def train(rank, a, h): if h.num_gpus > 1: init_process_group(backend=h.dist_config['dist_backend'], init_method=h.dist_config['dist_url'], world_size=h.dist_config['world_size'] * h.num_gpus, rank=rank) torch.cuda.manual_seed(h.seed) device = torch.device('cuda:{:d}'.format(rank)) generator = Generator(h).to(device) mpd = MultiPeriodDiscriminator( h["discriminator_periods"] if "discriminator_periods" in h.keys() else None).to(device) msd = MultiScaleDiscriminator().to(device) if rank == 0: print(generator) os.makedirs(a.checkpoint_path, exist_ok=True) print("checkpoints directory : ", a.checkpoint_path) if os.path.isdir(a.checkpoint_path): cp_g = scan_checkpoint(a.checkpoint_path, 'g_') cp_do = scan_checkpoint(a.checkpoint_path, 'do_') steps = 0 if cp_g is not None: state_dict_g = load_checkpoint(cp_g, device) gsd = generator.state_dict() gsd.update({ k: v for k, v in state_dict_g['generator'].items() if k in gsd and state_dict_g['generator'][k].shape == gsd[k].shape }) missing_keys = { k: v for k, v in state_dict_g['generator'].items() if not (k in gsd and state_dict_g['generator'][k].shape == gsd[k].shape) }.keys() generator.load_state_dict(gsd) del gsd, state_dict_g if cp_do is None or len(missing_keys) or a.from_zero: state_dict_do = None last_epoch = -1 else: state_dict_do = load_checkpoint(cp_do, device) mpd.load_state_dict(state_dict_do['mpd']) del state_dict_do['mpd'] msd.load_state_dict(state_dict_do['msd']) del state_dict_do['msd'] steps = state_dict_do['steps'] + 1 last_epoch = state_dict_do['epoch'] if h.num_gpus > 1: generator = DistributedDataParallel(generator, device_ids=[rank]).to(device) mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device) msd = DistributedDataParallel(msd, device_ids=[rank]).to(device) optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2]) optim_d = torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()), h.learning_rate, betas=[h.adam_b1, h.adam_b2]) if state_dict_do is not None: optim_g.load_state_dict(state_dict_do['optim_g']) optim_d.load_state_dict(state_dict_do['optim_d']) del state_dict_do scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch) scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch) training_filelist, validation_filelist = get_dataset_filelist( a, h.segment_size, h.sampling_rate) trainset = MelDataset(training_filelist, h.segment_size, h.n_fft, h.num_mels, h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0, shuffle=False if h.num_gpus > 1 else True, fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning, trim_non_voiced=a.trim_non_voiced) STFT = STFT_Class(h.sampling_rate, h.num_mels, h.n_fft, h.win_size, h.hop_size, h.fmin, h.fmax) train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False, sampler=train_sampler, batch_size=h.batch_size, pin_memory=True, drop_last=True) assert len(train_loader), 'No audio files in dataset!' if rank == 0: validset = MelDataset(validation_filelist, h.segment_size, h.n_fft, h.num_mels, h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, False, False, n_cache_reuse=0, fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning, trim_non_voiced=a.trim_non_voiced) validation_loader = DataLoader(validset, num_workers=h.num_workers, shuffle=False, sampler=None, batch_size=1, pin_memory=True, drop_last=True) sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs'), max_queue=10000) sw.logged_gt_plots = False if h.num_gpus > 1: import gc gc.collect() torch.cuda.empty_cache() generator.train() mpd.train() msd.train() for epoch in range(max(0, last_epoch), a.training_epochs): if rank == 0: start = time.time() print("Epoch: {}".format(epoch + 1)) if h.num_gpus > 1: train_sampler.set_epoch(epoch) for i, batch in enumerate(train_loader): if rank == 0: start_b = time.time() x, y, _, y_mel = batch x = torch.autograd.Variable(x.to(device, non_blocking=True)) y = torch.autograd.Variable(y.to(device, non_blocking=True)) y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True)) y = y.unsqueeze(1) y_g_hat = generator(x) y_g_hat_mel = STFT.get_mel(y_g_hat.squeeze(1)) optim_d.zero_grad() # MPD y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach()) loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss( y_df_hat_r, y_df_hat_g) # MSD y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach()) loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss( y_ds_hat_r, y_ds_hat_g) loss_disc_all = loss_disc_s + loss_disc_f loss_disc_all.backward() optim_d.step() # Generator optim_g.zero_grad() # L1 Mel-Spectrogram Loss loss_mel = F.l1_loss(y_mel, y_g_hat_mel) y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat) y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat) loss_fm_f = feature_loss(fmap_f_r, fmap_f_g) loss_fm_s = feature_loss(fmap_s_r, fmap_s_g) loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g) loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g) loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel * 45 loss_gen_all.backward() optim_g.step() if rank == 0: torch.set_grad_enabled(False) # STDOUT logging if steps % a.stdout_interval == 0: print( 'Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}' .format(steps, loss_gen_all, loss_mel.item(), time.time() - start_b)) # checkpointing if steps % a.checkpoint_interval == 0 and steps != 0: checkpoint_path = "{}/g_{:08d}".format( a.checkpoint_path, steps) save_checkpoint( checkpoint_path, { 'generator': (generator.module if h.num_gpus > 1 else generator).state_dict() }) checkpoint_path = "{}/do_{:08d}".format( a.checkpoint_path, steps) save_checkpoint( checkpoint_path, { 'mpd': (mpd.module if h.num_gpus > 1 else mpd).state_dict(), 'msd': (msd.module if h.num_gpus > 1 else msd).state_dict(), 'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps, 'epoch': epoch }) del_old_checkpoints(a.checkpoint_path, 'g_', a.n_models_to_keep) del_old_checkpoints(a.checkpoint_path, 'do_', a.n_models_to_keep) # Tensorboard summary logging if steps % a.summary_interval == 0: sw.add_scalar("training/gen_loss_total", loss_gen_all, steps) sw.add_scalar("training/mel_spec_error", loss_mel.item(), steps) # Validation if steps % a.validation_interval == 0: # and steps != 0: print("Validating...") n_audios_to_plot = 6 generator.eval() torch.cuda.empty_cache() val_err_tot = 0 for j, batch in tqdm(enumerate(validation_loader), total=len(validation_loader)): x, y, _, y_mel = batch y_g_hat = generator(x.to(device)) y_hat_spec = STFT.get_mel(y_g_hat.squeeze(1)) val_err_tot += F.l1_loss(y_mel, y_hat_spec.to(y_mel)).item() if j < n_audios_to_plot and not sw.logged_gt_plots: sw.add_audio(f'gt/y_{j}', y[0], steps, h.sampling_rate) sw.add_figure(f'spec_{j:02}/gt_spec', plot_spectrogram(y_mel[0]), steps) if j < n_audios_to_plot: sw.add_audio(f'generated/y_hat_{j}', y_g_hat[0], steps, h.sampling_rate) sw.add_figure( f'spec_{j:02}/pred_spec', plot_spectrogram( y_hat_spec.squeeze(0).cpu().numpy()), steps) if j > 64: # I am NOT patient enough to complete an entire validation cycle with over 1536 files. break sw.logged_gt_plots = True val_err = val_err_tot / (j + 1) sw.add_scalar("validation/mel_spec_error", val_err, steps) generator.train() print(f"Done. Val_loss = {val_err}") torch.set_grad_enabled(True) steps += 1 scheduler_g.step() scheduler_d.step() if rank == 0: print('Time taken for epoch {} is {} sec\n'.format( epoch + 1, int(time.time() - start)))