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import argparse
import os
import shutil
import torch
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
from accelerate import Accelerator
accelerator = Accelerator()
print(f"Using mixed precision: {accelerator.mixed_precision}")
# -------------------------- 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=700, help="Number of training epochs")
parser.add_argument("--num_warmup_updates", type=int, default=1500, help="Warmup steps")
parser.add_argument("--save_per_updates", type=int, default=4000, help="Save checkpoint every X steps")
parser.add_argument("--last_per_steps", type=int, default=40000, 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",
)
parser.add_argument("--ckpt_dir", required=True, type=str)
parser.add_argument("--data_dir", required=True, type=str)
parser.add_argument("--wandb_resume_id", type=str, default=None)
parser.add_argument("--resume", type=bool, default=False, help="Resume Finetune")
return parser.parse_args()
# -------------------------- Training Settings -------------------------- #
def main():
args = parse_args()
# checkpoint_path = str(files("f5_tts").joinpath(f"../../ckpts/{args.dataset_name}"))
checkpoint_path = args.ckpt_dir
# Model parameters based on experiment name
if args.exp_name == "F5TTS_Base":
wandb_resume_id = args.wandb_resume_id
print("wandb resume id is: ", wandb_resume_id)
model_cls = DiT
model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
# ckpt_path = "/home/tts/ttsteam/repos/F5-TTS/runs/indic_langs_11_1hr/ckpt/model_1200000.pt"
# 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 and not args.resume:
if not os.path.isdir(checkpoint_path):
os.makedirs(checkpoint_path, exist_ok=True)
file_checkpoint = os.path.join(checkpoint_path, 'model_last.pt')
# if not os.path.isfile(file_checkpoint): ## UNRELIABLE, if too slow on Multinode, can lead to some nodes training from scratch
# # shutil.copy2(load_from, file_checkpoint)
# ckpt = torch.load(args.load_from, weights_only=True, map_location="cpu")
# del ckpt['step']
# torch.save(ckpt, file_checkpoint)
# del ckpt
# print("copy checkpoint for finetune", load_from, file_checkpoint)
# 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, data_dir=args.data_dir)
trainer.train(
train_dataset,
resumable_with_seed=666, # seed for shuffling dataset
num_workers=16
)
if __name__ == "__main__":
main()
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