def evaluate(model, step, vocoder=None):
# Get dataset
print('evaluating..')
# Get dataset
if hp.with_hanzi:
dataset = Dataset(filename_py="val_pinyin.txt",vocab_file_py = 'vocab_pinyin.txt',
filename_hz = "val_hanzi.txt",
vocab_file_hz = 'vocab_hanzi.txt')
py_vocab_size = len(dataset.py_vocab)
hz_vocab_size = len(dataset.hz_vocab)
else:
dataset = Dataset(filename_py="val_pinyin.txt",vocab_file_py = 'vocab_pinyin.txt',
filename_hz = None,
vocab_file_hz = None)
py_vocab_size = len(dataset.py_vocab)
hz_vocab_size = None
loader = DataLoader(dataset, batch_size=hp.batch_size**2, shuffle=False,
collate_fn=dataset.collate_fn, drop_last=False, num_workers=0, )
# Get loss function
Loss = FastSpeech2Loss().to(device)
# Evaluation
d_l = []
f_l = []
e_l = []
mel_l = []
mel_p_l = []
current_step = 0
idx = 0
bar = tqdm.tqdm_notebook(total=len(dataset)//hp.batch_size)
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
bar.update(1)
# Get Data
id_ = data_of_batch["id"]
text = torch.from_numpy(data_of_batch["text"]).long().to(device)
if hp.with_hanzi:
hz_text = torch.from_numpy(
data_of_batch["hz_text"]).long().to(device)
else:
hz_text = None
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
log_D = torch.from_numpy(data_of_batch["log_D"]).int().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
with torch.no_grad():
mel_output, mel_postnet_output, log_duration_output, src_mask, mel_mask, out_mel_len = model(
src_seq=text, src_len=src_len, hz_seq=hz_text,mel_len=mel_len,
d_target=D, max_src_len=max_src_len, max_mel_len=max_mel_len)
# Cal Loss
mel_loss, mel_postnet_loss, d_loss = Loss(
log_duration_output, log_D, mel_output, mel_postnet_output, mel_target-hp.mel_mean, ~src_mask, ~mel_mask)
d_l.append(d_loss.item())
# f_l.append(f_loss.item())
# e_l.append(e_loss.item())
mel_l.append(mel_loss.item())
mel_p_l.append(mel_postnet_loss.item())
if vocoder is not None:
# Run vocoding and plotting spectrogram only when the vocoder is defined
for k in range(len(mel_target)):
basename = id_[k]
gt_length = mel_len[k]
out_length = out_mel_len[k]
mel_target_torch = mel_target[k:k+1,
:gt_length].transpose(1, 2).detach()
mel_target_ = mel_target[k, :gt_length].cpu(
).transpose(0, 1).detach()
mel_postnet_torch = mel_postnet_output[k:k +
1, :out_length].transpose(1, 2).detach()
mel_postnet = mel_postnet_output[k, :out_length].cpu(
).transpose(0, 1).detach()
if hp.vocoder == 'melgan':
utils.melgan_infer(mel_target_torch, vocoder, os.path.join(
hp.eval_path, 'ground-truth_{}_{}.wav'.format(basename, hp.vocoder)))
utils.melgan_infer(mel_postnet_torch+hp.mel_mean, vocoder, os.path.join(
hp.eval_path, 'eval_{}_{}.wav'.format(basename, hp.vocoder)))
elif hp.vocoder == 'waveglow':
utils.waveglow_infer(mel_target_torch, vocoder, os.path.join(
hp.eval_path, 'ground-truth_{}_{}.wav'.format(basename, hp.vocoder)))
utils.waveglow_infer(mel_postnet_torch+hp.mel_mean, vocoder, os.path.join(
hp.eval_path, 'eval_{}_{}.wav'.format(basename, hp.vocoder)))
# np.save(os.path.join(hp.eval_path, 'eval_{}_mel.npy'.format(
# basename)), mel_postnet.numpy()+hp.mel_mean)
# f0_ = f0[k, :gt_length].detach().cpu().numpy()
# energy_ = energy[k, :gt_length].detach().cpu().numpy()
# f0_output_ = f0_output[k,
# :out_length].detach().cpu().numpy()
# energy_output_ = energy_output[k, :out_length].detach(
# ).cpu().numpy()
utils.plot_data([mel_postnet.numpy()+hp.mel_mean,mel_target_.numpy()],
['Synthesized Spectrogram', 'Ground-Truth Spectrogram'], filename=os.path.join(hp.eval_path, 'eval_{}.png'.format(basename)))
idx += 1
current_step += 1
d_l = sum(d_l) / len(d_l)
# f_l = sum(f_l) / len(f_l)
# e_l = sum(e_l) / len(e_l)
mel_l = sum(mel_l) / len(mel_l)
mel_p_l = sum(mel_p_l) / len(mel_p_l)
str1 = "FastSpeech2 Step {},".format(step)
str2 = "Duration Loss: {}".format(d_l)
#str3 = "F0 Loss: {}".format(f_l)
# str4 = "Energy Loss: {}".format(e_l)
str4 = "Mel Loss: {}".format(mel_l)
str5 = "Mel Postnet Loss: {}".format(mel_p_l)
str6 = "total Loss: {}".format(mel_p_l+mel_l+d_l)
print("\n" + str1)
print(str2)
# print(str3)
print(str4)
print(str5)
print(str6)
with open(os.path.join(hp.log_path, "eval.txt"), "a") as f_log:
f_log.write(str1 + "\n")
f_log.write(str2 + "\n")
# f_log.write(str3 + "\n")
f_log.write(str4 + "\n")
f_log.write(str5 + "\n")
f_log.write(str6 + "\n")
f_log.write("\n")
return d_l, mel_l, mel_p_l
def evaluate(model, step):
torch.manual_seed(0)
# Get dataset
dataset = Dataset("val.txt", sort=False)
loader = DataLoader(dataset, batch_size=hp.batch_size*4, shuffle=False, collate_fn=dataset.collate_fn, drop_last=False, num_workers=0, )
# Get loss function
Loss = FastSpeech2Loss().to(device)
# Evaluation
d_l = []
f_l = []
e_l = []
if hp.vocoder=='WORLD':
ap = []
sp_l = []
sp_p_l = []
else:
mel_l = []
mel_p_l = []
current_step = 0
idx = 0
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
# Get Data
id_ = data_of_batch["id"]
condition = torch.from_numpy(data_of_batch["condition"]).long().to(device)
mel_refer = torch.from_numpy(data_of_batch["mel_refer"]).float().to(device)
if hp.vocoder=='WORLD':
ap_target = torch.from_numpy(data_of_batch["ap_target"]).float().to(device)
sp_target = torch.from_numpy(data_of_batch["sp_target"]).float().to(device)
else:
mel_target = torch.from_numpy(data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).long().to(device)
log_D = torch.from_numpy(data_of_batch["log_D"]).float().to(device)
#print(D,log_D)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(data_of_batch["energy"]).float().to(device)
src_len = torch.from_numpy(data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
with torch.no_grad():
# Forward
if hp.vocoder=='WORLD':
# print(condition.shape,mel_refer.shape, src_len.shape, mel_len.shape, D.shape, f0.shape, energy.shape, max_src_len.shape, max_mel_len.shape)
ap_output, sp_output, sp_postnet_output, log_duration_output, f0_output,energy_output, src_mask, ap_mask,sp_mask ,variance_adaptor_output,decoder_output= model(
condition, src_len, mel_len, D, f0, energy, max_src_len, max_mel_len)
ap_loss, sp_loss, sp_postnet_loss, d_loss, f_loss, e_loss = Loss(
log_duration_output, D, f0_output, f0, energy_output, energy, ap_output=ap_output,
sp_output=sp_output, sp_postnet_output=sp_postnet_output, ap_target=ap_target,
sp_target=sp_target,src_mask=src_mask, ap_mask=ap_mask,sp_mask=sp_mask)
total_loss = ap_loss + sp_loss + sp_postnet_loss + d_loss + f_loss + e_loss
else:
mel_output, mel_postnet_output, log_duration_output, f0_output,energy_output, src_mask, mel_mask, _ = model(
condition,mel_refer, src_len, mel_len, D, f0, energy, max_src_len, max_mel_len)
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
log_duration_output, log_D, f0_output, f0, energy_output, energy, mel_output=mel_output,
mel_postnet_output=mel_postnet_output, mel_target=mel_target, src_mask=~src_mask, mel_mask=~mel_mask)
total_loss = mel_loss + mel_postnet_loss + d_loss + f_loss + e_loss
t_l = total_loss.item()
if hp.vocoder=='WORLD':
ap_l = ap_loss.item()
sp_l = sp_loss.item()
sp_p_l = sp_postnet_loss.item()
else:
m_l = mel_loss.item()
m_p_l = mel_postnet_loss.item()
d_l = d_loss.item()
f_l = f_loss.item()
e_l = e_loss.item()
# Run vocoding and plotting spectrogram only when the vocoder is defined
for k in range(len(mel_target)):
basename = id_[k]
gt_length = mel_len[k]
out_length = out_mel_len[k]
mel_target_torch = mel_target[k:k+1, :gt_length].transpose(1, 2).detach()
mel_postnet_torch = mel_postnet_output[k:k+1, :out_length].transpose(1, 2).detach()
if hp.vocoder == 'melgan':
utils.melgan_infer(mel_target_torch, vocoder, os.path.join(hp.eval_path, 'ground-truth_{}_{}.wav'.format(basename, hp.vocoder)))
utils.melgan_infer(mel_postnet_torch, vocoder, os.path.join(hp.eval_path, 'eval_{}_{}.wav'.format(basename, hp.vocoder)))
elif hp.vocoder == 'waveglow':
utils.waveglow_infer(mel_target_torch, vocoder, os.path.join(hp.eval_path, 'ground-truth_{}_{}.wav'.format(basename, hp.vocoder)))
utils.waveglow_infer(mel_postnet_torch, vocoder, os.path.join(hp.eval_path, 'eval_{}_{}.wav'.format(basename, hp.vocoder)))
elif hp.vocoder=='WORLD':
utils.world_infer(mel_postnet_torch.numpy(),f0_output, os.path.join(hp.eval_path, 'eval_{}_{}.wav'.format(basename, hp.vocoder)))
utils.world_infer(mel_target_torch.numpy(),f0, os.path.join(hp.eval_path, 'ground-truth_{}_{}.wav'.format(basename, hp.vocoder)))
np.save(os.path.join(hp.eval_path, 'eval_{}_mel.npy'.format(basename)), mel_postnet.numpy())
f0_ = f0[k, :gt_length].detach().cpu().numpy()
energy_ = energy[k, :gt_length].detach().cpu().numpy()
f0_output_ = f0_output[k, :out_length].detach().cpu().numpy()
energy_output_ = energy_output[k, :out_length].detach().cpu().numpy()
utils.plot_data([(mel_postnet[0].numpy(), f0_output_, energy_output_), (mel_target_.numpy(), f0_, energy_)],
['Synthesized Spectrogram', 'Ground-Truth Spectrogram'], filename=os.path.join(hp.eval_path, 'eval_{}.png'.format(basename)))
idx += 1
current_step += 1
d_l = sum(d_l) / len(d_l)
f_l = sum(f_l) / len(f_l)
e_l = sum(e_l) / len(e_l)
if hp.vocoder=='WORLD':
ap_l = sum(ap_l) / len(ap_l)
sp_l = sum(sp_l) / len(sp_l)
sp_p_l = sum(sp_p_l) / len(sp_p_l)
else:
mel_l = sum(mel_l) / len(mel_l)
mel_p_l = sum(mel_p_l) / len(mel_p_l)
str1 = "FastSpeech2 Step {},".format(step)
str2 = "Duration Loss: {}".format(d_l)
str3 = "F0 Loss: {}".format(f_l)
str4 = "Energy Loss: {}".format(e_l)
str5 = "Mel Loss: {}".format(mel_l)
str6 = "Mel Postnet Loss: {}".format(mel_p_l)
print("\n" + str1)
print(str2)
print(str3)
print(str4)
print(str5)
print(str6)
with open(os.path.join(hp.log_path, "eval.txt"), "a") as f_log:
f_log.write(str1 + "\n")
f_log.write(str2 + "\n")
f_log.write(str3 + "\n")
f_log.write(str4 + "\n")
f_log.write(str5 + "\n")
f_log.write(str6 + "\n")
f_log.write("\n")
return d_l, f_l, e_l, mel_l, mel_p_l
def main(args):
torch.manual_seed(0)
# Get device
# device = torch.device('cuda'if torch.cuda.is_available()else 'cpu')
device = 'cuda'
# Get dataset
dataset = Dataset("train.txt")
loader = DataLoaderX(dataset,
batch_size=hp.batch_size * 4,
shuffle=True,
collate_fn=dataset.collate_fn,
drop_last=True,
num_workers=8)
# Define model
model = nn.DataParallel(FastSpeech2()).to(device)
# model = FastSpeech2().to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of FastSpeech2 Parameters:', num_param)
# Optimizer and loss
optimizer = torch.optim.Adam(model.parameters(),
betas=hp.betas,
eps=hp.eps,
weight_decay=hp.weight_decay)
scheduled_optim = ScheduledOptim(optimizer, hp.decoder_hidden,
hp.n_warm_up_step, args.restore_step)
Loss = FastSpeech2Loss().to(device)
print("Optimizer and Loss Function Defined.")
# Load checkpoint if exists
checkpoint_path = os.path.join(hp.checkpoint_path)
try:
checkpoint = torch.load(
os.path.join(checkpoint_path,
'checkpoint_{}.pth.tar'.format(args.restore_step)))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step {}---\n".format(args.restore_step))
except:
print("\n---Start New Training---\n")
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
# Load vocoder
if hp.vocoder == 'melgan':
melgan = utils.get_melgan()
melgan.to(device)
elif hp.vocoder == 'waveglow':
waveglow = utils.get_waveglow()
waveglow.to(device)
# Init logger
log_path = hp.log_path
if not os.path.exists(log_path):
os.makedirs(log_path)
os.makedirs(os.path.join(log_path, 'train'))
os.makedirs(os.path.join(log_path, 'validation'))
current_time = time.strftime("%Y-%m-%dT%H:%M", time.localtime())
train_logger = SummaryWriter(log_dir='log/train/' + current_time)
val_logger = SummaryWriter(log_dir='log/validation/' + current_time)
# Init synthesis directory
synth_path = hp.synth_path
if not os.path.exists(synth_path):
os.makedirs(synth_path)
# Define Some Information
Time = np.array([])
Start = time.perf_counter()
current_step0 = 0
# Training
model = model.train()
for epoch in range(hp.epochs):
# Get Training Loader
total_step = hp.epochs * len(loader) * hp.batch_size
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
start_time = time.perf_counter()
current_step = i * len(batchs) + j + args.restore_step + \
epoch * len(loader)*len(batchs) + 1
# Get Data
condition = torch.from_numpy(
data_of_batch["condition"]).long().to(device)
mel_refer = torch.from_numpy(
data_of_batch["mel_refer"]).float().to(device)
if hp.vocoder == 'WORLD':
ap_target = torch.from_numpy(
data_of_batch["ap_target"]).float().to(device)
sp_target = torch.from_numpy(
data_of_batch["sp_target"]).float().to(device)
else:
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).long().to(device)
log_D = torch.from_numpy(
data_of_batch["log_D"]).float().to(device)
#print(D,log_D)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
if hp.vocoder == 'WORLD':
# print(condition.shape,mel_refer.shape, src_len.shape, mel_len.shape, D.shape, f0.shape, energy.shape, max_src_len.shape, max_mel_len.shape)
ap_output, sp_output, sp_postnet_output, log_duration_output, f0_output, energy_output, src_mask, ap_mask, sp_mask, variance_adaptor_output, decoder_output = model(
condition, src_len, mel_len, D, f0, energy,
max_src_len, max_mel_len)
ap_loss, sp_loss, sp_postnet_loss, d_loss, f_loss, e_loss = Loss(
log_duration_output,
D,
f0_output,
f0,
energy_output,
energy,
ap_output=ap_output,
sp_output=sp_output,
sp_postnet_output=sp_postnet_output,
ap_target=ap_target,
sp_target=sp_target,
src_mask=src_mask,
ap_mask=ap_mask,
sp_mask=sp_mask)
total_loss = ap_loss + sp_loss + sp_postnet_loss + d_loss + f_loss + e_loss
else:
mel_output, mel_postnet_output, log_duration_output, f0_output, energy_output, src_mask, mel_mask, _ = model(
condition, mel_refer, src_len, mel_len, D, f0, energy,
max_src_len, max_mel_len)
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
log_duration_output,
log_D,
f0_output,
f0,
energy_output,
energy,
mel_output=mel_output,
mel_postnet_output=mel_postnet_output,
mel_target=mel_target,
src_mask=~src_mask,
mel_mask=~mel_mask)
total_loss = mel_loss + mel_postnet_loss + d_loss + f_loss + e_loss
# Logger
t_l = total_loss.item()
if hp.vocoder == 'WORLD':
ap_l = ap_loss.item()
sp_l = sp_loss.item()
sp_p_l = sp_postnet_loss.item()
else:
m_l = mel_loss.item()
m_p_l = mel_postnet_loss.item()
d_l = d_loss.item()
f_l = f_loss.item()
e_l = e_loss.item()
# with open(os.path.join(log_path, "total_loss.txt"), "a") as f_total_loss:
# f_total_loss.write(str(t_l)+"\n")
# with open(os.path.join(log_path, "mel_loss.txt"), "a") as f_mel_loss:
# f_mel_loss.write(str(m_l)+"\n")
# with open(os.path.join(log_path, "mel_postnet_loss.txt"), "a") as f_mel_postnet_loss:
# f_mel_postnet_loss.write(str(m_p_l)+"\n")
# with open(os.path.join(log_path, "duration_loss.txt"), "a") as f_d_loss:
# f_d_loss.write(str(d_l)+"\n")
# with open(os.path.join(log_path, "f0_loss.txt"), "a") as f_f_loss:
# f_f_loss.write(str(f_l)+"\n")
# with open(os.path.join(log_path, "energy_loss.txt"), "a") as f_e_loss:
# f_e_loss.write(str(e_l)+"\n")
# Backward
total_loss = total_loss / hp.acc_steps
total_loss.backward()
if current_step % hp.acc_steps != 0:
continue
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(model.parameters(),
hp.grad_clip_thresh)
# Update weights
scheduled_optim.step_and_update_lr()
scheduled_optim.zero_grad()
# Print
if current_step % hp.log_step == 0:
Now = time.perf_counter()
str1 = "Epoch[{}/{}],Step[{}/{}]:".format(
epoch + 1, hp.epochs, current_step, total_step)
if hp.vocoder == 'WORLD':
str2 = "Loss:{:.4f},ap:{:.4f},sp:{:.4f},spPN:{:.4f},Dur:{:.4f},F0:{:.4f},Energy:{:.4f};".format(
t_l, ap_l, sp_l, sp_p_l, d_l, f_l, e_l)
else:
str2 = "Loss:{:.4f},Mel:{:.4f},MelPN:{:.4f},Dur:{:.4f},F0:{:.4f},Energy:{:.4f};".format(
t_l, m_l, m_p_l, d_l, f_l, e_l)
str3 = "T:{:.1f}s,ETA:{:.1f}s.".format(
(Now - Start) / (current_step - current_step0),
(total_step - current_step) * np.mean(Time))
print("" + str1 + str2 + str3 + '')
# with open(os.path.join(log_path, "log.txt"), "a") as f_log:
# f_log.write(str1 + "\n")
# f_log.write(str2 + "\n")
# f_log.write(str3 + "\n")
# f_log.write("\n")
train_logger.add_scalar('Loss/total_loss', t_l,
current_step)
if hp.vocoder == 'WORLD':
train_logger.add_scalar('Loss/ap_loss', ap_l,
current_step)
train_logger.add_scalar('Loss/sp_loss', sp_l,
current_step)
train_logger.add_scalar('Loss/sp_postnet_loss', sp_p_l,
current_step)
else:
train_logger.add_scalar('Loss/mel_loss', m_l,
current_step)
train_logger.add_scalar('Loss/mel_postnet_loss', m_p_l,
current_step)
train_logger.add_scalar('Loss/duration_loss', d_l,
current_step)
train_logger.add_scalar('Loss/F0_loss', f_l, current_step)
train_logger.add_scalar('Loss/energy_loss', e_l,
current_step)
if current_step % hp.save_step == 0 or current_step == 20:
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
},
os.path.join(
checkpoint_path,
'checkpoint_{}.pth.tar'.format(current_step)))
print("save model at step {} ...".format(current_step))
if current_step % hp.synth_step == 0 or current_step == 5:
length = mel_len[0].item()
if hp.vocoder == 'WORLD':
ap_target_torch = ap_target[
0, :length].detach().unsqueeze(0).transpose(1, 2)
ap_torch = ap_output[0, :length].detach().unsqueeze(
0).transpose(1, 2)
sp_target_torch = sp_target[
0, :length].detach().unsqueeze(0).transpose(1, 2)
sp_torch = sp_output[0, :length].detach().unsqueeze(
0).transpose(1, 2)
sp_postnet_torch = sp_postnet_output[
0, :length].detach().unsqueeze(0).transpose(1, 2)
else:
mel_target_torch = mel_target[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_target = mel_target[
0, :length].detach().cpu().transpose(0, 1)
mel_torch = mel_output[0, :length].detach().unsqueeze(
0).transpose(1, 2)
mel = mel_output[0, :length].detach().cpu().transpose(
0, 1)
mel_postnet_torch = mel_postnet_output[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_postnet = mel_postnet_output[
0, :length].detach().cpu().transpose(0, 1)
# Audio.tools.inv_mel_spec(mel, os.path.join(synth_path, "step_{}_griffin_lim.wav".format(current_step)))
# Audio.tools.inv_mel_spec(mel_postnet, os.path.join(synth_path, "step_{}_postnet_griffin_lim.wav".format(current_step)))
f0 = f0[0, :length].detach().cpu().numpy()
energy = energy[0, :length].detach().cpu().numpy()
f0_output = f0_output[0, :length].detach().cpu().numpy()
energy_output = energy_output[
0, :length].detach().cpu().numpy()
if hp.vocoder == 'melgan':
utils.melgan_infer(
mel_torch, melgan,
os.path.join(
hp.synth_path, 'step_{}_{}.wav'.format(
current_step, hp.vocoder)))
utils.melgan_infer(
mel_postnet_torch, melgan,
os.path.join(
hp.synth_path, 'step_{}_postnet_{}.wav'.format(
current_step, hp.vocoder)))
utils.melgan_infer(
mel_target_torch, melgan,
os.path.join(
hp.synth_path,
'step_{}_ground-truth_{}.wav'.format(
current_step, hp.vocoder)))
elif hp.vocoder == 'waveglow':
utils.waveglow_infer(
mel_torch, waveglow,
os.path.join(
hp.synth_path, 'step_{}_{}.wav'.format(
current_step, hp.vocoder)))
utils.waveglow_infer(
mel_postnet_torch, waveglow,
os.path.join(
hp.synth_path, 'step_{}_postnet_{}.wav'.format(
current_step, hp.vocoder)))
utils.waveglow_infer(
mel_target_torch, waveglow,
os.path.join(
hp.synth_path,
'step_{}_ground-truth_{}.wav'.format(
current_step, hp.vocoder)))
elif hp.vocoder == 'WORLD':
# ap=np.swapaxes(ap,0,1)
# sp=np.swapaxes(sp,0,1)
wav = utils.world_infer(
np.swapaxes(ap_torch[0].cpu().numpy(), 0, 1),
np.swapaxes(sp_postnet_torch[0].cpu().numpy(), 0,
1), f0_output)
sf.write(
os.path.join(
hp.synth_path, 'step_{}_postnet_{}.wav'.format(
current_step, hp.vocoder)), wav, 32000)
wav = utils.world_infer(
np.swapaxes(ap_target_torch[0].cpu().numpy(), 0,
1),
np.swapaxes(sp_target_torch[0].cpu().numpy(), 0,
1), f0)
sf.write(
os.path.join(
hp.synth_path,
'step_{}_ground-truth_{}.wav'.format(
current_step, hp.vocoder)), wav, 32000)
utils.plot_data([
(sp_postnet_torch[0].cpu().numpy(), f0_output,
energy_output),
(sp_target_torch[0].cpu().numpy(), f0, energy)
], ['Synthetized Spectrogram', 'Ground-Truth Spectrogram'],
filename=os.path.join(
synth_path,
'step_{}.png'.format(current_step)))
plt.matshow(sp_postnet_torch[0].cpu().numpy())
plt.savefig(
os.path.join(synth_path,
'sp_postnet_{}.png'.format(current_step)))
plt.matshow(ap_torch[0].cpu().numpy())
plt.savefig(
os.path.join(synth_path,
'ap_{}.png'.format(current_step)))
plt.matshow(
variance_adaptor_output[0].detach().cpu().numpy())
# plt.savefig(os.path.join(synth_path, 'va_{}.png'.format(current_step)))
# plt.matshow(decoder_output[0].detach().cpu().numpy())
# plt.savefig(os.path.join(synth_path, 'encoder_{}.png'.format(current_step)))
plt.cla()
fout = open(
os.path.join(synth_path,
'D_{}.txt'.format(current_step)), 'w')
fout.write(
str(log_duration_output[0].detach().cpu().numpy()) +
'\n')
fout.write(str(D[0].detach().cpu().numpy()) + '\n')
fout.write(
str(condition[0, :, 2].detach().cpu().numpy()) + '\n')
fout.close()
# if current_step % hp.eval_step == 0 or current_step==20:
# model.eval()
# with torch.no_grad():
# if hp.vocoder=='WORLD':
# d_l, f_l, e_l, ap_l, sp_l, sp_p_l = evaluate(model, current_step)
# t_l = d_l + f_l + e_l + ap_l + sp_l + sp_p_l
# val_logger.add_scalar('valLoss/total_loss', t_l, current_step)
# val_logger.add_scalar('valLoss/ap_loss', ap_l, current_step)
# val_logger.add_scalar('valLoss/sp_loss', sp_l, current_step)
# val_logger.add_scalar('valLoss/sp_postnet_loss', sp_p_l, current_step)
# val_logger.add_scalar('valLoss/duration_loss', d_l, current_step)
# val_logger.add_scalar('valLoss/F0_loss', f_l, current_step)
# val_logger.add_scalar('valLoss/energy_loss', e_l, current_step)
# else:
# d_l, f_l, e_l, m_l, m_p_l = evaluate(model, current_step)
# t_l = d_l + f_l + e_l + m_l + m_p_l
# val_logger.add_scalar('valLoss/total_loss', t_l, current_step)
# val_logger.add_scalar('valLoss/mel_loss', m_l, current_step)
# val_logger.add_scalar('valLoss/mel_postnet_loss', m_p_l, current_step)
# val_logger.add_scalar('valLoss/duration_loss', d_l, current_step)
# val_logger.add_scalar('valLoss/F0_loss', f_l, current_step)
# val_logger.add_scalar('valLoss/energy_loss', e_l, current_step)
# model.train()
# if current_step%10==0:
# print(energy_output[0],energy[0])
end_time = time.perf_counter()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(Time, [i for i in range(len(Time))],
axis=None)
Time = np.append(Time, temp_value)
def main(args):
torch.manual_seed(0)
# Get device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Get dataset
dataset = Dataset("train.txt")
loader = DataLoader(dataset,
batch_size=hp.batch_size**2,
shuffle=True,
collate_fn=dataset.collate_fn,
drop_last=True,
num_workers=0)
# Define model
if hp.use_spk_embed:
n_pkers = len(dataset.spk_table.keys())
model = nn.DataParallel(FastSpeech2(n_spkers=n_pkers)).to(device)
else:
model = nn.DataParallel(FastSpeech2()).to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of FastSpeech2 Parameters:', num_param)
# Optimizer and loss
optimizer = torch.optim.Adam(model.parameters(),
betas=hp.betas,
eps=hp.eps,
weight_decay=hp.weight_decay)
scheduled_optim = ScheduledOptim(optimizer, hp.decoder_hidden,
hp.n_warm_up_step, args.restore_step)
Loss = FastSpeech2Loss().to(device)
print("Optimizer and Loss Function Defined.")
# Load checkpoint if exists
checkpoint_path = os.path.join(hp.checkpoint_path)
try:
checkpoint = torch.load(
os.path.join(checkpoint_path,
'checkpoint_{}.pth.tar'.format(args.restore_step)))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step {}---\n".format(args.restore_step))
except:
print("\n---Start New Training---\n")
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
# Load vocoder
if hp.vocoder == 'melgan':
melgan = utils.get_melgan()
#melgan.to(device)
elif hp.vocoder == 'waveglow':
waveglow = utils.get_waveglow()
waveglow.to(device)
# Init logger
log_path = hp.log_path
if not os.path.exists(log_path):
os.makedirs(log_path)
os.makedirs(os.path.join(log_path, 'train'))
os.makedirs(os.path.join(log_path, 'validation'))
train_logger = SummaryWriter(os.path.join(log_path, 'train'))
val_logger = SummaryWriter(os.path.join(log_path, 'validation'))
# Init synthesis directory
synth_path = hp.synth_path
if not os.path.exists(synth_path):
os.makedirs(synth_path)
# Init evaluation directory
eval_path = hp.eval_path
if not os.path.exists(eval_path):
os.makedirs(eval_path)
# Define Some Information
Time = np.array([])
Start = time.perf_counter()
# Training
model = model.train()
for epoch in range(hp.epochs):
# Get Training Loader
total_step = hp.epochs * len(loader) * hp.batch_size
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
start_time = time.perf_counter()
current_step = i * hp.batch_size + j + args.restore_step + epoch * len(
loader) * hp.batch_size + 1
print("step : {}".format(current_step), end='\r', flush=True)
### Get Data ###
if hp.use_spk_embed:
spk_ids = torch.tensor(data_of_batch["spk_ids"]).to(
torch.int64).to(device)
else:
spk_ids = None
text = torch.from_numpy(
data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).long().to(device)
log_D = torch.from_numpy(
data_of_batch["log_D"]).float().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
### Forward ###
mel_output, mel_postnet_output, log_duration_output, f0_output, energy_output, src_mask, mel_mask, _ = model(
text, src_len, mel_len, D, f0, energy, max_src_len,
max_mel_len, spk_ids)
### Cal Loss ###
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
log_duration_output, log_D, f0_output, f0, energy_output,
energy, mel_output, mel_postnet_output, mel_target,
~src_mask, ~mel_mask)
total_loss = mel_loss + mel_postnet_loss + d_loss + 0.01 * f_loss + 0.1 * e_loss
t_l = total_loss.item()
m_l = mel_loss.item()
m_p_l = mel_postnet_loss.item()
d_l = d_loss.item()
f_l = f_loss.item()
e_l = e_loss.item()
with open(os.path.join(log_path, "total_loss.txt"),
"a") as f_total_loss:
f_total_loss.write(str(t_l) + "\n")
with open(os.path.join(log_path, "mel_loss.txt"),
"a") as f_mel_loss:
f_mel_loss.write(str(m_l) + "\n")
with open(os.path.join(log_path, "mel_postnet_loss.txt"),
"a") as f_mel_postnet_loss:
f_mel_postnet_loss.write(str(m_p_l) + "\n")
with open(os.path.join(log_path, "duration_loss.txt"),
"a") as f_d_loss:
f_d_loss.write(str(d_l) + "\n")
with open(os.path.join(log_path, "f0_loss.txt"),
"a") as f_f_loss:
f_f_loss.write(str(f_l) + "\n")
with open(os.path.join(log_path, "energy_loss.txt"),
"a") as f_e_loss:
f_e_loss.write(str(e_l) + "\n")
## Backward
total_loss = total_loss / hp.acc_steps
total_loss.backward()
if current_step % hp.acc_steps != 0:
continue
### Update weights ###
nn.utils.clip_grad_norm_(model.parameters(),
hp.grad_clip_thresh)
scheduled_optim.step_and_update_lr()
scheduled_optim.zero_grad()
### Print ###
if current_step % hp.log_step == 0:
Now = time.perf_counter()
str1 = "Epoch [{}/{}], Step [{}/{}]:".format(
epoch + 1, hp.epochs, current_step, total_step)
str2 = "Total Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Duration Loss: {:.4f}, F0 Loss: {:.4f}, Energy Loss: {:.4f};".format(
t_l, m_l, m_p_l, d_l, f_l, e_l)
str3 = "Time Used: {:.3f}s, Estimated Time Remaining: {:.3f}s.".format(
(Now - Start),
(total_step - current_step) * np.mean(Time))
print("\n" + str1)
print(str2)
print(str3)
with open(os.path.join(log_path, "log.txt"), "a") as f_log:
f_log.write(str1 + "\n")
f_log.write(str2 + "\n")
f_log.write(str3 + "\n")
f_log.write("\n")
train_logger.add_scalar('Loss/total_loss', t_l,
current_step)
train_logger.add_scalar('Loss/mel_loss', m_l, current_step)
train_logger.add_scalar('Loss/mel_postnet_loss', m_p_l,
current_step)
train_logger.add_scalar('Loss/duration_loss', d_l,
current_step)
train_logger.add_scalar('Loss/F0_loss', f_l, current_step)
train_logger.add_scalar('Loss/energy_loss', e_l,
current_step)
### Save model ###
if current_step % hp.save_step == 0:
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
},
os.path.join(
checkpoint_path,
'checkpoint_{}.pth.tar'.format(current_step)))
print("save model at step {} ...".format(current_step))
### Synth ###
if current_step % hp.synth_step == 0:
length = mel_len[0].item()
print("step: {} , length {}, {}".format(
current_step, length, mel_len))
mel_target_torch = mel_target[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_target = mel_target[
0, :length].detach().cpu().transpose(0, 1)
mel_torch = mel_output[0, :length].detach().unsqueeze(
0).transpose(1, 2)
mel = mel_output[0, :length].detach().cpu().transpose(0, 1)
mel_postnet_torch = mel_postnet_output[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_postnet = mel_postnet_output[
0, :length].detach().cpu().transpose(0, 1)
spk_id = dataset.inv_spk_table[int(spk_ids[0])]
if hp.vocoder == 'melgan':
vocoder.melgan_infer(
mel_torch, melgan,
os.path.join(
hp.synth_path, 'step_{}_spk_{}_{}.wav'.format(
current_step, spk_id, hp.vocoder)))
vocoder.melgan_infer(
mel_postnet_torch, melgan,
os.path.join(
hp.synth_path,
'step_{}_spk_{}_postnet_{}.wav'.format(
current_step, spk_id, hp.vocoder)))
vocoder.melgan_infer(
mel_target_torch, melgan,
os.path.join(
hp.synth_path,
'step_{}_spk_{}_ground-truth_{}.wav'.format(
current_step, spk_id, hp.vocoder)))
elif hp.vocoder == 'waveglow':
vocoder.waveglow_infer(
mel_torch, waveglow,
os.path.join(
hp.synth_path, 'step_{}_spk_{}_{}.wav'.format(
current_step, hp.vocoder)))
vocoder.waveglow_infer(
mel_postnet_torch, waveglow,
os.path.join(
hp.synth_path,
'step_{}_spk_{}_postnet_{}.wav'.format(
current_step, spk_id, hp.vocoder)))
vocoder.waveglow_infer(
mel_target_torch, waveglow,
os.path.join(
hp.synth_path,
'step_{}_spk_{}_ground-truth_{}.wav'.format(
current_step, spk_id, hp.vocoder)))
f0 = f0[0, :length].detach().cpu().numpy()
energy = energy[0, :length].detach().cpu().numpy()
f0_output = f0_output[0, :length].detach().cpu().numpy()
energy_output = energy_output[
0, :length].detach().cpu().numpy()
utils.plot_data([
(mel_postnet.numpy(), f0_output, energy_output),
(mel_target.numpy(), f0, energy)
], ['Synthetized Spectrogram', 'Ground-Truth Spectrogram'],
filename=os.path.join(
synth_path,
'step_{}.png'.format(current_step)))
### Evaluation ###
if current_step % hp.eval_step == 0:
model.eval()
with torch.no_grad():
d_l, f_l, e_l, m_l, m_p_l = evaluate(
model, current_step)
t_l = d_l + f_l + e_l + m_l + m_p_l
val_logger.add_scalar('Loss/total_loss', t_l,
current_step)
val_logger.add_scalar('Loss/mel_loss', m_l,
current_step)
val_logger.add_scalar('Loss/mel_postnet_loss', m_p_l,
current_step)
val_logger.add_scalar('Loss/duration_loss', d_l,
current_step)
val_logger.add_scalar('Loss/F0_loss', f_l,
current_step)
val_logger.add_scalar('Loss/energy_loss', e_l,
current_step)
model.train()
### Time ###
end_time = time.perf_counter()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(Time, [i for i in range(len(Time))],
axis=None)
Time = np.append(Time, temp_value)
def main(args):
torch.manual_seed(0)
# Get dataset
dataset = Dataset("val.txt", sort=False)
loader = DataLoader(
dataset,
batch_size=hp.batch_size**2,
shuffle=False,
collate_fn=dataset.collate_fn,
drop_last=False,
num_workers=0,
)
# Get model
model = get_FastSpeech2(args.step).to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of FastSpeech2 Parameters:', num_param)
# Init directories
if not os.path.exists(hp.logger_path):
os.makedirs(hp.logger_path)
if not os.path.exists(hp.eval_path):
os.makedirs(hp.eval_path)
# Get loss function
Loss = FastSpeech2Loss().to(device)
print("Loss Function Defined.")
# Load vocoder
wave_glow = utils.get_WaveGlow()
# Evaluation
d_l = []
f_l = []
e_l = []
mel_l = []
mel_p_l = []
current_step = 0
idx = 0
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
# Get Data
text = torch.from_numpy(data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
mel_pos = torch.from_numpy(
data_of_batch["mel_pos"]).long().to(device)
src_pos = torch.from_numpy(
data_of_batch["src_pos"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_len = max(data_of_batch["mel_len"]).astype(np.int16)
with torch.no_grad():
# Forward
mel_output, mel_postnet_output, duration_output, f0_output, energy_output = model(
text, src_pos, mel_pos, max_len, D)
# Cal Loss
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
duration_output, D, f0_output, f0, energy_output, energy,
mel_output, mel_postnet_output, mel_target)
d_l.append(d_loss.item())
f_l.append(f_loss.item())
e_l.append(e_loss.item())
mel_l.append(mel_loss.item())
mel_p_l.append(mel_postnet_loss.item())
for k in range(len(mel_target)):
length = mel_len[k]
mel_target_torch = mel_target[k:k + 1, :length].transpose(
1, 2).detach()
mel_target_ = mel_target[k, :length].cpu().transpose(
0, 1).detach()
waveglow.inference.inference(
mel_target_torch, wave_glow,
os.path.join(
hp.eval_path,
'ground-truth_{}_waveglow.wav'.format(idx)))
mel_postnet_torch = mel_postnet_output[
k:k + 1, :length].transpose(1, 2).detach()
mel_postnet = mel_postnet_output[
k, :length].cpu().transpose(0, 1).detach()
waveglow.inference.inference(
mel_postnet_torch, wave_glow,
os.path.join(hp.eval_path,
'eval_{}_waveglow.wav'.format(idx)))
utils.plot_data([
(mel_postnet.numpy(), None, None),
(mel_target_.numpy(), None, None)
], ['Synthesized Spectrogram', 'Ground-Truth Spectrogram'],
filename=os.path.join(
hp.eval_path,
'eval_{}.png'.format(idx)))
idx += 1
current_step += 1
d_l = sum(d_l) / len(d_l)
f_l = sum(f_l) / len(f_l)
e_l = sum(e_l) / len(e_l)
mel_l = sum(mel_l) / len(mel_l)
mel_p_l = sum(mel_p_l) / len(mel_p_l)
str1 = "FastSpeech2 Step {},".format(args.step)
str2 = "Duration Loss: {}".format(d_l)
str3 = "F0 Loss: {}".format(f_l)
str4 = "Energy Loss: {}".format(e_l)
str5 = "Mel Loss: {}".format(mel_l)
str6 = "Mel Postnet Loss: {}".format(mel_p_l)
print("\n" + str1)
print(str2)
print(str3)
print(str4)
print(str5)
print(str6)
with open(os.path.join(hp.logger_path, "eval.txt"), "a") as f_logger:
f_logger.write(str1 + "\n")
f_logger.write(str2 + "\n")
f_logger.write(str3 + "\n")
f_logger.write(str4 + "\n")
f_logger.write(str5 + "\n")
f_logger.write(str6 + "\n")
f_logger.write("\n")
def main(args):
torch.manual_seed(0)
# Get device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Get dataset
dataset = Dataset("train.txt")
loader = DataLoader(dataset,
batch_size=hp.batch_size**2,
shuffle=True,
collate_fn=dataset.collate_fn,
drop_last=True,
num_workers=0)
# Define model
model = nn.DataParallel(FastSpeech2()).to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of FastSpeech2 Parameters:', num_param)
# Optimizer and loss
optimizer = torch.optim.Adam(model.parameters(),
betas=hp.betas,
eps=hp.eps,
weight_decay=hp.weight_decay)
scheduled_optim = ScheduledOptim(optimizer, hp.decoder_hidden,
hp.n_warm_up_step, args.restore_step)
Loss = FastSpeech2Loss().to(device)
print("Optimizer and Loss Function Defined.")
# Load checkpoint if exists
checkpoint_path = os.path.join(hp.checkpoint_path)
try:
checkpoint = torch.load(
os.path.join(checkpoint_path,
'checkpoint_{}.pth.tar'.format(args.restore_step)))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step {}---\n".format(args.restore_step))
except:
print("\n---Start New Training---\n")
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
# read params
mean_mel, std_mel = torch.tensor(np.load(
os.path.join(hp.preprocessed_path, "mel_stat.npy")),
dtype=torch.float).to(device)
mean_f0, std_f0 = torch.tensor(np.load(
os.path.join(hp.preprocessed_path, "f0_stat.npy")),
dtype=torch.float).to(device)
mean_energy, std_energy = torch.tensor(np.load(
os.path.join(hp.preprocessed_path, "energy_stat.npy")),
dtype=torch.float).to(device)
mean_mel, std_mel = mean_mel.reshape(1, -1), std_mel.reshape(1, -1)
mean_f0, std_f0 = mean_f0.reshape(1, -1), std_f0.reshape(1, -1)
mean_energy, std_energy = mean_energy.reshape(1, -1), std_energy.reshape(
1, -1)
# Load vocoder
if hp.vocoder == 'vocgan':
vocoder = utils.get_vocgan(ckpt_path=hp.vocoder_pretrained_model_path)
vocoder.to(device)
else:
vocoder = None
# Init logger
log_path = hp.log_path
if not os.path.exists(log_path):
os.makedirs(log_path)
os.makedirs(os.path.join(log_path, 'train'))
os.makedirs(os.path.join(log_path, 'validation'))
train_logger = SummaryWriter(os.path.join(log_path, 'train'))
val_logger = SummaryWriter(os.path.join(log_path, 'validation'))
# Define Some Information
Time = np.array([])
Start = time.perf_counter()
# Training
model = model.train()
for epoch in range(hp.epochs):
# Get Training Loader
total_step = hp.epochs * len(loader) * hp.batch_size
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
start_time = time.perf_counter()
current_step = i * hp.batch_size + j + args.restore_step + epoch * len(
loader) * hp.batch_size + 1
# Get Data
text = torch.from_numpy(
data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).long().to(device)
log_D = torch.from_numpy(
data_of_batch["log_D"]).float().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
# Forward
mel_output, mel_postnet_output, log_duration_output, f0_output, energy_output, src_mask, mel_mask, _ = model(
text, src_len, mel_len, D, f0, energy, max_src_len,
max_mel_len)
# Cal Loss
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
log_duration_output, log_D, f0_output, f0, energy_output,
energy, mel_output, mel_postnet_output, mel_target,
~src_mask, ~mel_mask)
total_loss = mel_loss + mel_postnet_loss + d_loss + f_loss + e_loss
# Logger
t_l = total_loss.item()
m_l = mel_loss.item()
m_p_l = mel_postnet_loss.item()
d_l = d_loss.item()
f_l = f_loss.item()
e_l = e_loss.item()
with open(os.path.join(log_path, "total_loss.txt"),
"a") as f_total_loss:
f_total_loss.write(str(t_l) + "\n")
with open(os.path.join(log_path, "mel_loss.txt"),
"a") as f_mel_loss:
f_mel_loss.write(str(m_l) + "\n")
with open(os.path.join(log_path, "mel_postnet_loss.txt"),
"a") as f_mel_postnet_loss:
f_mel_postnet_loss.write(str(m_p_l) + "\n")
with open(os.path.join(log_path, "duration_loss.txt"),
"a") as f_d_loss:
f_d_loss.write(str(d_l) + "\n")
with open(os.path.join(log_path, "f0_loss.txt"),
"a") as f_f_loss:
f_f_loss.write(str(f_l) + "\n")
with open(os.path.join(log_path, "energy_loss.txt"),
"a") as f_e_loss:
f_e_loss.write(str(e_l) + "\n")
# Backward
total_loss = total_loss / hp.acc_steps
total_loss.backward()
if current_step % hp.acc_steps != 0:
continue
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(model.parameters(),
hp.grad_clip_thresh)
# Update weights
scheduled_optim.step_and_update_lr()
scheduled_optim.zero_grad()
# Print
if current_step % hp.log_step == 0:
Now = time.perf_counter()
str1 = "Epoch [{}/{}], Step [{}/{}]:".format(
epoch + 1, hp.epochs, current_step, total_step)
str2 = "Total Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Duration Loss: {:.4f}, F0 Loss: {:.4f}, Energy Loss: {:.4f};".format(
t_l, m_l, m_p_l, d_l, f_l, e_l)
str3 = "Time Used: {:.3f}s, Estimated Time Remaining: {:.3f}s.".format(
(Now - Start),
(total_step - current_step) * np.mean(Time))
print("\n" + str1)
print(str2)
print(str3)
with open(os.path.join(log_path, "log.txt"), "a") as f_log:
f_log.write(str1 + "\n")
f_log.write(str2 + "\n")
f_log.write(str3 + "\n")
f_log.write("\n")
train_logger.add_scalar('Loss/total_loss', t_l, current_step)
train_logger.add_scalar('Loss/mel_loss', m_l, current_step)
train_logger.add_scalar('Loss/mel_postnet_loss', m_p_l,
current_step)
train_logger.add_scalar('Loss/duration_loss', d_l,
current_step)
train_logger.add_scalar('Loss/F0_loss', f_l, current_step)
train_logger.add_scalar('Loss/energy_loss', e_l, current_step)
if current_step % hp.save_step == 0:
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
},
os.path.join(
checkpoint_path,
'checkpoint_{}.pth.tar'.format(current_step)))
print("save model at step {} ...".format(current_step))
if current_step % hp.eval_step == 0:
model.eval()
with torch.no_grad():
d_l, f_l, e_l, m_l, m_p_l = evaluate(
model, current_step, vocoder)
t_l = d_l + f_l + e_l + m_l + m_p_l
val_logger.add_scalar('Loss/total_loss', t_l,
current_step)
val_logger.add_scalar('Loss/mel_loss', m_l,
current_step)
val_logger.add_scalar('Loss/mel_postnet_loss', m_p_l,
current_step)
val_logger.add_scalar('Loss/duration_loss', d_l,
current_step)
val_logger.add_scalar('Loss/F0_loss', f_l,
current_step)
val_logger.add_scalar('Loss/energy_loss', e_l,
current_step)
model.train()
end_time = time.perf_counter()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(Time, [i for i in range(len(Time))],
axis=None)
Time = np.append(Time, temp_value)
def evaluate(model, step, vocoder=None):
model.eval()
torch.manual_seed(0)
mean_mel, std_mel = torch.tensor(np.load(
os.path.join(hp.preprocessed_path, "mel_stat.npy")),
dtype=torch.float).to(device)
mean_f0, std_f0 = torch.tensor(np.load(
os.path.join(hp.preprocessed_path, "f0_stat.npy")),
dtype=torch.float).to(device)
mean_energy, std_energy = torch.tensor(np.load(
os.path.join(hp.preprocessed_path, "energy_stat.npy")),
dtype=torch.float).to(device)
eval_path = hp.eval_path
if not os.path.exists(eval_path):
os.makedirs(eval_path)
# Get dataset
dataset = Dataset("val.txt", sort=False)
loader = DataLoader(
dataset,
batch_size=hp.batch_size**2,
shuffle=False,
collate_fn=dataset.collate_fn,
drop_last=False,
num_workers=0,
)
# Get loss function
Loss = FastSpeech2Loss().to(device)
# Evaluation
d_l = []
f_l = []
e_l = []
mel_l = []
mel_p_l = []
current_step = 0
idx = 0
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
# Get Data
id_ = data_of_batch["id"]
text = torch.from_numpy(data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
log_D = torch.from_numpy(data_of_batch["log_D"]).int().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
with torch.no_grad():
# Forward
mel_output, mel_postnet_output, log_duration_output, f0_output, energy_output, src_mask, mel_mask, out_mel_len = model(
text, src_len, mel_len, D, f0, energy, max_src_len,
max_mel_len)
# Cal Loss
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
log_duration_output, log_D, f0_output, f0, energy_output,
energy, mel_output, mel_postnet_output, mel_target,
~src_mask, ~mel_mask)
d_l.append(d_loss.item())
f_l.append(f_loss.item())
e_l.append(e_loss.item())
mel_l.append(mel_loss.item())
mel_p_l.append(mel_postnet_loss.item())
if idx == 0 and vocoder is not None:
# Run vocoding and plotting spectrogram only when the vocoder is defined
for k in range(1):
basename = id_[k]
gt_length = mel_len[k]
out_length = out_mel_len[k]
mel_target_torch = mel_target[k:k + 1, :gt_length]
mel_target_ = mel_target[k, :gt_length]
mel_postnet_torch = mel_postnet_output[k:k +
1, :out_length]
mel_postnet = mel_postnet_output[k, :out_length]
mel_target_torch = utils.de_norm(
mel_target_torch, mean_mel,
std_mel).transpose(1, 2).detach()
mel_target_ = utils.de_norm(mel_target_, mean_mel,
std_mel).cpu().transpose(
0, 1).detach()
mel_postnet_torch = utils.de_norm(
mel_postnet_torch, mean_mel,
std_mel).transpose(1, 2).detach()
mel_postnet = utils.de_norm(mel_postnet, mean_mel,
std_mel).cpu().transpose(
0, 1).detach()
if hp.vocoder == "vocgan":
utils.vocgan_infer(
mel_target_torch,
vocoder,
path=os.path.join(
hp.eval_path,
'eval_groundtruth_{}_{}.wav'.format(
basename, hp.vocoder)))
utils.vocgan_infer(mel_postnet_torch,
vocoder,
path=os.path.join(
hp.eval_path,
'eval_{}_{}_{}.wav'.format(
step, basename,
hp.vocoder)))
np.save(
os.path.join(
hp.eval_path, 'eval_step_{}_{}_mel.npy'.format(
step, basename)), mel_postnet.numpy())
f0_ = f0[k, :gt_length]
energy_ = energy[k, :gt_length]
f0_output_ = f0_output[k, :out_length]
energy_output_ = energy_output[k, :out_length]
f0_ = utils.de_norm(f0_, mean_f0,
std_f0).detach().cpu().numpy()
f0_output_ = utils.de_norm(
f0_output, mean_f0, std_f0).detach().cpu().numpy()
energy_ = utils.de_norm(
energy_, mean_energy,
std_energy).detach().cpu().numpy()
energy_output_ = utils.de_norm(
energy_output_, mean_energy,
std_energy).detach().cpu().numpy()
utils.plot_data(
[(mel_postnet.numpy(), f0_output_, energy_output_),
(mel_target_.numpy(), f0_, energy_)], [
'Synthesized Spectrogram',
'Ground-Truth Spectrogram'
],
filename=os.path.join(
hp.eval_path,
'eval_step_{}_{}.png'.format(step, basename)))
idx += 1
print("done")
current_step += 1
d_l = sum(d_l) / len(d_l)
f_l = sum(f_l) / len(f_l)
e_l = sum(e_l) / len(e_l)
mel_l = sum(mel_l) / len(mel_l)
mel_p_l = sum(mel_p_l) / len(mel_p_l)
str1 = "FastSpeech2 Step {},".format(step)
str2 = "Duration Loss: {}".format(d_l)
str3 = "F0 Loss: {}".format(f_l)
str4 = "Energy Loss: {}".format(e_l)
str5 = "Mel Loss: {}".format(mel_l)
str6 = "Mel Postnet Loss: {}".format(mel_p_l)
print("\n" + str1)
print(str2)
print(str3)
print(str4)
print(str5)
print(str6)
with open(os.path.join(hp.log_path, "eval.txt"), "a") as f_log:
f_log.write(str1 + "\n")
f_log.write(str2 + "\n")
f_log.write(str3 + "\n")
f_log.write(str4 + "\n")
f_log.write(str5 + "\n")
f_log.write(str6 + "\n")
f_log.write("\n")
model.train()
return d_l, f_l, e_l, mel_l, mel_p_l
collate_fn=dataset.collate_fn,
drop_last=False,
num_workers=8)
# Define model
model = FastSpeech2(py_vocab_size, hz_vocab_size).to(device)
num_param = utils.get_param_num(model)
# Optimizer and loss
optimizer = torch.optim.Adam(model.parameters(),
lr=hp.start_lr,
betas=hp.betas,
eps=hp.eps,
weight_decay=0)
scheduled_optim = ScheduledOptim(optimizer, hp.decoder_hidden,
hp.n_warm_up_step, args.restore_step)
Loss = FastSpeech2Loss().to(device)
print("Optimizer and Loss Function Defined.")
# Load checkpoint if exists
checkpoint_path = os.path.join(hp.checkpoint_path)
try:
checkpoint = torch.load(
os.path.join(checkpoint_path,
'checkpoint_{}.pth.tar'.format(args.restore_step)))
#temp = nn.DataParallel(model)
model.load_state_dict(checkpoint['model'])
#model.load_state_dict(temp.module.state_dict())
#del temp
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step {}---\n".format(args.restore_step))
except:
def evaluate(model, step, vocoder=None):
torch.manual_seed(0)
# Get dataset
dataset = Dataset("val.txt", sort=False)
loader = DataLoader(
dataset,
batch_size=hp.batch_size**2,
shuffle=False,
collate_fn=dataset.collate_fn,
drop_last=False,
num_workers=0,
)
# Get loss function
Loss = FastSpeech2Loss().to(device)
# Evaluation
d_l = []
f_l = []
e_l = []
mel_l = []
mel_p_l = []
current_step = 0
idx = 0
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
# Get Data
id_ = data_of_batch["id"]
text = torch.from_numpy(data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
log_D = torch.from_numpy(data_of_batch["log_D"]).int().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
with torch.no_grad():
# Forward
mel_output, mel_postnet_output, duration_output, src_mask, pred_mel_mask, enc_attns, dec_attns, W = model(
text, src_len, mel_len, max_src_len, max_mel_len)
# Cal Loss
mel_loss, mel_postnet_loss, d_loss = Loss(
duration_output, mel_len, mel_output, mel_postnet_output,
mel_target, src_mask, pred_mel_mask)
d_l.append(d_loss.item())
mel_l.append(mel_loss.item())
mel_p_l.append(mel_postnet_loss.item())
if vocoder is not None:
# Run vocoding and plotting spectrogram only when the vocoder is defined
for k in range(len(mel_target)):
basename = id_[k]
gt_length = mel_len[k]
out_length = out_mel_len[k]
mel_target_torch = mel_target[k:k +
1, :gt_length].transpose(
1, 2).detach()
mel_target_ = mel_target[
k, :gt_length].cpu().transpose(0, 1).detach()
mel_postnet_torch = mel_postnet_output[
k:k + 1, :out_length].transpose(1, 2).detach()
mel_postnet = mel_postnet_output[
k, :out_length].cpu().transpose(0, 1).detach()
if hp.vocoder == 'melgan':
utils.melgan_infer(
mel_target_torch, vocoder,
os.path.join(
hp.eval_path,
'ground-truth_{}_{}.wav'.format(
basename, hp.vocoder)))
utils.melgan_infer(
mel_postnet_torch, vocoder,
os.path.join(
hp.eval_path, 'eval_{}_{}.wav'.format(
basename, hp.vocoder)))
elif hp.vocoder == 'waveglow':
utils.waveglow_infer(
mel_target_torch, vocoder,
os.path.join(
hp.eval_path,
'ground-truth_{}_{}.wav'.format(
basename, hp.vocoder)))
utils.waveglow_infer(
mel_postnet_torch, vocoder,
os.path.join(
hp.eval_path, 'eval_{}_{}.wav'.format(
basename, hp.vocoder)))
np.save(
os.path.join(hp.eval_path,
'eval_{}_mel.npy'.format(basename)),
mel_postnet.numpy())
f0_ = f0[k, :gt_length].detach().cpu().numpy()
energy_ = energy[k, :gt_length].detach().cpu().numpy()
f0_output_ = f0_output[
k, :out_length].detach().cpu().numpy()
energy_output_ = energy_output[
k, :out_length].detach().cpu().numpy()
utils.plot_data(
[(mel_postnet.numpy(), f0_output_, energy_output_),
(mel_target_.numpy(), f0_, energy_)], [
'Synthesized Spectrogram',
'Ground-Truth Spectrogram'
],
filename=os.path.join(
hp.eval_path, 'eval_{}.png'.format(basename)))
idx += 1
current_step += 1
d_l = sum(d_l) / len(d_l)
mel_l = sum(mel_l) / len(mel_l)
mel_p_l = sum(mel_p_l) / len(mel_p_l)
str1 = "FastSpeech2 Step {},".format(step)
str2 = "Duration Loss: {}".format(d_l)
str5 = "Mel Loss: {}".format(mel_l)
str6 = "Mel Postnet Loss: {}".format(mel_p_l)
print("\n" + str1)
print(str2)
print(str5)
print(str6)
with open(os.path.join(hp.log_path, "eval.txt"), "a") as f_log:
f_log.write(str1 + "\n")
f_log.write(str2 + "\n")
f_log.write(str5 + "\n")
f_log.write(str6 + "\n")
f_log.write("\n")
return d_l, mel_l, mel_p_l
def main(args):
torch.manual_seed(0)
# Get device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Get dataset
dataset = Dataset("train.txt")
loader = DataLoader(dataset,
batch_size=hp.batch_size**2,
shuffle=True,
collate_fn=dataset.collate_fn,
drop_last=True,
num_workers=0)
# Define model
model = nn.DataParallel(FastSpeech2()).to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of FastSpeech2 Parameters:', num_param)
# Optimizer and loss
optimizer = torch.optim.Adam(model.parameters(),
betas=hp.betas,
eps=hp.eps,
weight_decay=hp.weight_decay)
scheduled_optim = ScheduledOptim(optimizer, hp.decoder_hidden,
hp.n_warm_up_step, args.restore_step)
Loss = FastSpeech2Loss().to(device)
print("Optimizer and Loss Function Defined.")
# Load checkpoint if exists
checkpoint_path = os.path.join(hp.checkpoint_path)
try:
checkpoint = torch.load(
os.path.join(checkpoint_path,
'checkpoint_{}.pth.tar'.format(args.restore_step)))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step {}---\n".format(args.restore_step))
except:
print("\n---Start New Training---\n")
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
# Load vocoder
wave_glow = utils.get_WaveGlow()
# Init logger
log_path = hp.log_path
if not os.path.exists(log_path):
os.makedirs(log_path)
logger = SummaryWriter(log_path)
# Init synthesis directory
synth_path = hp.synth_path
if not os.path.exists(synth_path):
os.makedirs(synth_path)
# Define Some Information
Time = np.array([])
Start = time.perf_counter()
# Training
model = model.train()
for epoch in range(hp.epochs):
# Get Training Loader
total_step = hp.epochs * len(loader) * hp.batch_size
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
start_time = time.perf_counter()
current_step = i * hp.batch_size + j + args.restore_step + epoch * len(
loader) * hp.batch_size + 1
# Init
scheduled_optim.zero_grad()
# Get Data
text = torch.from_numpy(
data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
mel_pos = torch.from_numpy(
data_of_batch["mel_pos"]).long().to(device)
src_pos = torch.from_numpy(
data_of_batch["src_pos"]).long().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_len = max(data_of_batch["mel_len"]).astype(np.int16)
# Forward
mel_output, mel_postnet_output, duration_output, f0_output, energy_output = model(
text, src_pos, mel_pos, max_len, D, f0, energy)
# Cal Loss
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
duration_output, D, f0_output, f0, energy_output, energy,
mel_output, mel_postnet_output, mel_target, src_len,
mel_len)
total_loss = mel_loss + mel_postnet_loss + d_loss + f_loss + e_loss
# Logger
t_l = total_loss.item()
m_l = mel_loss.item()
m_p_l = mel_postnet_loss.item()
d_l = d_loss.item()
f_l = f_loss.item()
e_l = e_loss.item()
with open(os.path.join(log_path, "total_loss.txt"),
"a") as f_total_loss:
f_total_loss.write(str(t_l) + "\n")
with open(os.path.join(log_path, "mel_loss.txt"),
"a") as f_mel_loss:
f_mel_loss.write(str(m_l) + "\n")
with open(os.path.join(log_path, "mel_postnet_loss.txt"),
"a") as f_mel_postnet_loss:
f_mel_postnet_loss.write(str(m_p_l) + "\n")
with open(os.path.join(log_path, "duration_loss.txt"),
"a") as f_d_loss:
f_d_loss.write(str(d_l) + "\n")
with open(os.path.join(log_path, "f0_loss.txt"),
"a") as f_f_loss:
f_f_loss.write(str(f_l) + "\n")
with open(os.path.join(log_path, "energy_loss.txt"),
"a") as f_e_loss:
f_e_loss.write(str(e_l) + "\n")
# Backward
total_loss.backward()
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(model.parameters(),
hp.grad_clip_thresh)
# Update weights
scheduled_optim.step_and_update_lr()
# Print
if current_step % hp.log_step == 0:
Now = time.perf_counter()
str1 = "Epoch [{}/{}], Step [{}/{}]:".format(
epoch + 1, hp.epochs, current_step, total_step)
str2 = "Total Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Duration Loss: {:.4f}, F0 Loss: {:.4f}, Energy Loss: {:.4f};".format(
t_l, m_l, m_p_l, d_l, f_l, e_l)
str3 = "Time Used: {:.3f}s, Estimated Time Remaining: {:.3f}s.".format(
(Now - Start),
(total_step - current_step) * np.mean(Time))
print("\n" + str1)
print(str2)
print(str3)
with open(os.path.join(log_path, "log.txt"), "a") as f_log:
f_log.write(str1 + "\n")
f_log.write(str2 + "\n")
f_log.write(str3 + "\n")
f_log.write("\n")
logger.add_scalars('Loss/total_loss', {'training': t_l},
current_step)
logger.add_scalars('Loss/mel_loss', {'training': m_l},
current_step)
logger.add_scalars('Loss/mel_postnet_loss',
{'training': m_p_l}, current_step)
logger.add_scalars('Loss/duration_loss', {'training': d_l},
current_step)
logger.add_scalars('Loss/F0_loss', {'training': f_l},
current_step)
logger.add_scalars('Loss/energy_loss', {'training': e_l},
current_step)
if current_step % hp.save_step == 0:
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
},
os.path.join(
checkpoint_path,
'checkpoint_{}.pth.tar'.format(current_step)))
print("save model at step {} ...".format(current_step))
if current_step % hp.synth_step == 0:
length = mel_len[0].item()
mel_target_torch = mel_target[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_target = mel_target[
0, :length].detach().cpu().transpose(0, 1)
mel_torch = mel_output[0, :length].detach().unsqueeze(
0).transpose(1, 2)
mel = mel_output[0, :length].detach().cpu().transpose(0, 1)
mel_postnet_torch = mel_postnet_output[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_postnet = mel_postnet_output[
0, :length].detach().cpu().transpose(0, 1)
Audio.tools.inv_mel_spec(
mel,
os.path.join(
synth_path,
"step_{}_griffin_lim.wav".format(current_step)))
Audio.tools.inv_mel_spec(
mel_postnet,
os.path.join(
synth_path,
"step_{}_postnet_griffin_lim.wav".format(
current_step)))
waveglow.inference.inference(
mel_torch, wave_glow,
os.path.join(
synth_path,
"step_{}_waveglow.wav".format(current_step)))
waveglow.inference.inference(
mel_postnet_torch, wave_glow,
os.path.join(
synth_path, "step_{}_postnet_waveglow.wav".format(
current_step)))
waveglow.inference.inference(
mel_target_torch, wave_glow,
os.path.join(
synth_path,
"step_{}_ground-truth_waveglow.wav".format(
current_step)))
f0 = f0[0, :length].detach().cpu().numpy()
energy = energy[0, :length].detach().cpu().numpy()
f0_output = f0_output[0, :length].detach().cpu().numpy()
energy_output = energy_output[
0, :length].detach().cpu().numpy()
utils.plot_data([
(mel_postnet.numpy(), f0_output, energy_output),
(mel_target.numpy(), f0, energy)
], ['Synthetized Spectrogram', 'Ground-Truth Spectrogram'],
filename=os.path.join(
synth_path,
'step_{}.png'.format(current_step)))
if current_step % hp.eval_step == 0:
model.eval()
with torch.no_grad():
d_l, f_l, e_l, m_l, m_p_l = evaluate(
model, current_step)
t_l = d_l + f_l + e_l + m_l + m_p_l
logger.add_scalars('Loss/total_loss',
{'validation': t_l}, current_step)
logger.add_scalars('Loss/mel_loss',
{'validation': m_l}, current_step)
logger.add_scalars('Loss/mel_postnet_loss',
{'validation': m_p_l}, current_step)
logger.add_scalars('Loss/duration_loss',
{'validation': d_l}, current_step)
logger.add_scalars('Loss/F0_loss', {'validation': f_l},
current_step)
logger.add_scalars('Loss/energy_loss',
{'validation': e_l}, current_step)
model.train()
end_time = time.perf_counter()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(Time, [i for i in range(len(Time))],
axis=None)
Time = np.append(Time, temp_value)
def evaluate(model, step, wave_glow=None):
torch.manual_seed(0)
# Get dataset
dataset = Dataset("val.txt", sort=False)
loader = DataLoader(
dataset,
batch_size=hp.batch_size**2,
shuffle=False,
collate_fn=dataset.collate_fn,
drop_last=False,
num_workers=0,
)
# Get loss function
Loss = FastSpeech2Loss().to(device)
# Evaluation
d_l = []
f_l = []
e_l = []
mel_l = []
mel_p_l = []
current_step = 0
idx = 0
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
# Get Data
text = torch.from_numpy(data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
mel_pos = torch.from_numpy(
data_of_batch["mel_pos"]).long().to(device)
src_pos = torch.from_numpy(
data_of_batch["src_pos"]).long().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_len = max(data_of_batch["mel_len"]).astype(np.int16)
with torch.no_grad():
# Forward
mel_output, mel_postnet_output, duration_output, f0_output, energy_output = model(
text, src_pos, mel_pos, max_len, D)
# Cal Loss
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
duration_output, D, f0_output, f0, energy_output, energy,
mel_output, mel_postnet_output, mel_target, src_len,
mel_len)
d_l.append(d_loss.item())
f_l.append(f_loss.item())
e_l.append(e_loss.item())
mel_l.append(mel_loss.item())
mel_p_l.append(mel_postnet_loss.item())
if wave_glow is not None:
# Run vocoding and plotting spectrogram only when the vocoder is defined
for k in range(len(mel_target)):
length = mel_len[k]
mel_target_torch = mel_target[k:k +
1, :length].transpose(
1, 2).detach()
mel_target_ = mel_target[k, :length].cpu().transpose(
0, 1).detach()
waveglow.inference.inference(
mel_target_torch, wave_glow,
os.path.join(
hp.eval_path,
'ground-truth_{}_waveglow.wav'.format(idx)))
mel_postnet_torch = mel_postnet_output[
k:k + 1, :length].transpose(1, 2).detach()
mel_postnet = mel_postnet_output[
k, :length].cpu().transpose(0, 1).detach()
waveglow.inference.inference(
mel_postnet_torch, wave_glow,
os.path.join(hp.eval_path,
'eval_{}_waveglow.wav'.format(idx)))
f0_ = f0[k, :length].detach().cpu().numpy()
energy_ = energy[k, :length].detach().cpu().numpy()
f0_output_ = f0_output[
k, :length].detach().cpu().numpy()
energy_output_ = energy_output[
k, :length].detach().cpu().numpy()
utils.plot_data(
[(mel_postnet.numpy(), f0_output_, energy_output_),
(mel_target_.numpy(), f0_, energy_)], [
'Synthesized Spectrogram',
'Ground-Truth Spectrogram'
],
filename=os.path.join(hp.eval_path,
'eval_{}.png'.format(idx)))
idx += 1
current_step += 1
d_l = sum(d_l) / len(d_l)
f_l = sum(f_l) / len(f_l)
e_l = sum(e_l) / len(e_l)
mel_l = sum(mel_l) / len(mel_l)
mel_p_l = sum(mel_p_l) / len(mel_p_l)
str1 = "FastSpeech2 Step {},".format(step)
str2 = "Duration Loss: {}".format(d_l)
str3 = "F0 Loss: {}".format(f_l)
str4 = "Energy Loss: {}".format(e_l)
str5 = "Mel Loss: {}".format(mel_l)
str6 = "Mel Postnet Loss: {}".format(mel_p_l)
print("\n" + str1)
print(str2)
print(str3)
print(str4)
print(str5)
print(str6)
with open(os.path.join(hp.log_path, "eval.txt"), "a") as f_log:
f_log.write(str1 + "\n")
f_log.write(str2 + "\n")
f_log.write(str3 + "\n")
f_log.write(str4 + "\n")
f_log.write(str5 + "\n")
f_log.write(str6 + "\n")
f_log.write("\n")
return d_l, f_l, e_l, mel_l, mel_p_l
