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| 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 ( | |
| TextAudioSpeakerLoader, | |
| TextAudioSpeakerCollate, | |
| 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'] = '8899' | |
| 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) | |
| print(hps) or 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=8, shuffle=False, pin_memory=True, | |
| collate_fn=collate_fn, batch_sampler=train_sampler) | |
| if rank == 0: | |
| eval_dataset = TextAudioSpeakerLoader(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, | |
| n_speakers=hps.data.n_speakers, | |
| **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, speakers, emo) 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) | |
| speakers = speakers.cuda(rank, non_blocking=True) | |
| emo = emo.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, speakers, emo) | |
| 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.float()).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))) | |
| print([x.item() for x in losses] + [global_step, lr]) or 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: | |
| print('====> Epoch: {}'.format(epoch)) or 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, speakers, emo) 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) | |
| speakers = speakers.cuda(0) | |
| emo = emo.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] | |
| speakers = speakers[:1] | |
| emo = emo[:1] | |
| break | |
| y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers,emo, 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() | |