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import os
from dataclasses import dataclass, field
from trainer import Trainer, TrainerArgs
from TTS.config import load_config, register_config
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models import setup_model
@dataclass
class TrainTTSArgs(TrainerArgs):
config_path: str = field(default=None, metadata={"help": "Path to the config file."})
def main():
"""Run `tts` model training directly by a `config.json` file."""
# init trainer args
train_args = TrainTTSArgs()
parser = train_args.init_argparse(arg_prefix="")
# override trainer args from comman-line args
args, config_overrides = parser.parse_known_args()
train_args.parse_args(args)
# load config.json and register
if args.config_path or args.continue_path:
if args.config_path:
# init from a file
config = load_config(args.config_path)
if len(config_overrides) > 0:
config.parse_known_args(config_overrides, relaxed_parser=True)
elif args.continue_path:
# continue from a prev experiment
config = load_config(os.path.join(args.continue_path, "config.json"))
if len(config_overrides) > 0:
config.parse_known_args(config_overrides, relaxed_parser=True)
else:
# init from console args
from TTS.config.shared_configs import BaseTrainingConfig # pylint: disable=import-outside-toplevel
config_base = BaseTrainingConfig()
config_base.parse_known_args(config_overrides)
config = register_config(config_base.model)()
# load training samples
train_samples, eval_samples = load_tts_samples(
config.datasets,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
# init the model from config
model = setup_model(config, train_samples + eval_samples)
# init the trainer and 🚀
trainer = Trainer(
train_args,
model.config,
config.output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
parse_command_line_args=False,
)
trainer.fit()
if __name__ == "__main__":
main()
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