#!/usr/bin/env python3 # Copyright 2023 Xiaomi Corp. (authors: Zengwei Yao) # # See ../../../../LICENSE for clarification regarding multiple authors # # 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. import argparse import logging from pathlib import Path from shutil import copyfile from typing import Any, Dict, Optional, Tuple, Union import k2 import numpy as np import torch import torch.multiprocessing as mp import torch.nn as nn from lhotse.cut import Cut from lhotse.utils import fix_random_seed 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 LJSpeechTtsDataModule from utils import MetricsTracker, plot_feature, save_checkpoint from vits import VITS from icefall import diagnostics from icefall.checkpoint import load_checkpoint from icefall.dist import cleanup_dist, setup_dist from icefall.env import get_env_info from icefall.hooks import register_inf_check_hooks from icefall.utils import AttributeDict, setup_logger, str2bool LRSchedulerType = torch.optim.lr_scheduler._LRScheduler 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=12354, 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=str, default="vits/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( "--lr", type=float, default=2.0e-4, help="The base learning rate." ) parser.add_argument( "--seed", type=int, default=42, help="The seed for random generators intended for reproducibility", ) parser.add_argument( "--print-diagnostics", type=str2bool, default=False, help="Accumulate stats on activations, print them and exit.", ) parser.add_argument( "--inf-check", type=str2bool, default=False, help="Add hooks to check for infinite module outputs and gradients.", ) parser.add_argument( "--save-every-n", type=int, default=20, 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_params() -> AttributeDict: """Return a dict containing training parameters. All training related parameters that are not passed from the commandline are saved in the variable `params`. Commandline options are merged into `params` after they are parsed, so you can also access them via `params`. Explanation of options saved in `params`: - best_train_loss: Best training loss so far. It is used to select the model that has the lowest training loss. It is updated during the training. - best_valid_loss: Best validation loss so far. It is used to select the model that has the lowest validation loss. It is updated during the training. - best_train_epoch: It is the epoch that has the best training loss. - best_valid_epoch: It is the epoch that has the best validation loss. - batch_idx_train: Used to writing statistics to tensorboard. It contains number of batches trained so far across epochs. - log_interval: Print training loss if batch_idx % log_interval` is 0 - valid_interval: Run validation if batch_idx % valid_interval is 0 - feature_dim: The model input dim. It has to match the one used in computing features. - subsampling_factor: The subsampling factor for the model. - encoder_dim: Hidden dim for multi-head attention model. - num_decoder_layers: Number of decoder layer of transformer decoder. - warm_step: The warmup period that dictates the decay of the scale on "simple" (un-pruned) loss. """ params = AttributeDict( { # training 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": 50, "valid_interval": 200, "env_info": get_env_info(), "sampling_rate": 22050, "frame_shift": 256, "frame_length": 1024, "feature_dim": 513, # 1024 // 2 + 1, 1024 is fft_length "n_mels": 80, "lambda_adv": 1.0, # loss scaling coefficient for adversarial loss "lambda_mel": 45.0, # loss scaling coefficient for Mel loss "lambda_feat_match": 2.0, # loss scaling coefficient for feat match loss "lambda_dur": 1.0, # loss scaling coefficient for duration loss "lambda_kl": 1.0, # loss scaling coefficient for KL divergence loss } ) return params 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 get_model(params: AttributeDict) -> nn.Module: mel_loss_params = { "n_mels": params.n_mels, "frame_length": params.frame_length, "frame_shift": params.frame_shift, } model = VITS( vocab_size=params.vocab_size, feature_dim=params.feature_dim, sampling_rate=params.sampling_rate, mel_loss_params=mel_loss_params, lambda_adv=params.lambda_adv, lambda_mel=params.lambda_mel, lambda_feat_match=params.lambda_feat_match, lambda_dur=params.lambda_dur, lambda_kl=params.lambda_kl, ) return model def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device): """Parse batch data""" 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.tokens_to_token_ids(tokens) 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.blank_id) return audio, audio_lens, features, features_lens, tokens, tokens_lens def train_one_epoch( params: AttributeDict, model: Union[nn.Module, DDP], tokenizer: Tokenizer, optimizer_g: Optimizer, optimizer_d: Optimizer, scheduler_g: LRSchedulerType, scheduler_d: LRSchedulerType, 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. tokenizer: Used to convert text to phonemes. optimizer_g: The optimizer for generator. optimizer_d: The optimizer for discriminator. scheduler_g: The learning rate scheduler for generator, we call step() every epoch. scheduler_d: The learning rate scheduler for discriminator, we call step() every epoch. 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. world_size: Number of nodes in DDP training. If it is 1, DDP is disabled. rank: The rank of the node in DDP training. If no DDP is used, it should be set to 0. """ model.train() device = model.device if isinstance(model, DDP) else next(model.parameters()).device # used to summary 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_g=optimizer_g, optimizer_d=optimizer_d, scheduler_g=scheduler_g, scheduler_d=scheduler_d, sampler=train_dl.sampler, scaler=scaler, rank=0, ) for batch_idx, batch in enumerate(train_dl): params.batch_idx_train += 1 batch_size = len(batch["tokens"]) audio, audio_lens, features, features_lens, tokens, tokens_lens = prepare_input( batch, tokenizer, device ) loss_info = MetricsTracker() loss_info["samples"] = batch_size try: with autocast(enabled=params.use_fp16): # forward discriminator loss_d, stats_d = model( text=tokens, text_lengths=tokens_lens, feats=features, feats_lengths=features_lens, speech=audio, speech_lengths=audio_lens, forward_generator=False, ) for k, v in stats_d.items(): loss_info[k] = v * batch_size # update discriminator optimizer_d.zero_grad() scaler.scale(loss_d).backward() scaler.step(optimizer_d) with autocast(enabled=params.use_fp16): # forward generator loss_g, stats_g = model( text=tokens, text_lengths=tokens_lens, feats=features, feats_lengths=features_lens, speech=audio, speech_lengths=audio_lens, forward_generator=True, return_sample=params.batch_idx_train % params.log_interval == 0, ) for k, v in stats_g.items(): if "returned_sample" not in k: loss_info[k] = v * batch_size # update generator optimizer_g.zero_grad() scaler.scale(loss_g).backward() scaler.step(optimizer_g) scaler.update() # summary stats tot_loss = tot_loss + loss_info except: # noqa save_bad_model() raise if params.print_diagnostics and batch_idx == 5: return 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_lr_g = max(scheduler_g.get_last_lr()) cur_lr_d = max(scheduler_d.get_last_lr()) 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}, batch size: {batch_size}, " f"loss[{loss_info}], tot_loss[{tot_loss}], " f"cur_lr_g: {cur_lr_g:.2e}, cur_lr_d: {cur_lr_d:.2e}, " + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "") ) if tb_writer is not None: tb_writer.add_scalar( "train/learning_rate_g", cur_lr_g, params.batch_idx_train ) tb_writer.add_scalar( "train/learning_rate_d", cur_lr_d, params.batch_idx_train ) 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 "returned_sample" in stats_g: speech_hat_, speech_, mel_hat_, mel_ = stats_g["returned_sample"] tb_writer.add_audio( "train/speech_hat_", speech_hat_, params.batch_idx_train, params.sampling_rate, ) tb_writer.add_audio( "train/speech_", speech_, params.batch_idx_train, params.sampling_rate, ) tb_writer.add_image( "train/mel_hat_", plot_feature(mel_hat_), params.batch_idx_train, dataformats="HWC", ) tb_writer.add_image( "train/mel_", plot_feature(mel_), params.batch_idx_train, dataformats="HWC", ) if ( params.batch_idx_train % params.valid_interval == 0 and not params.print_diagnostics ): logging.info("Computing validation loss") valid_info, (speech_hat, speech) = compute_validation_loss( params=params, model=model, tokenizer=tokenizer, valid_dl=valid_dl, world_size=world_size, ) model.train() logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") logging.info( f"Maximum memory allocated so far is {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 ) tb_writer.add_audio( "train/valdi_speech_hat", speech_hat, params.batch_idx_train, params.sampling_rate, ) tb_writer.add_audio( "train/valdi_speech", speech, params.batch_idx_train, params.sampling_rate, ) loss_value = tot_loss["generator_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 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, ) -> Tuple[MetricsTracker, Tuple[np.ndarray, np.ndarray]]: """Run the validation process.""" model.eval() device = model.device if isinstance(model, DDP) else next(model.parameters()).device # used to summary the stats over iterations tot_loss = MetricsTracker() returned_sample = None with torch.no_grad(): for batch_idx, batch in enumerate(valid_dl): batch_size = len(batch["tokens"]) ( audio, audio_lens, features, features_lens, tokens, tokens_lens, ) = prepare_input(batch, tokenizer, device) loss_info = MetricsTracker() loss_info["samples"] = batch_size # forward discriminator loss_d, stats_d = model( text=tokens, text_lengths=tokens_lens, feats=features, feats_lengths=features_lens, speech=audio, speech_lengths=audio_lens, forward_generator=False, ) assert loss_d.requires_grad is False for k, v in stats_d.items(): loss_info[k] = v * batch_size # forward generator loss_g, stats_g = model( text=tokens, text_lengths=tokens_lens, feats=features, feats_lengths=features_lens, speech=audio, speech_lengths=audio_lens, forward_generator=True, ) assert loss_g.requires_grad is False for k, v in stats_g.items(): loss_info[k] = v * batch_size # summary stats tot_loss = tot_loss + loss_info # infer for first batch: if batch_idx == 0 and rank == 0: inner_model = model.module if isinstance(model, DDP) else model audio_pred, _, duration = inner_model.inference( text=tokens[0, : tokens_lens[0].item()] ) audio_pred = audio_pred.data.cpu().numpy() audio_len_pred = ( (duration.sum(0) * params.frame_shift).to(dtype=torch.int64).item() ) assert audio_len_pred == len(audio_pred), ( audio_len_pred, len(audio_pred), ) audio_gt = audio[0, : audio_lens[0].item()].data.cpu().numpy() returned_sample = (audio_pred, audio_gt) if world_size > 1: tot_loss.reduce(device) loss_value = tot_loss["generator_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, returned_sample def scan_pessimistic_batches_for_oom( model: Union[nn.Module, DDP], train_dl: torch.utils.data.DataLoader, tokenizer: Tokenizer, optimizer_g: torch.optim.Optimizer, optimizer_d: torch.optim.Optimizer, params: AttributeDict, ): from lhotse.dataset import find_pessimistic_batches logging.info( "Sanity check -- see if any of the batches in epoch 1 would cause OOM." ) device = model.device if isinstance(model, DDP) else next(model.parameters()).device batches, crit_values = find_pessimistic_batches(train_dl.sampler) for criterion, cuts in batches.items(): batch = train_dl.dataset[cuts] audio, audio_lens, features, features_lens, tokens, tokens_lens = prepare_input( batch, tokenizer, device ) try: # for discriminator with autocast(enabled=params.use_fp16): loss_d, stats_d = model( text=tokens, text_lengths=tokens_lens, feats=features, feats_lengths=features_lens, speech=audio, speech_lengths=audio_lens, forward_generator=False, ) optimizer_d.zero_grad() loss_d.backward() # for generator with autocast(enabled=params.use_fp16): loss_g, stats_g = model( text=tokens, text_lengths=tokens_lens, feats=features, feats_lengths=features_lens, speech=audio, speech_lengths=audio_lens, forward_generator=True, ) optimizer_g.zero_grad() loss_g.backward() except Exception as e: if "CUDA out of memory" in str(e): logging.error( "Your GPU ran out of memory with the current " "max_duration setting. We recommend decreasing " "max_duration and trying again.\n" f"Failing criterion: {criterion} " f"(={crit_values[criterion]}) ..." ) raise logging.info( f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" ) def run(rank, world_size, args): """ Args: rank: It is a value between 0 and `world_size-1`, which is passed automatically by `mp.spawn()` in :func:`main`. The node with rank 0 is responsible for saving checkpoint. world_size: Number of GPUs for DDP training. args: The return value of get_parser().parse_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.blank_id = tokenizer.blank_id params.oov_id = tokenizer.oov_id params.vocab_size = tokenizer.vocab_size logging.info(params) logging.info("About to create model") model = get_model(params) generator = model.generator discriminator = model.discriminator num_param_g = sum([p.numel() for p in generator.parameters()]) logging.info(f"Number of parameters in generator: {num_param_g}") num_param_d = sum([p.numel() for p in discriminator.parameters()]) logging.info(f"Number of parameters in discriminator: {num_param_d}") logging.info(f"Total number of parameters: {num_param_g + num_param_d}") 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_g = torch.optim.AdamW( generator.parameters(), lr=params.lr, betas=(0.8, 0.99), eps=1e-9 ) optimizer_d = torch.optim.AdamW( discriminator.parameters(), lr=params.lr, betas=(0.8, 0.99), eps=1e-9 ) scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optimizer_g, gamma=0.999875) scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optimizer_d, gamma=0.999875) if checkpoints is not None: # load state_dict for optimizers if "optimizer_g" in checkpoints: logging.info("Loading optimizer_g state dict") optimizer_g.load_state_dict(checkpoints["optimizer_g"]) if "optimizer_d" in checkpoints: logging.info("Loading optimizer_d state dict") optimizer_d.load_state_dict(checkpoints["optimizer_d"]) # load state_dict for schedulers if "scheduler_g" in checkpoints: logging.info("Loading scheduler_g state dict") scheduler_g.load_state_dict(checkpoints["scheduler_g"]) if "scheduler_d" in checkpoints: logging.info("Loading scheduler_d state dict") scheduler_d.load_state_dict(checkpoints["scheduler_d"]) if params.print_diagnostics: opts = diagnostics.TensorDiagnosticOptions( 512 ) # allow 4 megabytes per sub-module diagnostic = diagnostics.attach_diagnostics(model, opts) if params.inf_check: register_inf_check_hooks(model) ljspeech = LJSpeechTtsDataModule(args) train_cuts = ljspeech.train_cuts() def remove_short_and_long_utt(c: Cut): # Keep only utterances with duration between 1 second and 20 seconds # You should use ../local/display_manifest_statistics.py to get # an utterance duration distribution for your dataset to select # the threshold if c.duration < 1.0 or c.duration > 20.0: # logging.warning( # f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" # ) return False return True train_cuts = train_cuts.filter(remove_short_and_long_utt) train_dl = ljspeech.train_dataloaders(train_cuts) valid_cuts = ljspeech.valid_cuts() valid_dl = ljspeech.valid_dataloaders(valid_cuts) if not params.print_diagnostics: scan_pessimistic_batches_for_oom( model=model, train_dl=train_dl, tokenizer=tokenizer, optimizer_g=optimizer_g, optimizer_d=optimizer_d, params=params, ) 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) 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_g=optimizer_g, optimizer_d=optimizer_d, scheduler_g=scheduler_g, scheduler_d=scheduler_d, train_dl=train_dl, valid_dl=valid_dl, scaler=scaler, tb_writer=tb_writer, world_size=world_size, rank=rank, ) if params.print_diagnostics: diagnostic.print_diagnostics() break 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_g=optimizer_g, optimizer_d=optimizer_d, scheduler_g=scheduler_g, scheduler_d=scheduler_d, sampler=train_dl.sampler, 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) # step per epoch scheduler_g.step() scheduler_d.step() logging.info("Done!") if world_size > 1: torch.distributed.barrier() cleanup_dist() def main(): parser = get_parser() LJSpeechTtsDataModule.add_arguments(parser) args = parser.parse_args() args.exp_dir = Path(args.exp_dir) 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) torch.set_num_threads(1) torch.set_num_interop_threads(1) if __name__ == "__main__": main()