mirror of https://github.com/coqui-ai/TTS.git
Merge pull request #526 from mueller91/dev
Fix: Check storage params only for speaker encoder
This commit is contained in:
Eren Gölge
GitHub
commit
8e2dc79c3a
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@ -11,11 +11,12 @@ import torch
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from torch.utils.data import DataLoader
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from TTS.speaker_encoder.dataset import MyDataset
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from TTS.speaker_encoder.utils.generic_utils import save_best_model
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from TTS.speaker_encoder.losses import GE2ELoss, AngleProtoLoss
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from TTS.speaker_encoder.model import SpeakerEncoder
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from TTS.speaker_encoder.utils.visual import plot_embeddings
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from TTS.speaker_encoder.utils import check_config_speaker_encoder
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from TTS.speaker_encoder.visuals import plot_embeddings
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from TTS.tts.datasets.preprocess import load_meta_data
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from TTS.tts.utils.io import save_best_model
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from TTS.utils.generic_utils import (
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create_experiment_folder, get_git_branch, remove_experiment_folder,
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set_init_dict)
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@ -235,6 +236,7 @@ if __name__ == '__main__':
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# setup output paths and read configs
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c = load_config(args.config_path)
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check_config_speaker_encoder(c)
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_ = os.path.dirname(os.path.realpath(__file__))
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if args.data_path != '':
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c.data_path = args.data_path
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@ -17,7 +17,7 @@ from TTS.tts.layers.losses import TacotronLoss
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from TTS.tts.utils.distribute import (DistributedSampler,
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apply_gradient_allreduce,
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init_distributed, reduce_tensor)
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from TTS.tts.utils.generic_utils import check_config, setup_model
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from TTS.tts.utils.generic_utils import setup_model, check_config_tts
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from TTS.tts.utils.io import save_best_model, save_checkpoint
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from TTS.tts.utils.measures import alignment_diagonal_score
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from TTS.tts.utils.speakers import (get_speakers, load_speaker_mapping,
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@ -670,7 +670,7 @@ if __name__ == '__main__':
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# setup output paths and read configs
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c = load_config(args.config_path)
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check_config(c)
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check_config_tts(c)
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_ = os.path.dirname(os.path.realpath(__file__))
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if c.apex_amp_level == 'O1':
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@ -0,0 +1,103 @@
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{
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"run_name": "mueller91",
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"run_description": "train speaker encoder with voxceleb1, voxceleb2 and libriSpeech ",
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"audio":{
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// Audio processing parameters
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"num_mels": 40, // size of the mel spec frame.
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"fft_size": 400, // number of stft frequency levels. Size of the linear spectogram frame.
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"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
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"win_length": 400, // stft window length in ms.
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"hop_length": 160, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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"preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"min_level_db": -100, // normalization range
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"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
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"power": 1.5, // value to sharpen wav signals after GL algorithm.
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"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
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// Normalization parameters
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"signal_norm": true, // normalize the spec values in range [0, 1]
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"symmetric_norm": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
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"do_trim_silence": true, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"trim_db": 60 // threshold for timming silence. Set this according to your dataset.
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},
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"reinit_layers": [],
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"loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
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"grad_clip": 3.0, // upper limit for gradients for clipping.
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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"steps_plot_stats": 10, // number of steps to plot embeddings.
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"num_speakers_in_batch": 64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
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"num_utters_per_speaker": 10, //
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"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"wd": 0.000001, // Weight decay weight.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
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"print_step": 20, // Number of steps to log traning on console.
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"output_path": "../../MozillaTTSOutput/checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
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"model": {
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"input_dim": 40,
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"proj_dim": 256,
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"lstm_dim": 768,
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"num_lstm_layers": 3,
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"use_lstm_with_projection": true
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},
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"storage": {
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"sample_from_storage_p": 0.66, // the probability with which we'll sample from the DataSet in-memory storage
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"storage_size": 15, // the size of the in-memory storage with respect to a single batch
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"additive_noise": 1e-5 // add very small gaussian noise to the data in order to increase robustness
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},
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"datasets":
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[
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{
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"name": "vctk_slim",
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"path": "../../../audio-datasets/en/VCTK-Corpus/",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "libri_tts",
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"path": "../../../audio-datasets/en/LibriTTS/train-clean-100",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "libri_tts",
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"path": "../../../audio-datasets/en/LibriTTS/train-clean-360",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "libri_tts",
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"path": "../../../audio-datasets/en/LibriTTS/train-other-500",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "voxceleb1",
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"path": "../../../audio-datasets/en/voxceleb1/",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "voxceleb2",
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"path": "../../../audio-datasets/en/voxceleb2/",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "common_voice",
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"path": "../../../audio-datasets/en/MozillaCommonVoice",
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"meta_file_train": "train.tsv",
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"meta_file_val": "test.tsv"
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}
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]
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}
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@ -0,0 +1,61 @@
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from TTS.utils.generic_utils import check_argument
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def check_config_speaker_encoder(c):
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"""Check the config.json file of the speaker encoder"""
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check_argument('run_name', c, restricted=True, val_type=str)
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check_argument('run_description', c, val_type=str)
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# audio processing parameters
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check_argument('audio', c, restricted=True, val_type=dict)
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check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
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check_argument('fft_size', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
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check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
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check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length')
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check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length')
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check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1)
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check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10)
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check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000)
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check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5)
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check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000)
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# training parameters
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check_argument('loss', c, enum_list=['ge2e', 'angleproto'], restricted=True, val_type=str)
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check_argument('grad_clip', c, restricted=True, val_type=float)
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check_argument('epochs', c, restricted=True, val_type=int, min_val=1)
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check_argument('lr', c, restricted=True, val_type=float, min_val=0)
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check_argument('lr_decay', c, restricted=True, val_type=bool)
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check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0)
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check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)
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check_argument('num_speakers_in_batch', c, restricted=True, val_type=int)
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check_argument('num_loader_workers', c, restricted=True, val_type=int)
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check_argument('wd', c, restricted=True, val_type=float, min_val=0.0, max_val=1.0)
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# checkpoint and output parameters
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check_argument('steps_plot_stats', c, restricted=True, val_type=int)
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check_argument('checkpoint', c, restricted=True, val_type=bool)
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check_argument('save_step', c, restricted=True, val_type=int)
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check_argument('print_step', c, restricted=True, val_type=int)
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check_argument('output_path', c, restricted=True, val_type=str)
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# model parameters
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check_argument('model', c, restricted=True, val_type=dict)
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check_argument('input_dim', c['model'], restricted=True, val_type=int)
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check_argument('proj_dim', c['model'], restricted=True, val_type=int)
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check_argument('lstm_dim', c['model'], restricted=True, val_type=int)
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check_argument('num_lstm_layers', c['model'], restricted=True, val_type=int)
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check_argument('use_lstm_with_projection', c['model'], restricted=True, val_type=bool)
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# in-memory storage parameters
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check_argument('storage', c, restricted=True, val_type=dict)
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check_argument('sample_from_storage_p', c['storage'], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
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check_argument('storage_size', c['storage'], restricted=True, val_type=int, min_val=1, max_val=100)
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check_argument('additive_noise', c['storage'], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
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# datasets - checking only the first entry
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check_argument('datasets', c, restricted=True, val_type=list)
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for dataset_entry in c['datasets']:
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check_argument('name', dataset_entry, restricted=True, val_type=str)
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check_argument('path', dataset_entry, restricted=True, val_type=str)
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check_argument('meta_file_train', dataset_entry, restricted=True, val_type=[str, list])
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check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)
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@ -0,0 +1,46 @@
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import umap
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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matplotlib.use("Agg")
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colormap = (
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np.array(
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[
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[76, 255, 0],
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[0, 127, 70],
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[255, 0, 0],
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[255, 217, 38],
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[0, 135, 255],
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[165, 0, 165],
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[255, 167, 255],
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[0, 255, 255],
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[255, 96, 38],
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[142, 76, 0],
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[33, 0, 127],
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[0, 0, 0],
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[183, 183, 183],
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],
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dtype=np.float,
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)
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/ 255
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)
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def plot_embeddings(embeddings, num_utter_per_speaker):
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embeddings = embeddings[: 10 * num_utter_per_speaker]
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model = umap.UMAP()
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projection = model.fit_transform(embeddings)
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num_speakers = embeddings.shape[0] // num_utter_per_speaker
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ground_truth = np.repeat(np.arange(num_speakers), num_utter_per_speaker)
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colors = [colormap[i] for i in ground_truth]
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fig, ax = plt.subplots(figsize=(16, 10))
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_ = ax.scatter(projection[:, 0], projection[:, 1], c=colors)
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plt.gca().set_aspect("equal", "datalim")
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plt.title("UMAP projection")
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plt.tight_layout()
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plt.savefig("umap")
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return fig
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@ -127,7 +127,7 @@ def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None):
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return model
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def check_config(c):
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def check_config_tts(c):
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check_argument('model', c, enum_list=['tacotron', 'tacotron2'], restricted=True, val_type=str)
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check_argument('run_name', c, restricted=True, val_type=str)
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check_argument('run_description', c, val_type=str)
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@ -167,11 +167,6 @@ def check_config(c):
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check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
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check_argument('trim_db', c['audio'], restricted=True, val_type=int)
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# storage parameters
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check_argument('sample_from_storage_p', c['storage'], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
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check_argument('storage_size', c['storage'], restricted=True, val_type=int, min_val=1, max_val=100)
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check_argument('additive_noise', c['storage'], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
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# training parameters
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check_argument('batch_size', c, restricted=True, val_type=int, min_val=1)
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check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1)
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@ -21,4 +21,5 @@ nose==1.3.7
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cardboardlint==1.3.0
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pylint==2.5.3
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gdown
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umap
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cython
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