import logging import multiprocessing import os import time import torch import torch.distributed as dist import torch.multiprocessing as mp from torch.cuda.amp import GradScaler, autocast from torch.nn import functional as F from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter import modules.commons as commons import utils from data_utils import TextAudioCollate, TextAudioSpeakerLoader from models import ( MultiPeriodDiscriminator, SynthesizerTrn, ) from modules.losses import discriminator_loss, feature_loss, generator_loss, kl_loss from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch logging.getLogger('matplotlib').setLevel(logging.WARNING) logging.getLogger('numba').setLevel(logging.WARNING) torch.backends.cudnn.benchmark = True global_step = 0 start_time = time.time() # os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO' def main(): """Assume Single Node Multi GPUs Training Only""" assert torch.cuda.is_available(), "CPU training is not allowed." hps = utils.get_hparams() n_gpus = torch.cuda.device_count() os.environ['MASTER_ADDR'] = 'localhost' os.environ['MASTER_PORT'] = hps.train.port 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")) # for pytorch on win, backend use gloo dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank) torch.manual_seed(hps.train.seed) torch.cuda.set_device(rank) collate_fn = TextAudioCollate() all_in_mem = hps.train.all_in_mem # If you have enough memory, turn on this option to avoid disk IO and speed up training. train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps, all_in_mem=all_in_mem) num_workers = 5 if multiprocessing.cpu_count() > 4 else multiprocessing.cpu_count() if all_in_mem: num_workers = 0 train_loader = DataLoader(train_dataset, num_workers=num_workers, shuffle=False, pin_memory=True, batch_size=hps.train.batch_size, collate_fn=collate_fn) if rank == 0: eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps, all_in_mem=all_in_mem,vol_aug = False) eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False, batch_size=1, pin_memory=False, drop_last=False, collate_fn=collate_fn) net_g = SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model).cuda(rank) net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) optim_g = torch.optim.AdamW( 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) net_g = DDP(net_g, device_ids=[rank]) # , find_unused_parameters=True) net_d = DDP(net_d, device_ids=[rank]) skip_optimizer = False try: _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g, skip_optimizer) _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d, skip_optimizer) epoch_str = max(epoch_str, 1) name=utils.latest_checkpoint_path(hps.model_dir, "D_*.pth") global_step=int(name[name.rfind("_")+1:name.rfind(".")])+1 #global_step = (epoch_str - 1) * len(train_loader) except Exception: print("load old checkpoint failed...") epoch_str = 1 global_step = 0 if skip_optimizer: epoch_str = 1 global_step = 0 warmup_epoch = hps.train.warmup_epochs 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) scaler = GradScaler(enabled=hps.train.fp16_run) for epoch in range(epoch_str, hps.train.epochs + 1): # set up warm-up learning rate if epoch <= warmup_epoch: for param_group in optim_g.param_groups: param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch for param_group in optim_d.param_groups: param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch # training if rank == 0: train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval]) else: train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None) # update learning rate scheduler_g.step() scheduler_d.step() def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): net_g, net_d = nets optim_g, optim_d = optims scheduler_g, scheduler_d = schedulers train_loader, eval_loader = loaders if writers is not None: writer, writer_eval = writers half_type = torch.bfloat16 if hps.train.half_type=="bf16" else torch.float16 # train_loader.batch_sampler.set_epoch(epoch) global global_step net_g.train() net_d.train() for batch_idx, items in enumerate(train_loader): c, f0, spec, y, spk, lengths, uv,volume = items g = spk.cuda(rank, non_blocking=True) spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True) c = c.cuda(rank, non_blocking=True) f0 = f0.cuda(rank, non_blocking=True) uv = uv.cuda(rank, non_blocking=True) lengths = lengths.cuda(rank, non_blocking=True) 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) with autocast(enabled=hps.train.fp16_run, dtype=half_type): y_hat, ids_slice, z_mask, \ (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths,vol = volume) 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, dtype=half_type): loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) loss_disc_all = loss_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, dtype=half_type): # Generator y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) with autocast(enabled=False, dtype=half_type): 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_lf0 = F.mse_loss(pred_lf0, lf0) if net_g.module.use_automatic_f0_prediction else 0 loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0 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_kl] reference_loss=0 for i in losses: reference_loss += i logger.info('Train Epoch: {} [{:.0f}%]'.format( epoch, 100. * batch_idx / len(train_loader))) logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}, reference_loss: {reference_loss}") 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/kl": loss_kl, "loss/g/lf0": loss_lf0}) # 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()) } if net_g.module.use_automatic_f0_prediction: image_dict.update({ "all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(), pred_lf0[0, 0, :].detach().cpu().numpy()), "all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(), norm_lf0[0, 0, :].detach().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))) keep_ckpts = getattr(hps.train, 'keep_ckpts', 0) 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: global start_time now = time.time() durtaion = format(now - start_time, '.2f') logger.info(f'====> Epoch: {epoch}, cost {durtaion} s') start_time = now def evaluate(hps, generator, eval_loader, writer_eval): generator.eval() image_dict = {} audio_dict = {} with torch.no_grad(): for batch_idx, items in enumerate(eval_loader): c, f0, spec, y, spk, _, uv,volume = items g = spk[:1].cuda(0) spec, y = spec[:1].cuda(0), y[:1].cuda(0) c = c[:1].cuda(0) f0 = f0[:1].cuda(0) uv= uv[:1].cuda(0) if volume is not None: volume = volume[:1].cuda(0) 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,_ = generator.module.infer(c, f0, uv, g=g,vol = volume) 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 ) audio_dict.update({ f"gen/audio_{batch_idx}": y_hat[0], f"gt/audio_{batch_idx}": y[0] }) image_dict.update({ "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()), "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()) }) 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()