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import argparse
import os
import shutil
from importlib.resources import files
from cached_path import cached_path
from f5_tts.model import CFM, DiT, Trainer, UNetT
from f5_tts.model.dataset import load_dataset
from f5_tts.model.utils import get_tokenizer
# -------------------------- 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():
parser = argparse.ArgumentParser(description="Train CFM Model")
parser.add_argument(
"--exp_name",
type=str,
default="F5TTS_v1_Base",
choices=["F5TTS_v1_Base", "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=20000, help="Warmup updates"
)
parser.add_argument(
"--save_per_updates",
type=int,
default=50000,
help="Save checkpoint every N updates",
)
parser.add_argument(
"--keep_last_n_checkpoints",
type=int,
default=-1,
help="-1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints",
)
parser.add_argument(
"--last_per_updates",
type=int,
default=5000,
help="Save last checkpoint every N updates",
)
parser.add_argument("--finetune", action="store_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",
action="store_true",
help="Log inferenced samples per ckpt save updates",
)
parser.add_argument(
"--logger",
type=str,
default=None,
choices=[None, "wandb", "tensorboard"],
help="logger",
)
parser.add_argument(
"--bnb_optimizer",
action="store_true",
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_v1_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_v1_Base/model_1250000.safetensors"
)
)
else:
ckpt_path = args.pretrain
elif 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,
text_mask_padding=False,
conv_layers=4,
pe_attn_head=1,
)
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,
text_mask_padding=False,
pe_attn_head=1,
)
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.basename(ckpt_path)
if not file_checkpoint.startswith(
"pretrained_"
): # Change: Add 'pretrained_' prefix to copied model
file_checkpoint = "pretrained_" + file_checkpoint
file_checkpoint = os.path.join(checkpoint_path, file_checkpoint)
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,
keep_last_n_checkpoints=args.keep_last_n_checkpoints,
checkpoint_path=checkpoint_path,
batch_size_per_gpu=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_updates=args.last_per_updates,
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()
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