bins/tts/train.py

# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import argparse

import torch

from models.tts.fastspeech2.fs2_trainer import FastSpeech2Trainer
from models.tts.vits.vits_trainer import VITSTrainer
from models.tts.valle.valle_trainer import VALLETrainer
from models.tts.naturalspeech2.ns2_trainer import NS2Trainer
from models.tts.valle_v2.valle_ar_trainer import ValleARTrainer as VALLE_V2_AR
from models.tts.valle_v2.valle_nar_trainer import ValleNARTrainer as VALLE_V2_NAR
from models.tts.jets.jets_trainer import JetsTrainer

from utils.util import load_config


def build_trainer(args, cfg):
    supported_trainer = {
        "FastSpeech2": FastSpeech2Trainer,
        "VITS": VITSTrainer,
        "VALLE": VALLETrainer,
        "NaturalSpeech2": NS2Trainer,
        "VALLE_V2_AR": VALLE_V2_AR,
        "VALLE_V2_NAR": VALLE_V2_NAR,
        "Jets": JetsTrainer,
    }

    trainer_class = supported_trainer[cfg.model_type]
    trainer = trainer_class(args, cfg)
    return trainer


def cuda_relevant(deterministic=False):
    torch.cuda.empty_cache()
    # TF32 on Ampere and above
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.allow_tf32 = True
    # Deterministic
    torch.backends.cudnn.deterministic = deterministic
    torch.backends.cudnn.benchmark = not deterministic
    torch.use_deterministic_algorithms(deterministic)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--config",
        default="config.json",
        help="json files for configurations.",
        required=True,
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=1234,
        help="random seed",
        required=False,
    )
    parser.add_argument(
        "--exp_name",
        type=str,
        default="exp_name",
        help="A specific name to note the experiment",
        required=True,
    )
    parser.add_argument(
        "--resume", action="store_true", help="The model name to restore"
    )
    parser.add_argument(
        "--test", action="store_true", default=False, help="Test the model"
    )
    parser.add_argument(
        "--log_level", default="warning", help="logging level (debug, info, warning)"
    )
    parser.add_argument(
        "--resume_type",
        type=str,
        default="resume",
        help="Resume training or finetuning.",
    )
    parser.add_argument(
        "--checkpoint_path",
        type=str,
        default=None,
        help="Checkpoint for resume training or finetuning.",
    )
    parser.add_argument(
        "--resume_from_ckpt_path",
        type=str,
        default="",
        help="Checkpoint for resume training or finetuning.",
    )
    # VALLETrainer.add_arguments(parser)
    args = parser.parse_args()
    cfg = load_config(args.config)

    # Data Augmentation
    if hasattr(cfg, "preprocess"):
        if hasattr(cfg.preprocess, "data_augment"):
            if (
                type(cfg.preprocess.data_augment) == list
                and len(cfg.preprocess.data_augment) > 0
            ):
                new_datasets_list = []
                for dataset in cfg.preprocess.data_augment:
                    new_datasets = [
                        (
                            f"{dataset}_pitch_shift"
                            if cfg.preprocess.use_pitch_shift
                            else None
                        ),
                        (
                            f"{dataset}_formant_shift"
                            if cfg.preprocess.use_formant_shift
                            else None
                        ),
                        (
                            f"{dataset}_equalizer"
                            if cfg.preprocess.use_equalizer
                            else None
                        ),
                        (
                            f"{dataset}_time_stretch"
                            if cfg.preprocess.use_time_stretch
                            else None
                        ),
                    ]
                    new_datasets_list.extend(filter(None, new_datasets))
                cfg.dataset.extend(new_datasets_list)

    print("experiment name: ", args.exp_name)
    # # CUDA settings
    cuda_relevant()

    # Build trainer
    print(f"Building {cfg.model_type} trainer")
    trainer = build_trainer(args, cfg)
    print(f"Start training {cfg.model_type} model")
    if args.test:
        trainer.test_loop()
    else:
        trainer.train_loop()


if __name__ == "__main__":
    main()