Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,943 Bytes
d37849f dd217c7 d37849f dd217c7 d37849f dd217c7 d37849f dd217c7 d37849f dd217c7 d37849f dd217c7 a674527 dd217c7 d37849f 9eac142 d37849f dd217c7 9eac142 d37849f dd217c7 d37849f dd217c7 d37849f dd217c7 d37849f dd217c7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
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()
|