def evaluate(model, criterion, ap, global_step, epoch, speaker_mapping=None):
data_loader = setup_loader(ap,
model.decoder.r,
is_val=True,
speaker_mapping=speaker_mapping)
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, speaker_embeddings, _, _ = format_data(
data, speaker_mapping)
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,
speaker_embeddings=speaker_embeddings)
else:
decoder_output, postnet_output, alignments, stop_tokens = model(
text_input,
text_lengths,
mel_input,
speaker_ids=speaker_ids,
speaker_embeddings=speaker_embeddings)
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,
output_fig=False),
"ground_truth": plot_spectrogram(gt_spec, ap,
output_fig=False),
"alignment": plot_alignment(align_img, output_fig=False)
}
# 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,
output_fig=False)
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
speaker_embedding = speaker_mapping[list(speaker_mapping.keys(
))[randrange(
len(speaker_mapping) - 1
)]]['embedding'] if c.use_external_speaker_embedding_file and c.use_speaker_embedding else None
style_wav = c.get("gst_style_input")
if style_wav is None and c.use_gst:
# inicialize GST with zero dict.
style_wav = {}
print(
"WARNING: You don't provided a gst style wav, for this reason we use a zero tensor!"
)
for i in range(c.gst['gst_style_tokens']):
style_wav[str(i)] = 0
style_wav = c.get("gst_style_input")
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,
speaker_embedding=speaker_embedding,
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, output_fig=False)
test_figures['{}-alignment'.format(idx)] = plot_alignment(
alignment, output_fig=False)
except: #pylint: disable=bare-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 evaluate(model, criterion, ap, global_step, epoch):
data_loader = setup_loader(ap, 1, 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, _,\
_, _, attn_mask = format_data(data)
# forward pass model
z, logdet, y_mean, y_log_scale, alignments, o_dur_log, o_total_dur = model.forward(
text_input, text_lengths, mel_input, mel_lengths, attn_mask)
# compute loss
loss_dict = criterion(z, y_mean, y_log_scale, logdet, mel_lengths,
o_dur_log, o_total_dur, 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['log_mle'] = reduce_tensor(loss_dict['log_mle'].data, num_gpus)
loss_dict['loss_dur'] = reduce_tensor(loss_dict['loss_dur'].data, num_gpus)
loss_dict['loss'] = reduce_tensor(loss_dict['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
# direct pass on model for spec predictions
if hasattr(model, 'module'):
spec_pred, *_ = model.module.inference(text_input[:1], text_lengths[:1])
else:
spec_pred, *_ = model.inference(text_input[:1], text_lengths[:1])
spec_pred = spec_pred.permute(0, 2, 1)
gt_spec = mel_input.permute(0, 2, 1)
const_spec = spec_pred[0].data.cpu().numpy()
gt_spec = gt_spec[0].data.cpu().numpy()
align_img = alignments[0].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
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
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, _, postnet_output, _, _ = 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: #pylint: disable=bare-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,
amp,
speaker_mapping=None):
data_loader = setup_loader(ap,
model.decoder.r,
is_val=False,
verbose=(epoch == 0),
speaker_mapping=speaker_mapping)
model.train()
epoch_time = 0
keep_avg = KeepAverage()
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, speaker_embeddings, avg_text_length, avg_spec_length = format_data(
data, speaker_mapping)
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 or c.double_decoder_consistency:
decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
text_input,
text_lengths,
mel_input,
mel_lengths,
speaker_ids=speaker_ids,
speaker_embeddings=speaker_embeddings)
else:
decoder_output, postnet_output, alignments, stop_tokens = model(
text_input,
text_lengths,
mel_input,
mel_lengths,
speaker_ids=speaker_ids,
speaker_embeddings=speaker_embeddings)
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)
# backward pass
if amp is not None:
with amp.scale_loss(loss_dict['loss'], optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss_dict['loss'].backward()
optimizer, current_lr = adam_weight_decay(optimizer)
if amp:
amp_opt_params = amp.master_params(optimizer)
else:
amp_opt_params = None
grad_norm, _ = check_update(model,
c.grad_clip,
ignore_stopnet=True,
amp_opt_params=amp_opt_params)
optimizer.step()
# compute alignment error (the lower the better )
align_error = 1 - alignment_diagonal_score(alignments)
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)
if amp:
amp_opt_params = amp.master_params(optimizer)
else:
amp_opt_params = None
grad_norm_st, _ = check_update(model.decoder.stopnet,
1.0,
amp_opt_params=amp_opt_params)
optimizer_st.step()
else:
grad_norm_st = 0
step_time = time.time() - start_time
epoch_time += step_time
# 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']
# 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
update_train_values['avg_loader_time'] = loader_time
update_train_values['avg_step_time'] = step_time
keep_avg.update_values(update_train_values)
# print training progress
if global_step % c.print_step == 0:
log_dict = {
"avg_spec_length": [avg_spec_length, 1], # value, precision
"avg_text_length": [avg_text_length, 1],
"step_time": [step_time, 4],
"loader_time": [loader_time, 2],
"current_lr": current_lr,
}
c_logger.print_train_step(batch_n_iter, num_iter, global_step,
log_dict, loss_dict, keep_avg.avg_values)
if args.rank == 0:
# Plot Training Iter Stats
# reduce TB load
if global_step % c.tb_plot_step == 0:
iter_stats = {
"lr": current_lr,
"grad_norm": grad_norm,
"grad_norm_st": grad_norm_st,
"step_time": step_time
}
iter_stats.update(loss_dict)
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'],
amp_state_dict=amp.state_dict() if amp else None)
# 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, output_fig=False),
"ground_truth":
plot_spectrogram(gt_spec, ap, output_fig=False),
"alignment":
plot_alignment(align_img, output_fig=False),
}
if c.bidirectional_decoder or c.double_decoder_consistency:
figures["alignment_backward"] = plot_alignment(
alignments_backward[0].data.cpu().numpy(),
output_fig=False)
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:
epoch_stats = {"epoch_time": epoch_time}
epoch_stats.update(keep_avg.avg_values)
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 train(model, criterion, optimizer, scheduler,
ap, global_step, epoch, amp):
data_loader = setup_loader(ap, 1, is_val=False,
verbose=(epoch == 0))
model.train()
epoch_time = 0
keep_avg = KeepAverage()
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, _,\
avg_text_length, avg_spec_length, attn_mask = format_data(data)
loader_time = time.time() - end_time
global_step += 1
# setup lr
if c.noam_schedule:
scheduler.step()
optimizer.zero_grad()
# forward pass model
z, logdet, y_mean, y_log_scale, alignments, o_dur_log, o_total_dur = model.forward(
text_input, text_lengths, mel_input, mel_lengths, attn_mask)
# compute loss
loss_dict = criterion(z, y_mean, y_log_scale, logdet, mel_lengths,
o_dur_log, o_total_dur, text_lengths)
# backward pass
if amp is not None:
with amp.scale_loss(loss_dict['loss'], optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss_dict['loss'].backward()
if amp:
amp_opt_params = amp.master_params(optimizer)
else:
amp_opt_params = None
grad_norm, _ = check_update(model, c.grad_clip, ignore_stopnet=True, amp_opt_params=amp_opt_params)
optimizer.step()
# current_lr
current_lr = optimizer.param_groups[0]['lr']
# compute alignment error (the lower the better )
align_error = 1 - alignment_diagonal_score(alignments)
loss_dict['align_error'] = align_error
step_time = time.time() - start_time
epoch_time += step_time
# aggregate losses from processes
if num_gpus > 1:
loss_dict['log_mle'] = reduce_tensor(loss_dict['log_mle'].data, num_gpus)
loss_dict['loss_dur'] = reduce_tensor(loss_dict['loss_dur'].data, num_gpus)
loss_dict['loss'] = reduce_tensor(loss_dict['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
update_train_values['avg_loader_time'] = loader_time
update_train_values['avg_step_time'] = step_time
keep_avg.update_values(update_train_values)
# print training progress
if global_step % c.print_step == 0:
log_dict = {
"avg_spec_length": [avg_spec_length, 1], # value, precision
"avg_text_length": [avg_text_length, 1],
"step_time": [step_time, 4],
"loader_time": [loader_time, 2],
"current_lr": current_lr,
}
c_logger.print_train_step(batch_n_iter, num_iter, global_step,
log_dict, loss_dict, keep_avg.avg_values)
if args.rank == 0:
# Plot Training Iter Stats
# reduce TB load
if global_step % c.tb_plot_step == 0:
iter_stats = {
"lr": current_lr,
"grad_norm": grad_norm,
"step_time": step_time
}
iter_stats.update(loss_dict)
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, 1, OUT_PATH,
model_loss=loss_dict['loss'],
amp_state_dict=amp.state_dict() if amp else None)
# Diagnostic visualizations
# direct pass on model for spec predictions
spec_pred, *_ = model.inference(text_input[:1], text_lengths[:1])
spec_pred = spec_pred.permute(0, 2, 1)
gt_spec = mel_input.permute(0, 2, 1)
const_spec = spec_pred[0].data.cpu().numpy()
gt_spec = gt_spec[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
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:
epoch_stats = {"epoch_time": epoch_time}
epoch_stats.update(keep_avg.avg_values)
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