mirror of https://github.com/coqui-ai/TTS.git
231 lines
10 KiB
Python
231 lines
10 KiB
Python
import os
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import torch
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from trainer import Trainer, TrainerArgs
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from TTS.bin.compute_embeddings import compute_embeddings
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from TTS.bin.resample import resample_files
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from TTS.config.shared_configs import BaseDatasetConfig
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from TTS.tts.configs.vits_config import VitsConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.models.vits import CharactersConfig, Vits, VitsArgs, VitsAudioConfig, VitsDataset
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from TTS.utils.downloaders import download_libri_tts
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from torch.utils.data import DataLoader
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from TTS.utils.samplers import PerfectBatchSampler
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torch.set_num_threads(24)
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# pylint: disable=W0105
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"""
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This recipe replicates the first experiment proposed in the CML-TTS paper (https://arxiv.org/abs/2306.10097). It uses the YourTTS model.
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YourTTS model is based on the VITS model however it uses external speaker embeddings extracted from a pre-trained speaker encoder and has small architecture changes.
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"""
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CURRENT_PATH = os.path.dirname(os.path.abspath(__file__))
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# Name of the run for the Trainer
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RUN_NAME = "YourTTS-CML-TTS"
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# Path where you want to save the models outputs (configs, checkpoints and tensorboard logs)
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OUT_PATH = os.path.dirname(os.path.abspath(__file__)) # "/raid/coqui/Checkpoints/original-YourTTS/"
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# If you want to do transfer learning and speedup your training you can set here the path to the CML-TTS available checkpoint that cam be downloaded here: https://drive.google.com/u/2/uc?id=1yDCSJ1pFZQTHhL09GMbOrdjcPULApa0p
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RESTORE_PATH = None # "/raid/edresson/CML_YourTTS/checkpoints_yourtts_cml_tts_dataset/best_model.pth" # Download the checkpoint here: https://drive.google.com/u/2/uc?id=1yDCSJ1pFZQTHhL09GMbOrdjcPULApa0p
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# This paramter is useful to debug, it skips the training epochs and just do the evaluation and produce the test sentences
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SKIP_TRAIN_EPOCH = False
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# Set here the batch size to be used in training and evaluation
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BATCH_SIZE = 6
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# Training Sampling rate and the target sampling rate for resampling the downloaded dataset (Note: If you change this you might need to redownload the dataset !!)
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# Note: If you add new datasets, please make sure that the dataset sampling rate and this parameter are matching, otherwise resample your audios
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SAMPLE_RATE = 24000
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# Max audio length in seconds to be used in training (every audio bigger than it will be ignored)
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MAX_AUDIO_LEN_IN_SECONDS = float("inf")
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# DEfine here the datasets config
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esd_train_config = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="esd",
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meta_file_train="metadata_with_basic_metrics.csv", # TODO: compute emotion and d-vectors for test and evaluation splits
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path="/raid/datasets/Emotion/ESD-44kHz-VAD-renormalized/",
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language="en"
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)
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savee_config = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="savee",
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path="/raid/datasets/SAVEE-44khz/",
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meta_file_train="metadata_with_basic_metrics.csv",
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language="pt"
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)
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game1_config = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="game1",
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path="/raid/datasets/new_game_data/game1/datasetbuilder_formatted/",
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meta_file_train="metadata_with_basic_metrics.csv",
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language="de",
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)
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DATASETS_CONFIG_LIST = [esd_train_config, savee_config, game1_config]
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### Extract speaker embeddings
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SPEAKER_ENCODER_CHECKPOINT_PATH = (
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"https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar"
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)
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SPEAKER_ENCODER_CONFIG_PATH = "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json"
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D_VECTOR_FILES = [] # List of speaker embeddings/d-vectors to be used during the training
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# Iterates all the dataset configs checking if the speakers embeddings are already computated, if not compute it
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for dataset_conf in DATASETS_CONFIG_LIST:
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# Check if the embeddings weren't already computed, if not compute it
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embeddings_file = os.path.join(dataset_conf.path, "H_ASP_speaker_embeddings.pth")
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if not os.path.isfile(embeddings_file):
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print(f">>> Computing the speaker embeddings for the {dataset_conf.dataset_name} dataset")
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compute_embeddings(
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SPEAKER_ENCODER_CHECKPOINT_PATH,
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SPEAKER_ENCODER_CONFIG_PATH,
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embeddings_file,
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old_speakers_file=None,
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config_dataset_path=None,
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formatter_name=dataset_conf.formatter,
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dataset_name=dataset_conf.dataset_name,
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dataset_path=dataset_conf.path,
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meta_file_train=dataset_conf.meta_file_train,
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meta_file_val=dataset_conf.meta_file_val,
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disable_cuda=False,
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no_eval=False,
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)
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D_VECTOR_FILES.append(embeddings_file)
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# Audio config used in training.
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audio_config = VitsAudioConfig(
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sample_rate=SAMPLE_RATE,
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hop_length=256,
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win_length=1024,
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fft_size=1024,
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mel_fmin=0.0,
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mel_fmax=None,
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num_mels=80,
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)
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# Init VITSArgs setting the arguments that are needed for the YourTTS model
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model_args = VitsArgs(
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spec_segment_size=62,
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hidden_channels=192,
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hidden_channels_ffn_text_encoder=768,
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num_heads_text_encoder=2,
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num_layers_text_encoder=10,
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kernel_size_text_encoder=3,
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dropout_p_text_encoder=0.1,
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d_vector_file=D_VECTOR_FILES,
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use_d_vector_file=True,
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d_vector_dim=512,
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speaker_encoder_model_path=SPEAKER_ENCODER_CHECKPOINT_PATH,
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speaker_encoder_config_path=SPEAKER_ENCODER_CONFIG_PATH,
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resblock_type_decoder="2", # In the paper, we accidentally trained the YourTTS using ResNet blocks type 2, if you like you can use the ResNet blocks type 1 like the VITS model
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# Useful parameters to enable the Speaker Consistency Loss (SCL) described in the paper
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use_speaker_encoder_as_loss=False,
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# Useful parameters to enable multilingual training
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use_language_embedding=False,
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embedded_language_dim=4,
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use_adaptive_weight_text_encoder=True,
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use_perfect_class_batch_sampler=True,
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perfect_class_batch_sampler_key="language"
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)
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# General training config, here you can change the batch size and others useful parameters
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config = VitsConfig(
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output_path=OUT_PATH,
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model_args=model_args,
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run_name=RUN_NAME,
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project_name="SYNTACC",
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run_description="""
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- YourTTS with SYNTACC text encoder
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""",
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dashboard_logger="tensorboard",
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logger_uri=None,
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audio=audio_config,
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batch_size=BATCH_SIZE,
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batch_group_size=48,
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eval_batch_size=BATCH_SIZE,
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num_loader_workers=8,
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eval_split_max_size=256,
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print_step=50,
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plot_step=100,
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log_model_step=1000,
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save_step=5000,
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save_n_checkpoints=2,
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save_checkpoints=True,
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# target_loss="loss_1",
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print_eval=False,
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use_phonemes=False,
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phonemizer="espeak",
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phoneme_language="en",
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compute_input_seq_cache=True,
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add_blank=True,
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text_cleaner="multilingual_cleaners",
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characters=CharactersConfig(
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characters_class="TTS.tts.models.vits.VitsCharacters",
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pad="_",
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eos="&",
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bos="*",
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blank=None,
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characters="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\u00a1\u00a3\u00b7\u00b8\u00c0\u00c1\u00c2\u00c3\u00c4\u00c5\u00c7\u00c8\u00c9\u00ca\u00cb\u00cc\u00cd\u00ce\u00cf\u00d1\u00d2\u00d3\u00d4\u00d5\u00d6\u00d9\u00da\u00db\u00dc\u00df\u00e0\u00e1\u00e2\u00e3\u00e4\u00e5\u00e7\u00e8\u00e9\u00ea\u00eb\u00ec\u00ed\u00ee\u00ef\u00f1\u00f2\u00f3\u00f4\u00f5\u00f6\u00f9\u00fa\u00fb\u00fc\u0101\u0104\u0105\u0106\u0107\u010b\u0119\u0141\u0142\u0143\u0144\u0152\u0153\u015a\u015b\u0161\u0178\u0179\u017a\u017b\u017c\u020e\u04e7\u05c2\u1b20",
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punctuations="\u2014!'(),-.:;?\u00bf ",
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phonemes="iy\u0268\u0289\u026fu\u026a\u028f\u028ae\u00f8\u0258\u0259\u0275\u0264o\u025b\u0153\u025c\u025e\u028c\u0254\u00e6\u0250a\u0276\u0251\u0252\u1d7b\u0298\u0253\u01c0\u0257\u01c3\u0284\u01c2\u0260\u01c1\u029bpbtd\u0288\u0256c\u025fk\u0261q\u0262\u0294\u0274\u014b\u0272\u0273n\u0271m\u0299r\u0280\u2c71\u027e\u027d\u0278\u03b2fv\u03b8\u00f0sz\u0283\u0292\u0282\u0290\u00e7\u029dx\u0263\u03c7\u0281\u0127\u0295h\u0266\u026c\u026e\u028b\u0279\u027bj\u0270l\u026d\u028e\u029f\u02c8\u02cc\u02d0\u02d1\u028dw\u0265\u029c\u02a2\u02a1\u0255\u0291\u027a\u0267\u025a\u02de\u026b'\u0303' ",
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is_unique=True,
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is_sorted=True,
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),
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phoneme_cache_path=None,
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precompute_num_workers=12,
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start_by_longest=True,
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datasets=DATASETS_CONFIG_LIST,
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cudnn_benchmark=False,
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max_audio_len=SAMPLE_RATE * MAX_AUDIO_LEN_IN_SECONDS,
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mixed_precision=False,
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test_sentences=[
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["Voc\u00ea ter\u00e1 a vista do topo da montanha que voc\u00ea escalar.", "ESD_0012", None, "pt"],
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["Quando voc\u00ea n\u00e3o corre nenhum risco, voc\u00ea arrisca tudo.", "ESD_0012", None, "pt"],
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],
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# Enable the weighted sampler
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use_weighted_sampler=True,
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# Ensures that all speakers are seen in the training batch equally no matter how many samples each speaker has
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# weighted_sampler_attrs={"language": 1.0, "speaker_name": 1.0},
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weighted_sampler_attrs={"language": 1.0},
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weighted_sampler_multipliers={
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# "speaker_name": {
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# you can force the batching scheme to give a higher weight to a certain speaker and then this speaker will appears more frequently on the batch.
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# It will speedup the speaker adaptation process. Considering the CML train dataset and "new_speaker" as the speaker name of the speaker that you want to adapt.
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# The line above will make the balancer consider the "new_speaker" as 106 speakers so 1/4 of the number of speakers present on CML dataset.
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# 'new_speaker': 106, # (CML tot. train speaker)/4 = (424/4) = 106
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# }
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},
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# It defines the Speaker Consistency Loss (SCL) α to 9 like the YourTTS paper
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speaker_encoder_loss_alpha=9.0,
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)
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# Load all the datasets samples and split traning and evaluation sets
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train_samples, eval_samples = load_tts_samples(
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config.datasets,
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eval_split=True,
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eval_split_max_size=config.eval_split_max_size,
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eval_split_size=config.eval_split_size,
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)
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# Init the model
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model = Vits.init_from_config(config)
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# Init the trainer and 🚀
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trainer = Trainer(
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TrainerArgs(restore_path=RESTORE_PATH, skip_train_epoch=SKIP_TRAIN_EPOCH, start_with_eval=True),
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config,
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output_path=OUT_PATH,
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model=model,
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train_samples=train_samples,
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eval_samples=eval_samples,
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)
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trainer.fit()
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