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 import commons import utils from data_utils import ( TextAudioLoader, TextAudioCollate, DistributedBucketSampler ) from models import ( SynthesizerTrn, MultiPeriodDiscriminator, ) 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 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'] = '80000' 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 = TextAudioLoader(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 = TextAudioCollate() train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True, collate_fn=collate_fn, batch_sampler=train_sampler) if rank == 0: eval_dataset = TextAudioLoader(hps.data.validation_files, hps.data) eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False, batch_size=hps.train.batch_size, pin_memory=True, drop_last=False, collate_fn=collate_fn) net_g = SynthesizerTrn( len(symbols), 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]) net_d = DDP(net_d, device_ids=[rank]) try: _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g) _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d) global_step = (epoch_str - 1) * len(train_loader) except: 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) 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], [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) 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 train_loader.batch_sampler.set_epoch(epoch) global global_step net_g.train() net_d.train() for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(train_loader): 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) 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) = net_g(x, x_lengths, spec, spec_lengths) 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 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) 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 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))) global_step += 1 if rank == 0: logger.info('====> Epoch: {}'.format(epoch)) def evaluate(hps, generator, eval_loader, writer_eval): generator.eval() with torch.no_grad(): for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(eval_loader): x, x_lengths = x.cuda(0), x_lengths.cuda(0) spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0) y, y_lengths = y.cuda(0), y_lengths.cuda(0) # remove else x = x[:1] x_lengths = x_lengths[:1] spec = spec[:1] spec_lengths = spec_lengths[:1] y = y[:1] y_lengths = y_lengths[:1] break y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, max_len=1000) 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 = { "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()) } audio_dict = { "gen/audio": y_hat[0,:,:y_hat_lengths[0]] } if global_step == 0: image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())}) audio_dict.update({"gt/audio": 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()