import argparse import itertools import json import os import time import torch import torch.multiprocessing as mp import torch.nn.functional as F from fastprogress import master_bar, progress_bar from torch.cuda.amp.grad_scaler import GradScaler from torch.distributed import init_process_group from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader, DistributedSampler from torch.utils.tensorboard import SummaryWriter from .meldataset import (LogMelSpectrogram, MelDataset, get_dataset_filelist, mel_spectrogram) from .models import (Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, discriminator_loss, feature_loss, generator_loss) from .utils import (AttrDict, build_env, load_checkpoint, plot_spectrogram, save_checkpoint, scan_checkpoint) torch.backends.cudnn.benchmark = True USE_ALT_MELCALC = True 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().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 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']) msd.load_state_dict(state_dict_do['msd']) steps = state_dict_do['steps'] + 1 last_epoch = state_dict_do['epoch'] print(f"Restored checkpoint from {cp_g} and {cp_do}") if h.num_gpus > 1: print("Multi-gpu detected") 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']) 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) if a.fp16: scaler_g = GradScaler() scaler_d = GradScaler() train_df, valid_df = get_dataset_filelist(a) trainset = MelDataset(train_df, 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, audio_root_path=a.audio_root_path, feat_root_path=a.feature_root_path, use_alt_melcalc=USE_ALT_MELCALC) 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, persistent_workers=True, drop_last=True) alt_melspec = LogMelSpectrogram(h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax).to(device) if rank == 0: validset = MelDataset(valid_df, 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, audio_root_path=a.audio_root_path, feat_root_path=a.feature_root_path, use_alt_melcalc=USE_ALT_MELCALC) validation_loader = DataLoader(validset, num_workers=1, shuffle=False, sampler=None, batch_size=1, pin_memory=True, persistent_workers=True, drop_last=True) sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs')) generator.train() mpd.train() msd.train() if rank == 0: mb = master_bar(range(max(0, last_epoch), a.training_epochs)) else: mb = range(max(0, last_epoch), a.training_epochs) for epoch in mb: if rank == 0: start = time.time() mb.write("Epoch: {}".format(epoch+1)) if h.num_gpus > 1: train_sampler.set_epoch(epoch) if rank == 0: pb = progress_bar(enumerate(train_loader), total=len(train_loader), parent=mb) else: pb = enumerate(train_loader) for i, batch in pb: 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) with torch.cuda.amp.autocast(enabled=a.fp16): y_g_hat = generator(x) if USE_ALT_MELCALC: y_g_hat_mel = alt_melspec(y_g_hat.squeeze(1)) else: 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) # print(x.shape, y_g_hat.shape, y_g_hat_mel.shape, y_mel.shape, y.shape) optim_d.zero_grad() with torch.cuda.amp.autocast(enabled=a.fp16): # 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 if a.fp16: scaler_d.scale(loss_disc_all).backward() scaler_d.step(optim_d) scaler_d.update() else: loss_disc_all.backward() optim_d.step() # Generator optim_g.zero_grad() with torch.cuda.amp.autocast(enabled=a.fp16): # L1 Mel-Spectrogram Loss loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45 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 if a.fp16: scaler_g.scale(loss_gen_all).backward() scaler_g.step(optim_g) scaler_g.update() else: loss_gen_all.backward() 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() mb.write('Steps : {:,d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, sec/batch : {:4.3f}, peak mem: {:5.2f}GB'. \ format(steps, loss_gen_all, mel_error, time.time() - start_b, torch.cuda.max_memory_allocated()/1e9)) mb.child.comment = "Steps : {:,d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}". \ format(steps, loss_gen_all, mel_error) # checkpointing if steps % a.checkpoint_interval == 0 and steps != 0: checkpoint_path = "{}/g_{:08d}.pt".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}.pt".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}) # 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/disc_loss_total", loss_disc_all, steps) # Validation if steps % a.validation_interval == 0: # and steps != 0: generator.eval() torch.cuda.empty_cache() val_err_tot = 0 with torch.no_grad(): for j, batch in progress_bar(enumerate(validation_loader), total=len(validation_loader), parent=mb): x, y, _, y_mel = batch y_g_hat = generator(x.to(device)) y_mel = y_mel.to(device, non_blocking=True) if USE_ALT_MELCALC: y_g_hat_mel = alt_melspec(y_g_hat.squeeze(1)) if y_g_hat_mel.shape[-1] != y_mel.shape[-1]: # pad it n_pad = h.hop_size y_g_hat = F.pad(y_g_hat, (n_pad//2, n_pad - n_pad//2)) y_g_hat_mel = alt_melspec(y_g_hat.squeeze(1)) else: 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) #print('valid', x.shape, y_g_hat.shape, y_g_hat_mel.shape, y_mel.shape, y.shape) val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item() if j <= 4: if steps == 0: sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate) sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(x[0]), steps) sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate) if USE_ALT_MELCALC: y_hat_spec = alt_melspec(y_g_hat.squeeze(1)) else: 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_for_loss) sw.add_figure('generated/y_hat_spec_{}'.format(j), plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps) val_err = val_err_tot / (j+1) sw.add_scalar("validation/mel_spec_error", val_err, steps) mb.write(f"validation run complete at {steps:,d} steps. validation mel spec error: {val_err:5.4f}") generator.train() sw.add_scalar("memory/max_allocated_gb", torch.cuda.max_memory_allocated()/1e9, steps) sw.add_scalar("memory/max_reserved_gb", torch.cuda.max_memory_reserved()/1e9, steps) torch.cuda.reset_peak_memory_stats() torch.cuda.reset_accumulated_memory_stats() 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('--audio_root_path', required=True) parser.add_argument('--feature_root_path', required=True) parser.add_argument('--input_training_file', default='LJSpeech-1.1/training.txt') parser.add_argument('--input_validation_file', default='LJSpeech-1.1/validation.txt') parser.add_argument('--checkpoint_path', default='cp_hifigan') parser.add_argument('--config', default='') parser.add_argument('--training_epochs', default=1500, type=int) parser.add_argument('--stdout_interval', default=5, type=int) parser.add_argument('--checkpoint_interval', default=5000, type=int) parser.add_argument('--summary_interval', default=25, type=int) parser.add_argument('--validation_interval', default=1000, type=int) parser.add_argument('--fp16', default=False, type=bool) parser.add_argument('--fine_tuning', action='store_true') a = parser.parse_args() print(a) 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()