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import argparse
from model import CFM, UNetT, DiT, Trainer
from model.utils import get_tokenizer
from model.dataset import load_dataset
from cached_path import cached_path
import shutil
import os
# -------------------------- Dataset Settings --------------------------- #
target_sample_rate = 24000
n_mel_channels = 100
hop_length = 256
# -------------------------- Argument Parsing --------------------------- #
def parse_args():
parser = argparse.ArgumentParser(description="Train CFM Model")
parser.add_argument(
"--exp_name", type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"], help="Experiment name"
)
parser.add_argument("--dataset_name", type=str, default="Emilia_ZH_EN", help="Name of the dataset to use")
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate for training")
parser.add_argument("--batch_size_per_gpu", type=int, default=256, help="Batch size per GPU")
parser.add_argument(
"--batch_size_type", type=str, default="frame", choices=["frame", "sample"], help="Batch size type"
)
parser.add_argument("--max_samples", type=int, default=16, help="Max sequences per batch")
parser.add_argument("--grad_accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping")
parser.add_argument("--epochs", type=int, default=10, help="Number of training epochs")
parser.add_argument("--num_warmup_updates", type=int, default=5, help="Warmup steps")
parser.add_argument("--save_per_updates", type=int, default=10, help="Save checkpoint every X steps")
parser.add_argument("--last_per_steps", type=int, default=10, help="Save last checkpoint every X steps")
parser.add_argument("--finetune", type=bool, default=True, help="Use Finetune")
parser.add_argument(
"--tokenizer", type=str, default="pinyin", choices=["pinyin", "char", "custom"], help="Tokenizer type"
)
parser.add_argument(
"--tokenizer_path",
type=str,
default=None,
help="Path to custom tokenizer vocab file (only used if tokenizer = 'custom')",
)
return parser.parse_args()
# -------------------------- Training Settings -------------------------- #
def main():
args = parse_args()
# Model parameters based on experiment name
if args.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)
if args.finetune:
ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
elif args.exp_name == "E2TTS_Base":
wandb_resume_id = None
model_cls = UNetT
model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
if args.finetune:
ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
if args.finetune:
path_ckpt = os.path.join("ckpts", args.dataset_name)
if not os.path.isdir(path_ckpt):
os.makedirs(path_ckpt, exist_ok=True)
shutil.copy2(ckpt_path, os.path.join(path_ckpt, os.path.basename(ckpt_path)))
checkpoint_path = os.path.join("ckpts", args.dataset_name)
# Use the tokenizer and tokenizer_path provided in the command line arguments
tokenizer = args.tokenizer
if tokenizer == "custom":
if not args.tokenizer_path:
raise ValueError("Custom tokenizer selected, but no tokenizer_path provided.")
tokenizer_path = args.tokenizer_path
else:
tokenizer_path = args.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,
args.epochs,
args.learning_rate,
num_warmup_updates=args.num_warmup_updates,
save_per_updates=args.save_per_updates,
checkpoint_path=checkpoint_path,
batch_size=args.batch_size_per_gpu,
batch_size_type=args.batch_size_type,
max_samples=args.max_samples,
grad_accumulation_steps=args.grad_accumulation_steps,
max_grad_norm=args.max_grad_norm,
wandb_project="CFM-TTS",
wandb_run_name=args.exp_name,
wandb_resume_id=wandb_resume_id,
last_per_steps=args.last_per_steps,
)
train_dataset = load_dataset(args.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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