Spaces:
Running
Running
File size: 4,766 Bytes
a674527 |
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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
import argparse
from model import CFM, UNetT, DiT, MMDiT, Trainer
from model.utils import get_tokenizer
from model.dataset import load_dataset
from cached_path import cached_path
import shutil,os
# -------------------------- 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)
# -------------------------- 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')
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(f"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(f"hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
if args.finetune:
path_ckpt = os.path.join("ckpts",args.dataset_name)
if os.path.isdir(path_ckpt)==False:
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 dataset_name provided in the command line
tokenizer_path = args.dataset_name if tokenizer != "custom" else tokenizer_path
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()
|