mirror of https://github.com/coqui-ai/TTS.git
Add XTTS v2.0 training recipe
This commit is contained in:
Edresson Casanova
Eren G??lge
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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.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
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from TTS.utils.manage import ModelManager
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# Logging parameters
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RUN_NAME = "GPT_XTTS_v2.0_LJSpeech_FT"
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PROJECT_NAME = "XTTS_trainer"
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DASHBOARD_LOGGER = "tensorboard"
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LOGGER_URI = None
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# Set here the path that the checkpoints will be saved. Default: ./run/training/
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OUT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "run", "training")
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# Training Parameters
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OPTIMIZER_WD_ONLY_ON_WEIGHTS = True # for multi-gpu training please make it False
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START_WITH_EVAL = True # if True it will star with evaluation
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BATCH_SIZE = 3 # set here the batch size
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GRAD_ACUMM_STEPS = 84 # set here the grad accumulation steps
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# Note: we recommend that BATCH_SIZE * GRAD_ACUMM_STEPS need to be at least 252 for more efficient training. You can increase/decrease BATCH_SIZE but then set GRAD_ACUMM_STEPS accordingly.
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# Define here the dataset that you want to use for the fine-tuning on.
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config_dataset = BaseDatasetConfig(
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formatter="ljspeech",
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dataset_name="ljspeech",
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path="/raid/datasets/LJSpeech-1.1_24khz/",
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meta_file_train="/raid/datasets/LJSpeech-1.1_24khz/metadata.csv",
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language="en",
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)
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# Add here the configs of the datasets
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DATASETS_CONFIG_LIST = [config_dataset]
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# Define the path where XTTS v2.0.1 files will be downloaded
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CHECKPOINTS_OUT_PATH = os.path.join(OUT_PATH, "XTTS_v2.0_original_model_files/")
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os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True)
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# DVAE files
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DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/dvae.pth"
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MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/mel_stats.pth"
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# Set the path to the downloaded files
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DVAE_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, DVAE_CHECKPOINT_LINK.split("/")[-1])
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MEL_NORM_FILE = os.path.join(CHECKPOINTS_OUT_PATH, MEL_NORM_LINK.split("/")[-1])
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# download DVAE files if needed
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if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(MEL_NORM_FILE):
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print(" > Downloading DVAE files!")
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ModelManager._download_model_files([MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True)
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# ToDo: Update links for XTTS v2.0
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# Download XTTS v2.0 checkpoint if needed
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TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v2.0/vocab.json"
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XTTS_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v2.0/model.pth"
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# XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning.
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TOKENIZER_FILE = os.path.join(CHECKPOINTS_OUT_PATH, TOKENIZER_FILE_LINK.split("/")[-1]) # vocab.json file
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XTTS_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, XTTS_CHECKPOINT_LINK.split("/")[-1]) # model.pth file
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# download XTTS v2.0 files if needed
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if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(XTTS_CHECKPOINT):
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print(" > Downloading XTTS v2.0 files!")
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ModelManager._download_model_files([TOKENIZER_FILE_LINK, XTTS_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True)
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# Training sentences generations
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SPEAKER_REFERENCE = (
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"./tests/data/ljspeech/wavs/LJ001-0002.wav" # speaker reference to be used in training test sentences
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)
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LANGUAGE = config_dataset.language
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def main():
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# init args and config
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model_args = GPTArgs(
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max_conditioning_length=132300, # 6 secs
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min_conditioning_length=66150, # 3 secs
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debug_loading_failures=False,
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max_wav_length=255995, # ~11.6 seconds
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max_text_length=200,
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mel_norm_file=MEL_NORM_FILE,
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dvae_checkpoint=DVAE_CHECKPOINT,
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xtts_checkpoint=XTTS_CHECKPOINT, # checkpoint path of the model that you want to fine-tune
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tokenizer_file=TOKENIZER_FILE,
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gpt_num_audio_tokens=8194,
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gpt_start_audio_token=8192,
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gpt_stop_audio_token=8193,
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use_ne_hifigan=True, # if it is true it will keep the non-enhanced keys on the output checkpoint
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gpt_use_masking_gt_prompt_approach=True,
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gpt_use_perceiver_resampler=True,
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)
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# define audio config
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audio_config = XttsAudioConfig(
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sample_rate=22050, dvae_sample_rate=22050, diffusion_sample_rate=24000, output_sample_rate=24000
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)
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# training parameters config
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config = GPTTrainerConfig(
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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=PROJECT_NAME,
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run_description="""
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GPT XTTS training
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""",
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dashboard_logger=DASHBOARD_LOGGER,
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logger_uri=LOGGER_URI,
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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=10000,
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save_n_checkpoints=1,
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save_checkpoints=True,
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# target_loss="loss",
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print_eval=False,
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# Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
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optimizer="AdamW",
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optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
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optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
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lr=5e-06, # learning rate
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lr_scheduler="MultiStepLR",
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# it was adjusted accordly for the new step scheme
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lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1},
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test_sentences=[
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{
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"text": "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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"speaker_wav": SPEAKER_REFERENCE,
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"language": LANGUAGE,
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},
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{
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"text": "This cake is great. It's so delicious and moist.",
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"speaker_wav": SPEAKER_REFERENCE,
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"language": LANGUAGE,
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},
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],
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)
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# init the model from config
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model = GPTTrainer.init_from_config(config)
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# load training samples
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train_samples, eval_samples = load_tts_samples(
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DATASETS_CONFIG_LIST,
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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 trainer and 🚀
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trainer = Trainer(
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TrainerArgs(
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restore_path=None, # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
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skip_train_epoch=False,
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start_with_eval=START_WITH_EVAL,
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grad_accum_steps=GRAD_ACUMM_STEPS,
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),
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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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if __name__ == "__main__":
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main()
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