def test_train_step(self):
input = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8, )).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
linear_spec = torch.rand(8, 30, c.audio['num_freq']).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
stop_targets = torch.zeros(8, 30, 1).float().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked().to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron(
num_chars=32,
num_speakers=5,
linear_dim=c.audio['num_freq'],
mel_dim=c.audio['num_mels'],
r=c.r,
memory_size=c.memory_size
).to(device) #FIXME: missing num_speakers parameter to Tacotron ctor
model.train()
print(" > Num parameters for Tacotron model:%s" %
(count_parameters(model)))
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for _ in range(5):
mel_out, linear_out, align, stop_tokens = model.forward(
input, input_lengths, mel_spec, speaker_ids)
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
loss = loss + criterion(linear_out, linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
# if count not in [145, 59]:
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def test_train_step(self):
input = torch.randint(0, 24, (8, 128)).long().to(device)
mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
linear_spec = torch.rand(8, 30, c.audio['num_freq']).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
stop_targets = torch.zeros(8, 30, 1).float().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float()
criterion = L1LossMasked().to(device)
criterion_st = nn.BCELoss().to(device)
model = Tacotron(c.embedding_size, c.audio['num_freq'],
c.audio['num_mels'], c.r).to(device)
model.train()
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for i in range(5):
mel_out, linear_out, align, stop_tokens = model.forward(
input, mel_spec)
assert stop_tokens.data.max() <= 1.0
assert stop_tokens.data.min() >= 0.0
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
loss = loss + criterion(linear_out, linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
# if count not in [145, 59]:
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def main(args):
# setup output paths and read configs
c = load_config(args.config_path)
_ = os.path.dirname(os.path.realpath(__file__))
OUT_PATH = os.path.join(_, c.output_path)
OUT_PATH = create_experiment_folder(OUT_PATH)
CHECKPOINT_PATH = os.path.join(OUT_PATH, 'checkpoints')
shutil.copyfile(args.config_path, os.path.join(OUT_PATH, 'config.json'))
# save config to tmp place to be loaded by subsequent modules.
file_name = str(os.getpid())
tmp_path = os.path.join("/tmp/", file_name+'_tts')
pickle.dump(c, open(tmp_path, "wb"))
# setup tensorboard
LOG_DIR = OUT_PATH
tb = SummaryWriter(LOG_DIR)
# Ctrl+C handler to remove empty experiment folder
def signal_handler(signal, frame):
print(" !! Pressed Ctrl+C !!")
remove_experiment_folder(OUT_PATH)
sys.exit(1)
signal.signal(signal.SIGINT, signal_handler)
# Setup the dataset
dataset = LJSpeechDataset(os.path.join(c.data_path, 'metadata.csv'),
os.path.join(c.data_path, 'wavs'),
c.r,
c.sample_rate,
c.text_cleaner,
c.num_mels,
c.min_level_db,
c.frame_shift_ms,
c.frame_length_ms,
c.preemphasis,
c.ref_level_db,
c.num_freq,
c.power
)
dataloader = DataLoader(dataset, batch_size=c.batch_size,
shuffle=True, collate_fn=dataset.collate_fn,
drop_last=True, num_workers=c.num_loader_workers)
# setup the model
model = Tacotron(c.embedding_size,
c.hidden_size,
c.num_mels,
c.num_freq,
c.r)
# plot model on tensorboard
dummy_input = dataset.get_dummy_data()
## TODO: onnx does not support RNN fully yet
# model_proto_path = os.path.join(OUT_PATH, "model.proto")
# onnx.export(model, dummy_input, model_proto_path, verbose=True)
# tb.add_graph_onnx(model_proto_path)
if use_cuda:
model = nn.DataParallel(model.cuda())
optimizer = optim.Adam(model.parameters(), lr=c.lr)
if args.restore_step:
checkpoint = torch.load(os.path.join(
args.restore_path, 'checkpoint_%d.pth.tar' % args.restore_step))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n > Model restored from step %d\n" % args.restore_step)
start_epoch = checkpoint['step'] // len(dataloader)
best_loss = checkpoint['linear_loss']
else:
start_epoch = 0
print("\n > Starting a new training")
num_params = count_parameters(model)
print(" | > Model has {} parameters".format(num_params))
model = model.train()
if not os.path.exists(CHECKPOINT_PATH):
os.mkdir(CHECKPOINT_PATH)
if use_cuda:
criterion = nn.L1Loss().cuda()
else:
criterion = nn.L1Loss()
n_priority_freq = int(3000 / (c.sample_rate * 0.5) * c.num_freq)
#lr_scheduler = ReduceLROnPlateau(optimizer, factor=c.lr_decay,
# patience=c.lr_patience, verbose=True)
epoch_time = 0
best_loss = float('inf')
for epoch in range(0, c.epochs):
print("\n | > Epoch {}/{}".format(epoch, c.epochs))
progbar = Progbar(len(dataset) / c.batch_size)
for num_iter, data in enumerate(dataloader):
start_time = time.time()
text_input = data[0]
text_lengths = data[1]
linear_input = data[2]
mel_input = data[3]
current_step = num_iter + args.restore_step + epoch * len(dataloader) + 1
# setup lr
current_lr = lr_decay(c.lr, current_step)
for params_group in optimizer.param_groups:
params_group['lr'] = current_lr
optimizer.zero_grad()
# Add a single frame of zeros to Mel Specs for better end detection
#try:
# mel_input = np.concatenate((np.zeros(
# [c.batch_size, 1, c.num_mels], dtype=np.float32),
# mel_input[:, 1:, :]), axis=1)
#except:
# raise TypeError("not same dimension")
# convert inputs to variables
text_input_var = Variable(text_input)
mel_spec_var = Variable(mel_input)
linear_spec_var = Variable(linear_input, volatile=True)
# sort sequence by length.
# TODO: might be unnecessary
sorted_lengths, indices = torch.sort(
text_lengths.view(-1), dim=0, descending=True)
sorted_lengths = sorted_lengths.long().numpy()
text_input_var = text_input_var[indices]
mel_spec_var = mel_spec_var[indices]
linear_spec_var = linear_spec_var[indices]
if use_cuda:
text_input_var = text_input_var.cuda()
mel_spec_var = mel_spec_var.cuda()
linear_spec_var = linear_spec_var.cuda()
mel_output, linear_output, alignments =\
model.forward(text_input_var, mel_spec_var,
input_lengths= torch.autograd.Variable(torch.cuda.LongTensor(sorted_lengths)))
mel_loss = criterion(mel_output, mel_spec_var)
#linear_loss = torch.abs(linear_output - linear_spec_var)
#linear_loss = 0.5 * \
#torch.mean(linear_loss) + 0.5 * \
#torch.mean(linear_loss[:, :n_priority_freq, :])
linear_loss = 0.5 * criterion(linear_output, linear_spec_var) \
+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
linear_spec_var[: ,: ,:n_priority_freq])
loss = mel_loss + linear_loss
# loss = loss.cuda()
loss.backward()
grad_norm = nn.utils.clip_grad_norm(model.parameters(), 1.) ## TODO: maybe no need
optimizer.step()
step_time = time.time() - start_time
epoch_time += step_time
progbar.update(num_iter+1, values=[('total_loss', loss.data[0]),
('linear_loss', linear_loss.data[0]),
('mel_loss', mel_loss.data[0]),
('grad_norm', grad_norm)])
# Plot Learning Stats
tb.add_scalar('Loss/TotalLoss', loss.data[0], current_step)
tb.add_scalar('Loss/LinearLoss', linear_loss.data[0],
current_step)
tb.add_scalar('Loss/MelLoss', mel_loss.data[0], current_step)
tb.add_scalar('Params/LearningRate', optimizer.param_groups[0]['lr'],
current_step)
tb.add_scalar('Params/GradNorm', grad_norm, current_step)
tb.add_scalar('Time/StepTime', step_time, current_step)
align_img = alignments[0].data.cpu().numpy()
align_img = plot_alignment(align_img)
tb.add_image('Attn/Alignment', align_img, current_step)
if current_step % c.save_step == 0:
if c.checkpoint:
# save model
save_checkpoint(model, optimizer, linear_loss.data[0],
OUT_PATH, current_step, epoch)
# Diagnostic visualizations
const_spec = linear_output[0].data.cpu().numpy()
gt_spec = linear_spec_var[0].data.cpu().numpy()
const_spec = plot_spectrogram(const_spec, dataset.ap)
gt_spec = plot_spectrogram(gt_spec, dataset.ap)
tb.add_image('Spec/Reconstruction', const_spec, current_step)
tb.add_image('Spec/GroundTruth', gt_spec, current_step)
align_img = alignments[0].data.cpu().numpy()
align_img = plot_alignment(align_img)
tb.add_image('Attn/Alignment', align_img, current_step)
# Sample audio
audio_signal = linear_output[0].data.cpu().numpy()
dataset.ap.griffin_lim_iters = 60
audio_signal = dataset.ap.inv_spectrogram(audio_signal.T)
try:
tb.add_audio('SampleAudio', audio_signal, current_step,
sample_rate=c.sample_rate)
except:
print("\n > Error at audio signal on TB!!")
print(audio_signal.max())
print(audio_signal.min())
# average loss after the epoch
avg_epoch_loss = np.mean(
progbar.sum_values['linear_loss'][0] / max(1, progbar.sum_values['linear_loss'][1]))
best_loss = save_best_model(model, optimizer, avg_epoch_loss,
best_loss, OUT_PATH,
current_step, epoch)
#lr_scheduler.step(loss.data[0])
tb.add_scalar('Time/EpochTime', epoch_time, epoch)
epoch_time = 0
def train_session(self, model: Tacotron, optimizer: Optimizer,
session: TTSSession) -> None:
current_step = model.get_step()
training_steps = session.max_step - current_step
total_iters = len(session.train_set)
epochs = training_steps // total_iters + 1
model.r = session.r
simple_table([(f'Steps with r={session.r}',
str(training_steps // 1000) + 'k Steps'),
('Batch Size', session.bs),
('Learning Rate', session.lr),
('Outputs/Step (r)', model.r)])
for g in optimizer.param_groups:
g['lr'] = session.lr
loss_avg = Averager()
duration_avg = Averager()
device = next(
model.parameters()).device # use same device as model parameters
for e in range(1, epochs + 1):
for i, (x, m, ids, x_lens,
mel_lens) in enumerate(session.train_set, 1):
start = time.time()
model.train()
x, m = x.to(device), m.to(device)
m1_hat, m2_hat, attention = model(x, m)
m1_loss = F.l1_loss(m1_hat, m)
m2_loss = F.l1_loss(m2_hat, m)
loss = m1_loss + m2_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
hp.tts_clip_grad_norm)
optimizer.step()
loss_avg.add(loss.item())
step = model.get_step()
k = step // 1000
duration_avg.add(time.time() - start)
speed = 1. / duration_avg.get()
msg = f'| Epoch: {e}/{epochs} ({i}/{total_iters}) | Loss: {loss_avg.get():#.4} ' \
f'| {speed:#.2} steps/s | Step: {k}k | '
if step % hp.tts_checkpoint_every == 0:
ckpt_name = f'taco_step{k}K'
save_checkpoint('tts',
self.paths,
model,
optimizer,
name=ckpt_name,
is_silent=True)
if step % hp.tts_plot_every == 0:
self.generate_plots(model, session)
_, att_score = attention_score(attention, mel_lens)
att_score = torch.mean(att_score)
self.writer.add_scalar('Attention_Score/train', att_score,
model.get_step())
self.writer.add_scalar('Loss/train', loss, model.get_step())
self.writer.add_scalar('Params/reduction_factor', session.r,
model.get_step())
self.writer.add_scalar('Params/batch_size', session.bs,
model.get_step())
self.writer.add_scalar('Params/learning_rate', session.lr,
model.get_step())
stream(msg)
val_loss, val_att_score = self.evaluate(model, session.val_set)
self.writer.add_scalar('Loss/val', val_loss, model.get_step())
self.writer.add_scalar('Attention_Score/val', val_att_score,
model.get_step())
save_checkpoint('tts',
self.paths,
model,
optimizer,
is_silent=True)
loss_avg.reset()
duration_avg.reset()
print(' ')
def main(args):
# setup output paths and read configs
c = load_config(args.config_path)
_ = os.path.dirname(os.path.realpath(__file__))
OUT_PATH = os.path.join(_, c.output_path)
OUT_PATH = create_experiment_folder(OUT_PATH)
CHECKPOINT_PATH = os.path.join(OUT_PATH, 'checkpoints')
shutil.copyfile(args.config_path, os.path.join(OUT_PATH, 'config.json'))
# Ctrl+C handler to remove empty experiment folder
def signal_handler(signal, frame):
print(" !! Pressed Ctrl+C !!")
remove_experiment_folder(OUT_PATH)
sys.exit(0)
signal.signal(signal.SIGINT, signal_handler)
dataset = LJSpeechDataset(os.path.join(c.data_path, 'metadata.csv'),
os.path.join(c.data_path, 'wavs'), c.r,
c.sample_rate, c.text_cleaner)
model = Tacotron(c.embedding_size, c.hidden_size, c.num_mels, c.num_freq,
c.r)
if use_cuda:
model = nn.DataParallel(model.cuda())
optimizer = optim.Adam(model.parameters(), lr=c.lr)
try:
checkpoint = torch.load(
os.path.join(CHECKPOINT_PATH,
'checkpoint_%d.pth.tar' % args.restore_step))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n > Model restored from step %d\n" % args.restore_step)
except:
print("\n > Starting a new training\n")
model = model.train()
if not os.path.exists(CHECKPOINT_PATH):
os.mkdir(CHECKPOINT_PATH)
if use_cuda:
criterion = nn.L1Loss().cuda()
else:
criterion = nn.L1Loss()
n_priority_freq = int(3000 / (c.sample_rate * 0.5) * c.num_freq)
for epoch in range(c.epochs):
dataloader = DataLoader(dataset,
batch_size=c.batch_size,
shuffle=True,
collate_fn=dataset.collate_fn,
drop_last=True,
num_workers=32)
progbar = Progbar(len(dataset) / c.batch_size)
for i, data in enumerate(dataloader):
text_input = data[0]
magnitude_input = data[1]
mel_input = data[2]
current_step = i + args.restore_step + epoch * len(dataloader) + 1
optimizer.zero_grad()
try:
mel_input = np.concatenate(
(np.zeros([c.batch_size, 1, c.num_mels],
dtype=np.float32), mel_input[:, 1:, :]),
axis=1)
except:
raise TypeError("not same dimension")
if use_cuda:
text_input_var = Variable(torch.from_numpy(text_input).type(
torch.cuda.LongTensor),
requires_grad=False).cuda()
mel_input_var = Variable(torch.from_numpy(mel_input).type(
torch.cuda.FloatTensor),
requires_grad=False).cuda()
mel_spec_var = Variable(torch.from_numpy(mel_input).type(
torch.cuda.FloatTensor),
requires_grad=False).cuda()
linear_spec_var = Variable(
torch.from_numpy(magnitude_input).type(
torch.cuda.FloatTensor),
requires_grad=False).cuda()
else:
text_input_var = Variable(torch.from_numpy(text_input).type(
torch.LongTensor),
requires_grad=False)
mel_input_var = Variable(torch.from_numpy(mel_input).type(
torch.FloatTensor),
requires_grad=False)
mel_spec_var = Variable(torch.from_numpy(mel_input).type(
torch.FloatTensor),
requires_grad=False)
linear_spec_var = Variable(
torch.from_numpy(magnitude_input).type(torch.FloatTensor),
requires_grad=False)
mel_output, linear_output, alignments =\
model.forward(text_input_var, mel_input_var)
mel_loss = criterion(mel_output, mel_spec_var)
linear_loss = torch.abs(linear_output - linear_spec_var)
linear_loss = 0.5 * \
torch.mean(linear_loss) + 0.5 * \
torch.mean(linear_loss[:, :n_priority_freq, :])
loss = mel_loss + linear_loss
loss = loss.cuda()
start_time = time.time()
loss.backward()
nn.utils.clip_grad_norm(model.parameters(), 1.)
optimizer.step()
time_per_step = time.time() - start_time
progbar.update(i,
values=[('total_loss', loss.data[0]),
('linear_loss', linear_loss.data[0]),
('mel_loss', mel_loss.data[0])])
if current_step % c.save_step == 0:
checkpoint_path = 'checkpoint_{}.pth.tar'.format(current_step)
checkpoint_path = os.path.join(OUT_PATH, checkpoint_path)
save_checkpoint(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'step': current_step,
'total_loss': loss.data[0],
'linear_loss': linear_loss.data[0],
'mel_loss': mel_loss.data[0],
'date': datetime.date.today().strftime("%B %d, %Y")
}, checkpoint_path)
print(" > Checkpoint is saved : {}".format(checkpoint_path))
if current_step in c.decay_step:
optimizer = adjust_learning_rate(optimizer, current_step)
