from __future__ import annotations import os import gc from tqdm import tqdm import wandb import torch import torchaudio from torch.optim import AdamW from torch.utils.data import DataLoader, Dataset, SequentialSampler from torch.optim.lr_scheduler import LinearLR, SequentialLR from accelerate import Accelerator from accelerate.utils import DistributedDataParallelKwargs from ema_pytorch import EMA from f5_tts.model import CFM from f5_tts.model.utils import exists, default from f5_tts.model.dataset import DynamicBatchSampler, collate_fn # trainer class Trainer: def __init__( self, model: CFM, 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, logger: str | None = "wandb", # "wandb" | "tensorboard" | None wandb_project="test_e2-tts", wandb_run_name="test_run", wandb_resume_id: str = None, log_samples: bool = False, last_per_steps=None, accelerate_kwargs: dict = dict(), ema_kwargs: dict = dict(), bnb_optimizer: bool = False, ): ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) if logger == "wandb" and not wandb.api.api_key: logger = None print(f"Using logger: {logger}") self.log_samples = log_samples self.accelerator = Accelerator( log_with=logger if logger == "wandb" else None, kwargs_handlers=[ddp_kwargs], gradient_accumulation_steps=grad_accumulation_steps, **accelerate_kwargs, ) self.logger = logger if self.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, }, ) elif self.logger == "tensorboard": from torch.utils.tensorboard import SummaryWriter self.writer = SummaryWriter(log_dir=f"runs/{wandb_run_name}") 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) 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.batch_size = batch_size self.batch_size_type = batch_size_type 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 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) self.model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer) @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=self.accelerator.device) # rather use accelerator.load_state ಥ_ಥ checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", weights_only=True, map_location="cpu") if self.is_main: self.ema_model.load_state_dict(checkpoint["ema_model_state_dict"]) if "step" in checkpoint: self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"]) self.accelerator.unwrap_model(self.optimizer).load_state_dict(checkpoint["optimizer_state_dict"]) if self.scheduler: self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"]) step = checkpoint["step"] else: checkpoint["model_state_dict"] = { k.replace("ema_model.", ""): v for k, v in checkpoint["ema_model_state_dict"].items() if k not in ["initted", "step"] } self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"]) step = 0 del checkpoint gc.collect() return step def train(self, train_dataset: Dataset, num_workers=16, resumable_with_seed: int = None): if self.log_samples: from f5_tts.infer.utils_infer import load_vocoder, nfe_step, cfg_strength, sway_sampling_coef vocoder = load_vocoder() target_sample_rate = self.accelerator.unwrap_model(self.model).mel_spec.mel_stft.sample_rate log_samples_path = f"{self.checkpoint_path}/samples" os.makedirs(log_samples_path, exist_ok=True) if exists(resumable_with_seed): generator = torch.Generator() generator.manual_seed(resumable_with_seed) else: generator = None if self.batch_size_type == "sample": train_dataloader = DataLoader( train_dataset, collate_fn=collate_fn, num_workers=num_workers, pin_memory=True, persistent_workers=True, batch_size=self.batch_size, shuffle=True, generator=generator, ) elif self.batch_size_type == "frame": self.accelerator.even_batches = False sampler = SequentialSampler(train_dataset) batch_sampler = DynamicBatchSampler( sampler, self.batch_size, max_samples=self.max_samples, random_seed=resumable_with_seed, drop_last=False ) train_dataloader = DataLoader( train_dataset, collate_fn=collate_fn, num_workers=num_workers, pin_memory=True, persistent_workers=True, batch_sampler=batch_sampler, ) else: raise ValueError(f"batch_size_type must be either 'sample' or 'frame', but received {self.batch_size_type}") # accelerator.prepare() dispatches batches to devices; # which means the length of dataloader calculated before, should consider the number of devices warmup_steps = ( self.num_warmup_updates * self.accelerator.num_processes ) # consider a fixed warmup steps while using accelerate multi-gpu ddp # otherwise by default with split_batches=False, warmup steps change with num_processes total_steps = len(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] ) train_dataloader, self.scheduler = self.accelerator.prepare( train_dataloader, self.scheduler ) # actual steps = 1 gpu steps / gpus start_step = self.load_checkpoint() global_step = start_step if exists(resumable_with_seed): 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 exists(resumable_with_seed) 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, ) else: progress_bar = tqdm( train_dataloader, desc=f"Epoch {epoch+1}/{self.epochs}", unit="step", disable=not self.accelerator.is_local_main_process, ) for batch in progress_bar: with self.accelerator.accumulate(self.model): text_inputs = batch["text"] mel_spec = batch["mel"].permute(0, 2, 1) mel_lengths = batch["mel_lengths"] # TODO. add duration predictor training if self.duration_predictor is not None and self.accelerator.is_local_main_process: dur_loss = self.duration_predictor(mel_spec, lens=batch.get("durations")) self.accelerator.log({"duration loss": dur_loss.item()}, step=global_step) loss, cond, pred = self.model( mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler ) 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) if self.logger == "tensorboard": self.writer.add_scalar("loss", loss.item(), global_step) self.writer.add_scalar("lr", self.scheduler.get_last_lr()[0], 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 self.log_samples and self.accelerator.is_local_main_process: ref_audio, ref_audio_len = vocoder.decode(batch["mel"][0].unsqueeze(0).cpu()), mel_lengths[0] torchaudio.save(f"{log_samples_path}/step_{global_step}_ref.wav", ref_audio, target_sample_rate) with torch.inference_mode(): generated, _ = self.accelerator.unwrap_model(self.model).sample( cond=mel_spec[0][:ref_audio_len].unsqueeze(0), text=[text_inputs[0] + [" "] + text_inputs[0]], duration=ref_audio_len * 2, steps=nfe_step, cfg_strength=cfg_strength, sway_sampling_coef=sway_sampling_coef, ) generated = generated.to(torch.float32) gen_audio = vocoder.decode(generated[:, ref_audio_len:, :].permute(0, 2, 1).cpu()) torchaudio.save(f"{log_samples_path}/step_{global_step}_gen.wav", gen_audio, target_sample_rate) 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()