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etdnn-vox2

speechbrain
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etdnn-vox2 / 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