#!/usr/bin/env python3 # Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang) import argparse import json import logging from pathlib import Path from shutil import copyfile from typing import Any, Dict, Optional, Union import k2 import torch import torch.multiprocessing as mp import torch.nn as nn from lhotse.utils import fix_random_seed from model import fix_len_compatibility from models.matcha_tts import MatchaTTS from tokenizer import Tokenizer from torch.cuda.amp import GradScaler, autocast from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import Optimizer from torch.utils.tensorboard import SummaryWriter from tts_datamodule import BakerZhTtsDataModule from utils import MetricsTracker from icefall.checkpoint import load_checkpoint, save_checkpoint from icefall.dist import cleanup_dist, setup_dist from icefall.env import get_env_info from icefall.utils import AttributeDict, setup_logger, str2bool def get_parser(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument( "--world-size", type=int, default=1, help="Number of GPUs for DDP training.", ) parser.add_argument( "--master-port", type=int, default=12335, help="Master port to use for DDP training.", ) parser.add_argument( "--tensorboard", type=str2bool, default=True, help="Should various information be logged in tensorboard.", ) parser.add_argument( "--num-epochs", type=int, default=1000, help="Number of epochs to train.", ) parser.add_argument( "--start-epoch", type=int, default=1, help="""Resume training from this epoch. It should be positive. If larger than 1, it will load checkpoint from exp-dir/epoch-{start_epoch-1}.pt """, ) parser.add_argument( "--exp-dir", type=Path, default="matcha/exp", help="""The experiment dir. It specifies the directory where all training related files, e.g., checkpoints, log, etc, are saved """, ) parser.add_argument( "--tokens", type=str, default="data/tokens.txt", help="""Path to vocabulary.""", ) parser.add_argument( "--cmvn", type=str, default="data/fbank/cmvn.json", help="""Path to vocabulary.""", ) parser.add_argument( "--seed", type=int, default=42, help="The seed for random generators intended for reproducibility", ) parser.add_argument( "--save-every-n", type=int, default=10, help="""Save checkpoint after processing this number of epochs" periodically. We save checkpoint to exp-dir/ whenever params.cur_epoch % save_every_n == 0. The checkpoint filename has the form: f'exp-dir/epoch-{params.cur_epoch}.pt'. Since it will take around 1000 epochs, we suggest using a large save_every_n to save disk space. """, ) parser.add_argument( "--use-fp16", type=str2bool, default=False, help="Whether to use half precision training.", ) return parser def get_data_statistics(): return AttributeDict( { "mel_mean": 0, "mel_std": 1, } ) def _get_data_params() -> AttributeDict: params = AttributeDict( { "name": "baker-zh", "train_filelist_path": "./filelists/ljs_audio_text_train_filelist.txt", "valid_filelist_path": "./filelists/ljs_audio_text_val_filelist.txt", # "batch_size": 64, # "num_workers": 1, # "pin_memory": False, "cleaners": ["english_cleaners2"], "add_blank": True, "n_spks": 1, "n_fft": 1024, "n_feats": 80, "sampling_rate": 22050, "hop_length": 256, "win_length": 1024, "f_min": 0, "f_max": 8000, "seed": 1234, "load_durations": False, "data_statistics": get_data_statistics(), } ) return params def _get_model_params() -> AttributeDict: n_feats = 80 filter_channels_dp = 256 encoder_params_p_dropout = 0.1 params = AttributeDict( { "n_spks": 1, # for baker-zh. "spk_emb_dim": 64, "n_feats": n_feats, "out_size": None, # or use 172 "prior_loss": True, "use_precomputed_durations": False, "data_statistics": get_data_statistics(), "encoder": AttributeDict( { "encoder_type": "RoPE Encoder", # not used "encoder_params": AttributeDict( { "n_feats": n_feats, "n_channels": 192, "filter_channels": 768, "filter_channels_dp": filter_channels_dp, "n_heads": 2, "n_layers": 6, "kernel_size": 3, "p_dropout": encoder_params_p_dropout, "spk_emb_dim": 64, "n_spks": 1, "prenet": True, } ), "duration_predictor_params": AttributeDict( { "filter_channels_dp": filter_channels_dp, "kernel_size": 3, "p_dropout": encoder_params_p_dropout, } ), } ), "decoder": AttributeDict( { "channels": [256, 256], "dropout": 0.05, "attention_head_dim": 64, "n_blocks": 1, "num_mid_blocks": 2, "num_heads": 2, "act_fn": "snakebeta", } ), "cfm": AttributeDict( { "name": "CFM", "solver": "euler", "sigma_min": 1e-4, } ), "optimizer": AttributeDict( { "lr": 1e-4, "weight_decay": 0.0, } ), } ) return params def get_params(): params = AttributeDict( { "model_args": _get_model_params(), "data_args": _get_data_params(), "best_train_loss": float("inf"), "best_valid_loss": float("inf"), "best_train_epoch": -1, "best_valid_epoch": -1, "batch_idx_train": -1, # 0 "log_interval": 10, "valid_interval": 1500, "env_info": get_env_info(), } ) return params def get_model(params): m = MatchaTTS(**params.model_args) return m def load_checkpoint_if_available( params: AttributeDict, model: nn.Module ) -> Optional[Dict[str, Any]]: """Load checkpoint from file. If params.start_epoch is larger than 1, it will load the checkpoint from `params.start_epoch - 1`. Apart from loading state dict for `model` and `optimizer` it also updates `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, and `best_valid_loss` in `params`. Args: params: The return value of :func:`get_params`. model: The training model. Returns: Return a dict containing previously saved training info. """ if params.start_epoch > 1: filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" else: return None assert filename.is_file(), f"{filename} does not exist!" saved_params = load_checkpoint(filename, model=model) keys = [ "best_train_epoch", "best_valid_epoch", "batch_idx_train", "best_train_loss", "best_valid_loss", ] for k in keys: params[k] = saved_params[k] return saved_params def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device, params): """Parse batch data""" mel_mean = params.data_args.data_statistics.mel_mean mel_std_inv = 1 / params.data_args.data_statistics.mel_std for i in range(batch["features"].shape[0]): n = batch["features_lens"][i] batch["features"][i : i + 1, :n, :] = ( batch["features"][i : i + 1, :n, :] - mel_mean ) * mel_std_inv batch["features"][i : i + 1, n:, :] = 0 audio = batch["audio"].to(device) features = batch["features"].to(device) audio_lens = batch["audio_lens"].to(device) features_lens = batch["features_lens"].to(device) tokens = batch["tokens"] tokens = tokenizer.texts_to_token_ids(tokens, intersperse_blank=True) tokens = k2.RaggedTensor(tokens) row_splits = tokens.shape.row_splits(1) tokens_lens = row_splits[1:] - row_splits[:-1] tokens = tokens.to(device) tokens_lens = tokens_lens.to(device) # a tensor of shape (B, T) tokens = tokens.pad(mode="constant", padding_value=tokenizer.pad_id) max_feature_length = fix_len_compatibility(features.shape[1]) if max_feature_length > features.shape[1]: pad = max_feature_length - features.shape[1] features = torch.nn.functional.pad(features, (0, 0, 0, pad)) # features_lens[features_lens.argmax()] += pad return audio, audio_lens, features, features_lens.long(), tokens, tokens_lens.long() def compute_validation_loss( params: AttributeDict, model: Union[nn.Module, DDP], tokenizer: Tokenizer, valid_dl: torch.utils.data.DataLoader, world_size: int = 1, rank: int = 0, ) -> MetricsTracker: """Run the validation process.""" model.eval() device = model.device if isinstance(model, DDP) else next(model.parameters()).device get_losses = model.module.get_losses if isinstance(model, DDP) else model.get_losses # used to summary the stats over iterations tot_loss = MetricsTracker() with torch.no_grad(): for batch_idx, batch in enumerate(valid_dl): ( audio, audio_lens, features, features_lens, tokens, tokens_lens, ) = prepare_input(batch, tokenizer, device, params) losses = get_losses( { "x": tokens, "x_lengths": tokens_lens, "y": features.permute(0, 2, 1), "y_lengths": features_lens, "spks": None, # should change it for multi-speakers "durations": None, } ) batch_size = len(batch["tokens"]) loss_info = MetricsTracker() loss_info["samples"] = batch_size s = 0 for key, value in losses.items(): v = value.detach().item() loss_info[key] = v * batch_size s += v * batch_size loss_info["tot_loss"] = s # summary stats tot_loss = tot_loss + loss_info if world_size > 1: tot_loss.reduce(device) loss_value = tot_loss["tot_loss"] / tot_loss["samples"] if loss_value < params.best_valid_loss: params.best_valid_epoch = params.cur_epoch params.best_valid_loss = loss_value return tot_loss def train_one_epoch( params: AttributeDict, model: Union[nn.Module, DDP], tokenizer: Tokenizer, optimizer: Optimizer, train_dl: torch.utils.data.DataLoader, valid_dl: torch.utils.data.DataLoader, scaler: GradScaler, tb_writer: Optional[SummaryWriter] = None, world_size: int = 1, rank: int = 0, ) -> None: """Train the model for one epoch. The training loss from the mean of all frames is saved in `params.train_loss`. It runs the validation process every `params.valid_interval` batches. Args: params: It is returned by :func:`get_params`. model: The model for training. optimizer: The optimizer. train_dl: Dataloader for the training dataset. valid_dl: Dataloader for the validation dataset. scaler: The scaler used for mix precision training. tb_writer: Writer to write log messages to tensorboard. """ model.train() device = model.device if isinstance(model, DDP) else next(model.parameters()).device get_losses = model.module.get_losses if isinstance(model, DDP) else model.get_losses # used to track the stats over iterations in one epoch tot_loss = MetricsTracker() saved_bad_model = False def save_bad_model(suffix: str = ""): save_checkpoint( filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt", model=model, params=params, optimizer=optimizer, scaler=scaler, rank=0, ) for batch_idx, batch in enumerate(train_dl): params.batch_idx_train += 1 # audio: (N, T), float32 # features: (N, T, C), float32 # audio_lens, (N,), int32 # features_lens, (N,), int32 # tokens: List[List[str]], len(tokens) == N batch_size = len(batch["tokens"]) ( audio, audio_lens, features, features_lens, tokens, tokens_lens, ) = prepare_input(batch, tokenizer, device, params) try: with autocast(enabled=params.use_fp16): losses = get_losses( { "x": tokens, "x_lengths": tokens_lens, "y": features.permute(0, 2, 1), "y_lengths": features_lens, "spks": None, # should change it for multi-speakers "durations": None, } ) loss = sum(losses.values()) scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() optimizer.zero_grad() loss_info = MetricsTracker() loss_info["samples"] = batch_size s = 0 for key, value in losses.items(): v = value.detach().item() loss_info[key] = v * batch_size s += v * batch_size loss_info["tot_loss"] = s tot_loss = tot_loss + loss_info except: # noqa save_bad_model() raise if params.batch_idx_train % 100 == 0 and params.use_fp16: # If the grad scale was less than 1, try increasing it. # The _growth_interval of the grad scaler is configurable, # but we can't configure it to have different # behavior depending on the current grad scale. cur_grad_scale = scaler._scale.item() if cur_grad_scale < 8.0 or ( cur_grad_scale < 32.0 and params.batch_idx_train % 400 == 0 ): scaler.update(cur_grad_scale * 2.0) if cur_grad_scale < 0.01: if not saved_bad_model: save_bad_model(suffix="-first-warning") saved_bad_model = True logging.warning(f"Grad scale is small: {cur_grad_scale}") if cur_grad_scale < 1.0e-05: save_bad_model() raise RuntimeError( f"grad_scale is too small, exiting: {cur_grad_scale}" ) if params.batch_idx_train % params.log_interval == 0: cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0 logging.info( f"Epoch {params.cur_epoch}, batch {batch_idx}, " f"global_batch_idx: {params.batch_idx_train}, " f"batch size: {batch_size}, " f"loss[{loss_info}], tot_loss[{tot_loss}], " + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "") ) if tb_writer is not None: loss_info.write_summary( tb_writer, "train/current_", params.batch_idx_train ) tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train) if params.use_fp16: tb_writer.add_scalar( "train/grad_scale", cur_grad_scale, params.batch_idx_train ) if params.batch_idx_train % params.valid_interval == 1: logging.info("Computing validation loss") valid_info = compute_validation_loss( params=params, model=model, tokenizer=tokenizer, valid_dl=valid_dl, world_size=world_size, rank=rank, ) model.train() logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") logging.info( "Maximum memory allocated so far is " f"{torch.cuda.max_memory_allocated()//1000000}MB" ) if tb_writer is not None: valid_info.write_summary( tb_writer, "train/valid_", params.batch_idx_train ) loss_value = tot_loss["tot_loss"] / tot_loss["samples"] params.train_loss = loss_value if params.train_loss < params.best_train_loss: params.best_train_epoch = params.cur_epoch params.best_train_loss = params.train_loss def run(rank, world_size, args): params = get_params() params.update(vars(args)) fix_random_seed(params.seed) if world_size > 1: setup_dist(rank, world_size, params.master_port) setup_logger(f"{params.exp_dir}/log/log-train") logging.info("Training started") if args.tensorboard and rank == 0: tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") else: tb_writer = None device = torch.device("cpu") if torch.cuda.is_available(): device = torch.device("cuda", rank) logging.info(f"Device: {device}") tokenizer = Tokenizer(params.tokens) params.pad_id = tokenizer.pad_id params.vocab_size = tokenizer.vocab_size params.model_args.n_vocab = params.vocab_size with open(params.cmvn) as f: stats = json.load(f) params.data_args.data_statistics.mel_mean = stats["fbank_mean"] params.data_args.data_statistics.mel_std = stats["fbank_std"] params.model_args.data_statistics.mel_mean = stats["fbank_mean"] params.model_args.data_statistics.mel_std = stats["fbank_std"] logging.info(params) print(params) logging.info("About to create model") model = get_model(params) num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of parameters: {num_param}") assert params.start_epoch > 0, params.start_epoch checkpoints = load_checkpoint_if_available(params=params, model=model) model.to(device) if world_size > 1: logging.info("Using DDP") model = DDP(model, device_ids=[rank], find_unused_parameters=True) optimizer = torch.optim.Adam(model.parameters(), **params.model_args.optimizer) logging.info("About to create datamodule") baker_zh = BakerZhTtsDataModule(args) train_cuts = baker_zh.train_cuts() train_dl = baker_zh.train_dataloaders(train_cuts) valid_cuts = baker_zh.valid_cuts() valid_dl = baker_zh.valid_dataloaders(valid_cuts) scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0) if checkpoints and "grad_scaler" in checkpoints: logging.info("Loading grad scaler state dict") scaler.load_state_dict(checkpoints["grad_scaler"]) for epoch in range(params.start_epoch, params.num_epochs + 1): logging.info(f"Start epoch {epoch}") fix_random_seed(params.seed + epoch - 1) if "sampler" in train_dl: train_dl.sampler.set_epoch(epoch - 1) params.cur_epoch = epoch if tb_writer is not None: tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) train_one_epoch( params=params, model=model, tokenizer=tokenizer, optimizer=optimizer, train_dl=train_dl, valid_dl=valid_dl, scaler=scaler, tb_writer=tb_writer, world_size=world_size, rank=rank, ) if epoch % params.save_every_n == 0 or epoch == params.num_epochs: filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" save_checkpoint( filename=filename, params=params, model=model, optimizer=optimizer, scaler=scaler, rank=rank, ) if rank == 0: if params.best_train_epoch == params.cur_epoch: best_train_filename = params.exp_dir / "best-train-loss.pt" copyfile(src=filename, dst=best_train_filename) if params.best_valid_epoch == params.cur_epoch: best_valid_filename = params.exp_dir / "best-valid-loss.pt" copyfile(src=filename, dst=best_valid_filename) logging.info("Done!") if world_size > 1: torch.distributed.barrier() cleanup_dist() def main(): parser = get_parser() BakerZhTtsDataModule.add_arguments(parser) args = parser.parse_args() world_size = args.world_size assert world_size >= 1 if world_size > 1: mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) else: run(rank=0, world_size=1, args=args) if __name__ == "__main__": torch.set_num_threads(1) torch.set_num_interop_threads(1) main()