def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
data_loader = setup_loader(ap, is_val=True)
if c.use_speaker_embedding:
speaker_mapping = load_speaker_mapping(OUT_PATH)
model.eval()
epoch_time = 0
avg_postnet_loss = 0
avg_decoder_loss = 0
avg_stop_loss = 0
print("\n > Validation")
if c.test_sentences_file is None:
test_sentences = [
"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
"Be a voice, not an echo.",
"I'm sorry Dave. I'm afraid I can't do that.",
"This cake is great. It's so delicious and moist."
]
else:
with open(c.test_sentences_file, "r") as f:
test_sentences = [s.strip() for s in f.readlines()]
with torch.no_grad():
if data_loader is not None:
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# setup input data
text_input = data[0]
text_lengths = data[1]
speaker_names = data[2]
linear_input = data[3] if c.model in [
"Tacotron", "TacotronGST"
] else None
mel_input = data[4]
mel_lengths = data[5]
stop_targets = data[6]
if c.use_speaker_embedding:
speaker_ids = [
speaker_mapping[speaker_name]
for speaker_name in speaker_names
]
speaker_ids = torch.LongTensor(speaker_ids)
else:
speaker_ids = None
# set stop targets view, we predict a single stop token per r frames prediction
stop_targets = stop_targets.view(text_input.shape[0],
stop_targets.size(1) // c.r,
-1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze(2)
# dispatch data to GPU
if use_cuda:
text_input = text_input.cuda()
mel_input = mel_input.cuda()
mel_lengths = mel_lengths.cuda()
linear_input = linear_input.cuda() if c.model in [
"Tacotron", "TacotronGST"
] else None
stop_targets = stop_targets.cuda()
if speaker_ids is not None:
speaker_ids = speaker_ids.cuda()
# forward pass
decoder_output, postnet_output, alignments, stop_tokens =\
model.forward(text_input, text_lengths, mel_input,
speaker_ids=speaker_ids)
# loss computation
stop_loss = criterion_st(
stop_tokens, stop_targets) if c.stopnet else torch.zeros(1)
if c.loss_masking:
decoder_loss = criterion(decoder_output, mel_input,
mel_lengths)
if c.model in ["Tacotron", "TacotronGST"]:
postnet_loss = criterion(postnet_output, linear_input,
mel_lengths)
else:
postnet_loss = criterion(postnet_output, mel_input,
mel_lengths)
else:
decoder_loss = criterion(decoder_output, mel_input)
if c.model in ["Tacotron", "TacotronGST"]:
postnet_loss = criterion(postnet_output, linear_input)
else:
postnet_loss = criterion(postnet_output, mel_input)
loss = decoder_loss + postnet_loss + stop_loss
step_time = time.time() - start_time
epoch_time += step_time
if num_iter % c.print_step == 0:
print(
" | > TotalLoss: {:.5f} PostnetLoss: {:.5f} DecoderLoss:{:.5f} "
"StopLoss: {:.5f} ".format(loss.item(),
postnet_loss.item(),
decoder_loss.item(),
stop_loss.item()),
flush=True)
# aggregate losses from processes
if num_gpus > 1:
postnet_loss = reduce_tensor(postnet_loss.data, num_gpus)
decoder_loss = reduce_tensor(decoder_loss.data, num_gpus)
if c.stopnet:
stop_loss = reduce_tensor(stop_loss.data, num_gpus)
avg_postnet_loss += float(postnet_loss.item())
avg_decoder_loss += float(decoder_loss.item())
avg_stop_loss += stop_loss.item()
if args.rank == 0:
# Diagnostic visualizations
idx = np.random.randint(mel_input.shape[0])
const_spec = postnet_output[idx].data.cpu().numpy()
gt_spec = linear_input[idx].data.cpu().numpy() if c.model in [
"Tacotron", "TacotronGST"
] else mel_input[idx].data.cpu().numpy()
align_img = alignments[idx].data.cpu().numpy()
eval_figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img)
}
tb_logger.tb_eval_figures(global_step, eval_figures)
# Sample audio
if c.model in ["Tacotron", "TacotronGST"]:
eval_audio = ap.inv_spectrogram(const_spec.T)
else:
eval_audio = ap.inv_mel_spectrogram(const_spec.T)
tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio},
c.audio["sample_rate"])
# compute average losses
avg_postnet_loss /= (num_iter + 1)
avg_decoder_loss /= (num_iter + 1)
avg_stop_loss /= (num_iter + 1)
# Plot Validation Stats
epoch_stats = {
"loss_postnet": avg_postnet_loss,
"loss_decoder": avg_decoder_loss,
"stop_loss": avg_stop_loss
}
tb_logger.tb_eval_stats(global_step, epoch_stats)
if args.rank == 0 and epoch > c.test_delay_epochs:
# test sentences
test_audios = {}
test_figures = {}
print(" | > Synthesizing test sentences")
speaker_id = 0 if c.use_speaker_embedding else None
style_wav = c.get("style_wav_for_test")
for idx, test_sentence in enumerate(test_sentences):
try:
wav, alignment, decoder_output, postnet_output, stop_tokens = synthesis(
model,
test_sentence,
c,
use_cuda,
ap,
speaker_id=speaker_id,
style_wav=style_wav)
file_path = os.path.join(AUDIO_PATH, str(global_step))
os.makedirs(file_path, exist_ok=True)
file_path = os.path.join(file_path,
"TestSentence_{}.wav".format(idx))
ap.save_wav(wav, file_path)
test_audios['{}-audio'.format(idx)] = wav
test_figures['{}-prediction'.format(idx)] = plot_spectrogram(
postnet_output, ap)
test_figures['{}-alignment'.format(idx)] = plot_alignment(
alignment)
except:
print(" !! Error creating Test Sentence -", idx)
traceback.print_exc()
tb_logger.tb_test_audios(global_step, test_audios,
c.audio['sample_rate'])
tb_logger.tb_test_figures(global_step, test_figures)
return avg_postnet_loss
def train(model,
criterion,
criterion_st,
optimizer,
optimizer_st,
scheduler,
ap,
epoch,
use_half=False):
data_loader = setup_loader(is_val=False,
verbose=(epoch == 0),
use_half=use_half)
model.train()
epoch_time = 0
avg_postnet_loss = 0
avg_decoder_loss = 0
avg_stop_loss = 0
avg_step_time = 0
print("\n > Epoch {}/{}".format(epoch, c.epochs), flush=True)
batch_n_iter = int(len(data_loader.dataset) / (c.batch_size * num_gpus))
start_time = time.time()
for num_iter, data in enumerate(data_loader):
# setup input data
text_input = data[0]
text_lengths = data[1]
linear_input = data[2] if c.model == "Tacotron" else None
mel_input = data[3] if not use_half else data[3].type(torch.half)
mel_lengths = data[4] if not use_half else data[4].type(torch.half)
stop_targets = data[5]
avg_text_length = torch.mean(text_lengths.float())
avg_spec_length = torch.mean(mel_lengths.float())
# set stop targets view, we predict a single stop token per r frames prediction
stop_targets = stop_targets.view(text_input.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze(2)
stop_targets = stop_targets if not use_half else stop_targets.type(
torch.half)
current_step = num_iter + args.restore_step + \
epoch * len(data_loader) + 1
# setup lr
if c.lr_decay:
scheduler.step()
optimizer.zero_grad()
if optimizer_st: optimizer_st.zero_grad()
# dispatch data to GPU
if use_cuda:
text_input = text_input.cuda(non_blocking=True)
text_lengths = text_lengths.cuda(non_blocking=True)
mel_input = mel_input.cuda(non_blocking=True)
mel_lengths = mel_lengths.cuda(non_blocking=True)
linear_input = linear_input.cuda(
non_blocking=True) if c.model == "Tacotron" else None
stop_targets = stop_targets.cuda(non_blocking=True)
decoder_output, postnet_output, alignments, stop_tokens = model(
text_input, text_lengths, mel_input)
# loss computation
stop_loss = criterion_st(stop_tokens,
stop_targets) if c.stopnet else torch.zeros(1)
if c.loss_masking:
decoder_loss = criterion(decoder_output, mel_input, mel_lengths)
if c.model == "Tacotron":
postnet_loss = criterion(postnet_output, linear_input,
mel_lengths)
else:
postnet_loss = criterion(postnet_output, mel_input,
mel_lengths)
else:
decoder_loss = criterion(decoder_output, mel_input)
if c.model == "Tacotron":
postnet_loss = criterion(postnet_output, linear_input)
else:
postnet_loss = criterion(postnet_output, mel_input)
USE_HALF_LOSS_SCALE = 10.0
if use_half:
postnet_loss = postnet_loss * USE_HALF_LOSS_SCALE
decoder_loss = decoder_loss * USE_HALF_LOSS_SCALE
loss = decoder_loss + postnet_loss
if not c.separate_stopnet and c.stopnet:
loss += stop_loss
loss.backward()
optimizer, current_lr = weight_decay(optimizer, c.wd)
grad_norm, _ = check_update(model, c.grad_clip)
optimizer.step()
# backpass and check the grad norm for stop loss
if c.separate_stopnet:
USE_HALF_STOP_LOSS_SCALE = 1
stop_loss = stop_loss * USE_HALF_STOP_LOSS_SCALE
stop_loss.backward()
optimizer_st, _ = weight_decay(optimizer_st, c.wd)
grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
optimizer_st.step()
else:
grad_norm_st = 0
step_time = time.time() - start_time
start_time = time.time()
epoch_time += step_time
if current_step % c.print_step == 0:
print(
" | > Step:{}/{} GlobalStep:{} TotalLoss:{:.5f} PostnetLoss:{:.5f} "
"DecoderLoss:{:.5f} StopLoss:{:.5f} GradNorm:{:.5f} "
"GradNormST:{:.5f} AvgTextLen:{:.1f} AvgSpecLen:{:.1f} StepTime:{:.2f} LR:{:.6f}"
.format(num_iter, batch_n_iter, current_step, loss.item(),
postnet_loss.item(), decoder_loss.item(),
stop_loss.item(), grad_norm, grad_norm_st,
avg_text_length, avg_spec_length, step_time,
current_lr),
flush=True)
# aggregate losses from processes
if num_gpus > 1:
postnet_loss = reduce_tensor(postnet_loss.data, num_gpus)
decoder_loss = reduce_tensor(decoder_loss.data, num_gpus)
loss = reduce_tensor(loss.data, num_gpus)
stop_loss = reduce_tensor(stop_loss.data,
num_gpus) if c.stopnet else stop_loss
if args.rank == 0:
avg_postnet_loss += float(postnet_loss.item())
avg_decoder_loss += float(decoder_loss.item())
avg_stop_loss += stop_loss if type(stop_loss) is float else float(
stop_loss.item())
avg_step_time += step_time
# Plot Training Iter Stats
iter_stats = {
"loss_posnet": postnet_loss.item(),
"loss_decoder": decoder_loss.item(),
"lr": current_lr,
"grad_norm": grad_norm,
"grad_norm_st": grad_norm_st,
"step_time": step_time
}
tb_logger.tb_train_iter_stats(current_step, iter_stats)
if current_step % c.save_step == 0:
if c.checkpoint:
# save model
save_checkpoint(model, optimizer, optimizer_st,
postnet_loss.item(), OUT_PATH,
current_step, epoch)
# Diagnostic visualizations
const_spec = postnet_output[0].data.cpu().type(
torch.float).numpy()
gt_spec = linear_input[0].data.cpu().type(torch.float).numpy(
) if c.model == "Tacotron" else mel_input[0].data.cpu().type(
torch.float).numpy()
align_img = alignments[0].data.cpu().type(torch.float).numpy()
figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img)
}
tb_logger.tb_train_figures(current_step, figures)
# Sample audio
if c.model == "Tacotron":
train_audio = ap.inv_spectrogram(const_spec.T)
else:
train_audio = ap.inv_mel_spectrogram(const_spec.T)
tb_logger.tb_train_audios(current_step,
{'TrainAudio': train_audio},
c.audio["sample_rate"])
avg_postnet_loss /= (num_iter + 1)
avg_decoder_loss /= (num_iter + 1)
avg_stop_loss /= (num_iter + 1)
avg_total_loss = avg_decoder_loss + avg_postnet_loss + avg_stop_loss
avg_step_time /= (num_iter + 1)
# print epoch stats
print(" | > EPOCH END -- GlobalStep:{} AvgTotalLoss:{:.5f} "
"AvgPostnetLoss:{:.5f} AvgDecoderLoss:{:.5f} "
"AvgStopLoss:{:.5f} EpochTime:{:.2f} "
"AvgStepTime:{:.2f}".format(current_step, avg_total_loss,
avg_postnet_loss, avg_decoder_loss,
avg_stop_loss, epoch_time,
avg_step_time),
flush=True)
# Plot Epoch Stats
if args.rank == 0:
# Plot Training Epoch Stats
epoch_stats = {
"loss_postnet": avg_postnet_loss,
"loss_decoder": avg_decoder_loss,
"stop_loss": avg_stop_loss,
"epoch_time": epoch_time
}
tb_logger.tb_train_epoch_stats(current_step, epoch_stats)
if c.tb_model_param_stats:
tb_logger.tb_model_weights(model, current_step)
return avg_postnet_loss, current_step
def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
data_loader = setup_loader(ap, model.decoder.r, is_val=True)
if c.use_speaker_embedding:
speaker_mapping = load_speaker_mapping(OUT_PATH)
model.eval()
epoch_time = 0
eval_values_dict = {
'avg_postnet_loss': 0,
'avg_decoder_loss': 0,
'avg_stop_loss': 0,
'avg_align_score': 0
}
if c.bidirectional_decoder:
eval_values_dict['avg_decoder_b_loss'] = 0 # decoder backward loss
eval_values_dict['avg_decoder_c_loss'] = 0 # decoder consistency loss
keep_avg = KeepAverage()
keep_avg.add_values(eval_values_dict)
print("\n > Validation")
with torch.no_grad():
if data_loader is not None:
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# format data
text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, _, _ = format_data(
data)
assert mel_input.shape[1] % model.decoder.r == 0
# forward pass model
if c.bidirectional_decoder:
decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
text_input,
text_lengths,
mel_input,
speaker_ids=speaker_ids)
else:
decoder_output, postnet_output, alignments, stop_tokens = model(
text_input,
text_lengths,
mel_input,
speaker_ids=speaker_ids)
# loss computation
stop_loss = criterion_st(
stop_tokens, stop_targets) if c.stopnet else torch.zeros(1)
if c.loss_masking:
decoder_loss = criterion(decoder_output, mel_input,
mel_lengths)
if c.model in ["Tacotron", "TacotronGST"]:
postnet_loss = criterion(postnet_output, linear_input,
mel_lengths)
else:
postnet_loss = criterion(postnet_output, mel_input,
mel_lengths)
else:
decoder_loss = criterion(decoder_output, mel_input)
if c.model in ["Tacotron", "TacotronGST"]:
postnet_loss = criterion(postnet_output, linear_input)
else:
postnet_loss = criterion(postnet_output, mel_input)
loss = decoder_loss + postnet_loss + stop_loss
# backward decoder loss
if c.bidirectional_decoder:
if c.loss_masking:
decoder_backward_loss = criterion(
torch.flip(decoder_backward_output, dims=(1, )),
mel_input, mel_lengths)
else:
decoder_backward_loss = criterion(
torch.flip(decoder_backward_output, dims=(1, )),
mel_input)
decoder_c_loss = torch.nn.functional.l1_loss(
torch.flip(decoder_backward_output, dims=(1, )),
decoder_output)
loss += decoder_backward_loss + decoder_c_loss
keep_avg.update_values({
'avg_decoder_b_loss':
decoder_backward_loss.item(),
'avg_decoder_c_loss':
decoder_c_loss.item()
})
step_time = time.time() - start_time
epoch_time += step_time
# compute alignment score
align_score = alignment_diagonal_score(alignments)
keep_avg.update_value('avg_align_score', align_score)
# aggregate losses from processes
if num_gpus > 1:
postnet_loss = reduce_tensor(postnet_loss.data, num_gpus)
decoder_loss = reduce_tensor(decoder_loss.data, num_gpus)
if c.stopnet:
stop_loss = reduce_tensor(stop_loss.data, num_gpus)
keep_avg.update_values({
'avg_postnet_loss':
float(postnet_loss.item()),
'avg_decoder_loss':
float(decoder_loss.item()),
'avg_stop_loss':
float(stop_loss.item()),
})
if num_iter % c.print_step == 0:
print(
" | > TotalLoss: {:.5f} PostnetLoss: {:.5f} - {:.5f} DecoderLoss:{:.5f} - {:.5f} "
"StopLoss: {:.5f} - {:.5f} AlignScore: {:.4f} : {:.4f}"
.format(loss.item(), postnet_loss.item(),
keep_avg['avg_postnet_loss'],
decoder_loss.item(),
keep_avg['avg_decoder_loss'], stop_loss.item(),
keep_avg['avg_stop_loss'], align_score,
keep_avg['avg_align_score']),
flush=True)
if args.rank == 0:
# Diagnostic visualizations
idx = np.random.randint(mel_input.shape[0])
const_spec = postnet_output[idx].data.cpu().numpy()
gt_spec = linear_input[idx].data.cpu().numpy() if c.model in [
"Tacotron", "TacotronGST"
] else mel_input[idx].data.cpu().numpy()
align_img = alignments[idx].data.cpu().numpy()
eval_figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img)
}
# Sample audio
if c.model in ["Tacotron", "TacotronGST"]:
eval_audio = ap.inv_spectrogram(const_spec.T)
else:
eval_audio = ap.inv_mel_spectrogram(const_spec.T)
tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio},
c.audio["sample_rate"])
# Plot Validation Stats
epoch_stats = {
"loss_postnet": keep_avg['avg_postnet_loss'],
"loss_decoder": keep_avg['avg_decoder_loss'],
"stop_loss": keep_avg['avg_stop_loss'],
"alignment_score": keep_avg['avg_align_score']
}
if c.bidirectional_decoder:
epoch_stats['loss_decoder_backward'] = keep_avg[
'avg_decoder_b_loss']
align_b_img = alignments_backward[idx].data.cpu().numpy()
eval_figures['alignment_backward'] = plot_alignment(
align_b_img)
tb_logger.tb_eval_stats(global_step, epoch_stats)
tb_logger.tb_eval_figures(global_step, eval_figures)
if args.rank == 0 and epoch > c.test_delay_epochs:
if c.test_sentences_file is None:
test_sentences = [
"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
"Be a voice, not an echo.",
"I'm sorry Dave. I'm afraid I can't do that.",
"This cake is great. It's so delicious and moist."
]
else:
with open(c.test_sentences_file, "r") as f:
test_sentences = [s.strip() for s in f.readlines()]
# test sentences
test_audios = {}
test_figures = {}
print(" | > Synthesizing test sentences")
speaker_id = 0 if c.use_speaker_embedding else None
style_wav = c.get("style_wav_for_test")
for idx, test_sentence in enumerate(test_sentences):
try:
wav, alignment, decoder_output, postnet_output, stop_tokens = synthesis(
model,
test_sentence,
c,
use_cuda,
ap,
speaker_id=speaker_id,
style_wav=style_wav)
file_path = os.path.join(AUDIO_PATH, str(global_step))
os.makedirs(file_path, exist_ok=True)
file_path = os.path.join(file_path,
"TestSentence_{}.wav".format(idx))
ap.save_wav(wav, file_path)
test_audios['{}-audio'.format(idx)] = wav
test_figures['{}-prediction'.format(idx)] = plot_spectrogram(
postnet_output, ap)
test_figures['{}-alignment'.format(idx)] = plot_alignment(
alignment)
except:
print(" !! Error creating Test Sentence -", idx)
traceback.print_exc()
tb_logger.tb_test_audios(global_step, test_audios,
c.audio['sample_rate'])
tb_logger.tb_test_figures(global_step, test_figures)
return keep_avg['avg_postnet_loss']
def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
ap, global_step, epoch, criterion_gst=None, optimizer_gst=None):
data_loader = setup_loader(ap, is_val=False, verbose=(epoch == 0))
if c.use_speaker_embedding:
speaker_mapping = load_speaker_mapping(OUT_PATH)
model.train()
epoch_time = 0
avg_postnet_loss = 0
avg_decoder_loss = 0
avg_stop_loss = 0
avg_gst_loss = 0
avg_step_time = 0
avg_loader_time = 0
print("\n > Epoch {}/{}".format(epoch, c.epochs), flush=True)
if use_cuda:
batch_n_iter = int(len(data_loader.dataset) / (c.batch_size * num_gpus))
else:
batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
end_time = time.time()
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# setup input data
text_input = data[0]
text_lengths = data[1]
speaker_names = data[2]
linear_input = data[3] if c.model in ["Tacotron", "TacotronGST"] else None
mel_input = data[4]
mel_lengths = data[5]
stop_targets = data[6]
avg_text_length = torch.mean(text_lengths.float())
avg_spec_length = torch.mean(mel_lengths.float())
loader_time = time.time() - end_time
if c.use_speaker_embedding:
speaker_ids = [speaker_mapping[speaker_name]
for speaker_name in speaker_names]
speaker_ids = torch.LongTensor(speaker_ids)
else:
speaker_ids = None
# set stop targets view, we predict a single stop token per r frames prediction
stop_targets = stop_targets.view(text_input.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
global_step += 1
# setup lr
if c.lr_decay:
scheduler.step()
optimizer.zero_grad()
if optimizer_gst:
optimizer_gst.zero_grad()
if optimizer_st:
optimizer_st.zero_grad()
# dispatch data to GPU
if use_cuda:
text_input = text_input.cuda(non_blocking=True)
text_lengths = text_lengths.cuda(non_blocking=True)
mel_input = mel_input.cuda(non_blocking=True)
mel_lengths = mel_lengths.cuda(non_blocking=True)
linear_input = linear_input.cuda(non_blocking=True) if c.model in ["Tacotron", "TacotronGST"] else None
stop_targets = stop_targets.cuda(non_blocking=True)
if speaker_ids is not None:
speaker_ids = speaker_ids.cuda(non_blocking=True)
# forward pass model
decoder_output, postnet_output, alignments, stop_tokens, text_gst = model(
text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
# loss computation
stop_loss = criterion_st(stop_tokens, stop_targets) if c.stopnet else torch.zeros(1)
gst_loss = torch.zeros(1)
if c.loss_masking:
decoder_loss = criterion(decoder_output, mel_input, mel_lengths)
if c.model in ["Tacotron", "TacotronGST"]:
postnet_loss = criterion(postnet_output, linear_input, mel_lengths)
else:
postnet_loss = criterion(postnet_output, mel_input, mel_lengths)
else:
decoder_loss = criterion(decoder_output, mel_input)
if c.model in ["Tacotron", "TacotronGST"]:
postnet_loss = criterion(postnet_output, linear_input)
else:
postnet_loss = criterion(postnet_output, mel_input)
loss = decoder_loss + postnet_loss
if not c.separate_stopnet and c.stopnet:
loss += stop_loss
if c.text_gst and criterion_gst and optimizer_gst:
mel_gst, _ = model.gst(mel_input)
gst_loss = criterion_gst(text_gst, mel_gst.squeeze().detach())
gst_loss.backward()
optimizer_gst.step()
loss.backward()
optimizer, current_lr = weight_decay(optimizer, c.wd)
grad_norm, _ = check_update(model, c.grad_clip)
optimizer.step()
# backpass and check the grad norm for stop loss
if c.separate_stopnet:
stop_loss.backward()
optimizer_st, _ = weight_decay(optimizer_st, c.wd)
grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
optimizer_st.step()
else:
grad_norm_st = 0
step_time = time.time() - start_time
epoch_time += step_time
if global_step % c.print_step == 0:
print(
" | > Step:{}/{} GlobalStep:{} TotalLoss:{:.5f} PostnetLoss:{:.5f} "
"DecoderLoss:{:.5f} StopLoss:{:.5f} GSTLoss:{:.5f} GradNorm:{:.5f} "
"GradNormST:{:.5f} AvgTextLen:{:.1f} AvgSpecLen:{:.1f} StepTime:{:.2f} "
"LoaderTime:{:.2f} LR:{:.6f}".format(
num_iter, batch_n_iter, global_step, loss.item(),
postnet_loss.item(), decoder_loss.item(), stop_loss.item(), gst_loss.item(),
grad_norm, grad_norm_st, avg_text_length, avg_spec_length, step_time,
loader_time, current_lr),
flush=True)
# aggregate losses from processes
if num_gpus > 1:
postnet_loss = reduce_tensor(postnet_loss.data, num_gpus)
decoder_loss = reduce_tensor(decoder_loss.data, num_gpus)
gst_loss = reduce_tensor(gst_loss.data, num_gpus) if c.text_gst else gst_loss
loss = reduce_tensor(loss.data, num_gpus)
stop_loss = reduce_tensor(stop_loss.data, num_gpus) if c.stopnet else stop_loss
if args.rank == 0:
avg_postnet_loss += float(postnet_loss.item())
avg_decoder_loss += float(decoder_loss.item())
avg_stop_loss += stop_loss if isinstance(stop_loss, float) else float(stop_loss.item())
avg_gst_loss += float(gst_loss.item())
avg_step_time += step_time
avg_loader_time += loader_time
# Plot Training Iter Stats
# reduce TB load
if global_step % 10 == 0:
iter_stats = {"loss_posnet": postnet_loss.item(),
"loss_decoder": decoder_loss.item(),
"gst_loss" : gst_loss.item(),
"lr": current_lr,
"grad_norm": grad_norm,
"grad_norm_st": grad_norm_st,
"step_time": step_time}
tb_logger.tb_train_iter_stats(global_step, iter_stats)
if global_step % c.save_step == 0:
if c.checkpoint:
# save model
save_checkpoint(model, optimizer, optimizer_st, optimizer_gst,
postnet_loss.item(), OUT_PATH, global_step,
epoch)
# Diagnostic visualizations
const_spec = postnet_output[0].data.cpu().numpy()
gt_spec = linear_input[0].data.cpu().numpy() if c.model in ["Tacotron", "TacotronGST"] else mel_input[0].data.cpu().numpy()
align_img = alignments[0].data.cpu().numpy()
figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img)
}
tb_logger.tb_train_figures(global_step, figures)
# Sample audio
if c.model in ["Tacotron", "TacotronGST"]:
train_audio = ap.inv_spectrogram(const_spec.T)
else:
train_audio = ap.inv_mel_spectrogram(const_spec.T)
tb_logger.tb_train_audios(global_step,
{'TrainAudio': train_audio},
c.audio["sample_rate"])
end_time = time.time()
avg_postnet_loss /= (num_iter + 1)
avg_decoder_loss /= (num_iter + 1)
avg_stop_loss /= (num_iter + 1)
avg_gst_loss /= (num_iter + 1)
avg_total_loss = avg_decoder_loss + avg_postnet_loss + avg_stop_loss
avg_step_time /= (num_iter + 1)
avg_loader_time /= (num_iter + 1)
# print epoch stats
print(
" | > EPOCH END -- GlobalStep:{} AvgTotalLoss:{:.5f} "
"AvgPostnetLoss:{:.5f} AvgDecoderLoss:{:.5f} AvgGSTLoss:{:.5f} "
"AvgStopLoss:{:.5f} EpochTime:{:.2f} "
"AvgStepTime:{:.2f} AvgLoaderTime:{:.2f}".format(global_step, avg_total_loss,
avg_postnet_loss, avg_decoder_loss, avg_gst_loss,
avg_stop_loss, epoch_time, avg_step_time,
avg_loader_time),
flush=True)
# Plot Epoch Stats
if args.rank == 0:
# Plot Training Epoch Stats
epoch_stats = {"loss_postnet": avg_postnet_loss,
"loss_decoder": avg_decoder_loss,
"stop_loss": avg_stop_loss,
"gst_loss" : avg_gst_loss,
"epoch_time": epoch_time}
tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
if c.tb_model_param_stats:
tb_logger.tb_model_weights(model, global_step)
return avg_postnet_loss, global_step
def evaluate(model, criterion, criterion_st, ap, current_step, epoch):
data_loader = setup_loader(is_val=True)
model.eval()
epoch_time = 0
avg_postnet_loss = 0
avg_decoder_loss = 0
avg_stop_loss = 0
print("\n > Validation")
if c.test_sentences_file is None:
test_sentences = [
"Evinizde çocuklar televizyonun karşısına dizilmiş oturuyorlar.",
"Karşınızda reklamlara çıkan çocukların elinde çikulatalar, püskevitler, birbirlerine ikram ediyorlar, birbirleriyle yiyorlar, şakalaşıyorlar.",
"O çocuk aklından geçiriyor 'benim de bir çikulatam olsa, benim de bir püskevitim olsa' diyor.",
"Anne bana niye almıyorsunuz diyor, bizde niye yok diyor."
]
else:
with open(c.test_sentences_file, "r") as f:
test_sentences = [s.strip() for s in f.readlines()]
with torch.no_grad():
if data_loader is not None:
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# setup input data
text_input = data[0]
text_lengths = data[1]
linear_input = data[2] if c.model == "Tacotron" else None
mel_input = data[3]
mel_lengths = data[4]
stop_targets = data[5]
# set stop targets view, we predict a single stop token per r frames prediction
stop_targets = stop_targets.view(text_input.shape[0],
stop_targets.size(1) // c.r,
-1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze(2)
# dispatch data to GPU
if use_cuda:
text_input = text_input.cuda()
mel_input = mel_input.cuda()
mel_lengths = mel_lengths.cuda()
linear_input = linear_input.cuda(
) if c.model == "Tacotron" else None
stop_targets = stop_targets.cuda()
# forward pass
decoder_output, postnet_output, alignments, stop_tokens =\
model.forward(text_input, text_lengths, mel_input)
# loss computation
stop_loss = criterion_st(
stop_tokens, stop_targets) if c.stopnet else torch.zeros(1)
if c.loss_masking:
decoder_loss = criterion(decoder_output, mel_input,
mel_lengths)
if c.model == "Tacotron":
postnet_loss = criterion(postnet_output, linear_input,
mel_lengths)
else:
postnet_loss = criterion(postnet_output, mel_input,
mel_lengths)
else:
decoder_loss = criterion(decoder_output, mel_input)
if c.model == "Tacotron":
postnet_loss = criterion(postnet_output, linear_input)
else:
postnet_loss = criterion(postnet_output, mel_input)
loss = decoder_loss + postnet_loss + stop_loss
step_time = time.time() - start_time
epoch_time += step_time
if num_iter % c.print_step == 0:
print(
" | > TotalLoss: {:.5f} PostnetLoss: {:.5f} DecoderLoss:{:.5f} "
"StopLoss: {:.5f} ".format(loss.item(),
postnet_loss.item(),
decoder_loss.item(),
stop_loss.item()),
flush=True)
# aggregate losses from processes
if num_gpus > 1:
postnet_loss = reduce_tensor(postnet_loss.data, num_gpus)
decoder_loss = reduce_tensor(decoder_loss.data, num_gpus)
if c.stopnet:
stop_loss = reduce_tensor(stop_loss.data, num_gpus)
avg_postnet_loss += float(postnet_loss.item())
avg_decoder_loss += float(decoder_loss.item())
avg_stop_loss += stop_loss.item()
if args.rank == 0:
# Diagnostic visualizations
idx = np.random.randint(mel_input.shape[0])
const_spec = postnet_output[idx].data.cpu().numpy()
gt_spec = linear_input[idx].data.cpu().numpy(
) if c.model == "Tacotron" else mel_input[idx].data.cpu(
).numpy()
align_img = alignments[idx].data.cpu().numpy()
eval_figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img)
}
tb_logger.tb_eval_figures(current_step, eval_figures)
# Sample audio
if c.model == "Tacotron":
eval_audio = ap.inv_spectrogram(const_spec.T)
else:
eval_audio = ap.inv_mel_spectrogram(const_spec.T)
tb_logger.tb_eval_audios(current_step,
{"ValAudio": eval_audio},
c.audio["sample_rate"])
# compute average losses
avg_postnet_loss /= (num_iter + 1)
avg_decoder_loss /= (num_iter + 1)
avg_stop_loss /= (num_iter + 1)
# Plot Validation Stats
epoch_stats = {
"loss_postnet": avg_postnet_loss,
"loss_decoder": avg_decoder_loss,
"stop_loss": avg_stop_loss
}
tb_logger.tb_eval_stats(current_step, epoch_stats)
if args.rank == 0 and epoch > c.test_delay_epochs:
# test sentences
test_audios = {}
test_figures = {}
print(" | > Synthesizing test sentences")
for idx, test_sentence in enumerate(test_sentences):
try:
wav, alignment, decoder_output, postnet_output, stop_tokens = synthesis(
model, test_sentence, c, use_cuda, ap)
file_path = os.path.join(AUDIO_PATH, str(current_step))
os.makedirs(file_path, exist_ok=True)
file_path = os.path.join(file_path,
"TestSentence_{}.wav".format(idx))
ap.save_wav(wav, file_path)
test_audios['{}-audio'.format(idx)] = wav
test_figures['{}-prediction'.format(idx)] = plot_spectrogram(
postnet_output, ap)
test_figures['{}-alignment'.format(idx)] = plot_alignment(
alignment)
except:
print(" !! Error creating Test Sentence -", idx)
traceback.print_exc()
tb_logger.tb_test_audios(current_step, test_audios,
c.audio['sample_rate'])
tb_logger.tb_test_figures(current_step, test_figures)
return avg_postnet_loss
def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
ap, global_step, epoch):
data_loader = setup_loader(ap,
model.decoder.r,
is_val=False,
verbose=(epoch == 0))
model.train()
epoch_time = 0
train_values = {
'avg_postnet_loss': 0,
'avg_decoder_loss': 0,
'avg_stop_loss': 0,
'avg_align_score': 0,
'avg_step_time': 0,
'avg_loader_time': 0,
'avg_alignment_score': 0
}
if c.bidirectional_decoder:
train_values['avg_decoder_b_loss'] = 0 # decoder backward loss
train_values['avg_decoder_c_loss'] = 0 # decoder consistency loss
keep_avg = KeepAverage()
keep_avg.add_values(train_values)
print("\n > Epoch {}/{}".format(epoch, c.epochs), flush=True)
if use_cuda:
batch_n_iter = int(
len(data_loader.dataset) / (c.batch_size * num_gpus))
else:
batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
end_time = time.time()
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# format data
text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, avg_text_length, avg_spec_length = format_data(
data)
loader_time = time.time() - end_time
global_step += 1
# setup lr
if c.noam_schedule:
scheduler.step()
optimizer.zero_grad()
if optimizer_st:
optimizer_st.zero_grad()
# forward pass model
if c.bidirectional_decoder:
decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
else:
decoder_output, postnet_output, alignments, stop_tokens = model(
text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
# loss computation
stop_loss = criterion_st(stop_tokens,
stop_targets) if c.stopnet else torch.zeros(1)
if c.loss_masking:
decoder_loss = criterion(decoder_output, mel_input, mel_lengths)
if c.model in ["Tacotron", "TacotronGST"]:
postnet_loss = criterion(postnet_output, linear_input,
mel_lengths)
else:
postnet_loss = criterion(postnet_output, mel_input,
mel_lengths)
else:
decoder_loss = criterion(decoder_output, mel_input)
if c.model in ["Tacotron", "TacotronGST"]:
postnet_loss = criterion(postnet_output, linear_input)
else:
postnet_loss = criterion(postnet_output, mel_input)
loss = decoder_loss + postnet_loss
if not c.separate_stopnet and c.stopnet:
loss += stop_loss
# backward decoder
if c.bidirectional_decoder:
if c.loss_masking:
decoder_backward_loss = criterion(
torch.flip(decoder_backward_output, dims=(1, )), mel_input,
mel_lengths)
else:
decoder_backward_loss = criterion(
torch.flip(decoder_backward_output, dims=(1, )), mel_input)
decoder_c_loss = torch.nn.functional.l1_loss(
torch.flip(decoder_backward_output, dims=(1, )),
decoder_output)
loss += decoder_backward_loss + decoder_c_loss
keep_avg.update_values({
'avg_decoder_b_loss':
decoder_backward_loss.item(),
'avg_decoder_c_loss':
decoder_c_loss.item()
})
loss.backward()
optimizer, current_lr = adam_weight_decay(optimizer)
grad_norm, grad_flag = check_update(model,
c.grad_clip,
ignore_stopnet=True)
optimizer.step()
# compute alignment score
align_score = alignment_diagonal_score(alignments)
keep_avg.update_value('avg_align_score', align_score)
# backpass and check the grad norm for stop loss
if c.separate_stopnet:
stop_loss.backward()
optimizer_st, _ = adam_weight_decay(optimizer_st)
grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
optimizer_st.step()
else:
grad_norm_st = 0
step_time = time.time() - start_time
epoch_time += step_time
if global_step % c.print_step == 0:
print(
" | > Step:{}/{} GlobalStep:{} PostnetLoss:{:.5f} "
"DecoderLoss:{:.5f} StopLoss:{:.5f} AlignScore:{:.4f} GradNorm:{:.5f} "
"GradNormST:{:.5f} AvgTextLen:{:.1f} AvgSpecLen:{:.1f} StepTime:{:.2f} "
"LoaderTime:{:.2f} LR:{:.6f}".format(
num_iter, batch_n_iter, global_step, postnet_loss.item(),
decoder_loss.item(), stop_loss.item(), align_score,
grad_norm, grad_norm_st, avg_text_length, avg_spec_length,
step_time, loader_time, current_lr),
flush=True)
# aggregate losses from processes
if num_gpus > 1:
postnet_loss = reduce_tensor(postnet_loss.data, num_gpus)
decoder_loss = reduce_tensor(decoder_loss.data, num_gpus)
loss = reduce_tensor(loss.data, num_gpus)
stop_loss = reduce_tensor(stop_loss.data,
num_gpus) if c.stopnet else stop_loss
if args.rank == 0:
update_train_values = {
'avg_postnet_loss':
float(postnet_loss.item()),
'avg_decoder_loss':
float(decoder_loss.item()),
'avg_stop_loss':
stop_loss
if isinstance(stop_loss, float) else float(stop_loss.item()),
'avg_step_time':
step_time,
'avg_loader_time':
loader_time
}
keep_avg.update_values(update_train_values)
# Plot Training Iter Stats
# reduce TB load
if global_step % 10 == 0:
iter_stats = {
"loss_posnet": postnet_loss.item(),
"loss_decoder": decoder_loss.item(),
"lr": current_lr,
"grad_norm": grad_norm,
"grad_norm_st": grad_norm_st,
"step_time": step_time
}
tb_logger.tb_train_iter_stats(global_step, iter_stats)
if global_step % c.save_step == 0:
if c.checkpoint:
# save model
save_checkpoint(model, optimizer, optimizer_st,
postnet_loss.item(), OUT_PATH, global_step,
epoch)
# Diagnostic visualizations
const_spec = postnet_output[0].data.cpu().numpy()
gt_spec = linear_input[0].data.cpu().numpy() if c.model in [
"Tacotron", "TacotronGST"
] else mel_input[0].data.cpu().numpy()
align_img = alignments[0].data.cpu().numpy()
figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img),
}
if c.bidirectional_decoder:
figures["alignment_backward"] = plot_alignment(
alignments_backward[0].data.cpu().numpy())
tb_logger.tb_train_figures(global_step, figures)
# Sample audio
if c.model in ["Tacotron", "TacotronGST"]:
train_audio = ap.inv_spectrogram(const_spec.T)
else:
train_audio = ap.inv_mel_spectrogram(const_spec.T)
tb_logger.tb_train_audios(global_step,
{'TrainAudio': train_audio},
c.audio["sample_rate"])
end_time = time.time()
# print epoch stats
print(" | > EPOCH END -- GlobalStep:{} "
"AvgPostnetLoss:{:.5f} AvgDecoderLoss:{:.5f} "
"AvgStopLoss:{:.5f} AvgAlignScore:{:3f} EpochTime:{:.2f} "
"AvgStepTime:{:.2f} AvgLoaderTime:{:.2f}".format(
global_step, keep_avg['avg_postnet_loss'],
keep_avg['avg_decoder_loss'], keep_avg['avg_stop_loss'],
keep_avg['avg_align_score'], epoch_time,
keep_avg['avg_step_time'], keep_avg['avg_loader_time']),
flush=True)
# Plot Epoch Stats
if args.rank == 0:
# Plot Training Epoch Stats
epoch_stats = {
"loss_postnet": keep_avg['avg_postnet_loss'],
"loss_decoder": keep_avg['avg_decoder_loss'],
"stop_loss": keep_avg['avg_stop_loss'],
"alignment_score": keep_avg['avg_align_score'],
"epoch_time": epoch_time
}
tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
if c.tb_model_param_stats:
tb_logger.tb_model_weights(model, global_step)
return keep_avg['avg_postnet_loss'], global_step
def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
ap, epoch):
data_loader = setup_loader(is_val=False, verbose=(epoch == 0))
model.train()
epoch_time = 0
avg_linear_loss = 0
avg_mel_loss = 0
avg_stop_loss = 0
avg_step_time = 0
print("\n > Epoch {}/{}".format(epoch, c.epochs), flush=True)
n_priority_freq = int(3000 / (c.audio['sample_rate'] * 0.5) *
c.audio['num_freq'])
if num_gpus > 0:
batch_n_iter = int(
len(data_loader.dataset) / (c.batch_size * num_gpus))
else:
batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# setup input data
text_input = data[0]
text_lengths = data[1]
linear_input = data[2]
mel_input = data[3]
mel_lengths = data[4]
stop_targets = data[5]
avg_text_length = torch.mean(text_lengths.float())
avg_spec_length = torch.mean(mel_lengths.float())
# set stop targets view, we predict a single stop token per r frames prediction
stop_targets = stop_targets.view(text_input.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float()
current_step = num_iter + args.restore_step + \
epoch * len(data_loader) + 1
# setup lr
if c.lr_decay:
scheduler.step()
optimizer.zero_grad()
optimizer_st.zero_grad()
# dispatch data to GPU
if use_cuda:
text_input = text_input.cuda(non_blocking=True)
text_lengths = text_lengths.cuda(non_blocking=True)
mel_input = mel_input.cuda(non_blocking=True)
mel_lengths = mel_lengths.cuda(non_blocking=True)
linear_input = linear_input.cuda(non_blocking=True)
stop_targets = stop_targets.cuda(non_blocking=True)
# compute mask for padding
mask = sequence_mask(text_lengths)
# forward pass
mel_output, linear_output, alignments, stop_tokens = model(
text_input, mel_input, mask)
# loss computation
stop_loss = criterion_st(stop_tokens, stop_targets)
mel_loss = criterion(mel_output, mel_input, mel_lengths)
linear_loss = (1 - c.loss_weight) * criterion(linear_output, linear_input, mel_lengths)\
+ c.loss_weight * criterion(linear_output[:, :, :n_priority_freq],
linear_input[:, :, :n_priority_freq],
mel_lengths)
loss = mel_loss + linear_loss
# backpass and check the grad norm for spec losses
loss.backward(retain_graph=True)
optimizer, current_lr = weight_decay(optimizer, c.wd)
grad_norm, _ = check_update(model, 1.0)
optimizer.step()
# backpass and check the grad norm for stop loss
stop_loss.backward()
optimizer_st, _ = weight_decay(optimizer_st, c.wd)
grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
optimizer_st.step()
step_time = time.time() - start_time
epoch_time += step_time
if current_step % c.print_step == 0:
print(
" | > Step:{}/{} GlobalStep:{} TotalLoss:{:.5f} LinearLoss:{:.5f} "
"MelLoss:{:.5f} StopLoss:{:.5f} GradNorm:{:.5f} "
"GradNormST:{:.5f} AvgTextLen:{:.1f} AvgSpecLen:{:.1f} StepTime:{:.2f} LR:{:.6f}"
.format(num_iter, batch_n_iter, current_step, loss.item(),
linear_loss.item(), mel_loss.item(), stop_loss.item(),
grad_norm, grad_norm_st, avg_text_length,
avg_spec_length, step_time, current_lr),
flush=True)
# aggregate losses from processes
if num_gpus > 1:
linear_loss = reduce_tensor(linear_loss.data, num_gpus)
mel_loss = reduce_tensor(mel_loss.data, num_gpus)
loss = reduce_tensor(loss.data, num_gpus)
stop_loss = reduce_tensor(stop_loss.data, num_gpus)
if args.rank == 0:
avg_linear_loss += float(linear_loss.item())
avg_mel_loss += float(mel_loss.item())
avg_stop_loss += stop_loss.item()
avg_step_time += step_time
# Plot Training Iter Stats
iter_stats = {
"loss_posnet": linear_loss.item(),
"loss_decoder": mel_loss.item(),
"lr": current_lr,
"grad_norm": grad_norm,
"grad_norm_st": grad_norm_st,
"step_time": step_time
}
tb_logger.tb_train_iter_stats(current_step, iter_stats)
if current_step % c.save_step == 0:
if c.checkpoint:
# save model
save_checkpoint(model, optimizer, optimizer_st,
linear_loss.item(), OUT_PATH, current_step,
epoch)
# Diagnostic visualizations
const_spec = linear_output[0].data.cpu().numpy()
gt_spec = linear_input[0].data.cpu().numpy()
align_img = alignments[0].data.cpu().numpy()
figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img)
}
tb_logger.tb_train_figures(current_step, figures)
# Sample audio
tb_logger.tb_train_audios(
current_step,
{'TrainAudio': ap.inv_spectrogram(const_spec.T)},
c.audio["sample_rate"])
avg_linear_loss /= (num_iter + 1)
avg_mel_loss /= (num_iter + 1)
avg_stop_loss /= (num_iter + 1)
avg_total_loss = avg_mel_loss + avg_linear_loss + avg_stop_loss
avg_step_time /= (num_iter + 1)
# print epoch stats
print(" | > EPOCH END -- GlobalStep:{} AvgTotalLoss:{:.5f} "
"AvgLinearLoss:{:.5f} AvgMelLoss:{:.5f} "
"AvgStopLoss:{:.5f} EpochTime:{:.2f} "
"AvgStepTime:{:.2f}".format(current_step, avg_total_loss,
avg_linear_loss, avg_mel_loss,
avg_stop_loss, epoch_time,
avg_step_time),
flush=True)
# Plot Epoch Stats
if args.rank == 0:
# Plot Training Epoch Stats
epoch_stats = {
"loss_postnet": avg_linear_loss,
"loss_decoder": avg_mel_loss,
"stop_loss": avg_stop_loss,
"epoch_time": epoch_time
}
tb_logger.tb_train_epoch_stats(current_step, epoch_stats)
if c.tb_model_param_stats:
tb_logger.tb_model_weights(model, current_step)
return avg_linear_loss, current_step
def evaluate(model, criterion, criterion_st, ap, current_step, epoch):
data_loader = setup_loader(is_val=True)
model.eval()
epoch_time = 0
avg_linear_loss = 0
avg_mel_loss = 0
avg_stop_loss = 0
print("\n > Validation")
test_sentences = [
"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
"Be a voice, not an echo.",
"I'm sorry Dave. I'm afraid I can't do that.",
"This cake is great. It's so delicious and moist."
]
n_priority_freq = int(3000 / (c.audio['sample_rate'] * 0.5) *
c.audio['num_freq'])
with torch.no_grad():
if data_loader is not None:
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# setup input data
text_input = data[0]
text_lengths = data[1]
linear_input = data[2]
mel_input = data[3]
mel_lengths = data[4]
stop_targets = data[5]
# set stop targets view, we predict a single stop token per r frames prediction
stop_targets = stop_targets.view(text_input.shape[0],
stop_targets.size(1) // c.r,
-1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float()
# dispatch data to GPU
if use_cuda:
text_input = text_input.cuda()
mel_input = mel_input.cuda()
mel_lengths = mel_lengths.cuda()
linear_input = linear_input.cuda()
stop_targets = stop_targets.cuda()
# forward pass
mel_output, linear_output, alignments, stop_tokens =\
model.forward(text_input, mel_input)
# loss computation
stop_loss = criterion_st(stop_tokens, stop_targets)
mel_loss = criterion(mel_output, mel_input, mel_lengths)
linear_loss = 0.5 * criterion(linear_output, linear_input, mel_lengths) \
+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
linear_input[:, :, :n_priority_freq],
mel_lengths)
loss = mel_loss + linear_loss + stop_loss
step_time = time.time() - start_time
epoch_time += step_time
if num_iter % c.print_step == 0:
print(
" | > TotalLoss: {:.5f} LinearLoss: {:.5f} MelLoss:{:.5f} "
"StopLoss: {:.5f} ".format(loss.item(),
linear_loss.item(),
mel_loss.item(),
stop_loss.item()),
flush=True)
# aggregate losses from processes
if num_gpus > 1:
linear_loss = reduce_tensor(linear_loss.data, num_gpus)
mel_loss = reduce_tensor(mel_loss.data, num_gpus)
stop_loss = reduce_tensor(stop_loss.data, num_gpus)
avg_linear_loss += float(linear_loss.item())
avg_mel_loss += float(mel_loss.item())
avg_stop_loss += stop_loss.item()
if args.rank == 0:
# Diagnostic visualizations
idx = np.random.randint(mel_input.shape[0])
const_spec = linear_output[idx].data.cpu().numpy()
gt_spec = linear_input[idx].data.cpu().numpy()
align_img = alignments[idx].data.cpu().numpy()
eval_figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img)
}
tb_logger.tb_eval_figures(current_step, eval_figures)
# Sample audio
tb_logger.tb_eval_audios(
current_step,
{"ValAudio": ap.inv_spectrogram(const_spec.T)},
c.audio["sample_rate"])
# compute average losses
avg_linear_loss /= (num_iter + 1)
avg_mel_loss /= (num_iter + 1)
avg_stop_loss /= (num_iter + 1)
# Plot Validation Stats
epoch_stats = {
"loss_postnet": avg_linear_loss,
"loss_decoder": avg_mel_loss,
"stop_loss": avg_stop_loss
}
tb_logger.tb_eval_stats(current_step, epoch_stats)
if args.rank == 0 and epoch > c.test_delay_epochs:
# test sentences
test_audios = {}
test_figures = {}
print(" | > Synthesizing test sentences")
for idx, test_sentence in enumerate(test_sentences):
try:
wav, alignment, linear_spec, _, stop_tokens = synthesis(
model, test_sentence, c, use_cuda, ap)
file_path = os.path.join(AUDIO_PATH, str(current_step))
os.makedirs(file_path, exist_ok=True)
file_path = os.path.join(file_path,
"TestSentence_{}.wav".format(idx))
ap.save_wav(wav, file_path)
test_audios['{}-audio'.format(idx)] = wav
test_figures['{}-prediction'.format(idx)] = plot_spectrogram(
linear_spec, ap)
test_figures['{}-alignment'.format(idx)] = plot_alignment(
alignment)
except:
print(" !! Error creating Test Sentence -", idx)
traceback.print_exc()
tb_logger.tb_test_audios(current_step, test_audios,
c.audio['sample_rate'])
tb_logger.tb_test_figures(current_step, test_figures)
return avg_linear_loss
def evaluate(model, criterion, ap, global_step, epoch):
data_loader = setup_loader(ap, model.decoder.r, is_val=True)
model.eval()
epoch_time = 0
eval_values_dict = {
'avg_postnet_loss': 0,
'avg_decoder_loss': 0,
'avg_stopnet_loss': 0,
'avg_align_error': 0
}
if c.bidirectional_decoder:
eval_values_dict['avg_decoder_b_loss'] = 0 # decoder backward loss
eval_values_dict['avg_decoder_c_loss'] = 0 # decoder consistency loss
if c.ga_alpha > 0:
eval_values_dict['avg_ga_loss'] = 0 # guidede attention loss
keep_avg = KeepAverage()
keep_avg.add_values(eval_values_dict)
c_logger.print_eval_start()
if data_loader is not None:
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# format data
text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, _, _ = format_data(
data)
assert mel_input.shape[1] % model.decoder.r == 0
# forward pass model
if c.bidirectional_decoder:
decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
text_input,
text_lengths,
mel_input,
speaker_ids=speaker_ids)
else:
decoder_output, postnet_output, alignments, stop_tokens = model(
text_input,
text_lengths,
mel_input,
speaker_ids=speaker_ids)
decoder_backward_output = None
# set the alignment lengths wrt reduction factor for guided attention
if mel_lengths.max() % model.decoder.r != 0:
alignment_lengths = (
mel_lengths +
(model.decoder.r -
(mel_lengths.max() % model.decoder.r))) // model.decoder.r
else:
alignment_lengths = mel_lengths // model.decoder.r
# compute loss
loss_dict = criterion(postnet_output, decoder_output, mel_input,
linear_input, stop_tokens, stop_targets,
mel_lengths, decoder_backward_output,
alignments, alignment_lengths, text_lengths)
if c.bidirectional_decoder:
keep_avg.update_values({
'avg_decoder_b_loss':
loss_dict['decoder_b_loss'].item(),
'avg_decoder_c_loss':
loss_dict['decoder_c_loss'].item()
})
if c.ga_alpha > 0:
keep_avg.update_values(
{'avg_ga_loss': loss_dict['ga_loss'].item()})
# step time
step_time = time.time() - start_time
epoch_time += step_time
# compute alignment score
align_error = 1 - alignment_diagonal_score(alignments)
keep_avg.update_value('avg_align_error', align_error)
# aggregate losses from processes
if num_gpus > 1:
loss_dict['postnet_loss'] = reduce_tensor(
loss_dict['postnet_loss'].data, num_gpus)
loss_dict['decoder_loss'] = reduce_tensor(
loss_dict['decoder_loss'].data, num_gpus)
if c.stopnet:
loss_dict['stopnet_loss'] = reduce_tensor(
loss_dict['stopnet_loss'].data, num_gpus)
keep_avg.update_values({
'avg_postnet_loss':
float(loss_dict['postnet_loss'].item()),
'avg_decoder_loss':
float(loss_dict['decoder_loss'].item()),
'avg_stopnet_loss':
float(loss_dict['stopnet_loss'].item()),
})
if c.print_eval:
c_logger.print_eval_step(num_iter, loss_dict,
keep_avg.avg_values)
if args.rank == 0:
# Diagnostic visualizations
idx = np.random.randint(mel_input.shape[0])
const_spec = postnet_output[idx].data.cpu().numpy()
gt_spec = linear_input[idx].data.cpu().numpy() if c.model in [
"Tacotron", "TacotronGST"
] else mel_input[idx].data.cpu().numpy()
align_img = alignments[idx].data.cpu().numpy()
eval_figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img)
}
# Sample audio
if c.model in ["Tacotron", "TacotronGST"]:
eval_audio = ap.inv_spectrogram(const_spec.T)
else:
eval_audio = ap.inv_melspectrogram(const_spec.T)
tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio},
c.audio["sample_rate"])
# Plot Validation Stats
epoch_stats = {
"loss_postnet": keep_avg['avg_postnet_loss'],
"loss_decoder": keep_avg['avg_decoder_loss'],
"stopnet_loss": keep_avg['avg_stopnet_loss'],
"alignment_score": keep_avg['avg_align_error'],
}
if c.bidirectional_decoder:
epoch_stats['loss_decoder_backward'] = keep_avg[
'avg_decoder_b_loss']
align_b_img = alignments_backward[idx].data.cpu().numpy()
eval_figures['alignment_backward'] = plot_alignment(
align_b_img)
if c.ga_alpha > 0:
epoch_stats['guided_attention_loss'] = keep_avg['avg_ga_loss']
tb_logger.tb_eval_stats(global_step, epoch_stats)
tb_logger.tb_eval_figures(global_step, eval_figures)
if args.rank == 0 and epoch > c.test_delay_epochs:
if c.test_sentences_file is None:
test_sentences = [
"Con la mia voce posso dire cose splendide.",
"Ciao Marco ed Alice, come state?",
"Ora che ho una voce, voglio solo parlare.",
"Tra tutte le cose che ho letto, in tanti anni, questo libro è davvero il mio preferito."
]
else:
with open(c.test_sentences_file, "r") as f:
test_sentences = [s.strip() for s in f.readlines()]
# test sentences
test_audios = {}
test_figures = {}
print(" | > Synthesizing test sentences")
speaker_id = 0 if c.use_speaker_embedding else None
style_wav = c.get("style_wav_for_test")
for idx, test_sentence in enumerate(test_sentences):
try:
wav, alignment, decoder_output, postnet_output, stop_tokens, inputs = synthesis(
model,
test_sentence,
c,
use_cuda,
ap,
speaker_id=speaker_id,
style_wav=style_wav,
truncated=False,
enable_eos_bos_chars=c.enable_eos_bos_chars, #pylint: disable=unused-argument
use_griffin_lim=True,
do_trim_silence=False)
file_path = os.path.join(AUDIO_PATH, str(global_step))
os.makedirs(file_path, exist_ok=True)
file_path = os.path.join(file_path,
"TestSentence_{}.wav".format(idx))
ap.save_wav(wav, file_path)
test_audios['{}-audio'.format(idx)] = wav
test_figures['{}-prediction'.format(idx)] = plot_spectrogram(
postnet_output, ap)
test_figures['{}-alignment'.format(idx)] = plot_alignment(
alignment)
except:
print(" !! Error creating Test Sentence -", idx)
traceback.print_exc()
tb_logger.tb_test_audios(global_step, test_audios,
c.audio['sample_rate'])
tb_logger.tb_test_figures(global_step, test_figures)
return keep_avg.avg_values
def train(model, criterion, optimizer, optimizer_st, scheduler, ap,
global_step, epoch):
data_loader = setup_loader(ap,
model.decoder.r,
is_val=False,
verbose=(epoch == 0))
model.train()
epoch_time = 0
train_values = {
'avg_postnet_loss': 0,
'avg_decoder_loss': 0,
'avg_stopnet_loss': 0,
'avg_align_error': 0,
'avg_step_time': 0,
'avg_loader_time': 0
}
if c.bidirectional_decoder:
train_values['avg_decoder_b_loss'] = 0 # decoder backward loss
train_values['avg_decoder_c_loss'] = 0 # decoder consistency loss
if c.ga_alpha > 0:
train_values['avg_ga_loss'] = 0 # guidede attention loss
keep_avg = KeepAverage()
keep_avg.add_values(train_values)
if use_cuda:
batch_n_iter = int(
len(data_loader.dataset) / (c.batch_size * num_gpus))
else:
batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
end_time = time.time()
c_logger.print_train_start()
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# format data
text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, avg_text_length, avg_spec_length = format_data(
data)
loader_time = time.time() - end_time
global_step += 1
# setup lr
if c.noam_schedule:
scheduler.step()
optimizer.zero_grad()
if optimizer_st:
optimizer_st.zero_grad()
# forward pass model
if c.bidirectional_decoder:
decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
else:
decoder_output, postnet_output, alignments, stop_tokens = model(
text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
decoder_backward_output = None
# set the alignment lengths wrt reduction factor for guided attention
if mel_lengths.max() % model.decoder.r != 0:
alignment_lengths = (
mel_lengths +
(model.decoder.r -
(mel_lengths.max() % model.decoder.r))) // model.decoder.r
else:
alignment_lengths = mel_lengths // model.decoder.r
# compute loss
loss_dict = criterion(postnet_output, decoder_output, mel_input,
linear_input, stop_tokens, stop_targets,
mel_lengths, decoder_backward_output, alignments,
alignment_lengths, text_lengths)
if c.bidirectional_decoder:
keep_avg.update_values({
'avg_decoder_b_loss':
loss_dict['decoder_backward_loss'].item(),
'avg_decoder_c_loss':
loss_dict['decoder_c_loss'].item()
})
if c.ga_alpha > 0:
keep_avg.update_values(
{'avg_ga_loss': loss_dict['ga_loss'].item()})
# backward pass
loss_dict['loss'].backward()
optimizer, current_lr = adam_weight_decay(optimizer)
grad_norm, _ = check_update(model, c.grad_clip, ignore_stopnet=True)
optimizer.step()
# compute alignment error (the lower the better )
align_error = 1 - alignment_diagonal_score(alignments)
keep_avg.update_value('avg_align_error', align_error)
loss_dict['align_error'] = align_error
# backpass and check the grad norm for stop loss
if c.separate_stopnet:
loss_dict['stopnet_loss'].backward()
optimizer_st, _ = adam_weight_decay(optimizer_st)
grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
optimizer_st.step()
else:
grad_norm_st = 0
step_time = time.time() - start_time
epoch_time += step_time
# update avg stats
update_train_values = {
'avg_postnet_loss': float(loss_dict['postnet_loss'].item()),
'avg_decoder_loss': float(loss_dict['decoder_loss'].item()),
'avg_stopnet_loss': loss_dict['stopnet_loss'].item() \
if isinstance(loss_dict['stopnet_loss'], float) else float(loss_dict['stopnet_loss'].item()),
'avg_step_time': step_time,
'avg_loader_time': loader_time
}
keep_avg.update_values(update_train_values)
if global_step % c.print_step == 0:
c_logger.print_train_step(batch_n_iter, num_iter, global_step,
avg_spec_length, avg_text_length,
step_time, loader_time, current_lr,
loss_dict, keep_avg.avg_values)
# aggregate losses from processes
if num_gpus > 1:
loss_dict['postnet_loss'] = reduce_tensor(
loss_dict['postnet_loss'].data, num_gpus)
loss_dict['decoder_loss'] = reduce_tensor(
loss_dict['decoder_loss'].data, num_gpus)
loss_dict['loss'] = reduce_tensor(loss_dict['loss'].data, num_gpus)
loss_dict['stopnet_loss'] = reduce_tensor(
loss_dict['stopnet_loss'].data,
num_gpus) if c.stopnet else loss_dict['stopnet_loss']
if args.rank == 0:
# Plot Training Iter Stats
# reduce TB load
if global_step % 10 == 0:
iter_stats = {
"loss_posnet": loss_dict['postnet_loss'].item(),
"loss_decoder": loss_dict['decoder_loss'].item(),
"lr": current_lr,
"grad_norm": grad_norm,
"grad_norm_st": grad_norm_st,
"step_time": step_time
}
tb_logger.tb_train_iter_stats(global_step, iter_stats)
if global_step % c.save_step == 0:
if c.checkpoint:
# save model
save_checkpoint(
model,
optimizer,
global_step,
epoch,
model.decoder.r,
OUT_PATH,
optimizer_st=optimizer_st,
model_loss=loss_dict['postnet_loss'].item())
# Diagnostic visualizations
const_spec = postnet_output[0].data.cpu().numpy()
gt_spec = linear_input[0].data.cpu().numpy() if c.model in [
"Tacotron", "TacotronGST"
] else mel_input[0].data.cpu().numpy()
align_img = alignments[0].data.cpu().numpy()
figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img),
}
if c.bidirectional_decoder:
figures["alignment_backward"] = plot_alignment(
alignments_backward[0].data.cpu().numpy())
tb_logger.tb_train_figures(global_step, figures)
# Sample audio
if c.model in ["Tacotron", "TacotronGST"]:
train_audio = ap.inv_spectrogram(const_spec.T)
else:
train_audio = ap.inv_melspectrogram(const_spec.T)
tb_logger.tb_train_audios(global_step,
{'TrainAudio': train_audio},
c.audio["sample_rate"])
end_time = time.time()
# print epoch stats
c_logger.print_train_epoch_end(global_step, epoch, epoch_time, keep_avg)
# Plot Epoch Stats
if args.rank == 0:
# Plot Training Epoch Stats
epoch_stats = {
"loss_postnet": keep_avg['avg_postnet_loss'],
"loss_decoder": keep_avg['avg_decoder_loss'],
"stopnet_loss": keep_avg['avg_stopnet_loss'],
"alignment_score": keep_avg['avg_align_error'],
"epoch_time": epoch_time
}
if c.ga_alpha > 0:
epoch_stats['guided_attention_loss'] = keep_avg['avg_ga_loss']
tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
if c.tb_model_param_stats:
tb_logger.tb_model_weights(model, global_step)
return keep_avg.avg_values, global_step
def evaluate(model, criterion, ap, global_step, epoch):
data_loader = setup_loader(ap, model.decoder.r, is_val=True)
model.eval()
epoch_time = 0
keep_avg = KeepAverage()
c_logger.print_eval_start()
if data_loader is not None:
for num_iter, data in enumerate(data_loader):
start_time = time.time()
# format data
text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, _, _ = format_data(
data)
assert mel_input.shape[1] % model.decoder.r == 0
# forward pass model
if c.bidirectional_decoder or c.double_decoder_consistency:
decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
text_input,
text_lengths,
mel_input,
speaker_ids=speaker_ids)
else:
decoder_output, postnet_output, alignments, stop_tokens = model(
text_input,
text_lengths,
mel_input,
speaker_ids=speaker_ids)
decoder_backward_output = None
alignments_backward = None
# set the alignment lengths wrt reduction factor for guided attention
if mel_lengths.max() % model.decoder.r != 0:
alignment_lengths = (
mel_lengths +
(model.decoder.r -
(mel_lengths.max() % model.decoder.r))) // model.decoder.r
else:
alignment_lengths = mel_lengths // model.decoder.r
# compute loss
loss_dict = criterion(postnet_output, decoder_output, mel_input,
linear_input, stop_tokens, stop_targets,
mel_lengths, decoder_backward_output,
alignments, alignment_lengths,
alignments_backward, text_lengths)
# step time
step_time = time.time() - start_time
epoch_time += step_time
# compute alignment score
align_error = 1 - alignment_diagonal_score(alignments)
loss_dict['align_error'] = align_error
# aggregate losses from processes
if num_gpus > 1:
loss_dict['postnet_loss'] = reduce_tensor(
loss_dict['postnet_loss'].data, num_gpus)
loss_dict['decoder_loss'] = reduce_tensor(
loss_dict['decoder_loss'].data, num_gpus)
if c.stopnet:
loss_dict['stopnet_loss'] = reduce_tensor(
loss_dict['stopnet_loss'].data, num_gpus)
# detach loss values
loss_dict_new = dict()
for key, value in loss_dict.items():
if isinstance(value, (int, float)):
loss_dict_new[key] = value
else:
loss_dict_new[key] = value.item()
loss_dict = loss_dict_new
# update avg stats
update_train_values = dict()
for key, value in loss_dict.items():
update_train_values['avg_' + key] = value
keep_avg.update_values(update_train_values)
if c.print_eval:
c_logger.print_eval_step(num_iter, loss_dict,
keep_avg.avg_values)
if args.rank == 0:
# Diagnostic visualizations
idx = np.random.randint(mel_input.shape[0])
const_spec = postnet_output[idx].data.cpu().numpy()
gt_spec = linear_input[idx].data.cpu().numpy() if c.model in [
"Tacotron", "TacotronGST"
] else mel_input[idx].data.cpu().numpy()
align_img = alignments[idx].data.cpu().numpy()
eval_figures = {
"prediction": plot_spectrogram(const_spec, ap),
"ground_truth": plot_spectrogram(gt_spec, ap),
"alignment": plot_alignment(align_img)
}
# Sample audio
if c.model in ["Tacotron", "TacotronGST"]:
eval_audio = ap.inv_spectrogram(const_spec.T)
else:
eval_audio = ap.inv_melspectrogram(const_spec.T)
tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio},
c.audio["sample_rate"])
# Plot Validation Stats
if c.bidirectional_decoder or c.double_decoder_consistency:
align_b_img = alignments_backward[idx].data.cpu().numpy()
eval_figures['alignment2'] = plot_alignment(align_b_img)
tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
tb_logger.tb_eval_figures(global_step, eval_figures)
if args.rank == 0 and epoch > c.test_delay_epochs:
if c.test_sentences_file is None:
test_sentences = [
"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
"Be a voice, not an echo.",
"I'm sorry Dave. I'm afraid I can't do that.",
"This cake is great. It's so delicious and moist.",
"Prior to November 22, 1963."
]
else:
with open(c.test_sentences_file, "r") as f:
test_sentences = [s.strip() for s in f.readlines()]
# test sentences
test_audios = {}
test_figures = {}
print(" | > Synthesizing test sentences")
speaker_id = 0 if c.use_speaker_embedding else None
style_wav = c.get("style_wav_for_test")
for idx, test_sentence in enumerate(test_sentences):
try:
wav, alignment, decoder_output, postnet_output, stop_tokens, inputs = synthesis(
model,
test_sentence,
c,
use_cuda,
ap,
speaker_id=speaker_id,
style_wav=style_wav,
truncated=False,
enable_eos_bos_chars=c.enable_eos_bos_chars, #pylint: disable=unused-argument
use_griffin_lim=True,
do_trim_silence=False)
file_path = os.path.join(AUDIO_PATH, str(global_step))
os.makedirs(file_path, exist_ok=True)
file_path = os.path.join(file_path,
"TestSentence_{}.wav".format(idx))
ap.save_wav(wav, file_path)
test_audios['{}-audio'.format(idx)] = wav
test_figures['{}-prediction'.format(idx)] = plot_spectrogram(
postnet_output, ap)
test_figures['{}-alignment'.format(idx)] = plot_alignment(
alignment)
except:
print(" !! Error creating Test Sentence -", idx)
traceback.print_exc()
tb_logger.tb_test_audios(global_step, test_audios,
c.audio['sample_rate'])
tb_logger.tb_test_figures(global_step, test_figures)
return keep_avg.avg_values
