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import os
from trainer import Trainer, TrainerArgs
from TTS.config.shared_configs import BaseDatasetConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
from TTS.utils.manage import ModelManager
# Logging parameters
RUN_NAME = "GPT_XTTS_v2.0_LJSpeech_FT"
PROJECT_NAME = "XTTS_trainer"
DASHBOARD_LOGGER = "tensorboard"
LOGGER_URI = None
# Set here the path that the checkpoints will be saved. Default: ./run/training/
OUT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "run", "training")
# Training Parameters
OPTIMIZER_WD_ONLY_ON_WEIGHTS = True # for multi-gpu training please make it False
START_WITH_EVAL = True # if True it will star with evaluation
BATCH_SIZE = 3 # set here the batch size
GRAD_ACUMM_STEPS = 84 # set here the grad accumulation steps
# 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.
# Define here the dataset that you want to use for the fine-tuning on.
config_dataset = BaseDatasetConfig(
formatter="ljspeech",
dataset_name="ljspeech",
path="/raid/datasets/LJSpeech-1.1_24khz/",
meta_file_train="/raid/datasets/LJSpeech-1.1_24khz/metadata.csv",
language="en",
)
# Add here the configs of the datasets
DATASETS_CONFIG_LIST = [config_dataset]
# Define the path where XTTS v2.0.1 files will be downloaded
CHECKPOINTS_OUT_PATH = os.path.join(OUT_PATH, "XTTS_v2.0_original_model_files/")
os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True)
# DVAE files
DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/dvae.pth"
MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/mel_stats.pth"
# Set the path to the downloaded files
DVAE_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(DVAE_CHECKPOINT_LINK))
MEL_NORM_FILE = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(MEL_NORM_LINK))
# download DVAE files if needed
if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(MEL_NORM_FILE):
print(" > Downloading DVAE files!")
ModelManager._download_model_files([MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True)
# Download XTTS v2.0 checkpoint if needed
TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/vocab.json"
XTTS_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/model.pth"
# XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning.
TOKENIZER_FILE = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(TOKENIZER_FILE_LINK)) # vocab.json file
XTTS_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(XTTS_CHECKPOINT_LINK)) # model.pth file
# download XTTS v2.0 files if needed
if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(XTTS_CHECKPOINT):
print(" > Downloading XTTS v2.0 files!")
ModelManager._download_model_files(
[TOKENIZER_FILE_LINK, XTTS_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True
)
# Training sentences generations
SPEAKER_REFERENCE = [
"./tests/data/ljspeech/wavs/LJ001-0002.wav" # speaker reference to be used in training test sentences
]
LANGUAGE = config_dataset.language
def main():
# init args and config
model_args = GPTArgs(
max_conditioning_length=132300, # 6 secs
min_conditioning_length=66150, # 3 secs
debug_loading_failures=False,
max_wav_length=255995, # ~11.6 seconds
max_text_length=200,
mel_norm_file=MEL_NORM_FILE,
dvae_checkpoint=DVAE_CHECKPOINT,
xtts_checkpoint=XTTS_CHECKPOINT, # checkpoint path of the model that you want to fine-tune
tokenizer_file=TOKENIZER_FILE,
gpt_num_audio_tokens=1026,
gpt_start_audio_token=1024,
gpt_stop_audio_token=1025,
gpt_use_masking_gt_prompt_approach=True,
gpt_use_perceiver_resampler=True,
)
# define audio config
audio_config = XttsAudioConfig(sample_rate=22050, dvae_sample_rate=22050, output_sample_rate=24000)
# training parameters config
config = GPTTrainerConfig(
output_path=OUT_PATH,
model_args=model_args,
run_name=RUN_NAME,
project_name=PROJECT_NAME,
run_description="""
GPT XTTS training
""",
dashboard_logger=DASHBOARD_LOGGER,
logger_uri=LOGGER_URI,
audio=audio_config,
batch_size=BATCH_SIZE,
batch_group_size=48,
eval_batch_size=BATCH_SIZE,
num_loader_workers=8,
eval_split_max_size=256,
print_step=50,
plot_step=100,
log_model_step=1000,
save_step=10000,
save_n_checkpoints=1,
save_checkpoints=True,
# target_loss="loss",
print_eval=False,
# Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
optimizer="AdamW",
optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
lr=5e-06, # learning rate
lr_scheduler="MultiStepLR",
# it was adjusted accordly for the new step scheme
lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1},
test_sentences=[
{
"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.",
"speaker_wav": SPEAKER_REFERENCE,
"language": LANGUAGE,
},
{
"text": "This cake is great. It's so delicious and moist.",
"speaker_wav": SPEAKER_REFERENCE,
"language": LANGUAGE,
},
],
)
# init the model from config
model = GPTTrainer.init_from_config(config)
# load training samples
train_samples, eval_samples = load_tts_samples(
DATASETS_CONFIG_LIST,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
# init the trainer and 🚀
trainer = Trainer(
TrainerArgs(
restore_path=None, # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
skip_train_epoch=False,
start_with_eval=START_WITH_EVAL,
grad_accum_steps=GRAD_ACUMM_STEPS,
),
config,
output_path=OUT_PATH,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
)
trainer.fit()
if __name__ == "__main__":
main()
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