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from model import CFM, UNetT, DiT, MMDiT, 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.
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,
)
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,
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()
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