mirror of https://github.com/coqui-ai/TTS.git
Add YourTTS VCTK recipe
This commit is contained in:
Edresson Casanova
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import torch
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import os
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from trainer import Trainer, TrainerArgs
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from TTS.config.shared_configs import BaseDatasetConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.models.vits import VitsArgs, VitsAudioConfig, Vits
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from TTS.tts.configs.vits_config import VitsConfig
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torch.set_num_threads(24)
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# Name of the run for the Trainer
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RUN_NAME = "YourTTS-EN-VCTK"
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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 original YourTTS model
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RESTORE_PATH = None # "/raid/coqui/Checkpoints/YourTTS/checkpoint.pth"
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# This paramter is usefull 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 = 32
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# To get the speakers.json or speakers.pth you need to follow the steps described at: https://github.com/Edresson/YourTTS#reproducibility
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# or you can check the extract embedding script guidelines here: https://github.com/coqui-ai/TTS/blob/dev/TTS/bin/compute_embeddings.py#L20
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D_VECTOR_FILES = [
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"/raid/datasets/VCTK/speakers.json",
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]
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# Change our dataset paths to the VCTK dataset or replace it for others
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# init configs
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vctk_config = BaseDatasetConfig(
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formatter="vctk", dataset_name="vctk", meta_file_train="metadata.csv", path="/raid/datasets/VCTK/", language="en"
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)
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# add here all datasets configs, in our case we just want to train with the VCTK dataset then we need to add just VCTK
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datasets_list = [vctk_config]
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# Audio config used in training. Please: Check if your dataset sampling rate and the parameter sample_rate here are matching, otherwise resample your audios
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audio_config = VitsAudioConfig(
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sample_rate=22050,
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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 is needed for the YourTTS model
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model_args = VitsArgs(
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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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num_layers_text_encoder=10,
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# usefull parameters to the enable multilingual training
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# use_language_embedding=True,
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# embedded_language_dim=4,
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)
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# General training config, here you can change the batch size and others usefull 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="YourTTS",
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run_description="""
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- Original YourTTS trained using VCTK dataset
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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="english_cleaners",
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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_list,
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cudnn_benchmark=False,
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max_audio_len=220500, # it should be: sampling rate * max audio in sec. So it is 22050 * 10 = 220500
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mixed_precision=False,
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test_sentences=[
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[
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"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
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"VCTK_p277",
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None,
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"en",
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],
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[
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"Be a voice, not an echo.",
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"VCTK_p239",
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None,
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"en",
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],
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[
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"I'm sorry Dave. I'm afraid I can't do that.",
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"VCTK_p258",
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None,
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"en",
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],
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[
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"This cake is great. It's so delicious and moist.",
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"VCTK_p244",
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None,
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"en",
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],
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[
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"Prior to November 22, 1963.",
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"VCTK_p305",
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None,
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"en",
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],
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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={"speaker_name": 1.0},
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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),
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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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