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# 2025 Ziqian Ning (ningziqian@mail.nwpu.edu.cn)
# 2025 Huakang Chen (huakang@mail.nwpu.edu.cn)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" This implementation is adapted from github repo:
https://github.com/SWivid/F5-TTS.
"""
from __future__ import annotations
import os
import gc
from tqdm import tqdm
import wandb
import torch
from torch.optim import AdamW
from torch.optim.lr_scheduler import LinearLR, SequentialLR, ConstantLR
from accelerate import Accelerator
from accelerate.utils import DistributedDataParallelKwargs
from dataset.dataset import DiffusionDataset
from torch.utils.data import DataLoader
from ema_pytorch import EMA
from model import CFM
from model.utils import exists, default
class Trainer:
def __init__(
self,
model: CFM,
args,
epochs,
learning_rate,
num_warmup_updates=20000,
save_per_updates=1000,
checkpoint_path=None,
batch_size=32,
batch_size_type: str = "sample",
max_samples=32,
grad_accumulation_steps=1,
max_grad_norm=1.0,
noise_scheduler: str | None = None,
duration_predictor: torch.nn.Module | None = None,
wandb_project="test_e2-tts",
wandb_run_name="test_run",
wandb_resume_id: str = None,
last_per_steps=None,
accelerate_kwargs: dict = dict(),
ema_kwargs: dict = dict(),
bnb_optimizer: bool = False,
reset_lr: bool = False,
use_style_prompt: bool = False,
grad_ckpt: bool = False
):
self.args = args
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=False, )
logger = "wandb" if wandb.api.api_key else None
self.accelerator = Accelerator(
log_with=logger,
kwargs_handlers=[ddp_kwargs],
gradient_accumulation_steps=grad_accumulation_steps,
**accelerate_kwargs,
)
if logger == "wandb":
if exists(wandb_resume_id):
init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name, "id": wandb_resume_id}}
else:
init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name}}
self.accelerator.init_trackers(
project_name=wandb_project,
init_kwargs=init_kwargs,
config={
"epochs": epochs,
"learning_rate": learning_rate,
"num_warmup_updates": num_warmup_updates,
"batch_size": batch_size,
"batch_size_type": batch_size_type,
"max_samples": max_samples,
"grad_accumulation_steps": grad_accumulation_steps,
"max_grad_norm": max_grad_norm,
"gpus": self.accelerator.num_processes,
"noise_scheduler": noise_scheduler,
},
)
self.precision = self.accelerator.state.mixed_precision
self.precision = self.precision.replace("no", "fp32")
self.model = model
if self.is_main:
self.ema_model = EMA(model, include_online_model=False, **ema_kwargs)
self.ema_model.to(self.accelerator.device)
if self.accelerator.state.distributed_type in ["DEEPSPEED", "FSDP"]:
self.ema_model.half()
self.epochs = epochs
self.num_warmup_updates = num_warmup_updates
self.save_per_updates = save_per_updates
self.last_per_steps = default(last_per_steps, save_per_updates * grad_accumulation_steps)
self.checkpoint_path = default(checkpoint_path, "ckpts/test_e2-tts")
self.max_samples = max_samples
self.grad_accumulation_steps = grad_accumulation_steps
self.max_grad_norm = max_grad_norm
self.noise_scheduler = noise_scheduler
self.duration_predictor = duration_predictor
self.reset_lr = reset_lr
self.use_style_prompt = use_style_prompt
self.grad_ckpt = grad_ckpt
if bnb_optimizer:
import bitsandbytes as bnb
self.optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=learning_rate)
else:
self.optimizer = AdamW(model.parameters(), lr=learning_rate)
if self.accelerator.state.distributed_type == "DEEPSPEED":
self.accelerator.state.deepspeed_plugin.deepspeed_config['train_micro_batch_size_per_gpu'] = batch_size
self.get_dataloader()
self.get_scheduler()
self.model, self.optimizer, self.scheduler, self.train_dataloader = self.accelerator.prepare(self.model, self.optimizer, self.scheduler, self.train_dataloader)
def get_scheduler(self):
warmup_steps = (
self.num_warmup_updates * self.accelerator.num_processes
) # consider a fixed warmup steps while using accelerate multi-gpu ddp
total_steps = len(self.train_dataloader) * self.epochs / self.grad_accumulation_steps
decay_steps = total_steps - warmup_steps
warmup_scheduler = LinearLR(self.optimizer, start_factor=1e-8, end_factor=1.0, total_iters=warmup_steps)
decay_scheduler = LinearLR(self.optimizer, start_factor=1.0, end_factor=1e-8, total_iters=decay_steps)
self.scheduler = SequentialLR(
self.optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[warmup_steps]
)
def get_constant_scheduler(self):
total_steps = len(self.train_dataloader) * self.epochs / self.grad_accumulation_steps
self.scheduler = ConstantLR(self.optimizer, factor=1, total_iters=total_steps)
def get_dataloader(self):
print(self.args)
dd = DiffusionDataset(self.args.file_path, self.args.max_frames, self.args.min_frames, self.args.sampling_rate, self.args.downsample_rate, self.precision)
self.train_dataloader = DataLoader(
dataset=dd,
batch_size=self.args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
collate_fn=dd.custom_collate_fn,
persistent_workers=True
)
@property
def is_main(self):
return self.accelerator.is_main_process
def save_checkpoint(self, step, last=False):
self.accelerator.wait_for_everyone()
if self.is_main:
checkpoint = dict(
model_state_dict=self.accelerator.unwrap_model(self.model).state_dict(),
optimizer_state_dict=self.accelerator.unwrap_model(self.optimizer).state_dict(),
ema_model_state_dict=self.ema_model.state_dict(),
scheduler_state_dict=self.scheduler.state_dict(),
step=step,
)
if not os.path.exists(self.checkpoint_path):
os.makedirs(self.checkpoint_path)
if last:
self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_last.pt")
print(f"Saved last checkpoint at step {step}")
else:
self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_{step}.pt")
def load_checkpoint(self):
if (
not exists(self.checkpoint_path)
or not os.path.exists(self.checkpoint_path)
or not os.listdir(self.checkpoint_path)
):
return 0
self.accelerator.wait_for_everyone()
if "model_last.pt" in os.listdir(self.checkpoint_path):
latest_checkpoint = "model_last.pt"
else:
latest_checkpoint = sorted(
[f for f in os.listdir(self.checkpoint_path) if f.endswith(".pt")],
key=lambda x: int("".join(filter(str.isdigit, x))),
)[-1]
checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location="cpu")
if self.is_main:
ema_dict = self.ema_model.state_dict()
ema_checkpoint_dict = checkpoint["ema_model_state_dict"]
filtered_ema_dict = {
k: v for k, v in ema_checkpoint_dict.items()
if k in ema_dict and ema_dict[k].shape == v.shape
}
self.ema_model.load_state_dict(filtered_ema_dict, strict=False)
model_dict = self.accelerator.unwrap_model(self.model).state_dict()
checkpoint_model_dict = checkpoint["model_state_dict"]
filtered_model_dict = {
k: v for k, v in checkpoint_model_dict.items()
if k in model_dict and model_dict[k].shape == v.shape
}
self.accelerator.unwrap_model(self.model).load_state_dict(filtered_model_dict, strict=False)
if "step" in checkpoint:
if self.scheduler and not self.reset_lr:
self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
step = checkpoint["step"]
else:
step = 0
del checkpoint
gc.collect()
print("Checkpoint loaded at step", step)
return step
def train(self, resumable_with_seed: int = None):
train_dataloader = self.train_dataloader
start_step = self.load_checkpoint()
global_step = start_step
if resumable_with_seed > 0:
orig_epoch_step = len(train_dataloader)
skipped_epoch = int(start_step // orig_epoch_step)
skipped_batch = start_step % orig_epoch_step
skipped_dataloader = self.accelerator.skip_first_batches(train_dataloader, num_batches=skipped_batch)
else:
skipped_epoch = 0
for epoch in range(skipped_epoch, self.epochs):
self.model.train()
if resumable_with_seed > 0 and epoch == skipped_epoch:
progress_bar = tqdm(
skipped_dataloader,
desc=f"Epoch {epoch+1}/{self.epochs}",
unit="step",
disable=not self.accelerator.is_local_main_process,
initial=skipped_batch,
total=orig_epoch_step,
smoothing=0.15
)
else:
progress_bar = tqdm(
train_dataloader,
desc=f"Epoch {epoch+1}/{self.epochs}",
unit="step",
disable=not self.accelerator.is_local_main_process,
smoothing=0.15
)
for batch in progress_bar:
with self.accelerator.accumulate(self.model):
text_inputs = batch["lrc"]
mel_spec = batch["latent"].permute(0, 2, 1)
mel_lengths = batch["latent_lengths"]
style_prompt = batch["prompt"]
style_prompt_lens = batch["prompt_lengths"]
start_time = batch["start_time"]
loss, cond, pred = self.model(
mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler,
style_prompt=style_prompt,
style_prompt_lens=style_prompt_lens,
grad_ckpt=self.grad_ckpt, start_time=start_time
)
self.accelerator.backward(loss)
if self.max_grad_norm > 0 and self.accelerator.sync_gradients:
self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
self.optimizer.step()
self.scheduler.step()
self.optimizer.zero_grad()
if self.is_main:
self.ema_model.update()
global_step += 1
if self.accelerator.is_local_main_process:
self.accelerator.log({"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_step)
progress_bar.set_postfix(step=str(global_step), loss=loss.item())
if global_step % (self.save_per_updates * self.grad_accumulation_steps) == 0:
self.save_checkpoint(global_step)
if global_step % self.last_per_steps == 0:
self.save_checkpoint(global_step, last=True)
self.save_checkpoint(global_step, last=True)
self.accelerator.end_training()
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