mirror of
https://github.com/SWivid/F5-TTS.git
synced 2025-12-05 20:40:12 -08:00
92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
from model import CFM, UNetT, DiT, MMDiT, 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"
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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.
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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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vocab_char_map, vocab_size = get_tokenizer(dataset_name, 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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e2tts = CFM(
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transformer = model_cls(
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**model_cfg,
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text_num_embeds = vocab_size,
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mel_dim = n_mel_channels
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),
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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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e2tts,
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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(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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