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embedding_model.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e5458351fcb867dd9fbb60822c91d302dcc2512e9a3be03f714668c3c026ffae
+size 26960925

hyperparams.yaml

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+# Generated 2022-12-15 from:
+# /home/pcp22wc/exps/speaker-recognition/hparams/train_etdnn.yaml
+# yamllint disable
+# ################################
+# Model: Speaker identification with ECAPA
+# Authors: Hwidong Na & Mirco Ravanelli
+# ################################
+
+# Basic parameters
+seed: 914
+__set_seed: !apply:torch.manual_seed [914]
+output_folder: results/etdnn_augment/914
+save_folder: results/etdnn_augment/914/save
+train_log: results/etdnn_augment/914/train_log.txt
+
+# Data files
+data_folder: /fastdata/pcp22wc/audio/VoxCeleb2/dev, /fastdata/pcp22wc/audio/VoxCeleb1/test  # e.g. /path/to/Voxceleb
+train_annotation: save/train.csv
+valid_annotation: save/dev.csv
+
+# Folder to extract data augmentation files
+rir_folder: /fastdata/pcp22wc/audio # Change it if needed
+musan_folder: /fastdata/pcp22wc/audio/musan
+music_csv: save/music.csv
+noise_csv: save/noise.csv
+speech_csv: save/speech.csv
+
+# Use the following links for the official voxceleb splits:
+# VoxCeleb1 (cleaned): https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt
+# VoxCeleb1-H (cleaned): https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/list_test_hard2.txt
+# VoxCeleb1-E (cleaned): https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/list_test_all2.txt.
+# VoxCeleb1-E and VoxCeleb1-H lists are drawn from the VoxCeleb1 training set.
+# Therefore you cannot use any files in VoxCeleb1 for training if you are using these lists for testing.
+verification_file: https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt
+
+skip_prep: true
+ckpt_interval_minutes: 15 # save checkpoint every N min
+
+# Training parameters
+number_of_epochs: 40
+batch_size: 512
+lr: 0.001
+lr_final: 0.0001
+step_size: 65000
+sample_rate: 16000
+sentence_len: 3.0 # seconds
+shuffle: true
+random_chunk: true
+
+# Feature parameters
+n_mels: 80
+deltas: false
+
+# Number of speakers
+out_n_neurons: 5994 #1211 for vox1  # 5994 for vox2, 7205 for vox1+vox2
+
+dataloader_options:
+  batch_size: 512
+  shuffle: true
+  num_workers: 8
+
+# Functions
+compute_features: &id007 !new:speechbrain.lobes.features.Fbank
+    # augment_wavedrop: !ref <augment_wavedrop>
+    # augment_speed: !ref <augment_speed>
+  n_mels: 80
+  deltas: false
+
+embedding_model: &id008 !new:speechbrain.lobes.models.Xvector.Xvector
+  in_channels: 80
+  activation: !name:torch.nn.LeakyReLU
+  tdnn_blocks: 10
+  tdnn_channels: [512, 512, 512, 512, 512, 512, 512, 512, 512, 1500]
+  tdnn_kernel_sizes: [5, 1, 3, 1, 3, 1, 5, 1, 1, 1]
+  tdnn_dilations: [2, 1, 1, 1, 1, 1, 2, 1, 1, 1]
+  lin_neurons: 512
+
+classifier: &id009 !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
+  input_size: 512
+  out_neurons: 5994
+
+epoch_counter: &id011 !new:speechbrain.utils.epoch_loop.EpochCounter
+  limit: 40
+
+
+augment_wavedrop: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
+  sample_rate: 16000
+  speeds: [100]
+
+augment_speed: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
+  sample_rate: 16000
+  speeds: [95, 100, 105]
+
+add_rev: &id001 !new:speechbrain.lobes.augment.EnvCorrupt
+  openrir_folder: /fastdata/pcp22wc/audio
+  openrir_max_noise_len: 3.0    # seconds
+  reverb_prob: 1.0
+  noise_prob: 0.0
+  noise_snr_low: 0
+  noise_snr_high: 15
+  rir_scale_factor: 1.0
+
+add_noise: &id002 !new:speechbrain.lobes.augment.EnvCorrupt
+  openrir_folder: /fastdata/pcp22wc/audio
+  openrir_max_noise_len: 3.0    # seconds
+  reverb_prob: 0.0
+  noise_prob: 1.0
+  noise_snr_low: 0
+  noise_snr_high: 15
+  rir_scale_factor: 1.0
+
+add_rev_noise: &id003 !new:speechbrain.lobes.augment.EnvCorrupt
+  openrir_folder: /fastdata/pcp22wc/audio
+  openrir_max_noise_len: 3.0    # seconds
+  reverb_prob: 1.0
+  noise_prob: 1.0
+  noise_snr_low: 0
+  noise_snr_high: 15
+  rir_scale_factor: 1.0
+
+add_noise_musan: &id004 !new:speechbrain.lobes.augment.EnvCorrupt
+  noise_csv: save/noise.csv
+  babble_prob: 0.0
+  reverb_prob: 0.0
+  noise_prob: 1.0
+  noise_snr_low: 0
+  noise_snr_high: 15
+
+add_music_musan: &id005 !new:speechbrain.lobes.augment.EnvCorrupt
+  noise_csv: save/music.csv
+  babble_prob: 0.0
+  reverb_prob: 0.0
+  noise_prob: 1.0
+  noise_snr_low: 0
+  noise_snr_high: 15
+
+add_speech_musan: &id006 !new:speechbrain.lobes.augment.EnvCorrupt
+  noise_csv: save/speech.csv
+  babble_prob: 0.0
+  reverb_prob: 0.0
+  noise_prob: 1.0
+  noise_snr_low: 0
+  noise_snr_high: 15
+
+# Definition of the augmentation pipeline.
+# If concat_augment = False, the augmentation techniques are applied
+# in sequence. If concat_augment = True, all the augmented signals
+# # are concatenated in a single big batch.
+
+augment_pipeline: [*id001, *id002, *id003, *id004, *id005, *id006]
+concat_augment: true
+
+mean_var_norm: &id010 !new:speechbrain.processing.features.InputNormalization
+
+  norm_type: sentence
+  std_norm: false
+
+modules:
+  compute_features: *id007
+  add_rev: *id001
+  add_noise: *id002
+  add_rev_noise: *id003
+  add_noise_musan: *id004
+  add_music_musan: *id005
+  add_speech_musan: *id006
+  embedding_model: *id008
+  classifier: *id009
+  mean_var_norm: *id010
+compute_cost: !new:speechbrain.nnet.losses.LogSoftmaxWrapper
+  loss_fn: !new:speechbrain.nnet.losses.AdditiveAngularMargin
+    margin: 0.2
+    scale: 30
+
+# compute_error: !name:speechbrain.nnet.losses.classification_error
+
+opt_class: !name:torch.optim.Adam
+  lr: 0.001
+  weight_decay: 0.000002
+
+lr_annealing: !new:speechbrain.nnet.schedulers.LinearScheduler
+  initial_value: 0.001
+  final_value: 0.0001
+  epoch_count: 40
+
+# Logging + checkpoints
+train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
+  save_file: results/etdnn_augment/914/train_log.txt
+
+error_stats: !name:speechbrain.utils.metric_stats.MetricStats
+  metric: !name:speechbrain.nnet.losses.classification_error
+    reduction: batch
+
+checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
+  checkpoints_dir: results/etdnn_augment/914/save
+  recoverables:
+    embedding_model: *id008
+    classifier: *id009
+    normalizer: *id010
+    counter: *id011