mirror of
https://github.com/SWivid/F5-TTS.git
synced 2025-12-30 06:31:54 -08:00
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
from model import CFM, UNetT, DiT, Trainer
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from model.utils import get_tokenizer
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from model.dataset import load_dataset
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# -------------------------- Dataset Settings --------------------------- #
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target_sample_rate = 24000
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n_mel_channels = 100
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hop_length = 256
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tokenizer = "pinyin" # 'pinyin', 'char', or 'custom'
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tokenizer_path = None # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt)
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dataset_name = "Emilia_ZH_EN"
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# -------------------------- Training Settings -------------------------- #
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exp_name = "F5TTS_Base" # F5TTS_Base | E2TTS_Base
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learning_rate = 7.5e-5
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batch_size_per_gpu = 38400 # 8 GPUs, 8 * 38400 = 307200
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batch_size_type = "frame" # "frame" or "sample"
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max_samples = 64 # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
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grad_accumulation_steps = 1 # note: updates = steps / grad_accumulation_steps
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max_grad_norm = 1.0
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epochs = 11 # use linear decay, thus epochs control the slope
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num_warmup_updates = 20000 # warmup steps
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save_per_updates = 50000 # save checkpoint per steps
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last_per_steps = 5000 # save last checkpoint per steps
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# model params
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if exp_name == "F5TTS_Base":
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wandb_resume_id = None
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model_cls = DiT
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model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
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elif exp_name == "E2TTS_Base":
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wandb_resume_id = None
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model_cls = UNetT
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model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
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# ----------------------------------------------------------------------- #
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def main():
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if tokenizer == "custom":
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tokenizer_path = tokenizer_path
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else:
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tokenizer_path = dataset_name
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vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
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mel_spec_kwargs = dict(
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target_sample_rate=target_sample_rate,
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n_mel_channels=n_mel_channels,
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hop_length=hop_length,
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)
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model = CFM(
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transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
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mel_spec_kwargs=mel_spec_kwargs,
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vocab_char_map=vocab_char_map,
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)
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trainer = Trainer(
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model,
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epochs,
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learning_rate,
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num_warmup_updates=num_warmup_updates,
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save_per_updates=save_per_updates,
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checkpoint_path=f"ckpts/{exp_name}",
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batch_size=batch_size_per_gpu,
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batch_size_type=batch_size_type,
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max_samples=max_samples,
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grad_accumulation_steps=grad_accumulation_steps,
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max_grad_norm=max_grad_norm,
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wandb_project="CFM-TTS",
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wandb_run_name=exp_name,
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wandb_resume_id=wandb_resume_id,
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last_per_steps=last_per_steps,
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)
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train_dataset = load_dataset(dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)
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trainer.train(
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train_dataset,
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resumable_with_seed=666, # seed for shuffling dataset
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)
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if __name__ == "__main__":
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main()
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