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 torchaudio.transforms import MelSpectrogram 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 academicodec.models.hificodec.env import AttrDict, build_env from academicodec.models.hificodec.meldataset import MelDataset, mel_spectrogram, get_dataset_filelist from academicodec.models.encodec.msstftd import MultiScaleSTFTDiscriminator from academicodec.models.hificodec.models import Generator from academicodec.models.hificodec.models import MultiPeriodDiscriminator from academicodec.models.hificodec.models import MultiScaleDiscriminator from academicodec.models.hificodec.models import feature_loss from academicodec.models.hificodec.models import generator_loss from academicodec.models.hificodec.models import discriminator_loss from academicodec.models.hificodec.models import Encoder from academicodec.models.hificodec.models import Quantizer from academicodec.utils import plot_spectrogram from academicodec.utils import scan_checkpoint from academicodec.utils import load_checkpoint from academicodec.utils import save_checkpoint torch.backends.cudnn.benchmark = True def reconstruction_loss(x, G_x, device, eps=1e-7): L = 100 * F.mse_loss(x, G_x) # wav L1 loss for i in range(6, 11): s = 2**i melspec = MelSpectrogram( sample_rate=24000, n_fft=s, hop_length=s // 4, n_mels=64, wkwargs={"device": device}).to(device) # 64, 16, 64 # 128, 32, 128 # 256, 64, 256 # 512, 128, 512 # 1024, 256, 1024 S_x = melspec(x) S_G_x = melspec(G_x) loss = ((S_x - S_G_x).abs().mean() + ( ((torch.log(S_x.abs() + eps) - torch.log(S_G_x.abs() + eps))**2 ).mean(dim=-2)**0.5).mean()) / (i) L += loss #print('i ,loss ', i, loss) #assert 1==2 return L 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)) encoder = Encoder(h).to(device) generator = Generator(h).to(device) quantizer = Quantizer(h).to(device) mpd = MultiPeriodDiscriminator().to(device) msd = MultiScaleDiscriminator().to(device) mstftd = MultiScaleSTFTDiscriminator(32).to(device) if rank == 0: print(encoder) print(quantizer) 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']) encoder.load_state_dict(state_dict_g['encoder']) quantizer.load_state_dict(state_dict_g['quantizer']) mpd.load_state_dict(state_dict_do['mpd']) msd.load_state_dict(state_dict_do['msd']) mstftd.load_state_dict(state_dict_do['mstftd']) 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) encoder = DistributedDataParallel(encoder, device_ids=[rank]).to(device) quantizer = DistributedDataParallel( quantizer, device_ids=[rank]).to(device) mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device) msd = DistributedDataParallel(msd, device_ids=[rank]).to(device) mstftd = DistributedDataParallel(mstftd, device_ids=[rank]).to(device) optim_g = torch.optim.Adam( itertools.chain(generator.parameters(), encoder.parameters(), quantizer.parameters()), h.learning_rate, betas=[h.adam_b1, h.adam_b2]) optim_d = torch.optim.Adam( itertools.chain(msd.parameters(), mpd.parameters(), mstftd.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) training_filelist, validation_filelist = get_dataset_filelist(a) 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, base_mels_path=a.input_mels_dir) 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.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) validation_loader = DataLoader( validset, num_workers=1, shuffle=False, sampler=None, batch_size=1, pin_memory=True, drop_last=True) sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs')) plot_gt_once = False generator.train() encoder.train() quantizer.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) c = encoder(y) # print("c.shape: ", c.shape) q, loss_q, c = quantizer(c) # print("q.shape: ", q.shape) y_g_hat = generator(q) 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) # 1024, 80, 24000, 240,1024 y_r_mel_1 = mel_spectrogram( y.squeeze(1), 512, h.num_mels, h.sampling_rate, 120, 512, h.fmin, h.fmax_for_loss) y_g_mel_1 = mel_spectrogram( y_g_hat.squeeze(1), 512, h.num_mels, h.sampling_rate, 120, 512, h.fmin, h.fmax_for_loss) y_r_mel_2 = mel_spectrogram( y.squeeze(1), 256, h.num_mels, h.sampling_rate, 60, 256, h.fmin, h.fmax_for_loss) y_g_mel_2 = mel_spectrogram( y_g_hat.squeeze(1), 256, h.num_mels, h.sampling_rate, 60, 256, h.fmin, h.fmax_for_loss) y_r_mel_3 = mel_spectrogram( y.squeeze(1), 128, h.num_mels, h.sampling_rate, 30, 128, h.fmin, h.fmax_for_loss) y_g_mel_3 = mel_spectrogram( y_g_hat.squeeze(1), 128, h.num_mels, h.sampling_rate, 30, 128, h.fmin, h.fmax_for_loss) # print("x.shape: ", x.shape) # print("y.shape: ", y.shape) # print("y_g_hat.shape: ", y_g_hat.shape) 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) y_disc_r, fmap_r = mstftd(y) y_disc_gen, fmap_gen = mstftd(y_g_hat.detach()) loss_disc_stft, losses_disc_stft_r, losses_disc_stft_g = discriminator_loss( y_disc_r, y_disc_gen) loss_disc_all = loss_disc_s + loss_disc_f + loss_disc_stft loss_disc_all.backward() optim_d.step() # Generator optim_g.zero_grad() # L1 Mel-Spectrogram Loss loss_mel1 = F.l1_loss(y_r_mel_1, y_g_mel_1) loss_mel2 = F.l1_loss(y_r_mel_2, y_g_mel_2) loss_mel3 = F.l1_loss(y_r_mel_3, y_g_mel_3) #print('loss_mel1, loss_mel2 ', loss_mel1, loss_mel2) loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45 + loss_mel1 + loss_mel2 # print('loss_mel ', loss_mel) # assert 1==2 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) y_stftd_hat_r, fmap_stftd_r = mstftd(y) y_stftd_hat_g, fmap_stftd_g = mstftd(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_fm_stft = feature_loss(fmap_stftd_r, fmap_stftd_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_stft, losses_gen_stft = generator_loss(y_stftd_hat_g) loss_gen_all = loss_gen_s + loss_gen_f + loss_gen_stft + loss_fm_s + loss_fm_f + loss_fm_stft + loss_mel + loss_q * 10 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() print( 'Steps : {:d}, Gen Loss Total : {:4.3f}, Loss Q : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'. format(steps, loss_gen_all, loss_q, 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(), 'encoder': (encoder.module if h.num_gpus > 1 else encoder).state_dict(), 'quantizer': (quantizer.module if h.num_gpus > 1 else quantizer).state_dict() }, num_ckpt_keep=a.num_ckpt_keep) 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(), 'mstftd': (mstftd.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 }, num_ckpt_keep=a.num_ckpt_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", mel_error, steps) # Validation if steps % a.validation_interval == 0 and steps != 0: generator.eval() encoder.eval() quantizer.eval() torch.cuda.empty_cache() val_err_tot = 0 with torch.no_grad(): for j, batch in enumerate(validation_loader): x, y, _, y_mel = batch c = encoder(y.to(device).unsqueeze(1)) q, loss_q, c = quantizer(c) y_g_hat = generator(q) y_mel = torch.autograd.Variable(y_mel.to(device)) 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) i_size = min(y_mel.size(2), y_g_hat_mel.size(2)) val_err_tot += F.l1_loss( y_mel[:, :, :i_size], y_g_hat_mel[:, :, :i_size]).item() if j <= 8: # if steps == 0: if not plot_gt_once: 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) 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(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) if not plot_gt_once: plot_gt_once = True generator.train() 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='../datasets/audios') parser.add_argument('--input_mels_dir', default=None) parser.add_argument('--input_training_file', required=True) parser.add_argument('--input_validation_file', required=True) parser.add_argument('--checkpoint_path', default='checkpoints') parser.add_argument('--config', default='') parser.add_argument('--training_epochs', default=2000, 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=100, type=int) parser.add_argument('--validation_interval', default=5000, type=int) parser.add_argument('--num_ckpt_keep', default=5, type=int) parser.add_argument('--fine_tuning', default=False, type=bool) 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()