# Copyright (c) 2022 NVIDIA CORPORATION. # Licensed under the MIT license. # Adapted from https://github.com/jik876/hifi-gan under the MIT license. # LICENSE is in incl_licenses directory. import warnings warnings.simplefilter(action='ignore', category=FutureWarning) import itertools import os import time import argparse import json import torch import torch.nn.functional as F from torch.utils.tensorboard import SummaryWriter from torch.utils.data import DistributedSampler, DataLoader import torch.multiprocessing as mp from torch.distributed import init_process_group from torch.nn.parallel import DistributedDataParallel from env import AttrDict, build_env from meldataset import MelDataset, mel_spectrogram, get_dataset_filelist, MAX_WAV_VALUE from models import BigVGAN, MultiPeriodDiscriminator, MultiResolutionDiscriminator,\ feature_loss, generator_loss, discriminator_loss from utils import plot_spectrogram, plot_spectrogram_clipped, scan_checkpoint, load_checkpoint, save_checkpoint, save_audio import torchaudio as ta from pesq import pesq from tqdm import tqdm import auraloss torch.backends.cudnn.benchmark = False def train(rank, a, h): if h.num_gpus > 1: # initialize distributed 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) # set seed and device torch.cuda.manual_seed(h.seed) torch.cuda.set_device(rank) device = torch.device('cuda:{:d}'.format(rank)) # define BigVGAN generator generator = BigVGAN(h).to(device) print("Generator params: {}".format(sum(p.numel() for p in generator.parameters()))) # define discriminators. MPD is used by default mpd = MultiPeriodDiscriminator(h).to(device) print("Discriminator mpd params: {}".format(sum(p.numel() for p in mpd.parameters()))) # define additional discriminators. BigVGAN uses MRD as default mrd = MultiResolutionDiscriminator(h).to(device) print("Discriminator mrd params: {}".format(sum(p.numel() for p in mrd.parameters()))) # create or scan the latest checkpoint from checkpoints directory 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_') # load the latest checkpoint if exists steps = 0 if cp_g is None or cp_do is None: state_dict_do = None last_epoch = -1 else: state_dict_g = load_checkpoint(cp_g, device) state_dict_do = load_checkpoint(cp_do, device) generator.load_state_dict(state_dict_g['generator']) mpd.load_state_dict(state_dict_do['mpd']) mrd.load_state_dict(state_dict_do['mrd']) steps = state_dict_do['steps'] + 1 last_epoch = state_dict_do['epoch'] # initialize DDP, optimizers, and schedulers if h.num_gpus > 1: generator = DistributedDataParallel(generator, device_ids=[rank]).to(device) mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device) mrd = DistributedDataParallel(mrd, 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(mrd.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']) 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) # define training and validation datasets # unseen_validation_filelist will contain sample filepaths outside the seen training & validation dataset # example: trained on LibriTTS, validate on VCTK training_filelist, validation_filelist, list_unseen_validation_filelist = get_dataset_filelist(a) trainset = MelDataset(training_filelist, h, 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, base_mels_path=a.input_mels_dir, is_seen=True) 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) if rank == 0: validset = MelDataset(validation_filelist, h, 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, base_mels_path=a.input_mels_dir, is_seen=True) validation_loader = DataLoader(validset, num_workers=1, shuffle=False, sampler=None, batch_size=1, pin_memory=True, drop_last=True) list_unseen_validset = [] list_unseen_validation_loader = [] for i in range(len(list_unseen_validation_filelist)): unseen_validset = MelDataset(list_unseen_validation_filelist[i], h, 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, base_mels_path=a.input_mels_dir, is_seen=False) unseen_validation_loader = DataLoader(unseen_validset, num_workers=1, shuffle=False, sampler=None, batch_size=1, pin_memory=True, drop_last=True) list_unseen_validset.append(unseen_validset) list_unseen_validation_loader.append(unseen_validation_loader) # Tensorboard logger sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs')) if a.save_audio: # also save audio to disk if --save_audio is set to True os.makedirs(os.path.join(a.checkpoint_path, 'samples'), exist_ok=True) # validation loop # "mode" parameter is automatically defined as (seen or unseen)_(name of the dataset) # if the name of the dataset contains "nonspeech", it skips PESQ calculation to prevent errors def validate(rank, a, h, loader, mode="seen"): assert rank == 0, "validate should only run on rank=0" generator.eval() torch.cuda.empty_cache() val_err_tot = 0 val_pesq_tot = 0 val_mrstft_tot = 0 # modules for evaluation metrics pesq_resampler = ta.transforms.Resample(h.sampling_rate, 16000).cuda() loss_mrstft = auraloss.freq.MultiResolutionSTFTLoss(device="cuda") if a.save_audio: # also save audio to disk if --save_audio is set to True os.makedirs(os.path.join(a.checkpoint_path, 'samples', 'gt_{}'.format(mode)), exist_ok=True) os.makedirs(os.path.join(a.checkpoint_path, 'samples', '{}_{:08d}'.format(mode, steps)), exist_ok=True) with torch.no_grad(): print("step {} {} speaker validation...".format(steps, mode)) # loop over validation set and compute metrics for j, batch in tqdm(enumerate(loader)): x, y, _, y_mel = batch y = y.to(device) if hasattr(generator, 'module'): y_g_hat = generator.module(x.to(device)) else: y_g_hat = generator(x.to(device)) y_mel = y_mel.to(device, non_blocking=True) y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax_for_loss) val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item() # PESQ calculation. only evaluate PESQ if it's speech signal (nonspeech PESQ will error out) if not "nonspeech" in mode: # skips if the name of dataset (in mode string) contains "nonspeech" # resample to 16000 for pesq y_16k = pesq_resampler(y) y_g_hat_16k = pesq_resampler(y_g_hat.squeeze(1)) y_int_16k = (y_16k[0] * MAX_WAV_VALUE).short().cpu().numpy() y_g_hat_int_16k = (y_g_hat_16k[0] * MAX_WAV_VALUE).short().cpu().numpy() val_pesq_tot += pesq(16000, y_int_16k, y_g_hat_int_16k, 'wb') # MRSTFT calculation val_mrstft_tot += loss_mrstft(y_g_hat.squeeze(1), y).item() # log audio and figures to Tensorboard if j % a.eval_subsample == 0: # subsample every nth from validation set if steps >= 0: sw.add_audio('gt_{}/y_{}'.format(mode, j), y[0], steps, h.sampling_rate) if a.save_audio: # also save audio to disk if --save_audio is set to True save_audio(y[0], os.path.join(a.checkpoint_path, 'samples', 'gt_{}'.format(mode), '{:04d}.wav'.format(j)), h.sampling_rate) sw.add_figure('gt_{}/y_spec_{}'.format(mode, j), plot_spectrogram(x[0]), steps) sw.add_audio('generated_{}/y_hat_{}'.format(mode, j), y_g_hat[0], steps, h.sampling_rate) if a.save_audio: # also save audio to disk if --save_audio is set to True save_audio(y_g_hat[0, 0], os.path.join(a.checkpoint_path, 'samples', '{}_{:08d}'.format(mode, steps), '{:04d}.wav'.format(j)), h.sampling_rate) # spectrogram of synthesized audio y_hat_spec = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax) sw.add_figure('generated_{}/y_hat_spec_{}'.format(mode, j), plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps) # visualization of spectrogram difference between GT and synthesized audio # difference higher than 1 is clipped for better visualization spec_delta = torch.clamp(torch.abs(x[0] - y_hat_spec.squeeze(0).cpu()), min=1e-6, max=1.) sw.add_figure('delta_dclip1_{}/spec_{}'.format(mode, j), plot_spectrogram_clipped(spec_delta.numpy(), clip_max=1.), steps) val_err = val_err_tot / (j + 1) val_pesq = val_pesq_tot / (j + 1) val_mrstft = val_mrstft_tot / (j + 1) # log evaluation metrics to Tensorboard sw.add_scalar("validation_{}/mel_spec_error".format(mode), val_err, steps) sw.add_scalar("validation_{}/pesq".format(mode), val_pesq, steps) sw.add_scalar("validation_{}/mrstft".format(mode), val_mrstft, steps) generator.train() # if the checkpoint is loaded, start with validation loop if steps != 0 and rank == 0 and not a.debug: if not a.skip_seen: validate(rank, a, h, validation_loader, mode="seen_{}".format(train_loader.dataset.name)) for i in range(len(list_unseen_validation_loader)): validate(rank, a, h, list_unseen_validation_loader[i], mode="unseen_{}".format(list_unseen_validation_loader[i].dataset.name)) # exit the script if --evaluate is set to True if a.evaluate: exit() # main training loop generator.train() mpd.train() mrd.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 = x.to(device, non_blocking=True) y = y.to(device, non_blocking=True) y_mel = y_mel.to(device, non_blocking=True) y = y.unsqueeze(1) y_g_hat = generator(x) y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax_for_loss) 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) # MRD y_ds_hat_r, y_ds_hat_g, _, _ = mrd(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 # whether to freeze D for initial training steps if steps >= a.freeze_step: loss_disc_all.backward() grad_norm_mpd = torch.nn.utils.clip_grad_norm_(mpd.parameters(), 1000.) grad_norm_mrd = torch.nn.utils.clip_grad_norm_(mrd.parameters(), 1000.) optim_d.step() else: print("WARNING: skipping D training for the first {} steps".format(a.freeze_step)) grad_norm_mpd = 0. grad_norm_mrd = 0. # generator optim_g.zero_grad() # L1 Mel-Spectrogram Loss loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45 # MPD loss y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat) loss_fm_f = feature_loss(fmap_f_r, fmap_f_g) loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g) # MRD loss y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = mrd(y, y_g_hat) loss_fm_s = feature_loss(fmap_s_r, fmap_s_g) loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g) if steps >= a.freeze_step: loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel else: print("WARNING: using regression loss only for G for the first {} steps".format(a.freeze_step)) loss_gen_all = loss_mel loss_gen_all.backward() grad_norm_g = torch.nn.utils.clip_grad_norm_(generator.parameters(), 1000.) optim_g.step() if rank == 0: # STDOUT logging if steps % a.stdout_interval == 0: with torch.no_grad(): mel_error = F.l1_loss(y_mel, y_g_hat_mel).item() print('Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'. format(steps, loss_gen_all, mel_error, 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(), 'mrd': (mrd.module if h.num_gpus > 1 else mrd).state_dict(), 'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps, 'epoch': epoch}) # 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", mel_error, steps) sw.add_scalar("training/fm_loss_mpd", loss_fm_f.item(), steps) sw.add_scalar("training/gen_loss_mpd", loss_gen_f.item(), steps) sw.add_scalar("training/disc_loss_mpd", loss_disc_f.item(), steps) sw.add_scalar("training/grad_norm_mpd", grad_norm_mpd, steps) sw.add_scalar("training/fm_loss_mrd", loss_fm_s.item(), steps) sw.add_scalar("training/gen_loss_mrd", loss_gen_s.item(), steps) sw.add_scalar("training/disc_loss_mrd", loss_disc_s.item(), steps) sw.add_scalar("training/grad_norm_mrd", grad_norm_mrd, steps) sw.add_scalar("training/grad_norm_g", grad_norm_g, steps) sw.add_scalar("training/learning_rate_d", scheduler_d.get_last_lr()[0], steps) sw.add_scalar("training/learning_rate_g", scheduler_g.get_last_lr()[0], steps) sw.add_scalar("training/epoch", epoch+1, steps) # validation if steps % a.validation_interval == 0: # plot training input x so far used for i_x in range(x.shape[0]): sw.add_figure('training_input/x_{}'.format(i_x), plot_spectrogram(x[i_x].cpu()), steps) sw.add_audio('training_input/y_{}'.format(i_x), y[i_x][0], steps, h.sampling_rate) # seen and unseen speakers validation loops if not a.debug and steps != 0: validate(rank, a, h, validation_loader, mode="seen_{}".format(train_loader.dataset.name)) for i in range(len(list_unseen_validation_loader)): validate(rank, a, h, list_unseen_validation_loader[i], mode="unseen_{}".format(list_unseen_validation_loader[i].dataset.name)) 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))) def main(): print('Initializing Training Process..') parser = argparse.ArgumentParser() parser.add_argument('--group_name', default=None) parser.add_argument('--input_wavs_dir', default='LibriTTS') parser.add_argument('--input_mels_dir', default='ft_dataset') parser.add_argument('--input_training_file', default='LibriTTS/train-full.txt') parser.add_argument('--input_validation_file', default='LibriTTS/val-full.txt') parser.add_argument('--list_input_unseen_wavs_dir', nargs='+', default=['LibriTTS', 'LibriTTS']) parser.add_argument('--list_input_unseen_validation_file', nargs='+', default=['LibriTTS/dev-clean.txt', 'LibriTTS/dev-other.txt']) parser.add_argument('--checkpoint_path', default='exp/bigvgan') parser.add_argument('--config', default='') parser.add_argument('--training_epochs', default=100000, type=int) parser.add_argument('--stdout_interval', default=5, type=int) parser.add_argument('--checkpoint_interval', default=50000, type=int) parser.add_argument('--summary_interval', default=100, type=int) parser.add_argument('--validation_interval', default=50000, type=int) parser.add_argument('--freeze_step', default=0, type=int, help='freeze D for the first specified steps. G only uses regression loss for these steps.') parser.add_argument('--fine_tuning', default=False, type=bool) parser.add_argument('--debug', default=False, type=bool, help="debug mode. skips validation loop throughout training") parser.add_argument('--evaluate', default=False, type=bool, help="only run evaluation from checkpoint and exit") parser.add_argument('--eval_subsample', default=5, type=int, help="subsampling during evaluation loop") parser.add_argument('--skip_seen', default=False, type=bool, help="skip seen dataset. useful for test set inference") parser.add_argument('--save_audio', default=False, type=bool, help="save audio of test set inference to disk") a = parser.parse_args() with open(a.config) as f: data = f.read() json_config = json.loads(data) h = AttrDict(json_config) build_env(a.config, 'config.json', a.checkpoint_path) torch.manual_seed(h.seed) if torch.cuda.is_available(): torch.cuda.manual_seed(h.seed) h.num_gpus = torch.cuda.device_count() h.batch_size = int(h.batch_size / h.num_gpus) print('Batch size per GPU :', h.batch_size) else: pass if h.num_gpus > 1: mp.spawn(train, nprocs=h.num_gpus, args=(a, h,)) else: train(0, a, h) if __name__ == '__main__': main()