# 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()