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# Copyright (c) 2025 ASLP-LAB
# 2025 Ziqian Ning (ningziqian@mail.nwpu.edu.cn)
# 2025 Huakang Chen (huakang@mail.nwpu.edu.cn)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from importlib.resources import files
from model import CFM, DiT, Trainer
from prefigure.prefigure import get_all_args
import json
import os
os.environ['OMP_NUM_THREADS']="1"
os.environ['MKL_NUM_THREADS']="1"
def main():
args = get_all_args("config/default.ini")
with open(args.model_config) as f:
model_config = json.load(f)
if model_config["model_type"] == "diffrhythm":
wandb_resume_id = None
model_cls = DiT
model = CFM(
transformer=model_cls(**model_config["model"], max_frames=args.max_frames),
num_channels=model_config["model"]['mel_dim'],
audio_drop_prob=args.audio_drop_prob,
cond_drop_prob=args.cond_drop_prob,
style_drop_prob=args.style_drop_prob,
lrc_drop_prob=args.lrc_drop_prob,
max_frames=args.max_frames
)
total_params = sum(p.numel() for p in model.parameters())
print(f"Total parameters: {total_params}")
trainer = Trainer(
model,
args,
args.epochs,
args.learning_rate,
num_warmup_updates=args.num_warmup_updates,
save_per_updates=args.save_per_updates,
checkpoint_path=f"ckpts/{args.exp_name}",
grad_accumulation_steps=args.grad_accumulation_steps,
max_grad_norm=args.max_grad_norm,
wandb_project="diffrhythm-test",
wandb_run_name=args.exp_name,
wandb_resume_id=wandb_resume_id,
last_per_steps=args.last_per_steps,
bnb_optimizer=False,
reset_lr=args.reset_lr,
batch_size=args.batch_size,
grad_ckpt=args.grad_ckpt
)
trainer.train(
resumable_with_seed=args.resumable_with_seed, # seed for shuffling dataset
)
if __name__ == "__main__":
main()
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