Merge pull request #125 from lpscr/main

gradio_finetune

finetune-cli.py

@@ -0,0 +1,103 @@
+import argparse
+from model import CFM, UNetT, DiT, MMDiT, Trainer
+from model.utils import get_tokenizer
+from model.dataset import load_dataset
+from cached_path import cached_path
+import shutil,os
+# -------------------------- Dataset Settings --------------------------- #
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+
+tokenizer = "pinyin"  # 'pinyin', 'char', or 'custom'
+tokenizer_path = None  # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt)
+
+# -------------------------- Argument Parsing --------------------------- #
+def parse_args():
+    parser = argparse.ArgumentParser(description='Train CFM Model')
+
+    parser.add_argument('--exp_name', type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"],help='Experiment name')
+    parser.add_argument('--dataset_name', type=str, default="Emilia_ZH_EN", help='Name of the dataset to use')
+    parser.add_argument('--learning_rate', type=float, default=1e-4, help='Learning rate for training')
+    parser.add_argument('--batch_size_per_gpu', type=int, default=256, help='Batch size per GPU')
+    parser.add_argument('--batch_size_type', type=str, default="frame", choices=["frame", "sample"],help='Batch size type')
+    parser.add_argument('--max_samples', type=int, default=16, help='Max sequences per batch')
+    parser.add_argument('--grad_accumulation_steps', type=int, default=1,help='Gradient accumulation steps')
+    parser.add_argument('--max_grad_norm', type=float, default=1.0, help='Max gradient norm for clipping')
+    parser.add_argument('--epochs', type=int, default=10, help='Number of training epochs')
+    parser.add_argument('--num_warmup_updates', type=int, default=5, help='Warmup steps')
+    parser.add_argument('--save_per_updates', type=int, default=10, help='Save checkpoint every X steps')
+    parser.add_argument('--last_per_steps', type=int, default=10, help='Save last checkpoint every X steps')
+
+    return parser.parse_args()
+
+# -------------------------- Training Settings -------------------------- #
+
+def main():
+    args = parse_args()
+
+
+    # Model parameters based on experiment name
+    if args.exp_name == "F5TTS_Base":
+        wandb_resume_id = None
+        model_cls = DiT
+        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+        ckpt_path = str(cached_path(f"hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
+    elif args.exp_name == "E2TTS_Base":
+        wandb_resume_id = None
+        model_cls = UNetT
+        model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+        ckpt_path = str(cached_path(f"hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
+
+    path_ckpt = os.path.join("ckpts",args.dataset_name)
+    if os.path.isdir(path_ckpt)==False:
+       os.makedirs(path_ckpt,exist_ok=True)
+       shutil.copy2(ckpt_path,os.path.join(path_ckpt,os.path.basename(ckpt_path)))
+    checkpoint_path=os.path.join("ckpts",args.dataset_name)
+
+    # Use the dataset_name provided in the command line
+    tokenizer_path = args.dataset_name if tokenizer != "custom" else tokenizer_path
+    vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
+
+    mel_spec_kwargs = dict(
+        target_sample_rate=target_sample_rate,
+        n_mel_channels=n_mel_channels,
+        hop_length=hop_length,
+    )
+
+    e2tts = CFM(
+        transformer=model_cls(
+            **model_cfg,
+            text_num_embeds=vocab_size,
+            mel_dim=n_mel_channels
+        ),
+        mel_spec_kwargs=mel_spec_kwargs,
+        vocab_char_map=vocab_char_map,
+    )
+
+    trainer = Trainer(
+        e2tts,
+        args.epochs,
+        args.learning_rate,
+        num_warmup_updates=args.num_warmup_updates,
+        save_per_updates=args.save_per_updates,
+        checkpoint_path=checkpoint_path,
+        batch_size=args.batch_size_per_gpu,
+        batch_size_type=args.batch_size_type,
+        max_samples=args.max_samples,
+        grad_accumulation_steps=args.grad_accumulation_steps,
+        max_grad_norm=args.max_grad_norm,
+        wandb_project="CFM-TTS",
+        wandb_run_name=args.exp_name,
+        wandb_resume_id=wandb_resume_id,
+        last_per_steps=args.last_per_steps,
+    )
+
+    train_dataset = load_dataset(args.dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)
+    trainer.train(train_dataset,
+                  resumable_with_seed=666  # seed for shuffling dataset
+                  )
+
+
+if __name__ == '__main__':
+    main()