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Add Base encoder config

This commit is contained in:
Edresson Casanova 2022-03-01 14:11:38 -03:00
parent 33ac13e44e
commit f811af7651
9 changed files with 25 additions and 2054 deletions
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1
TTS/bin/train_encoder.py
View File

@ -173,7 +173,6 @@ def main(args): # pylint: disable=redefined-outer-name
# update config with the class map
c.map_classid_to_classname = map_classid_to_classname
copy_model_files(c, OUT_PATH)
print(OUT_PATH)
else:
raise Exception("The %s not is a loss supported" % c.loss)

2
TTS/config/__init__.py
View File

@ -37,7 +37,7 @@ def register_config(model_name: str) -> Coqpit:
"""
config_class = None
config_name = model_name + "_config"
paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.encoder"]
paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.encoder.configs"]
for path in paths:
try:
config_class = find_module(path, config_name)

6
TTS/encoder/speaker_encoder_config.py → TTS/encoder/configs/base_encoder_config.py
View File

@ -7,10 +7,10 @@ from TTS.config.shared_configs import BaseAudioConfig, BaseDatasetConfig, BaseTr
@dataclass
class SpeakerEncoderConfig(BaseTrainingConfig):
"""Defines parameters for Speaker Encoder model."""
class BaseEncoderConfig(BaseTrainingConfig):
"""Defines parameters for a Generic Encoder model."""
model: str = "speaker_encoder"
model: str = None
audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
# model params

118
TTS/encoder/configs/config.json
View File

@ -1,118 +0,0 @@
{
"model_name": "lstm",
"run_name": "mueller91",
"run_description": "train speaker encoder with voxceleb1, voxceleb2 and libriSpeech ",
"audio":{
// Audio processing parameters
"num_mels": 40, // size of the mel spec frame.
"fft_size": 400, // number of stft frequency levels. Size of the linear spectogram frame.
"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
"win_length": 400, // stft window length in ms.
"hop_length": 160, // stft window hop-lengh in ms.
"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
"preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
"min_level_db": -100, // normalization range
"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
"power": 1.5, // value to sharpen wav signals after GL algorithm.
"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
// Normalization parameters
"signal_norm": true, // normalize the spec values in range [0, 1]
"symmetric_norm": true, // move normalization to range [-1, 1]
"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"clip_norm": true, // clip normalized values into the range.
"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
"do_trim_silence": true, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
"trim_db": 60, // threshold for timming silence. Set this according to your dataset.
"stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
},
"reinit_layers": [],
"loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
"grad_clip": 3.0, // upper limit for gradients for clipping.
"epochs": 1000, // total number of epochs to train.
"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
"steps_plot_stats": 10, // number of steps to plot embeddings.
"num_classes_in_batch": 64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
"num_utter_per_class": 10, //
"skip_classes": false, // skip speakers with samples less than "num_utter_per_class"
"voice_len": 1.6, // number of seconds for each training instance
"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"wd": 0.000001, // Weight decay weight.
"checkpoint": true, // If true, it saves checkpoints per "save_step"
"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
"print_step": 20, // Number of steps to log traning on console.
"output_path": "../../MozillaTTSOutput/checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
"model": {
"input_dim": 40,
"proj_dim": 256,
"lstm_dim": 768,
"num_lstm_layers": 3,
"use_lstm_with_projection": true
},
"audio_augmentation": {
"p": 0,
//add a gaussian noise to the data in order to increase robustness
"gaussian":{ // as the insertion of Gaussian noise is quick to be calculated, we added it after loading the wav file, this way, even audios that were reused with the cache can receive this noise
"p": 1, // propability of apply this method, 0 is disable
"min_amplitude": 0.0,
"max_amplitude": 1e-5
}
},
"storage": {
"sample_from_storage_p": 0.66, // the probability with which we'll sample from the DataSet in-memory storage
"storage_size": 15, // the size of the in-memory storage with respect to a single batch
"additive_noise": 1e-5 // add very small gaussian noise to the data in order to increase robustness
},
"datasets":
[
{
"name": "vctk_slim",
"path": "../../../audio-datasets/en/VCTK-Corpus/",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "libri_tts",
"path": "../../../audio-datasets/en/LibriTTS/train-clean-100",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "libri_tts",
"path": "../../../audio-datasets/en/LibriTTS/train-clean-360",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "libri_tts",
"path": "../../../audio-datasets/en/LibriTTS/train-other-500",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "voxceleb1",
"path": "../../../audio-datasets/en/voxceleb1/",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "voxceleb2",
"path": "../../../audio-datasets/en/voxceleb2/",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "common_voice",
"path": "../../../audio-datasets/en/MozillaCommonVoice",
"meta_file_train": "train.tsv",
"meta_file_val": "test.tsv"
}
]
}

956
TTS/encoder/configs/config_resnet_angleproto.json
View File

@ -1,956 +0,0 @@
{
"model": "speaker_encoder",
"run_name": "speaker_encoder",
"run_description": "resnet speaker encoder trained with commonvoice all languages dev and train, Voxceleb 1 dev and Voxceleb 2 dev",
// AUDIO PARAMETERS
"audio":{
// Audio processing parameters
"num_mels": 80, // size of the mel spec frame.
"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
"win_length": 1024, // stft window length in ms.
"hop_length": 256, // stft window hop-lengh in ms.
"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
"preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
"min_level_db": -100, // normalization range
"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
"power": 1.5, // value to sharpen wav signals after GL algorithm.
"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
"stft_pad_mode": "reflect",
// Normalization parameters
"signal_norm": true, // normalize the spec values in range [0, 1]
"symmetric_norm": true, // move normalization to range [-1, 1]
"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"clip_norm": true, // clip normalized values into the range.
"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
"spec_gain": 20.0,
"do_trim_silence": false, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
"trim_db": 60, // threshold for timming silence. Set this according to your dataset.
"stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
},
"reinit_layers": [],
"loss": "angleproto", // "ge2e" to use Generalized End-to-End loss, "angleproto" to use Angular Prototypical loss and "softmaxproto" to use Softmax with Angular Prototypical loss
"grad_clip": 3.0, // upper limit for gradients for clipping.
"max_train_step": 1000000, // total number of steps to train.
"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
"steps_plot_stats": 100, // number of steps to plot embeddings.
// Speakers config
"num_classes_in_batch": 200, // Batch size for training.
"num_utter_per_class": 2, //
"skip_classes": true, // skip speakers with samples less than "num_utter_per_class"
"voice_len": 2, // number of seconds for each training instance
"num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"wd": 0.000001, // Weight decay weight.
"checkpoint": true, // If true, it saves checkpoints per "save_step"
"save_step": 1000, // Number of training steps expected to save the best checkpoints in training.
"print_step": 50, // Number of steps to log traning on console.
"output_path": "../checkpoints/speaker_encoder/angleproto/resnet_voxceleb1_and_voxceleb2-and-common-voice-all-using-angleproto/", // DATASET-RELATED: output path for all training outputs.
"audio_augmentation": {
"p": 0.5, // propability of apply this method, 0 is disable rir and additive noise augmentation
"rir":{
"rir_path": "/workspace/store/ecasanova/ComParE/RIRS_NOISES/simulated_rirs/",
"conv_mode": "full"
},
"additive":{
"sounds_path": "/workspace/store/ecasanova/ComParE/musan/",
// list of each of the directories in your data augmentation, if a directory is in "sounds_path" but is not listed here it will be ignored
"speech":{
"min_snr_in_db": 13,
"max_snr_in_db": 20,
"min_num_noises": 2,
"max_num_noises": 3
},
"noise":{
"min_snr_in_db": 0,
"max_snr_in_db": 15,
"min_num_noises": 1,
"max_num_noises": 1
},
"music":{
"min_snr_in_db": 5,
"max_snr_in_db": 15,
"min_num_noises": 1,
"max_num_noises": 1
}
},
//add a gaussian noise to the data in order to increase robustness
"gaussian":{ // as the insertion of Gaussian noise is quick to be calculated, we added it after loading the wav file, this way, even audios that were reused with the cache can receive this noise
"p": 0.5, // propability of apply this method, 0 is disable
"min_amplitude": 0.0,
"max_amplitude": 1e-5
}
},
"model_params": {
"model_name": "resnet",
"input_dim": 80,
"proj_dim": 512
},
"storage": {
"sample_from_storage_p": 0.5, // the probability with which we'll sample from the DataSet in-memory storage
"storage_size": 35 // the size of the in-memory storage with respect to a single batch
},
"datasets":
[
{
"name": "voxceleb2",
"path": "/workspace/scratch/ecasanova/datasets/VoxCeleb/vox2_dev_aac/",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "voxceleb1",
"path": "/workspace/scratch/ecasanova/datasets/VoxCeleb/vox1_dev_wav/",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/fi",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/fi",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/zh-CN",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/zh-CN",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/rm-sursilv",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/rm-sursilv",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/lt",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/lt",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ka",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ka",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/sv-SE",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/sv-SE",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pl",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pl",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ru",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ru",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/mn",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/mn",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/nl",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/nl",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/sl",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/sl",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/es",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/es",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pt",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pt",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/hi",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/hi",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ja",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ja",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ia",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ia",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/br",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/br",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/id",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/id",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/dv",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/dv",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ta",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ta",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/or",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/or",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/zh-HK",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
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{
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{
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{
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{
"name": "common_voice",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"name": "common_voice",
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},
{
"name": "common_voice",
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{
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{
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},
{
"name": "common_voice",
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},
{
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"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/hu",
"meta_file_train": "dev.tsv",
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{
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{
"name": "common_voice",
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{
"name": "common_voice",
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},
{
"name": "common_voice",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ca",
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{
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{
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{
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{
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"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/cy",
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{
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"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/cv",
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{
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{
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"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/hsb",
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{
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"meta_file_train": "train.tsv",
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{
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}
]
}

957
TTS/encoder/configs/config_resnet_softmax_angleproto.json
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@ -1,957 +0,0 @@
{
"model": "speaker_encoder",
"run_name": "speaker_encoder",
"run_description": "resnet speaker encoder trained with commonvoice all languages dev and train, Voxceleb 1 dev and Voxceleb 2 dev",
// AUDIO PARAMETERS
"audio":{
// Audio processing parameters
"num_mels": 80, // size of the mel spec frame.
"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
"win_length": 1024, // stft window length in ms.
"hop_length": 256, // stft window hop-lengh in ms.
"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
"preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
"min_level_db": -100, // normalization range
"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
"power": 1.5, // value to sharpen wav signals after GL algorithm.
"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
"stft_pad_mode": "reflect",
// Normalization parameters
"signal_norm": true, // normalize the spec values in range [0, 1]
"symmetric_norm": true, // move normalization to range [-1, 1]
"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"clip_norm": true, // clip normalized values into the range.
"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
"spec_gain": 20.0,
"do_trim_silence": false, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
"trim_db": 60, // threshold for timming silence. Set this according to your dataset.
"stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
},
"reinit_layers": [],
"loss": "softmaxproto", // "ge2e" to use Generalized End-to-End loss, "angleproto" to use Angular Prototypical loss and "softmaxproto" to use Softmax with Angular Prototypical loss
"grad_clip": 3.0, // upper limit for gradients for clipping.
"max_train_step": 1000000, // total number of steps to train.
"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
"steps_plot_stats": 100, // number of steps to plot embeddings.
// Speakers config
"num_classes_in_batch": 200, // Batch size for training.
"num_utter_per_class": 2, //
"skip_classes": true, // skip speakers with samples less than "num_utter_per_class"
"voice_len": 2, // number of seconds for each training instance
"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"wd": 0.000001, // Weight decay weight.
"checkpoint": true, // If true, it saves checkpoints per "save_step"
"save_step": 1000, // Number of training steps expected to save the best checkpoints in training.
"print_step": 50, // Number of steps to log traning on console.
"output_path": "../../../checkpoints/speaker_encoder/resnet_voxceleb1_and_voxceleb2-and-common-voice-all/", // DATASET-RELATED: output path for all training outputs.
"audio_augmentation": {
"p": 0.5, // propability of apply this method, 0 is disable rir and additive noise augmentation
"rir":{
"rir_path": "/workspace/store/ecasanova/ComParE/RIRS_NOISES/simulated_rirs/",
"conv_mode": "full"
},
"additive":{
"sounds_path": "/workspace/store/ecasanova/ComParE/musan/",
// list of each of the directories in your data augmentation, if a directory is in "sounds_path" but is not listed here it will be ignored
"speech":{
"min_snr_in_db": 13,
"max_snr_in_db": 20,
"min_num_noises": 2,
"max_num_noises": 3
},
"noise":{
"min_snr_in_db": 0,
"max_snr_in_db": 15,
"min_num_noises": 1,
"max_num_noises": 1
},
"music":{
"min_snr_in_db": 5,
"max_snr_in_db": 15,
"min_num_noises": 1,
"max_num_noises": 1
}
},
//add a gaussian noise to the data in order to increase robustness
"gaussian":{ // as the insertion of Gaussian noise is quick to be calculated, we added it after loading the wav file, this way, even audios that were reused with the cache can receive this noise
"p": 0.5, // propability of apply this method, 0 is disable
"min_amplitude": 0.0,
"max_amplitude": 1e-5
}
},
"model_params": {
"model_name": "resnet",
"input_dim": 80,
"proj_dim": 512
},
"storage": {
"sample_from_storage_p": 0.66, // the probability with which we'll sample from the DataSet in-memory storage
"storage_size": 35 // the size of the in-memory storage with respect to a single batch
},
"datasets":
[
{
"name": "voxceleb2",
"path": "/workspace/scratch/ecasanova/datasets/VoxCeleb/vox2_dev_aac/",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "voxceleb1",
"path": "/workspace/scratch/ecasanova/datasets/VoxCeleb/vox1_dev_wav/",
"meta_file_train": null,
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/fi",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/fi",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/zh-CN",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/zh-CN",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/rm-sursilv",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/rm-sursilv",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/lt",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
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"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ka",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ka",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/sv-SE",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/sv-SE",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pl",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pl",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ru",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ru",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
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"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
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"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/nl",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/nl",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
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"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/sl",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/es",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/es",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pt",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pt",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/hi",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/hi",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ja",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ja",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ia",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ia",
"meta_file_train": "dev.tsv",
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},
{
"name": "common_voice",
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},
{
"name": "common_voice",
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},
{
"name": "common_voice",
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},
{
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},
{
"name": "common_voice",
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},
{
"name": "common_voice",
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},
{
"name": "common_voice",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"name": "common_voice",
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},
{
"name": "common_voice",
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},
{
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},
{
"name": "common_voice",
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},
{
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},
{
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},
{
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{
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{
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{
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{
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{
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{
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},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/eo",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pa-IN",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/pa-IN",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/th",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/th",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/it",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/it",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ga-IE",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ga-IE",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/cnh",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/cnh",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ky",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ky",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ar",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ar",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/eu",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/eu",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ca",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/ca",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/kab",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/kab",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/cy",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/cy",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/cv",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/cv",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/hsb",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/hsb",
"meta_file_train": "dev.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/lv",
"meta_file_train": "train.tsv",
"meta_file_val": null
},
{
"name": "common_voice",
"path": "/workspace/scratch/ecasanova/datasets/common-voice/cv-corpus-6.1-2020-12-11_16khz/lv",
"meta_file_train": "dev.tsv",
"meta_file_val": null
}
]
}

11
TTS/encoder/configs/emotion_encoder_config.py Normal file
View File

@ -0,0 +1,11 @@
from dataclasses import asdict, dataclass
from TTS.encoder.configs.base_encoder_config import BaseEncoderConfig
@dataclass
class EmotionEncoderConfig(BaseEncoderConfig):
"""Defines parameters for Emotion Encoder model."""
model: str = "emotion_encoder"
map_classid_to_classname: dict = None

10
TTS/encoder/configs/speaker_encoder_config.py Normal file
View File

@ -0,0 +1,10 @@
from dataclasses import asdict, dataclass
from TTS.encoder.configs.base_encoder_config import BaseEncoderConfig
@dataclass
class SpeakerEncoderConfig(BaseEncoderConfig):
"""Defines parameters for Speaker Encoder model."""
model: str = "speaker_encoder"

18
TTS/encoder/emotion_encoder_config.py
View File

@ -1,18 +0,0 @@
from dataclasses import asdict, dataclass
from TTS.encoder.speaker_encoder_config import SpeakerEncoderConfig
@dataclass
class EmotionEncoderConfig(SpeakerEncoderConfig):
"""Defines parameters for Speaker Encoder model."""
model: str = "emotion_encoder"
map_classid_to_classname: dict = None
def check_values(self):
super().check_values()
c = asdict(self)
assert (
c["model_params"]["input_dim"] == self.audio.num_mels
), " [!] model input dimendion must be equal to melspectrogram dimension."