def main():
args = parser.parse_args()
# Load a config file (.yml)
params = load_config(join(args.model_path, 'config.yml'), is_eval=True)
# Setting for logging
logger = set_logger(args.model_path)
for i, data_type in enumerate(['dev', 'test']):
# Load dataset
dataset = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type=data_type,
label_type=params['label_type'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
tool=params['tool'])
if i == 0:
params['num_classes'] = dataset.num_classes
# Load model
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Restore the saved parameters
epoch, _, _, _ = model.load_checkpoint(save_path=args.model_path,
epoch=args.epoch)
# GPU setting
model.set_cuda(deterministic=False, benchmark=True)
logger.info('beam width: %d' % args.beam_width)
logger.info('epoch: %d' % (epoch - 1))
per, df = eval_phone(model=model,
dataset=dataset,
map_file_path='./conf/phones.60-48-39.map',
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_PHONE,
min_decode_len=MIN_DECODE_LEN_PHONE,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty,
progressbar=True)
logger.info(' PER (%s): %.3f %%' % (data_type, (per * 100)))
logger.info(df)
def main():
args = parser.parse_args()
# Load a config file (.yml)
params = load_config(join(args.model_path, 'config.yml'), is_eval=True)
# Load dataset
test_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test',
label_type=params['label_type'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=True,
reverse=True,
tool=params['tool'])
params['num_classes'] = test_data.num_classes
# Load model
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Restore the saved parameters
model.load_checkpoint(save_path=args.model_path, epoch=args.epoch)
# GPU setting
model.set_cuda(deterministic=False, benchmark=True)
# Visualize
decode(model=model,
dataset=test_data,
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
length_penalty=args.length_penalty,
save_path=None)
def main():
args = parser.parse_args()
# Load a config file (.yml)
params = load_config(join(args.model_path, 'config.yml'), is_eval=True)
# Load dataset
dataset = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test',
label_type=params['label_type'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=True,
reverse=True,
tool=params['tool'])
params['num_classes'] = dataset.num_classes
# Load model
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Restore the saved parameters
model.load_checkpoint(save_path=args.model_path, epoch=args.epoch)
# GPU setting
model.set_cuda(deterministic=False, benchmark=True)
save_path = mkdir_join(args.model_path, 'ctc_probs')
######################################################################
# Clean directory
if save_path is not None and isdir(save_path):
shutil.rmtree(save_path)
mkdir(save_path)
for batch, is_new_epoch in dataset:
# Get CTC probs
probs, x_lens, _ = model.posteriors(batch['xs'],
batch['x_lens'],
temperature=1)
# NOTE: probs: '[B, T, num_classes]'
# Visualize
for b in range(len(batch['xs'])):
plot_ctc_probs(probs[b, :x_lens[b], :],
frame_num=x_lens[b],
num_stack=dataset.num_stack,
spectrogram=batch['xs'][b, :, :40],
save_path=join(save_path,
batch['input_names'][b] + '.png'),
figsize=(14, 7))
if is_new_epoch:
break
def main():
args = parser.parse_args()
# Load a config file (.yml)
params = load_config(join(args.model_path, 'config.yml'), is_eval=True)
# Load dataset
dev_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='dev',
label_type=params['label_type'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
tool=params['tool'])
test_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test',
label_type=params['label_type'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
tool=params['tool'])
params['num_classes'] = test_data.num_classes
# Load model
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Restore the saved parameters
model.load_checkpoint(save_path=args.model_path, epoch=args.epoch)
# GPU setting
model.set_cuda(deterministic=False, benchmark=True)
print('beam width: %d' % args.beam_width)
# dev
per_dev, df_dev = eval_phone(model=model,
dataset=dev_data,
map_file_path='./conf/phones.60-48-39.map',
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_PHONE,
length_penalty=args.length_penalty,
progressbar=True)
print(' PER (dev): %.3f %%' % (per_dev * 100))
print(df_dev)
# test
per_test, df_test = eval_phone(model=model,
dataset=test_data,
map_file_path='./conf/phones.60-48-39.map',
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_PHONE,
length_penalty=args.length_penalty,
progressbar=True)
print(' PER (test): %.3f %%' % (per_test * 100))
print(df_test)
with open(join(args.model_path, 'RESULTS'), 'w') as f:
f.write('beam width: %d\n' % args.beam_width)
f.write(' PER (dev): %.3f %%' % (per_dev * 100))
f.write(' PER (test): %.3f %%' % (per_test * 100))
def main():
args = parser.parse_args()
# Load a config file (.yml)
params = load_config(join(args.model_path, 'config.yml'), is_eval=True)
# Load dataset
dataset = Dataset(
data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test',
label_type=params['label_type'],
batch_size=args.eval_batch_size, splice=params['splice'],
num_stack=params['num_stack'], num_skip=params['num_skip'],
sort_utt=True, reverse=True, tool=params['tool'])
params['num_classes'] = dataset.num_classes
# Load model
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Restore the saved parameters
model.load_checkpoint(save_path=args.model_path, epoch=args.epoch)
# GPU setting
model.set_cuda(deterministic=False, benchmark=True)
# sys.stdout = open(join(model.model_dir, 'decode.txt'), 'w')
######################################################################
for batch, is_new_epoch in dataset:
# Decode
best_hyps, _, perm_idx = model.decode(
batch['xs'], batch['x_lens'],
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_PHONE,
min_decode_len=MIN_DECODE_LEN_PHONE,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty)
if model.model_type == 'attention' and model.ctc_loss_weight > 0:
best_hyps_ctc, perm_idx = model.decode_ctc(
batch['xs'], batch['x_lens'],
beam_width=args.beam_width)
ys = batch['ys'][perm_idx]
y_lens = batch['y_lens'][perm_idx]
for b in range(len(batch['xs'])):
##############################
# Reference
##############################
if dataset.is_test:
str_ref = ys[b][0]
# NOTE: transcript is seperated by space(' ')
else:
# Convert from list of index to string
str_ref = dataset.idx2phone(ys[b][: y_lens[b]])
##############################
# Hypothesis
##############################
# Convert from list of index to string
str_hyp = dataset.idx2phone(best_hyps[b])
print('----- wav: %s -----' % batch['input_names'][b])
print('Ref : %s' % str_ref)
print('Hyp : %s' % str_hyp)
if model.model_type == 'attention' and model.ctc_loss_weight > 0:
str_hyp_ctc = dataset.idx2phone(best_hyps_ctc[b])
print('Hyp (CTC): %s' % str_hyp_ctc)
# Compute PER
per, _, _, _ = compute_wer(ref=str_ref.split(' '),
hyp=re.sub(r'(.*) >(.*)', r'\1',
str_hyp).split(' '),
normalize=True)
print('PER: %.3f %%' % (per * 100))
if model.model_type == 'attention' and model.ctc_loss_weight > 0:
per_ctc, _, _, _ = compute_wer(ref=str_ref.split(' '),
hyp=str_hyp_ctc.split(' '),
normalize=True)
print('PER (CTC): %.3f %%' % (per_ctc * 100))
if is_new_epoch:
break
def main():
args = parser.parse_args()
##################################################
# DATSET
##################################################
if args.model_save_path is not None:
# Load a config file (.yml)
params = load_config(args.config_path)
# NOTE: Retrain the saved model from the last checkpoint
elif args.saved_model_path is not None:
params = load_config(os.path.join(args.saved_model_path, 'config.yml'))
else:
raise ValueError("Set model_save_path or saved_model_path.")
# Load dataset
train_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='train',
label_type=params['label_type'],
batch_size=params['batch_size'],
max_epoch=params['num_epoch'],
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=True,
sort_stop_epoch=params['sort_stop_epoch'],
tool=params['tool'],
num_enque=None,
dynamic_batching=params['dynamic_batching'])
dev_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='dev',
label_type=params['label_type'],
batch_size=params['batch_size'],
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
shuffle=True,
tool=params['tool'])
test_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test',
label_type=params['label_type'],
batch_size=1,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
tool=params['tool'])
params['num_classes'] = train_data.num_classes
##################################################
# MODEL
##################################################
if args.model_save_path is not None:
# Model setting
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Set save path
save_path = mkdir_join(args.model_save_path, params['backend'],
params['model_type'], params['label_type'],
model.name)
model.set_save_path(save_path)
# Save config file
save_config(config_path=args.config_path, save_path=model.save_path)
# Setting for logging
logger = set_logger(model.save_path)
if os.path.isdir(params['char_init']):
# NOTE: Start training from the pre-trained character model
model.load_checkpoint(save_path=params['char_init'],
epoch=-1,
load_pretrained_model=True)
# Count total parameters
for name in sorted(list(model.num_params_dict.keys())):
num_params = model.num_params_dict[name]
logger.info("%s %d" % (name, num_params))
logger.info("Total %.3f M parameters" %
(model.total_parameters / 1000000))
# Define optimizer
model.set_optimizer(optimizer=params['optimizer'],
learning_rate_init=float(params['learning_rate']),
weight_decay=float(params['weight_decay']),
clip_grad_norm=params['clip_grad_norm'],
lr_schedule=False,
factor=params['decay_rate'],
patience_epoch=params['decay_patient_epoch'])
epoch, step = 1, 0
learning_rate = float(params['learning_rate'])
metric_dev_best = 1
# NOTE: Retrain the saved model from the last checkpoint
elif args.saved_model_path is not None:
# Load model
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Set save path
model.save_path = args.saved_model_path
# Setting for logging
logger = set_logger(model.save_path)
# Define optimizer
model.set_optimizer(
optimizer=params['optimizer'],
learning_rate_init=float(params['learning_rate']), # on-the-fly
weight_decay=float(params['weight_decay']),
clip_grad_norm=params['clip_grad_norm'],
lr_schedule=False,
factor=params['decay_rate'],
patience_epoch=params['decay_patient_epoch'])
# Restore the last saved model
epoch, step, learning_rate, metric_dev_best = model.load_checkpoint(
save_path=args.saved_model_path, epoch=-1, restart=True)
else:
raise ValueError("Set model_save_path or saved_model_path.")
train_data.epoch = epoch - 1
# GPU setting
model.set_cuda(deterministic=False, benchmark=True)
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
# Set process name
setproctitle('timit_' + params['backend'] + '_' + params['model_type'] +
'_' + params['label_type'])
##################################################
# TRAINING LOOP
##################################################
# Define learning rate controller
lr_controller = Controller(
learning_rate_init=params['learning_rate'],
backend=params['backend'],
decay_type=params['decay_type'],
decay_start_epoch=params['decay_start_epoch'],
decay_rate=params['decay_rate'],
decay_patient_epoch=params['decay_patient_epoch'],
lower_better=True)
# Setting for tensorboard
if params['backend'] == 'pytorch':
tf_writer = SummaryWriter(model.save_path)
# Train model
csv_steps, csv_loss_train, csv_loss_dev = [], [], []
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
not_improved_epoch = 0
loss_train_mean = 0.
pbar_epoch = tqdm(total=len(train_data))
best_model = None
while True:
# Compute loss in the training set (including parameter update)
batch_train, is_new_epoch = train_data.next()
model, loss_train_val = train_step(model, batch_train,
params['clip_grad_norm'],
params['backend'])
loss_train_mean += loss_train_val
pbar_epoch.update(len(batch_train['xs']))
if (step + 1) % params['print_step'] == 0:
# Compute loss in the dev set
batch_dev = dev_data.next()[0]
loss_dev = model(batch_dev['xs'],
batch_dev['ys'],
batch_dev['x_lens'],
batch_dev['y_lens'],
is_eval=True)
loss_train_mean /= params['print_step']
csv_steps.append(step)
csv_loss_train.append(loss_train_mean)
csv_loss_dev.append(loss_dev)
# Logging by tensorboard
if params['backend'] == 'pytorch':
tf_writer.add_scalar('train/loss', loss_train_mean, step + 1)
tf_writer.add_scalar('dev/loss', loss_dev, step + 1)
for name, param in model.named_parameters():
name = name.replace('.', '/')
tf_writer.add_histogram(name,
param.data.cpu().numpy(), step + 1)
tf_writer.add_histogram(name + '/grad',
param.grad.data.cpu().numpy(),
step + 1)
duration_step = time.time() - start_time_step
logger.info(
"...Step:%d(epoch:%.3f) loss:%.3f(%.3f)/lr:%.5f/batch:%d/x_lens:%d (%.3f min)"
% (step + 1, train_data.epoch_detail, loss_train_mean,
loss_dev, learning_rate, train_data.current_batch_size,
max(batch_train['x_lens']) * params['num_stack'],
duration_step / 60))
start_time_step = time.time()
loss_train_mean = 0.
step += 1
# Save checkpoint and evaluate model per epoch
if is_new_epoch:
duration_epoch = time.time() - start_time_epoch
logger.info('===== EPOCH:%d (%.3f min) =====' %
(epoch, duration_epoch / 60))
# Save fugure of loss
plot_loss(csv_loss_train,
csv_loss_dev,
csv_steps,
save_path=model.save_path)
if epoch < params['eval_start_epoch']:
# Save the model
model.save_checkpoint(model.save_path, epoch, step,
learning_rate, metric_dev_best)
else:
start_time_eval = time.time()
# dev
per_dev_epoch, _ = eval_phone(
model=model,
dataset=dev_data,
map_file_path='./conf/phones.60-48-39.map',
eval_batch_size=1,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_PHONE)
logger.info(' PER (dev): %.3f %%' % (per_dev_epoch * 100))
if per_dev_epoch < metric_dev_best:
metric_dev_best = per_dev_epoch
not_improved_epoch = 0
best_model = copy.deepcopy(model)
logger.info('||||| Best Score (PER) |||||')
# Save the model
model.save_checkpoint(model.save_path, epoch, step,
learning_rate, metric_dev_best)
# test
per_test, _ = eval_phone(
model=model,
dataset=test_data,
map_file_path='./conf/phones.60-48-39.map',
eval_batch_size=1,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_PHONE)
logger.info(' PER (test): %.3f %%' % (per_test * 100))
else:
not_improved_epoch += 1
duration_eval = time.time() - start_time_eval
logger.info('Evaluation time: %.3f min' % (duration_eval / 60))
# Early stopping
if not_improved_epoch == params['not_improved_patient_epoch']:
break
# Update learning rate
model.optimizer, learning_rate = lr_controller.decay_lr(
optimizer=model.optimizer,
learning_rate=learning_rate,
epoch=epoch,
value=per_dev_epoch)
if epoch == params['convert_to_sgd_epoch']:
# Convert to fine-tuning stage
model.set_optimizer(
'sgd',
learning_rate_init=learning_rate,
weight_decay=float(params['weight_decay']),
clip_grad_norm=params['clip_grad_norm'],
lr_schedule=False,
factor=params['decay_rate'],
patience_epoch=params['decay_patient_epoch'])
logger.info('========== Convert to SGD ==========')
# Inject Gaussian noise to all parameters
if float(params['weight_noise_std']) > 0:
model.weight_noise_injection = True
pbar_epoch = tqdm(total=len(train_data))
print('========== EPOCH:%d (%.3f min) ==========' %
(epoch, duration_epoch / 60))
if epoch == params['num_epoch']:
break
start_time_step = time.time()
start_time_epoch = time.time()
epoch += 1
duration_train = time.time() - start_time_train
logger.info('Total time: %.3f hour' % (duration_train / 3600))
if params['backend'] == 'pytorch':
tf_writer.close()
pbar_epoch.close()
# Evaluate the best model by beam search
per_test_best, _ = eval_phone(model=best_model,
dataset=test_data,
beam_width=10,
max_decode_len=MAX_DECODE_LEN_PHONE,
eval_batch_size=1,
map_file_path='./conf/phones.60-48-39.map')
logger.info(' PER (test, beam: 10): %.3f %%' % (per_test_best * 100))
# Training was finished correctly
with open(os.path.join(model.save_path, 'COMPLETE'), 'w') as f:
f.write('')
def check(self,
label_type,
data_type='dev',
backend='pytorch',
shuffle=False,
sort_utt=False,
sort_stop_epoch=None,
frame_stacking=False,
splice=1):
print('========================================')
print(' backend: %s' % backend)
print(' label_type: %s' % label_type)
print(' data_type: %s' % data_type)
print(' shuffle: %s' % str(shuffle))
print(' sort_utt: %s' % str(sort_utt))
print(' sort_stop_epoch: %s' % str(sort_stop_epoch))
print(' frame_stacking: %s' % str(frame_stacking))
print(' splice: %d' % splice)
print('========================================')
num_stack = 3 if frame_stacking else 1
num_skip = 3 if frame_stacking else 1
dataset = Dataset(data_save_path='/n/sd8/inaguma/corpus/timit/kaldi',
backend=backend,
input_freq=41,
use_delta=True,
use_double_delta=True,
data_type=data_type,
label_type=label_type,
batch_size=64,
max_epoch=2,
splice=splice,
num_stack=num_stack,
num_skip=num_skip,
shuffle=shuffle,
sort_utt=sort_utt,
sort_stop_epoch=sort_stop_epoch,
tool='htk',
num_enque=None)
print('=> Loading mini-batch...')
idx2phone = Idx2phone(dataset.vocab_file_path)
for batch, is_new_epoch in dataset:
if data_type == 'train' and backend == 'pytorch':
for i in range(len(batch['xs'])):
if batch['xs'].shape[1] < batch['ys'].shape[1]:
raise ValueError(
'input length must be longer than label length.')
if dataset.is_test:
str_true = batch['ys'][0][0]
else:
str_true = idx2phone(batch['ys'][0][:batch['y_lens'][0]])
print('----- %s (epoch: %.3f, batch: %d) -----' %
(batch['input_names'][0], dataset.epoch_detail,
len(batch['xs'])))
print(str_true)
print('x_lens: %d' % (batch['x_lens'][0] * num_stack))
if not dataset.is_test:
print('y_lens: %d' % batch['y_lens'][0])
def main():
args = parser.parse_args()
# Load a config file (.yml)
params = load_config(join(args.model_path, 'config.yml'), is_eval=True)
# Load dataset
dataset = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test',
label_type=params['label_type'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
shuffle=False,
tool=params['tool'])
params['num_classes'] = dataset.num_classes
# Load model
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Restore the saved parameters
model.load_checkpoint(save_path=args.model_path, epoch=args.epoch)
# GPU setting
model.set_cuda(deterministic=False, benchmark=True)
save_path = mkdir_join(args.model_path, 'att_weights')
######################################################################
# Clean directory
if save_path is not None and isdir(save_path):
shutil.rmtree(save_path)
mkdir(save_path)
for batch, is_new_epoch in dataset:
# Decode
best_hyps, aw, perm_idx = model.decode(
batch['xs'],
batch['x_lens'],
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_PHONE,
min_decode_len=MIN_DECODE_LEN_PHONE,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty)
ys = batch['ys'][perm_idx]
y_lens = batch['y_lens'][perm_idx]
for b in range(len(batch['xs'])):
##############################
# Reference
##############################
if dataset.is_test:
str_ref = ys[b][0]
# NOTE: transcript is seperated by space(' ')
else:
# Convert from list of index to string
str_ref = dataset.idx2phone(ys[b][:y_lens[b]])
token_list = dataset.idx2phone(best_hyps[b], return_list=True)
plot_attention_weights(
aw[b][:len(token_list), :batch['x_lens'][b]],
# label_list=token_list,
label_list=[],
spectrogram=batch['xs'][b, :, :dataset.input_freq],
str_ref=str_ref,
save_path=join(save_path, batch['input_names'][b] + '.png'),
figsize=(20, 8))
if is_new_epoch:
break