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# 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.svc.diffusion.diffusion_trainer import DiffusionTrainer
from models.svc.comosvc.comosvc_trainer import ComoSVCTrainer
from models.svc.transformer.transformer_trainer import TransformerTrainer
from utils.util import load_config
def build_trainer(args, cfg):
supported_trainer = {
"DiffWaveNetSVC": DiffusionTrainer,
"DiffComoSVC": ComoSVCTrainer,
"TransformerSVC": TransformerTrainer,
}
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.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(
"--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="If specified, to resume from the existing checkpoint.",
)
parser.add_argument(
"--resume_from_ckpt_path",
type=str,
default="",
help="The specific checkpoint path that you want to resume from.",
)
parser.add_argument(
"--resume_type",
type=str,
default="",
help="`resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights",
)
parser.add_argument(
"--log_level", default="warning", help="logging level (debug, info, warning)"
)
args = parser.parse_args()
cfg = load_config(args.config)
# Data Augmentation
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)
# CUDA settings
cuda_relevant()
# Build trainer
trainer = build_trainer(args, cfg)
trainer.train_loop()
if __name__ == "__main__":
main()
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