def check(self,
label_type,
data_type='dev',
shuffle=False,
sort_utt=False,
sort_stop_epoch=None,
frame_stacking=False,
splice=1):
print('========================================')
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_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,
progressbar=True)
print('=> Loading mini-batch...')
map_fn = Idx2phone(map_file_path='../../metrics/mapping_files/' +
label_type + '.txt')
for data, is_new_epoch in dataset:
inputs, labels, inputs_seq_len, input_names = data
if data_type == 'train':
for i_batch, l_batch in zip(inputs[0], labels[0]):
if len(np.where(l_batch == dataset.padded_value)[0]) > 0:
if i_batch.shape[0] < np.where(
l_batch == dataset.padded_value)[0][0]:
raise ValueError(
'input length must be longer than label length.'
)
else:
if i_batch.shape[0] < len(l_batch):
raise ValueError(
'input length must be longer than label length.'
)
str_true = map_fn(labels[0][0])
print('----- %s ----- (epoch: %.3f)' %
(input_names[0][0], dataset.epoch_detail))
print(inputs[0][0].shape)
print(str_true)
def check_loading(self, label_type, data_type='dev',
shuffle=False, sort_utt=False, sort_stop_epoch=None,
frame_stacking=False, splice=1):
print('========================================')
print(' label_type: %s' % label_type)
print(' data_type: %s' % data_type)
print(' shuffle: %s' % str(sort_utt))
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_type=data_type, label_type=label_type,
batch_size=64, eos_index=1, 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,
progressbar=True)
print('=> Loading mini-batch...')
if label_type in ['character', 'character_capital_divide']:
map_fn_ctc = Idx2char(
map_file_path='../../metrics/mapping_files/ctc/' + label_type + '.txt')
map_fn_att = Idx2char(
map_file_path='../../metrics/mapping_files/attention/' + label_type + '.txt')
else:
map_fn_ctc = Idx2phone(
map_file_path='../../metrics/mapping_files/ctc/' + label_type + '.txt')
map_fn_att = Idx2phone(
map_file_path='../../metrics/mapping_files/attention/' + label_type + '.txt')
for data, is_new_epoch in dataset:
inputs, att_labels, ctc_labels, inputs_seq_len, att_labels_seq_len, input_names = data
att_str_true = map_fn_att(att_labels[0][0: att_labels_seq_len[0]])
ctc_str_true = map_fn_ctc(ctc_labels[0])
att_str_true = re.sub(r'_', ' ', att_str_true)
ctc_str_true = re.sub(r'_', ' ', ctc_str_true)
print('----- %s ----- (epoch: %.3f)' %
(input_names[0], dataset.epoch_detail))
print(att_str_true)
print(ctc_str_true)
def plot_attention(model,
dataset,
eval_batch_size,
beam_width,
length_penalty,
save_path=None):
"""Visualize attention weights of the attetnion-based model.
Args:
model: model to evaluate
dataset: An instance of a `Dataset` class
eval_batch_size (int): the batch size when evaluating the model
beam_width: (int): the size of beam
length_penalty (float):
save_path (string, optional): path to save attention weights plotting
"""
# Clean directory
if save_path is not None and isdir(save_path):
shutil.rmtree(save_path)
mkdir(save_path)
idx2phone = Idx2phone(dataset.vocab_file_path)
for batch, is_new_epoch in dataset:
# Decode
best_hyps, aw, perm_idx = model.decode(
batch['xs'],
batch['x_lens'],
beam_width=beam_width,
max_decode_len=MAX_DECODE_LEN_PHONE,
length_penalty=length_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 = idx2phone(ys[b][:y_lens[b]])
token_list = idx2phone(best_hyps[b])
plot_attention_weights(
aw[b][:len(token_list), :batch['x_lens'][b]],
label_list=token_list,
spectrogram=batch['xs'][b, :, :40],
str_ref=str_ref,
save_path=join(save_path, batch['input_names'][b] + '.png'),
figsize=(20, 8))
if is_new_epoch:
break
def check(self,
label_type_main,
data_type='dev',
shuffle=False,
sort_utt=False,
sort_stop_epoch=None,
frame_stacking=False,
splice=1):
print('========================================')
print(' label_type_main: %s' % label_type_main)
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_type=data_type,
label_type_main=label_type_main,
label_type_sub='phone61',
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,
progressbar=True)
print('=> Loading mini-batch...')
idx2char = Idx2char(map_file_path='../../metrics/mapping_files/' +
label_type_main + '.txt')
idx2phone = Idx2phone(
map_file_path='../../metrics/mapping_files/phone61.txt')
for data, is_new_epoch in dataset:
inputs, labels_char, labels_phone, inputs_seq_len, input_names = data
if data_type != 'test':
str_true_char = idx2char(labels_char[0][0])
str_true_phone = idx2phone(labels_phone[0][0])
else:
str_true_char = labels_char[0][0][0]
str_true_phone = labels_phone[0][0][0]
print('----- %s ----- (epoch: %.3f)' %
(input_names[0][0], dataset.epoch_detail))
print(str_true_char)
print(str_true_phone)
def check(self,
label_type,
data_type='dev',
shuffle=False,
sort_utt=False,
sort_stop_epoch=None,
frame_stacking=False,
splice=1):
print('========================================')
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('========================================')
map_file_path = '../../metrics/mapping_files/' + label_type + '.txt'
num_stack = 3 if frame_stacking else 1
num_skip = 3 if frame_stacking else 1
dataset = Dataset(data_type=data_type,
label_type=label_type,
batch_size=64,
map_file_path=map_file_path,
max_epoch=1,
splice=splice,
num_stack=num_stack,
num_skip=num_skip,
shuffle=shuffle,
sort_utt=sort_utt,
sort_stop_epoch=sort_stop_epoch,
progressbar=True)
print('=> Loading mini-batch...')
map_fn = Idx2phone(map_file_path=map_file_path)
for data, is_new_epoch in dataset:
inputs, labels, inputs_seq_len, labels_seq_len, input_names = data
str_true = map_fn(labels[0][0][0:labels_seq_len[0][0]])
print('----- %s ----- (epoch: %.3f)' %
(input_names[0][0], dataset.epoch_detail))
print(inputs[0][0].shape)
print(str_true)
def check(self, encoder_type, lstm_impl, time_major=False):
print('==================================================')
print(' encoder_type: %s' % str(encoder_type))
print(' lstm_impl: %s' % str(lstm_impl))
print(' time_major: %s' % str(time_major))
print('==================================================')
tf.reset_default_graph()
with tf.Graph().as_default():
# Load batch data
batch_size = 2
inputs, labels_char, labels_phone, inputs_seq_len = generate_data(
label_type='multitask', model='ctc', batch_size=batch_size)
# Define model graph
num_classes_main = 27
num_classes_sub = 61
model = MultitaskCTC(
encoder_type=encoder_type,
input_size=inputs[0].shape[1],
num_units=256,
num_layers_main=2,
num_layers_sub=1,
num_classes_main=num_classes_main,
num_classes_sub=num_classes_sub,
main_task_weight=0.8,
lstm_impl=lstm_impl,
parameter_init=0.1,
clip_grad_norm=5.0,
clip_activation=50,
num_proj=256,
weight_decay=1e-8,
# bottleneck_dim=50,
bottleneck_dim=None,
time_major=time_major)
# Define placeholders
model.create_placeholders()
learning_rate_pl = tf.placeholder(tf.float32, name='learning_rate')
# Add to the graph each operation
loss_op, logits_main, logits_sub = model.compute_loss(
model.inputs_pl_list[0], model.labels_pl_list[0],
model.labels_sub_pl_list[0], model.inputs_seq_len_pl_list[0],
model.keep_prob_pl_list[0])
train_op = model.train(loss_op,
optimizer='adam',
learning_rate=learning_rate_pl)
decode_op_main, decode_op_sub = model.decoder(
logits_main,
logits_sub,
model.inputs_seq_len_pl_list[0],
beam_width=20)
ler_op_main, ler_op_sub = model.compute_ler(
decode_op_main, decode_op_sub, model.labels_pl_list[0],
model.labels_sub_pl_list[0])
# Define learning rate controller
learning_rate = 1e-3
lr_controller = Controller(learning_rate_init=learning_rate,
decay_start_epoch=20,
decay_rate=0.9,
decay_patient_epoch=5,
lower_better=True)
# Add the variable initializer operation
init_op = tf.global_variables_initializer()
# Count total parameters
parameters_dict, total_parameters = count_total_parameters(
tf.trainable_variables())
for parameter_name in sorted(parameters_dict.keys()):
print("%s %d" %
(parameter_name, parameters_dict[parameter_name]))
print("Total %d variables, %s M parameters" %
(len(parameters_dict.keys()), "{:,}".format(
total_parameters / 1000000)))
# Make feed dict
feed_dict = {
model.inputs_pl_list[0]:
inputs,
model.labels_pl_list[0]:
list2sparsetensor(labels_char, padded_value=-1),
model.labels_sub_pl_list[0]:
list2sparsetensor(labels_phone, padded_value=-1),
model.inputs_seq_len_pl_list[0]:
inputs_seq_len,
model.keep_prob_pl_list[0]:
0.9,
learning_rate_pl:
learning_rate
}
idx2phone = Idx2phone(map_file_path='./phone61.txt')
with tf.Session() as sess:
# Initialize parameters
sess.run(init_op)
# Wrapper for tfdbg
# sess = tf_debug.LocalCLIDebugWrapperSession(sess)
# Train model
max_steps = 1000
start_time_step = time.time()
for step in range(max_steps):
# Compute loss
_, loss_train = sess.run([train_op, loss_op],
feed_dict=feed_dict)
# Gradient check
# grads = sess.run(model.clipped_grads,
# feed_dict=feed_dict)
# for grad in grads:
# print(np.max(grad))
if (step + 1) % 10 == 0:
# Change to evaluation mode
feed_dict[model.keep_prob_pl_list[0]] = 1.0
# Compute accuracy
ler_train_char, ler_train_phone = sess.run(
[ler_op_main, ler_op_sub], feed_dict=feed_dict)
duration_step = time.time() - start_time_step
print(
'Step %d: loss = %.3f / cer = %.3f / per = %.3f (%.3f sec) / lr = %.5f'
% (step + 1, loss_train, ler_train_char,
ler_train_phone, duration_step, learning_rate))
start_time_step = time.time()
# Visualize
labels_pred_char_st, labels_pred_phone_st = sess.run(
[decode_op_main, decode_op_sub],
feed_dict=feed_dict)
labels_pred_char = sparsetensor2list(
labels_pred_char_st, batch_size=batch_size)
labels_pred_phone = sparsetensor2list(
labels_pred_phone_st, batch_size=batch_size)
print('Character')
try:
print(' Ref: %s' % idx2alpha(labels_char[0]))
print(' Hyp: %s' % idx2alpha(labels_pred_char[0]))
except IndexError:
print('Character')
print(' Ref: %s' % idx2alpha(labels_char[0]))
print(' Hyp: %s' % '')
print('Phone')
try:
print(' Ref: %s' % idx2phone(labels_phone[0]))
print(' Hyp: %s' %
idx2phone(labels_pred_phone[0]))
except IndexError:
print(' Ref: %s' % idx2phone(labels_phone[0]))
print(' Hyp: %s' % '')
# NOTE: This is for no prediction
print('-' * 30)
if ler_train_char < 0.1:
print('Modle is Converged.')
break
# Update learning rate
learning_rate = lr_controller.decay_lr(
learning_rate=learning_rate,
epoch=step,
value=ler_train_char)
feed_dict[learning_rate_pl] = learning_rate
def decode(session,
decode_op_main,
decode_op_sub,
model,
dataset,
label_type_main,
label_type_sub,
is_test=True,
save_path=None):
"""Visualize label outputs of Multi-task CTC model.
Args:
session: session of training model
decode_op_main: operation for decoding in the main task
decode_op_sub: operation for decoding in the sub task
model: the model to evaluate
dataset: An instance of a `Dataset` class
label_type_main (string): character or character_capital_divide
label_type_sub (string): phone39 or phone48 or phone61
is_test (bool, optional):
save_path (string, optional): path to save decoding results
"""
idx2char = Idx2char(map_file_path='../metrics/mapping_files/' +
label_type_main + '.txt')
idx2phone = Idx2phone(map_file_path='../metrics/mapping_files/' +
label_type_sub + '.txt')
if save_path is not None:
sys.stdout = open(join(model.model_dir, 'decode.txt'), 'w')
for data, is_new_epoch in dataset:
# Create feed dictionary for next mini batch
inputs, labels_true_char, labels_true_phone, inputs_seq_len, input_names = data
feed_dict = {
model.inputs_pl_list[0]: inputs[0],
model.inputs_seq_len_pl_list[0]: inputs_seq_len[0],
model.keep_prob_pl_list[0]: 1.0
}
batch_size = inputs[0].shape[0]
labels_pred_char_st, labels_pred_phone_st = session.run(
[decode_op_main, decode_op_sub], feed_dict=feed_dict)
try:
labels_pred_char = sparsetensor2list(labels_pred_char_st,
batch_size=batch_size)
except:
# no output
labels_pred_char = ['']
try:
labels_pred_phone = sparsetensor2list(labels_pred_char_st,
batch_size=batch_size)
except:
# no output
labels_pred_phone = ['']
for i_batch in range(batch_size):
print('----- wav: %s -----' % input_names[0][i_batch])
if is_test:
str_true_char = labels_true_char[0][i_batch][0].replace(
'_', ' ')
str_true_phone = labels_true_phone[0][i_batch][0]
else:
str_true_char = idx2char(labels_true_char[0][i_batch])
str_true_phone = idx2phone(labels_true_phone[0][i_batch])
str_pred_char = idx2char(labels_pred_char[i_batch])
str_pred_phone = idx2phone(labels_pred_phone[i_batch])
print('Ref (char): %s' % str_true_char)
print('Hyp (char): %s' % str_pred_char)
print('Ref (phone): %s' % str_true_phone)
print('Hyp (phone): %s' % str_pred_phone)
if is_new_epoch:
break
def check_training(self,
encoder_type,
label_type,
lstm_impl='LSTMBlockCell',
save_params=False):
print('==================================================')
print(' encoder_type: %s' % encoder_type)
print(' label_type: %s' % label_type)
print(' lstm_impl: %s' % lstm_impl)
print('==================================================')
tf.reset_default_graph()
with tf.Graph().as_default():
# Load batch data
batch_size = 1
splice = 11 if encoder_type in [
'vgg_blstm', 'vgg_lstm', 'vgg_wang', 'resnet_wang', 'cnn_zhang'
] else 1
inputs, labels_true_st, inputs_seq_len = generate_data(
label_type=label_type,
model='ctc',
batch_size=batch_size,
splice=splice)
# NOTE: input_size must be even number when using CudnnLSTM
# Define model graph
num_classes = 26 if label_type == 'character' else 61
model = CTC(
encoder_type=encoder_type,
input_size=inputs[0].shape[-1] // splice,
splice=splice,
num_units=256,
num_layers=2,
num_classes=num_classes,
lstm_impl=lstm_impl,
parameter_init=0.1,
clip_grad=5.0,
clip_activation=50,
num_proj=256,
# bottleneck_dim=50,
bottleneck_dim=None,
weight_decay=1e-8)
# Define placeholders
model.create_placeholders()
learning_rate_pl = tf.placeholder(tf.float32, name='learning_rate')
# Add to the graph each operation
loss_op, logits = model.compute_loss(
model.inputs_pl_list[0], model.labels_pl_list[0],
model.inputs_seq_len_pl_list[0],
model.keep_prob_input_pl_list[0],
model.keep_prob_hidden_pl_list[0],
model.keep_prob_output_pl_list[0])
train_op = model.train(loss_op,
optimizer='adam',
learning_rate=learning_rate_pl)
# NOTE: Adam does not run on CudnnLSTM
decode_op = model.decoder(logits,
model.inputs_seq_len_pl_list[0],
beam_width=20)
ler_op = model.compute_ler(decode_op, model.labels_pl_list[0])
# Define learning rate controller
learning_rate = 1e-3
lr_controller = Controller(learning_rate_init=learning_rate,
decay_start_epoch=10,
decay_rate=0.98,
decay_patient_epoch=5,
lower_better=True)
if save_params:
# Create a saver for writing training checkpoints
saver = tf.train.Saver(max_to_keep=None)
# Add the variable initializer operation
init_op = tf.global_variables_initializer()
# Count total parameters
if lstm_impl != 'CudnnLSTM':
parameters_dict, total_parameters = count_total_parameters(
tf.trainable_variables())
for parameter_name in sorted(parameters_dict.keys()):
print("%s %d" %
(parameter_name, parameters_dict[parameter_name]))
print("Total %d variables, %s M parameters" %
(len(parameters_dict.keys()), "{:,}".format(
total_parameters / 1000000)))
# Make feed dict
feed_dict = {
model.inputs_pl_list[0]: inputs,
model.labels_pl_list[0]: labels_true_st,
model.inputs_seq_len_pl_list[0]: inputs_seq_len,
model.keep_prob_input_pl_list[0]: 1.0,
model.keep_prob_hidden_pl_list[0]: 1.0,
model.keep_prob_output_pl_list[0]: 1.0,
learning_rate_pl: learning_rate
}
idx2phone = Idx2phone(map_file_path='./phone61_ctc.txt')
with tf.Session() as sess:
# Initialize parameters
sess.run(init_op)
# Wrapper for tfdbg
# sess = tf_debug.LocalCLIDebugWrapperSession(sess)
# Train model
max_steps = 1000
start_time_global = time.time()
start_time_step = time.time()
ler_train_pre = 1
not_improved_count = 0
for step in range(max_steps):
# Compute loss
_, loss_train = sess.run([train_op, loss_op],
feed_dict=feed_dict)
# Gradient check
# grads = sess.run(model.clipped_grads,
# feed_dict=feed_dict)
# for grad in grads:
# print(np.max(grad))
if (step + 1) % 10 == 0:
# Change to evaluation mode
feed_dict[model.keep_prob_input_pl_list[0]] = 1.0
feed_dict[model.keep_prob_hidden_pl_list[0]] = 1.0
feed_dict[model.keep_prob_output_pl_list[0]] = 1.0
# Compute accuracy
ler_train = sess.run(ler_op, feed_dict=feed_dict)
duration_step = time.time() - start_time_step
print(
'Step %d: loss = %.3f / ler = %.3f (%.3f sec) / lr = %.5f'
% (step + 1, loss_train, ler_train, duration_step,
learning_rate))
start_time_step = time.time()
# Decode
labels_pred_st = sess.run(decode_op,
feed_dict=feed_dict)
labels_true = sparsetensor2list(labels_true_st,
batch_size=batch_size)
# Visualize
try:
labels_pred = sparsetensor2list(
labels_pred_st, batch_size=batch_size)
if label_type == 'character':
print('Ref: %s' % idx2alpha(labels_true[0]))
print('Hyp: %s' % idx2alpha(labels_pred[0]))
else:
print('Ref: %s' % idx2phone(labels_true[0]))
print('Hyp: %s' % idx2phone(labels_pred[0]))
except IndexError:
if label_type == 'character':
print('Ref: %s' % idx2alpha(labels_true[0]))
print('Hyp: %s' % '')
else:
print('Ref: %s' % idx2phone(labels_true[0]))
print('Hyp: %s' % '')
# NOTE: This is for no prediction
if ler_train >= ler_train_pre:
not_improved_count += 1
else:
not_improved_count = 0
if ler_train < 0.05:
print('Modle is Converged.')
if save_params:
# Save model (check point)
checkpoint_file = './model.ckpt'
save_path = saver.save(sess,
checkpoint_file,
global_step=1)
print("Model saved in file: %s" % save_path)
break
ler_train_pre = ler_train
# Update learning rate
learning_rate = lr_controller.decay_lr(
learning_rate=learning_rate,
epoch=step,
value=ler_train)
feed_dict[learning_rate_pl] = learning_rate
duration_global = time.time() - start_time_global
print('Total time: %.3f sec' % (duration_global))
def decode(session,
decode_op,
model,
dataset,
label_type,
is_test=False,
save_path=None):
"""Visualize label outputs of Attention-based model.
Args:
session: session of training model
decode_op: operation for decoding
model: the model to evaluate
dataset: An instance of a `Dataset` class
label_type (string): phone39 or phone48 or phone61 or character or
character_capital_divide
is_test (bool, optional):
save_path (string): path to save decoding results
"""
if label_type == 'character':
map_fn = Idx2char(
map_file_path='../metrics/mapping_files/character.txt')
elif label_type == 'character_capital_divide':
map_fn = Idx2char(
map_file_path=
'../metrics/mapping_files/character_capital_divide.txt',
capital_divide=True)
else:
map_fn = Idx2phone(map_file_path='../metrics/mapping_files/' +
label_type + '.txt')
if save_path is not None:
sys.stdout = open(join(model.model_dir, 'decode.txt'), 'w')
for data, is_new_epoch in dataset:
# Create feed dictionary for next mini batch
inputs, labels_true, inputs_seq_len, labels_seq_len, input_names = data
feed_dict = {
model.inputs_pl_list[0]: inputs[0],
model.inputs_seq_len_pl_list[0]: inputs_seq_len[0],
model.keep_prob_encoder_pl_list[0]: 1.0,
model.keep_prob_decoder_pl_list[0]: 1.0,
model.keep_prob_embedding_pl_list[0]: 1.0
}
batch_size = inputs[0].shape[0]
labels_pred = session.run(decode_op, feed_dict=feed_dict)
for i_batch in range(batch_size):
print('----- wav: %s -----' % input_names[0][i_batch])
if is_test:
str_true = labels_true[0][i_batch][0]
else:
str_true = map_fn(
labels_true[0][i_batch][1:labels_seq_len[0][i_batch] - 1])
# NOTE: Exclude <SOS> and <EOS>
str_pred = map_fn(labels_pred[i_batch]).split('>')[0]
# NOTE: Trancate by <EOS>
if 'phone' in label_type:
# Remove the last space
if str_pred[-1] == ' ':
str_pred = str_pred[:-1]
print('Ref: %s' % str_true)
print('Hyp: %s' % str_pred)
if is_new_epoch:
break
def decode(session,
decode_op,
model,
dataset,
label_type,
is_test=True,
save_path=None):
"""Visualize label outputs of CTC model.
Args:
session: session of training model
decode_op: operation for decoding
model: the model to evaluate
dataset: An instance of a `Dataset` class
label_type (string): phone39 or phone48 or phone61 or character or
character_capital_divide
is_test (bool, optional):
save_path (string, optional): path to save decoding results
"""
if label_type == 'character':
map_fn = Idx2char(
map_file_path='../metrics/mapping_files/character.txt')
elif label_type == 'character_capital_divide':
map_fn = Idx2char(
map_file_path=
'../metrics/mapping_files/character_capital_divide.txt',
capital_divide=True)
else:
map_fn = Idx2phone(map_file_path='../metrics/mapping_files/' +
label_type + '.txt')
if save_path is not None:
sys.stdout = open(join(model.model_dir, 'decode.txt'), 'w')
for data, is_new_epoch in dataset:
# Create feed dictionary for next mini batch
inputs, labels_true, inputs_seq_len, input_names = data
feed_dict = {
model.inputs_pl_list[0]: inputs[0],
model.inputs_seq_len_pl_list[0]: inputs_seq_len[0],
model.keep_prob_pl_list[0]: 1.0
}
batch_size = inputs[0].shape[0]
labels_pred_st = session.run(decode_op, feed_dict=feed_dict)
try:
labels_pred = sparsetensor2list(labels_pred_st,
batch_size=batch_size)
except IndexError:
# no output
labels_pred = ['']
for i_batch in range(batch_size):
print('----- wav: %s -----' % input_names[0][i_batch])
if 'char' in label_type:
if is_test:
str_true = labels_true[0][i_batch][0]
else:
str_true = map_fn(labels_true[0][i_batch])
str_pred = map_fn(labels_pred[i_batch])
else:
if is_test:
str_true = labels_true[0][i_batch][0]
else:
str_true = map_fn(labels_true[0][i_batch])
str_pred = map_fn(labels_pred[i_batch])
print('Ref: %s' % str_true)
print('Hyp: %s' % str_pred)
if is_new_epoch:
break
def do_eval_per(session, decode_op, per_op, model, dataset, label_type,
is_test=False, eval_batch_size=None, progressbar=False,
is_multitask=False, is_jointctcatt=False):
"""Evaluate trained model by Phone Error Rate.
Args:
session: session of training model
decode_op: operation for decoding
per_op: operation for computing phone error rate
model: the model to evaluate
dataset: An instance of a `Dataset' class
label_type (string): phone39 or phone48 or phone61
is_test (bool, optional): set to True when evaluating by the test set
eval_batch_size (int, optional): the batch size when evaluating the model
progressbar (bool, optional): if True, visualize the progressbar
is_multitask (bool, optional): if True, evaluate the multitask model
is_jointctcatt (bool, optional): if True, evaluate the joint
CTC-Attention model
Returns:
per_mean (float): An average of PER
"""
batch_size_original = dataset.batch_size
# Reset data counter
dataset.reset()
# Set batch size in the evaluation
if eval_batch_size is not None:
dataset.batch_size = eval_batch_size
train_label_type = label_type
eval_label_type = dataset.label_type_sub if is_multitask else dataset.label_type
idx2phone_train = Idx2phone(
map_file_path='../metrics/mapping_files/' + train_label_type + '.txt')
idx2phone_eval = Idx2phone(
map_file_path='../metrics/mapping_files/' + eval_label_type + '.txt')
map2phone39_train = Map2phone39(
label_type=train_label_type,
map_file_path='../metrics/mapping_files/phone2phone.txt')
map2phone39_eval = Map2phone39(
label_type=eval_label_type,
map_file_path='../metrics/mapping_files/phone2phone.txt')
per_mean = 0
if progressbar:
pbar = tqdm(total=len(dataset))
for data, is_new_epoch in dataset:
# Create feed dictionary for next mini-batch
if is_multitask:
inputs, _, labels_true, inputs_seq_len, labels_seq_len, _ = data
elif is_jointctcatt:
inputs, labels_true, _, inputs_seq_len, labels_seq_len, _ = data
else:
inputs, labels_true, inputs_seq_len, labels_seq_len, _ = data
feed_dict = {
model.inputs_pl_list[0]: inputs[0],
model.inputs_seq_len_pl_list[0]: inputs_seq_len[0],
model.keep_prob_encoder_pl_list[0]: 1.0,
model.keep_prob_decoder_pl_list[0]: 1.0,
model.keep_prob_embedding_pl_list[0]: 1.0
}
batch_size = inputs[0].shape[0]
# Evaluate by 39 phones
labels_pred = session.run(decode_op, feed_dict=feed_dict)
for i_batch in range(batch_size):
###############
# Hypothesis
###############
# Convert from index to phone (-> list of phone strings)
str_pred = idx2phone_train(labels_pred[i_batch]).split('>')[0]
# NOTE: Trancate by <EOS>
# Remove the last space
if str_pred[-1] == ' ':
str_pred = str_pred[:-1]
phone_pred_list = str_pred.split(' ')
###############
# Reference
###############
if is_test:
phone_true_list = labels_true[0][i_batch][0].split(' ')
else:
# Convert from index to phone (-> list of phone strings)
phone_true_list = idx2phone_eval(
labels_true[0][i_batch][1:labels_seq_len[0][i_batch] - 1]).split(' ')
# NOTE: Exclude <SOS> and <EOS>
# Mapping to 39 phones (-> list of phone strings)
phone_pred_list = map2phone39_train(phone_pred_list)
phone_true_list = map2phone39_eval(phone_true_list)
# Compute PER
per_mean += compute_per(ref=phone_true_list,
hyp=phone_pred_list,
normalize=True)
if progressbar:
pbar.update(1)
if is_new_epoch:
break
per_mean /= len(dataset)
# Register original batch size
if eval_batch_size is not None:
dataset.batch_size = batch_size_original
return per_mean
def plot(session,
decode_op,
attention_weights_op,
model,
dataset,
label_type,
is_test=False,
save_path=None,
show=False):
"""Visualize attention weights of Attetnion-based model.
Args:
session: session of training model
decode_op: operation for decoding
attention_weights_op: operation for computing attention weights
model: model to evaluate
dataset: An instance of a `Dataset` class
label_type (string, optional): phone39 or phone48 or phone61 or character or
character_capital_divide
is_test (bool, optional):
save_path (string, optional): path to save attention weights plotting
show (bool, optional): if True, show each figure
"""
# Clean directory
if save_path is not None and isdir(save_path):
shutil.rmtree(save_path)
mkdir(save_path)
if label_type == 'character':
map_fn = Idx2char(
map_file_path='../metrics/mapping_files/character.txt')
elif label_type == 'character_capital_divide':
map_fn = Idx2char(
map_file_path=
'../metrics/mapping_files/character_capital_divide.txt',
capital_divide=True)
else:
map_fn = Idx2phone(map_file_path='../metrics/mapping_files/' +
label_type + '.txt')
for data, is_new_epoch in dataset:
# Create feed dictionary for next mini batch
inputs, labels_true, inputs_seq_len, _, input_names = data
feed_dict = {
model.inputs_pl_list[0]: inputs[0],
model.inputs_seq_len_pl_list[0]: inputs_seq_len[0],
model.keep_prob_encoder_pl_list[0]: 1.0,
model.keep_prob_decoder_pl_list[0]: 1.0,
model.keep_prob_embedding_pl_list[0]: 1.0
}
# Visualize
batch_size, max_frame_num = inputs.shape[:2]
attention_weights, labels_pred = session.run(
[attention_weights_op, decode_op], feed_dict=feed_dict)
for i_batch in range(batch_size):
# t_out, t_in = attention_weights[i_batch].shape
# Check if the sum of attention weights equals to 1
# print(np.sum(attention_weights[i_batch], axis=1))
# Convert from index to label
str_pred = map_fn(labels_pred[i_batch])
if 'phone' in label_type:
label_list = str_pred.split(' ')
else:
raise NotImplementedError
plt.clf()
plt.figure(figsize=(10, 4))
sns.heatmap(attention_weights[i_batch],
cmap='Blues',
xticklabels=False,
yticklabels=label_list)
plt.xlabel('Input frames', fontsize=12)
plt.ylabel('Output labels (top to bottom)', fontsize=12)
if show:
plt.show()
# Save as a png file
if save_path is not None:
plt.savefig(join(save_path, input_names[0] + '.png'), dvi=500)
if is_new_epoch:
break
def decode_test_multitask(session,
decode_op_main,
decode_op_sub,
model,
dataset,
label_type_main,
label_type_sub,
save_path=None):
"""Visualize label outputs of Multi-task CTC model.
Args:
session: session of training model
decode_op_main: operation for decoding in the main task
decode_op_sub: operation for decoding in the sub task
model: the model to evaluate
dataset: An instance of a `Dataset` class
label_type_main (string): character or character_capital_divide
label_type_sub (string): phone39 or phone48 or phone61
save_path (string, optional): path to save decoding results
"""
# TODO: fix
if save_path is not None:
sys.stdout = open(join(model.model_dir, 'decode.txt'), 'w')
# Decode character
print('===== ' + label_type_main + ' =====')
idx2char = Idx2char(map_file_path='../metrics/mapping_files/ctc/' +
label_type_main + '.txt')
while True:
# Create feed dictionary for next mini batch
data, is_new_epoch = dataset.next(batch_size=1)
inputs, labels_true, _, inputs_seq_len, input_names = data
# NOTE: Batch size is expected to be 1
feed_dict = {
model.inputs_pl_list[0]: inputs,
model.inputs_seq_len_pl_list[0]: inputs_seq_len,
model.keep_prob_input_pl_list[0]: 1.0,
model.keep_prob_hidden_pl_list[0]: 1.0,
model.keep_prob_output_pl_list[0]: 1.0
}
# Visualize
labels_pred_st = session.run(decode_op_main, feed_dict=feed_dict)
labels_pred = sparsetensor2list(labels_pred_st, batch_size=1)
print('----- wav: %s -----' % input_names[0])
print('Ref: %s' % idx2char(labels_true[0]))
print('Hyp: %s' % idx2char(labels_pred[0]))
if is_new_epoch:
break
# Decode phone
print('\n===== ' + label_type_sub + ' =====')
idx2phone = Idx2phone(map_file_path='../metrics/mapping_files/ctc/' +
label_type_sub + '.txt')
while True:
# Create feed dictionary for next mini batch
data, is_new_epoch = dataset.next(batch_size=1)
inputs, _, labels_true, inputs_seq_len, input_names = data
feed_dict = {
model.inputs_pl_list[0]: inputs,
model.inputs_seq_len_pl_list[0]: inputs_seq_len,
model.keep_prob_input_pl_list[0]: 1.0,
model.keep_prob_hidden_pl_list[0]: 1.0,
model.keep_prob_output_pl_list[0]: 1.0
}
# Visualize
labels_pred_st = session.run(decode_op_sub, feed_dict=feed_dict)
try:
labels_pred = sparsetensor2list(labels_pred_st, batch_size=1)
except IndexError:
# no output
labels_pred = ['']
finally:
print('----- wav: %s -----' % input_names[0])
print('Ref: %s' % idx2phone(labels_true[0]))
print('Hyp: %s' % idx2phone(labels_pred[0]))
if is_new_epoch:
break
def decode_test(session,
decode_op,
model,
dataset,
label_type,
save_path=None):
"""Visualize label outputs of CTC model.
Args:
session: session of training model
decode_op: operation for decoding
model: the model to evaluate
dataset: An instance of a `Dataset` class
label_type (string): phone39 or phone48 or phone61 or character or
character_capital_divide
save_path (string, optional): path to save decoding results
"""
if label_type == 'character':
map_fn = Idx2char(
map_file_path='../metrics/mapping_files/ctc/character.txt')
elif label_type == 'character_capital_divide':
map_fn = Idx2char(
map_file_path=
'../metrics/mapping_files/ctc/character_capital_divide.txt',
capital_divide=True)
else:
map_fn = Idx2phone(map_file_path='../metrics/mapping_files/ctc/' +
label_type + '.txt')
if save_path is not None:
sys.stdout = open(join(model.model_dir, 'decode.txt'), 'w')
while True:
# Create feed dictionary for next mini batch
data, is_new_epoch = dataset.next(batch_size=1)
inputs, labels_true, inputs_seq_len, input_names = data
# NOTE: Batch size is expected to be 1
feed_dict = {
model.inputs_pl_list[0]: inputs,
model.inputs_seq_len_pl_list[0]: inputs_seq_len,
model.keep_prob_input_pl_list[0]: 1.0,
model.keep_prob_hidden_pl_list[0]: 1.0,
model.keep_prob_output_pl_list[0]: 1.0
}
# Visualize
labels_pred_st = session.run(decode_op, feed_dict=feed_dict)
try:
labels_pred = sparsetensor2list(labels_pred_st, batch_size=1)
except IndexError:
# no output
labels_pred = ['']
finally:
print('----- wav: %s -----' % input_names[0])
if label_type == 'character':
true_seq = map_fn(labels_true[0]).replace('_', ' ')
pred_seq = map_fn(labels_pred[0]).replace('_', ' ')
else:
true_seq = map_fn(labels_true[0])
pred_seq = map_fn(labels_pred[0])
print('Ref: %s' % true_seq)
print('Hyp: %s' % pred_seq)
if is_new_epoch:
break
def do_eval_per(session,
decode_op,
per_op,
model,
dataset,
label_type,
eval_batch_size=None,
progressbar=False,
is_multitask=False):
"""Evaluate trained model by Phone Error Rate.
Args:
session: session of training model
decode_op: operation for decoding
per_op: operation for computing phone error rate
model: the model to evaluate
dataset: An instance of a `Dataset' class
label_type (string): phone39 or phone48 or phone61
eval_batch_size (int, optional): the batch size when evaluating the model
progressbar (bool, optional): if True, visualize the progressbar
is_multitask (bool, optional): if True, evaluate the multitask model
Returns:
per_mean (float): An average of PER
"""
# Reset data counter
dataset.reset()
train_label_type = label_type
eval_label_type = dataset.label_type_sub if is_multitask else dataset.label_type
# phone2idx_39_map_file_path = '../metrics/mapping_files/ctc/phone39.txt'
idx2phone_train = Idx2phone(map_file_path='../metrics/mapping_files/ctc/' +
train_label_type + '.txt')
idx2phone_eval = Idx2phone(map_file_path='../metrics/mapping_files/ctc/' +
eval_label_type + '.txt')
map2phone39_train = Map2phone39(
label_type=train_label_type,
map_file_path='../metrics/mapping_files/phone2phone.txt')
map2phone39_eval = Map2phone39(
label_type=eval_label_type,
map_file_path='../metrics/mapping_files/phone2phone.txt')
per_mean = 0
if progressbar:
pbar = tqdm(total=len(dataset))
for data, is_new_epoch in dataset:
# Create feed dictionary for next mini batch
if is_multitask:
inputs, _, labels_true, inputs_seq_len, _ = data
else:
inputs, labels_true, inputs_seq_len, _ = data
feed_dict = {
model.inputs_pl_list[0]: inputs,
model.inputs_seq_len_pl_list[0]: inputs_seq_len,
model.keep_prob_input_pl_list[0]: 1.0,
model.keep_prob_hidden_pl_list[0]: 1.0,
model.keep_prob_output_pl_list[0]: 1.0
}
batch_size_each = len(inputs)
# Evaluate by 39 phones
labels_pred_st = session.run(decode_op, feed_dict=feed_dict)
labels_pred = sparsetensor2list(labels_pred_st, batch_size_each)
for i_batch in range(batch_size_each):
###############
# Hypothesis
###############
# Convert from index to phone (-> list of phone strings)
phone_pred_list = idx2phone_train(labels_pred[i_batch]).split(' ')
# Mapping to 39 phones (-> list of phone strings)
phone_pred_list = map2phone39_train(phone_pred_list)
###############
# Reference
###############
# Convert from index to phone (-> list of phone strings)
phone_true_list = idx2phone_eval(labels_true[i_batch]).split(' ')
# Mapping to 39 phones (-> list of phone strings)
phone_true_list = map2phone39_eval(phone_true_list)
# Compute PER
per_mean += compute_per(ref=phone_pred_list,
hyp=phone_true_list,
normalize=True)
if progressbar:
pbar.update(1)
if is_new_epoch:
break
per_mean /= len(dataset)
return per_mean
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 __init__(self,
data_save_path,
backend,
input_freq,
use_delta,
use_double_delta,
data_type,
label_type,
batch_size,
max_epoch=None,
splice=1,
num_stack=1,
num_skip=1,
shuffle=False,
sort_utt=False,
reverse=False,
sort_stop_epoch=None,
tool='htk',
num_enque=None,
dynamic_batching=False):
"""A class for loading dataset.
Args:
data_save_path (string): path to saved data
backend (string): pytorch or chainer
input_freq (int): the number of dimensions of acoustics
use_delta (bool): if True, use the delta feature
use_double_delta (bool): if True, use the acceleration feature
data_type (string): train or dev or test
label_type (string): phone39 or phone48 or phone61
batch_size (int): the size of mini-batch
max_epoch (int): the max epoch. None means infinite loop.
splice (int): frames to splice. Default is 1 frame.
num_stack (int): the number of frames to stack
num_skip (int): the number of frames to skip
shuffle (bool): if True, shuffle utterances. This is
disabled when sort_utt is True.
sort_utt (bool): if True, sort all utterances in the
ascending order
reverse (bool): if True, sort utteraces in the
descending order
sort_stop_epoch (int): After sort_stop_epoch, training
will revert back to a random order
tool (string): htk or librosa or python_speech_features
num_enque (int): the number of elements to enqueue
dynamic_batching (bool): if True, batch size will be
chainged dynamically in training
"""
self.backend = backend
self.input_freq = input_freq
self.use_delta = use_delta
self.use_double_delta = use_double_delta
self.data_type = data_type
self.label_type = label_type
self.batch_size = batch_size
self.max_epoch = max_epoch
self.splice = splice
self.num_stack = num_stack
self.num_skip = num_skip
self.shuffle = shuffle
self.sort_utt = sort_utt
self.sort_stop_epoch = sort_stop_epoch
self.num_gpus = 1
self.tool = tool
self.num_enque = num_enque
self.dynamic_batching = dynamic_batching
self.is_test = True if data_type == 'test' else False
self.vocab_file_path = join(data_save_path, 'vocab',
label_type + '.txt')
self.idx2phone = Idx2phone(self.vocab_file_path)
super(Dataset, self).__init__(vocab_file_path=self.vocab_file_path)
# Load dataset file
dataset_path = join(data_save_path, 'dataset', tool, data_type,
label_type + '.csv')
df = pd.read_csv(dataset_path)
df = df.loc[:, ['frame_num', 'input_path', 'transcript']]
# Sort paths to input & label
if sort_utt:
df = df.sort_values(by='frame_num', ascending=not reverse)
else:
df = df.sort_values(by='input_path', ascending=True)
self.df = df
self.rest = set(list(df.index))
def eval_phone(model,
dataset,
map_file_path,
eval_batch_size,
beam_width,
max_decode_len,
length_penalty=0,
progressbar=False):
"""Evaluate trained model by Phone Error Rate.
Args:
model: the model to evaluate
dataset: An instance of a `Dataset' class
map_file_path (string): path to phones.60-48-39.map
eval_batch_size (int): the batch size when evaluating the model
beam_width: (int): the size of beam
max_decode_len (int): the length of output sequences
to stop prediction. This is used for seq2seq models.
length_penalty (float, optional):
progressbar (bool, optional): if True, visualize the progressbar
Returns:
per (float): Phone error rate
df_per (pd.DataFrame): dataframe of substitution, insertion, and deletion
"""
# Reset data counter
dataset.reset()
idx2phone = Idx2phone(vocab_file_path=dataset.vocab_file_path)
map2phone39 = Map2phone39(label_type=dataset.label_type,
map_file_path=map_file_path)
per = 0
sub, ins, dele = 0, 0, 0
num_phones = 0
if progressbar:
pbar = tqdm(total=len(dataset)) # TODO: fix this
while True:
batch, is_new_epoch = dataset.next(batch_size=eval_batch_size)
# Decode
best_hyps, _, perm_idx = model.decode(batch['xs'],
batch['x_lens'],
beam_width=beam_width,
max_decode_len=max_decode_len,
length_penalty=length_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:
phone_ref_list = ys[b][0].split(' ')
# NOTE: transcript is seperated by space(' ')
else:
# Convert from index to phone (-> list of phone strings)
phone_ref_list = idx2phone(ys[b][:y_lens[b]]).split(' ')
##############################
# Hypothesis
##############################
# Convert from index to phone (-> list of phone strings)
str_hyp = idx2phone(best_hyps[b])
str_hyp = re.sub(r'(.*) >(.*)', r'\1', str_hyp)
# NOTE: Trancate by the first <EOS>
phone_hyp_list = str_hyp.split(' ')
# Mapping to 39 phones (-> list of phone strings)
if dataset.label_type != 'phone39':
phone_ref_list = map2phone39(phone_ref_list)
phone_hyp_list = map2phone39(phone_hyp_list)
# Compute PER
try:
per_b, sub_b, ins_b, del_b = compute_wer(ref=phone_ref_list,
hyp=phone_hyp_list,
normalize=False)
per += per_b
sub += sub_b
ins += ins_b
dele += del_b
num_phones += len(phone_ref_list)
except:
pass
if progressbar:
pbar.update(1)
if is_new_epoch:
break
if progressbar:
pbar.close()
# Reset data counters
dataset.reset()
per /= num_phones
sub /= num_phones
ins /= num_phones
dele /= num_phones
df_per = pd.DataFrame(
{
'SUB': [sub * 100],
'INS': [ins * 100],
'DEL': [dele * 100]
},
columns=['SUB', 'INS', 'DEL'],
index=['PER'])
return per, df_per
def check(self, encoder_type, attention_type, label_type='character'):
print('==================================================')
print(' encoder_type: %s' % encoder_type)
print(' attention_type: %s' % attention_type)
print(' label_type: %s' % label_type)
print('==================================================')
tf.reset_default_graph()
with tf.Graph().as_default():
# Load batch data
batch_size = 4
inputs, labels, inputs_seq_len, labels_seq_len = generate_data(
label_type=label_type,
model='attention',
batch_size=batch_size)
# Define model graph
num_classes = 27 if label_type == 'character' else 61
model = AttentionSeq2Seq(input_size=inputs[0].shape[1],
encoder_type=encoder_type,
encoder_num_units=256,
encoder_num_layers=2,
encoder_num_proj=None,
attention_type=attention_type,
attention_dim=128,
decoder_type='lstm',
decoder_num_units=256,
decoder_num_layers=1,
embedding_dim=64,
num_classes=num_classes,
sos_index=num_classes,
eos_index=num_classes + 1,
max_decode_length=100,
use_peephole=True,
splice=1,
parameter_init=0.1,
clip_grad_norm=5.0,
clip_activation_encoder=50,
clip_activation_decoder=50,
weight_decay=1e-8,
time_major=True,
sharpening_factor=1.0,
logits_temperature=1.0)
# Define placeholders
model.create_placeholders()
learning_rate_pl = tf.placeholder(tf.float32, name='learning_rate')
# Add to the graph each operation
loss_op, logits, decoder_outputs_train, decoder_outputs_infer = model.compute_loss(
model.inputs_pl_list[0],
model.labels_pl_list[0],
model.inputs_seq_len_pl_list[0],
model.labels_seq_len_pl_list[0],
model.keep_prob_encoder_pl_list[0],
model.keep_prob_decoder_pl_list[0],
model.keep_prob_embedding_pl_list[0])
train_op = model.train(loss_op,
optimizer='adam',
learning_rate=learning_rate_pl)
decode_op_train, decode_op_infer = model.decode(
decoder_outputs_train, decoder_outputs_infer)
ler_op = model.compute_ler(model.labels_st_true_pl,
model.labels_st_pred_pl)
# Define learning rate controller
learning_rate = 1e-3
lr_controller = Controller(learning_rate_init=learning_rate,
decay_start_epoch=20,
decay_rate=0.9,
decay_patient_epoch=10,
lower_better=True)
# Add the variable initializer operation
init_op = tf.global_variables_initializer()
# Count total parameters
parameters_dict, total_parameters = count_total_parameters(
tf.trainable_variables())
for parameter_name in sorted(parameters_dict.keys()):
print("%s %d" %
(parameter_name, parameters_dict[parameter_name]))
print("Total %d variables, %s M parameters" %
(len(parameters_dict.keys()),
"{:,}".format(total_parameters / 1000000)))
# Make feed dict
feed_dict = {
model.inputs_pl_list[0]: inputs,
model.labels_pl_list[0]: labels,
model.inputs_seq_len_pl_list[0]: inputs_seq_len,
model.labels_seq_len_pl_list[0]: labels_seq_len,
model.keep_prob_encoder_pl_list[0]: 0.8,
model.keep_prob_decoder_pl_list[0]: 1.0,
model.keep_prob_embedding_pl_list[0]: 1.0,
learning_rate_pl: learning_rate
}
idx2phone = Idx2phone(map_file_path='./phone61.txt')
with tf.Session() as sess:
# Initialize parameters
sess.run(init_op)
# Wrapper for tfdbg
# sess = tf_debug.LocalCLIDebugWrapperSession(sess)
# Train model
max_steps = 1000
start_time_step = time.time()
for step in range(max_steps):
# Compute loss
_, loss_train = sess.run(
[train_op, loss_op], feed_dict=feed_dict)
# Gradient check
# grads = sess.run(model.clipped_grads,
# feed_dict=feed_dict)
# for grad in grads:
# print(np.max(grad))
if (step + 1) % 10 == 0:
# Change to evaluation mode
feed_dict[model.keep_prob_encoder_pl_list[0]] = 1.0
feed_dict[model.keep_prob_decoder_pl_list[0]] = 1.0
feed_dict[model.keep_prob_embedding_pl_list[0]] = 1.0
# Predict class ids
predicted_ids_train, predicted_ids_infer = sess.run(
[decode_op_train, decode_op_infer],
feed_dict=feed_dict)
# Compute accuracy
try:
feed_dict_ler = {
model.labels_st_true_pl: list2sparsetensor(
labels, padded_value=model.eos_index),
model.labels_st_pred_pl: list2sparsetensor(
predicted_ids_infer, padded_value=model.eos_index)
}
ler_train = sess.run(
ler_op, feed_dict=feed_dict_ler)
except IndexError:
ler_train = 1
duration_step = time.time() - start_time_step
print('Step %d: loss = %.3f / ler = %.3f (%.3f sec) / lr = %.5f' %
(step + 1, loss_train, ler_train, duration_step, learning_rate))
start_time_step = time.time()
# Visualize
if label_type == 'character':
print('True : %s' %
idx2alpha(labels[0]))
print('Pred (Training) : <%s' %
idx2alpha(predicted_ids_train[0]))
print('Pred (Inference): <%s' %
idx2alpha(predicted_ids_infer[0]))
else:
print('True : %s' %
idx2phone(labels[0]))
print('Pred (Training) : < %s' %
idx2phone(predicted_ids_train[0]))
print('Pred (Inference): < %s' %
idx2phone(predicted_ids_infer[0]))
if ler_train < 0.1:
print('Model is Converged.')
break
# Update learning rate
learning_rate = lr_controller.decay_lr(
learning_rate=learning_rate,
epoch=step,
value=ler_train)
feed_dict[learning_rate_pl] = learning_rate
def decode(model,
dataset,
eval_batch_size,
beam_width,
length_penalty,
save_path=None):
"""Visualize label outputs.
Args:
model: the model to evaluate
dataset: An instance of a `Dataset` class
eval_batch_size (int): the batch size when evaluating the model
beam_width: (int): the size of beam
length_penalty (float):
save_path (string): path to save decoding results
"""
idx2phone = Idx2phone(dataset.vocab_file_path)
if save_path is not None:
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=beam_width,
max_decode_len=MAX_DECODE_LEN_PHONE,
length_penalty=length_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=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 = idx2phone(ys[b][:y_lens[b]])
##############################
# Hypothesis
##############################
# Convert from list of index to string
str_hyp = 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 = 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
