Upload train.py

train.py

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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()