.822, generator_adv_loss=1.828, generator_feat_match_loss=5.6, over 80.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=29, generator_mel_loss=17.86, generator_kl_loss=1.44, generator_dur_loss=1.745, generator_adv_loss=1.982, generator_feat_match_loss=5.968, over 1958.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:30:15,714 INFO [train.py:527] (5/6) Epoch 810, batch 84, global_batch_idx: 100400, batch size: 36, loss[discriminator_loss=2.711, discriminator_real_loss=1.394, discriminator_fake_loss=1.317, generator_loss=27.61, generator_mel_loss=17.55, generator_kl_loss=1.517, generator_dur_loss=1.72, generator_adv_loss=1.93, generator_feat_match_loss=4.898, over 36.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=28.93, generator_mel_loss=17.8, generator_kl_loss=1.428, generator_dur_loss=1.743, generator_adv_loss=1.992, generator_feat_match_loss=5.967, over 4892.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:30:15,715 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 11:30:24,305 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 11:32:15,845 INFO [train.py:919] (5/6) Start epoch 811 +2024-03-15 11:33:08,715 INFO [train.py:527] (5/6) Epoch 811, batch 10, global_batch_idx: 100450, batch size: 52, loss[discriminator_loss=2.691, discriminator_real_loss=1.32, discriminator_fake_loss=1.37, generator_loss=29.39, generator_mel_loss=17.96, generator_kl_loss=1.587, generator_dur_loss=1.651, generator_adv_loss=1.915, generator_feat_match_loss=6.273, over 52.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.374, discriminator_fake_loss=1.313, generator_loss=28.93, generator_mel_loss=17.87, generator_kl_loss=1.434, generator_dur_loss=1.716, generator_adv_loss=2.035, generator_feat_match_loss=5.879, over 517.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:35:26,313 INFO [train.py:527] (5/6) Epoch 811, batch 60, global_batch_idx: 100500, batch size: 64, loss[discriminator_loss=2.675, discriminator_real_loss=1.368, discriminator_fake_loss=1.307, generator_loss=28.94, generator_mel_loss=17.75, generator_kl_loss=1.358, generator_dur_loss=1.769, generator_adv_loss=2.075, generator_feat_match_loss=5.995, over 64.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=28.96, generator_mel_loss=17.86, generator_kl_loss=1.418, generator_dur_loss=1.746, generator_adv_loss=2.008, generator_feat_match_loss=5.93, over 3403.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:37:43,203 INFO [train.py:527] (5/6) Epoch 811, batch 110, global_batch_idx: 100550, batch size: 56, loss[discriminator_loss=2.701, discriminator_real_loss=1.352, discriminator_fake_loss=1.349, generator_loss=29.02, generator_mel_loss=18.07, generator_kl_loss=1.499, generator_dur_loss=1.737, generator_adv_loss=1.908, generator_feat_match_loss=5.81, over 56.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=28.92, generator_mel_loss=17.83, generator_kl_loss=1.416, generator_dur_loss=1.747, generator_adv_loss=1.999, generator_feat_match_loss=5.928, over 6422.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:38:20,740 INFO [train.py:919] (5/6) Start epoch 812 +2024-03-15 11:40:25,027 INFO [train.py:527] (5/6) Epoch 812, batch 36, global_batch_idx: 100600, batch size: 59, loss[discriminator_loss=2.738, discriminator_real_loss=1.336, discriminator_fake_loss=1.402, generator_loss=28.7, generator_mel_loss=17.77, generator_kl_loss=1.342, generator_dur_loss=1.689, generator_adv_loss=1.961, generator_feat_match_loss=5.938, over 59.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=29.08, generator_mel_loss=17.86, generator_kl_loss=1.446, generator_dur_loss=1.731, generator_adv_loss=2.006, generator_feat_match_loss=6.039, over 2033.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:40:25,029 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 11:40:33,078 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 11:42:53,735 INFO [train.py:527] (5/6) Epoch 812, batch 86, global_batch_idx: 100650, batch size: 96, loss[discriminator_loss=2.694, discriminator_real_loss=1.399, discriminator_fake_loss=1.295, generator_loss=28.61, generator_mel_loss=17.76, generator_kl_loss=1.36, generator_dur_loss=1.838, generator_adv_loss=1.949, generator_feat_match_loss=5.698, over 96.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.359, discriminator_fake_loss=1.319, generator_loss=29.07, generator_mel_loss=17.84, generator_kl_loss=1.436, generator_dur_loss=1.748, generator_adv_loss=2.01, generator_feat_match_loss=6.027, over 5086.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:44:33,055 INFO [train.py:919] (5/6) Start epoch 813 +2024-03-15 11:45:30,615 INFO [train.py:527] (5/6) Epoch 813, batch 12, global_batch_idx: 100700, batch size: 14, loss[discriminator_loss=2.635, discriminator_real_loss=1.426, discriminator_fake_loss=1.209, generator_loss=29.5, generator_mel_loss=18.26, generator_kl_loss=1.56, generator_dur_loss=1.657, generator_adv_loss=2.084, generator_feat_match_loss=5.936, over 14.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.347, discriminator_fake_loss=1.338, generator_loss=28.91, generator_mel_loss=17.85, generator_kl_loss=1.434, generator_dur_loss=1.76, generator_adv_loss=2.015, generator_feat_match_loss=5.857, over 673.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:47:51,315 INFO [train.py:527] (5/6) Epoch 813, batch 62, global_batch_idx: 100750, batch size: 59, loss[discriminator_loss=2.692, discriminator_real_loss=1.434, discriminator_fake_loss=1.259, generator_loss=28.3, generator_mel_loss=17.77, generator_kl_loss=1.374, generator_dur_loss=1.727, generator_adv_loss=1.975, generator_feat_match_loss=5.455, over 59.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=29.09, generator_mel_loss=17.88, generator_kl_loss=1.422, generator_dur_loss=1.755, generator_adv_loss=2.014, generator_feat_match_loss=6.019, over 3594.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:50:11,237 INFO [train.py:527] (5/6) Epoch 813, batch 112, global_batch_idx: 100800, batch size: 74, loss[discriminator_loss=2.683, discriminator_real_loss=1.35, discriminator_fake_loss=1.333, generator_loss=29.12, generator_mel_loss=17.55, generator_kl_loss=1.281, generator_dur_loss=1.814, generator_adv_loss=2.08, generator_feat_match_loss=6.399, over 74.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.356, discriminator_fake_loss=1.324, generator_loss=29.04, generator_mel_loss=17.85, generator_kl_loss=1.429, generator_dur_loss=1.753, generator_adv_loss=2.005, generator_feat_match_loss=6.003, over 6505.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:50:11,238 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 11:50:19,988 INFO [train.py:591] (5/6) 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 11:50:19,989 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 11:50:51,402 INFO [train.py:919] (5/6) Start epoch 814 +2024-03-15 11:52:57,891 INFO [train.py:527] (5/6) Epoch 814, batch 38, global_batch_idx: 100850, batch size: 44, loss[discriminator_loss=2.648, discriminator_real_loss=1.292, discriminator_fake_loss=1.356, generator_loss=29.22, generator_mel_loss=18.09, generator_kl_loss=1.44, generator_dur_loss=1.691, generator_adv_loss=2.079, generator_feat_match_loss=5.924, over 44.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=28.91, generator_mel_loss=17.81, generator_kl_loss=1.419, generator_dur_loss=1.745, generator_adv_loss=1.989, generator_feat_match_loss=5.945, over 2186.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:55:16,900 INFO [train.py:527] (5/6) Epoch 814, batch 88, global_batch_idx: 100900, batch size: 59, loss[discriminator_loss=2.749, discriminator_real_loss=1.368, discriminator_fake_loss=1.381, generator_loss=28.69, generator_mel_loss=17.77, generator_kl_loss=1.451, generator_dur_loss=1.722, generator_adv_loss=2.097, generator_feat_match_loss=5.652, over 59.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.327, generator_loss=28.98, generator_mel_loss=17.84, generator_kl_loss=1.413, generator_dur_loss=1.743, generator_adv_loss=2.001, generator_feat_match_loss=5.98, over 5089.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 11:56:58,297 INFO [train.py:919] (5/6) Start epoch 815 +2024-03-15 11:58:01,577 INFO [train.py:527] (5/6) Epoch 815, batch 14, global_batch_idx: 100950, batch size: 77, loss[discriminator_loss=2.717, discriminator_real_loss=1.379, discriminator_fake_loss=1.338, generator_loss=29.6, generator_mel_loss=18.01, generator_kl_loss=1.329, generator_dur_loss=1.793, generator_adv_loss=1.971, generator_feat_match_loss=6.49, over 77.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.357, discriminator_fake_loss=1.338, generator_loss=29.01, generator_mel_loss=17.92, generator_kl_loss=1.44, generator_dur_loss=1.763, generator_adv_loss=1.991, generator_feat_match_loss=5.902, over 942.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:00:23,956 INFO [train.py:527] (5/6) Epoch 815, batch 64, global_batch_idx: 101000, batch size: 77, loss[discriminator_loss=2.712, discriminator_real_loss=1.381, discriminator_fake_loss=1.331, generator_loss=28.36, generator_mel_loss=17.75, generator_kl_loss=1.387, generator_dur_loss=1.831, generator_adv_loss=1.894, generator_feat_match_loss=5.493, over 77.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=29.01, generator_mel_loss=17.88, generator_kl_loss=1.427, generator_dur_loss=1.757, generator_adv_loss=1.995, generator_feat_match_loss=5.945, over 3913.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:00:23,958 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 12:00:32,151 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 12:02:51,517 INFO [train.py:527] (5/6) Epoch 815, batch 114, global_batch_idx: 101050, batch size: 31, loss[discriminator_loss=2.691, discriminator_real_loss=1.441, discriminator_fake_loss=1.25, generator_loss=29.29, generator_mel_loss=17.9, generator_kl_loss=1.499, generator_dur_loss=1.683, generator_adv_loss=1.995, generator_feat_match_loss=6.217, over 31.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.326, generator_loss=28.94, generator_mel_loss=17.85, generator_kl_loss=1.425, generator_dur_loss=1.756, generator_adv_loss=1.989, generator_feat_match_loss=5.923, over 6837.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:03:20,257 INFO [train.py:919] (5/6) Start epoch 816 +2024-03-15 12:05:40,845 INFO [train.py:527] (5/6) Epoch 816, batch 40, global_batch_idx: 101100, batch size: 59, loss[discriminator_loss=2.701, discriminator_real_loss=1.375, discriminator_fake_loss=1.326, generator_loss=29.8, generator_mel_loss=17.97, generator_kl_loss=1.406, generator_dur_loss=1.783, generator_adv_loss=1.999, generator_feat_match_loss=6.64, over 59.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.357, discriminator_fake_loss=1.339, generator_loss=28.86, generator_mel_loss=17.84, generator_kl_loss=1.389, generator_dur_loss=1.771, generator_adv_loss=1.99, generator_feat_match_loss=5.876, over 2558.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:07:55,595 INFO [train.py:527] (5/6) Epoch 816, batch 90, global_batch_idx: 101150, batch size: 45, loss[discriminator_loss=2.693, discriminator_real_loss=1.312, discriminator_fake_loss=1.381, generator_loss=29.12, generator_mel_loss=18.22, generator_kl_loss=1.506, generator_dur_loss=1.698, generator_adv_loss=2.012, generator_feat_match_loss=5.677, over 45.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.352, discriminator_fake_loss=1.331, generator_loss=28.92, generator_mel_loss=17.87, generator_kl_loss=1.41, generator_dur_loss=1.753, generator_adv_loss=1.992, generator_feat_match_loss=5.897, over 5287.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:09:28,427 INFO [train.py:919] (5/6) Start epoch 817 +2024-03-15 12:10:35,939 INFO [train.py:527] (5/6) Epoch 817, batch 16, global_batch_idx: 101200, batch size: 74, loss[discriminator_loss=2.719, discriminator_real_loss=1.299, discriminator_fake_loss=1.42, generator_loss=29.5, generator_mel_loss=18.18, generator_kl_loss=1.333, generator_dur_loss=1.806, generator_adv_loss=2.162, generator_feat_match_loss=6.017, over 74.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.353, discriminator_fake_loss=1.338, generator_loss=29.05, generator_mel_loss=17.86, generator_kl_loss=1.416, generator_dur_loss=1.759, generator_adv_loss=2.007, generator_feat_match_loss=6.008, over 997.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:10:35,940 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 12:10:44,064 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 12:13:03,851 INFO [train.py:527] (5/6) Epoch 817, batch 66, global_batch_idx: 101250, batch size: 74, loss[discriminator_loss=2.683, discriminator_real_loss=1.48, discriminator_fake_loss=1.203, generator_loss=28.15, generator_mel_loss=17.7, generator_kl_loss=1.378, generator_dur_loss=1.841, generator_adv_loss=1.836, generator_feat_match_loss=5.399, over 74.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.364, discriminator_fake_loss=1.322, generator_loss=29.03, generator_mel_loss=17.9, generator_kl_loss=1.424, generator_dur_loss=1.749, generator_adv_loss=2.005, generator_feat_match_loss=5.954, over 4024.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:15:22,483 INFO [train.py:527] (5/6) Epoch 817, batch 116, global_batch_idx: 101300, batch size: 13, loss[discriminator_loss=2.584, discriminator_real_loss=1.237, discriminator_fake_loss=1.346, generator_loss=32.75, generator_mel_loss=18.55, generator_kl_loss=1.781, generator_dur_loss=1.564, generator_adv_loss=2.129, generator_feat_match_loss=8.721, over 13.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=29, generator_mel_loss=17.88, generator_kl_loss=1.434, generator_dur_loss=1.748, generator_adv_loss=2.006, generator_feat_match_loss=5.932, over 6729.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:15:43,913 INFO [train.py:919] (5/6) Start epoch 818 +2024-03-15 12:18:05,825 INFO [train.py:527] (5/6) Epoch 818, batch 42, global_batch_idx: 101350, batch size: 50, loss[discriminator_loss=2.664, discriminator_real_loss=1.254, discriminator_fake_loss=1.411, generator_loss=30.02, generator_mel_loss=18.16, generator_kl_loss=1.561, generator_dur_loss=1.655, generator_adv_loss=2.178, generator_feat_match_loss=6.461, over 50.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.357, discriminator_fake_loss=1.329, generator_loss=28.92, generator_mel_loss=17.85, generator_kl_loss=1.413, generator_dur_loss=1.751, generator_adv_loss=2.009, generator_feat_match_loss=5.9, over 2499.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:20:26,214 INFO [train.py:527] (5/6) Epoch 818, batch 92, global_batch_idx: 101400, batch size: 52, loss[discriminator_loss=2.72, discriminator_real_loss=1.335, discriminator_fake_loss=1.385, generator_loss=28.92, generator_mel_loss=17.52, generator_kl_loss=1.531, generator_dur_loss=1.728, generator_adv_loss=1.89, generator_feat_match_loss=6.243, over 52.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.355, discriminator_fake_loss=1.33, generator_loss=28.87, generator_mel_loss=17.8, generator_kl_loss=1.427, generator_dur_loss=1.748, generator_adv_loss=1.994, generator_feat_match_loss=5.903, over 5354.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:20:26,216 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 12:20:34,993 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 12:22:01,685 INFO [train.py:919] (5/6) Start epoch 819 +2024-03-15 12:23:14,768 INFO [train.py:527] (5/6) Epoch 819, batch 18, global_batch_idx: 101450, batch size: 61, loss[discriminator_loss=2.698, discriminator_real_loss=1.415, discriminator_fake_loss=1.283, generator_loss=29.47, generator_mel_loss=18.19, generator_kl_loss=1.543, generator_dur_loss=1.768, generator_adv_loss=1.917, generator_feat_match_loss=6.053, over 61.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=29.14, generator_mel_loss=17.83, generator_kl_loss=1.405, generator_dur_loss=1.761, generator_adv_loss=2.009, generator_feat_match_loss=6.127, over 1152.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:25:33,156 INFO [train.py:527] (5/6) Epoch 819, batch 68, global_batch_idx: 101500, batch size: 72, loss[discriminator_loss=2.668, discriminator_real_loss=1.311, discriminator_fake_loss=1.358, generator_loss=28.97, generator_mel_loss=17.69, generator_kl_loss=1.439, generator_dur_loss=1.758, generator_adv_loss=2.029, generator_feat_match_loss=6.053, over 72.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.355, discriminator_fake_loss=1.326, generator_loss=29, generator_mel_loss=17.81, generator_kl_loss=1.41, generator_dur_loss=1.758, generator_adv_loss=2.001, generator_feat_match_loss=6.018, over 4091.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:27:51,583 INFO [train.py:527] (5/6) Epoch 819, batch 118, global_batch_idx: 101550, batch size: 39, loss[discriminator_loss=2.769, discriminator_real_loss=1.389, discriminator_fake_loss=1.381, generator_loss=28.12, generator_mel_loss=17.67, generator_kl_loss=1.536, generator_dur_loss=1.714, generator_adv_loss=1.995, generator_feat_match_loss=5.2, over 39.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=28.95, generator_mel_loss=17.79, generator_kl_loss=1.419, generator_dur_loss=1.757, generator_adv_loss=1.996, generator_feat_match_loss=5.99, over 7039.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:28:08,628 INFO [train.py:919] (5/6) Start epoch 820 +2024-03-15 12:30:35,351 INFO [train.py:527] (5/6) Epoch 820, batch 44, global_batch_idx: 101600, batch size: 39, loss[discriminator_loss=2.694, discriminator_real_loss=1.323, discriminator_fake_loss=1.371, generator_loss=29.6, generator_mel_loss=18.16, generator_kl_loss=1.517, generator_dur_loss=1.702, generator_adv_loss=2.087, generator_feat_match_loss=6.126, over 39.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.342, discriminator_fake_loss=1.328, generator_loss=29.21, generator_mel_loss=17.84, generator_kl_loss=1.414, generator_dur_loss=1.751, generator_adv_loss=2.03, generator_feat_match_loss=6.175, over 2554.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:30:35,353 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 12:30:43,545 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 12:33:03,588 INFO [train.py:527] (5/6) Epoch 820, batch 94, global_batch_idx: 101650, batch size: 47, loss[discriminator_loss=2.686, discriminator_real_loss=1.379, discriminator_fake_loss=1.307, generator_loss=27.79, generator_mel_loss=17.62, generator_kl_loss=1.505, generator_dur_loss=1.652, generator_adv_loss=1.874, generator_feat_match_loss=5.145, over 47.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.346, discriminator_fake_loss=1.325, generator_loss=29.12, generator_mel_loss=17.85, generator_kl_loss=1.415, generator_dur_loss=1.753, generator_adv_loss=2.018, generator_feat_match_loss=6.078, over 5514.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:34:23,550 INFO [train.py:919] (5/6) Start epoch 821 +2024-03-15 12:35:45,139 INFO [train.py:527] (5/6) Epoch 821, batch 20, global_batch_idx: 101700, batch size: 61, loss[discriminator_loss=2.644, discriminator_real_loss=1.268, discriminator_fake_loss=1.376, generator_loss=29.69, generator_mel_loss=18.06, generator_kl_loss=1.477, generator_dur_loss=1.705, generator_adv_loss=2.045, generator_feat_match_loss=6.405, over 61.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.352, discriminator_fake_loss=1.327, generator_loss=29.02, generator_mel_loss=17.9, generator_kl_loss=1.421, generator_dur_loss=1.731, generator_adv_loss=1.993, generator_feat_match_loss=5.976, over 1153.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:38:06,253 INFO [train.py:527] (5/6) Epoch 821, batch 70, global_batch_idx: 101750, batch size: 47, loss[discriminator_loss=2.64, discriminator_real_loss=1.349, discriminator_fake_loss=1.291, generator_loss=27.7, generator_mel_loss=17.41, generator_kl_loss=1.424, generator_dur_loss=1.656, generator_adv_loss=1.876, generator_feat_match_loss=5.336, over 47.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.358, discriminator_fake_loss=1.329, generator_loss=29.08, generator_mel_loss=17.92, generator_kl_loss=1.422, generator_dur_loss=1.743, generator_adv_loss=1.999, generator_feat_match_loss=5.99, over 4100.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:40:20,004 INFO [train.py:527] (5/6) Epoch 821, batch 120, global_batch_idx: 101800, batch size: 15, loss[discriminator_loss=2.747, discriminator_real_loss=1.459, discriminator_fake_loss=1.288, generator_loss=29.26, generator_mel_loss=18.18, generator_kl_loss=1.901, generator_dur_loss=1.532, generator_adv_loss=2.043, generator_feat_match_loss=5.604, over 15.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.357, discriminator_fake_loss=1.332, generator_loss=29.01, generator_mel_loss=17.88, generator_kl_loss=1.423, generator_dur_loss=1.749, generator_adv_loss=1.998, generator_feat_match_loss=5.963, over 7098.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, +2024-03-15 12:40:20,005 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 12:40:28,990 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 12:40:38,331 INFO [train.py:919] (5/6) Start epoch 822 +2024-03-15 12:43:10,198 INFO [train.py:527] (5/6) Epoch 822, batch 46, global_batch_idx: 101850, batch size: 42, loss[discriminator_loss=2.671, discriminator_real_loss=1.355, discriminator_fake_loss=1.316, generator_loss=29.31, generator_mel_loss=18.06, generator_kl_loss=1.548, generator_dur_loss=1.709, generator_adv_loss=1.938, generator_feat_match_loss=6.058, over 42.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.358, discriminator_fake_loss=1.324, generator_loss=29.02, generator_mel_loss=17.84, generator_kl_loss=1.388, generator_dur_loss=1.776, generator_adv_loss=2.019, generator_feat_match_loss=5.999, over 2901.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:45:28,314 INFO [train.py:527] (5/6) Epoch 822, batch 96, global_batch_idx: 101900, batch size: 25, loss[discriminator_loss=2.656, discriminator_real_loss=1.305, discriminator_fake_loss=1.351, generator_loss=30.86, generator_mel_loss=18.69, generator_kl_loss=1.957, generator_dur_loss=1.568, generator_adv_loss=2.006, generator_feat_match_loss=6.646, over 25.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=28.98, generator_mel_loss=17.83, generator_kl_loss=1.4, generator_dur_loss=1.762, generator_adv_loss=2.009, generator_feat_match_loss=5.981, over 5634.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:46:44,727 INFO [train.py:919] (5/6) Start epoch 823 +2024-03-15 12:48:11,350 INFO [train.py:527] (5/6) Epoch 823, batch 22, global_batch_idx: 101950, batch size: 45, loss[discriminator_loss=2.721, discriminator_real_loss=1.372, discriminator_fake_loss=1.349, generator_loss=29.6, generator_mel_loss=17.92, generator_kl_loss=1.408, generator_dur_loss=1.681, generator_adv_loss=2.002, generator_feat_match_loss=6.583, over 45.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.362, discriminator_fake_loss=1.331, generator_loss=28.95, generator_mel_loss=17.86, generator_kl_loss=1.42, generator_dur_loss=1.752, generator_adv_loss=2.008, generator_feat_match_loss=5.913, over 1365.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:50:31,078 INFO [train.py:527] (5/6) Epoch 823, batch 72, global_batch_idx: 102000, batch size: 62, loss[discriminator_loss=2.754, discriminator_real_loss=1.346, discriminator_fake_loss=1.408, generator_loss=29.1, generator_mel_loss=17.8, generator_kl_loss=1.514, generator_dur_loss=1.722, generator_adv_loss=2.024, generator_feat_match_loss=6.04, over 62.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=28.98, generator_mel_loss=17.79, generator_kl_loss=1.416, generator_dur_loss=1.751, generator_adv_loss=2.017, generator_feat_match_loss=6.002, over 4339.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:50:31,079 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 12:50:39,871 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 12:52:57,055 INFO [train.py:527] (5/6) Epoch 823, batch 122, global_batch_idx: 102050, batch size: 77, loss[discriminator_loss=2.713, discriminator_real_loss=1.377, discriminator_fake_loss=1.336, generator_loss=28.52, generator_mel_loss=17.85, generator_kl_loss=1.193, generator_dur_loss=1.753, generator_adv_loss=1.971, generator_feat_match_loss=5.755, over 77.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29, generator_mel_loss=17.8, generator_kl_loss=1.412, generator_dur_loss=1.75, generator_adv_loss=2.017, generator_feat_match_loss=6.024, over 7232.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:53:02,011 INFO [train.py:919] (5/6) Start epoch 824 +2024-03-15 12:55:40,380 INFO [train.py:527] (5/6) Epoch 824, batch 48, global_batch_idx: 102100, batch size: 25, loss[discriminator_loss=2.566, discriminator_real_loss=1.279, discriminator_fake_loss=1.286, generator_loss=31.97, generator_mel_loss=18.37, generator_kl_loss=1.987, generator_dur_loss=1.504, generator_adv_loss=1.947, generator_feat_match_loss=8.161, over 25.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.323, generator_loss=29.02, generator_mel_loss=17.74, generator_kl_loss=1.412, generator_dur_loss=1.752, generator_adv_loss=2.008, generator_feat_match_loss=6.108, over 2879.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:57:58,924 INFO [train.py:527] (5/6) Epoch 824, batch 98, global_batch_idx: 102150, batch size: 96, loss[discriminator_loss=2.677, discriminator_real_loss=1.275, discriminator_fake_loss=1.402, generator_loss=28.03, generator_mel_loss=17.31, generator_kl_loss=1.15, generator_dur_loss=1.882, generator_adv_loss=2.112, generator_feat_match_loss=5.574, over 96.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.347, discriminator_fake_loss=1.323, generator_loss=29.06, generator_mel_loss=17.78, generator_kl_loss=1.41, generator_dur_loss=1.746, generator_adv_loss=2.014, generator_feat_match_loss=6.11, over 5702.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 12:59:04,943 INFO [train.py:919] (5/6) Start epoch 825 +2024-03-15 13:00:36,445 INFO [train.py:527] (5/6) Epoch 825, batch 24, global_batch_idx: 102200, batch size: 55, loss[discriminator_loss=2.676, discriminator_real_loss=1.383, discriminator_fake_loss=1.293, generator_loss=28.47, generator_mel_loss=17.84, generator_kl_loss=1.444, generator_dur_loss=1.681, generator_adv_loss=1.852, generator_feat_match_loss=5.653, over 55.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.359, discriminator_fake_loss=1.317, generator_loss=28.8, generator_mel_loss=17.74, generator_kl_loss=1.397, generator_dur_loss=1.744, generator_adv_loss=2.006, generator_feat_match_loss=5.905, over 1468.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:00:36,446 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 13:00:44,503 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 13:03:03,004 INFO [train.py:527] (5/6) Epoch 825, batch 74, global_batch_idx: 102250, batch size: 72, loss[discriminator_loss=2.704, discriminator_real_loss=1.431, discriminator_fake_loss=1.273, generator_loss=28.7, generator_mel_loss=18.02, generator_kl_loss=1.336, generator_dur_loss=1.82, generator_adv_loss=1.922, generator_feat_match_loss=5.607, over 72.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.324, generator_loss=28.86, generator_mel_loss=17.81, generator_kl_loss=1.402, generator_dur_loss=1.746, generator_adv_loss=1.997, generator_feat_match_loss=5.899, over 4272.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:05:20,445 INFO [train.py:919] (5/6) Start epoch 826 +2024-03-15 13:05:44,595 INFO [train.py:527] (5/6) Epoch 826, batch 0, global_batch_idx: 102300, batch size: 53, loss[discriminator_loss=2.611, discriminator_real_loss=1.338, discriminator_fake_loss=1.273, generator_loss=28.35, generator_mel_loss=17.71, generator_kl_loss=1.307, generator_dur_loss=1.721, generator_adv_loss=1.918, generator_feat_match_loss=5.693, over 53.00 samples.], tot_loss[discriminator_loss=2.611, discriminator_real_loss=1.338, discriminator_fake_loss=1.273, generator_loss=28.35, generator_mel_loss=17.71, generator_kl_loss=1.307, generator_dur_loss=1.721, generator_adv_loss=1.918, generator_feat_match_loss=5.693, over 53.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:08:00,684 INFO [train.py:527] (5/6) Epoch 826, batch 50, global_batch_idx: 102350, batch size: 45, loss[discriminator_loss=2.668, discriminator_real_loss=1.307, discriminator_fake_loss=1.361, generator_loss=28.78, generator_mel_loss=17.4, generator_kl_loss=1.517, generator_dur_loss=1.701, generator_adv_loss=2.152, generator_feat_match_loss=6.009, over 45.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.353, discriminator_fake_loss=1.327, generator_loss=28.93, generator_mel_loss=17.84, generator_kl_loss=1.411, generator_dur_loss=1.744, generator_adv_loss=1.99, generator_feat_match_loss=5.94, over 2875.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:10:20,751 INFO [train.py:527] (5/6) Epoch 826, batch 100, global_batch_idx: 102400, batch size: 15, loss[discriminator_loss=2.773, discriminator_real_loss=1.296, discriminator_fake_loss=1.477, generator_loss=31.38, generator_mel_loss=18.26, generator_kl_loss=1.816, generator_dur_loss=1.596, generator_adv_loss=1.962, generator_feat_match_loss=7.751, over 15.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=28.98, generator_mel_loss=17.87, generator_kl_loss=1.408, generator_dur_loss=1.743, generator_adv_loss=1.995, generator_feat_match_loss=5.96, over 5602.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:10:20,753 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 13:10:29,554 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 13:11:33,726 INFO [train.py:919] (5/6) Start epoch 827 +2024-03-15 13:13:08,350 INFO [train.py:527] (5/6) Epoch 827, batch 26, global_batch_idx: 102450, batch size: 77, loss[discriminator_loss=2.655, discriminator_real_loss=1.319, discriminator_fake_loss=1.336, generator_loss=29.47, generator_mel_loss=17.82, generator_kl_loss=1.318, generator_dur_loss=1.804, generator_adv_loss=1.972, generator_feat_match_loss=6.56, over 77.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.36, discriminator_fake_loss=1.319, generator_loss=28.96, generator_mel_loss=17.77, generator_kl_loss=1.407, generator_dur_loss=1.755, generator_adv_loss=2.006, generator_feat_match_loss=6.018, over 1502.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:15:26,040 INFO [train.py:527] (5/6) Epoch 827, batch 76, global_batch_idx: 102500, batch size: 55, loss[discriminator_loss=2.602, discriminator_real_loss=1.263, discriminator_fake_loss=1.339, generator_loss=29.94, generator_mel_loss=18.11, generator_kl_loss=1.413, generator_dur_loss=1.648, generator_adv_loss=2.104, generator_feat_match_loss=6.659, over 55.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.347, discriminator_fake_loss=1.32, generator_loss=29, generator_mel_loss=17.85, generator_kl_loss=1.41, generator_dur_loss=1.742, generator_adv_loss=2.001, generator_feat_match_loss=5.996, over 4232.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:17:40,492 INFO [train.py:919] (5/6) Start epoch 828 +2024-03-15 13:18:09,392 INFO [train.py:527] (5/6) Epoch 828, batch 2, global_batch_idx: 102550, batch size: 53, loss[discriminator_loss=2.662, discriminator_real_loss=1.307, discriminator_fake_loss=1.354, generator_loss=29.45, generator_mel_loss=17.94, generator_kl_loss=1.43, generator_dur_loss=1.676, generator_adv_loss=2.056, generator_feat_match_loss=6.352, over 53.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.326, discriminator_fake_loss=1.343, generator_loss=29.65, generator_mel_loss=17.95, generator_kl_loss=1.451, generator_dur_loss=1.736, generator_adv_loss=2.014, generator_feat_match_loss=6.505, over 164.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:20:27,722 INFO [train.py:527] (5/6) Epoch 828, batch 52, global_batch_idx: 102600, batch size: 55, loss[discriminator_loss=2.625, discriminator_real_loss=1.441, discriminator_fake_loss=1.184, generator_loss=29.04, generator_mel_loss=17.37, generator_kl_loss=1.567, generator_dur_loss=1.679, generator_adv_loss=1.93, generator_feat_match_loss=6.493, over 55.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.363, discriminator_fake_loss=1.323, generator_loss=28.94, generator_mel_loss=17.77, generator_kl_loss=1.429, generator_dur_loss=1.738, generator_adv_loss=2.006, generator_feat_match_loss=5.992, over 2947.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:20:27,724 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 13:20:35,763 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 13:22:54,500 INFO [train.py:527] (5/6) Epoch 828, batch 102, global_batch_idx: 102650, batch size: 70, loss[discriminator_loss=2.729, discriminator_real_loss=1.365, discriminator_fake_loss=1.364, generator_loss=28.72, generator_mel_loss=17.92, generator_kl_loss=1.346, generator_dur_loss=1.82, generator_adv_loss=1.913, generator_feat_match_loss=5.721, over 70.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=29, generator_mel_loss=17.8, generator_kl_loss=1.449, generator_dur_loss=1.737, generator_adv_loss=2.003, generator_feat_match_loss=6.016, over 5588.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:23:55,278 INFO [train.py:919] (5/6) Start epoch 829 +2024-03-15 13:25:38,746 INFO [train.py:527] (5/6) Epoch 829, batch 28, global_batch_idx: 102700, batch size: 77, loss[discriminator_loss=2.722, discriminator_real_loss=1.304, discriminator_fake_loss=1.419, generator_loss=29.2, generator_mel_loss=18.11, generator_kl_loss=1.28, generator_dur_loss=1.815, generator_adv_loss=1.996, generator_feat_match_loss=5.993, over 77.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.353, discriminator_fake_loss=1.334, generator_loss=29.03, generator_mel_loss=17.93, generator_kl_loss=1.451, generator_dur_loss=1.725, generator_adv_loss=1.979, generator_feat_match_loss=5.947, over 1625.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:27:57,163 INFO [train.py:527] (5/6) Epoch 829, batch 78, global_batch_idx: 102750, batch size: 61, loss[discriminator_loss=2.655, discriminator_real_loss=1.334, discriminator_fake_loss=1.32, generator_loss=29.87, generator_mel_loss=18.01, generator_kl_loss=1.353, generator_dur_loss=1.744, generator_adv_loss=2, generator_feat_match_loss=6.763, over 61.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.356, discriminator_fake_loss=1.323, generator_loss=28.92, generator_mel_loss=17.81, generator_kl_loss=1.446, generator_dur_loss=1.721, generator_adv_loss=1.995, generator_feat_match_loss=5.95, over 4479.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:30:03,606 INFO [train.py:919] (5/6) Start epoch 830 +2024-03-15 13:30:37,865 INFO [train.py:527] (5/6) Epoch 830, batch 4, global_batch_idx: 102800, batch size: 66, loss[discriminator_loss=2.815, discriminator_real_loss=1.49, discriminator_fake_loss=1.325, generator_loss=29.02, generator_mel_loss=18.36, generator_kl_loss=1.481, generator_dur_loss=1.74, generator_adv_loss=1.939, generator_feat_match_loss=5.504, over 66.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.344, discriminator_fake_loss=1.365, generator_loss=29.1, generator_mel_loss=17.95, generator_kl_loss=1.417, generator_dur_loss=1.753, generator_adv_loss=2.091, generator_feat_match_loss=5.885, over 314.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:30:37,868 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 13:30:45,668 INFO [train.py:591] (5/6) 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 13:30:45,670 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 13:33:07,715 INFO [train.py:527] (5/6) Epoch 830, batch 54, global_batch_idx: 102850, batch size: 48, loss[discriminator_loss=2.547, discriminator_real_loss=1.286, discriminator_fake_loss=1.261, generator_loss=30.78, generator_mel_loss=18.36, generator_kl_loss=1.576, generator_dur_loss=1.689, generator_adv_loss=2.176, generator_feat_match_loss=6.983, over 48.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=28.8, generator_mel_loss=17.74, generator_kl_loss=1.407, generator_dur_loss=1.752, generator_adv_loss=2.016, generator_feat_match_loss=5.879, over 3232.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:35:25,286 INFO [train.py:527] (5/6) Epoch 830, batch 104, global_batch_idx: 102900, batch size: 70, loss[discriminator_loss=2.636, discriminator_real_loss=1.397, discriminator_fake_loss=1.239, generator_loss=29.17, generator_mel_loss=17.86, generator_kl_loss=1.321, generator_dur_loss=1.782, generator_adv_loss=1.927, generator_feat_match_loss=6.275, over 70.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.357, discriminator_fake_loss=1.322, generator_loss=28.9, generator_mel_loss=17.77, generator_kl_loss=1.417, generator_dur_loss=1.746, generator_adv_loss=2.017, generator_feat_match_loss=5.951, over 6079.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:36:16,573 INFO [train.py:919] (5/6) Start epoch 831 +2024-03-15 13:38:02,916 INFO [train.py:527] (5/6) Epoch 831, batch 30, global_batch_idx: 102950, batch size: 50, loss[discriminator_loss=2.626, discriminator_real_loss=1.331, discriminator_fake_loss=1.295, generator_loss=28.66, generator_mel_loss=17.65, generator_kl_loss=1.421, generator_dur_loss=1.668, generator_adv_loss=2.076, generator_feat_match_loss=5.847, over 50.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.369, discriminator_fake_loss=1.334, generator_loss=28.96, generator_mel_loss=17.83, generator_kl_loss=1.443, generator_dur_loss=1.739, generator_adv_loss=1.994, generator_feat_match_loss=5.955, over 1739.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:40:21,244 INFO [train.py:527] (5/6) Epoch 831, batch 80, global_batch_idx: 103000, batch size: 39, loss[discriminator_loss=2.704, discriminator_real_loss=1.453, discriminator_fake_loss=1.251, generator_loss=28.76, generator_mel_loss=17.62, generator_kl_loss=1.461, generator_dur_loss=1.656, generator_adv_loss=2.08, generator_feat_match_loss=5.943, over 39.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.361, discriminator_fake_loss=1.332, generator_loss=28.99, generator_mel_loss=17.83, generator_kl_loss=1.43, generator_dur_loss=1.736, generator_adv_loss=2.001, generator_feat_match_loss=5.998, over 4551.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:40:21,246 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 13:40:30,034 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 13:42:32,001 INFO [train.py:919] (5/6) Start epoch 832 +2024-03-15 13:43:12,225 INFO [train.py:527] (5/6) Epoch 832, batch 6, global_batch_idx: 103050, batch size: 25, loss[discriminator_loss=2.748, discriminator_real_loss=1.417, discriminator_fake_loss=1.331, generator_loss=29.51, generator_mel_loss=18.25, generator_kl_loss=1.887, generator_dur_loss=1.604, generator_adv_loss=1.903, generator_feat_match_loss=5.864, over 25.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.366, discriminator_fake_loss=1.322, generator_loss=28.72, generator_mel_loss=17.68, generator_kl_loss=1.399, generator_dur_loss=1.706, generator_adv_loss=2.005, generator_feat_match_loss=5.934, over 363.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:45:31,512 INFO [train.py:527] (5/6) Epoch 832, batch 56, global_batch_idx: 103100, batch size: 58, loss[discriminator_loss=2.676, discriminator_real_loss=1.364, discriminator_fake_loss=1.312, generator_loss=29.54, generator_mel_loss=17.89, generator_kl_loss=1.299, generator_dur_loss=1.722, generator_adv_loss=2.031, generator_feat_match_loss=6.593, over 58.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.362, discriminator_fake_loss=1.329, generator_loss=29.01, generator_mel_loss=17.81, generator_kl_loss=1.437, generator_dur_loss=1.737, generator_adv_loss=2.001, generator_feat_match_loss=6.029, over 3272.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:47:51,703 INFO [train.py:527] (5/6) Epoch 832, batch 106, global_batch_idx: 103150, batch size: 42, loss[discriminator_loss=2.62, discriminator_real_loss=1.348, discriminator_fake_loss=1.272, generator_loss=29.87, generator_mel_loss=18.25, generator_kl_loss=1.744, generator_dur_loss=1.612, generator_adv_loss=1.936, generator_feat_match_loss=6.321, over 42.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.322, generator_loss=28.98, generator_mel_loss=17.8, generator_kl_loss=1.429, generator_dur_loss=1.745, generator_adv_loss=2.01, generator_feat_match_loss=5.994, over 6068.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:48:36,939 INFO [train.py:919] (5/6) Start epoch 833 +2024-03-15 13:50:31,969 INFO [train.py:527] (5/6) Epoch 833, batch 32, global_batch_idx: 103200, batch size: 80, loss[discriminator_loss=2.683, discriminator_real_loss=1.388, discriminator_fake_loss=1.294, generator_loss=28.09, generator_mel_loss=17.59, generator_kl_loss=1.357, generator_dur_loss=1.775, generator_adv_loss=2.044, generator_feat_match_loss=5.332, over 80.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.353, discriminator_fake_loss=1.33, generator_loss=29.09, generator_mel_loss=17.85, generator_kl_loss=1.384, generator_dur_loss=1.754, generator_adv_loss=2.011, generator_feat_match_loss=6.092, over 1977.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:50:31,971 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 13:50:39,868 INFO [train.py:591] (5/6) 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 13:50:39,869 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 13:52:59,997 INFO [train.py:527] (5/6) Epoch 833, batch 82, global_batch_idx: 103250, batch size: 42, loss[discriminator_loss=2.698, discriminator_real_loss=1.351, discriminator_fake_loss=1.347, generator_loss=28.97, generator_mel_loss=17.82, generator_kl_loss=1.598, generator_dur_loss=1.713, generator_adv_loss=1.912, generator_feat_match_loss=5.925, over 42.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=29, generator_mel_loss=17.85, generator_kl_loss=1.413, generator_dur_loss=1.747, generator_adv_loss=2.003, generator_feat_match_loss=5.98, over 4841.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:54:51,875 INFO [train.py:919] (5/6) Start epoch 834 +2024-03-15 13:55:35,738 INFO [train.py:527] (5/6) Epoch 834, batch 8, global_batch_idx: 103300, batch size: 55, loss[discriminator_loss=2.644, discriminator_real_loss=1.247, discriminator_fake_loss=1.396, generator_loss=29.42, generator_mel_loss=17.9, generator_kl_loss=1.41, generator_dur_loss=1.678, generator_adv_loss=2.187, generator_feat_match_loss=6.249, over 55.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.331, discriminator_fake_loss=1.346, generator_loss=29.2, generator_mel_loss=18.05, generator_kl_loss=1.348, generator_dur_loss=1.734, generator_adv_loss=1.98, generator_feat_match_loss=6.09, over 537.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 13:57:53,714 INFO [train.py:527] (5/6) Epoch 834, batch 58, global_batch_idx: 103350, batch size: 52, loss[discriminator_loss=2.629, discriminator_real_loss=1.334, discriminator_fake_loss=1.295, generator_loss=28.95, generator_mel_loss=17.73, generator_kl_loss=1.427, generator_dur_loss=1.697, generator_adv_loss=1.929, generator_feat_match_loss=6.175, over 52.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.36, discriminator_fake_loss=1.331, generator_loss=28.9, generator_mel_loss=17.84, generator_kl_loss=1.434, generator_dur_loss=1.732, generator_adv_loss=1.987, generator_feat_match_loss=5.905, over 3383.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:00:12,049 INFO [train.py:527] (5/6) Epoch 834, batch 108, global_batch_idx: 103400, batch size: 68, loss[discriminator_loss=2.755, discriminator_real_loss=1.372, discriminator_fake_loss=1.383, generator_loss=28.85, generator_mel_loss=17.62, generator_kl_loss=1.398, generator_dur_loss=1.754, generator_adv_loss=1.975, generator_feat_match_loss=6.107, over 68.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=29.06, generator_mel_loss=17.83, generator_kl_loss=1.446, generator_dur_loss=1.726, generator_adv_loss=2.013, generator_feat_match_loss=6.047, over 6109.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:00:12,050 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 14:00:20,873 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 14:01:05,332 INFO [train.py:919] (5/6) Start epoch 835 +2024-03-15 14:03:05,104 INFO [train.py:527] (5/6) Epoch 835, batch 34, global_batch_idx: 103450, batch size: 25, loss[discriminator_loss=2.526, discriminator_real_loss=1.24, discriminator_fake_loss=1.286, generator_loss=31.97, generator_mel_loss=18.39, generator_kl_loss=1.832, generator_dur_loss=1.531, generator_adv_loss=2.309, generator_feat_match_loss=7.905, over 25.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.321, generator_loss=29.13, generator_mel_loss=17.79, generator_kl_loss=1.47, generator_dur_loss=1.73, generator_adv_loss=2.003, generator_feat_match_loss=6.142, over 1989.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:05:22,175 INFO [train.py:527] (5/6) Epoch 835, batch 84, global_batch_idx: 103500, batch size: 77, loss[discriminator_loss=2.732, discriminator_real_loss=1.312, discriminator_fake_loss=1.42, generator_loss=27.1, generator_mel_loss=17.02, generator_kl_loss=1.278, generator_dur_loss=1.821, generator_adv_loss=2.048, generator_feat_match_loss=4.937, over 77.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.1, generator_mel_loss=17.82, generator_kl_loss=1.458, generator_dur_loss=1.725, generator_adv_loss=1.998, generator_feat_match_loss=6.097, over 4667.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:07:10,830 INFO [train.py:919] (5/6) Start epoch 836 +2024-03-15 14:08:02,021 INFO [train.py:527] (5/6) Epoch 836, batch 10, global_batch_idx: 103550, batch size: 47, loss[discriminator_loss=2.69, discriminator_real_loss=1.302, discriminator_fake_loss=1.388, generator_loss=29.1, generator_mel_loss=18.25, generator_kl_loss=1.556, generator_dur_loss=1.672, generator_adv_loss=2.037, generator_feat_match_loss=5.588, over 47.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.357, discriminator_fake_loss=1.335, generator_loss=28.73, generator_mel_loss=17.83, generator_kl_loss=1.396, generator_dur_loss=1.753, generator_adv_loss=2.008, generator_feat_match_loss=5.745, over 597.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:10:26,522 INFO [train.py:527] (5/6) Epoch 836, batch 60, global_batch_idx: 103600, batch size: 96, loss[discriminator_loss=2.636, discriminator_real_loss=1.27, discriminator_fake_loss=1.366, generator_loss=28.41, generator_mel_loss=17.59, generator_kl_loss=1.361, generator_dur_loss=1.811, generator_adv_loss=1.98, generator_feat_match_loss=5.666, over 96.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.344, discriminator_fake_loss=1.329, generator_loss=28.99, generator_mel_loss=17.75, generator_kl_loss=1.422, generator_dur_loss=1.756, generator_adv_loss=2.003, generator_feat_match_loss=6.063, over 3550.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:10:26,524 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 14:10:34,774 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 14:12:51,432 INFO [train.py:527] (5/6) Epoch 836, batch 110, global_batch_idx: 103650, batch size: 83, loss[discriminator_loss=2.711, discriminator_real_loss=1.368, discriminator_fake_loss=1.343, generator_loss=29.16, generator_mel_loss=17.95, generator_kl_loss=1.393, generator_dur_loss=1.828, generator_adv_loss=1.934, generator_feat_match_loss=6.05, over 83.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.349, discriminator_fake_loss=1.327, generator_loss=29.01, generator_mel_loss=17.77, generator_kl_loss=1.442, generator_dur_loss=1.743, generator_adv_loss=2.005, generator_feat_match_loss=6.052, over 6370.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:13:25,845 INFO [train.py:919] (5/6) Start epoch 837 +2024-03-15 14:15:31,704 INFO [train.py:527] (5/6) Epoch 837, batch 36, global_batch_idx: 103700, batch size: 31, loss[discriminator_loss=2.631, discriminator_real_loss=1.337, discriminator_fake_loss=1.294, generator_loss=29.69, generator_mel_loss=18.16, generator_kl_loss=1.577, generator_dur_loss=1.644, generator_adv_loss=2.111, generator_feat_match_loss=6.193, over 31.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.362, discriminator_fake_loss=1.318, generator_loss=29.07, generator_mel_loss=17.85, generator_kl_loss=1.43, generator_dur_loss=1.727, generator_adv_loss=2.031, generator_feat_match_loss=6.028, over 2034.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:17:48,985 INFO [train.py:527] (5/6) Epoch 837, batch 86, global_batch_idx: 103750, batch size: 61, loss[discriminator_loss=2.707, discriminator_real_loss=1.381, discriminator_fake_loss=1.326, generator_loss=29.1, generator_mel_loss=17.83, generator_kl_loss=1.396, generator_dur_loss=1.749, generator_adv_loss=1.952, generator_feat_match_loss=6.175, over 61.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.1, generator_mel_loss=17.89, generator_kl_loss=1.418, generator_dur_loss=1.738, generator_adv_loss=2.015, generator_feat_match_loss=6.03, over 5093.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:19:30,346 INFO [train.py:919] (5/6) Start epoch 838 +2024-03-15 14:20:25,575 INFO [train.py:527] (5/6) Epoch 838, batch 12, global_batch_idx: 103800, batch size: 72, loss[discriminator_loss=2.678, discriminator_real_loss=1.322, discriminator_fake_loss=1.355, generator_loss=29.95, generator_mel_loss=18.25, generator_kl_loss=1.483, generator_dur_loss=1.748, generator_adv_loss=1.986, generator_feat_match_loss=6.485, over 72.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.357, discriminator_fake_loss=1.314, generator_loss=29.02, generator_mel_loss=17.82, generator_kl_loss=1.384, generator_dur_loss=1.752, generator_adv_loss=2.023, generator_feat_match_loss=6.035, over 832.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:20:25,578 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 14:20:33,315 INFO [train.py:591] (5/6) 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 14:20:33,316 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 14:22:51,851 INFO [train.py:527] (5/6) Epoch 838, batch 62, global_batch_idx: 103850, batch size: 50, loss[discriminator_loss=2.678, discriminator_real_loss=1.336, discriminator_fake_loss=1.343, generator_loss=28.8, generator_mel_loss=17.85, generator_kl_loss=1.642, generator_dur_loss=1.686, generator_adv_loss=1.95, generator_feat_match_loss=5.675, over 50.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=28.94, generator_mel_loss=17.82, generator_kl_loss=1.434, generator_dur_loss=1.726, generator_adv_loss=2.007, generator_feat_match_loss=5.955, over 3414.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:25:14,099 INFO [train.py:527] (5/6) Epoch 838, batch 112, global_batch_idx: 103900, batch size: 68, loss[discriminator_loss=2.705, discriminator_real_loss=1.396, discriminator_fake_loss=1.309, generator_loss=28.06, generator_mel_loss=17.76, generator_kl_loss=1.403, generator_dur_loss=1.795, generator_adv_loss=1.951, generator_feat_match_loss=5.155, over 68.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.321, generator_loss=29.01, generator_mel_loss=17.83, generator_kl_loss=1.419, generator_dur_loss=1.738, generator_adv_loss=2.01, generator_feat_match_loss=6.015, over 6293.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:25:44,323 INFO [train.py:919] (5/6) Start epoch 839 +2024-03-15 14:27:55,879 INFO [train.py:527] (5/6) Epoch 839, batch 38, global_batch_idx: 103950, batch size: 80, loss[discriminator_loss=2.718, discriminator_real_loss=1.481, discriminator_fake_loss=1.236, generator_loss=28.41, generator_mel_loss=17.52, generator_kl_loss=1.423, generator_dur_loss=1.813, generator_adv_loss=1.879, generator_feat_match_loss=5.775, over 80.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.35, discriminator_fake_loss=1.333, generator_loss=29.12, generator_mel_loss=17.93, generator_kl_loss=1.427, generator_dur_loss=1.75, generator_adv_loss=1.988, generator_feat_match_loss=6.026, over 2130.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:30:14,701 INFO [train.py:527] (5/6) Epoch 839, batch 88, global_batch_idx: 104000, batch size: 53, loss[discriminator_loss=2.749, discriminator_real_loss=1.41, discriminator_fake_loss=1.339, generator_loss=29, generator_mel_loss=17.79, generator_kl_loss=1.493, generator_dur_loss=1.682, generator_adv_loss=2.004, generator_feat_match_loss=6.035, over 53.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=29.02, generator_mel_loss=17.86, generator_kl_loss=1.412, generator_dur_loss=1.754, generator_adv_loss=1.993, generator_feat_match_loss=5.995, over 5155.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:30:14,703 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 14:30:23,580 INFO [train.py:591] (5/6) 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 14:30:23,581 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 14:31:59,499 INFO [train.py:919] (5/6) Start epoch 840 +2024-03-15 14:33:02,569 INFO [train.py:527] (5/6) Epoch 840, batch 14, global_batch_idx: 104050, batch size: 55, loss[discriminator_loss=2.669, discriminator_real_loss=1.331, discriminator_fake_loss=1.338, generator_loss=30.94, generator_mel_loss=18.48, generator_kl_loss=1.546, generator_dur_loss=1.68, generator_adv_loss=2.064, generator_feat_match_loss=7.164, over 55.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.345, discriminator_fake_loss=1.323, generator_loss=29.61, generator_mel_loss=18.23, generator_kl_loss=1.463, generator_dur_loss=1.731, generator_adv_loss=2.027, generator_feat_match_loss=6.156, over 761.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:35:22,491 INFO [train.py:527] (5/6) Epoch 840, batch 64, global_batch_idx: 104100, batch size: 70, loss[discriminator_loss=2.659, discriminator_real_loss=1.338, discriminator_fake_loss=1.321, generator_loss=28.64, generator_mel_loss=17.66, generator_kl_loss=1.357, generator_dur_loss=1.801, generator_adv_loss=2.009, generator_feat_match_loss=5.812, over 70.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.33, generator_loss=29.1, generator_mel_loss=17.93, generator_kl_loss=1.432, generator_dur_loss=1.749, generator_adv_loss=2.006, generator_feat_match_loss=5.986, over 3684.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:37:43,200 INFO [train.py:527] (5/6) Epoch 840, batch 114, global_batch_idx: 104150, batch size: 53, loss[discriminator_loss=2.72, discriminator_real_loss=1.354, discriminator_fake_loss=1.366, generator_loss=29.86, generator_mel_loss=18.55, generator_kl_loss=1.515, generator_dur_loss=1.739, generator_adv_loss=1.999, generator_feat_match_loss=6.054, over 53.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.362, discriminator_fake_loss=1.327, generator_loss=29.05, generator_mel_loss=17.92, generator_kl_loss=1.423, generator_dur_loss=1.753, generator_adv_loss=2.004, generator_feat_match_loss=5.951, over 6661.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:38:10,497 INFO [train.py:919] (5/6) Start epoch 841 +2024-03-15 14:40:29,575 INFO [train.py:527] (5/6) Epoch 841, batch 40, global_batch_idx: 104200, batch size: 13, loss[discriminator_loss=2.726, discriminator_real_loss=1.337, discriminator_fake_loss=1.389, generator_loss=28.9, generator_mel_loss=17.99, generator_kl_loss=1.805, generator_dur_loss=1.548, generator_adv_loss=1.726, generator_feat_match_loss=5.832, over 13.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.366, discriminator_fake_loss=1.33, generator_loss=28.76, generator_mel_loss=17.74, generator_kl_loss=1.394, generator_dur_loss=1.77, generator_adv_loss=1.998, generator_feat_match_loss=5.858, over 2559.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:40:29,576 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 14:40:37,610 INFO [train.py:591] (5/6) 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 14:40:37,611 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 14:42:54,702 INFO [train.py:527] (5/6) Epoch 841, batch 90, global_batch_idx: 104250, batch size: 77, loss[discriminator_loss=2.677, discriminator_real_loss=1.364, discriminator_fake_loss=1.314, generator_loss=28.1, generator_mel_loss=17.35, generator_kl_loss=1.452, generator_dur_loss=1.824, generator_adv_loss=1.905, generator_feat_match_loss=5.564, over 77.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.357, discriminator_fake_loss=1.329, generator_loss=28.88, generator_mel_loss=17.76, generator_kl_loss=1.416, generator_dur_loss=1.759, generator_adv_loss=1.994, generator_feat_match_loss=5.945, over 5258.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:44:26,486 INFO [train.py:919] (5/6) Start epoch 842 +2024-03-15 14:45:35,149 INFO [train.py:527] (5/6) Epoch 842, batch 16, global_batch_idx: 104300, batch size: 47, loss[discriminator_loss=2.654, discriminator_real_loss=1.265, discriminator_fake_loss=1.389, generator_loss=29.44, generator_mel_loss=18.07, generator_kl_loss=1.465, generator_dur_loss=1.642, generator_adv_loss=2.039, generator_feat_match_loss=6.219, over 47.00 samples.], tot_loss[discriminator_loss=2.656, discriminator_real_loss=1.349, discriminator_fake_loss=1.307, generator_loss=29.28, generator_mel_loss=17.89, generator_kl_loss=1.438, generator_dur_loss=1.729, generator_adv_loss=2.009, generator_feat_match_loss=6.213, over 961.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:47:53,236 INFO [train.py:527] (5/6) Epoch 842, batch 66, global_batch_idx: 104350, batch size: 74, loss[discriminator_loss=2.734, discriminator_real_loss=1.317, discriminator_fake_loss=1.417, generator_loss=28.1, generator_mel_loss=17.52, generator_kl_loss=1.181, generator_dur_loss=1.821, generator_adv_loss=2.016, generator_feat_match_loss=5.568, over 74.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.365, discriminator_fake_loss=1.318, generator_loss=28.94, generator_mel_loss=17.8, generator_kl_loss=1.44, generator_dur_loss=1.746, generator_adv_loss=1.997, generator_feat_match_loss=5.959, over 3832.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:50:14,359 INFO [train.py:527] (5/6) Epoch 842, batch 116, global_batch_idx: 104400, batch size: 48, loss[discriminator_loss=2.69, discriminator_real_loss=1.348, discriminator_fake_loss=1.342, generator_loss=30.44, generator_mel_loss=18.43, generator_kl_loss=1.525, generator_dur_loss=1.649, generator_adv_loss=2.068, generator_feat_match_loss=6.773, over 48.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.36, discriminator_fake_loss=1.318, generator_loss=29.09, generator_mel_loss=17.82, generator_kl_loss=1.442, generator_dur_loss=1.74, generator_adv_loss=2.004, generator_feat_match_loss=6.088, over 6549.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:50:14,360 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 14:50:23,187 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 14:50:44,028 INFO [train.py:919] (5/6) Start epoch 843 +2024-03-15 14:53:01,805 INFO [train.py:527] (5/6) Epoch 843, batch 42, global_batch_idx: 104450, batch size: 72, loss[discriminator_loss=2.727, discriminator_real_loss=1.404, discriminator_fake_loss=1.323, generator_loss=30.06, generator_mel_loss=18.32, generator_kl_loss=1.476, generator_dur_loss=1.784, generator_adv_loss=1.953, generator_feat_match_loss=6.524, over 72.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.367, discriminator_fake_loss=1.331, generator_loss=29, generator_mel_loss=17.85, generator_kl_loss=1.436, generator_dur_loss=1.741, generator_adv_loss=1.991, generator_feat_match_loss=5.991, over 2345.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:55:21,071 INFO [train.py:527] (5/6) Epoch 843, batch 92, global_batch_idx: 104500, batch size: 58, loss[discriminator_loss=2.72, discriminator_real_loss=1.497, discriminator_fake_loss=1.223, generator_loss=28.37, generator_mel_loss=17.71, generator_kl_loss=1.39, generator_dur_loss=1.719, generator_adv_loss=1.925, generator_feat_match_loss=5.63, over 58.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.365, discriminator_fake_loss=1.331, generator_loss=29.04, generator_mel_loss=17.86, generator_kl_loss=1.447, generator_dur_loss=1.741, generator_adv_loss=1.99, generator_feat_match_loss=6.005, over 5150.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 14:56:46,916 INFO [train.py:919] (5/6) Start epoch 844 +2024-03-15 14:58:00,524 INFO [train.py:527] (5/6) Epoch 844, batch 18, global_batch_idx: 104550, batch size: 42, loss[discriminator_loss=2.619, discriminator_real_loss=1.236, discriminator_fake_loss=1.383, generator_loss=29.08, generator_mel_loss=17.85, generator_kl_loss=1.547, generator_dur_loss=1.661, generator_adv_loss=1.866, generator_feat_match_loss=6.158, over 42.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.344, discriminator_fake_loss=1.327, generator_loss=29.12, generator_mel_loss=17.83, generator_kl_loss=1.409, generator_dur_loss=1.752, generator_adv_loss=2.057, generator_feat_match_loss=6.067, over 1236.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:00:22,323 INFO [train.py:527] (5/6) Epoch 844, batch 68, global_batch_idx: 104600, batch size: 72, loss[discriminator_loss=2.671, discriminator_real_loss=1.384, discriminator_fake_loss=1.286, generator_loss=28.7, generator_mel_loss=17.8, generator_kl_loss=1.439, generator_dur_loss=1.79, generator_adv_loss=2.09, generator_feat_match_loss=5.577, over 72.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.367, discriminator_fake_loss=1.319, generator_loss=28.92, generator_mel_loss=17.8, generator_kl_loss=1.441, generator_dur_loss=1.732, generator_adv_loss=2.022, generator_feat_match_loss=5.927, over 3962.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:00:22,324 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 15:00:31,248 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 15:02:49,510 INFO [train.py:527] (5/6) Epoch 844, batch 118, global_batch_idx: 104650, batch size: 31, loss[discriminator_loss=2.639, discriminator_real_loss=1.319, discriminator_fake_loss=1.319, generator_loss=29.38, generator_mel_loss=17.87, generator_kl_loss=1.504, generator_dur_loss=1.671, generator_adv_loss=2.018, generator_feat_match_loss=6.315, over 31.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.364, discriminator_fake_loss=1.322, generator_loss=28.86, generator_mel_loss=17.78, generator_kl_loss=1.424, generator_dur_loss=1.739, generator_adv_loss=2.003, generator_feat_match_loss=5.91, over 7002.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:03:04,547 INFO [train.py:919] (5/6) Start epoch 845 +2024-03-15 15:05:32,901 INFO [train.py:527] (5/6) Epoch 845, batch 44, global_batch_idx: 104700, batch size: 55, loss[discriminator_loss=2.654, discriminator_real_loss=1.334, discriminator_fake_loss=1.32, generator_loss=28.86, generator_mel_loss=17.51, generator_kl_loss=1.423, generator_dur_loss=1.667, generator_adv_loss=1.97, generator_feat_match_loss=6.288, over 55.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.358, discriminator_fake_loss=1.322, generator_loss=28.98, generator_mel_loss=17.85, generator_kl_loss=1.419, generator_dur_loss=1.735, generator_adv_loss=2.003, generator_feat_match_loss=5.971, over 2499.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:07:50,471 INFO [train.py:527] (5/6) Epoch 845, batch 94, global_batch_idx: 104750, batch size: 48, loss[discriminator_loss=2.736, discriminator_real_loss=1.341, discriminator_fake_loss=1.395, generator_loss=28.08, generator_mel_loss=17.57, generator_kl_loss=1.41, generator_dur_loss=1.696, generator_adv_loss=1.956, generator_feat_match_loss=5.446, over 48.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.318, generator_loss=28.98, generator_mel_loss=17.83, generator_kl_loss=1.422, generator_dur_loss=1.737, generator_adv_loss=1.998, generator_feat_match_loss=5.993, over 5300.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:09:11,533 INFO [train.py:919] (5/6) Start epoch 846 +2024-03-15 15:10:31,964 INFO [train.py:527] (5/6) Epoch 846, batch 20, global_batch_idx: 104800, batch size: 53, loss[discriminator_loss=2.657, discriminator_real_loss=1.277, discriminator_fake_loss=1.38, generator_loss=29.31, generator_mel_loss=17.73, generator_kl_loss=1.503, generator_dur_loss=1.669, generator_adv_loss=1.881, generator_feat_match_loss=6.531, over 53.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.326, generator_loss=28.99, generator_mel_loss=17.8, generator_kl_loss=1.415, generator_dur_loss=1.753, generator_adv_loss=1.997, generator_feat_match_loss=6.019, over 1289.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:10:31,965 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 15:10:40,200 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 15:12:56,303 INFO [train.py:527] (5/6) Epoch 846, batch 70, global_batch_idx: 104850, batch size: 47, loss[discriminator_loss=2.642, discriminator_real_loss=1.234, discriminator_fake_loss=1.408, generator_loss=28.27, generator_mel_loss=17.7, generator_kl_loss=1.425, generator_dur_loss=1.68, generator_adv_loss=2.139, generator_feat_match_loss=5.328, over 47.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=28.92, generator_mel_loss=17.8, generator_kl_loss=1.429, generator_dur_loss=1.732, generator_adv_loss=1.995, generator_feat_match_loss=5.963, over 4081.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:15:15,439 INFO [train.py:527] (5/6) Epoch 846, batch 120, global_batch_idx: 104900, batch size: 56, loss[discriminator_loss=2.791, discriminator_real_loss=1.54, discriminator_fake_loss=1.252, generator_loss=27.66, generator_mel_loss=17.44, generator_kl_loss=1.441, generator_dur_loss=1.726, generator_adv_loss=1.955, generator_feat_match_loss=5.101, over 56.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.326, generator_loss=28.91, generator_mel_loss=17.81, generator_kl_loss=1.445, generator_dur_loss=1.731, generator_adv_loss=2.002, generator_feat_match_loss=5.923, over 6780.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:15:25,240 INFO [train.py:919] (5/6) Start epoch 847 +2024-03-15 15:17:57,103 INFO [train.py:527] (5/6) Epoch 847, batch 46, global_batch_idx: 104950, batch size: 55, loss[discriminator_loss=2.636, discriminator_real_loss=1.321, discriminator_fake_loss=1.315, generator_loss=30.01, generator_mel_loss=18.21, generator_kl_loss=1.571, generator_dur_loss=1.681, generator_adv_loss=1.918, generator_feat_match_loss=6.629, over 55.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.351, discriminator_fake_loss=1.318, generator_loss=29.08, generator_mel_loss=17.8, generator_kl_loss=1.438, generator_dur_loss=1.746, generator_adv_loss=2.033, generator_feat_match_loss=6.061, over 2721.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:20:17,168 INFO [train.py:527] (5/6) Epoch 847, batch 96, global_batch_idx: 105000, batch size: 66, loss[discriminator_loss=2.698, discriminator_real_loss=1.39, discriminator_fake_loss=1.307, generator_loss=28.41, generator_mel_loss=17.68, generator_kl_loss=1.404, generator_dur_loss=1.737, generator_adv_loss=1.924, generator_feat_match_loss=5.663, over 66.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.353, discriminator_fake_loss=1.319, generator_loss=29.1, generator_mel_loss=17.83, generator_kl_loss=1.433, generator_dur_loss=1.747, generator_adv_loss=2.014, generator_feat_match_loss=6.072, over 5720.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:20:17,169 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 15:20:26,203 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 15:21:42,515 INFO [train.py:919] (5/6) Start epoch 848 +2024-03-15 15:23:07,268 INFO [train.py:527] (5/6) Epoch 848, batch 22, global_batch_idx: 105050, batch size: 25, loss[discriminator_loss=2.682, discriminator_real_loss=1.321, discriminator_fake_loss=1.362, generator_loss=30.41, generator_mel_loss=19.12, generator_kl_loss=1.758, generator_dur_loss=1.603, generator_adv_loss=1.994, generator_feat_match_loss=5.927, over 25.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.344, discriminator_fake_loss=1.318, generator_loss=29.07, generator_mel_loss=17.74, generator_kl_loss=1.451, generator_dur_loss=1.743, generator_adv_loss=2.007, generator_feat_match_loss=6.122, over 1259.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:25:25,217 INFO [train.py:527] (5/6) Epoch 848, batch 72, global_batch_idx: 105100, batch size: 56, loss[discriminator_loss=2.666, discriminator_real_loss=1.403, discriminator_fake_loss=1.263, generator_loss=29.06, generator_mel_loss=17.83, generator_kl_loss=1.508, generator_dur_loss=1.734, generator_adv_loss=2.003, generator_feat_match_loss=5.985, over 56.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.09, generator_mel_loss=17.83, generator_kl_loss=1.424, generator_dur_loss=1.747, generator_adv_loss=1.994, generator_feat_match_loss=6.094, over 4108.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:27:45,021 INFO [train.py:527] (5/6) Epoch 848, batch 122, global_batch_idx: 105150, batch size: 70, loss[discriminator_loss=2.699, discriminator_real_loss=1.434, discriminator_fake_loss=1.265, generator_loss=28.62, generator_mel_loss=17.49, generator_kl_loss=1.382, generator_dur_loss=1.805, generator_adv_loss=2.036, generator_feat_match_loss=5.899, over 70.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.07, generator_mel_loss=17.83, generator_kl_loss=1.429, generator_dur_loss=1.746, generator_adv_loss=1.998, generator_feat_match_loss=6.059, over 6881.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:27:49,968 INFO [train.py:919] (5/6) Start epoch 849 +2024-03-15 15:30:29,697 INFO [train.py:527] (5/6) Epoch 849, batch 48, global_batch_idx: 105200, batch size: 42, loss[discriminator_loss=2.641, discriminator_real_loss=1.387, discriminator_fake_loss=1.254, generator_loss=28.94, generator_mel_loss=17.83, generator_kl_loss=1.59, generator_dur_loss=1.68, generator_adv_loss=1.907, generator_feat_match_loss=5.928, over 42.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.36, discriminator_fake_loss=1.319, generator_loss=28.88, generator_mel_loss=17.73, generator_kl_loss=1.418, generator_dur_loss=1.75, generator_adv_loss=2.01, generator_feat_match_loss=5.968, over 2755.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:30:29,698 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 15:30:37,816 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 15:32:57,495 INFO [train.py:527] (5/6) Epoch 849, batch 98, global_batch_idx: 105250, batch size: 50, loss[discriminator_loss=2.714, discriminator_real_loss=1.331, discriminator_fake_loss=1.383, generator_loss=29.22, generator_mel_loss=17.93, generator_kl_loss=1.514, generator_dur_loss=1.667, generator_adv_loss=2.015, generator_feat_match_loss=6.089, over 50.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.354, discriminator_fake_loss=1.319, generator_loss=28.96, generator_mel_loss=17.76, generator_kl_loss=1.419, generator_dur_loss=1.742, generator_adv_loss=2.008, generator_feat_match_loss=6.036, over 5733.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:34:06,046 INFO [train.py:919] (5/6) Start epoch 850 +2024-03-15 15:35:36,262 INFO [train.py:527] (5/6) Epoch 850, batch 24, global_batch_idx: 105300, batch size: 70, loss[discriminator_loss=2.67, discriminator_real_loss=1.36, discriminator_fake_loss=1.31, generator_loss=30.35, generator_mel_loss=18.25, generator_kl_loss=1.528, generator_dur_loss=1.73, generator_adv_loss=2.104, generator_feat_match_loss=6.739, over 70.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.351, discriminator_fake_loss=1.333, generator_loss=29.12, generator_mel_loss=17.87, generator_kl_loss=1.398, generator_dur_loss=1.703, generator_adv_loss=2.014, generator_feat_match_loss=6.134, over 1339.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:37:54,297 INFO [train.py:527] (5/6) Epoch 850, batch 74, global_batch_idx: 105350, batch size: 58, loss[discriminator_loss=2.688, discriminator_real_loss=1.429, discriminator_fake_loss=1.259, generator_loss=29.13, generator_mel_loss=17.72, generator_kl_loss=1.455, generator_dur_loss=1.733, generator_adv_loss=2.057, generator_feat_match_loss=6.161, over 58.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.356, discriminator_fake_loss=1.324, generator_loss=29, generator_mel_loss=17.81, generator_kl_loss=1.418, generator_dur_loss=1.711, generator_adv_loss=2.01, generator_feat_match_loss=6.05, over 4066.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:40:12,124 INFO [train.py:919] (5/6) Start epoch 851 +2024-03-15 15:40:36,974 INFO [train.py:527] (5/6) Epoch 851, batch 0, global_batch_idx: 105400, batch size: 62, loss[discriminator_loss=2.676, discriminator_real_loss=1.257, discriminator_fake_loss=1.418, generator_loss=29.59, generator_mel_loss=18.05, generator_kl_loss=1.519, generator_dur_loss=1.693, generator_adv_loss=2.211, generator_feat_match_loss=6.111, over 62.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.257, discriminator_fake_loss=1.418, generator_loss=29.59, generator_mel_loss=18.05, generator_kl_loss=1.519, generator_dur_loss=1.693, generator_adv_loss=2.211, generator_feat_match_loss=6.111, over 62.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:40:36,977 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 15:40:45,128 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 15:43:04,953 INFO [train.py:527] (5/6) Epoch 851, batch 50, global_batch_idx: 105450, batch size: 13, loss[discriminator_loss=2.671, discriminator_real_loss=1.293, discriminator_fake_loss=1.378, generator_loss=32.4, generator_mel_loss=18.5, generator_kl_loss=1.936, generator_dur_loss=1.637, generator_adv_loss=2.066, generator_feat_match_loss=8.259, over 13.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.368, discriminator_fake_loss=1.326, generator_loss=28.77, generator_mel_loss=17.69, generator_kl_loss=1.425, generator_dur_loss=1.725, generator_adv_loss=1.987, generator_feat_match_loss=5.938, over 2758.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:45:30,066 INFO [train.py:527] (5/6) Epoch 851, batch 100, global_batch_idx: 105500, batch size: 68, loss[discriminator_loss=2.669, discriminator_real_loss=1.305, discriminator_fake_loss=1.364, generator_loss=29.61, generator_mel_loss=17.95, generator_kl_loss=1.464, generator_dur_loss=1.79, generator_adv_loss=1.921, generator_feat_match_loss=6.483, over 68.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.365, discriminator_fake_loss=1.322, generator_loss=28.85, generator_mel_loss=17.73, generator_kl_loss=1.437, generator_dur_loss=1.734, generator_adv_loss=1.998, generator_feat_match_loss=5.948, over 5550.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:46:30,718 INFO [train.py:919] (5/6) Start epoch 852 +2024-03-15 15:48:06,105 INFO [train.py:527] (5/6) Epoch 852, batch 26, global_batch_idx: 105550, batch size: 72, loss[discriminator_loss=2.632, discriminator_real_loss=1.31, discriminator_fake_loss=1.322, generator_loss=29.05, generator_mel_loss=17.49, generator_kl_loss=1.534, generator_dur_loss=1.826, generator_adv_loss=2.034, generator_feat_match_loss=6.17, over 72.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.351, discriminator_fake_loss=1.327, generator_loss=29.18, generator_mel_loss=17.8, generator_kl_loss=1.471, generator_dur_loss=1.733, generator_adv_loss=2.02, generator_feat_match_loss=6.159, over 1434.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:50:24,284 INFO [train.py:527] (5/6) Epoch 852, batch 76, global_batch_idx: 105600, batch size: 45, loss[discriminator_loss=2.735, discriminator_real_loss=1.395, discriminator_fake_loss=1.34, generator_loss=29.51, generator_mel_loss=17.78, generator_kl_loss=1.591, generator_dur_loss=1.624, generator_adv_loss=1.961, generator_feat_match_loss=6.562, over 45.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.35, discriminator_fake_loss=1.323, generator_loss=29.12, generator_mel_loss=17.79, generator_kl_loss=1.446, generator_dur_loss=1.735, generator_adv_loss=2.026, generator_feat_match_loss=6.117, over 4424.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:50:24,286 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 15:50:33,122 INFO [train.py:591] (5/6) 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 15:50:33,124 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 15:52:44,755 INFO [train.py:919] (5/6) Start epoch 853 +2024-03-15 15:53:14,697 INFO [train.py:527] (5/6) Epoch 853, batch 2, global_batch_idx: 105650, batch size: 80, loss[discriminator_loss=2.702, discriminator_real_loss=1.347, discriminator_fake_loss=1.355, generator_loss=29.48, generator_mel_loss=17.82, generator_kl_loss=1.315, generator_dur_loss=1.819, generator_adv_loss=2.061, generator_feat_match_loss=6.469, over 80.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.346, discriminator_fake_loss=1.346, generator_loss=29.36, generator_mel_loss=17.82, generator_kl_loss=1.486, generator_dur_loss=1.743, generator_adv_loss=1.984, generator_feat_match_loss=6.328, over 195.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:55:34,470 INFO [train.py:527] (5/6) Epoch 853, batch 52, global_batch_idx: 105700, batch size: 55, loss[discriminator_loss=2.609, discriminator_real_loss=1.39, discriminator_fake_loss=1.219, generator_loss=29.18, generator_mel_loss=17.93, generator_kl_loss=1.488, generator_dur_loss=1.729, generator_adv_loss=2.213, generator_feat_match_loss=5.821, over 55.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.355, discriminator_fake_loss=1.316, generator_loss=29.11, generator_mel_loss=17.8, generator_kl_loss=1.442, generator_dur_loss=1.738, generator_adv_loss=2.009, generator_feat_match_loss=6.127, over 3132.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:57:55,579 INFO [train.py:527] (5/6) Epoch 853, batch 102, global_batch_idx: 105750, batch size: 66, loss[discriminator_loss=2.675, discriminator_real_loss=1.342, discriminator_fake_loss=1.333, generator_loss=28.74, generator_mel_loss=18.25, generator_kl_loss=1.325, generator_dur_loss=1.79, generator_adv_loss=1.898, generator_feat_match_loss=5.48, over 66.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.359, discriminator_fake_loss=1.318, generator_loss=29.03, generator_mel_loss=17.79, generator_kl_loss=1.431, generator_dur_loss=1.734, generator_adv_loss=2.001, generator_feat_match_loss=6.083, over 5944.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 15:58:51,352 INFO [train.py:919] (5/6) Start epoch 854 +2024-03-15 16:00:31,942 INFO [train.py:527] (5/6) Epoch 854, batch 28, global_batch_idx: 105800, batch size: 61, loss[discriminator_loss=2.69, discriminator_real_loss=1.353, discriminator_fake_loss=1.337, generator_loss=28.79, generator_mel_loss=17.78, generator_kl_loss=1.459, generator_dur_loss=1.726, generator_adv_loss=1.908, generator_feat_match_loss=5.919, over 61.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.342, discriminator_fake_loss=1.327, generator_loss=29.12, generator_mel_loss=17.83, generator_kl_loss=1.441, generator_dur_loss=1.714, generator_adv_loss=1.99, generator_feat_match_loss=6.146, over 1563.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:00:31,943 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 16:00:40,053 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 16:03:01,601 INFO [train.py:527] (5/6) Epoch 854, batch 78, global_batch_idx: 105850, batch size: 39, loss[discriminator_loss=2.665, discriminator_real_loss=1.349, discriminator_fake_loss=1.316, generator_loss=28.7, generator_mel_loss=17.43, generator_kl_loss=1.494, generator_dur_loss=1.632, generator_adv_loss=2.116, generator_feat_match_loss=6.027, over 39.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.347, discriminator_fake_loss=1.322, generator_loss=29.05, generator_mel_loss=17.82, generator_kl_loss=1.422, generator_dur_loss=1.737, generator_adv_loss=2, generator_feat_match_loss=6.073, over 4560.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:05:08,967 INFO [train.py:919] (5/6) Start epoch 855 +2024-03-15 16:05:42,788 INFO [train.py:527] (5/6) Epoch 855, batch 4, global_batch_idx: 105900, batch size: 72, loss[discriminator_loss=2.691, discriminator_real_loss=1.394, discriminator_fake_loss=1.297, generator_loss=29.46, generator_mel_loss=18.18, generator_kl_loss=1.343, generator_dur_loss=1.79, generator_adv_loss=2.022, generator_feat_match_loss=6.124, over 72.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.366, discriminator_fake_loss=1.314, generator_loss=29.31, generator_mel_loss=18.09, generator_kl_loss=1.418, generator_dur_loss=1.711, generator_adv_loss=2.001, generator_feat_match_loss=6.087, over 255.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:08:01,108 INFO [train.py:527] (5/6) Epoch 855, batch 54, global_batch_idx: 105950, batch size: 45, loss[discriminator_loss=2.714, discriminator_real_loss=1.356, discriminator_fake_loss=1.358, generator_loss=28.77, generator_mel_loss=18.2, generator_kl_loss=1.592, generator_dur_loss=1.623, generator_adv_loss=1.94, generator_feat_match_loss=5.419, over 45.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.356, discriminator_fake_loss=1.316, generator_loss=28.92, generator_mel_loss=17.79, generator_kl_loss=1.42, generator_dur_loss=1.731, generator_adv_loss=2.012, generator_feat_match_loss=5.973, over 3125.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:10:21,472 INFO [train.py:527] (5/6) Epoch 855, batch 104, global_batch_idx: 106000, batch size: 62, loss[discriminator_loss=2.662, discriminator_real_loss=1.264, discriminator_fake_loss=1.398, generator_loss=29.85, generator_mel_loss=18.03, generator_kl_loss=1.476, generator_dur_loss=1.744, generator_adv_loss=1.95, generator_feat_match_loss=6.651, over 62.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.359, discriminator_fake_loss=1.319, generator_loss=28.95, generator_mel_loss=17.8, generator_kl_loss=1.428, generator_dur_loss=1.738, generator_adv_loss=2.006, generator_feat_match_loss=5.976, over 5967.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:10:21,474 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 16:10:30,229 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 16:11:22,544 INFO [train.py:919] (5/6) Start epoch 856 +2024-03-15 16:13:09,273 INFO [train.py:527] (5/6) Epoch 856, batch 30, global_batch_idx: 106050, batch size: 68, loss[discriminator_loss=2.679, discriminator_real_loss=1.345, discriminator_fake_loss=1.334, generator_loss=29.3, generator_mel_loss=18.28, generator_kl_loss=1.457, generator_dur_loss=1.777, generator_adv_loss=1.968, generator_feat_match_loss=5.817, over 68.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.345, discriminator_fake_loss=1.324, generator_loss=29.11, generator_mel_loss=17.74, generator_kl_loss=1.407, generator_dur_loss=1.724, generator_adv_loss=2.055, generator_feat_match_loss=6.183, over 1723.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:15:28,342 INFO [train.py:527] (5/6) Epoch 856, batch 80, global_batch_idx: 106100, batch size: 56, loss[discriminator_loss=2.705, discriminator_real_loss=1.308, discriminator_fake_loss=1.397, generator_loss=28.91, generator_mel_loss=17.53, generator_kl_loss=1.398, generator_dur_loss=1.635, generator_adv_loss=2.034, generator_feat_match_loss=6.311, over 56.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.36, discriminator_fake_loss=1.322, generator_loss=29, generator_mel_loss=17.77, generator_kl_loss=1.426, generator_dur_loss=1.725, generator_adv_loss=2.023, generator_feat_match_loss=6.054, over 4516.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:17:28,235 INFO [train.py:919] (5/6) Start epoch 857 +2024-03-15 16:18:09,583 INFO [train.py:527] (5/6) Epoch 857, batch 6, global_batch_idx: 106150, batch size: 58, loss[discriminator_loss=2.673, discriminator_real_loss=1.313, discriminator_fake_loss=1.359, generator_loss=29.14, generator_mel_loss=17.72, generator_kl_loss=1.395, generator_dur_loss=1.753, generator_adv_loss=2.132, generator_feat_match_loss=6.141, over 58.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=28.95, generator_mel_loss=17.83, generator_kl_loss=1.42, generator_dur_loss=1.726, generator_adv_loss=2.021, generator_feat_match_loss=5.955, over 361.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:20:27,281 INFO [train.py:527] (5/6) Epoch 857, batch 56, global_batch_idx: 106200, batch size: 50, loss[discriminator_loss=2.735, discriminator_real_loss=1.397, discriminator_fake_loss=1.338, generator_loss=30.18, generator_mel_loss=18.09, generator_kl_loss=1.501, generator_dur_loss=1.684, generator_adv_loss=2.069, generator_feat_match_loss=6.84, over 50.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.352, discriminator_fake_loss=1.329, generator_loss=28.95, generator_mel_loss=17.8, generator_kl_loss=1.442, generator_dur_loss=1.742, generator_adv_loss=2.011, generator_feat_match_loss=5.962, over 3281.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:20:27,283 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 16:20:35,561 INFO [train.py:591] (5/6) 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 16:20:35,562 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 16:22:55,616 INFO [train.py:527] (5/6) Epoch 857, batch 106, global_batch_idx: 106250, batch size: 16, loss[discriminator_loss=2.577, discriminator_real_loss=1.266, discriminator_fake_loss=1.311, generator_loss=29.96, generator_mel_loss=17.53, generator_kl_loss=1.939, generator_dur_loss=1.553, generator_adv_loss=2.121, generator_feat_match_loss=6.817, over 16.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.351, discriminator_fake_loss=1.329, generator_loss=28.94, generator_mel_loss=17.79, generator_kl_loss=1.451, generator_dur_loss=1.741, generator_adv_loss=2, generator_feat_match_loss=5.951, over 6088.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:23:42,481 INFO [train.py:919] (5/6) Start epoch 858 +2024-03-15 16:25:35,212 INFO [train.py:527] (5/6) Epoch 858, batch 32, global_batch_idx: 106300, batch size: 45, loss[discriminator_loss=2.724, discriminator_real_loss=1.428, discriminator_fake_loss=1.296, generator_loss=28.09, generator_mel_loss=17.59, generator_kl_loss=1.566, generator_dur_loss=1.69, generator_adv_loss=1.964, generator_feat_match_loss=5.285, over 45.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.36, discriminator_fake_loss=1.319, generator_loss=29.12, generator_mel_loss=17.82, generator_kl_loss=1.424, generator_dur_loss=1.748, generator_adv_loss=2.01, generator_feat_match_loss=6.118, over 1813.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:27:50,705 INFO [train.py:527] (5/6) Epoch 858, batch 82, global_batch_idx: 106350, batch size: 58, loss[discriminator_loss=2.655, discriminator_real_loss=1.294, discriminator_fake_loss=1.361, generator_loss=29.13, generator_mel_loss=18.16, generator_kl_loss=1.439, generator_dur_loss=1.747, generator_adv_loss=1.982, generator_feat_match_loss=5.8, over 58.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.324, generator_loss=29.09, generator_mel_loss=17.85, generator_kl_loss=1.434, generator_dur_loss=1.734, generator_adv_loss=2.004, generator_feat_match_loss=6.068, over 4332.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:29:49,279 INFO [train.py:919] (5/6) Start epoch 859 +2024-03-15 16:30:36,452 INFO [train.py:527] (5/6) Epoch 859, batch 8, global_batch_idx: 106400, batch size: 48, loss[discriminator_loss=2.752, discriminator_real_loss=1.367, discriminator_fake_loss=1.385, generator_loss=28.93, generator_mel_loss=17.92, generator_kl_loss=1.439, generator_dur_loss=1.691, generator_adv_loss=1.915, generator_feat_match_loss=5.961, over 48.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=28.86, generator_mel_loss=17.82, generator_kl_loss=1.441, generator_dur_loss=1.75, generator_adv_loss=1.99, generator_feat_match_loss=5.862, over 541.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:30:36,455 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 16:30:44,309 INFO [train.py:591] (5/6) 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 16:30:44,311 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 16:33:02,139 INFO [train.py:527] (5/6) Epoch 859, batch 58, global_batch_idx: 106450, batch size: 15, loss[discriminator_loss=2.673, discriminator_real_loss=1.315, discriminator_fake_loss=1.359, generator_loss=28.1, generator_mel_loss=18.43, generator_kl_loss=1.76, generator_dur_loss=1.528, generator_adv_loss=1.952, generator_feat_match_loss=4.436, over 15.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=29.01, generator_mel_loss=17.82, generator_kl_loss=1.42, generator_dur_loss=1.74, generator_adv_loss=2.017, generator_feat_match_loss=6.014, over 3241.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:35:20,869 INFO [train.py:527] (5/6) Epoch 859, batch 108, global_batch_idx: 106500, batch size: 96, loss[discriminator_loss=2.685, discriminator_real_loss=1.386, discriminator_fake_loss=1.3, generator_loss=28.94, generator_mel_loss=17.47, generator_kl_loss=1.358, generator_dur_loss=1.811, generator_adv_loss=1.761, generator_feat_match_loss=6.547, over 96.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.353, discriminator_fake_loss=1.322, generator_loss=29.04, generator_mel_loss=17.82, generator_kl_loss=1.427, generator_dur_loss=1.735, generator_adv_loss=2.012, generator_feat_match_loss=6.047, over 6088.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:36:03,304 INFO [train.py:919] (5/6) Start epoch 860 +2024-03-15 16:38:00,638 INFO [train.py:527] (5/6) Epoch 860, batch 34, global_batch_idx: 106550, batch size: 16, loss[discriminator_loss=2.722, discriminator_real_loss=1.447, discriminator_fake_loss=1.275, generator_loss=31.05, generator_mel_loss=18.46, generator_kl_loss=2.06, generator_dur_loss=1.499, generator_adv_loss=1.849, generator_feat_match_loss=7.183, over 16.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.351, discriminator_fake_loss=1.318, generator_loss=29.02, generator_mel_loss=17.71, generator_kl_loss=1.457, generator_dur_loss=1.729, generator_adv_loss=2.03, generator_feat_match_loss=6.088, over 1990.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:40:16,798 INFO [train.py:527] (5/6) Epoch 860, batch 84, global_batch_idx: 106600, batch size: 62, loss[discriminator_loss=2.637, discriminator_real_loss=1.305, discriminator_fake_loss=1.332, generator_loss=29.91, generator_mel_loss=18.19, generator_kl_loss=1.388, generator_dur_loss=1.758, generator_adv_loss=2.057, generator_feat_match_loss=6.522, over 62.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=28.97, generator_mel_loss=17.76, generator_kl_loss=1.435, generator_dur_loss=1.734, generator_adv_loss=2.021, generator_feat_match_loss=6.024, over 4882.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:40:16,800 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 16:40:25,518 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 16:42:19,214 INFO [train.py:919] (5/6) Start epoch 861 +2024-03-15 16:43:12,706 INFO [train.py:527] (5/6) Epoch 861, batch 10, global_batch_idx: 106650, batch size: 61, loss[discriminator_loss=2.677, discriminator_real_loss=1.318, discriminator_fake_loss=1.359, generator_loss=29, generator_mel_loss=18.31, generator_kl_loss=1.338, generator_dur_loss=1.809, generator_adv_loss=1.951, generator_feat_match_loss=5.591, over 61.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.356, discriminator_fake_loss=1.325, generator_loss=28.99, generator_mel_loss=17.81, generator_kl_loss=1.443, generator_dur_loss=1.785, generator_adv_loss=2.01, generator_feat_match_loss=5.945, over 655.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:45:32,309 INFO [train.py:527] (5/6) Epoch 861, batch 60, global_batch_idx: 106700, batch size: 88, loss[discriminator_loss=2.671, discriminator_real_loss=1.322, discriminator_fake_loss=1.349, generator_loss=29.37, generator_mel_loss=17.53, generator_kl_loss=1.546, generator_dur_loss=1.852, generator_adv_loss=1.965, generator_feat_match_loss=6.472, over 88.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.364, discriminator_fake_loss=1.321, generator_loss=29.12, generator_mel_loss=17.8, generator_kl_loss=1.488, generator_dur_loss=1.735, generator_adv_loss=2.025, generator_feat_match_loss=6.072, over 3353.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:47:49,923 INFO [train.py:527] (5/6) Epoch 861, batch 110, global_batch_idx: 106750, batch size: 42, loss[discriminator_loss=2.634, discriminator_real_loss=1.379, discriminator_fake_loss=1.255, generator_loss=30.15, generator_mel_loss=18.45, generator_kl_loss=1.411, generator_dur_loss=1.68, generator_adv_loss=2.039, generator_feat_match_loss=6.569, over 42.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=29.11, generator_mel_loss=17.82, generator_kl_loss=1.477, generator_dur_loss=1.735, generator_adv_loss=2.015, generator_feat_match_loss=6.065, over 5912.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:48:28,653 INFO [train.py:919] (5/6) Start epoch 862 +2024-03-15 16:50:33,815 INFO [train.py:527] (5/6) Epoch 862, batch 36, global_batch_idx: 106800, batch size: 53, loss[discriminator_loss=2.724, discriminator_real_loss=1.496, discriminator_fake_loss=1.228, generator_loss=28.1, generator_mel_loss=17.28, generator_kl_loss=1.357, generator_dur_loss=1.752, generator_adv_loss=1.968, generator_feat_match_loss=5.744, over 53.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.99, generator_mel_loss=17.8, generator_kl_loss=1.408, generator_dur_loss=1.759, generator_adv_loss=2.013, generator_feat_match_loss=6.009, over 2163.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:50:33,816 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 16:50:41,934 INFO [train.py:591] (5/6) 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 16:50:41,935 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 16:53:01,228 INFO [train.py:527] (5/6) Epoch 862, batch 86, global_batch_idx: 106850, batch size: 74, loss[discriminator_loss=2.797, discriminator_real_loss=1.399, discriminator_fake_loss=1.398, generator_loss=27.94, generator_mel_loss=17.4, generator_kl_loss=1.36, generator_dur_loss=1.79, generator_adv_loss=2.002, generator_feat_match_loss=5.382, over 74.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.366, discriminator_fake_loss=1.327, generator_loss=28.95, generator_mel_loss=17.75, generator_kl_loss=1.435, generator_dur_loss=1.747, generator_adv_loss=2.01, generator_feat_match_loss=6.007, over 4960.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:54:43,642 INFO [train.py:919] (5/6) Start epoch 863 +2024-03-15 16:55:40,720 INFO [train.py:527] (5/6) Epoch 863, batch 12, global_batch_idx: 106900, batch size: 61, loss[discriminator_loss=2.649, discriminator_real_loss=1.376, discriminator_fake_loss=1.273, generator_loss=29.33, generator_mel_loss=17.82, generator_kl_loss=1.386, generator_dur_loss=1.818, generator_adv_loss=2.117, generator_feat_match_loss=6.193, over 61.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.366, discriminator_fake_loss=1.313, generator_loss=28.88, generator_mel_loss=17.72, generator_kl_loss=1.464, generator_dur_loss=1.715, generator_adv_loss=2.002, generator_feat_match_loss=5.975, over 668.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 16:58:01,661 INFO [train.py:527] (5/6) Epoch 863, batch 62, global_batch_idx: 106950, batch size: 56, loss[discriminator_loss=2.64, discriminator_real_loss=1.331, discriminator_fake_loss=1.309, generator_loss=28.89, generator_mel_loss=17.47, generator_kl_loss=1.399, generator_dur_loss=1.708, generator_adv_loss=1.995, generator_feat_match_loss=6.322, over 56.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.362, discriminator_fake_loss=1.322, generator_loss=28.87, generator_mel_loss=17.69, generator_kl_loss=1.452, generator_dur_loss=1.732, generator_adv_loss=1.997, generator_feat_match_loss=6.006, over 3417.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:00:22,763 INFO [train.py:527] (5/6) Epoch 863, batch 112, global_batch_idx: 107000, batch size: 72, loss[discriminator_loss=2.73, discriminator_real_loss=1.453, discriminator_fake_loss=1.276, generator_loss=28.21, generator_mel_loss=17.49, generator_kl_loss=1.314, generator_dur_loss=1.786, generator_adv_loss=2.036, generator_feat_match_loss=5.587, over 72.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.92, generator_mel_loss=17.72, generator_kl_loss=1.452, generator_dur_loss=1.733, generator_adv_loss=2, generator_feat_match_loss=6.025, over 6129.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:00:22,765 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 17:00:31,750 INFO [train.py:591] (5/6) 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 17:00:31,751 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 17:01:03,472 INFO [train.py:919] (5/6) Start epoch 864 +2024-03-15 17:03:14,840 INFO [train.py:527] (5/6) Epoch 864, batch 38, global_batch_idx: 107050, batch size: 77, loss[discriminator_loss=2.697, discriminator_real_loss=1.266, discriminator_fake_loss=1.431, generator_loss=29.22, generator_mel_loss=17.89, generator_kl_loss=1.324, generator_dur_loss=1.808, generator_adv_loss=1.99, generator_feat_match_loss=6.206, over 77.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=29.01, generator_mel_loss=17.83, generator_kl_loss=1.443, generator_dur_loss=1.743, generator_adv_loss=2.002, generator_feat_match_loss=6.002, over 2289.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:05:35,940 INFO [train.py:527] (5/6) Epoch 864, batch 88, global_batch_idx: 107100, batch size: 52, loss[discriminator_loss=2.674, discriminator_real_loss=1.337, discriminator_fake_loss=1.337, generator_loss=28.62, generator_mel_loss=17.62, generator_kl_loss=1.62, generator_dur_loss=1.684, generator_adv_loss=2.1, generator_feat_match_loss=5.6, over 52.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.357, discriminator_fake_loss=1.321, generator_loss=29.08, generator_mel_loss=17.84, generator_kl_loss=1.432, generator_dur_loss=1.738, generator_adv_loss=2.008, generator_feat_match_loss=6.056, over 5178.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:07:16,151 INFO [train.py:919] (5/6) Start epoch 865 +2024-03-15 17:08:19,454 INFO [train.py:527] (5/6) Epoch 865, batch 14, global_batch_idx: 107150, batch size: 74, loss[discriminator_loss=2.69, discriminator_real_loss=1.381, discriminator_fake_loss=1.308, generator_loss=28.55, generator_mel_loss=17.78, generator_kl_loss=1.389, generator_dur_loss=1.797, generator_adv_loss=1.899, generator_feat_match_loss=5.681, over 74.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.16, generator_mel_loss=17.92, generator_kl_loss=1.477, generator_dur_loss=1.717, generator_adv_loss=2.005, generator_feat_match_loss=6.045, over 801.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:10:41,858 INFO [train.py:527] (5/6) Epoch 865, batch 64, global_batch_idx: 107200, batch size: 45, loss[discriminator_loss=2.605, discriminator_real_loss=1.303, discriminator_fake_loss=1.302, generator_loss=29.85, generator_mel_loss=17.99, generator_kl_loss=1.451, generator_dur_loss=1.641, generator_adv_loss=2.142, generator_feat_match_loss=6.633, over 45.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.348, discriminator_fake_loss=1.319, generator_loss=29.14, generator_mel_loss=17.82, generator_kl_loss=1.438, generator_dur_loss=1.739, generator_adv_loss=2.02, generator_feat_match_loss=6.123, over 3612.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:10:41,860 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 17:10:49,761 INFO [train.py:591] (5/6) 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 17:10:49,762 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 17:13:13,611 INFO [train.py:527] (5/6) Epoch 865, batch 114, global_batch_idx: 107250, batch size: 53, loss[discriminator_loss=2.715, discriminator_real_loss=1.457, discriminator_fake_loss=1.258, generator_loss=28.75, generator_mel_loss=17.76, generator_kl_loss=1.378, generator_dur_loss=1.709, generator_adv_loss=2.072, generator_feat_match_loss=5.83, over 53.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.352, discriminator_fake_loss=1.322, generator_loss=29.15, generator_mel_loss=17.85, generator_kl_loss=1.434, generator_dur_loss=1.738, generator_adv_loss=2.016, generator_feat_match_loss=6.117, over 6537.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:13:40,893 INFO [train.py:919] (5/6) Start epoch 866 +2024-03-15 17:15:59,848 INFO [train.py:527] (5/6) Epoch 866, batch 40, global_batch_idx: 107300, batch size: 64, loss[discriminator_loss=2.645, discriminator_real_loss=1.349, discriminator_fake_loss=1.296, generator_loss=29.26, generator_mel_loss=18.01, generator_kl_loss=1.336, generator_dur_loss=1.721, generator_adv_loss=1.929, generator_feat_match_loss=6.271, over 64.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.346, discriminator_fake_loss=1.32, generator_loss=29.17, generator_mel_loss=17.85, generator_kl_loss=1.429, generator_dur_loss=1.741, generator_adv_loss=2.016, generator_feat_match_loss=6.134, over 2352.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:18:21,256 INFO [train.py:527] (5/6) Epoch 866, batch 90, global_batch_idx: 107350, batch size: 58, loss[discriminator_loss=2.675, discriminator_real_loss=1.333, discriminator_fake_loss=1.342, generator_loss=28.85, generator_mel_loss=17.91, generator_kl_loss=1.499, generator_dur_loss=1.746, generator_adv_loss=2.018, generator_feat_match_loss=5.672, over 58.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.346, discriminator_fake_loss=1.321, generator_loss=29.08, generator_mel_loss=17.82, generator_kl_loss=1.434, generator_dur_loss=1.733, generator_adv_loss=2.016, generator_feat_match_loss=6.082, over 5189.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, +2024-03-15 17:19:50,827 INFO [train.py:919] (5/6) Start epoch 867 +2024-03-15 17:20:58,848 INFO [train.py:527] (5/6) Epoch 867, batch 16, global_batch_idx: 107400, batch size: 72, loss[discriminator_loss=2.617, discriminator_real_loss=1.369, discriminator_fake_loss=1.248, generator_loss=27.98, generator_mel_loss=17.48, generator_kl_loss=1.384, generator_dur_loss=1.809, generator_adv_loss=1.98, generator_feat_match_loss=5.325, over 72.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.37, discriminator_fake_loss=1.311, generator_loss=28.38, generator_mel_loss=17.63, generator_kl_loss=1.461, generator_dur_loss=1.729, generator_adv_loss=2.001, generator_feat_match_loss=5.556, over 1004.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:20:58,850 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 17:21:06,797 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 17:23:24,409 INFO [train.py:527] (5/6) Epoch 867, batch 66, global_batch_idx: 107450, batch size: 88, loss[discriminator_loss=2.667, discriminator_real_loss=1.31, discriminator_fake_loss=1.356, generator_loss=30.09, generator_mel_loss=17.79, generator_kl_loss=1.253, generator_dur_loss=1.815, generator_adv_loss=2.212, generator_feat_match_loss=7.02, over 88.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.35, discriminator_fake_loss=1.324, generator_loss=28.96, generator_mel_loss=17.81, generator_kl_loss=1.425, generator_dur_loss=1.734, generator_adv_loss=2.009, generator_feat_match_loss=5.982, over 3771.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:25:42,125 INFO [train.py:527] (5/6) Epoch 867, batch 116, global_batch_idx: 107500, batch size: 74, loss[discriminator_loss=2.742, discriminator_real_loss=1.367, discriminator_fake_loss=1.375, generator_loss=28.46, generator_mel_loss=17.59, generator_kl_loss=1.354, generator_dur_loss=1.754, generator_adv_loss=2.097, generator_feat_match_loss=5.667, over 74.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.321, generator_loss=28.92, generator_mel_loss=17.79, generator_kl_loss=1.412, generator_dur_loss=1.741, generator_adv_loss=2.008, generator_feat_match_loss=5.964, over 6668.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:26:05,470 INFO [train.py:919] (5/6) Start epoch 868 +2024-03-15 17:28:24,484 INFO [train.py:527] (5/6) Epoch 868, batch 42, global_batch_idx: 107550, batch size: 70, loss[discriminator_loss=2.672, discriminator_real_loss=1.292, discriminator_fake_loss=1.379, generator_loss=28.9, generator_mel_loss=17.63, generator_kl_loss=1.354, generator_dur_loss=1.806, generator_adv_loss=2.142, generator_feat_match_loss=5.965, over 70.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.354, discriminator_fake_loss=1.319, generator_loss=29.04, generator_mel_loss=17.78, generator_kl_loss=1.422, generator_dur_loss=1.752, generator_adv_loss=2.007, generator_feat_match_loss=6.083, over 2414.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:30:41,027 INFO [train.py:527] (5/6) Epoch 868, batch 92, global_batch_idx: 107600, batch size: 68, loss[discriminator_loss=2.693, discriminator_real_loss=1.412, discriminator_fake_loss=1.28, generator_loss=28.96, generator_mel_loss=17.83, generator_kl_loss=1.355, generator_dur_loss=1.737, generator_adv_loss=2.04, generator_feat_match_loss=6.001, over 68.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.352, discriminator_fake_loss=1.324, generator_loss=29.11, generator_mel_loss=17.8, generator_kl_loss=1.423, generator_dur_loss=1.747, generator_adv_loss=2.005, generator_feat_match_loss=6.128, over 5320.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:30:41,028 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 17:30:49,715 INFO [train.py:591] (5/6) 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 17:30:49,716 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 17:32:20,009 INFO [train.py:919] (5/6) Start epoch 869 +2024-03-15 17:33:34,688 INFO [train.py:527] (5/6) Epoch 869, batch 18, global_batch_idx: 107650, batch size: 80, loss[discriminator_loss=2.707, discriminator_real_loss=1.393, discriminator_fake_loss=1.314, generator_loss=28.1, generator_mel_loss=17.62, generator_kl_loss=1.165, generator_dur_loss=1.834, generator_adv_loss=2.046, generator_feat_match_loss=5.433, over 80.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.369, discriminator_fake_loss=1.322, generator_loss=28.66, generator_mel_loss=17.71, generator_kl_loss=1.371, generator_dur_loss=1.732, generator_adv_loss=2.001, generator_feat_match_loss=5.851, over 1122.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:35:52,984 INFO [train.py:527] (5/6) Epoch 869, batch 68, global_batch_idx: 107700, batch size: 53, loss[discriminator_loss=2.714, discriminator_real_loss=1.362, discriminator_fake_loss=1.352, generator_loss=27.97, generator_mel_loss=17.43, generator_kl_loss=1.642, generator_dur_loss=1.669, generator_adv_loss=1.961, generator_feat_match_loss=5.275, over 53.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.11, generator_mel_loss=17.81, generator_kl_loss=1.429, generator_dur_loss=1.723, generator_adv_loss=2.026, generator_feat_match_loss=6.12, over 3907.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:38:10,261 INFO [train.py:527] (5/6) Epoch 869, batch 118, global_batch_idx: 107750, batch size: 15, loss[discriminator_loss=2.609, discriminator_real_loss=1.333, discriminator_fake_loss=1.276, generator_loss=29.26, generator_mel_loss=18.23, generator_kl_loss=1.867, generator_dur_loss=1.583, generator_adv_loss=1.912, generator_feat_match_loss=5.677, over 15.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=29.04, generator_mel_loss=17.79, generator_kl_loss=1.426, generator_dur_loss=1.738, generator_adv_loss=2.013, generator_feat_match_loss=6.071, over 6959.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:38:26,188 INFO [train.py:919] (5/6) Start epoch 870 +2024-03-15 17:40:55,621 INFO [train.py:527] (5/6) Epoch 870, batch 44, global_batch_idx: 107800, batch size: 96, loss[discriminator_loss=2.711, discriminator_real_loss=1.355, discriminator_fake_loss=1.356, generator_loss=28.06, generator_mel_loss=17.54, generator_kl_loss=1.177, generator_dur_loss=1.865, generator_adv_loss=1.939, generator_feat_match_loss=5.539, over 96.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=29.15, generator_mel_loss=17.78, generator_kl_loss=1.42, generator_dur_loss=1.757, generator_adv_loss=2.015, generator_feat_match_loss=6.172, over 2642.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:40:55,623 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 17:41:03,898 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 17:43:23,787 INFO [train.py:527] (5/6) Epoch 870, batch 94, global_batch_idx: 107850, batch size: 39, loss[discriminator_loss=2.627, discriminator_real_loss=1.259, discriminator_fake_loss=1.368, generator_loss=28.99, generator_mel_loss=17.73, generator_kl_loss=1.589, generator_dur_loss=1.658, generator_adv_loss=2.206, generator_feat_match_loss=5.798, over 39.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=29.03, generator_mel_loss=17.78, generator_kl_loss=1.426, generator_dur_loss=1.748, generator_adv_loss=2.005, generator_feat_match_loss=6.073, over 5418.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:44:43,211 INFO [train.py:919] (5/6) Start epoch 871 +2024-03-15 17:46:03,844 INFO [train.py:527] (5/6) Epoch 871, batch 20, global_batch_idx: 107900, batch size: 68, loss[discriminator_loss=2.723, discriminator_real_loss=1.368, discriminator_fake_loss=1.355, generator_loss=29.01, generator_mel_loss=17.95, generator_kl_loss=1.408, generator_dur_loss=1.751, generator_adv_loss=1.813, generator_feat_match_loss=6.092, over 68.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.368, discriminator_fake_loss=1.317, generator_loss=28.84, generator_mel_loss=17.72, generator_kl_loss=1.438, generator_dur_loss=1.756, generator_adv_loss=2.002, generator_feat_match_loss=5.916, over 1271.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:48:24,321 INFO [train.py:527] (5/6) Epoch 871, batch 70, global_batch_idx: 107950, batch size: 36, loss[discriminator_loss=2.738, discriminator_real_loss=1.406, discriminator_fake_loss=1.332, generator_loss=29.02, generator_mel_loss=17.75, generator_kl_loss=1.536, generator_dur_loss=1.695, generator_adv_loss=1.96, generator_feat_match_loss=6.072, over 36.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.363, discriminator_fake_loss=1.326, generator_loss=29, generator_mel_loss=17.79, generator_kl_loss=1.427, generator_dur_loss=1.746, generator_adv_loss=2.002, generator_feat_match_loss=6.037, over 4096.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:50:40,156 INFO [train.py:527] (5/6) Epoch 871, batch 120, global_batch_idx: 108000, batch size: 70, loss[discriminator_loss=2.661, discriminator_real_loss=1.277, discriminator_fake_loss=1.383, generator_loss=28.53, generator_mel_loss=17.95, generator_kl_loss=1.366, generator_dur_loss=1.795, generator_adv_loss=2.008, generator_feat_match_loss=5.411, over 70.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.362, discriminator_fake_loss=1.324, generator_loss=29.01, generator_mel_loss=17.78, generator_kl_loss=1.439, generator_dur_loss=1.747, generator_adv_loss=2.005, generator_feat_match_loss=6.04, over 6965.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:50:40,158 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 17:50:49,222 INFO [train.py:591] (5/6) 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 17:50:49,223 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 17:50:59,265 INFO [train.py:919] (5/6) Start epoch 872 +2024-03-15 17:53:28,742 INFO [train.py:527] (5/6) Epoch 872, batch 46, global_batch_idx: 108050, batch size: 56, loss[discriminator_loss=2.77, discriminator_real_loss=1.503, discriminator_fake_loss=1.267, generator_loss=28.82, generator_mel_loss=17.63, generator_kl_loss=1.492, generator_dur_loss=1.744, generator_adv_loss=1.821, generator_feat_match_loss=6.131, over 56.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.354, discriminator_fake_loss=1.316, generator_loss=29.14, generator_mel_loss=17.79, generator_kl_loss=1.434, generator_dur_loss=1.738, generator_adv_loss=2.008, generator_feat_match_loss=6.168, over 2563.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:55:49,381 INFO [train.py:527] (5/6) Epoch 872, batch 96, global_batch_idx: 108100, batch size: 77, loss[discriminator_loss=2.626, discriminator_real_loss=1.324, discriminator_fake_loss=1.302, generator_loss=29.44, generator_mel_loss=17.55, generator_kl_loss=1.375, generator_dur_loss=1.867, generator_adv_loss=1.987, generator_feat_match_loss=6.661, over 77.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=29, generator_mel_loss=17.75, generator_kl_loss=1.433, generator_dur_loss=1.754, generator_adv_loss=2.004, generator_feat_match_loss=6.067, over 5563.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 17:57:05,231 INFO [train.py:919] (5/6) Start epoch 873 +2024-03-15 17:58:29,775 INFO [train.py:527] (5/6) Epoch 873, batch 22, global_batch_idx: 108150, batch size: 42, loss[discriminator_loss=2.612, discriminator_real_loss=1.376, discriminator_fake_loss=1.236, generator_loss=29.29, generator_mel_loss=17.68, generator_kl_loss=1.622, generator_dur_loss=1.684, generator_adv_loss=2.152, generator_feat_match_loss=6.147, over 42.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.345, discriminator_fake_loss=1.327, generator_loss=29.26, generator_mel_loss=17.75, generator_kl_loss=1.422, generator_dur_loss=1.752, generator_adv_loss=2.092, generator_feat_match_loss=6.248, over 1402.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:00:46,079 INFO [train.py:527] (5/6) Epoch 873, batch 72, global_batch_idx: 108200, batch size: 25, loss[discriminator_loss=2.677, discriminator_real_loss=1.371, discriminator_fake_loss=1.306, generator_loss=30.05, generator_mel_loss=18.31, generator_kl_loss=1.715, generator_dur_loss=1.486, generator_adv_loss=1.996, generator_feat_match_loss=6.538, over 25.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.352, discriminator_fake_loss=1.317, generator_loss=29.17, generator_mel_loss=17.78, generator_kl_loss=1.455, generator_dur_loss=1.734, generator_adv_loss=2.048, generator_feat_match_loss=6.146, over 4180.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:00:46,080 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 18:00:54,110 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 18:03:12,113 INFO [train.py:527] (5/6) Epoch 873, batch 122, global_batch_idx: 108250, batch size: 15, loss[discriminator_loss=2.751, discriminator_real_loss=1.377, discriminator_fake_loss=1.374, generator_loss=29.7, generator_mel_loss=18.54, generator_kl_loss=1.744, generator_dur_loss=1.554, generator_adv_loss=1.91, generator_feat_match_loss=5.954, over 15.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.35, discriminator_fake_loss=1.318, generator_loss=29.18, generator_mel_loss=17.79, generator_kl_loss=1.45, generator_dur_loss=1.737, generator_adv_loss=2.033, generator_feat_match_loss=6.168, over 6942.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:03:17,193 INFO [train.py:919] (5/6) Start epoch 874 +2024-03-15 18:05:52,498 INFO [train.py:527] (5/6) Epoch 874, batch 48, global_batch_idx: 108300, batch size: 64, loss[discriminator_loss=2.703, discriminator_real_loss=1.36, discriminator_fake_loss=1.344, generator_loss=29.13, generator_mel_loss=17.92, generator_kl_loss=1.502, generator_dur_loss=1.752, generator_adv_loss=2.041, generator_feat_match_loss=5.917, over 64.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.35, discriminator_fake_loss=1.317, generator_loss=28.98, generator_mel_loss=17.76, generator_kl_loss=1.456, generator_dur_loss=1.726, generator_adv_loss=2.015, generator_feat_match_loss=6.022, over 2710.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:08:12,161 INFO [train.py:527] (5/6) Epoch 874, batch 98, global_batch_idx: 108350, batch size: 48, loss[discriminator_loss=2.679, discriminator_real_loss=1.205, discriminator_fake_loss=1.474, generator_loss=30.22, generator_mel_loss=18.28, generator_kl_loss=1.401, generator_dur_loss=1.679, generator_adv_loss=2.248, generator_feat_match_loss=6.612, over 48.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.355, discriminator_fake_loss=1.318, generator_loss=29.09, generator_mel_loss=17.77, generator_kl_loss=1.45, generator_dur_loss=1.73, generator_adv_loss=2.018, generator_feat_match_loss=6.125, over 5625.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:09:22,349 INFO [train.py:919] (5/6) Start epoch 875 +2024-03-15 18:10:53,771 INFO [train.py:527] (5/6) Epoch 875, batch 24, global_batch_idx: 108400, batch size: 47, loss[discriminator_loss=2.649, discriminator_real_loss=1.337, discriminator_fake_loss=1.312, generator_loss=28.63, generator_mel_loss=17.61, generator_kl_loss=1.536, generator_dur_loss=1.678, generator_adv_loss=1.992, generator_feat_match_loss=5.811, over 47.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.35, discriminator_fake_loss=1.327, generator_loss=29.04, generator_mel_loss=17.82, generator_kl_loss=1.414, generator_dur_loss=1.747, generator_adv_loss=2.008, generator_feat_match_loss=6.045, over 1505.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:10:53,773 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 18:11:01,727 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 18:13:20,862 INFO [train.py:527] (5/6) Epoch 875, batch 74, global_batch_idx: 108450, batch size: 31, loss[discriminator_loss=2.682, discriminator_real_loss=1.367, discriminator_fake_loss=1.315, generator_loss=27.99, generator_mel_loss=17.78, generator_kl_loss=1.592, generator_dur_loss=1.636, generator_adv_loss=2.025, generator_feat_match_loss=4.958, over 31.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.11, generator_mel_loss=17.83, generator_kl_loss=1.443, generator_dur_loss=1.734, generator_adv_loss=2.019, generator_feat_match_loss=6.082, over 4153.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:15:38,794 INFO [train.py:919] (5/6) Start epoch 876 +2024-03-15 18:16:02,422 INFO [train.py:527] (5/6) Epoch 876, batch 0, global_batch_idx: 108500, batch size: 45, loss[discriminator_loss=2.677, discriminator_real_loss=1.446, discriminator_fake_loss=1.232, generator_loss=30.07, generator_mel_loss=18.11, generator_kl_loss=1.548, generator_dur_loss=1.67, generator_adv_loss=2.043, generator_feat_match_loss=6.697, over 45.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.446, discriminator_fake_loss=1.232, generator_loss=30.07, generator_mel_loss=18.11, generator_kl_loss=1.548, generator_dur_loss=1.67, generator_adv_loss=2.043, generator_feat_match_loss=6.697, over 45.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:18:19,655 INFO [train.py:527] (5/6) Epoch 876, batch 50, global_batch_idx: 108550, batch size: 56, loss[discriminator_loss=2.686, discriminator_real_loss=1.355, discriminator_fake_loss=1.331, generator_loss=28.84, generator_mel_loss=18.02, generator_kl_loss=1.611, generator_dur_loss=1.708, generator_adv_loss=2.052, generator_feat_match_loss=5.453, over 56.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.357, discriminator_fake_loss=1.328, generator_loss=28.98, generator_mel_loss=17.78, generator_kl_loss=1.448, generator_dur_loss=1.729, generator_adv_loss=2.012, generator_feat_match_loss=6.007, over 2743.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:20:40,484 INFO [train.py:527] (5/6) Epoch 876, batch 100, global_batch_idx: 108600, batch size: 45, loss[discriminator_loss=2.61, discriminator_real_loss=1.391, discriminator_fake_loss=1.219, generator_loss=29.57, generator_mel_loss=17.76, generator_kl_loss=1.545, generator_dur_loss=1.614, generator_adv_loss=2.026, generator_feat_match_loss=6.623, over 45.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.354, discriminator_fake_loss=1.326, generator_loss=29.04, generator_mel_loss=17.81, generator_kl_loss=1.43, generator_dur_loss=1.742, generator_adv_loss=2.018, generator_feat_match_loss=6.037, over 5651.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:20:40,486 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 18:20:49,300 INFO [train.py:591] (5/6) 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 18:20:49,301 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 18:21:52,949 INFO [train.py:919] (5/6) Start epoch 877 +2024-03-15 18:23:24,987 INFO [train.py:527] (5/6) Epoch 877, batch 26, global_batch_idx: 108650, batch size: 70, loss[discriminator_loss=2.696, discriminator_real_loss=1.299, discriminator_fake_loss=1.397, generator_loss=29.04, generator_mel_loss=17.75, generator_kl_loss=1.411, generator_dur_loss=1.777, generator_adv_loss=2.017, generator_feat_match_loss=6.088, over 70.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.365, discriminator_fake_loss=1.32, generator_loss=29.22, generator_mel_loss=17.88, generator_kl_loss=1.448, generator_dur_loss=1.729, generator_adv_loss=2.002, generator_feat_match_loss=6.16, over 1425.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:25:44,599 INFO [train.py:527] (5/6) Epoch 877, batch 76, global_batch_idx: 108700, batch size: 48, loss[discriminator_loss=2.598, discriminator_real_loss=1.307, discriminator_fake_loss=1.291, generator_loss=29.96, generator_mel_loss=17.85, generator_kl_loss=1.635, generator_dur_loss=1.635, generator_adv_loss=2.078, generator_feat_match_loss=6.758, over 48.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.354, discriminator_fake_loss=1.312, generator_loss=29.09, generator_mel_loss=17.77, generator_kl_loss=1.43, generator_dur_loss=1.735, generator_adv_loss=2.013, generator_feat_match_loss=6.139, over 4297.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:27:56,003 INFO [train.py:919] (5/6) Start epoch 878 +2024-03-15 18:28:20,907 INFO [train.py:527] (5/6) Epoch 878, batch 2, global_batch_idx: 108750, batch size: 83, loss[discriminator_loss=2.633, discriminator_real_loss=1.371, discriminator_fake_loss=1.262, generator_loss=29.11, generator_mel_loss=17.6, generator_kl_loss=1.366, generator_dur_loss=1.853, generator_adv_loss=1.923, generator_feat_match_loss=6.373, over 83.00 samples.], tot_loss[discriminator_loss=2.648, discriminator_real_loss=1.376, discriminator_fake_loss=1.272, generator_loss=29.45, generator_mel_loss=17.88, generator_kl_loss=1.467, generator_dur_loss=1.763, generator_adv_loss=1.973, generator_feat_match_loss=6.37, over 161.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:30:42,801 INFO [train.py:527] (5/6) Epoch 878, batch 52, global_batch_idx: 108800, batch size: 56, loss[discriminator_loss=2.649, discriminator_real_loss=1.353, discriminator_fake_loss=1.296, generator_loss=29.44, generator_mel_loss=17.45, generator_kl_loss=1.451, generator_dur_loss=1.761, generator_adv_loss=2.172, generator_feat_match_loss=6.606, over 56.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.319, generator_loss=29.11, generator_mel_loss=17.77, generator_kl_loss=1.43, generator_dur_loss=1.741, generator_adv_loss=2.013, generator_feat_match_loss=6.154, over 3113.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:30:42,803 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 18:30:51,153 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 18:33:07,733 INFO [train.py:527] (5/6) Epoch 878, batch 102, global_batch_idx: 108850, batch size: 36, loss[discriminator_loss=2.619, discriminator_real_loss=1.292, discriminator_fake_loss=1.326, generator_loss=29.51, generator_mel_loss=17.75, generator_kl_loss=1.492, generator_dur_loss=1.683, generator_adv_loss=2.124, generator_feat_match_loss=6.461, over 36.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.354, discriminator_fake_loss=1.323, generator_loss=29.04, generator_mel_loss=17.75, generator_kl_loss=1.442, generator_dur_loss=1.74, generator_adv_loss=2.018, generator_feat_match_loss=6.093, over 6112.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:34:08,410 INFO [train.py:919] (5/6) Start epoch 879 +2024-03-15 18:35:51,410 INFO [train.py:527] (5/6) Epoch 879, batch 28, global_batch_idx: 108900, batch size: 64, loss[discriminator_loss=2.695, discriminator_real_loss=1.365, discriminator_fake_loss=1.33, generator_loss=29.08, generator_mel_loss=17.97, generator_kl_loss=1.433, generator_dur_loss=1.759, generator_adv_loss=2.043, generator_feat_match_loss=5.871, over 64.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.367, discriminator_fake_loss=1.323, generator_loss=29.09, generator_mel_loss=17.8, generator_kl_loss=1.444, generator_dur_loss=1.736, generator_adv_loss=2.006, generator_feat_match_loss=6.112, over 1642.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:38:13,271 INFO [train.py:527] (5/6) Epoch 879, batch 78, global_batch_idx: 108950, batch size: 55, loss[discriminator_loss=2.668, discriminator_real_loss=1.377, discriminator_fake_loss=1.291, generator_loss=29.1, generator_mel_loss=17.98, generator_kl_loss=1.373, generator_dur_loss=1.698, generator_adv_loss=1.995, generator_feat_match_loss=6.051, over 55.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.361, discriminator_fake_loss=1.325, generator_loss=29.07, generator_mel_loss=17.82, generator_kl_loss=1.434, generator_dur_loss=1.741, generator_adv_loss=1.999, generator_feat_match_loss=6.08, over 4612.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:40:18,756 INFO [train.py:919] (5/6) Start epoch 880 +2024-03-15 18:40:52,054 INFO [train.py:527] (5/6) Epoch 880, batch 4, global_batch_idx: 109000, batch size: 53, loss[discriminator_loss=2.574, discriminator_real_loss=1.213, discriminator_fake_loss=1.36, generator_loss=31.48, generator_mel_loss=18.28, generator_kl_loss=1.651, generator_dur_loss=1.599, generator_adv_loss=2.22, generator_feat_match_loss=7.728, over 53.00 samples.], tot_loss[discriminator_loss=2.656, discriminator_real_loss=1.334, discriminator_fake_loss=1.322, generator_loss=29.91, generator_mel_loss=18.06, generator_kl_loss=1.505, generator_dur_loss=1.712, generator_adv_loss=2.056, generator_feat_match_loss=6.577, over 270.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:40:52,057 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 18:40:59,969 INFO [train.py:591] (5/6) 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 18:40:59,972 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 18:43:19,279 INFO [train.py:527] (5/6) Epoch 880, batch 54, global_batch_idx: 109050, batch size: 80, loss[discriminator_loss=2.662, discriminator_real_loss=1.4, discriminator_fake_loss=1.263, generator_loss=28.45, generator_mel_loss=17.56, generator_kl_loss=1.321, generator_dur_loss=1.799, generator_adv_loss=1.942, generator_feat_match_loss=5.824, over 80.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=29.14, generator_mel_loss=17.81, generator_kl_loss=1.45, generator_dur_loss=1.721, generator_adv_loss=2.007, generator_feat_match_loss=6.157, over 2882.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:45:38,340 INFO [train.py:527] (5/6) Epoch 880, batch 104, global_batch_idx: 109100, batch size: 68, loss[discriminator_loss=2.644, discriminator_real_loss=1.306, discriminator_fake_loss=1.338, generator_loss=29.14, generator_mel_loss=17.65, generator_kl_loss=1.423, generator_dur_loss=1.78, generator_adv_loss=1.991, generator_feat_match_loss=6.299, over 68.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.351, discriminator_fake_loss=1.318, generator_loss=29.17, generator_mel_loss=17.81, generator_kl_loss=1.432, generator_dur_loss=1.735, generator_adv_loss=2.009, generator_feat_match_loss=6.183, over 5932.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:46:32,336 INFO [train.py:919] (5/6) Start epoch 881 +2024-03-15 18:48:21,165 INFO [train.py:527] (5/6) Epoch 881, batch 30, global_batch_idx: 109150, batch size: 61, loss[discriminator_loss=2.728, discriminator_real_loss=1.336, discriminator_fake_loss=1.392, generator_loss=28.78, generator_mel_loss=17.96, generator_kl_loss=1.496, generator_dur_loss=1.748, generator_adv_loss=1.987, generator_feat_match_loss=5.594, over 61.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.359, discriminator_fake_loss=1.322, generator_loss=29.02, generator_mel_loss=17.77, generator_kl_loss=1.42, generator_dur_loss=1.706, generator_adv_loss=2.017, generator_feat_match_loss=6.105, over 1639.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:50:40,777 INFO [train.py:527] (5/6) Epoch 881, batch 80, global_batch_idx: 109200, batch size: 50, loss[discriminator_loss=2.643, discriminator_real_loss=1.324, discriminator_fake_loss=1.319, generator_loss=29.29, generator_mel_loss=18.29, generator_kl_loss=1.585, generator_dur_loss=1.695, generator_adv_loss=1.918, generator_feat_match_loss=5.798, over 50.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=29.03, generator_mel_loss=17.76, generator_kl_loss=1.423, generator_dur_loss=1.731, generator_adv_loss=2.008, generator_feat_match_loss=6.114, over 4601.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:50:40,779 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 18:50:49,494 INFO [train.py:591] (5/6) 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 18:50:49,495 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 18:52:50,198 INFO [train.py:919] (5/6) Start epoch 882 +2024-03-15 18:53:31,227 INFO [train.py:527] (5/6) Epoch 882, batch 6, global_batch_idx: 109250, batch size: 88, loss[discriminator_loss=2.718, discriminator_real_loss=1.31, discriminator_fake_loss=1.408, generator_loss=29.06, generator_mel_loss=18, generator_kl_loss=1.353, generator_dur_loss=1.846, generator_adv_loss=2.003, generator_feat_match_loss=5.863, over 88.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.333, discriminator_fake_loss=1.356, generator_loss=29.29, generator_mel_loss=17.96, generator_kl_loss=1.424, generator_dur_loss=1.783, generator_adv_loss=2.04, generator_feat_match_loss=6.092, over 496.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:55:50,502 INFO [train.py:527] (5/6) Epoch 882, batch 56, global_batch_idx: 109300, batch size: 72, loss[discriminator_loss=2.706, discriminator_real_loss=1.421, discriminator_fake_loss=1.286, generator_loss=28.59, generator_mel_loss=17.49, generator_kl_loss=1.217, generator_dur_loss=1.807, generator_adv_loss=2.026, generator_feat_match_loss=6.054, over 72.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.345, discriminator_fake_loss=1.324, generator_loss=29.08, generator_mel_loss=17.78, generator_kl_loss=1.454, generator_dur_loss=1.742, generator_adv_loss=2.022, generator_feat_match_loss=6.078, over 3227.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:58:09,372 INFO [train.py:527] (5/6) Epoch 882, batch 106, global_batch_idx: 109350, batch size: 36, loss[discriminator_loss=2.687, discriminator_real_loss=1.32, discriminator_fake_loss=1.367, generator_loss=29.89, generator_mel_loss=17.57, generator_kl_loss=1.52, generator_dur_loss=1.653, generator_adv_loss=2.071, generator_feat_match_loss=7.084, over 36.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.351, discriminator_fake_loss=1.32, generator_loss=29.12, generator_mel_loss=17.8, generator_kl_loss=1.447, generator_dur_loss=1.738, generator_adv_loss=2.022, generator_feat_match_loss=6.114, over 6036.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 18:58:57,777 INFO [train.py:919] (5/6) Start epoch 883 +2024-03-15 19:00:51,730 INFO [train.py:527] (5/6) Epoch 883, batch 32, global_batch_idx: 109400, batch size: 70, loss[discriminator_loss=2.689, discriminator_real_loss=1.318, discriminator_fake_loss=1.371, generator_loss=29.66, generator_mel_loss=18.06, generator_kl_loss=1.369, generator_dur_loss=1.774, generator_adv_loss=2.133, generator_feat_match_loss=6.327, over 70.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.35, discriminator_fake_loss=1.318, generator_loss=29.11, generator_mel_loss=17.72, generator_kl_loss=1.422, generator_dur_loss=1.727, generator_adv_loss=2.025, generator_feat_match_loss=6.216, over 1871.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:00:51,731 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 19:00:59,684 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 19:03:19,612 INFO [train.py:527] (5/6) Epoch 883, batch 82, global_batch_idx: 109450, batch size: 72, loss[discriminator_loss=2.659, discriminator_real_loss=1.265, discriminator_fake_loss=1.394, generator_loss=30.39, generator_mel_loss=18.14, generator_kl_loss=1.484, generator_dur_loss=1.732, generator_adv_loss=2.031, generator_feat_match_loss=6.998, over 72.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.348, discriminator_fake_loss=1.319, generator_loss=29.12, generator_mel_loss=17.79, generator_kl_loss=1.423, generator_dur_loss=1.729, generator_adv_loss=2.021, generator_feat_match_loss=6.155, over 4837.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:05:12,495 INFO [train.py:919] (5/6) Start epoch 884 +2024-03-15 19:05:58,847 INFO [train.py:527] (5/6) Epoch 884, batch 8, global_batch_idx: 109500, batch size: 80, loss[discriminator_loss=2.602, discriminator_real_loss=1.28, discriminator_fake_loss=1.321, generator_loss=29.05, generator_mel_loss=17.75, generator_kl_loss=1.357, generator_dur_loss=1.774, generator_adv_loss=2.078, generator_feat_match_loss=6.084, over 80.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.34, discriminator_fake_loss=1.324, generator_loss=29.14, generator_mel_loss=17.8, generator_kl_loss=1.447, generator_dur_loss=1.682, generator_adv_loss=2.003, generator_feat_match_loss=6.211, over 482.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:08:17,077 INFO [train.py:527] (5/6) Epoch 884, batch 58, global_batch_idx: 109550, batch size: 70, loss[discriminator_loss=2.724, discriminator_real_loss=1.42, discriminator_fake_loss=1.304, generator_loss=27.53, generator_mel_loss=17.42, generator_kl_loss=1.353, generator_dur_loss=1.769, generator_adv_loss=1.88, generator_feat_match_loss=5.105, over 70.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.346, discriminator_fake_loss=1.323, generator_loss=29.05, generator_mel_loss=17.74, generator_kl_loss=1.438, generator_dur_loss=1.729, generator_adv_loss=2.007, generator_feat_match_loss=6.134, over 3434.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:10:35,867 INFO [train.py:527] (5/6) Epoch 884, batch 108, global_batch_idx: 109600, batch size: 59, loss[discriminator_loss=2.631, discriminator_real_loss=1.3, discriminator_fake_loss=1.33, generator_loss=29.82, generator_mel_loss=18.06, generator_kl_loss=1.531, generator_dur_loss=1.677, generator_adv_loss=2.187, generator_feat_match_loss=6.369, over 59.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.1, generator_mel_loss=17.79, generator_kl_loss=1.438, generator_dur_loss=1.731, generator_adv_loss=2.013, generator_feat_match_loss=6.127, over 6301.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:10:35,869 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 19:10:44,693 INFO [train.py:591] (5/6) 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 19:10:44,695 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 19:11:27,722 INFO [train.py:919] (5/6) Start epoch 885 +2024-03-15 19:13:25,328 INFO [train.py:527] (5/6) Epoch 885, batch 34, global_batch_idx: 109650, batch size: 52, loss[discriminator_loss=2.727, discriminator_real_loss=1.44, discriminator_fake_loss=1.286, generator_loss=29.1, generator_mel_loss=17.6, generator_kl_loss=1.575, generator_dur_loss=1.666, generator_adv_loss=1.923, generator_feat_match_loss=6.334, over 52.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.36, discriminator_fake_loss=1.32, generator_loss=29.31, generator_mel_loss=17.81, generator_kl_loss=1.452, generator_dur_loss=1.721, generator_adv_loss=2.024, generator_feat_match_loss=6.294, over 1984.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:15:43,330 INFO [train.py:527] (5/6) Epoch 885, batch 84, global_batch_idx: 109700, batch size: 52, loss[discriminator_loss=2.704, discriminator_real_loss=1.351, discriminator_fake_loss=1.353, generator_loss=29.58, generator_mel_loss=18.11, generator_kl_loss=1.595, generator_dur_loss=1.735, generator_adv_loss=1.998, generator_feat_match_loss=6.14, over 52.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.36, discriminator_fake_loss=1.332, generator_loss=29.18, generator_mel_loss=17.81, generator_kl_loss=1.438, generator_dur_loss=1.737, generator_adv_loss=2.006, generator_feat_match_loss=6.187, over 4935.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:17:32,021 INFO [train.py:919] (5/6) Start epoch 886 +2024-03-15 19:18:22,969 INFO [train.py:527] (5/6) Epoch 886, batch 10, global_batch_idx: 109750, batch size: 83, loss[discriminator_loss=2.683, discriminator_real_loss=1.373, discriminator_fake_loss=1.309, generator_loss=29.42, generator_mel_loss=17.94, generator_kl_loss=1.278, generator_dur_loss=1.831, generator_adv_loss=1.93, generator_feat_match_loss=6.441, over 83.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.369, discriminator_fake_loss=1.314, generator_loss=29.23, generator_mel_loss=17.86, generator_kl_loss=1.401, generator_dur_loss=1.777, generator_adv_loss=2.004, generator_feat_match_loss=6.192, over 708.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:20:43,179 INFO [train.py:527] (5/6) Epoch 886, batch 60, global_batch_idx: 109800, batch size: 64, loss[discriminator_loss=2.653, discriminator_real_loss=1.393, discriminator_fake_loss=1.259, generator_loss=28.04, generator_mel_loss=17.45, generator_kl_loss=1.328, generator_dur_loss=1.753, generator_adv_loss=2.071, generator_feat_match_loss=5.445, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=29.14, generator_mel_loss=17.75, generator_kl_loss=1.41, generator_dur_loss=1.756, generator_adv_loss=2.007, generator_feat_match_loss=6.22, over 3725.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:20:43,181 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 19:20:51,344 INFO [train.py:591] (5/6) 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 19:20:51,345 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 19:23:09,717 INFO [train.py:527] (5/6) Epoch 886, batch 110, global_batch_idx: 109850, batch size: 66, loss[discriminator_loss=2.682, discriminator_real_loss=1.361, discriminator_fake_loss=1.32, generator_loss=29.14, generator_mel_loss=17.83, generator_kl_loss=1.298, generator_dur_loss=1.763, generator_adv_loss=2.044, generator_feat_match_loss=6.213, over 66.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=29.05, generator_mel_loss=17.74, generator_kl_loss=1.395, generator_dur_loss=1.76, generator_adv_loss=2.003, generator_feat_match_loss=6.157, over 6971.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:23:43,760 INFO [train.py:919] (5/6) Start epoch 887 +2024-03-15 19:25:52,524 INFO [train.py:527] (5/6) Epoch 887, batch 36, global_batch_idx: 109900, batch size: 13, loss[discriminator_loss=2.584, discriminator_real_loss=1.276, discriminator_fake_loss=1.308, generator_loss=32.53, generator_mel_loss=19.11, generator_kl_loss=1.738, generator_dur_loss=1.527, generator_adv_loss=2.209, generator_feat_match_loss=7.946, over 13.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=29, generator_mel_loss=17.72, generator_kl_loss=1.426, generator_dur_loss=1.745, generator_adv_loss=2.018, generator_feat_match_loss=6.091, over 2095.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:28:10,996 INFO [train.py:527] (5/6) Epoch 887, batch 86, global_batch_idx: 109950, batch size: 42, loss[discriminator_loss=2.642, discriminator_real_loss=1.32, discriminator_fake_loss=1.321, generator_loss=29.7, generator_mel_loss=17.89, generator_kl_loss=1.509, generator_dur_loss=1.613, generator_adv_loss=2.297, generator_feat_match_loss=6.396, over 42.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.319, generator_loss=29.14, generator_mel_loss=17.79, generator_kl_loss=1.433, generator_dur_loss=1.738, generator_adv_loss=2.017, generator_feat_match_loss=6.16, over 4790.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:29:51,016 INFO [train.py:919] (5/6) Start epoch 888 +2024-03-15 19:30:47,044 INFO [train.py:527] (5/6) Epoch 888, batch 12, global_batch_idx: 110000, batch size: 39, loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.11, generator_mel_loss=17.31, generator_kl_loss=1.421, generator_dur_loss=1.7, generator_adv_loss=1.941, generator_feat_match_loss=5.734, over 39.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.379, discriminator_fake_loss=1.31, generator_loss=28.58, generator_mel_loss=17.69, generator_kl_loss=1.429, generator_dur_loss=1.727, generator_adv_loss=2.013, generator_feat_match_loss=5.729, over 681.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:30:47,047 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 19:30:54,876 INFO [train.py:591] (5/6) 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 19:30:54,877 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 19:33:14,042 INFO [train.py:527] (5/6) Epoch 888, batch 62, global_batch_idx: 110050, batch size: 48, loss[discriminator_loss=2.653, discriminator_real_loss=1.306, discriminator_fake_loss=1.347, generator_loss=29.3, generator_mel_loss=17.86, generator_kl_loss=1.627, generator_dur_loss=1.639, generator_adv_loss=2.129, generator_feat_match_loss=6.041, over 48.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.353, discriminator_fake_loss=1.332, generator_loss=28.91, generator_mel_loss=17.74, generator_kl_loss=1.419, generator_dur_loss=1.739, generator_adv_loss=1.999, generator_feat_match_loss=6.007, over 3601.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:35:33,591 INFO [train.py:527] (5/6) Epoch 888, batch 112, global_batch_idx: 110100, batch size: 70, loss[discriminator_loss=2.691, discriminator_real_loss=1.336, discriminator_fake_loss=1.354, generator_loss=28.13, generator_mel_loss=17.49, generator_kl_loss=1.411, generator_dur_loss=1.768, generator_adv_loss=1.921, generator_feat_match_loss=5.542, over 70.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.98, generator_mel_loss=17.77, generator_kl_loss=1.431, generator_dur_loss=1.742, generator_adv_loss=1.996, generator_feat_match_loss=6.044, over 6661.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:36:05,109 INFO [train.py:919] (5/6) Start epoch 889 +2024-03-15 19:38:16,364 INFO [train.py:527] (5/6) Epoch 889, batch 38, global_batch_idx: 110150, batch size: 83, loss[discriminator_loss=2.617, discriminator_real_loss=1.259, discriminator_fake_loss=1.358, generator_loss=29.95, generator_mel_loss=18.08, generator_kl_loss=1.254, generator_dur_loss=1.82, generator_adv_loss=2.06, generator_feat_match_loss=6.734, over 83.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=29.12, generator_mel_loss=17.83, generator_kl_loss=1.41, generator_dur_loss=1.749, generator_adv_loss=1.998, generator_feat_match_loss=6.135, over 2406.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:40:33,954 INFO [train.py:527] (5/6) Epoch 889, batch 88, global_batch_idx: 110200, batch size: 52, loss[discriminator_loss=2.684, discriminator_real_loss=1.35, discriminator_fake_loss=1.334, generator_loss=29.12, generator_mel_loss=17.85, generator_kl_loss=1.454, generator_dur_loss=1.668, generator_adv_loss=2.067, generator_feat_match_loss=6.08, over 52.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.35, discriminator_fake_loss=1.331, generator_loss=29.18, generator_mel_loss=17.82, generator_kl_loss=1.434, generator_dur_loss=1.735, generator_adv_loss=2.008, generator_feat_match_loss=6.186, over 5119.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:40:33,955 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 19:40:42,834 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 19:42:21,809 INFO [train.py:919] (5/6) Start epoch 890 +2024-03-15 19:43:21,690 INFO [train.py:527] (5/6) Epoch 890, batch 14, global_batch_idx: 110250, batch size: 74, loss[discriminator_loss=2.674, discriminator_real_loss=1.308, discriminator_fake_loss=1.367, generator_loss=29.52, generator_mel_loss=17.99, generator_kl_loss=1.434, generator_dur_loss=1.81, generator_adv_loss=2.025, generator_feat_match_loss=6.262, over 74.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.342, discriminator_fake_loss=1.328, generator_loss=29.19, generator_mel_loss=17.87, generator_kl_loss=1.448, generator_dur_loss=1.734, generator_adv_loss=2.016, generator_feat_match_loss=6.128, over 868.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:45:41,048 INFO [train.py:527] (5/6) Epoch 890, batch 64, global_batch_idx: 110300, batch size: 74, loss[discriminator_loss=2.675, discriminator_real_loss=1.411, discriminator_fake_loss=1.265, generator_loss=28.81, generator_mel_loss=17.89, generator_kl_loss=1.303, generator_dur_loss=1.798, generator_adv_loss=1.986, generator_feat_match_loss=5.835, over 74.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.363, discriminator_fake_loss=1.315, generator_loss=29.06, generator_mel_loss=17.76, generator_kl_loss=1.458, generator_dur_loss=1.72, generator_adv_loss=2.016, generator_feat_match_loss=6.113, over 3558.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:48:00,216 INFO [train.py:527] (5/6) Epoch 890, batch 114, global_batch_idx: 110350, batch size: 68, loss[discriminator_loss=2.657, discriminator_real_loss=1.355, discriminator_fake_loss=1.302, generator_loss=28.67, generator_mel_loss=17.77, generator_kl_loss=1.286, generator_dur_loss=1.8, generator_adv_loss=2.148, generator_feat_match_loss=5.667, over 68.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.36, discriminator_fake_loss=1.318, generator_loss=29.1, generator_mel_loss=17.79, generator_kl_loss=1.446, generator_dur_loss=1.732, generator_adv_loss=2.013, generator_feat_match_loss=6.12, over 6522.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:48:25,345 INFO [train.py:919] (5/6) Start epoch 891 +2024-03-15 19:50:39,188 INFO [train.py:527] (5/6) Epoch 891, batch 40, global_batch_idx: 110400, batch size: 36, loss[discriminator_loss=2.709, discriminator_real_loss=1.359, discriminator_fake_loss=1.35, generator_loss=30.03, generator_mel_loss=17.89, generator_kl_loss=1.654, generator_dur_loss=1.676, generator_adv_loss=1.971, generator_feat_match_loss=6.834, over 36.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.346, discriminator_fake_loss=1.318, generator_loss=28.98, generator_mel_loss=17.74, generator_kl_loss=1.435, generator_dur_loss=1.744, generator_adv_loss=2.016, generator_feat_match_loss=6.048, over 2366.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:50:39,189 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 19:50:47,089 INFO [train.py:591] (5/6) 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 19:50:47,090 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 19:53:05,753 INFO [train.py:527] (5/6) Epoch 891, batch 90, global_batch_idx: 110450, batch size: 50, loss[discriminator_loss=2.65, discriminator_real_loss=1.337, discriminator_fake_loss=1.313, generator_loss=28.12, generator_mel_loss=17.46, generator_kl_loss=1.499, generator_dur_loss=1.645, generator_adv_loss=1.968, generator_feat_match_loss=5.543, over 50.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.348, discriminator_fake_loss=1.321, generator_loss=28.99, generator_mel_loss=17.7, generator_kl_loss=1.442, generator_dur_loss=1.744, generator_adv_loss=2.005, generator_feat_match_loss=6.094, over 5265.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:54:38,360 INFO [train.py:919] (5/6) Start epoch 892 +2024-03-15 19:55:47,516 INFO [train.py:527] (5/6) Epoch 892, batch 16, global_batch_idx: 110500, batch size: 96, loss[discriminator_loss=2.673, discriminator_real_loss=1.298, discriminator_fake_loss=1.375, generator_loss=28.36, generator_mel_loss=17.44, generator_kl_loss=1.282, generator_dur_loss=1.89, generator_adv_loss=2.036, generator_feat_match_loss=5.713, over 96.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.336, discriminator_fake_loss=1.325, generator_loss=29.37, generator_mel_loss=17.81, generator_kl_loss=1.477, generator_dur_loss=1.752, generator_adv_loss=2.016, generator_feat_match_loss=6.321, over 1041.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 19:58:05,735 INFO [train.py:527] (5/6) Epoch 892, batch 66, global_batch_idx: 110550, batch size: 64, loss[discriminator_loss=2.664, discriminator_real_loss=1.336, discriminator_fake_loss=1.328, generator_loss=29.11, generator_mel_loss=17.62, generator_kl_loss=1.425, generator_dur_loss=1.707, generator_adv_loss=2.103, generator_feat_match_loss=6.261, over 64.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=29.1, generator_mel_loss=17.73, generator_kl_loss=1.488, generator_dur_loss=1.736, generator_adv_loss=2.006, generator_feat_match_loss=6.144, over 3870.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:00:25,558 INFO [train.py:527] (5/6) Epoch 892, batch 116, global_batch_idx: 110600, batch size: 25, loss[discriminator_loss=2.607, discriminator_real_loss=1.337, discriminator_fake_loss=1.27, generator_loss=30.64, generator_mel_loss=18.66, generator_kl_loss=1.815, generator_dur_loss=1.528, generator_adv_loss=2.181, generator_feat_match_loss=6.456, over 25.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.353, discriminator_fake_loss=1.328, generator_loss=29.11, generator_mel_loss=17.75, generator_kl_loss=1.494, generator_dur_loss=1.727, generator_adv_loss=2.002, generator_feat_match_loss=6.137, over 6386.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:00:25,560 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 20:00:34,389 INFO [train.py:591] (5/6) 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 20:00:34,390 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 20:00:55,374 INFO [train.py:919] (5/6) Start epoch 893 +2024-03-15 20:03:17,308 INFO [train.py:527] (5/6) Epoch 893, batch 42, global_batch_idx: 110650, batch size: 56, loss[discriminator_loss=2.682, discriminator_real_loss=1.28, discriminator_fake_loss=1.402, generator_loss=29.18, generator_mel_loss=17.55, generator_kl_loss=1.428, generator_dur_loss=1.762, generator_adv_loss=2.195, generator_feat_match_loss=6.251, over 56.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=29.27, generator_mel_loss=17.83, generator_kl_loss=1.452, generator_dur_loss=1.735, generator_adv_loss=2.022, generator_feat_match_loss=6.235, over 2296.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:05:36,757 INFO [train.py:527] (5/6) Epoch 893, batch 92, global_batch_idx: 110700, batch size: 36, loss[discriminator_loss=2.737, discriminator_real_loss=1.383, discriminator_fake_loss=1.354, generator_loss=29.36, generator_mel_loss=17.67, generator_kl_loss=1.532, generator_dur_loss=1.631, generator_adv_loss=1.883, generator_feat_match_loss=6.643, over 36.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.357, discriminator_fake_loss=1.321, generator_loss=29.26, generator_mel_loss=17.82, generator_kl_loss=1.451, generator_dur_loss=1.738, generator_adv_loss=2.037, generator_feat_match_loss=6.211, over 4974.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:07:01,265 INFO [train.py:919] (5/6) Start epoch 894 +2024-03-15 20:08:14,950 INFO [train.py:527] (5/6) Epoch 894, batch 18, global_batch_idx: 110750, batch size: 48, loss[discriminator_loss=2.725, discriminator_real_loss=1.377, discriminator_fake_loss=1.348, generator_loss=29.93, generator_mel_loss=18.2, generator_kl_loss=1.575, generator_dur_loss=1.668, generator_adv_loss=1.947, generator_feat_match_loss=6.547, over 48.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.344, discriminator_fake_loss=1.326, generator_loss=29, generator_mel_loss=17.73, generator_kl_loss=1.402, generator_dur_loss=1.741, generator_adv_loss=1.988, generator_feat_match_loss=6.137, over 1134.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:10:34,362 INFO [train.py:527] (5/6) Epoch 894, batch 68, global_batch_idx: 110800, batch size: 39, loss[discriminator_loss=2.776, discriminator_real_loss=1.369, discriminator_fake_loss=1.407, generator_loss=28.23, generator_mel_loss=17.55, generator_kl_loss=1.552, generator_dur_loss=1.662, generator_adv_loss=1.789, generator_feat_match_loss=5.68, over 39.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.355, discriminator_fake_loss=1.325, generator_loss=28.92, generator_mel_loss=17.72, generator_kl_loss=1.454, generator_dur_loss=1.728, generator_adv_loss=1.989, generator_feat_match_loss=6.026, over 3747.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:10:34,364 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 20:10:42,208 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 20:12:59,096 INFO [train.py:527] (5/6) Epoch 894, batch 118, global_batch_idx: 110850, batch size: 55, loss[discriminator_loss=2.662, discriminator_real_loss=1.322, discriminator_fake_loss=1.341, generator_loss=28.83, generator_mel_loss=17.56, generator_kl_loss=1.734, generator_dur_loss=1.684, generator_adv_loss=2.032, generator_feat_match_loss=5.821, over 55.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.324, generator_loss=28.94, generator_mel_loss=17.73, generator_kl_loss=1.455, generator_dur_loss=1.729, generator_adv_loss=1.998, generator_feat_match_loss=6.031, over 6530.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:13:16,868 INFO [train.py:919] (5/6) Start epoch 895 +2024-03-15 20:15:43,948 INFO [train.py:527] (5/6) Epoch 895, batch 44, global_batch_idx: 110900, batch size: 72, loss[discriminator_loss=2.678, discriminator_real_loss=1.387, discriminator_fake_loss=1.291, generator_loss=30.02, generator_mel_loss=18.09, generator_kl_loss=1.456, generator_dur_loss=1.757, generator_adv_loss=2.14, generator_feat_match_loss=6.577, over 72.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.328, generator_loss=29.08, generator_mel_loss=17.74, generator_kl_loss=1.438, generator_dur_loss=1.733, generator_adv_loss=2.011, generator_feat_match_loss=6.154, over 2546.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:18:04,611 INFO [train.py:527] (5/6) Epoch 895, batch 94, global_batch_idx: 110950, batch size: 52, loss[discriminator_loss=2.616, discriminator_real_loss=1.277, discriminator_fake_loss=1.339, generator_loss=29.68, generator_mel_loss=17.75, generator_kl_loss=1.475, generator_dur_loss=1.774, generator_adv_loss=2.159, generator_feat_match_loss=6.513, over 52.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=29.14, generator_mel_loss=17.81, generator_kl_loss=1.435, generator_dur_loss=1.733, generator_adv_loss=2.006, generator_feat_match_loss=6.156, over 5146.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:19:26,742 INFO [train.py:919] (5/6) Start epoch 896 +2024-03-15 20:20:46,848 INFO [train.py:527] (5/6) Epoch 896, batch 20, global_batch_idx: 111000, batch size: 66, loss[discriminator_loss=2.666, discriminator_real_loss=1.254, discriminator_fake_loss=1.412, generator_loss=29.72, generator_mel_loss=18.06, generator_kl_loss=1.455, generator_dur_loss=1.778, generator_adv_loss=1.98, generator_feat_match_loss=6.451, over 66.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.354, discriminator_fake_loss=1.328, generator_loss=29.54, generator_mel_loss=17.98, generator_kl_loss=1.443, generator_dur_loss=1.715, generator_adv_loss=2.023, generator_feat_match_loss=6.381, over 1124.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:20:46,850 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 20:20:54,706 INFO [train.py:591] (5/6) 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 20:20:54,707 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 20:23:10,992 INFO [train.py:527] (5/6) Epoch 896, batch 70, global_batch_idx: 111050, batch size: 61, loss[discriminator_loss=2.726, discriminator_real_loss=1.361, discriminator_fake_loss=1.364, generator_loss=29.37, generator_mel_loss=18.32, generator_kl_loss=1.391, generator_dur_loss=1.736, generator_adv_loss=1.984, generator_feat_match_loss=5.94, over 61.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.359, discriminator_fake_loss=1.324, generator_loss=29.12, generator_mel_loss=17.83, generator_kl_loss=1.444, generator_dur_loss=1.732, generator_adv_loss=2.005, generator_feat_match_loss=6.107, over 3802.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:25:30,779 INFO [train.py:527] (5/6) Epoch 896, batch 120, global_batch_idx: 111100, batch size: 52, loss[discriminator_loss=2.634, discriminator_real_loss=1.4, discriminator_fake_loss=1.234, generator_loss=30.56, generator_mel_loss=18.11, generator_kl_loss=1.628, generator_dur_loss=1.657, generator_adv_loss=2.085, generator_feat_match_loss=7.081, over 52.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.357, discriminator_fake_loss=1.322, generator_loss=29.1, generator_mel_loss=17.79, generator_kl_loss=1.431, generator_dur_loss=1.745, generator_adv_loss=2.01, generator_feat_match_loss=6.125, over 6837.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:25:41,238 INFO [train.py:919] (5/6) Start epoch 897 +2024-03-15 20:28:12,132 INFO [train.py:527] (5/6) Epoch 897, batch 46, global_batch_idx: 111150, batch size: 61, loss[discriminator_loss=2.724, discriminator_real_loss=1.369, discriminator_fake_loss=1.355, generator_loss=27.72, generator_mel_loss=17.31, generator_kl_loss=1.589, generator_dur_loss=1.73, generator_adv_loss=1.908, generator_feat_match_loss=5.178, over 61.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.365, discriminator_fake_loss=1.322, generator_loss=29, generator_mel_loss=17.74, generator_kl_loss=1.422, generator_dur_loss=1.745, generator_adv_loss=2.017, generator_feat_match_loss=6.069, over 2685.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:30:31,898 INFO [train.py:527] (5/6) Epoch 897, batch 96, global_batch_idx: 111200, batch size: 83, loss[discriminator_loss=2.698, discriminator_real_loss=1.296, discriminator_fake_loss=1.402, generator_loss=29.13, generator_mel_loss=18.08, generator_kl_loss=1.465, generator_dur_loss=1.853, generator_adv_loss=2.132, generator_feat_match_loss=5.597, over 83.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=29.11, generator_mel_loss=17.78, generator_kl_loss=1.437, generator_dur_loss=1.743, generator_adv_loss=2.018, generator_feat_match_loss=6.136, over 5543.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:30:31,899 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 20:30:40,685 INFO [train.py:591] (5/6) 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 20:30:40,686 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 20:31:58,568 INFO [train.py:919] (5/6) Start epoch 898 +2024-03-15 20:33:24,442 INFO [train.py:527] (5/6) Epoch 898, batch 22, global_batch_idx: 111250, batch size: 52, loss[discriminator_loss=2.657, discriminator_real_loss=1.338, discriminator_fake_loss=1.318, generator_loss=30.3, generator_mel_loss=18.39, generator_kl_loss=1.512, generator_dur_loss=1.664, generator_adv_loss=2.185, generator_feat_match_loss=6.546, over 52.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=29.06, generator_mel_loss=17.78, generator_kl_loss=1.438, generator_dur_loss=1.732, generator_adv_loss=2.007, generator_feat_match_loss=6.096, over 1314.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:35:45,787 INFO [train.py:527] (5/6) Epoch 898, batch 72, global_batch_idx: 111300, batch size: 55, loss[discriminator_loss=2.68, discriminator_real_loss=1.373, discriminator_fake_loss=1.307, generator_loss=28.49, generator_mel_loss=17.54, generator_kl_loss=1.338, generator_dur_loss=1.717, generator_adv_loss=2.083, generator_feat_match_loss=5.814, over 55.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.354, discriminator_fake_loss=1.33, generator_loss=29.05, generator_mel_loss=17.72, generator_kl_loss=1.426, generator_dur_loss=1.746, generator_adv_loss=2.008, generator_feat_match_loss=6.149, over 4398.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:38:00,203 INFO [train.py:527] (5/6) Epoch 898, batch 122, global_batch_idx: 111350, batch size: 66, loss[discriminator_loss=2.69, discriminator_real_loss=1.444, discriminator_fake_loss=1.247, generator_loss=28.33, generator_mel_loss=17.29, generator_kl_loss=1.415, generator_dur_loss=1.773, generator_adv_loss=1.964, generator_feat_match_loss=5.888, over 66.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.356, discriminator_fake_loss=1.324, generator_loss=29.09, generator_mel_loss=17.72, generator_kl_loss=1.44, generator_dur_loss=1.738, generator_adv_loss=2.013, generator_feat_match_loss=6.175, over 7112.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:38:04,484 INFO [train.py:919] (5/6) Start epoch 899 +2024-03-15 20:40:44,569 INFO [train.py:527] (5/6) Epoch 899, batch 48, global_batch_idx: 111400, batch size: 50, loss[discriminator_loss=2.733, discriminator_real_loss=1.386, discriminator_fake_loss=1.346, generator_loss=28.66, generator_mel_loss=17.51, generator_kl_loss=1.561, generator_dur_loss=1.664, generator_adv_loss=2.031, generator_feat_match_loss=5.893, over 50.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=29.03, generator_mel_loss=17.75, generator_kl_loss=1.462, generator_dur_loss=1.736, generator_adv_loss=2.001, generator_feat_match_loss=6.079, over 2860.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:40:44,570 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 20:40:52,598 INFO [train.py:591] (5/6) 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 20:40:52,599 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 20:43:09,863 INFO [train.py:527] (5/6) Epoch 899, batch 98, global_batch_idx: 111450, batch size: 72, loss[discriminator_loss=2.696, discriminator_real_loss=1.326, discriminator_fake_loss=1.37, generator_loss=28.63, generator_mel_loss=17.63, generator_kl_loss=1.408, generator_dur_loss=1.752, generator_adv_loss=1.946, generator_feat_match_loss=5.893, over 72.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.36, discriminator_fake_loss=1.323, generator_loss=28.86, generator_mel_loss=17.71, generator_kl_loss=1.457, generator_dur_loss=1.738, generator_adv_loss=1.993, generator_feat_match_loss=5.969, over 5642.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:44:18,353 INFO [train.py:919] (5/6) Start epoch 900 +2024-03-15 20:45:47,529 INFO [train.py:527] (5/6) Epoch 900, batch 24, global_batch_idx: 111500, batch size: 74, loss[discriminator_loss=2.754, discriminator_real_loss=1.453, discriminator_fake_loss=1.3, generator_loss=28.55, generator_mel_loss=17.46, generator_kl_loss=1.399, generator_dur_loss=1.821, generator_adv_loss=1.956, generator_feat_match_loss=5.919, over 74.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.342, discriminator_fake_loss=1.328, generator_loss=29.16, generator_mel_loss=17.79, generator_kl_loss=1.486, generator_dur_loss=1.733, generator_adv_loss=2.002, generator_feat_match_loss=6.152, over 1412.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:48:02,743 INFO [train.py:527] (5/6) Epoch 900, batch 74, global_batch_idx: 111550, batch size: 50, loss[discriminator_loss=2.672, discriminator_real_loss=1.324, discriminator_fake_loss=1.348, generator_loss=29.34, generator_mel_loss=17.28, generator_kl_loss=1.392, generator_dur_loss=1.675, generator_adv_loss=2.084, generator_feat_match_loss=6.907, over 50.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.348, discriminator_fake_loss=1.329, generator_loss=29.21, generator_mel_loss=17.84, generator_kl_loss=1.443, generator_dur_loss=1.736, generator_adv_loss=2.008, generator_feat_match_loss=6.178, over 4391.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:50:22,837 INFO [train.py:919] (5/6) Start epoch 901 +2024-03-15 20:50:46,729 INFO [train.py:527] (5/6) Epoch 901, batch 0, global_batch_idx: 111600, batch size: 58, loss[discriminator_loss=2.675, discriminator_real_loss=1.341, discriminator_fake_loss=1.334, generator_loss=29.03, generator_mel_loss=18.14, generator_kl_loss=1.455, generator_dur_loss=1.732, generator_adv_loss=1.91, generator_feat_match_loss=5.793, over 58.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.341, discriminator_fake_loss=1.334, generator_loss=29.03, generator_mel_loss=18.14, generator_kl_loss=1.455, generator_dur_loss=1.732, generator_adv_loss=1.91, generator_feat_match_loss=5.793, over 58.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:50:46,732 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 20:50:54,867 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 20:53:15,352 INFO [train.py:527] (5/6) Epoch 901, batch 50, global_batch_idx: 111650, batch size: 55, loss[discriminator_loss=2.645, discriminator_real_loss=1.419, discriminator_fake_loss=1.226, generator_loss=27.19, generator_mel_loss=17.16, generator_kl_loss=1.45, generator_dur_loss=1.695, generator_adv_loss=1.846, generator_feat_match_loss=5.039, over 55.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.358, discriminator_fake_loss=1.318, generator_loss=29.02, generator_mel_loss=17.72, generator_kl_loss=1.481, generator_dur_loss=1.745, generator_adv_loss=2, generator_feat_match_loss=6.076, over 2832.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:55:32,926 INFO [train.py:527] (5/6) Epoch 901, batch 100, global_batch_idx: 111700, batch size: 74, loss[discriminator_loss=2.632, discriminator_real_loss=1.273, discriminator_fake_loss=1.359, generator_loss=29.04, generator_mel_loss=17.67, generator_kl_loss=1.442, generator_dur_loss=1.821, generator_adv_loss=2.125, generator_feat_match_loss=5.987, over 74.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.06, generator_mel_loss=17.72, generator_kl_loss=1.469, generator_dur_loss=1.747, generator_adv_loss=2.01, generator_feat_match_loss=6.116, over 5771.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 20:56:39,878 INFO [train.py:919] (5/6) Start epoch 902 +2024-03-15 20:58:14,284 INFO [train.py:527] (5/6) Epoch 902, batch 26, global_batch_idx: 111750, batch size: 88, loss[discriminator_loss=2.648, discriminator_real_loss=1.264, discriminator_fake_loss=1.385, generator_loss=30.73, generator_mel_loss=18.12, generator_kl_loss=1.419, generator_dur_loss=1.843, generator_adv_loss=2.04, generator_feat_match_loss=7.303, over 88.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.347, discriminator_fake_loss=1.325, generator_loss=29.2, generator_mel_loss=17.77, generator_kl_loss=1.429, generator_dur_loss=1.733, generator_adv_loss=2.022, generator_feat_match_loss=6.248, over 1612.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:00:32,591 INFO [train.py:527] (5/6) Epoch 902, batch 76, global_batch_idx: 111800, batch size: 31, loss[discriminator_loss=2.762, discriminator_real_loss=1.316, discriminator_fake_loss=1.446, generator_loss=28.12, generator_mel_loss=17.37, generator_kl_loss=1.334, generator_dur_loss=1.653, generator_adv_loss=2.076, generator_feat_match_loss=5.678, over 31.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.351, discriminator_fake_loss=1.326, generator_loss=29.15, generator_mel_loss=17.79, generator_kl_loss=1.446, generator_dur_loss=1.731, generator_adv_loss=2.012, generator_feat_match_loss=6.17, over 4347.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:00:32,592 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 21:00:41,437 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 21:02:53,435 INFO [train.py:919] (5/6) Start epoch 903 +2024-03-15 21:03:24,032 INFO [train.py:527] (5/6) Epoch 903, batch 2, global_batch_idx: 111850, batch size: 77, loss[discriminator_loss=2.724, discriminator_real_loss=1.316, discriminator_fake_loss=1.407, generator_loss=28.95, generator_mel_loss=17.66, generator_kl_loss=1.359, generator_dur_loss=1.795, generator_adv_loss=1.988, generator_feat_match_loss=6.143, over 77.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.354, discriminator_fake_loss=1.348, generator_loss=29.08, generator_mel_loss=17.86, generator_kl_loss=1.429, generator_dur_loss=1.758, generator_adv_loss=1.989, generator_feat_match_loss=6.048, over 199.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:05:40,708 INFO [train.py:527] (5/6) Epoch 903, batch 52, global_batch_idx: 111900, batch size: 59, loss[discriminator_loss=2.715, discriminator_real_loss=1.338, discriminator_fake_loss=1.377, generator_loss=27.39, generator_mel_loss=17.36, generator_kl_loss=1.444, generator_dur_loss=1.719, generator_adv_loss=1.949, generator_feat_match_loss=4.915, over 59.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=29.08, generator_mel_loss=17.78, generator_kl_loss=1.451, generator_dur_loss=1.735, generator_adv_loss=2.017, generator_feat_match_loss=6.097, over 2967.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:08:00,018 INFO [train.py:527] (5/6) Epoch 903, batch 102, global_batch_idx: 111950, batch size: 25, loss[discriminator_loss=2.757, discriminator_real_loss=1.526, discriminator_fake_loss=1.231, generator_loss=28.47, generator_mel_loss=18.02, generator_kl_loss=1.691, generator_dur_loss=1.54, generator_adv_loss=1.906, generator_feat_match_loss=5.308, over 25.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=29.04, generator_mel_loss=17.74, generator_kl_loss=1.46, generator_dur_loss=1.732, generator_adv_loss=2.011, generator_feat_match_loss=6.096, over 5672.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:08:59,334 INFO [train.py:919] (5/6) Start epoch 904 +2024-03-15 21:10:44,553 INFO [train.py:527] (5/6) Epoch 904, batch 28, global_batch_idx: 112000, batch size: 83, loss[discriminator_loss=2.611, discriminator_real_loss=1.309, discriminator_fake_loss=1.302, generator_loss=29.75, generator_mel_loss=17.76, generator_kl_loss=1.367, generator_dur_loss=1.812, generator_adv_loss=2.009, generator_feat_match_loss=6.796, over 83.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.347, discriminator_fake_loss=1.316, generator_loss=29.27, generator_mel_loss=17.82, generator_kl_loss=1.405, generator_dur_loss=1.743, generator_adv_loss=2.015, generator_feat_match_loss=6.285, over 1660.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:10:44,555 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 21:10:52,259 INFO [train.py:591] (5/6) 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 21:10:52,264 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 21:13:12,938 INFO [train.py:527] (5/6) Epoch 904, batch 78, global_batch_idx: 112050, batch size: 77, loss[discriminator_loss=2.677, discriminator_real_loss=1.371, discriminator_fake_loss=1.306, generator_loss=29.25, generator_mel_loss=17.89, generator_kl_loss=1.204, generator_dur_loss=1.856, generator_adv_loss=2.056, generator_feat_match_loss=6.237, over 77.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=29.26, generator_mel_loss=17.8, generator_kl_loss=1.424, generator_dur_loss=1.74, generator_adv_loss=2.02, generator_feat_match_loss=6.281, over 4660.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:15:17,278 INFO [train.py:919] (5/6) Start epoch 905 +2024-03-15 21:15:52,513 INFO [train.py:527] (5/6) Epoch 905, batch 4, global_batch_idx: 112100, batch size: 64, loss[discriminator_loss=2.703, discriminator_real_loss=1.305, discriminator_fake_loss=1.398, generator_loss=29.18, generator_mel_loss=17.75, generator_kl_loss=1.311, generator_dur_loss=1.751, generator_adv_loss=2.157, generator_feat_match_loss=6.206, over 64.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.357, discriminator_fake_loss=1.342, generator_loss=29.06, generator_mel_loss=17.73, generator_kl_loss=1.304, generator_dur_loss=1.786, generator_adv_loss=2.013, generator_feat_match_loss=6.223, over 345.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:18:11,891 INFO [train.py:527] (5/6) Epoch 905, batch 54, global_batch_idx: 112150, batch size: 52, loss[discriminator_loss=2.615, discriminator_real_loss=1.228, discriminator_fake_loss=1.386, generator_loss=29.67, generator_mel_loss=18.12, generator_kl_loss=1.5, generator_dur_loss=1.71, generator_adv_loss=2.012, generator_feat_match_loss=6.33, over 52.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.326, generator_loss=29.03, generator_mel_loss=17.71, generator_kl_loss=1.435, generator_dur_loss=1.733, generator_adv_loss=2.008, generator_feat_match_loss=6.144, over 3161.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:20:30,343 INFO [train.py:527] (5/6) Epoch 905, batch 104, global_batch_idx: 112200, batch size: 83, loss[discriminator_loss=2.691, discriminator_real_loss=1.264, discriminator_fake_loss=1.426, generator_loss=29.26, generator_mel_loss=17.47, generator_kl_loss=1.43, generator_dur_loss=1.788, generator_adv_loss=2.136, generator_feat_match_loss=6.436, over 83.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=29.16, generator_mel_loss=17.75, generator_kl_loss=1.444, generator_dur_loss=1.736, generator_adv_loss=2.017, generator_feat_match_loss=6.217, over 6080.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:20:30,345 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 21:20:39,139 INFO [train.py:591] (5/6) 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 21:20:39,139 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 21:21:31,778 INFO [train.py:919] (5/6) Start epoch 906 +2024-03-15 21:23:20,332 INFO [train.py:527] (5/6) Epoch 906, batch 30, global_batch_idx: 112250, batch size: 70, loss[discriminator_loss=2.691, discriminator_real_loss=1.378, discriminator_fake_loss=1.313, generator_loss=28.51, generator_mel_loss=17.64, generator_kl_loss=1.208, generator_dur_loss=1.833, generator_adv_loss=1.929, generator_feat_match_loss=5.895, over 70.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.346, discriminator_fake_loss=1.336, generator_loss=29.2, generator_mel_loss=17.8, generator_kl_loss=1.403, generator_dur_loss=1.745, generator_adv_loss=2.003, generator_feat_match_loss=6.24, over 1821.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:25:40,512 INFO [train.py:527] (5/6) Epoch 906, batch 80, global_batch_idx: 112300, batch size: 96, loss[discriminator_loss=2.633, discriminator_real_loss=1.345, discriminator_fake_loss=1.289, generator_loss=29.31, generator_mel_loss=17.53, generator_kl_loss=1.471, generator_dur_loss=1.836, generator_adv_loss=2.099, generator_feat_match_loss=6.378, over 96.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.351, discriminator_fake_loss=1.325, generator_loss=29.15, generator_mel_loss=17.77, generator_kl_loss=1.422, generator_dur_loss=1.749, generator_adv_loss=2.011, generator_feat_match_loss=6.196, over 4696.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:27:38,080 INFO [train.py:919] (5/6) Start epoch 907 +2024-03-15 21:28:22,173 INFO [train.py:527] (5/6) Epoch 907, batch 6, global_batch_idx: 112350, batch size: 70, loss[discriminator_loss=2.651, discriminator_real_loss=1.362, discriminator_fake_loss=1.289, generator_loss=29.07, generator_mel_loss=17.39, generator_kl_loss=1.293, generator_dur_loss=1.779, generator_adv_loss=2.126, generator_feat_match_loss=6.486, over 70.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.348, discriminator_fake_loss=1.321, generator_loss=29.29, generator_mel_loss=17.73, generator_kl_loss=1.479, generator_dur_loss=1.713, generator_adv_loss=2.068, generator_feat_match_loss=6.299, over 395.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:30:38,151 INFO [train.py:527] (5/6) Epoch 907, batch 56, global_batch_idx: 112400, batch size: 42, loss[discriminator_loss=2.707, discriminator_real_loss=1.408, discriminator_fake_loss=1.299, generator_loss=30.09, generator_mel_loss=18.31, generator_kl_loss=1.704, generator_dur_loss=1.66, generator_adv_loss=2.125, generator_feat_match_loss=6.292, over 42.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.348, discriminator_fake_loss=1.325, generator_loss=29.08, generator_mel_loss=17.74, generator_kl_loss=1.472, generator_dur_loss=1.723, generator_adv_loss=2.006, generator_feat_match_loss=6.135, over 3268.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:30:38,152 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 21:30:45,988 INFO [train.py:591] (5/6) 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 21:30:45,989 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 21:33:05,443 INFO [train.py:527] (5/6) Epoch 907, batch 106, global_batch_idx: 112450, batch size: 53, loss[discriminator_loss=2.671, discriminator_real_loss=1.343, discriminator_fake_loss=1.328, generator_loss=30.06, generator_mel_loss=17.97, generator_kl_loss=1.655, generator_dur_loss=1.674, generator_adv_loss=2.031, generator_feat_match_loss=6.732, over 53.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.32, generator_loss=29.02, generator_mel_loss=17.72, generator_kl_loss=1.462, generator_dur_loss=1.722, generator_adv_loss=2.012, generator_feat_match_loss=6.106, over 6052.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:33:56,334 INFO [train.py:919] (5/6) Start epoch 908 +2024-03-15 21:35:50,124 INFO [train.py:527] (5/6) Epoch 908, batch 32, global_batch_idx: 112500, batch size: 31, loss[discriminator_loss=2.712, discriminator_real_loss=1.344, discriminator_fake_loss=1.368, generator_loss=29.06, generator_mel_loss=18.29, generator_kl_loss=1.521, generator_dur_loss=1.607, generator_adv_loss=2.038, generator_feat_match_loss=5.607, over 31.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=29.25, generator_mel_loss=17.83, generator_kl_loss=1.425, generator_dur_loss=1.738, generator_adv_loss=2.011, generator_feat_match_loss=6.244, over 1929.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:38:12,198 INFO [train.py:527] (5/6) Epoch 908, batch 82, global_batch_idx: 112550, batch size: 61, loss[discriminator_loss=2.687, discriminator_real_loss=1.364, discriminator_fake_loss=1.323, generator_loss=29.32, generator_mel_loss=17.84, generator_kl_loss=1.366, generator_dur_loss=1.751, generator_adv_loss=2.066, generator_feat_match_loss=6.298, over 61.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.35, discriminator_fake_loss=1.325, generator_loss=29.1, generator_mel_loss=17.8, generator_kl_loss=1.403, generator_dur_loss=1.748, generator_adv_loss=2.008, generator_feat_match_loss=6.15, over 5211.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:40:05,856 INFO [train.py:919] (5/6) Start epoch 909 +2024-03-15 21:40:51,661 INFO [train.py:527] (5/6) Epoch 909, batch 8, global_batch_idx: 112600, batch size: 74, loss[discriminator_loss=2.64, discriminator_real_loss=1.311, discriminator_fake_loss=1.329, generator_loss=29.08, generator_mel_loss=17.93, generator_kl_loss=1.417, generator_dur_loss=1.793, generator_adv_loss=1.964, generator_feat_match_loss=5.984, over 74.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.35, discriminator_fake_loss=1.317, generator_loss=29.18, generator_mel_loss=17.88, generator_kl_loss=1.481, generator_dur_loss=1.716, generator_adv_loss=2.016, generator_feat_match_loss=6.089, over 482.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:40:51,664 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 21:40:59,652 INFO [train.py:591] (5/6) 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 21:40:59,654 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 21:43:17,129 INFO [train.py:527] (5/6) Epoch 909, batch 58, global_batch_idx: 112650, batch size: 59, loss[discriminator_loss=2.67, discriminator_real_loss=1.29, discriminator_fake_loss=1.38, generator_loss=29.44, generator_mel_loss=17.76, generator_kl_loss=1.397, generator_dur_loss=1.739, generator_adv_loss=2.048, generator_feat_match_loss=6.49, over 59.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.346, discriminator_fake_loss=1.324, generator_loss=29.13, generator_mel_loss=17.83, generator_kl_loss=1.44, generator_dur_loss=1.735, generator_adv_loss=2.012, generator_feat_match_loss=6.115, over 3336.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:45:36,728 INFO [train.py:527] (5/6) Epoch 909, batch 108, global_batch_idx: 112700, batch size: 96, loss[discriminator_loss=2.67, discriminator_real_loss=1.299, discriminator_fake_loss=1.371, generator_loss=29.2, generator_mel_loss=17.8, generator_kl_loss=1.293, generator_dur_loss=1.894, generator_adv_loss=2.077, generator_feat_match_loss=6.136, over 96.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.347, discriminator_fake_loss=1.322, generator_loss=29.13, generator_mel_loss=17.77, generator_kl_loss=1.42, generator_dur_loss=1.742, generator_adv_loss=2.023, generator_feat_match_loss=6.181, over 6417.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:46:19,211 INFO [train.py:919] (5/6) Start epoch 910 +2024-03-15 21:48:18,736 INFO [train.py:527] (5/6) Epoch 910, batch 34, global_batch_idx: 112750, batch size: 50, loss[discriminator_loss=2.707, discriminator_real_loss=1.347, discriminator_fake_loss=1.36, generator_loss=29.44, generator_mel_loss=17.68, generator_kl_loss=1.471, generator_dur_loss=1.691, generator_adv_loss=2.14, generator_feat_match_loss=6.459, over 50.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.364, discriminator_fake_loss=1.325, generator_loss=28.91, generator_mel_loss=17.71, generator_kl_loss=1.442, generator_dur_loss=1.725, generator_adv_loss=2.008, generator_feat_match_loss=6.024, over 1969.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:50:36,712 INFO [train.py:527] (5/6) Epoch 910, batch 84, global_batch_idx: 112800, batch size: 74, loss[discriminator_loss=2.594, discriminator_real_loss=1.359, discriminator_fake_loss=1.236, generator_loss=29.08, generator_mel_loss=17.6, generator_kl_loss=1.353, generator_dur_loss=1.794, generator_adv_loss=1.978, generator_feat_match_loss=6.349, over 74.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.359, discriminator_fake_loss=1.319, generator_loss=29.04, generator_mel_loss=17.76, generator_kl_loss=1.422, generator_dur_loss=1.735, generator_adv_loss=2.008, generator_feat_match_loss=6.11, over 4840.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, +2024-03-15 21:50:36,713 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 21:50:45,551 INFO [train.py:591] (5/6) 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 21:50:45,551 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 21:52:33,605 INFO [train.py:919] (5/6) Start epoch 911 +2024-03-15 21:53:21,780 INFO [train.py:527] (5/6) Epoch 911, batch 10, global_batch_idx: 112850, batch size: 83, loss[discriminator_loss=2.649, discriminator_real_loss=1.354, discriminator_fake_loss=1.295, generator_loss=29.77, generator_mel_loss=17.82, generator_kl_loss=1.434, generator_dur_loss=1.781, generator_adv_loss=2.102, generator_feat_match_loss=6.63, over 83.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.362, discriminator_fake_loss=1.324, generator_loss=29.28, generator_mel_loss=17.87, generator_kl_loss=1.47, generator_dur_loss=1.735, generator_adv_loss=2.008, generator_feat_match_loss=6.192, over 676.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 21:55:41,963 INFO [train.py:527] (5/6) Epoch 911, batch 60, global_batch_idx: 112900, batch size: 48, loss[discriminator_loss=2.626, discriminator_real_loss=1.359, discriminator_fake_loss=1.267, generator_loss=28.53, generator_mel_loss=17.05, generator_kl_loss=1.497, generator_dur_loss=1.681, generator_adv_loss=2.088, generator_feat_match_loss=6.218, over 48.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=29.15, generator_mel_loss=17.77, generator_kl_loss=1.449, generator_dur_loss=1.727, generator_adv_loss=2.009, generator_feat_match_loss=6.191, over 3413.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 21:58:01,561 INFO [train.py:527] (5/6) Epoch 911, batch 110, global_batch_idx: 112950, batch size: 70, loss[discriminator_loss=2.659, discriminator_real_loss=1.402, discriminator_fake_loss=1.258, generator_loss=29, generator_mel_loss=17.82, generator_kl_loss=1.265, generator_dur_loss=1.764, generator_adv_loss=1.901, generator_feat_match_loss=6.243, over 70.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.2, generator_mel_loss=17.79, generator_kl_loss=1.445, generator_dur_loss=1.735, generator_adv_loss=2.013, generator_feat_match_loss=6.223, over 6288.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 21:58:37,933 INFO [train.py:919] (5/6) Start epoch 912 +2024-03-15 22:00:41,160 INFO [train.py:527] (5/6) Epoch 912, batch 36, global_batch_idx: 113000, batch size: 59, loss[discriminator_loss=2.654, discriminator_real_loss=1.365, discriminator_fake_loss=1.29, generator_loss=28.65, generator_mel_loss=17.64, generator_kl_loss=1.543, generator_dur_loss=1.746, generator_adv_loss=1.914, generator_feat_match_loss=5.813, over 59.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.356, discriminator_fake_loss=1.315, generator_loss=29.19, generator_mel_loss=17.7, generator_kl_loss=1.471, generator_dur_loss=1.75, generator_adv_loss=2.022, generator_feat_match_loss=6.241, over 2174.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:00:41,162 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 22:00:49,098 INFO [train.py:591] (5/6) 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 22:00:49,099 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 22:03:06,640 INFO [train.py:527] (5/6) Epoch 912, batch 86, global_batch_idx: 113050, batch size: 62, loss[discriminator_loss=2.736, discriminator_real_loss=1.382, discriminator_fake_loss=1.354, generator_loss=27.8, generator_mel_loss=17.27, generator_kl_loss=1.438, generator_dur_loss=1.72, generator_adv_loss=1.955, generator_feat_match_loss=5.42, over 62.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.352, discriminator_fake_loss=1.317, generator_loss=29.12, generator_mel_loss=17.67, generator_kl_loss=1.463, generator_dur_loss=1.742, generator_adv_loss=2.015, generator_feat_match_loss=6.233, over 4999.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:04:50,367 INFO [train.py:919] (5/6) Start epoch 913 +2024-03-15 22:05:47,812 INFO [train.py:527] (5/6) Epoch 913, batch 12, global_batch_idx: 113100, batch size: 80, loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=28.41, generator_mel_loss=17.79, generator_kl_loss=1.393, generator_dur_loss=1.802, generator_adv_loss=1.98, generator_feat_match_loss=5.437, over 80.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.335, discriminator_fake_loss=1.342, generator_loss=28.84, generator_mel_loss=17.58, generator_kl_loss=1.34, generator_dur_loss=1.787, generator_adv_loss=2.011, generator_feat_match_loss=6.115, over 956.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:08:07,178 INFO [train.py:527] (5/6) Epoch 913, batch 62, global_batch_idx: 113150, batch size: 80, loss[discriminator_loss=2.65, discriminator_real_loss=1.377, discriminator_fake_loss=1.273, generator_loss=28.56, generator_mel_loss=17.71, generator_kl_loss=1.369, generator_dur_loss=1.774, generator_adv_loss=2.142, generator_feat_match_loss=5.568, over 80.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.357, discriminator_fake_loss=1.316, generator_loss=29.12, generator_mel_loss=17.7, generator_kl_loss=1.419, generator_dur_loss=1.745, generator_adv_loss=2.048, generator_feat_match_loss=6.206, over 3790.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:10:23,147 INFO [train.py:527] (5/6) Epoch 913, batch 112, global_batch_idx: 113200, batch size: 59, loss[discriminator_loss=2.698, discriminator_real_loss=1.334, discriminator_fake_loss=1.363, generator_loss=29.19, generator_mel_loss=17.46, generator_kl_loss=1.605, generator_dur_loss=1.717, generator_adv_loss=2.009, generator_feat_match_loss=6.394, over 59.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.355, discriminator_fake_loss=1.318, generator_loss=29.09, generator_mel_loss=17.7, generator_kl_loss=1.433, generator_dur_loss=1.738, generator_adv_loss=2.029, generator_feat_match_loss=6.184, over 6569.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:10:23,149 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 22:10:32,025 INFO [train.py:591] (5/6) 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 22:10:32,026 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 22:11:03,052 INFO [train.py:919] (5/6) Start epoch 914 +2024-03-15 22:13:12,225 INFO [train.py:527] (5/6) Epoch 914, batch 38, global_batch_idx: 113250, batch size: 74, loss[discriminator_loss=2.673, discriminator_real_loss=1.311, discriminator_fake_loss=1.362, generator_loss=28.49, generator_mel_loss=17.98, generator_kl_loss=1.303, generator_dur_loss=1.836, generator_adv_loss=1.954, generator_feat_match_loss=5.414, over 74.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.351, discriminator_fake_loss=1.327, generator_loss=29.21, generator_mel_loss=17.84, generator_kl_loss=1.473, generator_dur_loss=1.739, generator_adv_loss=1.997, generator_feat_match_loss=6.157, over 2169.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:15:32,703 INFO [train.py:527] (5/6) Epoch 914, batch 88, global_batch_idx: 113300, batch size: 83, loss[discriminator_loss=2.637, discriminator_real_loss=1.333, discriminator_fake_loss=1.303, generator_loss=28.52, generator_mel_loss=17.41, generator_kl_loss=1.32, generator_dur_loss=1.776, generator_adv_loss=2.156, generator_feat_match_loss=5.857, over 83.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.352, discriminator_fake_loss=1.322, generator_loss=29.17, generator_mel_loss=17.76, generator_kl_loss=1.457, generator_dur_loss=1.745, generator_adv_loss=2.004, generator_feat_match_loss=6.198, over 5198.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:17:09,755 INFO [train.py:919] (5/6) Start epoch 915 +2024-03-15 22:18:12,915 INFO [train.py:527] (5/6) Epoch 915, batch 14, global_batch_idx: 113350, batch size: 72, loss[discriminator_loss=2.723, discriminator_real_loss=1.382, discriminator_fake_loss=1.341, generator_loss=28.21, generator_mel_loss=17.69, generator_kl_loss=1.36, generator_dur_loss=1.773, generator_adv_loss=2.1, generator_feat_match_loss=5.281, over 72.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.363, discriminator_fake_loss=1.317, generator_loss=28.79, generator_mel_loss=17.61, generator_kl_loss=1.394, generator_dur_loss=1.771, generator_adv_loss=1.994, generator_feat_match_loss=6.015, over 987.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:20:30,718 INFO [train.py:527] (5/6) Epoch 915, batch 64, global_batch_idx: 113400, batch size: 58, loss[discriminator_loss=2.69, discriminator_real_loss=1.376, discriminator_fake_loss=1.315, generator_loss=28.4, generator_mel_loss=17.29, generator_kl_loss=1.501, generator_dur_loss=1.73, generator_adv_loss=1.961, generator_feat_match_loss=5.915, over 58.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.354, discriminator_fake_loss=1.318, generator_loss=29.03, generator_mel_loss=17.66, generator_kl_loss=1.476, generator_dur_loss=1.745, generator_adv_loss=1.999, generator_feat_match_loss=6.145, over 3842.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:20:30,720 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 22:20:38,829 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 22:22:58,977 INFO [train.py:527] (5/6) Epoch 915, batch 114, global_batch_idx: 113450, batch size: 96, loss[discriminator_loss=2.698, discriminator_real_loss=1.384, discriminator_fake_loss=1.314, generator_loss=28.76, generator_mel_loss=17.54, generator_kl_loss=1.299, generator_dur_loss=1.806, generator_adv_loss=2.085, generator_feat_match_loss=6.032, over 96.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.35, discriminator_fake_loss=1.319, generator_loss=29.16, generator_mel_loss=17.7, generator_kl_loss=1.479, generator_dur_loss=1.743, generator_adv_loss=2.009, generator_feat_match_loss=6.231, over 6712.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:23:24,307 INFO [train.py:919] (5/6) Start epoch 916 +2024-03-15 22:25:38,667 INFO [train.py:527] (5/6) Epoch 916, batch 40, global_batch_idx: 113500, batch size: 66, loss[discriminator_loss=2.675, discriminator_real_loss=1.327, discriminator_fake_loss=1.347, generator_loss=28.33, generator_mel_loss=17.65, generator_kl_loss=1.504, generator_dur_loss=1.724, generator_adv_loss=1.982, generator_feat_match_loss=5.463, over 66.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.351, discriminator_fake_loss=1.321, generator_loss=29.16, generator_mel_loss=17.81, generator_kl_loss=1.473, generator_dur_loss=1.739, generator_adv_loss=2.003, generator_feat_match_loss=6.138, over 2388.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:27:58,744 INFO [train.py:527] (5/6) Epoch 916, batch 90, global_batch_idx: 113550, batch size: 60, loss[discriminator_loss=2.622, discriminator_real_loss=1.252, discriminator_fake_loss=1.37, generator_loss=29.5, generator_mel_loss=17.69, generator_kl_loss=1.502, generator_dur_loss=1.761, generator_adv_loss=1.95, generator_feat_match_loss=6.597, over 60.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.35, discriminator_fake_loss=1.327, generator_loss=29.1, generator_mel_loss=17.76, generator_kl_loss=1.46, generator_dur_loss=1.746, generator_adv_loss=2, generator_feat_match_loss=6.135, over 5377.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:29:32,288 INFO [train.py:919] (5/6) Start epoch 917 +2024-03-15 22:30:40,796 INFO [train.py:527] (5/6) Epoch 917, batch 16, global_batch_idx: 113600, batch size: 83, loss[discriminator_loss=2.684, discriminator_real_loss=1.349, discriminator_fake_loss=1.335, generator_loss=29.13, generator_mel_loss=18, generator_kl_loss=1.474, generator_dur_loss=1.843, generator_adv_loss=1.866, generator_feat_match_loss=5.951, over 83.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.342, discriminator_fake_loss=1.327, generator_loss=29.26, generator_mel_loss=17.79, generator_kl_loss=1.461, generator_dur_loss=1.728, generator_adv_loss=2.003, generator_feat_match_loss=6.276, over 911.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:30:40,797 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 22:30:48,825 INFO [train.py:591] (5/6) 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 22:30:48,826 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 22:33:08,167 INFO [train.py:527] (5/6) Epoch 917, batch 66, global_batch_idx: 113650, batch size: 66, loss[discriminator_loss=2.676, discriminator_real_loss=1.411, discriminator_fake_loss=1.265, generator_loss=27.85, generator_mel_loss=17.52, generator_kl_loss=1.442, generator_dur_loss=1.783, generator_adv_loss=1.967, generator_feat_match_loss=5.135, over 66.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.356, discriminator_fake_loss=1.323, generator_loss=29.18, generator_mel_loss=17.75, generator_kl_loss=1.438, generator_dur_loss=1.743, generator_adv_loss=2.011, generator_feat_match_loss=6.236, over 3860.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:35:24,451 INFO [train.py:527] (5/6) Epoch 917, batch 116, global_batch_idx: 113700, batch size: 72, loss[discriminator_loss=2.614, discriminator_real_loss=1.258, discriminator_fake_loss=1.356, generator_loss=28.67, generator_mel_loss=17.36, generator_kl_loss=1.309, generator_dur_loss=1.794, generator_adv_loss=2.027, generator_feat_match_loss=6.174, over 72.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.352, discriminator_fake_loss=1.322, generator_loss=29.15, generator_mel_loss=17.74, generator_kl_loss=1.446, generator_dur_loss=1.736, generator_adv_loss=2.013, generator_feat_match_loss=6.21, over 6483.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:35:45,739 INFO [train.py:919] (5/6) Start epoch 918 +2024-03-15 22:38:06,137 INFO [train.py:527] (5/6) Epoch 918, batch 42, global_batch_idx: 113750, batch size: 15, loss[discriminator_loss=2.595, discriminator_real_loss=1.353, discriminator_fake_loss=1.241, generator_loss=29.19, generator_mel_loss=17.83, generator_kl_loss=1.918, generator_dur_loss=1.551, generator_adv_loss=1.953, generator_feat_match_loss=5.933, over 15.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.344, discriminator_fake_loss=1.322, generator_loss=29.29, generator_mel_loss=17.79, generator_kl_loss=1.5, generator_dur_loss=1.714, generator_adv_loss=2.006, generator_feat_match_loss=6.274, over 2260.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:40:23,950 INFO [train.py:527] (5/6) Epoch 918, batch 92, global_batch_idx: 113800, batch size: 39, loss[discriminator_loss=2.634, discriminator_real_loss=1.322, discriminator_fake_loss=1.313, generator_loss=29.55, generator_mel_loss=17.7, generator_kl_loss=1.507, generator_dur_loss=1.623, generator_adv_loss=1.988, generator_feat_match_loss=6.733, over 39.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.346, discriminator_fake_loss=1.318, generator_loss=29.32, generator_mel_loss=17.76, generator_kl_loss=1.473, generator_dur_loss=1.731, generator_adv_loss=2.012, generator_feat_match_loss=6.34, over 5117.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:40:23,952 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 22:40:32,680 INFO [train.py:591] (5/6) 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 22:40:32,681 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 22:42:01,378 INFO [train.py:919] (5/6) Start epoch 919 +2024-03-15 22:43:15,194 INFO [train.py:527] (5/6) Epoch 919, batch 18, global_batch_idx: 113850, batch size: 53, loss[discriminator_loss=2.668, discriminator_real_loss=1.322, discriminator_fake_loss=1.346, generator_loss=30, generator_mel_loss=18.17, generator_kl_loss=1.569, generator_dur_loss=1.653, generator_adv_loss=2.185, generator_feat_match_loss=6.427, over 53.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.358, discriminator_fake_loss=1.336, generator_loss=28.87, generator_mel_loss=17.72, generator_kl_loss=1.427, generator_dur_loss=1.74, generator_adv_loss=2.036, generator_feat_match_loss=5.943, over 1124.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:45:35,800 INFO [train.py:527] (5/6) Epoch 919, batch 68, global_batch_idx: 113900, batch size: 47, loss[discriminator_loss=2.634, discriminator_real_loss=1.318, discriminator_fake_loss=1.317, generator_loss=30.1, generator_mel_loss=17.93, generator_kl_loss=1.488, generator_dur_loss=1.644, generator_adv_loss=2.094, generator_feat_match_loss=6.946, over 47.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=29.01, generator_mel_loss=17.67, generator_kl_loss=1.443, generator_dur_loss=1.75, generator_adv_loss=2.01, generator_feat_match_loss=6.137, over 4109.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:47:55,610 INFO [train.py:527] (5/6) Epoch 919, batch 118, global_batch_idx: 113950, batch size: 17, loss[discriminator_loss=2.697, discriminator_real_loss=1.334, discriminator_fake_loss=1.363, generator_loss=30.05, generator_mel_loss=18.26, generator_kl_loss=1.774, generator_dur_loss=1.554, generator_adv_loss=2.068, generator_feat_match_loss=6.391, over 17.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.357, discriminator_fake_loss=1.328, generator_loss=29.05, generator_mel_loss=17.69, generator_kl_loss=1.449, generator_dur_loss=1.746, generator_adv_loss=2.009, generator_feat_match_loss=6.156, over 6884.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:48:11,228 INFO [train.py:919] (5/6) Start epoch 920 +2024-03-15 22:50:39,311 INFO [train.py:527] (5/6) Epoch 920, batch 44, global_batch_idx: 114000, batch size: 36, loss[discriminator_loss=2.668, discriminator_real_loss=1.357, discriminator_fake_loss=1.311, generator_loss=28.54, generator_mel_loss=17.78, generator_kl_loss=1.575, generator_dur_loss=1.695, generator_adv_loss=2.011, generator_feat_match_loss=5.48, over 36.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.356, discriminator_fake_loss=1.329, generator_loss=29.22, generator_mel_loss=17.79, generator_kl_loss=1.461, generator_dur_loss=1.722, generator_adv_loss=2.009, generator_feat_match_loss=6.23, over 2403.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:50:39,313 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 22:50:47,287 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 22:53:07,373 INFO [train.py:527] (5/6) Epoch 920, batch 94, global_batch_idx: 114050, batch size: 59, loss[discriminator_loss=2.67, discriminator_real_loss=1.391, discriminator_fake_loss=1.279, generator_loss=29.09, generator_mel_loss=17.39, generator_kl_loss=1.512, generator_dur_loss=1.735, generator_adv_loss=2.011, generator_feat_match_loss=6.44, over 59.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=29.13, generator_mel_loss=17.73, generator_kl_loss=1.453, generator_dur_loss=1.743, generator_adv_loss=2.01, generator_feat_match_loss=6.189, over 5403.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:54:27,338 INFO [train.py:919] (5/6) Start epoch 921 +2024-03-15 22:55:45,846 INFO [train.py:527] (5/6) Epoch 921, batch 20, global_batch_idx: 114100, batch size: 68, loss[discriminator_loss=2.731, discriminator_real_loss=1.367, discriminator_fake_loss=1.364, generator_loss=29.23, generator_mel_loss=18, generator_kl_loss=1.315, generator_dur_loss=1.763, generator_adv_loss=2.064, generator_feat_match_loss=6.091, over 68.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.357, discriminator_fake_loss=1.33, generator_loss=29.25, generator_mel_loss=17.85, generator_kl_loss=1.421, generator_dur_loss=1.729, generator_adv_loss=2.002, generator_feat_match_loss=6.25, over 1132.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 22:58:02,014 INFO [train.py:527] (5/6) Epoch 921, batch 70, global_batch_idx: 114150, batch size: 39, loss[discriminator_loss=2.714, discriminator_real_loss=1.311, discriminator_fake_loss=1.403, generator_loss=28.93, generator_mel_loss=17.53, generator_kl_loss=1.508, generator_dur_loss=1.659, generator_adv_loss=2.004, generator_feat_match_loss=6.228, over 39.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.361, discriminator_fake_loss=1.326, generator_loss=29.16, generator_mel_loss=17.81, generator_kl_loss=1.416, generator_dur_loss=1.738, generator_adv_loss=1.999, generator_feat_match_loss=6.197, over 4074.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:00:22,574 INFO [train.py:527] (5/6) Epoch 921, batch 120, global_batch_idx: 114200, batch size: 52, loss[discriminator_loss=2.674, discriminator_real_loss=1.367, discriminator_fake_loss=1.307, generator_loss=29.5, generator_mel_loss=18.08, generator_kl_loss=1.578, generator_dur_loss=1.683, generator_adv_loss=2.057, generator_feat_match_loss=6.103, over 52.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=29.15, generator_mel_loss=17.81, generator_kl_loss=1.41, generator_dur_loss=1.744, generator_adv_loss=2.017, generator_feat_match_loss=6.164, over 6959.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:00:22,575 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 23:00:31,611 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 23:00:40,830 INFO [train.py:919] (5/6) Start epoch 922 +2024-03-15 23:03:11,613 INFO [train.py:527] (5/6) Epoch 922, batch 46, global_batch_idx: 114250, batch size: 83, loss[discriminator_loss=2.703, discriminator_real_loss=1.351, discriminator_fake_loss=1.352, generator_loss=28.78, generator_mel_loss=17.58, generator_kl_loss=1.375, generator_dur_loss=1.85, generator_adv_loss=1.949, generator_feat_match_loss=6.02, over 83.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.353, discriminator_fake_loss=1.32, generator_loss=29.08, generator_mel_loss=17.75, generator_kl_loss=1.45, generator_dur_loss=1.739, generator_adv_loss=2.001, generator_feat_match_loss=6.142, over 2654.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:05:29,973 INFO [train.py:527] (5/6) Epoch 922, batch 96, global_batch_idx: 114300, batch size: 39, loss[discriminator_loss=2.71, discriminator_real_loss=1.282, discriminator_fake_loss=1.427, generator_loss=30.33, generator_mel_loss=18.35, generator_kl_loss=1.59, generator_dur_loss=1.723, generator_adv_loss=2.088, generator_feat_match_loss=6.577, over 39.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.356, discriminator_fake_loss=1.322, generator_loss=29.09, generator_mel_loss=17.77, generator_kl_loss=1.438, generator_dur_loss=1.743, generator_adv_loss=2.002, generator_feat_match_loss=6.134, over 5664.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:06:47,745 INFO [train.py:919] (5/6) Start epoch 923 +2024-03-15 23:08:16,241 INFO [train.py:527] (5/6) Epoch 923, batch 22, global_batch_idx: 114350, batch size: 61, loss[discriminator_loss=2.69, discriminator_real_loss=1.341, discriminator_fake_loss=1.35, generator_loss=29.13, generator_mel_loss=17.88, generator_kl_loss=1.447, generator_dur_loss=1.71, generator_adv_loss=2.01, generator_feat_match_loss=6.081, over 61.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.359, discriminator_fake_loss=1.331, generator_loss=28.9, generator_mel_loss=17.76, generator_kl_loss=1.394, generator_dur_loss=1.755, generator_adv_loss=2.005, generator_feat_match_loss=5.988, over 1512.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:10:38,422 INFO [train.py:527] (5/6) Epoch 923, batch 72, global_batch_idx: 114400, batch size: 44, loss[discriminator_loss=2.704, discriminator_real_loss=1.323, discriminator_fake_loss=1.381, generator_loss=29.36, generator_mel_loss=17.61, generator_kl_loss=1.583, generator_dur_loss=1.689, generator_adv_loss=2.027, generator_feat_match_loss=6.446, over 44.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.12, generator_mel_loss=17.78, generator_kl_loss=1.406, generator_dur_loss=1.74, generator_adv_loss=2.019, generator_feat_match_loss=6.184, over 4428.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:10:38,423 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 23:10:47,329 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 23:13:02,724 INFO [train.py:527] (5/6) Epoch 923, batch 122, global_batch_idx: 114450, batch size: 64, loss[discriminator_loss=2.633, discriminator_real_loss=1.397, discriminator_fake_loss=1.236, generator_loss=28.64, generator_mel_loss=17.89, generator_kl_loss=1.273, generator_dur_loss=1.742, generator_adv_loss=1.846, generator_feat_match_loss=5.897, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.32, generator_loss=29.13, generator_mel_loss=17.78, generator_kl_loss=1.421, generator_dur_loss=1.729, generator_adv_loss=2.012, generator_feat_match_loss=6.187, over 7098.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:13:08,024 INFO [train.py:919] (5/6) Start epoch 924 +2024-03-15 23:15:46,467 INFO [train.py:527] (5/6) Epoch 924, batch 48, global_batch_idx: 114500, batch size: 68, loss[discriminator_loss=2.691, discriminator_real_loss=1.374, discriminator_fake_loss=1.317, generator_loss=27.84, generator_mel_loss=17.51, generator_kl_loss=1.238, generator_dur_loss=1.758, generator_adv_loss=2.027, generator_feat_match_loss=5.308, over 68.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.357, discriminator_fake_loss=1.31, generator_loss=29.1, generator_mel_loss=17.74, generator_kl_loss=1.435, generator_dur_loss=1.733, generator_adv_loss=2.015, generator_feat_match_loss=6.175, over 2930.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:18:06,619 INFO [train.py:527] (5/6) Epoch 924, batch 98, global_batch_idx: 114550, batch size: 70, loss[discriminator_loss=2.671, discriminator_real_loss=1.284, discriminator_fake_loss=1.387, generator_loss=29.48, generator_mel_loss=17.88, generator_kl_loss=1.262, generator_dur_loss=1.804, generator_adv_loss=2.022, generator_feat_match_loss=6.518, over 70.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.353, discriminator_fake_loss=1.319, generator_loss=29.15, generator_mel_loss=17.77, generator_kl_loss=1.445, generator_dur_loss=1.723, generator_adv_loss=2.012, generator_feat_match_loss=6.196, over 5769.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:19:16,044 INFO [train.py:919] (5/6) Start epoch 925 +2024-03-15 23:20:43,874 INFO [train.py:527] (5/6) Epoch 925, batch 24, global_batch_idx: 114600, batch size: 50, loss[discriminator_loss=2.743, discriminator_real_loss=1.377, discriminator_fake_loss=1.367, generator_loss=28.03, generator_mel_loss=17.68, generator_kl_loss=1.427, generator_dur_loss=1.693, generator_adv_loss=1.836, generator_feat_match_loss=5.401, over 50.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.354, discriminator_fake_loss=1.326, generator_loss=28.78, generator_mel_loss=17.56, generator_kl_loss=1.434, generator_dur_loss=1.737, generator_adv_loss=2.016, generator_feat_match_loss=6.026, over 1517.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:20:43,876 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 23:20:52,331 INFO [train.py:591] (5/6) 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 23:20:52,333 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 23:23:12,823 INFO [train.py:527] (5/6) Epoch 925, batch 74, global_batch_idx: 114650, batch size: 42, loss[discriminator_loss=2.689, discriminator_real_loss=1.331, discriminator_fake_loss=1.359, generator_loss=28.28, generator_mel_loss=17.66, generator_kl_loss=1.473, generator_dur_loss=1.657, generator_adv_loss=2.028, generator_feat_match_loss=5.465, over 42.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=29.08, generator_mel_loss=17.67, generator_kl_loss=1.449, generator_dur_loss=1.725, generator_adv_loss=2.009, generator_feat_match_loss=6.227, over 4276.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:25:30,281 INFO [train.py:919] (5/6) Start epoch 926 +2024-03-15 23:25:55,290 INFO [train.py:527] (5/6) Epoch 926, batch 0, global_batch_idx: 114700, batch size: 77, loss[discriminator_loss=2.694, discriminator_real_loss=1.422, discriminator_fake_loss=1.272, generator_loss=27.31, generator_mel_loss=17.32, generator_kl_loss=1.29, generator_dur_loss=1.828, generator_adv_loss=1.898, generator_feat_match_loss=4.977, over 77.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.422, discriminator_fake_loss=1.272, generator_loss=27.31, generator_mel_loss=17.32, generator_kl_loss=1.29, generator_dur_loss=1.828, generator_adv_loss=1.898, generator_feat_match_loss=4.977, over 77.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:28:12,628 INFO [train.py:527] (5/6) Epoch 926, batch 50, global_batch_idx: 114750, batch size: 62, loss[discriminator_loss=2.653, discriminator_real_loss=1.361, discriminator_fake_loss=1.292, generator_loss=29.84, generator_mel_loss=17.71, generator_kl_loss=1.333, generator_dur_loss=1.714, generator_adv_loss=2.022, generator_feat_match_loss=7.065, over 62.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.349, discriminator_fake_loss=1.312, generator_loss=29.16, generator_mel_loss=17.74, generator_kl_loss=1.44, generator_dur_loss=1.727, generator_adv_loss=2.035, generator_feat_match_loss=6.222, over 2788.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:30:32,407 INFO [train.py:527] (5/6) Epoch 926, batch 100, global_batch_idx: 114800, batch size: 56, loss[discriminator_loss=2.708, discriminator_real_loss=1.342, discriminator_fake_loss=1.367, generator_loss=29.11, generator_mel_loss=17.72, generator_kl_loss=1.407, generator_dur_loss=1.709, generator_adv_loss=1.958, generator_feat_match_loss=6.314, over 56.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.13, generator_mel_loss=17.74, generator_kl_loss=1.441, generator_dur_loss=1.721, generator_adv_loss=2.018, generator_feat_match_loss=6.209, over 5507.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:30:32,409 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 23:30:41,174 INFO [train.py:591] (5/6) 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 23:30:41,175 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 23:31:47,241 INFO [train.py:919] (5/6) Start epoch 927 +2024-03-15 23:33:20,830 INFO [train.py:527] (5/6) Epoch 927, batch 26, global_batch_idx: 114850, batch size: 16, loss[discriminator_loss=2.688, discriminator_real_loss=1.286, discriminator_fake_loss=1.402, generator_loss=30.59, generator_mel_loss=18.14, generator_kl_loss=1.957, generator_dur_loss=1.522, generator_adv_loss=2.112, generator_feat_match_loss=6.857, over 16.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.349, discriminator_fake_loss=1.34, generator_loss=29.09, generator_mel_loss=17.78, generator_kl_loss=1.44, generator_dur_loss=1.735, generator_adv_loss=2.006, generator_feat_match_loss=6.127, over 1520.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:35:40,892 INFO [train.py:527] (5/6) Epoch 927, batch 76, global_batch_idx: 114900, batch size: 56, loss[discriminator_loss=2.714, discriminator_real_loss=1.34, discriminator_fake_loss=1.374, generator_loss=28.44, generator_mel_loss=17.62, generator_kl_loss=1.373, generator_dur_loss=1.688, generator_adv_loss=2.002, generator_feat_match_loss=5.756, over 56.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=29.12, generator_mel_loss=17.74, generator_kl_loss=1.44, generator_dur_loss=1.742, generator_adv_loss=2.009, generator_feat_match_loss=6.189, over 4417.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:37:52,578 INFO [train.py:919] (5/6) Start epoch 928 +2024-03-15 23:38:21,862 INFO [train.py:527] (5/6) Epoch 928, batch 2, global_batch_idx: 114950, batch size: 36, loss[discriminator_loss=2.748, discriminator_real_loss=1.398, discriminator_fake_loss=1.35, generator_loss=29.62, generator_mel_loss=18.15, generator_kl_loss=1.494, generator_dur_loss=1.679, generator_adv_loss=1.886, generator_feat_match_loss=6.407, over 36.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=29.64, generator_mel_loss=18.05, generator_kl_loss=1.518, generator_dur_loss=1.673, generator_adv_loss=1.957, generator_feat_match_loss=6.437, over 103.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:40:44,044 INFO [train.py:527] (5/6) Epoch 928, batch 52, global_batch_idx: 115000, batch size: 36, loss[discriminator_loss=2.705, discriminator_real_loss=1.398, discriminator_fake_loss=1.307, generator_loss=29.36, generator_mel_loss=17.95, generator_kl_loss=1.642, generator_dur_loss=1.683, generator_adv_loss=1.947, generator_feat_match_loss=6.133, over 36.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.32, generator_loss=29.13, generator_mel_loss=17.69, generator_kl_loss=1.423, generator_dur_loss=1.734, generator_adv_loss=2.024, generator_feat_match_loss=6.261, over 2903.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:40:44,045 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 23:40:52,364 INFO [train.py:591] (5/6) 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 23:40:52,365 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 23:43:11,573 INFO [train.py:527] (5/6) Epoch 928, batch 102, global_batch_idx: 115050, batch size: 45, loss[discriminator_loss=2.647, discriminator_real_loss=1.371, discriminator_fake_loss=1.276, generator_loss=28.91, generator_mel_loss=17.57, generator_kl_loss=1.418, generator_dur_loss=1.689, generator_adv_loss=1.995, generator_feat_match_loss=6.239, over 45.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.357, discriminator_fake_loss=1.321, generator_loss=29.16, generator_mel_loss=17.72, generator_kl_loss=1.428, generator_dur_loss=1.745, generator_adv_loss=2.037, generator_feat_match_loss=6.225, over 5830.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:44:09,456 INFO [train.py:919] (5/6) Start epoch 929 +2024-03-15 23:45:49,398 INFO [train.py:527] (5/6) Epoch 929, batch 28, global_batch_idx: 115100, batch size: 47, loss[discriminator_loss=2.688, discriminator_real_loss=1.328, discriminator_fake_loss=1.361, generator_loss=29.37, generator_mel_loss=17.57, generator_kl_loss=1.682, generator_dur_loss=1.675, generator_adv_loss=2.127, generator_feat_match_loss=6.316, over 47.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.35, discriminator_fake_loss=1.32, generator_loss=29.14, generator_mel_loss=17.74, generator_kl_loss=1.444, generator_dur_loss=1.737, generator_adv_loss=2.012, generator_feat_match_loss=6.203, over 1640.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:48:09,891 INFO [train.py:527] (5/6) Epoch 929, batch 78, global_batch_idx: 115150, batch size: 55, loss[discriminator_loss=2.662, discriminator_real_loss=1.376, discriminator_fake_loss=1.286, generator_loss=28.74, generator_mel_loss=17.77, generator_kl_loss=1.37, generator_dur_loss=1.67, generator_adv_loss=2.021, generator_feat_match_loss=5.916, over 55.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.358, discriminator_fake_loss=1.316, generator_loss=28.96, generator_mel_loss=17.73, generator_kl_loss=1.434, generator_dur_loss=1.724, generator_adv_loss=2.002, generator_feat_match_loss=6.075, over 4343.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:50:17,470 INFO [train.py:919] (5/6) Start epoch 930 +2024-03-15 23:50:51,635 INFO [train.py:527] (5/6) Epoch 930, batch 4, global_batch_idx: 115200, batch size: 83, loss[discriminator_loss=2.675, discriminator_real_loss=1.404, discriminator_fake_loss=1.271, generator_loss=29.21, generator_mel_loss=18, generator_kl_loss=1.313, generator_dur_loss=1.814, generator_adv_loss=1.939, generator_feat_match_loss=6.151, over 83.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.352, discriminator_fake_loss=1.339, generator_loss=29.21, generator_mel_loss=17.76, generator_kl_loss=1.438, generator_dur_loss=1.741, generator_adv_loss=2.026, generator_feat_match_loss=6.251, over 295.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:50:51,638 INFO [train.py:581] (5/6) Computing validation loss +2024-03-15 23:50:59,572 INFO [train.py:591] (5/6) 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 23:50:59,572 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-15 23:53:20,137 INFO [train.py:527] (5/6) Epoch 930, batch 54, global_batch_idx: 115250, batch size: 72, loss[discriminator_loss=2.721, discriminator_real_loss=1.319, discriminator_fake_loss=1.403, generator_loss=28.86, generator_mel_loss=17.75, generator_kl_loss=1.428, generator_dur_loss=1.783, generator_adv_loss=1.939, generator_feat_match_loss=5.955, over 72.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.357, discriminator_fake_loss=1.312, generator_loss=29.21, generator_mel_loss=17.73, generator_kl_loss=1.475, generator_dur_loss=1.733, generator_adv_loss=2.052, generator_feat_match_loss=6.213, over 2943.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:55:39,473 INFO [train.py:527] (5/6) Epoch 930, batch 104, global_batch_idx: 115300, batch size: 48, loss[discriminator_loss=2.65, discriminator_real_loss=1.327, discriminator_fake_loss=1.323, generator_loss=29.89, generator_mel_loss=18, generator_kl_loss=1.684, generator_dur_loss=1.692, generator_adv_loss=2.101, generator_feat_match_loss=6.41, over 48.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.359, discriminator_fake_loss=1.314, generator_loss=29.09, generator_mel_loss=17.7, generator_kl_loss=1.46, generator_dur_loss=1.738, generator_adv_loss=2.029, generator_feat_match_loss=6.168, over 5870.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-15 23:56:31,372 INFO [train.py:919] (5/6) Start epoch 931 +2024-03-15 23:58:20,367 INFO [train.py:527] (5/6) Epoch 931, batch 30, global_batch_idx: 115350, batch size: 36, loss[discriminator_loss=2.683, discriminator_real_loss=1.386, discriminator_fake_loss=1.297, generator_loss=29.93, generator_mel_loss=17.61, generator_kl_loss=1.57, generator_dur_loss=1.674, generator_adv_loss=2.104, generator_feat_match_loss=6.975, over 36.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.353, discriminator_fake_loss=1.309, generator_loss=29.02, generator_mel_loss=17.69, generator_kl_loss=1.426, generator_dur_loss=1.732, generator_adv_loss=1.996, generator_feat_match_loss=6.168, over 1675.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:00:41,313 INFO [train.py:527] (5/6) Epoch 931, batch 80, global_batch_idx: 115400, batch size: 70, loss[discriminator_loss=2.715, discriminator_real_loss=1.42, discriminator_fake_loss=1.296, generator_loss=27.79, generator_mel_loss=16.89, generator_kl_loss=1.435, generator_dur_loss=1.714, generator_adv_loss=1.969, generator_feat_match_loss=5.777, over 70.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.36, discriminator_fake_loss=1.316, generator_loss=29.05, generator_mel_loss=17.69, generator_kl_loss=1.45, generator_dur_loss=1.726, generator_adv_loss=2.004, generator_feat_match_loss=6.188, over 4507.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:00:41,316 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 00:00:50,266 INFO [train.py:591] (5/6) 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 00:00:50,268 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 00:02:49,520 INFO [train.py:919] (5/6) Start epoch 932 +2024-03-16 00:03:30,019 INFO [train.py:527] (5/6) Epoch 932, batch 6, global_batch_idx: 115450, batch size: 55, loss[discriminator_loss=2.711, discriminator_real_loss=1.362, discriminator_fake_loss=1.349, generator_loss=27.68, generator_mel_loss=17.43, generator_kl_loss=1.338, generator_dur_loss=1.729, generator_adv_loss=1.879, generator_feat_match_loss=5.303, over 55.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.337, discriminator_fake_loss=1.33, generator_loss=29.16, generator_mel_loss=17.65, generator_kl_loss=1.41, generator_dur_loss=1.755, generator_adv_loss=2.007, generator_feat_match_loss=6.34, over 437.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:05:49,187 INFO [train.py:527] (5/6) Epoch 932, batch 56, global_batch_idx: 115500, batch size: 48, loss[discriminator_loss=2.672, discriminator_real_loss=1.383, discriminator_fake_loss=1.289, generator_loss=29.37, generator_mel_loss=17.93, generator_kl_loss=1.523, generator_dur_loss=1.69, generator_adv_loss=1.99, generator_feat_match_loss=6.236, over 48.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.321, generator_loss=29.13, generator_mel_loss=17.69, generator_kl_loss=1.466, generator_dur_loss=1.729, generator_adv_loss=2.011, generator_feat_match_loss=6.241, over 3035.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:08:05,239 INFO [train.py:527] (5/6) Epoch 932, batch 106, global_batch_idx: 115550, batch size: 42, loss[discriminator_loss=2.63, discriminator_real_loss=1.336, discriminator_fake_loss=1.293, generator_loss=30.97, generator_mel_loss=18.4, generator_kl_loss=1.707, generator_dur_loss=1.727, generator_adv_loss=2.02, generator_feat_match_loss=7.118, over 42.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.323, generator_loss=29.15, generator_mel_loss=17.72, generator_kl_loss=1.46, generator_dur_loss=1.733, generator_adv_loss=2.006, generator_feat_match_loss=6.229, over 5892.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:08:54,843 INFO [train.py:919] (5/6) Start epoch 933 +2024-03-16 00:10:46,918 INFO [train.py:527] (5/6) Epoch 933, batch 32, global_batch_idx: 115600, batch size: 52, loss[discriminator_loss=2.64, discriminator_real_loss=1.326, discriminator_fake_loss=1.314, generator_loss=28.74, generator_mel_loss=17.65, generator_kl_loss=1.519, generator_dur_loss=1.725, generator_adv_loss=2.001, generator_feat_match_loss=5.851, over 52.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.1, generator_mel_loss=17.73, generator_kl_loss=1.48, generator_dur_loss=1.711, generator_adv_loss=2.004, generator_feat_match_loss=6.172, over 1793.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:10:46,920 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 00:10:55,125 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 00:13:11,340 INFO [train.py:527] (5/6) Epoch 933, batch 82, global_batch_idx: 115650, batch size: 16, loss[discriminator_loss=2.738, discriminator_real_loss=1.499, discriminator_fake_loss=1.239, generator_loss=31.1, generator_mel_loss=18.27, generator_kl_loss=1.925, generator_dur_loss=1.562, generator_adv_loss=2.014, generator_feat_match_loss=7.335, over 16.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.353, discriminator_fake_loss=1.323, generator_loss=29.17, generator_mel_loss=17.73, generator_kl_loss=1.47, generator_dur_loss=1.724, generator_adv_loss=2.013, generator_feat_match_loss=6.235, over 4586.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:15:07,480 INFO [train.py:919] (5/6) Start epoch 934 +2024-03-16 00:15:53,484 INFO [train.py:527] (5/6) Epoch 934, batch 8, global_batch_idx: 115700, batch size: 25, loss[discriminator_loss=2.608, discriminator_real_loss=1.342, discriminator_fake_loss=1.266, generator_loss=30.12, generator_mel_loss=18.24, generator_kl_loss=1.872, generator_dur_loss=1.599, generator_adv_loss=2.242, generator_feat_match_loss=6.161, over 25.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.42, discriminator_fake_loss=1.296, generator_loss=29.55, generator_mel_loss=17.77, generator_kl_loss=1.447, generator_dur_loss=1.744, generator_adv_loss=2.113, generator_feat_match_loss=6.477, over 474.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:18:14,910 INFO [train.py:527] (5/6) Epoch 934, batch 58, global_batch_idx: 115750, batch size: 31, loss[discriminator_loss=2.585, discriminator_real_loss=1.273, discriminator_fake_loss=1.312, generator_loss=30.71, generator_mel_loss=18.11, generator_kl_loss=1.728, generator_dur_loss=1.599, generator_adv_loss=2.106, generator_feat_match_loss=7.167, over 31.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.362, discriminator_fake_loss=1.307, generator_loss=29.11, generator_mel_loss=17.72, generator_kl_loss=1.432, generator_dur_loss=1.743, generator_adv_loss=2.033, generator_feat_match_loss=6.18, over 3382.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:20:34,092 INFO [train.py:527] (5/6) Epoch 934, batch 108, global_batch_idx: 115800, batch size: 88, loss[discriminator_loss=2.682, discriminator_real_loss=1.309, discriminator_fake_loss=1.373, generator_loss=29.7, generator_mel_loss=17.83, generator_kl_loss=1.355, generator_dur_loss=1.801, generator_adv_loss=2.093, generator_feat_match_loss=6.619, over 88.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.364, discriminator_fake_loss=1.314, generator_loss=29.08, generator_mel_loss=17.72, generator_kl_loss=1.437, generator_dur_loss=1.744, generator_adv_loss=2.021, generator_feat_match_loss=6.166, over 6350.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:20:34,094 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 00:20:42,782 INFO [train.py:591] (5/6) 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 00:20:42,783 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 00:21:20,870 INFO [train.py:919] (5/6) Start epoch 935 +2024-03-16 00:23:20,363 INFO [train.py:527] (5/6) Epoch 935, batch 34, global_batch_idx: 115850, batch size: 50, loss[discriminator_loss=2.66, discriminator_real_loss=1.377, discriminator_fake_loss=1.283, generator_loss=29.07, generator_mel_loss=17.92, generator_kl_loss=1.698, generator_dur_loss=1.641, generator_adv_loss=2.007, generator_feat_match_loss=5.804, over 50.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.353, discriminator_fake_loss=1.319, generator_loss=29.13, generator_mel_loss=17.74, generator_kl_loss=1.462, generator_dur_loss=1.733, generator_adv_loss=2.012, generator_feat_match_loss=6.181, over 1939.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:25:38,968 INFO [train.py:527] (5/6) Epoch 935, batch 84, global_batch_idx: 115900, batch size: 47, loss[discriminator_loss=2.628, discriminator_real_loss=1.264, discriminator_fake_loss=1.364, generator_loss=30.61, generator_mel_loss=18.16, generator_kl_loss=1.517, generator_dur_loss=1.687, generator_adv_loss=2.146, generator_feat_match_loss=7.099, over 47.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.35, discriminator_fake_loss=1.327, generator_loss=29.2, generator_mel_loss=17.76, generator_kl_loss=1.451, generator_dur_loss=1.739, generator_adv_loss=2.034, generator_feat_match_loss=6.221, over 4790.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:27:27,632 INFO [train.py:919] (5/6) Start epoch 936 +2024-03-16 00:28:19,995 INFO [train.py:527] (5/6) Epoch 936, batch 10, global_batch_idx: 115950, batch size: 53, loss[discriminator_loss=2.738, discriminator_real_loss=1.4, discriminator_fake_loss=1.338, generator_loss=29.5, generator_mel_loss=17.83, generator_kl_loss=1.578, generator_dur_loss=1.697, generator_adv_loss=2.053, generator_feat_match_loss=6.341, over 53.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.371, discriminator_fake_loss=1.341, generator_loss=29.22, generator_mel_loss=17.93, generator_kl_loss=1.403, generator_dur_loss=1.77, generator_adv_loss=1.999, generator_feat_match_loss=6.123, over 712.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:30:39,192 INFO [train.py:527] (5/6) Epoch 936, batch 60, global_batch_idx: 116000, batch size: 48, loss[discriminator_loss=2.77, discriminator_real_loss=1.467, discriminator_fake_loss=1.303, generator_loss=28.66, generator_mel_loss=17.78, generator_kl_loss=1.571, generator_dur_loss=1.72, generator_adv_loss=1.91, generator_feat_match_loss=5.681, over 48.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.358, discriminator_fake_loss=1.332, generator_loss=29.14, generator_mel_loss=17.72, generator_kl_loss=1.418, generator_dur_loss=1.749, generator_adv_loss=2.007, generator_feat_match_loss=6.245, over 3605.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:30:39,194 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 00:30:47,171 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 00:33:04,964 INFO [train.py:527] (5/6) Epoch 936, batch 110, global_batch_idx: 116050, batch size: 15, loss[discriminator_loss=2.577, discriminator_real_loss=1.352, discriminator_fake_loss=1.224, generator_loss=30.53, generator_mel_loss=18.07, generator_kl_loss=1.869, generator_dur_loss=1.597, generator_adv_loss=2.139, generator_feat_match_loss=6.86, over 15.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=29.11, generator_mel_loss=17.7, generator_kl_loss=1.427, generator_dur_loss=1.745, generator_adv_loss=2.006, generator_feat_match_loss=6.23, over 6462.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:33:40,157 INFO [train.py:919] (5/6) Start epoch 937 +2024-03-16 00:35:43,925 INFO [train.py:527] (5/6) Epoch 937, batch 36, global_batch_idx: 116100, batch size: 31, loss[discriminator_loss=2.581, discriminator_real_loss=1.293, discriminator_fake_loss=1.288, generator_loss=30.36, generator_mel_loss=18.23, generator_kl_loss=1.803, generator_dur_loss=1.622, generator_adv_loss=1.986, generator_feat_match_loss=6.713, over 31.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.341, discriminator_fake_loss=1.323, generator_loss=29.17, generator_mel_loss=17.75, generator_kl_loss=1.478, generator_dur_loss=1.741, generator_adv_loss=2.004, generator_feat_match_loss=6.2, over 2081.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:38:02,925 INFO [train.py:527] (5/6) Epoch 937, batch 86, global_batch_idx: 116150, batch size: 45, loss[discriminator_loss=2.652, discriminator_real_loss=1.361, discriminator_fake_loss=1.291, generator_loss=29.96, generator_mel_loss=17.9, generator_kl_loss=1.437, generator_dur_loss=1.671, generator_adv_loss=1.903, generator_feat_match_loss=7.047, over 45.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.14, generator_mel_loss=17.76, generator_kl_loss=1.45, generator_dur_loss=1.745, generator_adv_loss=2.011, generator_feat_match_loss=6.178, over 4969.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:39:46,330 INFO [train.py:919] (5/6) Start epoch 938 +2024-03-16 00:40:43,636 INFO [train.py:527] (5/6) Epoch 938, batch 12, global_batch_idx: 116200, batch size: 50, loss[discriminator_loss=2.66, discriminator_real_loss=1.34, discriminator_fake_loss=1.319, generator_loss=30.16, generator_mel_loss=17.87, generator_kl_loss=1.423, generator_dur_loss=1.68, generator_adv_loss=2.01, generator_feat_match_loss=7.174, over 50.00 samples.], tot_loss[discriminator_loss=2.655, discriminator_real_loss=1.342, discriminator_fake_loss=1.313, generator_loss=29.18, generator_mel_loss=17.53, generator_kl_loss=1.445, generator_dur_loss=1.742, generator_adv_loss=2.031, generator_feat_match_loss=6.436, over 838.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:40:43,639 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 00:40:51,534 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 00:43:09,092 INFO [train.py:527] (5/6) Epoch 938, batch 62, global_batch_idx: 116250, batch size: 31, loss[discriminator_loss=2.693, discriminator_real_loss=1.411, discriminator_fake_loss=1.281, generator_loss=28.26, generator_mel_loss=17.5, generator_kl_loss=1.62, generator_dur_loss=1.617, generator_adv_loss=2.078, generator_feat_match_loss=5.437, over 31.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.356, discriminator_fake_loss=1.325, generator_loss=29.11, generator_mel_loss=17.68, generator_kl_loss=1.471, generator_dur_loss=1.729, generator_adv_loss=1.999, generator_feat_match_loss=6.23, over 3544.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:45:29,111 INFO [train.py:527] (5/6) Epoch 938, batch 112, global_batch_idx: 116300, batch size: 68, loss[discriminator_loss=2.722, discriminator_real_loss=1.34, discriminator_fake_loss=1.382, generator_loss=29.29, generator_mel_loss=17.79, generator_kl_loss=1.381, generator_dur_loss=1.809, generator_adv_loss=2.009, generator_feat_match_loss=6.301, over 68.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.354, discriminator_fake_loss=1.326, generator_loss=29.11, generator_mel_loss=17.68, generator_kl_loss=1.451, generator_dur_loss=1.742, generator_adv_loss=2.005, generator_feat_match_loss=6.229, over 6545.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:46:01,027 INFO [train.py:919] (5/6) Start epoch 939 +2024-03-16 00:48:11,724 INFO [train.py:527] (5/6) Epoch 939, batch 38, global_batch_idx: 116350, batch size: 25, loss[discriminator_loss=2.591, discriminator_real_loss=1.245, discriminator_fake_loss=1.346, generator_loss=31.38, generator_mel_loss=18.01, generator_kl_loss=1.857, generator_dur_loss=1.523, generator_adv_loss=2.126, generator_feat_match_loss=7.86, over 25.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.342, discriminator_fake_loss=1.325, generator_loss=29.14, generator_mel_loss=17.74, generator_kl_loss=1.463, generator_dur_loss=1.734, generator_adv_loss=1.999, generator_feat_match_loss=6.206, over 2242.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:50:30,583 INFO [train.py:527] (5/6) Epoch 939, batch 88, global_batch_idx: 116400, batch size: 88, loss[discriminator_loss=2.675, discriminator_real_loss=1.376, discriminator_fake_loss=1.299, generator_loss=29.59, generator_mel_loss=17.59, generator_kl_loss=1.36, generator_dur_loss=1.826, generator_adv_loss=2.077, generator_feat_match_loss=6.739, over 88.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.321, generator_loss=29.1, generator_mel_loss=17.74, generator_kl_loss=1.456, generator_dur_loss=1.744, generator_adv_loss=2.012, generator_feat_match_loss=6.149, over 5122.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:50:30,584 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 00:50:39,490 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 00:52:14,720 INFO [train.py:919] (5/6) Start epoch 940 +2024-03-16 00:53:18,588 INFO [train.py:527] (5/6) Epoch 940, batch 14, global_batch_idx: 116450, batch size: 15, loss[discriminator_loss=2.673, discriminator_real_loss=1.416, discriminator_fake_loss=1.257, generator_loss=27.82, generator_mel_loss=16.84, generator_kl_loss=1.856, generator_dur_loss=1.599, generator_adv_loss=2.216, generator_feat_match_loss=5.303, over 15.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.354, discriminator_fake_loss=1.312, generator_loss=29.11, generator_mel_loss=17.65, generator_kl_loss=1.429, generator_dur_loss=1.743, generator_adv_loss=2.036, generator_feat_match_loss=6.25, over 824.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:55:37,738 INFO [train.py:527] (5/6) Epoch 940, batch 64, global_batch_idx: 116500, batch size: 96, loss[discriminator_loss=2.663, discriminator_real_loss=1.308, discriminator_fake_loss=1.355, generator_loss=28.77, generator_mel_loss=17.54, generator_kl_loss=1.41, generator_dur_loss=1.849, generator_adv_loss=2.038, generator_feat_match_loss=5.933, over 96.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.351, discriminator_fake_loss=1.32, generator_loss=29.16, generator_mel_loss=17.69, generator_kl_loss=1.442, generator_dur_loss=1.738, generator_adv_loss=2.022, generator_feat_match_loss=6.263, over 3591.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:57:56,315 INFO [train.py:527] (5/6) Epoch 940, batch 114, global_batch_idx: 116550, batch size: 66, loss[discriminator_loss=2.631, discriminator_real_loss=1.373, discriminator_fake_loss=1.258, generator_loss=29.45, generator_mel_loss=17.64, generator_kl_loss=1.647, generator_dur_loss=1.724, generator_adv_loss=2.034, generator_feat_match_loss=6.407, over 66.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.24, generator_mel_loss=17.74, generator_kl_loss=1.456, generator_dur_loss=1.742, generator_adv_loss=2.025, generator_feat_match_loss=6.28, over 6652.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 00:58:23,204 INFO [train.py:919] (5/6) Start epoch 941 +2024-03-16 01:00:37,593 INFO [train.py:527] (5/6) Epoch 941, batch 40, global_batch_idx: 116600, batch size: 48, loss[discriminator_loss=2.66, discriminator_real_loss=1.274, discriminator_fake_loss=1.385, generator_loss=30.12, generator_mel_loss=17.76, generator_kl_loss=1.528, generator_dur_loss=1.682, generator_adv_loss=2.195, generator_feat_match_loss=6.956, over 48.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.35, discriminator_fake_loss=1.315, generator_loss=29.2, generator_mel_loss=17.71, generator_kl_loss=1.467, generator_dur_loss=1.734, generator_adv_loss=2.019, generator_feat_match_loss=6.269, over 2403.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:00:37,595 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 01:00:45,811 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 01:03:04,719 INFO [train.py:527] (5/6) Epoch 941, batch 90, global_batch_idx: 116650, batch size: 39, loss[discriminator_loss=2.673, discriminator_real_loss=1.317, discriminator_fake_loss=1.356, generator_loss=29.22, generator_mel_loss=18.02, generator_kl_loss=1.565, generator_dur_loss=1.655, generator_adv_loss=2.067, generator_feat_match_loss=5.913, over 39.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.319, generator_loss=29.09, generator_mel_loss=17.7, generator_kl_loss=1.461, generator_dur_loss=1.724, generator_adv_loss=2.016, generator_feat_match_loss=6.191, over 5184.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:04:36,596 INFO [train.py:919] (5/6) Start epoch 942 +2024-03-16 01:05:45,314 INFO [train.py:527] (5/6) Epoch 942, batch 16, global_batch_idx: 116700, batch size: 45, loss[discriminator_loss=2.688, discriminator_real_loss=1.435, discriminator_fake_loss=1.253, generator_loss=28.24, generator_mel_loss=17.6, generator_kl_loss=1.529, generator_dur_loss=1.658, generator_adv_loss=1.894, generator_feat_match_loss=5.563, over 45.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.347, discriminator_fake_loss=1.326, generator_loss=28.94, generator_mel_loss=17.71, generator_kl_loss=1.408, generator_dur_loss=1.755, generator_adv_loss=2.007, generator_feat_match_loss=6.057, over 1092.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:08:05,310 INFO [train.py:527] (5/6) Epoch 942, batch 66, global_batch_idx: 116750, batch size: 74, loss[discriminator_loss=2.715, discriminator_real_loss=1.491, discriminator_fake_loss=1.224, generator_loss=28.06, generator_mel_loss=17.42, generator_kl_loss=1.45, generator_dur_loss=1.787, generator_adv_loss=1.832, generator_feat_match_loss=5.565, over 74.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.347, discriminator_fake_loss=1.325, generator_loss=29.15, generator_mel_loss=17.73, generator_kl_loss=1.45, generator_dur_loss=1.739, generator_adv_loss=2.011, generator_feat_match_loss=6.216, over 4029.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:10:21,019 INFO [train.py:527] (5/6) Epoch 942, batch 116, global_batch_idx: 116800, batch size: 42, loss[discriminator_loss=2.663, discriminator_real_loss=1.345, discriminator_fake_loss=1.318, generator_loss=29.97, generator_mel_loss=18.02, generator_kl_loss=1.626, generator_dur_loss=1.689, generator_adv_loss=1.965, generator_feat_match_loss=6.678, over 42.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.319, generator_loss=29.1, generator_mel_loss=17.72, generator_kl_loss=1.446, generator_dur_loss=1.738, generator_adv_loss=2.011, generator_feat_match_loss=6.187, over 6804.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:10:21,020 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 01:10:29,980 INFO [train.py:591] (5/6) 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 01:10:29,981 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 01:10:50,686 INFO [train.py:919] (5/6) Start epoch 943 +2024-03-16 01:13:12,505 INFO [train.py:527] (5/6) Epoch 943, batch 42, global_batch_idx: 116850, batch size: 53, loss[discriminator_loss=2.675, discriminator_real_loss=1.34, discriminator_fake_loss=1.335, generator_loss=29.1, generator_mel_loss=17.35, generator_kl_loss=1.365, generator_dur_loss=1.688, generator_adv_loss=2.043, generator_feat_match_loss=6.654, over 53.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.348, discriminator_fake_loss=1.327, generator_loss=29.35, generator_mel_loss=17.73, generator_kl_loss=1.438, generator_dur_loss=1.734, generator_adv_loss=2.021, generator_feat_match_loss=6.425, over 2310.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:15:30,956 INFO [train.py:527] (5/6) Epoch 943, batch 92, global_batch_idx: 116900, batch size: 42, loss[discriminator_loss=2.757, discriminator_real_loss=1.382, discriminator_fake_loss=1.374, generator_loss=30.49, generator_mel_loss=18.34, generator_kl_loss=1.662, generator_dur_loss=1.714, generator_adv_loss=1.913, generator_feat_match_loss=6.867, over 42.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.347, discriminator_fake_loss=1.33, generator_loss=29.27, generator_mel_loss=17.73, generator_kl_loss=1.448, generator_dur_loss=1.737, generator_adv_loss=2.026, generator_feat_match_loss=6.328, over 5053.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:16:56,881 INFO [train.py:919] (5/6) Start epoch 944 +2024-03-16 01:18:10,357 INFO [train.py:527] (5/6) Epoch 944, batch 18, global_batch_idx: 116950, batch size: 88, loss[discriminator_loss=2.67, discriminator_real_loss=1.29, discriminator_fake_loss=1.38, generator_loss=28.74, generator_mel_loss=17.5, generator_kl_loss=1.347, generator_dur_loss=1.861, generator_adv_loss=1.938, generator_feat_match_loss=6.1, over 88.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.346, discriminator_fake_loss=1.314, generator_loss=29.02, generator_mel_loss=17.67, generator_kl_loss=1.417, generator_dur_loss=1.762, generator_adv_loss=1.994, generator_feat_match_loss=6.172, over 1167.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:20:29,909 INFO [train.py:527] (5/6) Epoch 944, batch 68, global_batch_idx: 117000, batch size: 31, loss[discriminator_loss=2.645, discriminator_real_loss=1.277, discriminator_fake_loss=1.368, generator_loss=29.71, generator_mel_loss=17.97, generator_kl_loss=1.567, generator_dur_loss=1.681, generator_adv_loss=2.031, generator_feat_match_loss=6.462, over 31.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.1, generator_mel_loss=17.71, generator_kl_loss=1.439, generator_dur_loss=1.745, generator_adv_loss=2.021, generator_feat_match_loss=6.182, over 4004.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:20:29,910 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 01:20:38,012 INFO [train.py:591] (5/6) 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 01:20:38,013 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 01:22:56,878 INFO [train.py:527] (5/6) Epoch 944, batch 118, global_batch_idx: 117050, batch size: 62, loss[discriminator_loss=2.69, discriminator_real_loss=1.357, discriminator_fake_loss=1.333, generator_loss=29.54, generator_mel_loss=18.23, generator_kl_loss=1.371, generator_dur_loss=1.737, generator_adv_loss=1.921, generator_feat_match_loss=6.284, over 62.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=29.08, generator_mel_loss=17.74, generator_kl_loss=1.439, generator_dur_loss=1.746, generator_adv_loss=2.019, generator_feat_match_loss=6.137, over 6908.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:23:10,538 INFO [train.py:919] (5/6) Start epoch 945 +2024-03-16 01:25:37,122 INFO [train.py:527] (5/6) Epoch 945, batch 44, global_batch_idx: 117100, batch size: 45, loss[discriminator_loss=2.707, discriminator_real_loss=1.373, discriminator_fake_loss=1.334, generator_loss=29.69, generator_mel_loss=18.03, generator_kl_loss=1.338, generator_dur_loss=1.645, generator_adv_loss=2.006, generator_feat_match_loss=6.677, over 45.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.25, generator_mel_loss=17.77, generator_kl_loss=1.439, generator_dur_loss=1.745, generator_adv_loss=2.014, generator_feat_match_loss=6.276, over 2618.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:27:55,882 INFO [train.py:527] (5/6) Epoch 945, batch 94, global_batch_idx: 117150, batch size: 74, loss[discriminator_loss=2.654, discriminator_real_loss=1.251, discriminator_fake_loss=1.403, generator_loss=29.27, generator_mel_loss=17.89, generator_kl_loss=1.51, generator_dur_loss=1.746, generator_adv_loss=2.197, generator_feat_match_loss=5.925, over 74.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.18, generator_mel_loss=17.73, generator_kl_loss=1.44, generator_dur_loss=1.743, generator_adv_loss=2.023, generator_feat_match_loss=6.244, over 5543.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:29:15,074 INFO [train.py:919] (5/6) Start epoch 946 +2024-03-16 01:30:35,120 INFO [train.py:527] (5/6) Epoch 946, batch 20, global_batch_idx: 117200, batch size: 50, loss[discriminator_loss=2.636, discriminator_real_loss=1.303, discriminator_fake_loss=1.333, generator_loss=29, generator_mel_loss=18.24, generator_kl_loss=1.563, generator_dur_loss=1.697, generator_adv_loss=2.048, generator_feat_match_loss=5.454, over 50.00 samples.], tot_loss[discriminator_loss=2.656, discriminator_real_loss=1.34, discriminator_fake_loss=1.315, generator_loss=29.21, generator_mel_loss=17.76, generator_kl_loss=1.485, generator_dur_loss=1.703, generator_adv_loss=2.014, generator_feat_match_loss=6.246, over 1068.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:30:35,122 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 01:30:43,265 INFO [train.py:591] (5/6) 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 01:30:43,266 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 01:33:02,956 INFO [train.py:527] (5/6) Epoch 946, batch 70, global_batch_idx: 117250, batch size: 53, loss[discriminator_loss=2.693, discriminator_real_loss=1.419, discriminator_fake_loss=1.275, generator_loss=27.65, generator_mel_loss=16.99, generator_kl_loss=1.473, generator_dur_loss=1.685, generator_adv_loss=1.923, generator_feat_match_loss=5.582, over 53.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.35, discriminator_fake_loss=1.322, generator_loss=29.15, generator_mel_loss=17.76, generator_kl_loss=1.461, generator_dur_loss=1.736, generator_adv_loss=2.005, generator_feat_match_loss=6.18, over 4082.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:35:20,566 INFO [train.py:527] (5/6) Epoch 946, batch 120, global_batch_idx: 117300, batch size: 25, loss[discriminator_loss=2.694, discriminator_real_loss=1.325, discriminator_fake_loss=1.37, generator_loss=28.92, generator_mel_loss=18.01, generator_kl_loss=1.809, generator_dur_loss=1.505, generator_adv_loss=1.983, generator_feat_match_loss=5.621, over 25.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.346, discriminator_fake_loss=1.322, generator_loss=29.15, generator_mel_loss=17.77, generator_kl_loss=1.456, generator_dur_loss=1.736, generator_adv_loss=2.006, generator_feat_match_loss=6.189, over 6807.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:35:30,323 INFO [train.py:919] (5/6) Start epoch 947 +2024-03-16 01:38:02,457 INFO [train.py:527] (5/6) Epoch 947, batch 46, global_batch_idx: 117350, batch size: 64, loss[discriminator_loss=2.688, discriminator_real_loss=1.366, discriminator_fake_loss=1.323, generator_loss=28.3, generator_mel_loss=17.36, generator_kl_loss=1.347, generator_dur_loss=1.732, generator_adv_loss=1.98, generator_feat_match_loss=5.878, over 64.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=29.24, generator_mel_loss=17.76, generator_kl_loss=1.457, generator_dur_loss=1.738, generator_adv_loss=2.022, generator_feat_match_loss=6.267, over 2607.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:40:21,636 INFO [train.py:527] (5/6) Epoch 947, batch 96, global_batch_idx: 117400, batch size: 64, loss[discriminator_loss=2.708, discriminator_real_loss=1.405, discriminator_fake_loss=1.303, generator_loss=28.39, generator_mel_loss=17.21, generator_kl_loss=1.395, generator_dur_loss=1.755, generator_adv_loss=1.955, generator_feat_match_loss=6.071, over 64.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.355, discriminator_fake_loss=1.317, generator_loss=29.27, generator_mel_loss=17.76, generator_kl_loss=1.437, generator_dur_loss=1.742, generator_adv_loss=2.014, generator_feat_match_loss=6.311, over 5382.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:40:21,637 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 01:40:30,417 INFO [train.py:591] (5/6) 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 01:40:30,417 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 01:41:46,410 INFO [train.py:919] (5/6) Start epoch 948 +2024-03-16 01:43:10,130 INFO [train.py:527] (5/6) Epoch 948, batch 22, global_batch_idx: 117450, batch size: 48, loss[discriminator_loss=2.634, discriminator_real_loss=1.3, discriminator_fake_loss=1.334, generator_loss=29.95, generator_mel_loss=17.81, generator_kl_loss=1.516, generator_dur_loss=1.653, generator_adv_loss=2.011, generator_feat_match_loss=6.966, over 48.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.35, discriminator_fake_loss=1.326, generator_loss=29.17, generator_mel_loss=17.7, generator_kl_loss=1.432, generator_dur_loss=1.734, generator_adv_loss=2.014, generator_feat_match_loss=6.291, over 1309.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:45:27,430 INFO [train.py:527] (5/6) Epoch 948, batch 72, global_batch_idx: 117500, batch size: 53, loss[discriminator_loss=2.631, discriminator_real_loss=1.357, discriminator_fake_loss=1.274, generator_loss=28.74, generator_mel_loss=17.74, generator_kl_loss=1.477, generator_dur_loss=1.679, generator_adv_loss=1.951, generator_feat_match_loss=5.893, over 53.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.346, discriminator_fake_loss=1.319, generator_loss=29.16, generator_mel_loss=17.74, generator_kl_loss=1.43, generator_dur_loss=1.745, generator_adv_loss=2.01, generator_feat_match_loss=6.237, over 4307.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:47:44,905 INFO [train.py:527] (5/6) Epoch 948, batch 122, global_batch_idx: 117550, batch size: 77, loss[discriminator_loss=2.655, discriminator_real_loss=1.403, discriminator_fake_loss=1.252, generator_loss=29.15, generator_mel_loss=17.83, generator_kl_loss=1.39, generator_dur_loss=1.758, generator_adv_loss=1.999, generator_feat_match_loss=6.177, over 77.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.352, discriminator_fake_loss=1.316, generator_loss=29.16, generator_mel_loss=17.72, generator_kl_loss=1.43, generator_dur_loss=1.745, generator_adv_loss=2.016, generator_feat_match_loss=6.244, over 7292.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:47:50,697 INFO [train.py:919] (5/6) Start epoch 949 +2024-03-16 01:50:29,818 INFO [train.py:527] (5/6) Epoch 949, batch 48, global_batch_idx: 117600, batch size: 55, loss[discriminator_loss=2.718, discriminator_real_loss=1.45, discriminator_fake_loss=1.268, generator_loss=28.71, generator_mel_loss=17.71, generator_kl_loss=1.544, generator_dur_loss=1.72, generator_adv_loss=2.01, generator_feat_match_loss=5.728, over 55.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.358, discriminator_fake_loss=1.321, generator_loss=29.38, generator_mel_loss=17.81, generator_kl_loss=1.456, generator_dur_loss=1.722, generator_adv_loss=2.027, generator_feat_match_loss=6.357, over 2818.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:50:29,820 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 01:50:37,970 INFO [train.py:591] (5/6) 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 01:50:37,971 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 01:52:58,055 INFO [train.py:527] (5/6) Epoch 949, batch 98, global_batch_idx: 117650, batch size: 25, loss[discriminator_loss=2.659, discriminator_real_loss=1.328, discriminator_fake_loss=1.33, generator_loss=31.03, generator_mel_loss=18.9, generator_kl_loss=1.794, generator_dur_loss=1.55, generator_adv_loss=2.082, generator_feat_match_loss=6.707, over 25.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.361, discriminator_fake_loss=1.321, generator_loss=29.25, generator_mel_loss=17.79, generator_kl_loss=1.446, generator_dur_loss=1.724, generator_adv_loss=2.022, generator_feat_match_loss=6.271, over 5590.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:54:07,040 INFO [train.py:919] (5/6) Start epoch 950 +2024-03-16 01:55:37,218 INFO [train.py:527] (5/6) Epoch 950, batch 24, global_batch_idx: 117700, batch size: 72, loss[discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.341, generator_loss=28.72, generator_mel_loss=17.33, generator_kl_loss=1.281, generator_dur_loss=1.78, generator_adv_loss=2, generator_feat_match_loss=6.329, over 72.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.365, discriminator_fake_loss=1.319, generator_loss=28.87, generator_mel_loss=17.63, generator_kl_loss=1.381, generator_dur_loss=1.764, generator_adv_loss=1.982, generator_feat_match_loss=6.116, over 1508.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 01:58:01,010 INFO [train.py:527] (5/6) Epoch 950, batch 74, global_batch_idx: 117750, batch size: 61, loss[discriminator_loss=2.65, discriminator_real_loss=1.281, discriminator_fake_loss=1.369, generator_loss=29.49, generator_mel_loss=17.97, generator_kl_loss=1.348, generator_dur_loss=1.735, generator_adv_loss=2.031, generator_feat_match_loss=6.405, over 61.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=29.08, generator_mel_loss=17.67, generator_kl_loss=1.411, generator_dur_loss=1.755, generator_adv_loss=2, generator_feat_match_loss=6.238, over 4435.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:00:19,477 INFO [train.py:919] (5/6) Start epoch 951 +2024-03-16 02:00:44,341 INFO [train.py:527] (5/6) Epoch 951, batch 0, global_batch_idx: 117800, batch size: 59, loss[discriminator_loss=2.699, discriminator_real_loss=1.375, discriminator_fake_loss=1.324, generator_loss=28.32, generator_mel_loss=17.46, generator_kl_loss=1.376, generator_dur_loss=1.723, generator_adv_loss=2.085, generator_feat_match_loss=5.67, over 59.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.375, discriminator_fake_loss=1.324, generator_loss=28.32, generator_mel_loss=17.46, generator_kl_loss=1.376, generator_dur_loss=1.723, generator_adv_loss=2.085, generator_feat_match_loss=5.67, over 59.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:00:44,343 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 02:00:52,408 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 02:03:13,035 INFO [train.py:527] (5/6) Epoch 951, batch 50, global_batch_idx: 117850, batch size: 66, loss[discriminator_loss=2.647, discriminator_real_loss=1.331, discriminator_fake_loss=1.316, generator_loss=29.93, generator_mel_loss=18, generator_kl_loss=1.347, generator_dur_loss=1.788, generator_adv_loss=2.176, generator_feat_match_loss=6.622, over 66.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.26, generator_mel_loss=17.78, generator_kl_loss=1.406, generator_dur_loss=1.741, generator_adv_loss=2.029, generator_feat_match_loss=6.301, over 2911.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:05:35,460 INFO [train.py:527] (5/6) Epoch 951, batch 100, global_batch_idx: 117900, batch size: 88, loss[discriminator_loss=2.687, discriminator_real_loss=1.455, discriminator_fake_loss=1.232, generator_loss=29.16, generator_mel_loss=17.53, generator_kl_loss=1.486, generator_dur_loss=1.821, generator_adv_loss=2.01, generator_feat_match_loss=6.318, over 88.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.358, discriminator_fake_loss=1.32, generator_loss=29.26, generator_mel_loss=17.79, generator_kl_loss=1.424, generator_dur_loss=1.747, generator_adv_loss=2.019, generator_feat_match_loss=6.28, over 5892.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:06:41,494 INFO [train.py:919] (5/6) Start epoch 952 +2024-03-16 02:08:20,300 INFO [train.py:527] (5/6) Epoch 952, batch 26, global_batch_idx: 117950, batch size: 47, loss[discriminator_loss=2.737, discriminator_real_loss=1.317, discriminator_fake_loss=1.421, generator_loss=30.51, generator_mel_loss=17.89, generator_kl_loss=1.714, generator_dur_loss=1.618, generator_adv_loss=2.212, generator_feat_match_loss=7.08, over 47.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.348, discriminator_fake_loss=1.312, generator_loss=29.43, generator_mel_loss=17.8, generator_kl_loss=1.429, generator_dur_loss=1.733, generator_adv_loss=2.027, generator_feat_match_loss=6.434, over 1577.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:10:40,355 INFO [train.py:527] (5/6) Epoch 952, batch 76, global_batch_idx: 118000, batch size: 50, loss[discriminator_loss=2.611, discriminator_real_loss=1.315, discriminator_fake_loss=1.296, generator_loss=30.43, generator_mel_loss=17.82, generator_kl_loss=1.655, generator_dur_loss=1.669, generator_adv_loss=2.029, generator_feat_match_loss=7.257, over 50.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.35, discriminator_fake_loss=1.318, generator_loss=29.41, generator_mel_loss=17.81, generator_kl_loss=1.452, generator_dur_loss=1.733, generator_adv_loss=2.022, generator_feat_match_loss=6.399, over 4518.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:10:40,356 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 02:10:49,137 INFO [train.py:591] (5/6) 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 02:10:49,138 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 02:13:03,145 INFO [train.py:919] (5/6) Start epoch 953 +2024-03-16 02:13:33,408 INFO [train.py:527] (5/6) Epoch 953, batch 2, global_batch_idx: 118050, batch size: 52, loss[discriminator_loss=2.727, discriminator_real_loss=1.326, discriminator_fake_loss=1.4, generator_loss=30.23, generator_mel_loss=17.93, generator_kl_loss=1.348, generator_dur_loss=1.699, generator_adv_loss=2.03, generator_feat_match_loss=7.215, over 52.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.346, discriminator_fake_loss=1.335, generator_loss=29.81, generator_mel_loss=17.85, generator_kl_loss=1.512, generator_dur_loss=1.669, generator_adv_loss=2.033, generator_feat_match_loss=6.744, over 136.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:15:54,582 INFO [train.py:527] (5/6) Epoch 953, batch 52, global_batch_idx: 118100, batch size: 25, loss[discriminator_loss=2.63, discriminator_real_loss=1.33, discriminator_fake_loss=1.3, generator_loss=31.96, generator_mel_loss=18.46, generator_kl_loss=1.745, generator_dur_loss=1.601, generator_adv_loss=2.168, generator_feat_match_loss=7.992, over 25.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.345, discriminator_fake_loss=1.322, generator_loss=29.26, generator_mel_loss=17.78, generator_kl_loss=1.413, generator_dur_loss=1.745, generator_adv_loss=2.011, generator_feat_match_loss=6.311, over 3126.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:18:17,752 INFO [train.py:527] (5/6) Epoch 953, batch 102, global_batch_idx: 118150, batch size: 62, loss[discriminator_loss=2.61, discriminator_real_loss=1.26, discriminator_fake_loss=1.35, generator_loss=30.01, generator_mel_loss=17.8, generator_kl_loss=1.417, generator_dur_loss=1.706, generator_adv_loss=2.126, generator_feat_match_loss=6.96, over 62.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.25, generator_mel_loss=17.77, generator_kl_loss=1.434, generator_dur_loss=1.736, generator_adv_loss=2.011, generator_feat_match_loss=6.305, over 5850.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:19:16,747 INFO [train.py:919] (5/6) Start epoch 954 +2024-03-16 02:21:03,225 INFO [train.py:527] (5/6) Epoch 954, batch 28, global_batch_idx: 118200, batch size: 77, loss[discriminator_loss=2.7, discriminator_real_loss=1.353, discriminator_fake_loss=1.347, generator_loss=28.83, generator_mel_loss=17.87, generator_kl_loss=1.249, generator_dur_loss=1.824, generator_adv_loss=2.044, generator_feat_match_loss=5.849, over 77.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.339, discriminator_fake_loss=1.322, generator_loss=29.26, generator_mel_loss=17.79, generator_kl_loss=1.431, generator_dur_loss=1.737, generator_adv_loss=2.009, generator_feat_match_loss=6.294, over 1607.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:21:03,226 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 02:21:11,359 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 02:23:33,030 INFO [train.py:527] (5/6) Epoch 954, batch 78, global_batch_idx: 118250, batch size: 42, loss[discriminator_loss=2.754, discriminator_real_loss=1.239, discriminator_fake_loss=1.515, generator_loss=29.48, generator_mel_loss=17.73, generator_kl_loss=1.64, generator_dur_loss=1.707, generator_adv_loss=2.215, generator_feat_match_loss=6.194, over 42.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.343, discriminator_fake_loss=1.319, generator_loss=29.32, generator_mel_loss=17.75, generator_kl_loss=1.454, generator_dur_loss=1.738, generator_adv_loss=2.015, generator_feat_match_loss=6.365, over 4345.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:25:41,800 INFO [train.py:919] (5/6) Start epoch 955 +2024-03-16 02:26:18,612 INFO [train.py:527] (5/6) Epoch 955, batch 4, global_batch_idx: 118300, batch size: 52, loss[discriminator_loss=2.692, discriminator_real_loss=1.343, discriminator_fake_loss=1.35, generator_loss=29.5, generator_mel_loss=17.75, generator_kl_loss=1.612, generator_dur_loss=1.697, generator_adv_loss=2.088, generator_feat_match_loss=6.353, over 52.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.383, discriminator_fake_loss=1.309, generator_loss=29.46, generator_mel_loss=17.62, generator_kl_loss=1.522, generator_dur_loss=1.715, generator_adv_loss=2.031, generator_feat_match_loss=6.576, over 267.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:28:40,829 INFO [train.py:527] (5/6) Epoch 955, batch 54, global_batch_idx: 118350, batch size: 88, loss[discriminator_loss=2.711, discriminator_real_loss=1.365, discriminator_fake_loss=1.346, generator_loss=28.76, generator_mel_loss=17.39, generator_kl_loss=1.371, generator_dur_loss=1.886, generator_adv_loss=2.069, generator_feat_match_loss=6.043, over 88.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.36, discriminator_fake_loss=1.315, generator_loss=29.26, generator_mel_loss=17.73, generator_kl_loss=1.457, generator_dur_loss=1.742, generator_adv_loss=2.025, generator_feat_match_loss=6.298, over 3069.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:31:04,370 INFO [train.py:527] (5/6) Epoch 955, batch 104, global_batch_idx: 118400, batch size: 74, loss[discriminator_loss=2.685, discriminator_real_loss=1.405, discriminator_fake_loss=1.28, generator_loss=28.47, generator_mel_loss=17.5, generator_kl_loss=1.351, generator_dur_loss=1.756, generator_adv_loss=2.04, generator_feat_match_loss=5.831, over 74.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.36, discriminator_fake_loss=1.315, generator_loss=29.16, generator_mel_loss=17.72, generator_kl_loss=1.45, generator_dur_loss=1.746, generator_adv_loss=2.021, generator_feat_match_loss=6.228, over 5977.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, +2024-03-16 02:31:04,372 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 02:31:13,205 INFO [train.py:591] (5/6) 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 02:31:13,205 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 02:32:05,331 INFO [train.py:919] (5/6) Start epoch 956 +2024-03-16 02:33:54,936 INFO [train.py:527] (5/6) Epoch 956, batch 30, global_batch_idx: 118450, batch size: 53, loss[discriminator_loss=2.668, discriminator_real_loss=1.415, discriminator_fake_loss=1.253, generator_loss=27.77, generator_mel_loss=17.46, generator_kl_loss=1.311, generator_dur_loss=1.742, generator_adv_loss=2.022, generator_feat_match_loss=5.239, over 53.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.32, generator_mel_loss=17.75, generator_kl_loss=1.44, generator_dur_loss=1.746, generator_adv_loss=2.018, generator_feat_match_loss=6.363, over 1778.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:36:16,827 INFO [train.py:527] (5/6) Epoch 956, batch 80, global_batch_idx: 118500, batch size: 72, loss[discriminator_loss=2.664, discriminator_real_loss=1.359, discriminator_fake_loss=1.305, generator_loss=28.75, generator_mel_loss=17.36, generator_kl_loss=1.424, generator_dur_loss=1.788, generator_adv_loss=1.956, generator_feat_match_loss=6.215, over 72.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=29.12, generator_mel_loss=17.69, generator_kl_loss=1.432, generator_dur_loss=1.736, generator_adv_loss=2.011, generator_feat_match_loss=6.248, over 4726.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:38:19,805 INFO [train.py:919] (5/6) Start epoch 957 +2024-03-16 02:39:00,513 INFO [train.py:527] (5/6) Epoch 957, batch 6, global_batch_idx: 118550, batch size: 50, loss[discriminator_loss=2.676, discriminator_real_loss=1.315, discriminator_fake_loss=1.361, generator_loss=29.48, generator_mel_loss=17.7, generator_kl_loss=1.603, generator_dur_loss=1.637, generator_adv_loss=2.098, generator_feat_match_loss=6.44, over 50.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.357, discriminator_fake_loss=1.318, generator_loss=29.06, generator_mel_loss=17.67, generator_kl_loss=1.477, generator_dur_loss=1.706, generator_adv_loss=2.024, generator_feat_match_loss=6.176, over 350.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:41:20,841 INFO [train.py:527] (5/6) Epoch 957, batch 56, global_batch_idx: 118600, batch size: 88, loss[discriminator_loss=2.59, discriminator_real_loss=1.289, discriminator_fake_loss=1.301, generator_loss=29.99, generator_mel_loss=17.76, generator_kl_loss=1.463, generator_dur_loss=1.843, generator_adv_loss=2.034, generator_feat_match_loss=6.891, over 88.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.35, discriminator_fake_loss=1.319, generator_loss=29.31, generator_mel_loss=17.77, generator_kl_loss=1.458, generator_dur_loss=1.741, generator_adv_loss=2.018, generator_feat_match_loss=6.328, over 3233.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:41:20,842 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 02:41:28,895 INFO [train.py:591] (5/6) 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 02:41:28,896 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 02:43:50,868 INFO [train.py:527] (5/6) Epoch 957, batch 106, global_batch_idx: 118650, batch size: 55, loss[discriminator_loss=2.665, discriminator_real_loss=1.293, discriminator_fake_loss=1.372, generator_loss=29.14, generator_mel_loss=17.6, generator_kl_loss=1.548, generator_dur_loss=1.678, generator_adv_loss=2.053, generator_feat_match_loss=6.264, over 55.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.348, discriminator_fake_loss=1.322, generator_loss=29.32, generator_mel_loss=17.77, generator_kl_loss=1.462, generator_dur_loss=1.738, generator_adv_loss=2.014, generator_feat_match_loss=6.337, over 6011.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:44:40,670 INFO [train.py:919] (5/6) Start epoch 958 +2024-03-16 02:46:36,137 INFO [train.py:527] (5/6) Epoch 958, batch 32, global_batch_idx: 118700, batch size: 36, loss[discriminator_loss=2.66, discriminator_real_loss=1.31, discriminator_fake_loss=1.35, generator_loss=29.83, generator_mel_loss=17.85, generator_kl_loss=1.511, generator_dur_loss=1.569, generator_adv_loss=2.093, generator_feat_match_loss=6.805, over 36.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.344, discriminator_fake_loss=1.33, generator_loss=29.14, generator_mel_loss=17.71, generator_kl_loss=1.444, generator_dur_loss=1.736, generator_adv_loss=2.036, generator_feat_match_loss=6.216, over 1912.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:49:02,428 INFO [train.py:527] (5/6) Epoch 958, batch 82, global_batch_idx: 118750, batch size: 25, loss[discriminator_loss=2.615, discriminator_real_loss=1.278, discriminator_fake_loss=1.336, generator_loss=31.16, generator_mel_loss=18.06, generator_kl_loss=1.866, generator_dur_loss=1.57, generator_adv_loss=2.093, generator_feat_match_loss=7.575, over 25.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.348, discriminator_fake_loss=1.324, generator_loss=29.08, generator_mel_loss=17.69, generator_kl_loss=1.434, generator_dur_loss=1.753, generator_adv_loss=2.02, generator_feat_match_loss=6.183, over 4936.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:50:54,439 INFO [train.py:919] (5/6) Start epoch 959 +2024-03-16 02:51:42,578 INFO [train.py:527] (5/6) Epoch 959, batch 8, global_batch_idx: 118800, batch size: 50, loss[discriminator_loss=2.669, discriminator_real_loss=1.3, discriminator_fake_loss=1.369, generator_loss=29.77, generator_mel_loss=17.89, generator_kl_loss=1.573, generator_dur_loss=1.68, generator_adv_loss=1.948, generator_feat_match_loss=6.678, over 50.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.339, discriminator_fake_loss=1.371, generator_loss=29.49, generator_mel_loss=17.82, generator_kl_loss=1.495, generator_dur_loss=1.709, generator_adv_loss=1.999, generator_feat_match_loss=6.467, over 399.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:51:42,580 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 02:51:50,404 INFO [train.py:591] (5/6) 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 02:51:50,406 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 02:54:11,241 INFO [train.py:527] (5/6) Epoch 959, batch 58, global_batch_idx: 118850, batch size: 50, loss[discriminator_loss=2.688, discriminator_real_loss=1.401, discriminator_fake_loss=1.287, generator_loss=29.15, generator_mel_loss=18.28, generator_kl_loss=1.55, generator_dur_loss=1.662, generator_adv_loss=1.897, generator_feat_match_loss=5.762, over 50.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.347, discriminator_fake_loss=1.333, generator_loss=29.3, generator_mel_loss=17.74, generator_kl_loss=1.489, generator_dur_loss=1.734, generator_adv_loss=2.017, generator_feat_match_loss=6.323, over 3047.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:56:33,454 INFO [train.py:527] (5/6) Epoch 959, batch 108, global_batch_idx: 118900, batch size: 45, loss[discriminator_loss=2.678, discriminator_real_loss=1.334, discriminator_fake_loss=1.344, generator_loss=30.38, generator_mel_loss=18.21, generator_kl_loss=1.617, generator_dur_loss=1.668, generator_adv_loss=2.006, generator_feat_match_loss=6.874, over 45.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.325, generator_loss=29.26, generator_mel_loss=17.76, generator_kl_loss=1.464, generator_dur_loss=1.738, generator_adv_loss=2.013, generator_feat_match_loss=6.28, over 5939.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 02:57:14,821 INFO [train.py:919] (5/6) Start epoch 960 +2024-03-16 02:59:14,262 INFO [train.py:527] (5/6) Epoch 960, batch 34, global_batch_idx: 118950, batch size: 59, loss[discriminator_loss=2.614, discriminator_real_loss=1.267, discriminator_fake_loss=1.347, generator_loss=29.81, generator_mel_loss=17.96, generator_kl_loss=1.328, generator_dur_loss=1.806, generator_adv_loss=2.048, generator_feat_match_loss=6.67, over 59.00 samples.], tot_loss[discriminator_loss=2.647, discriminator_real_loss=1.341, discriminator_fake_loss=1.306, generator_loss=29.43, generator_mel_loss=17.74, generator_kl_loss=1.44, generator_dur_loss=1.761, generator_adv_loss=2.113, generator_feat_match_loss=6.374, over 2072.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:01:36,110 INFO [train.py:527] (5/6) Epoch 960, batch 84, global_batch_idx: 119000, batch size: 13, loss[discriminator_loss=2.567, discriminator_real_loss=1.319, discriminator_fake_loss=1.248, generator_loss=30.45, generator_mel_loss=17.95, generator_kl_loss=1.745, generator_dur_loss=1.569, generator_adv_loss=2.218, generator_feat_match_loss=6.971, over 13.00 samples.], tot_loss[discriminator_loss=2.652, discriminator_real_loss=1.343, discriminator_fake_loss=1.309, generator_loss=29.38, generator_mel_loss=17.75, generator_kl_loss=1.442, generator_dur_loss=1.746, generator_adv_loss=2.064, generator_feat_match_loss=6.38, over 4774.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:01:36,111 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 03:01:43,956 INFO [train.py:591] (5/6) 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 03:01:43,957 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 03:03:35,953 INFO [train.py:919] (5/6) Start epoch 961 +2024-03-16 03:04:32,352 INFO [train.py:527] (5/6) Epoch 961, batch 10, global_batch_idx: 119050, batch size: 77, loss[discriminator_loss=2.724, discriminator_real_loss=1.371, discriminator_fake_loss=1.353, generator_loss=29.15, generator_mel_loss=17.64, generator_kl_loss=1.385, generator_dur_loss=1.786, generator_adv_loss=1.992, generator_feat_match_loss=6.338, over 77.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.367, discriminator_fake_loss=1.329, generator_loss=29.09, generator_mel_loss=17.65, generator_kl_loss=1.42, generator_dur_loss=1.751, generator_adv_loss=2.014, generator_feat_match_loss=6.25, over 707.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:06:51,803 INFO [train.py:527] (5/6) Epoch 961, batch 60, global_batch_idx: 119100, batch size: 12, loss[discriminator_loss=2.639, discriminator_real_loss=1.264, discriminator_fake_loss=1.375, generator_loss=31.58, generator_mel_loss=18.44, generator_kl_loss=2.057, generator_dur_loss=1.605, generator_adv_loss=2.208, generator_feat_match_loss=7.264, over 12.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.351, discriminator_fake_loss=1.331, generator_loss=29.15, generator_mel_loss=17.73, generator_kl_loss=1.436, generator_dur_loss=1.743, generator_adv_loss=2.005, generator_feat_match_loss=6.241, over 3484.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:09:12,452 INFO [train.py:527] (5/6) Epoch 961, batch 110, global_batch_idx: 119150, batch size: 55, loss[discriminator_loss=2.62, discriminator_real_loss=1.362, discriminator_fake_loss=1.257, generator_loss=29.44, generator_mel_loss=17.85, generator_kl_loss=1.601, generator_dur_loss=1.706, generator_adv_loss=2.149, generator_feat_match_loss=6.133, over 55.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=29.15, generator_mel_loss=17.73, generator_kl_loss=1.462, generator_dur_loss=1.742, generator_adv_loss=2.012, generator_feat_match_loss=6.201, over 6186.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:09:51,124 INFO [train.py:919] (5/6) Start epoch 962 +2024-03-16 03:11:56,425 INFO [train.py:527] (5/6) Epoch 962, batch 36, global_batch_idx: 119200, batch size: 36, loss[discriminator_loss=2.654, discriminator_real_loss=1.395, discriminator_fake_loss=1.259, generator_loss=29.24, generator_mel_loss=17.79, generator_kl_loss=1.347, generator_dur_loss=1.721, generator_adv_loss=2.11, generator_feat_match_loss=6.28, over 36.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=29.27, generator_mel_loss=17.75, generator_kl_loss=1.46, generator_dur_loss=1.729, generator_adv_loss=2.019, generator_feat_match_loss=6.319, over 2057.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:11:56,427 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 03:12:04,407 INFO [train.py:591] (5/6) 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 03:12:04,408 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 03:14:22,640 INFO [train.py:527] (5/6) Epoch 962, batch 86, global_batch_idx: 119250, batch size: 68, loss[discriminator_loss=2.739, discriminator_real_loss=1.317, discriminator_fake_loss=1.422, generator_loss=28.69, generator_mel_loss=17.58, generator_kl_loss=1.39, generator_dur_loss=1.701, generator_adv_loss=1.973, generator_feat_match_loss=6.049, over 68.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.356, discriminator_fake_loss=1.322, generator_loss=29.18, generator_mel_loss=17.72, generator_kl_loss=1.46, generator_dur_loss=1.725, generator_adv_loss=2.015, generator_feat_match_loss=6.255, over 4785.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:16:09,614 INFO [train.py:919] (5/6) Start epoch 963 +2024-03-16 03:17:08,123 INFO [train.py:527] (5/6) Epoch 963, batch 12, global_batch_idx: 119300, batch size: 59, loss[discriminator_loss=2.629, discriminator_real_loss=1.249, discriminator_fake_loss=1.381, generator_loss=30.52, generator_mel_loss=17.87, generator_kl_loss=1.465, generator_dur_loss=1.744, generator_adv_loss=2.063, generator_feat_match_loss=7.378, over 59.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.357, discriminator_fake_loss=1.317, generator_loss=29.36, generator_mel_loss=17.78, generator_kl_loss=1.445, generator_dur_loss=1.722, generator_adv_loss=2.012, generator_feat_match_loss=6.397, over 762.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:19:29,451 INFO [train.py:527] (5/6) Epoch 963, batch 62, global_batch_idx: 119350, batch size: 74, loss[discriminator_loss=2.639, discriminator_real_loss=1.293, discriminator_fake_loss=1.346, generator_loss=30.18, generator_mel_loss=17.71, generator_kl_loss=1.469, generator_dur_loss=1.794, generator_adv_loss=2.07, generator_feat_match_loss=7.132, over 74.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.349, discriminator_fake_loss=1.319, generator_loss=29.29, generator_mel_loss=17.7, generator_kl_loss=1.446, generator_dur_loss=1.733, generator_adv_loss=2.015, generator_feat_match_loss=6.394, over 3696.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:21:49,920 INFO [train.py:527] (5/6) Epoch 963, batch 112, global_batch_idx: 119400, batch size: 25, loss[discriminator_loss=2.678, discriminator_real_loss=1.448, discriminator_fake_loss=1.23, generator_loss=31.14, generator_mel_loss=18.67, generator_kl_loss=1.758, generator_dur_loss=1.538, generator_adv_loss=2.169, generator_feat_match_loss=7.008, over 25.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.35, discriminator_fake_loss=1.32, generator_loss=29.25, generator_mel_loss=17.72, generator_kl_loss=1.433, generator_dur_loss=1.741, generator_adv_loss=2.016, generator_feat_match_loss=6.342, over 6756.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:21:49,922 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 03:21:59,011 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 03:22:30,338 INFO [train.py:919] (5/6) Start epoch 964 +2024-03-16 03:24:45,527 INFO [train.py:527] (5/6) Epoch 964, batch 38, global_batch_idx: 119450, batch size: 13, loss[discriminator_loss=2.691, discriminator_real_loss=1.302, discriminator_fake_loss=1.389, generator_loss=31.48, generator_mel_loss=18.34, generator_kl_loss=1.998, generator_dur_loss=1.59, generator_adv_loss=1.99, generator_feat_match_loss=7.567, over 13.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.343, discriminator_fake_loss=1.324, generator_loss=29.39, generator_mel_loss=17.83, generator_kl_loss=1.456, generator_dur_loss=1.729, generator_adv_loss=2.021, generator_feat_match_loss=6.357, over 2054.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:27:09,176 INFO [train.py:527] (5/6) Epoch 964, batch 88, global_batch_idx: 119500, batch size: 44, loss[discriminator_loss=2.633, discriminator_real_loss=1.319, discriminator_fake_loss=1.314, generator_loss=30.02, generator_mel_loss=17.83, generator_kl_loss=1.651, generator_dur_loss=1.679, generator_adv_loss=2.111, generator_feat_match_loss=6.748, over 44.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.347, discriminator_fake_loss=1.325, generator_loss=29.32, generator_mel_loss=17.76, generator_kl_loss=1.454, generator_dur_loss=1.735, generator_adv_loss=2.04, generator_feat_match_loss=6.334, over 4827.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:28:46,633 INFO [train.py:919] (5/6) Start epoch 965 +2024-03-16 03:29:50,758 INFO [train.py:527] (5/6) Epoch 965, batch 14, global_batch_idx: 119550, batch size: 74, loss[discriminator_loss=2.709, discriminator_real_loss=1.343, discriminator_fake_loss=1.366, generator_loss=29.04, generator_mel_loss=17.89, generator_kl_loss=1.306, generator_dur_loss=1.767, generator_adv_loss=2.048, generator_feat_match_loss=6.027, over 74.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.355, discriminator_fake_loss=1.315, generator_loss=28.97, generator_mel_loss=17.68, generator_kl_loss=1.37, generator_dur_loss=1.758, generator_adv_loss=2.018, generator_feat_match_loss=6.141, over 1049.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:32:10,237 INFO [train.py:527] (5/6) Epoch 965, batch 64, global_batch_idx: 119600, batch size: 14, loss[discriminator_loss=2.767, discriminator_real_loss=1.25, discriminator_fake_loss=1.517, generator_loss=30.29, generator_mel_loss=18.75, generator_kl_loss=1.894, generator_dur_loss=1.59, generator_adv_loss=2.053, generator_feat_match_loss=6.006, over 14.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.16, generator_mel_loss=17.71, generator_kl_loss=1.401, generator_dur_loss=1.75, generator_adv_loss=2.01, generator_feat_match_loss=6.286, over 4060.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:32:10,238 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 03:32:18,313 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 03:34:35,888 INFO [train.py:527] (5/6) Epoch 965, batch 114, global_batch_idx: 119650, batch size: 42, loss[discriminator_loss=2.75, discriminator_real_loss=1.461, discriminator_fake_loss=1.289, generator_loss=27.79, generator_mel_loss=17.19, generator_kl_loss=1.548, generator_dur_loss=1.678, generator_adv_loss=1.764, generator_feat_match_loss=5.616, over 42.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.26, generator_mel_loss=17.74, generator_kl_loss=1.422, generator_dur_loss=1.743, generator_adv_loss=2.015, generator_feat_match_loss=6.344, over 6768.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:35:02,472 INFO [train.py:919] (5/6) Start epoch 966 +2024-03-16 03:37:17,030 INFO [train.py:527] (5/6) Epoch 966, batch 40, global_batch_idx: 119700, batch size: 88, loss[discriminator_loss=2.685, discriminator_real_loss=1.403, discriminator_fake_loss=1.282, generator_loss=28.62, generator_mel_loss=17.43, generator_kl_loss=1.352, generator_dur_loss=1.795, generator_adv_loss=1.939, generator_feat_match_loss=6.111, over 88.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.342, discriminator_fake_loss=1.327, generator_loss=29.18, generator_mel_loss=17.68, generator_kl_loss=1.458, generator_dur_loss=1.725, generator_adv_loss=2.008, generator_feat_match_loss=6.307, over 2272.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:39:35,431 INFO [train.py:527] (5/6) Epoch 966, batch 90, global_batch_idx: 119750, batch size: 31, loss[discriminator_loss=2.716, discriminator_real_loss=1.445, discriminator_fake_loss=1.27, generator_loss=29.14, generator_mel_loss=18.14, generator_kl_loss=1.551, generator_dur_loss=1.659, generator_adv_loss=1.984, generator_feat_match_loss=5.8, over 31.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.346, discriminator_fake_loss=1.319, generator_loss=29.21, generator_mel_loss=17.7, generator_kl_loss=1.468, generator_dur_loss=1.726, generator_adv_loss=2.018, generator_feat_match_loss=6.289, over 4874.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:41:10,987 INFO [train.py:919] (5/6) Start epoch 967 +2024-03-16 03:42:20,768 INFO [train.py:527] (5/6) Epoch 967, batch 16, global_batch_idx: 119800, batch size: 61, loss[discriminator_loss=2.682, discriminator_real_loss=1.319, discriminator_fake_loss=1.362, generator_loss=29.9, generator_mel_loss=17.58, generator_kl_loss=1.415, generator_dur_loss=1.75, generator_adv_loss=2.077, generator_feat_match_loss=7.074, over 61.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.344, discriminator_fake_loss=1.325, generator_loss=29.14, generator_mel_loss=17.64, generator_kl_loss=1.417, generator_dur_loss=1.766, generator_adv_loss=2.005, generator_feat_match_loss=6.313, over 1080.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:42:20,770 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 03:42:29,728 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 03:44:48,639 INFO [train.py:527] (5/6) Epoch 967, batch 66, global_batch_idx: 119850, batch size: 50, loss[discriminator_loss=2.657, discriminator_real_loss=1.266, discriminator_fake_loss=1.391, generator_loss=30.35, generator_mel_loss=18.11, generator_kl_loss=1.633, generator_dur_loss=1.701, generator_adv_loss=1.824, generator_feat_match_loss=7.086, over 50.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.351, discriminator_fake_loss=1.324, generator_loss=29.18, generator_mel_loss=17.7, generator_kl_loss=1.437, generator_dur_loss=1.746, generator_adv_loss=2.007, generator_feat_match_loss=6.295, over 3883.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:47:04,616 INFO [train.py:527] (5/6) Epoch 967, batch 116, global_batch_idx: 119900, batch size: 61, loss[discriminator_loss=2.642, discriminator_real_loss=1.365, discriminator_fake_loss=1.277, generator_loss=29.5, generator_mel_loss=17.79, generator_kl_loss=1.598, generator_dur_loss=1.699, generator_adv_loss=1.969, generator_feat_match_loss=6.439, over 61.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.15, generator_mel_loss=17.71, generator_kl_loss=1.437, generator_dur_loss=1.75, generator_adv_loss=2.007, generator_feat_match_loss=6.247, over 6989.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:47:27,524 INFO [train.py:919] (5/6) Start epoch 968 +2024-03-16 03:49:48,365 INFO [train.py:527] (5/6) Epoch 968, batch 42, global_batch_idx: 119950, batch size: 31, loss[discriminator_loss=2.697, discriminator_real_loss=1.43, discriminator_fake_loss=1.267, generator_loss=29.52, generator_mel_loss=17.68, generator_kl_loss=1.627, generator_dur_loss=1.637, generator_adv_loss=1.97, generator_feat_match_loss=6.599, over 31.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.362, discriminator_fake_loss=1.317, generator_loss=29.18, generator_mel_loss=17.62, generator_kl_loss=1.453, generator_dur_loss=1.732, generator_adv_loss=2.069, generator_feat_match_loss=6.31, over 2244.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:52:07,678 INFO [train.py:527] (5/6) Epoch 968, batch 92, global_batch_idx: 120000, batch size: 59, loss[discriminator_loss=2.646, discriminator_real_loss=1.344, discriminator_fake_loss=1.302, generator_loss=28.97, generator_mel_loss=17.69, generator_kl_loss=1.523, generator_dur_loss=1.732, generator_adv_loss=1.952, generator_feat_match_loss=6.064, over 59.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.352, discriminator_fake_loss=1.314, generator_loss=29.19, generator_mel_loss=17.68, generator_kl_loss=1.432, generator_dur_loss=1.74, generator_adv_loss=2.033, generator_feat_match_loss=6.313, over 5199.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:52:07,681 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 03:52:16,509 INFO [train.py:591] (5/6) 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 03:52:16,510 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 03:53:43,798 INFO [train.py:919] (5/6) Start epoch 969 +2024-03-16 03:54:57,284 INFO [train.py:527] (5/6) Epoch 969, batch 18, global_batch_idx: 120050, batch size: 48, loss[discriminator_loss=2.673, discriminator_real_loss=1.336, discriminator_fake_loss=1.338, generator_loss=29.43, generator_mel_loss=17.49, generator_kl_loss=1.462, generator_dur_loss=1.678, generator_adv_loss=1.984, generator_feat_match_loss=6.818, over 48.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.361, discriminator_fake_loss=1.321, generator_loss=29.2, generator_mel_loss=17.76, generator_kl_loss=1.41, generator_dur_loss=1.748, generator_adv_loss=2.012, generator_feat_match_loss=6.271, over 1144.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:57:17,529 INFO [train.py:527] (5/6) Epoch 969, batch 68, global_batch_idx: 120100, batch size: 14, loss[discriminator_loss=2.651, discriminator_real_loss=1.408, discriminator_fake_loss=1.243, generator_loss=30.34, generator_mel_loss=18.23, generator_kl_loss=1.668, generator_dur_loss=1.596, generator_adv_loss=2.005, generator_feat_match_loss=6.843, over 14.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.352, discriminator_fake_loss=1.314, generator_loss=29.21, generator_mel_loss=17.76, generator_kl_loss=1.439, generator_dur_loss=1.728, generator_adv_loss=2.02, generator_feat_match_loss=6.266, over 3724.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:59:34,222 INFO [train.py:527] (5/6) Epoch 969, batch 118, global_batch_idx: 120150, batch size: 25, loss[discriminator_loss=2.554, discriminator_real_loss=1.324, discriminator_fake_loss=1.23, generator_loss=30.94, generator_mel_loss=18.36, generator_kl_loss=2.002, generator_dur_loss=1.554, generator_adv_loss=2.138, generator_feat_match_loss=6.883, over 25.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.352, discriminator_fake_loss=1.319, generator_loss=29.21, generator_mel_loss=17.75, generator_kl_loss=1.445, generator_dur_loss=1.728, generator_adv_loss=2.016, generator_feat_match_loss=6.276, over 6515.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 03:59:49,961 INFO [train.py:919] (5/6) Start epoch 970 +2024-03-16 04:02:16,060 INFO [train.py:527] (5/6) Epoch 970, batch 44, global_batch_idx: 120200, batch size: 31, loss[discriminator_loss=2.595, discriminator_real_loss=1.3, discriminator_fake_loss=1.295, generator_loss=30.63, generator_mel_loss=18.55, generator_kl_loss=1.64, generator_dur_loss=1.549, generator_adv_loss=2.09, generator_feat_match_loss=6.794, over 31.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=29.2, generator_mel_loss=17.77, generator_kl_loss=1.407, generator_dur_loss=1.757, generator_adv_loss=2.014, generator_feat_match_loss=6.251, over 2772.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:02:16,061 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 04:02:23,798 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 04:04:41,787 INFO [train.py:527] (5/6) Epoch 970, batch 94, global_batch_idx: 120250, batch size: 25, loss[discriminator_loss=2.622, discriminator_real_loss=1.294, discriminator_fake_loss=1.328, generator_loss=30.83, generator_mel_loss=18.55, generator_kl_loss=1.85, generator_dur_loss=1.543, generator_adv_loss=2.213, generator_feat_match_loss=6.673, over 25.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.2, generator_mel_loss=17.73, generator_kl_loss=1.431, generator_dur_loss=1.742, generator_adv_loss=2.017, generator_feat_match_loss=6.278, over 5476.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:06:00,708 INFO [train.py:919] (5/6) Start epoch 971 +2024-03-16 04:07:16,753 INFO [train.py:527] (5/6) Epoch 971, batch 20, global_batch_idx: 120300, batch size: 44, loss[discriminator_loss=2.638, discriminator_real_loss=1.281, discriminator_fake_loss=1.357, generator_loss=29.02, generator_mel_loss=17.63, generator_kl_loss=1.508, generator_dur_loss=1.672, generator_adv_loss=2.106, generator_feat_match_loss=6.105, over 44.00 samples.], tot_loss[discriminator_loss=2.641, discriminator_real_loss=1.334, discriminator_fake_loss=1.307, generator_loss=29.27, generator_mel_loss=17.6, generator_kl_loss=1.465, generator_dur_loss=1.748, generator_adv_loss=2.058, generator_feat_match_loss=6.403, over 1283.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:09:34,979 INFO [train.py:527] (5/6) Epoch 971, batch 70, global_batch_idx: 120350, batch size: 48, loss[discriminator_loss=2.672, discriminator_real_loss=1.355, discriminator_fake_loss=1.317, generator_loss=28.83, generator_mel_loss=17.54, generator_kl_loss=1.47, generator_dur_loss=1.66, generator_adv_loss=1.996, generator_feat_match_loss=6.163, over 48.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.353, discriminator_fake_loss=1.31, generator_loss=29.16, generator_mel_loss=17.62, generator_kl_loss=1.471, generator_dur_loss=1.739, generator_adv_loss=2.027, generator_feat_match_loss=6.304, over 4065.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:11:53,991 INFO [train.py:527] (5/6) Epoch 971, batch 120, global_batch_idx: 120400, batch size: 80, loss[discriminator_loss=2.62, discriminator_real_loss=1.338, discriminator_fake_loss=1.282, generator_loss=29.33, generator_mel_loss=17.54, generator_kl_loss=1.331, generator_dur_loss=1.801, generator_adv_loss=2.082, generator_feat_match_loss=6.575, over 80.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.353, discriminator_fake_loss=1.313, generator_loss=29.1, generator_mel_loss=17.61, generator_kl_loss=1.464, generator_dur_loss=1.74, generator_adv_loss=2.017, generator_feat_match_loss=6.272, over 6861.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:11:53,993 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 04:12:02,758 INFO [train.py:591] (5/6) 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 04:12:02,759 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 04:12:12,194 INFO [train.py:919] (5/6) Start epoch 972 +2024-03-16 04:14:42,818 INFO [train.py:527] (5/6) Epoch 972, batch 46, global_batch_idx: 120450, batch size: 58, loss[discriminator_loss=2.627, discriminator_real_loss=1.355, discriminator_fake_loss=1.272, generator_loss=28.18, generator_mel_loss=17.15, generator_kl_loss=1.276, generator_dur_loss=1.765, generator_adv_loss=2.008, generator_feat_match_loss=5.983, over 58.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.352, discriminator_fake_loss=1.327, generator_loss=29.02, generator_mel_loss=17.67, generator_kl_loss=1.422, generator_dur_loss=1.751, generator_adv_loss=2, generator_feat_match_loss=6.18, over 2921.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:17:02,585 INFO [train.py:527] (5/6) Epoch 972, batch 96, global_batch_idx: 120500, batch size: 74, loss[discriminator_loss=2.696, discriminator_real_loss=1.43, discriminator_fake_loss=1.266, generator_loss=28.74, generator_mel_loss=17.66, generator_kl_loss=1.33, generator_dur_loss=1.759, generator_adv_loss=1.9, generator_feat_match_loss=6.09, over 74.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.347, discriminator_fake_loss=1.323, generator_loss=29.14, generator_mel_loss=17.7, generator_kl_loss=1.443, generator_dur_loss=1.739, generator_adv_loss=2.01, generator_feat_match_loss=6.241, over 5627.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:18:19,256 INFO [train.py:919] (5/6) Start epoch 973 +2024-03-16 04:19:42,309 INFO [train.py:527] (5/6) Epoch 973, batch 22, global_batch_idx: 120550, batch size: 25, loss[discriminator_loss=2.651, discriminator_real_loss=1.208, discriminator_fake_loss=1.443, generator_loss=32.38, generator_mel_loss=18.11, generator_kl_loss=1.834, generator_dur_loss=1.538, generator_adv_loss=2.328, generator_feat_match_loss=8.567, over 25.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.341, discriminator_fake_loss=1.325, generator_loss=29.34, generator_mel_loss=17.75, generator_kl_loss=1.448, generator_dur_loss=1.705, generator_adv_loss=1.999, generator_feat_match_loss=6.436, over 1229.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:22:01,989 INFO [train.py:527] (5/6) Epoch 973, batch 72, global_batch_idx: 120600, batch size: 55, loss[discriminator_loss=2.67, discriminator_real_loss=1.443, discriminator_fake_loss=1.227, generator_loss=28.91, generator_mel_loss=17.51, generator_kl_loss=1.555, generator_dur_loss=1.664, generator_adv_loss=1.977, generator_feat_match_loss=6.195, over 55.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.347, discriminator_fake_loss=1.316, generator_loss=29.3, generator_mel_loss=17.7, generator_kl_loss=1.457, generator_dur_loss=1.718, generator_adv_loss=2.01, generator_feat_match_loss=6.411, over 4162.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:22:01,991 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 04:22:10,204 INFO [train.py:591] (5/6) 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 04:22:10,205 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 04:24:28,784 INFO [train.py:527] (5/6) Epoch 973, batch 122, global_batch_idx: 120650, batch size: 25, loss[discriminator_loss=2.612, discriminator_real_loss=1.38, discriminator_fake_loss=1.231, generator_loss=29.8, generator_mel_loss=17.66, generator_kl_loss=1.85, generator_dur_loss=1.557, generator_adv_loss=2.154, generator_feat_match_loss=6.574, over 25.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.348, discriminator_fake_loss=1.319, generator_loss=29.34, generator_mel_loss=17.74, generator_kl_loss=1.455, generator_dur_loss=1.72, generator_adv_loss=2.016, generator_feat_match_loss=6.408, over 6808.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:24:35,050 INFO [train.py:919] (5/6) Start epoch 974 +2024-03-16 04:27:14,400 INFO [train.py:527] (5/6) Epoch 974, batch 48, global_batch_idx: 120700, batch size: 47, loss[discriminator_loss=2.604, discriminator_real_loss=1.409, discriminator_fake_loss=1.196, generator_loss=28.66, generator_mel_loss=17.24, generator_kl_loss=1.46, generator_dur_loss=1.622, generator_adv_loss=2.193, generator_feat_match_loss=6.144, over 47.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.347, discriminator_fake_loss=1.313, generator_loss=29.25, generator_mel_loss=17.65, generator_kl_loss=1.445, generator_dur_loss=1.74, generator_adv_loss=2.033, generator_feat_match_loss=6.378, over 2778.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:29:32,825 INFO [train.py:527] (5/6) Epoch 974, batch 98, global_batch_idx: 120750, batch size: 48, loss[discriminator_loss=2.699, discriminator_real_loss=1.329, discriminator_fake_loss=1.371, generator_loss=28.83, generator_mel_loss=17.46, generator_kl_loss=1.401, generator_dur_loss=1.638, generator_adv_loss=1.989, generator_feat_match_loss=6.345, over 48.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.351, discriminator_fake_loss=1.314, generator_loss=29.31, generator_mel_loss=17.7, generator_kl_loss=1.448, generator_dur_loss=1.729, generator_adv_loss=2.039, generator_feat_match_loss=6.39, over 5382.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:30:42,109 INFO [train.py:919] (5/6) Start epoch 975 +2024-03-16 04:32:14,161 INFO [train.py:527] (5/6) Epoch 975, batch 24, global_batch_idx: 120800, batch size: 45, loss[discriminator_loss=2.65, discriminator_real_loss=1.289, discriminator_fake_loss=1.361, generator_loss=29, generator_mel_loss=17.91, generator_kl_loss=1.429, generator_dur_loss=1.686, generator_adv_loss=2.037, generator_feat_match_loss=5.94, over 45.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.364, discriminator_fake_loss=1.32, generator_loss=29.12, generator_mel_loss=17.58, generator_kl_loss=1.394, generator_dur_loss=1.773, generator_adv_loss=2.001, generator_feat_match_loss=6.378, over 1545.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:32:14,162 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 04:32:22,021 INFO [train.py:591] (5/6) 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 04:32:22,022 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 04:34:43,078 INFO [train.py:527] (5/6) Epoch 975, batch 74, global_batch_idx: 120850, batch size: 59, loss[discriminator_loss=2.672, discriminator_real_loss=1.41, discriminator_fake_loss=1.261, generator_loss=29.34, generator_mel_loss=17.7, generator_kl_loss=1.392, generator_dur_loss=1.772, generator_adv_loss=2.09, generator_feat_match_loss=6.383, over 59.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.356, discriminator_fake_loss=1.322, generator_loss=29.15, generator_mel_loss=17.62, generator_kl_loss=1.437, generator_dur_loss=1.749, generator_adv_loss=2, generator_feat_match_loss=6.34, over 4355.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:36:58,805 INFO [train.py:919] (5/6) Start epoch 976 +2024-03-16 04:37:24,086 INFO [train.py:527] (5/6) Epoch 976, batch 0, global_batch_idx: 120900, batch size: 58, loss[discriminator_loss=2.66, discriminator_real_loss=1.288, discriminator_fake_loss=1.372, generator_loss=29.18, generator_mel_loss=18.02, generator_kl_loss=1.437, generator_dur_loss=1.74, generator_adv_loss=2.088, generator_feat_match_loss=5.895, over 58.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.288, discriminator_fake_loss=1.372, generator_loss=29.18, generator_mel_loss=18.02, generator_kl_loss=1.437, generator_dur_loss=1.74, generator_adv_loss=2.088, generator_feat_match_loss=5.895, over 58.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:39:43,137 INFO [train.py:527] (5/6) Epoch 976, batch 50, global_batch_idx: 120950, batch size: 74, loss[discriminator_loss=2.639, discriminator_real_loss=1.372, discriminator_fake_loss=1.266, generator_loss=29.43, generator_mel_loss=17.71, generator_kl_loss=1.328, generator_dur_loss=1.782, generator_adv_loss=1.945, generator_feat_match_loss=6.66, over 74.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.369, discriminator_fake_loss=1.315, generator_loss=29.18, generator_mel_loss=17.73, generator_kl_loss=1.437, generator_dur_loss=1.737, generator_adv_loss=2.031, generator_feat_match_loss=6.241, over 2871.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:42:03,696 INFO [train.py:527] (5/6) Epoch 976, batch 100, global_batch_idx: 121000, batch size: 72, loss[discriminator_loss=2.684, discriminator_real_loss=1.316, discriminator_fake_loss=1.368, generator_loss=29.39, generator_mel_loss=17.98, generator_kl_loss=1.449, generator_dur_loss=1.837, generator_adv_loss=1.868, generator_feat_match_loss=6.256, over 72.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.36, discriminator_fake_loss=1.314, generator_loss=29.2, generator_mel_loss=17.75, generator_kl_loss=1.439, generator_dur_loss=1.739, generator_adv_loss=2.018, generator_feat_match_loss=6.249, over 5647.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:42:03,697 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 04:42:12,325 INFO [train.py:591] (5/6) 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 04:42:12,325 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 04:43:17,284 INFO [train.py:919] (5/6) Start epoch 977 +2024-03-16 04:44:52,764 INFO [train.py:527] (5/6) Epoch 977, batch 26, global_batch_idx: 121050, batch size: 58, loss[discriminator_loss=2.689, discriminator_real_loss=1.353, discriminator_fake_loss=1.336, generator_loss=28.49, generator_mel_loss=17.39, generator_kl_loss=1.542, generator_dur_loss=1.767, generator_adv_loss=1.981, generator_feat_match_loss=5.803, over 58.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.326, generator_loss=29.21, generator_mel_loss=17.72, generator_kl_loss=1.532, generator_dur_loss=1.757, generator_adv_loss=2.005, generator_feat_match_loss=6.2, over 1668.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:47:15,544 INFO [train.py:527] (5/6) Epoch 977, batch 76, global_batch_idx: 121100, batch size: 74, loss[discriminator_loss=2.709, discriminator_real_loss=1.309, discriminator_fake_loss=1.4, generator_loss=29.11, generator_mel_loss=17.55, generator_kl_loss=1.388, generator_dur_loss=1.747, generator_adv_loss=2.05, generator_feat_match_loss=6.377, over 74.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.349, discriminator_fake_loss=1.322, generator_loss=29.28, generator_mel_loss=17.75, generator_kl_loss=1.494, generator_dur_loss=1.761, generator_adv_loss=2.013, generator_feat_match_loss=6.259, over 4820.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:49:23,602 INFO [train.py:919] (5/6) Start epoch 978 +2024-03-16 04:49:52,891 INFO [train.py:527] (5/6) Epoch 978, batch 2, global_batch_idx: 121150, batch size: 72, loss[discriminator_loss=2.635, discriminator_real_loss=1.371, discriminator_fake_loss=1.264, generator_loss=30.42, generator_mel_loss=17.95, generator_kl_loss=1.598, generator_dur_loss=1.76, generator_adv_loss=2.002, generator_feat_match_loss=7.117, over 72.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.325, discriminator_fake_loss=1.359, generator_loss=30.22, generator_mel_loss=18, generator_kl_loss=1.486, generator_dur_loss=1.798, generator_adv_loss=2.084, generator_feat_match_loss=6.85, over 207.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:52:09,656 INFO [train.py:527] (5/6) Epoch 978, batch 52, global_batch_idx: 121200, batch size: 72, loss[discriminator_loss=2.664, discriminator_real_loss=1.356, discriminator_fake_loss=1.308, generator_loss=29.3, generator_mel_loss=17.31, generator_kl_loss=1.409, generator_dur_loss=1.779, generator_adv_loss=2.042, generator_feat_match_loss=6.759, over 72.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.354, discriminator_fake_loss=1.332, generator_loss=29.22, generator_mel_loss=17.76, generator_kl_loss=1.495, generator_dur_loss=1.734, generator_adv_loss=2.008, generator_feat_match_loss=6.224, over 2893.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:52:09,657 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 04:52:17,759 INFO [train.py:591] (5/6) 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 04:52:17,760 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 04:54:33,275 INFO [train.py:527] (5/6) Epoch 978, batch 102, global_batch_idx: 121250, batch size: 31, loss[discriminator_loss=2.715, discriminator_real_loss=1.426, discriminator_fake_loss=1.289, generator_loss=28.71, generator_mel_loss=17.78, generator_kl_loss=1.49, generator_dur_loss=1.655, generator_adv_loss=1.981, generator_feat_match_loss=5.811, over 31.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=29.16, generator_mel_loss=17.71, generator_kl_loss=1.455, generator_dur_loss=1.742, generator_adv_loss=2.01, generator_feat_match_loss=6.241, over 5821.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:55:35,646 INFO [train.py:919] (5/6) Start epoch 979 +2024-03-16 04:57:14,741 INFO [train.py:527] (5/6) Epoch 979, batch 28, global_batch_idx: 121300, batch size: 64, loss[discriminator_loss=2.642, discriminator_real_loss=1.287, discriminator_fake_loss=1.355, generator_loss=30, generator_mel_loss=17.88, generator_kl_loss=1.434, generator_dur_loss=1.765, generator_adv_loss=1.963, generator_feat_match_loss=6.953, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.338, discriminator_fake_loss=1.338, generator_loss=29.54, generator_mel_loss=17.78, generator_kl_loss=1.449, generator_dur_loss=1.723, generator_adv_loss=2.013, generator_feat_match_loss=6.579, over 1540.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 04:59:31,913 INFO [train.py:527] (5/6) Epoch 979, batch 78, global_batch_idx: 121350, batch size: 53, loss[discriminator_loss=2.714, discriminator_real_loss=1.46, discriminator_fake_loss=1.254, generator_loss=29.49, generator_mel_loss=18.04, generator_kl_loss=1.458, generator_dur_loss=1.685, generator_adv_loss=1.954, generator_feat_match_loss=6.349, over 53.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.324, generator_loss=29.27, generator_mel_loss=17.7, generator_kl_loss=1.449, generator_dur_loss=1.735, generator_adv_loss=2.01, generator_feat_match_loss=6.369, over 4306.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:01:39,358 INFO [train.py:919] (5/6) Start epoch 980 +2024-03-16 05:02:15,970 INFO [train.py:527] (5/6) Epoch 980, batch 4, global_batch_idx: 121400, batch size: 48, loss[discriminator_loss=2.708, discriminator_real_loss=1.404, discriminator_fake_loss=1.304, generator_loss=29.55, generator_mel_loss=17.24, generator_kl_loss=1.518, generator_dur_loss=1.698, generator_adv_loss=1.935, generator_feat_match_loss=7.164, over 48.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.377, discriminator_fake_loss=1.31, generator_loss=29.6, generator_mel_loss=17.81, generator_kl_loss=1.441, generator_dur_loss=1.698, generator_adv_loss=2.052, generator_feat_match_loss=6.596, over 270.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:02:15,972 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 05:02:23,771 INFO [train.py:591] (5/6) 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 05:02:23,773 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 05:04:44,217 INFO [train.py:527] (5/6) Epoch 980, batch 54, global_batch_idx: 121450, batch size: 62, loss[discriminator_loss=2.661, discriminator_real_loss=1.341, discriminator_fake_loss=1.321, generator_loss=29.21, generator_mel_loss=17.71, generator_kl_loss=1.327, generator_dur_loss=1.752, generator_adv_loss=2.074, generator_feat_match_loss=6.346, over 62.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=29.21, generator_mel_loss=17.73, generator_kl_loss=1.443, generator_dur_loss=1.737, generator_adv_loss=2.005, generator_feat_match_loss=6.299, over 3193.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:07:02,449 INFO [train.py:527] (5/6) Epoch 980, batch 104, global_batch_idx: 121500, batch size: 25, loss[discriminator_loss=2.674, discriminator_real_loss=1.241, discriminator_fake_loss=1.433, generator_loss=31.95, generator_mel_loss=18.66, generator_kl_loss=1.54, generator_dur_loss=1.565, generator_adv_loss=2.13, generator_feat_match_loss=8.057, over 25.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=29.25, generator_mel_loss=17.75, generator_kl_loss=1.445, generator_dur_loss=1.728, generator_adv_loss=2.007, generator_feat_match_loss=6.311, over 5856.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:07:54,983 INFO [train.py:919] (5/6) Start epoch 981 +2024-03-16 05:09:42,431 INFO [train.py:527] (5/6) Epoch 981, batch 30, global_batch_idx: 121550, batch size: 31, loss[discriminator_loss=2.719, discriminator_real_loss=1.364, discriminator_fake_loss=1.356, generator_loss=28.7, generator_mel_loss=17.75, generator_kl_loss=1.738, generator_dur_loss=1.648, generator_adv_loss=2.058, generator_feat_match_loss=5.499, over 31.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.351, discriminator_fake_loss=1.319, generator_loss=29.11, generator_mel_loss=17.66, generator_kl_loss=1.452, generator_dur_loss=1.716, generator_adv_loss=2.014, generator_feat_match_loss=6.264, over 1644.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:12:00,816 INFO [train.py:527] (5/6) Epoch 981, batch 80, global_batch_idx: 121600, batch size: 62, loss[discriminator_loss=2.711, discriminator_real_loss=1.292, discriminator_fake_loss=1.419, generator_loss=29.65, generator_mel_loss=17.75, generator_kl_loss=1.4, generator_dur_loss=1.71, generator_adv_loss=2.142, generator_feat_match_loss=6.648, over 62.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.19, generator_mel_loss=17.69, generator_kl_loss=1.447, generator_dur_loss=1.72, generator_adv_loss=2.013, generator_feat_match_loss=6.315, over 4388.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:12:00,818 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 05:12:09,456 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 05:14:12,778 INFO [train.py:919] (5/6) Start epoch 982 +2024-03-16 05:14:52,280 INFO [train.py:527] (5/6) Epoch 982, batch 6, global_batch_idx: 121650, batch size: 50, loss[discriminator_loss=2.667, discriminator_real_loss=1.325, discriminator_fake_loss=1.343, generator_loss=29.16, generator_mel_loss=18.26, generator_kl_loss=1.511, generator_dur_loss=1.734, generator_adv_loss=2.022, generator_feat_match_loss=5.636, over 50.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.383, discriminator_fake_loss=1.294, generator_loss=29.44, generator_mel_loss=17.94, generator_kl_loss=1.455, generator_dur_loss=1.753, generator_adv_loss=2.037, generator_feat_match_loss=6.252, over 373.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:17:09,849 INFO [train.py:527] (5/6) Epoch 982, batch 56, global_batch_idx: 121700, batch size: 15, loss[discriminator_loss=2.686, discriminator_real_loss=1.097, discriminator_fake_loss=1.589, generator_loss=32.28, generator_mel_loss=18.01, generator_kl_loss=1.942, generator_dur_loss=1.547, generator_adv_loss=1.942, generator_feat_match_loss=8.839, over 15.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.32, generator_loss=29.28, generator_mel_loss=17.77, generator_kl_loss=1.446, generator_dur_loss=1.729, generator_adv_loss=2.021, generator_feat_match_loss=6.318, over 3156.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:19:30,274 INFO [train.py:527] (5/6) Epoch 982, batch 106, global_batch_idx: 121750, batch size: 47, loss[discriminator_loss=2.677, discriminator_real_loss=1.352, discriminator_fake_loss=1.325, generator_loss=29.9, generator_mel_loss=17.87, generator_kl_loss=1.677, generator_dur_loss=1.67, generator_adv_loss=2.207, generator_feat_match_loss=6.48, over 47.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.359, discriminator_fake_loss=1.319, generator_loss=29.29, generator_mel_loss=17.78, generator_kl_loss=1.448, generator_dur_loss=1.728, generator_adv_loss=2.019, generator_feat_match_loss=6.311, over 6039.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:20:20,847 INFO [train.py:919] (5/6) Start epoch 983 +2024-03-16 05:22:11,840 INFO [train.py:527] (5/6) Epoch 983, batch 32, global_batch_idx: 121800, batch size: 15, loss[discriminator_loss=2.782, discriminator_real_loss=1.442, discriminator_fake_loss=1.34, generator_loss=28.62, generator_mel_loss=17.6, generator_kl_loss=1.725, generator_dur_loss=1.539, generator_adv_loss=2.116, generator_feat_match_loss=5.643, over 15.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.35, discriminator_fake_loss=1.324, generator_loss=29.24, generator_mel_loss=17.81, generator_kl_loss=1.461, generator_dur_loss=1.726, generator_adv_loss=2.02, generator_feat_match_loss=6.222, over 1833.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:22:11,842 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 05:22:19,997 INFO [train.py:591] (5/6) 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 05:22:19,998 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 05:24:35,224 INFO [train.py:527] (5/6) Epoch 983, batch 82, global_batch_idx: 121850, batch size: 77, loss[discriminator_loss=2.682, discriminator_real_loss=1.365, discriminator_fake_loss=1.316, generator_loss=27.86, generator_mel_loss=17.2, generator_kl_loss=1.138, generator_dur_loss=1.779, generator_adv_loss=2.043, generator_feat_match_loss=5.699, over 77.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.355, discriminator_fake_loss=1.316, generator_loss=29.24, generator_mel_loss=17.77, generator_kl_loss=1.478, generator_dur_loss=1.714, generator_adv_loss=2.026, generator_feat_match_loss=6.254, over 4273.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:26:30,250 INFO [train.py:919] (5/6) Start epoch 984 +2024-03-16 05:27:17,842 INFO [train.py:527] (5/6) Epoch 984, batch 8, global_batch_idx: 121900, batch size: 83, loss[discriminator_loss=2.644, discriminator_real_loss=1.351, discriminator_fake_loss=1.293, generator_loss=29.18, generator_mel_loss=17.74, generator_kl_loss=1.259, generator_dur_loss=1.779, generator_adv_loss=1.958, generator_feat_match_loss=6.448, over 83.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.337, discriminator_fake_loss=1.322, generator_loss=29.4, generator_mel_loss=17.87, generator_kl_loss=1.449, generator_dur_loss=1.72, generator_adv_loss=2.005, generator_feat_match_loss=6.356, over 507.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:29:35,372 INFO [train.py:527] (5/6) Epoch 984, batch 58, global_batch_idx: 121950, batch size: 58, loss[discriminator_loss=2.715, discriminator_real_loss=1.334, discriminator_fake_loss=1.381, generator_loss=28.73, generator_mel_loss=17.55, generator_kl_loss=1.567, generator_dur_loss=1.717, generator_adv_loss=2.057, generator_feat_match_loss=5.842, over 58.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.348, discriminator_fake_loss=1.318, generator_loss=29.29, generator_mel_loss=17.74, generator_kl_loss=1.454, generator_dur_loss=1.728, generator_adv_loss=2.018, generator_feat_match_loss=6.354, over 3341.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:31:53,542 INFO [train.py:527] (5/6) Epoch 984, batch 108, global_batch_idx: 122000, batch size: 72, loss[discriminator_loss=2.67, discriminator_real_loss=1.346, discriminator_fake_loss=1.325, generator_loss=29.01, generator_mel_loss=17.41, generator_kl_loss=1.42, generator_dur_loss=1.81, generator_adv_loss=1.985, generator_feat_match_loss=6.383, over 72.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.353, discriminator_fake_loss=1.323, generator_loss=29.15, generator_mel_loss=17.71, generator_kl_loss=1.448, generator_dur_loss=1.725, generator_adv_loss=2.005, generator_feat_match_loss=6.266, over 6049.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:31:53,544 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 05:32:02,175 INFO [train.py:591] (5/6) 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 05:32:02,175 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 05:32:45,054 INFO [train.py:919] (5/6) Start epoch 985 +2024-03-16 05:34:45,311 INFO [train.py:527] (5/6) Epoch 985, batch 34, global_batch_idx: 122050, batch size: 80, loss[discriminator_loss=2.684, discriminator_real_loss=1.402, discriminator_fake_loss=1.282, generator_loss=28.89, generator_mel_loss=17.69, generator_kl_loss=1.414, generator_dur_loss=1.798, generator_adv_loss=1.956, generator_feat_match_loss=6.029, over 80.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.356, discriminator_fake_loss=1.322, generator_loss=29.23, generator_mel_loss=17.73, generator_kl_loss=1.441, generator_dur_loss=1.75, generator_adv_loss=2, generator_feat_match_loss=6.305, over 2074.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:37:03,529 INFO [train.py:527] (5/6) Epoch 985, batch 84, global_batch_idx: 122100, batch size: 36, loss[discriminator_loss=2.672, discriminator_real_loss=1.42, discriminator_fake_loss=1.251, generator_loss=29.99, generator_mel_loss=17.85, generator_kl_loss=1.613, generator_dur_loss=1.7, generator_adv_loss=2.105, generator_feat_match_loss=6.724, over 36.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.22, generator_mel_loss=17.71, generator_kl_loss=1.469, generator_dur_loss=1.738, generator_adv_loss=2.008, generator_feat_match_loss=6.297, over 4727.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:38:51,228 INFO [train.py:919] (5/6) Start epoch 986 +2024-03-16 05:39:43,684 INFO [train.py:527] (5/6) Epoch 986, batch 10, global_batch_idx: 122150, batch size: 61, loss[discriminator_loss=2.725, discriminator_real_loss=1.359, discriminator_fake_loss=1.366, generator_loss=29.59, generator_mel_loss=18.06, generator_kl_loss=1.463, generator_dur_loss=1.753, generator_adv_loss=2.024, generator_feat_match_loss=6.292, over 61.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.387, discriminator_fake_loss=1.335, generator_loss=29.33, generator_mel_loss=17.85, generator_kl_loss=1.494, generator_dur_loss=1.713, generator_adv_loss=1.972, generator_feat_match_loss=6.301, over 505.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:42:01,279 INFO [train.py:527] (5/6) Epoch 986, batch 60, global_batch_idx: 122200, batch size: 36, loss[discriminator_loss=2.659, discriminator_real_loss=1.305, discriminator_fake_loss=1.354, generator_loss=28.49, generator_mel_loss=17.38, generator_kl_loss=1.57, generator_dur_loss=1.688, generator_adv_loss=2.035, generator_feat_match_loss=5.815, over 36.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.364, discriminator_fake_loss=1.327, generator_loss=29.03, generator_mel_loss=17.67, generator_kl_loss=1.472, generator_dur_loss=1.725, generator_adv_loss=2, generator_feat_match_loss=6.162, over 3094.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:42:01,280 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 05:42:09,353 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 05:44:30,364 INFO [train.py:527] (5/6) Epoch 986, batch 110, global_batch_idx: 122250, batch size: 36, loss[discriminator_loss=2.638, discriminator_real_loss=1.424, discriminator_fake_loss=1.214, generator_loss=29.57, generator_mel_loss=17.5, generator_kl_loss=1.599, generator_dur_loss=1.652, generator_adv_loss=1.899, generator_feat_match_loss=6.919, over 36.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.322, generator_loss=29.06, generator_mel_loss=17.66, generator_kl_loss=1.47, generator_dur_loss=1.733, generator_adv_loss=2.008, generator_feat_match_loss=6.195, over 6054.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:45:07,290 INFO [train.py:919] (5/6) Start epoch 987 +2024-03-16 05:47:13,330 INFO [train.py:527] (5/6) Epoch 987, batch 36, global_batch_idx: 122300, batch size: 68, loss[discriminator_loss=2.643, discriminator_real_loss=1.339, discriminator_fake_loss=1.304, generator_loss=29.81, generator_mel_loss=18.19, generator_kl_loss=1.374, generator_dur_loss=1.791, generator_adv_loss=1.963, generator_feat_match_loss=6.486, over 68.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.346, discriminator_fake_loss=1.325, generator_loss=29.14, generator_mel_loss=17.71, generator_kl_loss=1.445, generator_dur_loss=1.72, generator_adv_loss=2.026, generator_feat_match_loss=6.243, over 2180.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:49:36,728 INFO [train.py:527] (5/6) Epoch 987, batch 86, global_batch_idx: 122350, batch size: 55, loss[discriminator_loss=2.676, discriminator_real_loss=1.323, discriminator_fake_loss=1.353, generator_loss=28.35, generator_mel_loss=17.37, generator_kl_loss=1.41, generator_dur_loss=1.679, generator_adv_loss=2.052, generator_feat_match_loss=5.843, over 55.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.348, discriminator_fake_loss=1.322, generator_loss=29.21, generator_mel_loss=17.7, generator_kl_loss=1.47, generator_dur_loss=1.723, generator_adv_loss=2.025, generator_feat_match_loss=6.297, over 5063.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:51:16,999 INFO [train.py:919] (5/6) Start epoch 988 +2024-03-16 05:52:15,210 INFO [train.py:527] (5/6) Epoch 988, batch 12, global_batch_idx: 122400, batch size: 59, loss[discriminator_loss=2.68, discriminator_real_loss=1.336, discriminator_fake_loss=1.344, generator_loss=29.32, generator_mel_loss=17.6, generator_kl_loss=1.356, generator_dur_loss=1.757, generator_adv_loss=2.027, generator_feat_match_loss=6.571, over 59.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.344, discriminator_fake_loss=1.328, generator_loss=28.98, generator_mel_loss=17.52, generator_kl_loss=1.46, generator_dur_loss=1.747, generator_adv_loss=1.99, generator_feat_match_loss=6.256, over 856.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:52:15,213 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 05:52:23,028 INFO [train.py:591] (5/6) 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 05:52:23,030 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 05:54:41,325 INFO [train.py:527] (5/6) Epoch 988, batch 62, global_batch_idx: 122450, batch size: 56, loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=28.8, generator_mel_loss=17.61, generator_kl_loss=1.558, generator_dur_loss=1.746, generator_adv_loss=1.963, generator_feat_match_loss=5.919, over 56.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.323, generator_loss=29.11, generator_mel_loss=17.71, generator_kl_loss=1.459, generator_dur_loss=1.73, generator_adv_loss=2.004, generator_feat_match_loss=6.207, over 3602.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:57:00,775 INFO [train.py:527] (5/6) Epoch 988, batch 112, global_batch_idx: 122500, batch size: 88, loss[discriminator_loss=2.687, discriminator_real_loss=1.306, discriminator_fake_loss=1.38, generator_loss=28.87, generator_mel_loss=17.67, generator_kl_loss=1.469, generator_dur_loss=1.794, generator_adv_loss=2.146, generator_feat_match_loss=5.786, over 88.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.348, discriminator_fake_loss=1.322, generator_loss=29.17, generator_mel_loss=17.71, generator_kl_loss=1.445, generator_dur_loss=1.739, generator_adv_loss=2.018, generator_feat_match_loss=6.256, over 6742.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 05:57:31,438 INFO [train.py:919] (5/6) Start epoch 989 +2024-03-16 05:59:43,202 INFO [train.py:527] (5/6) Epoch 989, batch 38, global_batch_idx: 122550, batch size: 66, loss[discriminator_loss=2.658, discriminator_real_loss=1.307, discriminator_fake_loss=1.35, generator_loss=28.89, generator_mel_loss=17.49, generator_kl_loss=1.387, generator_dur_loss=1.715, generator_adv_loss=1.901, generator_feat_match_loss=6.401, over 66.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.351, discriminator_fake_loss=1.329, generator_loss=29.14, generator_mel_loss=17.68, generator_kl_loss=1.449, generator_dur_loss=1.732, generator_adv_loss=1.996, generator_feat_match_loss=6.285, over 2315.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:02:03,371 INFO [train.py:527] (5/6) Epoch 989, batch 88, global_batch_idx: 122600, batch size: 61, loss[discriminator_loss=2.673, discriminator_real_loss=1.284, discriminator_fake_loss=1.39, generator_loss=29.29, generator_mel_loss=17.59, generator_kl_loss=1.319, generator_dur_loss=1.709, generator_adv_loss=2.117, generator_feat_match_loss=6.555, over 61.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.24, generator_mel_loss=17.69, generator_kl_loss=1.451, generator_dur_loss=1.728, generator_adv_loss=2.007, generator_feat_match_loss=6.359, over 5233.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:02:03,372 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 06:02:12,383 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 06:03:48,445 INFO [train.py:919] (5/6) Start epoch 990 +2024-03-16 06:04:49,359 INFO [train.py:527] (5/6) Epoch 990, batch 14, global_batch_idx: 122650, batch size: 83, loss[discriminator_loss=2.602, discriminator_real_loss=1.241, discriminator_fake_loss=1.361, generator_loss=30.23, generator_mel_loss=17.81, generator_kl_loss=1.388, generator_dur_loss=1.814, generator_adv_loss=2.185, generator_feat_match_loss=7.042, over 83.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.33, discriminator_fake_loss=1.327, generator_loss=29.26, generator_mel_loss=17.66, generator_kl_loss=1.425, generator_dur_loss=1.757, generator_adv_loss=2.005, generator_feat_match_loss=6.409, over 961.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:07:08,624 INFO [train.py:527] (5/6) Epoch 990, batch 64, global_batch_idx: 122700, batch size: 88, loss[discriminator_loss=2.676, discriminator_real_loss=1.339, discriminator_fake_loss=1.337, generator_loss=29.8, generator_mel_loss=17.74, generator_kl_loss=1.366, generator_dur_loss=1.837, generator_adv_loss=1.99, generator_feat_match_loss=6.866, over 88.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.341, discriminator_fake_loss=1.325, generator_loss=29.54, generator_mel_loss=17.78, generator_kl_loss=1.456, generator_dur_loss=1.743, generator_adv_loss=2.005, generator_feat_match_loss=6.553, over 3778.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:09:27,435 INFO [train.py:527] (5/6) Epoch 990, batch 114, global_batch_idx: 122750, batch size: 61, loss[discriminator_loss=2.689, discriminator_real_loss=1.445, discriminator_fake_loss=1.244, generator_loss=28.95, generator_mel_loss=17.65, generator_kl_loss=1.448, generator_dur_loss=1.714, generator_adv_loss=2.011, generator_feat_match_loss=6.12, over 61.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.345, discriminator_fake_loss=1.324, generator_loss=29.41, generator_mel_loss=17.76, generator_kl_loss=1.455, generator_dur_loss=1.737, generator_adv_loss=2.008, generator_feat_match_loss=6.452, over 6569.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:09:52,887 INFO [train.py:919] (5/6) Start epoch 991 +2024-03-16 06:12:07,880 INFO [train.py:527] (5/6) Epoch 991, batch 40, global_batch_idx: 122800, batch size: 59, loss[discriminator_loss=2.688, discriminator_real_loss=1.414, discriminator_fake_loss=1.275, generator_loss=29.13, generator_mel_loss=17.96, generator_kl_loss=1.571, generator_dur_loss=1.734, generator_adv_loss=2.012, generator_feat_match_loss=5.844, over 59.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.346, discriminator_fake_loss=1.324, generator_loss=29.3, generator_mel_loss=17.71, generator_kl_loss=1.474, generator_dur_loss=1.747, generator_adv_loss=2.015, generator_feat_match_loss=6.354, over 2499.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:12:07,882 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 06:12:15,877 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 06:14:34,124 INFO [train.py:527] (5/6) Epoch 991, batch 90, global_batch_idx: 122850, batch size: 83, loss[discriminator_loss=2.72, discriminator_real_loss=1.336, discriminator_fake_loss=1.384, generator_loss=29.73, generator_mel_loss=17.85, generator_kl_loss=1.417, generator_dur_loss=1.813, generator_adv_loss=2.089, generator_feat_match_loss=6.56, over 83.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.2, generator_mel_loss=17.67, generator_kl_loss=1.459, generator_dur_loss=1.743, generator_adv_loss=2.012, generator_feat_match_loss=6.324, over 5568.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:16:06,392 INFO [train.py:919] (5/6) Start epoch 992 +2024-03-16 06:17:12,684 INFO [train.py:527] (5/6) Epoch 992, batch 16, global_batch_idx: 122900, batch size: 13, loss[discriminator_loss=2.733, discriminator_real_loss=1.422, discriminator_fake_loss=1.311, generator_loss=25.98, generator_mel_loss=17.43, generator_kl_loss=1.595, generator_dur_loss=1.625, generator_adv_loss=2.055, generator_feat_match_loss=3.28, over 13.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.344, discriminator_fake_loss=1.325, generator_loss=29.46, generator_mel_loss=17.73, generator_kl_loss=1.428, generator_dur_loss=1.741, generator_adv_loss=2.012, generator_feat_match_loss=6.552, over 958.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:19:33,883 INFO [train.py:527] (5/6) Epoch 992, batch 66, global_batch_idx: 122950, batch size: 66, loss[discriminator_loss=2.698, discriminator_real_loss=1.334, discriminator_fake_loss=1.363, generator_loss=28.41, generator_mel_loss=17.82, generator_kl_loss=1.231, generator_dur_loss=1.763, generator_adv_loss=1.919, generator_feat_match_loss=5.675, over 66.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=29.23, generator_mel_loss=17.71, generator_kl_loss=1.422, generator_dur_loss=1.738, generator_adv_loss=2.014, generator_feat_match_loss=6.343, over 3888.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:21:53,130 INFO [train.py:527] (5/6) Epoch 992, batch 116, global_batch_idx: 123000, batch size: 31, loss[discriminator_loss=2.644, discriminator_real_loss=1.261, discriminator_fake_loss=1.382, generator_loss=30.05, generator_mel_loss=17.92, generator_kl_loss=1.779, generator_dur_loss=1.629, generator_adv_loss=2.165, generator_feat_match_loss=6.557, over 31.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.352, discriminator_fake_loss=1.324, generator_loss=29.23, generator_mel_loss=17.68, generator_kl_loss=1.437, generator_dur_loss=1.735, generator_adv_loss=2.017, generator_feat_match_loss=6.358, over 6685.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:21:53,132 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 06:22:02,305 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 06:22:21,626 INFO [train.py:919] (5/6) Start epoch 993 +2024-03-16 06:24:43,629 INFO [train.py:527] (5/6) Epoch 993, batch 42, global_batch_idx: 123050, batch size: 25, loss[discriminator_loss=2.679, discriminator_real_loss=1.399, discriminator_fake_loss=1.281, generator_loss=28.75, generator_mel_loss=18.07, generator_kl_loss=1.696, generator_dur_loss=1.579, generator_adv_loss=1.954, generator_feat_match_loss=5.45, over 25.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=29.41, generator_mel_loss=17.81, generator_kl_loss=1.457, generator_dur_loss=1.733, generator_adv_loss=2.014, generator_feat_match_loss=6.395, over 2451.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:27:03,415 INFO [train.py:527] (5/6) Epoch 993, batch 92, global_batch_idx: 123100, batch size: 58, loss[discriminator_loss=2.633, discriminator_real_loss=1.327, discriminator_fake_loss=1.306, generator_loss=29.37, generator_mel_loss=17.88, generator_kl_loss=1.46, generator_dur_loss=1.713, generator_adv_loss=1.956, generator_feat_match_loss=6.357, over 58.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.346, discriminator_fake_loss=1.324, generator_loss=29.25, generator_mel_loss=17.76, generator_kl_loss=1.445, generator_dur_loss=1.738, generator_adv_loss=2.019, generator_feat_match_loss=6.286, over 5356.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:28:28,027 INFO [train.py:919] (5/6) Start epoch 994 +2024-03-16 06:29:43,572 INFO [train.py:527] (5/6) Epoch 994, batch 18, global_batch_idx: 123150, batch size: 61, loss[discriminator_loss=2.673, discriminator_real_loss=1.298, discriminator_fake_loss=1.376, generator_loss=28.96, generator_mel_loss=18.09, generator_kl_loss=1.337, generator_dur_loss=1.723, generator_adv_loss=1.942, generator_feat_match_loss=5.861, over 61.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.335, discriminator_fake_loss=1.326, generator_loss=29.43, generator_mel_loss=17.81, generator_kl_loss=1.463, generator_dur_loss=1.728, generator_adv_loss=2.013, generator_feat_match_loss=6.417, over 1072.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:32:04,842 INFO [train.py:527] (5/6) Epoch 994, batch 68, global_batch_idx: 123200, batch size: 88, loss[discriminator_loss=2.713, discriminator_real_loss=1.445, discriminator_fake_loss=1.268, generator_loss=28.73, generator_mel_loss=17.51, generator_kl_loss=1.324, generator_dur_loss=1.797, generator_adv_loss=2.045, generator_feat_match_loss=6.06, over 88.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.353, discriminator_fake_loss=1.318, generator_loss=29.26, generator_mel_loss=17.71, generator_kl_loss=1.457, generator_dur_loss=1.743, generator_adv_loss=2.021, generator_feat_match_loss=6.332, over 4057.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:32:04,843 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 06:32:12,729 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 06:34:30,931 INFO [train.py:527] (5/6) Epoch 994, batch 118, global_batch_idx: 123250, batch size: 53, loss[discriminator_loss=2.697, discriminator_real_loss=1.314, discriminator_fake_loss=1.383, generator_loss=30.54, generator_mel_loss=17.99, generator_kl_loss=1.711, generator_dur_loss=1.674, generator_adv_loss=2.166, generator_feat_match_loss=6.997, over 53.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.352, discriminator_fake_loss=1.317, generator_loss=29.27, generator_mel_loss=17.71, generator_kl_loss=1.451, generator_dur_loss=1.741, generator_adv_loss=2.023, generator_feat_match_loss=6.349, over 6837.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:34:46,099 INFO [train.py:919] (5/6) Start epoch 995 +2024-03-16 06:37:11,332 INFO [train.py:527] (5/6) Epoch 995, batch 44, global_batch_idx: 123300, batch size: 50, loss[discriminator_loss=2.653, discriminator_real_loss=1.333, discriminator_fake_loss=1.32, generator_loss=29.45, generator_mel_loss=17.94, generator_kl_loss=1.531, generator_dur_loss=1.627, generator_adv_loss=2.033, generator_feat_match_loss=6.317, over 50.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.326, generator_loss=29.47, generator_mel_loss=17.78, generator_kl_loss=1.447, generator_dur_loss=1.742, generator_adv_loss=2.016, generator_feat_match_loss=6.479, over 2499.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:39:29,429 INFO [train.py:527] (5/6) Epoch 995, batch 94, global_batch_idx: 123350, batch size: 60, loss[discriminator_loss=2.668, discriminator_real_loss=1.406, discriminator_fake_loss=1.262, generator_loss=27.88, generator_mel_loss=17.52, generator_kl_loss=1.231, generator_dur_loss=1.766, generator_adv_loss=2.014, generator_feat_match_loss=5.346, over 60.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.35, discriminator_fake_loss=1.322, generator_loss=29.42, generator_mel_loss=17.7, generator_kl_loss=1.433, generator_dur_loss=1.741, generator_adv_loss=2.033, generator_feat_match_loss=6.508, over 5267.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:40:52,563 INFO [train.py:919] (5/6) Start epoch 996 +2024-03-16 06:42:12,822 INFO [train.py:527] (5/6) Epoch 996, batch 20, global_batch_idx: 123400, batch size: 68, loss[discriminator_loss=2.628, discriminator_real_loss=1.267, discriminator_fake_loss=1.361, generator_loss=30.26, generator_mel_loss=18.03, generator_kl_loss=1.28, generator_dur_loss=1.77, generator_adv_loss=1.965, generator_feat_match_loss=7.215, over 68.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=29.15, generator_mel_loss=17.6, generator_kl_loss=1.452, generator_dur_loss=1.742, generator_adv_loss=2.004, generator_feat_match_loss=6.358, over 1215.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:42:12,823 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 06:42:20,825 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 06:44:41,161 INFO [train.py:527] (5/6) Epoch 996, batch 70, global_batch_idx: 123450, batch size: 59, loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.331, generator_loss=29.11, generator_mel_loss=17.57, generator_kl_loss=1.315, generator_dur_loss=1.763, generator_adv_loss=1.996, generator_feat_match_loss=6.476, over 59.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.349, discriminator_fake_loss=1.322, generator_loss=29.22, generator_mel_loss=17.63, generator_kl_loss=1.442, generator_dur_loss=1.751, generator_adv_loss=2.006, generator_feat_match_loss=6.391, over 4265.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:46:58,089 INFO [train.py:527] (5/6) Epoch 996, batch 120, global_batch_idx: 123500, batch size: 36, loss[discriminator_loss=2.756, discriminator_real_loss=1.467, discriminator_fake_loss=1.289, generator_loss=27.56, generator_mel_loss=17.41, generator_kl_loss=1.496, generator_dur_loss=1.694, generator_adv_loss=1.975, generator_feat_match_loss=4.983, over 36.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.352, discriminator_fake_loss=1.324, generator_loss=29.25, generator_mel_loss=17.66, generator_kl_loss=1.446, generator_dur_loss=1.745, generator_adv_loss=2.009, generator_feat_match_loss=6.391, over 7011.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:47:07,830 INFO [train.py:919] (5/6) Start epoch 997 +2024-03-16 06:49:38,290 INFO [train.py:527] (5/6) Epoch 997, batch 46, global_batch_idx: 123550, batch size: 36, loss[discriminator_loss=2.642, discriminator_real_loss=1.366, discriminator_fake_loss=1.277, generator_loss=29.86, generator_mel_loss=17.68, generator_kl_loss=1.61, generator_dur_loss=1.611, generator_adv_loss=1.896, generator_feat_match_loss=7.06, over 36.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=29.25, generator_mel_loss=17.73, generator_kl_loss=1.415, generator_dur_loss=1.736, generator_adv_loss=2.015, generator_feat_match_loss=6.352, over 2740.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:51:52,897 INFO [train.py:527] (5/6) Epoch 997, batch 96, global_batch_idx: 123600, batch size: 77, loss[discriminator_loss=2.633, discriminator_real_loss=1.339, discriminator_fake_loss=1.294, generator_loss=28.52, generator_mel_loss=17.3, generator_kl_loss=1.258, generator_dur_loss=1.78, generator_adv_loss=2.107, generator_feat_match_loss=6.074, over 77.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.353, discriminator_fake_loss=1.327, generator_loss=29.24, generator_mel_loss=17.7, generator_kl_loss=1.439, generator_dur_loss=1.738, generator_adv_loss=2.015, generator_feat_match_loss=6.345, over 5704.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:51:52,899 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 06:52:00,767 INFO [train.py:591] (5/6) 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] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 06:53:18,849 INFO [train.py:919] (5/6) Start epoch 998 +2024-03-16 06:54:45,881 INFO [train.py:527] (5/6) Epoch 998, batch 22, global_batch_idx: 123650, batch size: 50, loss[discriminator_loss=2.63, discriminator_real_loss=1.252, discriminator_fake_loss=1.378, generator_loss=29.4, generator_mel_loss=18.22, generator_kl_loss=1.441, generator_dur_loss=1.673, generator_adv_loss=2.091, generator_feat_match_loss=5.977, over 50.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=29.39, generator_mel_loss=17.77, generator_kl_loss=1.459, generator_dur_loss=1.723, generator_adv_loss=2.019, generator_feat_match_loss=6.42, over 1300.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:57:01,492 INFO [train.py:527] (5/6) Epoch 998, batch 72, global_batch_idx: 123700, batch size: 68, loss[discriminator_loss=2.743, discriminator_real_loss=1.411, discriminator_fake_loss=1.332, generator_loss=28.57, generator_mel_loss=17.49, generator_kl_loss=1.28, generator_dur_loss=1.763, generator_adv_loss=2.029, generator_feat_match_loss=6.009, over 68.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.25, generator_mel_loss=17.71, generator_kl_loss=1.443, generator_dur_loss=1.73, generator_adv_loss=2.014, generator_feat_match_loss=6.352, over 4105.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:59:20,804 INFO [train.py:527] (5/6) Epoch 998, batch 122, global_batch_idx: 123750, batch size: 47, loss[discriminator_loss=2.687, discriminator_real_loss=1.428, discriminator_fake_loss=1.259, generator_loss=28.94, generator_mel_loss=17.68, generator_kl_loss=1.617, generator_dur_loss=1.651, generator_adv_loss=1.926, generator_feat_match_loss=6.06, over 47.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.348, discriminator_fake_loss=1.322, generator_loss=29.3, generator_mel_loss=17.71, generator_kl_loss=1.448, generator_dur_loss=1.729, generator_adv_loss=2.012, generator_feat_match_loss=6.396, over 6773.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 06:59:26,318 INFO [train.py:919] (5/6) Start epoch 999 +2024-03-16 07:02:04,274 INFO [train.py:527] (5/6) Epoch 999, batch 48, global_batch_idx: 123800, batch size: 52, loss[discriminator_loss=2.658, discriminator_real_loss=1.4, discriminator_fake_loss=1.258, generator_loss=28.91, generator_mel_loss=17.76, generator_kl_loss=1.355, generator_dur_loss=1.633, generator_adv_loss=1.957, generator_feat_match_loss=6.203, over 52.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.21, generator_mel_loss=17.71, generator_kl_loss=1.433, generator_dur_loss=1.741, generator_adv_loss=2.03, generator_feat_match_loss=6.297, over 2804.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 07:02:04,275 INFO [train.py:581] (5/6) Computing validation loss +2024-03-16 07:02:12,162 INFO [train.py:591] (5/6) 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 07:02:12,163 INFO [train.py:592] (5/6) Maximum memory allocated so far is 29073MB +2024-03-16 07:04:29,763 INFO [train.py:527] (5/6) Epoch 999, batch 98, global_batch_idx: 123850, batch size: 96, loss[discriminator_loss=2.648, discriminator_real_loss=1.242, discriminator_fake_loss=1.407, generator_loss=29.24, generator_mel_loss=17.57, generator_kl_loss=1.493, generator_dur_loss=1.82, generator_adv_loss=2.06, generator_feat_match_loss=6.299, over 96.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.29, generator_mel_loss=17.67, generator_kl_loss=1.433, generator_dur_loss=1.742, generator_adv_loss=2.024, generator_feat_match_loss=6.419, over 5798.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 07:05:43,040 INFO [train.py:919] (5/6) Start epoch 1000 +2024-03-16 07:07:13,391 INFO [train.py:527] (5/6) Epoch 1000, batch 24, global_batch_idx: 123900, batch size: 66, loss[discriminator_loss=2.612, discriminator_real_loss=1.303, discriminator_fake_loss=1.309, generator_loss=31.55, generator_mel_loss=18.32, generator_kl_loss=1.287, generator_dur_loss=1.713, generator_adv_loss=2.228, generator_feat_match_loss=8.005, over 66.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.348, discriminator_fake_loss=1.328, generator_loss=29.34, generator_mel_loss=17.82, generator_kl_loss=1.446, generator_dur_loss=1.721, generator_adv_loss=2.037, generator_feat_match_loss=6.313, over 1343.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 07:09:33,611 INFO [train.py:527] (5/6) Epoch 1000, batch 74, global_batch_idx: 123950, batch size: 25, loss[discriminator_loss=2.678, discriminator_real_loss=1.371, discriminator_fake_loss=1.307, generator_loss=30, generator_mel_loss=17.71, generator_kl_loss=2.053, generator_dur_loss=1.575, generator_adv_loss=1.977, generator_feat_match_loss=6.689, over 25.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.353, discriminator_fake_loss=1.317, generator_loss=29.36, generator_mel_loss=17.74, generator_kl_loss=1.448, generator_dur_loss=1.732, generator_adv_loss=2.026, generator_feat_match_loss=6.416, over 4328.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, +2024-03-16 07:11:48,746 INFO [train.py:977] (5/6) Done!