Delete train.py

train.py

@@ -1,92 +0,0 @@
-from model import CFM, UNetT, DiT, Trainer
-from model.utils import get_tokenizer
-from model.dataset import load_dataset
-
-
-# -------------------------- 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)
-dataset_name = "Emilia_ZH_EN"
-
-# -------------------------- Training Settings -------------------------- #
-
-exp_name = "F5TTS_Base"  # F5TTS_Base | E2TTS_Base
-
-learning_rate = 7.5e-5
-
-batch_size_per_gpu = 38400  # 8 GPUs, 8 * 38400 = 307200
-batch_size_type = "frame"  # "frame" or "sample"
-max_samples = 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
-grad_accumulation_steps = 1  # note: updates = steps / grad_accumulation_steps
-max_grad_norm = 1.0
-
-epochs = 11  # use linear decay, thus epochs control the slope
-num_warmup_updates = 20000  # warmup steps
-save_per_updates = 50000  # save checkpoint per steps
-last_per_steps = 5000  # save last checkpoint per steps
-
-# model params
-if 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)
-elif exp_name == "E2TTS_Base":
-    wandb_resume_id = None
-    model_cls = UNetT
-    model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
-
-
-# ----------------------------------------------------------------------- #
-
-
-def main():
-    if tokenizer == "custom":
-        tokenizer_path = tokenizer_path
-    else:
-        tokenizer_path = dataset_name
-    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,
-    )
-
-    model = 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(
-        model,
-        epochs,
-        learning_rate,
-        num_warmup_updates=num_warmup_updates,
-        save_per_updates=save_per_updates,
-        checkpoint_path=f"ckpts/{exp_name}",
-        batch_size=batch_size_per_gpu,
-        batch_size_type=batch_size_type,
-        max_samples=max_samples,
-        grad_accumulation_steps=grad_accumulation_steps,
-        max_grad_norm=max_grad_norm,
-        wandb_project="CFM-TTS",
-        wandb_run_name=exp_name,
-        wandb_resume_id=wandb_resume_id,
-        last_per_steps=last_per_steps,
-    )
-
-    train_dataset = load_dataset(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()