Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,646 Bytes
4dab15f 79086d9 4dab15f c0fb8c8 4dab15f c0fb8c8 4dab15f 392ff83 4dab15f b6584c2 aa59806 b6584c2 4dab15f b6584c2 79086d9 4dab15f 79086d9 392ff83 4dab15f f0d11e3 c0fb8c8 f0d11e3 4dab15f c0fb8c8 4dab15f c0fb8c8 4dab15f b6584c2 4dab15f b6584c2 4dab15f aa59806 4dab15f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
import argparse
import os
import shutil
from cached_path import cached_path
from f5_tts.model import CFM, UNetT, DiT, Trainer
from f5_tts.model.utils import get_tokenizer
from f5_tts.model.dataset import load_dataset
from importlib.resources import files
# -------------------------- Dataset Settings --------------------------- #
target_sample_rate = 24000
n_mel_channels = 100
hop_length = 256
win_length = 1024
n_fft = 1024
mel_spec_type = "vocos" # 'vocos' or 'bigvgan'
# -------------------------- Argument Parsing --------------------------- #
def parse_args():
# batch_size_per_gpu = 1000 settting for gpu 8GB
# batch_size_per_gpu = 1600 settting for gpu 12GB
# batch_size_per_gpu = 2000 settting for gpu 16GB
# batch_size_per_gpu = 3200 settting for gpu 24GB
# num_warmup_updates = 300 for 5000 sample about 10 hours
# change save_per_updates , last_per_steps change this value what you need ,
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-5, help="Learning rate for training")
parser.add_argument("--batch_size_per_gpu", type=int, default=3200, 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=64, 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=100, help="Number of training epochs")
parser.add_argument("--num_warmup_updates", type=int, default=300, help="Warmup steps")
parser.add_argument("--save_per_updates", type=int, default=10000, help="Save checkpoint every X steps")
parser.add_argument("--last_per_steps", type=int, default=50000, help="Save last checkpoint every X steps")
parser.add_argument("--finetune", type=bool, default=True, help="Use Finetune")
parser.add_argument("--pretrain", type=str, default=None, help="the path to the checkpoint")
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')",
)
parser.add_argument(
"--log_samples",
type=bool,
default=False,
help="Log inferenced samples per ckpt save steps",
)
parser.add_argument("--logger", type=str, default=None, choices=["wandb", "tensorboard"], help="logger")
parser.add_argument(
"--bnb_optimizer",
type=bool,
default=False,
help="Use 8-bit Adam optimizer from bitsandbytes",
)
return parser.parse_args()
# -------------------------- Training Settings -------------------------- #
def main():
args = parse_args()
checkpoint_path = str(files("f5_tts").joinpath(f"../../ckpts/{args.dataset_name}"))
# 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:
if args.pretrain is None:
ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
else:
ckpt_path = args.pretrain
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:
if args.pretrain is None:
ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
else:
ckpt_path = args.pretrain
if args.finetune:
if not os.path.isdir(checkpoint_path):
os.makedirs(checkpoint_path, exist_ok=True)
file_checkpoint = os.path.join(checkpoint_path, os.path.basename(ckpt_path))
if not os.path.isfile(file_checkpoint):
shutil.copy2(ckpt_path, file_checkpoint)
print("copy checkpoint for finetune")
# 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)
print("\nvocab : ", vocab_size)
print("\nvocoder : ", mel_spec_type)
mel_spec_kwargs = dict(
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
n_mel_channels=n_mel_channels,
target_sample_rate=target_sample_rate,
mel_spec_type=mel_spec_type,
)
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,
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,
logger=args.logger,
wandb_project=args.dataset_name,
wandb_run_name=args.exp_name,
wandb_resume_id=wandb_resume_id,
log_samples=args.log_samples,
last_per_steps=args.last_per_steps,
bnb_optimizer=args.bnb_optimizer,
)
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()
|