import os import json import argparse import itertools import math import torch from torch import nn, optim from torch.nn import functional as F from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter import torch.multiprocessing as mp import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP from torch.cuda.amp import autocast, GradScaler from tqdm import tqdm import logging logging.getLogger('numba').setLevel(logging.WARNING) import commons import utils from data_utils import ( TextAudioSpeakerLoader, TextAudioSpeakerCollate, DistributedBucketSampler ) from models import ( SynthesizerTrn, MultiPeriodDiscriminator, DurationDiscriminator, ) from losses import ( generator_loss, discriminator_loss, feature_loss, kl_loss ) from mel_processing import mel_spectrogram_torch, spec_to_mel_torch from text.symbols import symbols torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True # If encontered training problem,please try to disable TF32. torch.set_float32_matmul_precision('medium') torch.backends.cuda.sdp_kernel("flash") torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) # Not avaliable if torch version is lower than 2.0 torch.backends.cuda.enable_math_sdp(True) global_step = 0 def main(): """Assume Single Node Multi GPUs Training Only""" assert torch.cuda.is_available(), "CPU training is not allowed." n_gpus = torch.cuda.device_count() os.environ['MASTER_ADDR'] = 'localhost' os.environ['MASTER_PORT'] = '65280' hps = utils.get_hparams() mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,)) def run(rank, n_gpus, hps): global global_step if rank == 0: logger = utils.get_logger(hps.model_dir) logger.info(hps) utils.check_git_hash(hps.model_dir) writer = SummaryWriter(log_dir=hps.model_dir) writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval")) dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank) torch.manual_seed(hps.train.seed) torch.cuda.set_device(rank) train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data) train_sampler = DistributedBucketSampler( train_dataset, hps.train.batch_size, [32, 300, 400, 500, 600, 700, 800, 900, 1000], num_replicas=n_gpus, rank=rank, shuffle=True) collate_fn = TextAudioSpeakerCollate() train_loader = DataLoader(train_dataset, num_workers=24, shuffle=False, pin_memory=True, collate_fn=collate_fn, batch_sampler=train_sampler, persistent_workers=True,prefetch_factor=4) #256G Memory suitable loader. if rank == 0: eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data) eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False, batch_size=1, pin_memory=True, drop_last=False, collate_fn=collate_fn) if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True: print("Using noise scaled MAS for VITS2") use_noise_scaled_mas = True mas_noise_scale_initial = 0.01 noise_scale_delta = 2e-6 else: print("Using normal MAS for VITS1") use_noise_scaled_mas = False mas_noise_scale_initial = 0.0 noise_scale_delta = 0.0 if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True: print("Using duration discriminator for VITS2") use_duration_discriminator = True net_dur_disc = DurationDiscriminator( hps.model.hidden_channels, hps.model.hidden_channels, 3, 0.1, gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0, ).cuda(rank) if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True: if hps.data.n_speakers == 0: raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model") use_spk_conditioned_encoder = True else: print("Using normal encoder for VITS1") use_spk_conditioned_encoder = False net_g = SynthesizerTrn( len(symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, mas_noise_scale_initial = mas_noise_scale_initial, noise_scale_delta = noise_scale_delta, **hps.model).cuda(rank) freeze_enc = getattr(hps.model, "freeze_enc", False) if freeze_enc: print("freeze encoder !!!") for param in net_g.enc_p.parameters(): param.requires_grad = False net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) optim_g = torch.optim.AdamW( filter(lambda p: p.requires_grad, net_g.parameters()), hps.train.learning_rate, betas=hps.train.betas, eps=hps.train.eps) optim_d = torch.optim.AdamW( net_d.parameters(), hps.train.learning_rate, betas=hps.train.betas, eps=hps.train.eps) if net_dur_disc is not None: optim_dur_disc = torch.optim.AdamW( net_dur_disc.parameters(), hps.train.learning_rate, betas=hps.train.betas, eps=hps.train.eps) else: optim_dur_disc = None net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True) net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True) if net_dur_disc is not None: net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True) try: if net_dur_disc is not None: _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=True) _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g, skip_optimizer=True) _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d, skip_optimizer=True) epoch_str = max(epoch_str, 1) global_step = (epoch_str - 1) * len(train_loader) except Exception as e: print(e) epoch_str = 1 global_step = 0 scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) if net_dur_disc is not None: scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2) else: scheduler_dur_disc = None scaler = GradScaler(enabled=hps.train.fp16_run) for epoch in range(epoch_str, hps.train.epochs + 1): if rank == 0: train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval]) else: train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None) scheduler_g.step() scheduler_d.step() if net_dur_disc is not None: scheduler_dur_disc.step() def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): net_g, net_d, net_dur_disc = nets optim_g, optim_d, optim_dur_disc = optims scheduler_g, scheduler_d, scheduler_dur_disc = schedulers train_loader, eval_loader = loaders if writers is not None: writer, writer_eval = writers train_loader.batch_sampler.set_epoch(epoch) global global_step net_g.train() net_d.train() if net_dur_disc is not None: net_dur_disc.train() for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)): if net_g.module.use_noise_scaled_mas: current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0) x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True) spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True) y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True) speakers = speakers.cuda(rank, non_blocking=True) tone = tone.cuda(rank, non_blocking=True) language = language.cuda(rank, non_blocking=True) bert = bert.cuda(rank, non_blocking=True) with autocast(enabled=hps.train.fp16_run): y_hat, l_length, attn, ids_slice, x_mask, z_mask, \ (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert) mel = spec_to_mel_torch( spec, hps.data.filter_length, hps.data.n_mel_channels, hps.data.sampling_rate, hps.data.mel_fmin, hps.data.mel_fmax) y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length) y_hat_mel = mel_spectrogram_torch( y_hat.squeeze(1), hps.data.filter_length, hps.data.n_mel_channels, hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, hps.data.mel_fmin, hps.data.mel_fmax ) y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice # Discriminator y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) with autocast(enabled=False): loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) loss_disc_all = loss_disc if net_dur_disc is not None: y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()) with autocast(enabled=False): # TODO: I think need to mean using the mask, but for now, just mean all loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g) loss_dur_disc_all = loss_dur_disc optim_dur_disc.zero_grad() scaler.scale(loss_dur_disc_all).backward() scaler.unscale_(optim_dur_disc) grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None) scaler.step(optim_dur_disc) optim_d.zero_grad() scaler.scale(loss_disc_all).backward() scaler.unscale_(optim_d) grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None) scaler.step(optim_d) with autocast(enabled=hps.train.fp16_run): # Generator y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) if net_dur_disc is not None: y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_) with autocast(enabled=False): loss_dur = torch.sum(l_length.float()) loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl loss_fm = feature_loss(fmap_r, fmap_g) loss_gen, losses_gen = generator_loss(y_d_hat_g) loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl if net_dur_disc is not None: loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g) loss_gen_all += loss_dur_gen optim_g.zero_grad() scaler.scale(loss_gen_all).backward() scaler.unscale_(optim_g) grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) scaler.step(optim_g) scaler.update() if rank == 0: if global_step % hps.train.log_interval == 0: lr = optim_g.param_groups[0]['lr'] losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl] logger.info('Train Epoch: {} [{:.0f}%]'.format( epoch, 100. * batch_idx / len(train_loader))) logger.info([x.item() for x in losses] + [global_step, lr]) scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g} scalar_dict.update( {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl}) scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}) scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}) scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}) image_dict = { "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()), "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()), "all/attn": utils.plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy()) } utils.summarize( writer=writer, global_step=global_step, images=image_dict, scalars=scalar_dict) if global_step % hps.train.eval_interval == 0: evaluate(hps, net_g, eval_loader, writer_eval) utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step))) utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step))) if net_dur_disc is not None: utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step))) keep_ckpts = getattr(hps.train, 'keep_ckpts', 5) if keep_ckpts > 0: utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True) global_step += 1 if rank == 0: logger.info('====> Epoch: {}'.format(epoch)) def evaluate(hps, generator, eval_loader, writer_eval): generator.eval() image_dict = {} audio_dict = {} print("Evaluating ...") with torch.no_grad(): for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader): x, x_lengths = x.cuda(), x_lengths.cuda() spec, spec_lengths = spec.cuda(), spec_lengths.cuda() y, y_lengths = y.cuda(), y_lengths.cuda() speakers = speakers.cuda() bert = bert.cuda() tone = tone.cuda() language = language.cuda() for use_sdp in [True, False]: y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0) y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length mel = spec_to_mel_torch( spec, hps.data.filter_length, hps.data.n_mel_channels, hps.data.sampling_rate, hps.data.mel_fmin, hps.data.mel_fmax) y_hat_mel = mel_spectrogram_torch( y_hat.squeeze(1).float(), hps.data.filter_length, hps.data.n_mel_channels, hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, hps.data.mel_fmin, hps.data.mel_fmax ) image_dict.update({ f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()) }) audio_dict.update({ f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]] }) image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())}) audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]}) utils.summarize( writer=writer_eval, global_step=global_step, images=image_dict, audios=audio_dict, audio_sampling_rate=hps.data.sampling_rate ) generator.train() if __name__ == "__main__": main()