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='eval1',
# data_type='eval2',
# data_type='eval3',
data_size=params['data_size'],
label_type=params['label_type'],
label_type_sub=params['label_type_sub'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
reverse=False,
tool=params['tool'])
params['num_classes'] = test_data.num_classes
params['num_classes_sub'] = test_data.num_classes_sub
# 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)
a2c_oracle = False
# Visualize
plot(model=model,
dataset=test_data,
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
beam_width_sub=args.beam_width_sub,
length_penalty=args.length_penalty,
a2c_oracle=a2c_oracle,
save_path=mkdir_join(args.model_path, 'att_weights'))
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
vocab_file_path = '../metrics/vocab_files/' + \
params['label_type'] + '_' + params['data_size'] + '.txt'
vocab_file_path_sub = '../metrics/vocab_files/' + \
params['label_type_sub'] + '_' + params['data_size'] + '.txt'
test_data = Dataset(
backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test_clean',
# data_type='test_other',
data_size=params['data_size'],
label_type=params['label_type'],
label_type_sub=params['label_type_sub'],
vocab_file_path=vocab_file_path,
vocab_file_path_sub=vocab_file_path_sub,
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=True,
reverse=True,
save_format=params['save_format'])
params['num_classes'] = test_data.num_classes
params['num_classes_sub'] = test_data.num_classes_sub
# 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,
beam_width=args.beam_width,
max_decode_len=args.max_decode_len,
max_decode_len_sub=args.max_decode_len_sub,
eval_batch_size=args.eval_batch_size,
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
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='eval2000_swbd',
data_type='eval2000_ch',
data_size=params['data_size'],
label_type=params['label_type'], label_type_sub=params['label_type_sub'],
batch_size=args.eval_batch_size, splice=params['splice'],
num_stack=params['num_stack'], num_skip=params['num_skip'],
# sort_utt=True, reverse=True,
sort_utt=False, reverse=False, tool=params['tool'])
params['num_classes'] = test_data.num_classes
params['num_classes_sub'] = test_data.num_classes_sub
# 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,
beam_width=args.beam_width,
beam_width_sub=args.beam_width_sub,
eval_batch_size=args.eval_batch_size,
save_path=None
# save_path=args.model_path
resolving_unk=False)
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
vocab_file_path = '../metrics/vocab_files/' + \
params['label_type'] + '_' + params['data_size'] + '.txt'
test_data = Dataset(
backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test_clean',
# data_type='test_other',
data_size=params['data_size'],
label_type=params['label_type'], vocab_file_path=vocab_file_path,
batch_size=args.eval_batch_size, splice=params['splice'],
num_stack=params['num_stack'], num_skip=params['num_skip'],
sort_utt=True, reverse=False, save_format=params['save_format'])
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)
space_index = 27 if params['label_type'] == 'character' else None
# NOTE: index 0 is reserved for blank in warpctc_pytorch
# Visualize
plot(model=model,
dataset=test_data,
eval_batch_size=args.eval_batch_size,
save_path=mkdir_join(args.model_path, 'ctc_probs'),
space_index=space_index)
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
plot_probs(model=model,
dataset=test_data,
eval_batch_size=args.eval_batch_size,
save_path=mkdir_join(args.model_path, 'ctc_probs'))
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)
# Setting for logging
logger = set_logger(args.model_path)
wer_mean, wer_sub_mean, cer_sub_mean = 0, 0, 0
for i, data_type in enumerate(['eval1', 'eval2', 'eval3']):
# 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,
data_size=params['data_size'],
label_type=params['label_type'],
label_type_sub=params['label_type_sub'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
shuffle=False,
tool=params['tool'])
if i == 0:
params['num_classes'] = dataset.num_classes
params['num_classes_sub'] = dataset.num_classes_sub
# 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 (main): %d\n' % args.beam_width)
logger.info('beam width (sub) : %d\n' % args.beam_width_sub)
logger.info('epoch: %d' % (epoch - 1))
logger.info('a2c oracle: %s\n' % str(args.a2c_oracle))
logger.info('resolving_unk: %s\n' % str(args.resolving_unk))
logger.info('joint_decoding: %s\n' % str(args.joint_decoding))
logger.info('score_sub_weight : %f' % args.score_sub_weight)
wer, df = eval_word(models=[model],
dataset=dataset,
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_WORD,
min_decode_len=MIN_DECODE_LEN_WORD,
beam_width_sub=args.beam_width_sub,
max_decode_len_sub=MAX_DECODE_LEN_CHAR,
min_decode_len_sub=MIN_DECODE_LEN_CHAR,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty,
progressbar=True,
resolving_unk=args.resolving_unk,
a2c_oracle=args.a2c_oracle,
joint_decoding=args.joint_decoding,
score_sub_weight=args.score_sub_weight)
wer_mean += wer
logger.info(' WER (%s, main): %.3f %%' % (data_type, (wer * 100)))
logger.info(df)
wer_sub, cer_sub, df_sub = eval_char(
models=[model],
dataset=dataset,
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width_sub,
max_decode_len=MAX_DECODE_LEN_CHAR,
min_decode_len=MIN_DECODE_LEN_CHAR,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty,
progressbar=True)
wer_sub_mean += wer_sub
cer_sub_mean += cer_sub
logger.info(' WER / CER (%s, sub): %.3f / %.3f %%' % (data_type,
(wer_sub * 100),
(cer_sub * 100)))
logger.info(df_sub)
logger.info(' WER (mean, main): %.3f %%' % (wer_mean * 100 / 3))
logger.info(' WER / CER (mean, sub): %.3f / %.3f %%' %
((wer_sub_mean * 100 / 3), (cer_sub_mean * 100 / 3)))
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='eval1',
# data_type='eval2',
# data_type='eval3',
data_size=params['data_size'],
label_type=params['label_type'],
label_type_sub=params['label_type_sub'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
reverse=False,
tool=params['tool'])
params['num_classes'] = dataset.num_classes
params['num_classes_sub'] = dataset.num_classes_sub
# 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')
######################################################################
word2char = Word2char(dataset.vocab_file_path, dataset.vocab_file_path_sub)
for batch, is_new_epoch in dataset:
# Decode
if model.model_type == 'nested_attention':
best_hyps, aw, best_hyps_sub, aw_sub, _, perm_idx = model.decode(
batch['xs'],
batch['x_lens'],
beam_width=args.beam_width,
beam_width_sub=args.beam_width_sub,
max_decode_len=MAX_DECODE_LEN_WORD,
max_decode_len_sub=MAX_DECODE_LEN_CHAR,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty)
else:
best_hyps, aw, perm_idx = model.decode(
batch['xs'],
batch['x_lens'],
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_WORD,
min_decode_len=MIN_DECODE_LEN_WORD,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty)
best_hyps_sub, aw_sub, _ = model.decode(
batch['xs'],
batch['x_lens'],
beam_width=args.beam_width_sub,
max_decode_len=MAX_DECODE_LEN_CHAR,
min_decode_len=MIN_DECODE_LEN_CHAR,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty,
task_index=1)
if model.model_type == 'hierarchical_attention' and args.joint_decoding is not None:
best_hyps_joint, aw_joint, best_hyps_sub_joint, aw_sub_joint, _ = model.decode(
batch['xs'],
batch['x_lens'],
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_WORD,
min_decode_len=MIN_DECODE_LEN_WORD,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty,
joint_decoding=args.joint_decoding,
space_index=dataset.char2idx('_')[0],
oov_index=dataset.word2idx('OOV')[0],
word2char=word2char,
idx2word=dataset.idx2word,
idx2char=dataset.idx2char,
score_sub_weight=args.score_sub_weight)
ys = batch['ys'][perm_idx]
y_lens = batch['y_lens'][perm_idx]
ys_sub = batch['ys_sub'][perm_idx]
y_lens_sub = batch['y_lens_sub'][perm_idx]
for b in range(len(batch['xs'])):
##############################
# Reference
##############################
if dataset.is_test:
str_ref = ys[b][0]
str_ref_sub = ys_sub[b][0]
# NOTE: transcript is seperated by space('_')
else:
# Convert from list of index to string
str_ref = dataset.idx2word(ys[b][:y_lens[b]])
str_ref_sub = dataset.idx2char(ys_sub[b][:y_lens_sub[b]])
##############################
# Hypothesis
##############################
# Convert from list of index to string
str_hyp = dataset.idx2word(best_hyps[b])
str_hyp_sub = dataset.idx2char(best_hyps_sub[b])
if model.model_type == 'hierarchical_attention' and args.joint_decoding is not None:
str_hyp_joint = dataset.idx2word(best_hyps_joint[b])
str_hyp_sub_joint = dataset.idx2char(best_hyps_sub_joint[b])
##############################
# Resolving UNK
##############################
if 'OOV' in str_hyp and args.resolving_unk:
str_hyp_no_unk = resolve_unk(str_hyp, best_hyps_sub[b], aw[b],
aw_sub[b], dataset.idx2char)
if model.model_type == 'hierarchical_attention' and args.joint_decoding is not None:
if 'OOV' in str_hyp_joint and args.resolving_unk:
str_hyp_no_unk_joint = resolve_unk(str_hyp_joint,
best_hyps_sub_joint[b],
aw_joint[b],
aw_sub_joint[b],
dataset.idx2char)
print('----- wav: %s -----' % batch['input_names'][b])
print('Ref : %s' % str_ref.replace('_', ' '))
print('Hyp (main) : %s' % str_hyp.replace('_', ' '))
print('Hyp (sub) : %s' % str_hyp_sub.replace('_', ' '))
if 'OOV' in str_hyp and args.resolving_unk:
print('Hyp (no UNK): %s' % str_hyp_no_unk.replace('_', ' '))
if model.model_type == 'hierarchical_attention' and args.joint_decoding is not None:
print('===== joint decoding =====')
print('Hyp (main) : %s' % str_hyp_joint.replace('_', ' '))
print('Hyp (sub) : %s' % str_hyp_sub_joint.replace('_', ' '))
if 'OOV' in str_hyp_joint and args.resolving_unk:
print('Hyp (no UNK): %s' %
str_hyp_no_unk_joint.replace('_', ' '))
try:
wer, _, _, _ = compute_wer(ref=str_ref.split('_'),
hyp=re.sub(r'(.*)_>(.*)', r'\1',
str_hyp).split('_'),
normalize=True)
print('WER (main) : %.3f %%' % (wer * 100))
if dataset.label_type_sub == 'character_wb':
wer_sub, _, _, _ = compute_wer(ref=str_ref_sub.split('_'),
hyp=re.sub(
r'(.*)>(.*)', r'\1',
str_hyp_sub).split('_'),
normalize=True)
print('WER (sub) : %.3f %%' % (wer_sub * 100))
else:
cer, _, _, _ = compute_wer(
ref=list(str_ref_sub.replace('_', '')),
hyp=list(
re.sub(r'(.*)>(.*)', r'\1',
str_hyp_sub).replace('_', '')),
normalize=True)
print('CER (sub) : %.3f %%' % (cer * 100))
if 'OOV' in str_hyp and args.resolving_unk:
wer_no_unk, _, _, _ = compute_wer(
ref=str_ref.split('_'),
hyp=re.sub(r'(.*)_>(.*)', r'\1',
str_hyp_no_unk.replace('*', '')).split('_'),
normalize=True)
print('WER (no UNK): %.3f %%' % (wer_no_unk * 100))
if model.model_type == 'hierarchical_attention' and args.joint_decoding is not None:
print('===== joint decoding =====')
wer_joint, _, _, _ = compute_wer(
ref=str_ref.split('_'),
hyp=re.sub(r'(.*)_>(.*)', r'\1',
str_hyp_joint).split('_'),
normalize=True)
print('WER (main) : %.3f %%' % (wer_joint * 100))
if 'OOV' in str_hyp_joint and args.resolving_unk:
wer_no_unk_joint, _, _, _ = compute_wer(
ref=str_ref.split('_'),
hyp=re.sub(r'(.*)_>(.*)', r'\1',
str_hyp_no_unk_joint.replace(
'*', '')).split('_'),
normalize=True)
print('WER (no UNK): %.3f %%' %
(wer_no_unk_joint * 100))
except:
print('--- skipped ---')
print('\n')
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
vocab_file_path = '../metrics/vocab_files/' + \
params['label_type'] + '_' + params['data_size'] + '.txt'
test_clean_data = Dataset(backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test_clean',
data_size=params['data_size'],
label_type=params['label_type'],
vocab_file_path=vocab_file_path,
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
save_format=params['save_format'])
test_other_data = Dataset(backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test_other',
data_size=params['data_size'],
label_type=params['label_type'],
vocab_file_path=vocab_file_path,
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
save_format=params['save_format'])
params['num_classes'] = test_clean_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)
if 'word' in params['label_type']:
wer_test_clean = do_eval_wer(model=model,
dataset=test_clean_data,
beam_width=args.beam_width,
max_decode_len=args.max_decode_len,
eval_batch_size=args.eval_batch_size,
progressbar=True)
print(' WER (clean): %f %%' % (wer_test_clean * 100))
wer_test_other = do_eval_wer(model=model,
dataset=test_other_data,
beam_width=args.beam_width,
max_decode_len=args.max_decode_len,
eval_batch_size=args.eval_batch_size,
progressbar=True)
print(' WER (other): %f %%' % (wer_test_other * 100))
print(' WER (mean): %f %%' %
((wer_test_clean + wer_test_other) * 100 / 2))
else:
cer_test_clean, wer_test_clean = do_eval_cer(
model=model,
dataset=test_clean_data,
beam_width=args.beam_width,
max_decode_len=args.max_decode_len,
eval_batch_size=args.eval_batch_size,
progressbar=True)
print(' CER (clean): %f %%' % (cer_test_clean * 100))
print(' WER (clean): %f %%' % (wer_test_clean * 100))
cer_test_other, wer_test_other = do_eval_cer(
model=model,
dataset=test_other_data,
beam_width=args.beam_width,
max_decode_len=args.max_decode_len,
eval_batch_size=args.eval_batch_size,
progressbar=True)
print(' CER (other): %f %%' % (cer_test_other * 100))
print(' WER (other): %f %%' % (wer_test_other * 100))
print(' CER (mean): %f %%' %
((cer_test_clean + cer_test_other) * 100 / 2))
print(' WER (mean): %f %%' %
((wer_test_clean + wer_test_other) * 100 / 2))
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
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_dev93',
data_type='test_eval92',
data_size=params['data_size'],
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)
if params['label_type'] == 'word':
wer_eval92, df_eval92 = eval_word(models=[model],
dataset=test_data,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_WORD,
eval_batch_size=args.eval_batch_size,
length_penalty=args.length_penalty,
progressbar=True)
print(' WER (eval92): %.3f %%' % (wer_eval92 * 100))
print(df_eval92)
else:
wer_eval92, cer_eval92, df_eval92 = eval_char(
models=[model],
dataset=test_data,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_CHAR,
eval_batch_size=args.eval_batch_size,
length_penalty=args.length_penalty,
progressbar=True)
print(' WER / CER (eval92): %.3f / %.3f %%' % ((wer_eval92 * 100),
(cer_eval92 * 100)))
print(df_eval92)
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
eval2000_swbd_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='eval2000_swbd',
data_size=params['data_size'],
label_type=params['label_type'],
label_type_sub=params['label_type_sub'],
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'])
eval2000_ch_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='eval2000_ch',
data_size=params['data_size'],
label_type=params['label_type'],
label_type_sub=params['label_type_sub'],
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'] = eval2000_swbd_data.num_classes
params['num_classes_sub'] = eval2000_swbd_data.num_classes_sub
# 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)
a2c_oracle = False
resolving_unk = False
##############################
# Switchboard
##############################
wer_eval2000_swbd, df_wer_eval2000_swbd = eval_word(
models=[model],
dataset=eval2000_swbd_data,
beam_width=args.beam_width,
beam_width_sub=args.beam_width_sub,
max_decode_len=MAX_DECODE_LEN_WORD,
max_decode_len_sub=MAX_DECODE_LEN_CHAR,
eval_batch_size=args.eval_batch_size,
progressbar=True,
resolving_unk=resolving_unk,
a2c_oracle=a2c_oracle)
print(' WER (SWB, main): %.3f %%' % (wer_eval2000_swbd * 100))
print(df_wer_eval2000_swbd)
wer_eval2000_swbd_sub, cer_eval2000_swbd_sub, _ = eval_char(
models=[model],
dataset=eval2000_swbd_data,
beam_width=args.beam_width_sub,
max_decode_len=MAX_DECODE_LEN_CHAR,
eval_batch_size=args.eval_batch_size,
progressbar=True)
print(' WER / CER (SWB, sub): %.3f / %.3f %%' %
((wer_eval2000_swbd_sub * 100), (cer_eval2000_swbd_sub * 100)))
##############################
# Callhome
##############################
wer_eval2000_ch, df_wer_eval2000_ch = eval_word(
models=[model],
dataset=eval2000_ch_data,
beam_width=args.beam_width,
beam_width_sub=args.beam_width_sub,
max_decode_len=MAX_DECODE_LEN_WORD,
max_decode_len_sub=MAX_DECODE_LEN_CHAR,
eval_batch_size=args.eval_batch_size,
progressbar=True,
resolving_unk=resolving_unk,
a2c_oracle=a2c_oracle)
print(' WER (CHE, main): %.3f %%' % (wer_eval2000_ch * 100))
print(df_wer_eval2000_ch)
wer_eval2000_ch_sub, cer_eval2000_ch_sub, _ = eval_char(
models=[model],
dataset=eval2000_ch_data,
beam_width=args.beam_width_sub,
max_decode_len=args.max_decode_len_sub,
eval_batch_size=args.eval_batch_size,
progressbar=True)
print(' WER / CER (CHE, sub): %.3f / %.3f %%' %
((wer_eval2000_ch_sub * 100), (cer_eval2000_ch_sub * 100)))
print(' WER (mean, main): %.3f %%' %
((wer_eval2000_swbd + wer_eval2000_ch) * 100 / 2))
print(' WER / CER (mean, sub): %.3f / %.3f %%' %
(((wer_eval2000_swbd_sub + wer_eval2000_ch_sub) * 100 / 2),
((cer_eval2000_swbd_sub + cer_eval2000_ch_sub) * 100 / 2)))
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='eval1',
# data_type='eval2',
# data_type='eval3',
data_size=params['data_size'],
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, reverse=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)
if dataset.label_type == 'word':
map_fn = dataset.idx2word
max_decode_len = MAX_DECODE_LEN_WORD
min_decode_len = MIN_DECODE_LEN_WORD
else:
map_fn = dataset.idx2char
max_decode_len = MAX_DECODE_LEN_CHAR
min_decode_len = MIN_DECODE_LEN_CHAR
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,
min_decode_len=min_decode_len,
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 = map_fn(ys[b][:y_lens[b]])
token_list = map_fn(best_hyps[b], return_list=True)
speaker = batch['input_names'][b].split('_')[0]
plot_attention_weights(
aw[b][:len(token_list), :batch['x_lens'][b]],
label_list=token_list,
spectrogram=batch['xs'][b, :, :dataset.input_freq],
str_ref=str_ref,
save_path=mkdir_join(save_path, speaker,
batch['input_names'][b] + '.png'),
figsize=(20, 8))
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
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_eval92',
data_size=params['data_size'],
label_type=params['label_type'],
label_type_sub=params['label_type_sub'],
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
params['num_classes_sub'] = test_data.num_classes_sub
# 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)
a2c_oracle = False
resolving_unk = True
print('beam width (main): %d' % args.beam_width)
print('beam width (sub) : %d' % args.beam_width_sub)
print('a2c oracle: %s' % str(a2c_oracle))
print('resolving_unk: %s' % str(resolving_unk))
wer_eval92, df_eval92 = eval_word(models=[model],
dataset=test_data,
beam_width=args.beam_width,
beam_width_sub=args.beam_width_sub,
max_decode_len=MAX_DECODE_LEN_WORD,
max_decode_len_sub=MAX_DECODE_LEN_CHAR,
eval_batch_size=args.eval_batch_size,
progressbar=True,
resolving_unk=resolving_unk,
a2c_oracle=a2c_oracle)
print(' WER (eval92, main): %.3f %%' % (wer_eval92 * 100))
print(df_eval92)
wer_eval92_sub, cer_eval92_sub, df_eval92_sub = eval_char(
models=[model],
dataset=test_data,
beam_width=args.beam_width_sub,
max_decode_len=MAX_DECODE_LEN_CHAR,
eval_batch_size=args.eval_batch_size,
progressbar=True)
print(' WER / CER (eval92, sub): %.3f / %.3f %%' %
((wer_eval92_sub * 100), (cer_eval92_sub * 100)))
print(df_eval92_sub)
def main():
args = parser.parse_args()
# Load a config file (.yml)
params = load_config(join(args.model_path, 'config.yml'), is_eval=True)
wer_mean, cer_mean = 0, 0
with open(join(args.model_path, 'RESULTS'), 'w') as f:
for i, data_type in enumerate(['eval1', 'eval2', 'eval3']):
# Load dataset
eval_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=data_type, data_size=params['data_size'],
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'])
if i == 0:
params['num_classes'] = eval_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)
f.write('beam width: %d\n' % args.beam_width)
if params['label_type'] == 'word':
wer, df = eval_word(
models=[model],
dataset=eval_data,
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_WORD,
length_penalty=args.length_penalty,
progressbar=True)
wer_mean += wer
print(' WER (%s): %.3f %%' % (data_type, (wer * 100)))
f.write(' WER (%s): %.3f %%' % (data_type, (wer * 100)))
print(df)
else:
wer, cer, df = eval_char(
models=[model],
dataset=eval_data,
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_CHAR,
length_penalty=args.length_penalty,
progressbar=True)
wer_mean += wer
cer_mean += cer
print(' WER / CER (%s, sub): %.3f / %.3f %%' %
(data_type, (wer * 100), (cer * 100)))
f.write(' WER / CER (%s, sub): %.3f / %.3f %%' %
(data_type, (wer * 100), (cer * 100)))
print(df)
if params['label_type'] == 'word':
print(' WER (mean): %.3f %%' % (wer * 100 / 3))
f.write(' WER (mean): %.3f %%' % (wer * 100 / 3))
else:
print(' WER / CER (mean): %.3f / %.3f %%' %
((wer * 100 / 3), (cer * 100 / 3)))
f.write(' WER / CER (mean): %.3f / %.3f %%' %
((wer * 100 / 3), (cer * 100 / 3)))
def main():
model_paths = [
path for path in glob(
join(
'/n/sd8/inaguma/result/pytorch/librispeech/ctc/character/100h',
'*'))
]
if len(model_paths) == 0:
raise ValueError('There are no model path.')
args = parser.parse_args()
# Load a config file (.yml)
params = load_config(join(model_paths[0], 'config.yml'), is_eval=True)
# Load dataset
vocab_file_path = '../metrics/vocab_files/' + \
params['label_type'] + '_' + params['data_size'] + '.txt'
test_clean_data = Dataset(backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test_clean',
data_size=params['data_size'],
label_type=params['label_type'],
vocab_file_path=vocab_file_path,
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
save_format=params['save_format'])
test_other_data = Dataset(backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test_other',
data_size=params['data_size'],
label_type=params['label_type'],
vocab_file_path=vocab_file_path,
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
save_format=params['save_format'])
models = []
for model_path in model_paths:
if isfile(join(model_path, 'complete.txt')):
# Load a config file (.yml)
params = load_config(join(model_path, 'config.yml'), is_eval=True)
params['num_classes'] = test_clean_data.num_classes
# Load model
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Restore the saved model
model.load_checkpoint(save_path=args.model_path, epoch=-1)
# GPU setting
model.set_cuda(deterministic=False, benchmark=True)
models.append(model)
print('=' * 30)
print(' frame stack %d' % int(params['num_stack']))
print(' beam width: %d' % args.beam_width)
print(' ensemble: %d' % len(models))
print(' temperature (training): %d' % params['logits_temperature'])
print(' temperature (inference): %d' % args.temperature)
print('=' * 30)
if 'char' in params['label_type']:
cer_test_clean, wer_test_clean = do_eval_cer(
models=models,
dataset=test_clean_data,
beam_width=args.beam_width,
max_decode_len=args.max_decode_len,
eval_batch_size=args.eval_batch_size,
temperature=args.temperature,
progressbar=True)
print(' CER (clean): %f %%' % (cer_test_clean * 100))
print(' WER (clean): %f %%' % (wer_test_clean * 100))
cer_test_other, wer_test_other = do_eval_cer(
models=models,
dataset=test_other_data,
beam_width=args.beam_width,
max_decode_len=args.max_decode_len,
eval_batch_size=args.eval_batch_size,
progressbar=True)
print(' CER (other): %f %%' % (cer_test_other * 100))
print(' WER (other): %f %%' % (wer_test_other * 100))
print(' CER (mean): %f %%' %
((cer_test_clean + cer_test_other) * 100 / 2))
print(' WER (mean): %f %%' %
((wer_test_clean + wer_test_other) * 100 / 2))
else:
raise NotImplementedError
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='eval1',
# data_type='eval2',
# data_type='eval3',
data_size=params['data_size'],
label_type=params['label_type'],
label_type_sub=params['label_type_sub'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
reverse=False,
tool=params['tool'])
params['num_classes'] = dataset.num_classes
params['num_classes_sub'] = dataset.num_classes_sub
# 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_WORD,
min_decode_len=MIN_DECODE_LEN_WORD,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty)
best_hyps_sub, aw_sub, _ = model.decode(
batch['xs'],
batch['x_lens'],
beam_width=args.beam_width_sub,
max_decode_len=MAX_DECODE_LEN_CHAR,
min_decode_len=MIN_DECODE_LEN_CHAR,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty,
task_index=1)
for b in range(len(batch['xs'])):
word_list = dataset.idx2word(best_hyps[b], return_list=True)
char_list = dataset.idx2char(best_hyps_sub[b], return_list=True)
speaker = batch['input_names'][b].split('_')[0]
plot_hierarchical_attention_weights(
aw[b][:len(word_list), :batch['x_lens'][b]],
aw_sub[b][:len(char_list), :batch['x_lens'][b]],
label_list=word_list,
label_list_sub=char_list,
spectrogram=batch['xs'][b, :, :dataset.input_freq],
save_path=mkdir_join(save_path, speaker,
batch['input_names'][b] + '.png'),
figsize=(40, 8))
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_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='train',
data_size=params['data_size'],
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_clean_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='dev_clean',
data_size=params['data_size'],
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'])
dev_other_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='dev_other',
data_size=params['data_size'],
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_clean_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test_clean',
data_size=params['data_size'],
label_type=params['label_type'],
batch_size=params['batch_size'],
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
tool=params['tool'])
test_other_data = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_channel=params['input_channel'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test_other',
data_size=params['data_size'],
label_type=params['label_type'],
batch_size=params['batch_size'],
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
tool=params['tool'])
params['num_classes'] = train_data.num_classes
##################################################
# MODEL
##################################################
# Model setting
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
if args.model_save_path is not None:
# Set save path
save_path = mkdir_join(args.model_save_path, params['backend'],
params['model_type'], params['label_type'],
params['data_size'], 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:
# Set save path
model.save_path = args.saved_model_path
# Setting for logging
logger = set_logger(model.save_path, restart=True)
# 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('libri_' + params['backend'] + '_' + params['model_type'] +
'_' + params['label_type'] + '_' + params['data_size'])
##################################################
# TRAINING LOOP
##################################################
# Define learning rate controller
lr_controller = Controller(
learning_rate_init=learning_rate,
backend=params['backend'],
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
best_model = model
loss_train_mean = 0.
pbar_epoch = tqdm(total=len(train_data))
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'],
backend=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_clean_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
if 'word' in params['label_type']:
metric_dev_epoch, _ = do_eval_wer(
models=[model],
dataset=dev_clean_data,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_WORD,
eval_batch_size=1)
logger.info(' WER (dev-clean): %.3f %%' %
(metric_dev_epoch * 100))
else:
metric_dev_epoch, wer_dev_clean_epoch, _ = do_eval_cer(
models=[model],
dataset=dev_clean_data,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_CHAR,
eval_batch_size=1)
logger.info(' CER / WER (dev-clean): %.3f %% / %.3f %%' %
((metric_dev_epoch * 100),
(wer_dev_clean_epoch * 100)))
if metric_dev_epoch < metric_dev_best:
metric_dev_best = metric_dev_epoch
not_improved_epoch = 0
best_model = copy.deepcopy(model)
logger.info('||||| Best Score |||||')
# Save the model
model.save_checkpoint(model.save_path, epoch, step,
learning_rate, metric_dev_best)
# dev-other & test
if 'word' in params['label_type']:
metric_dev_other_epoch, _ = do_eval_wer(
models=[model],
dataset=dev_other_data,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_WORD,
eval_batch_size=1)
logger.info(' WER (dev-other): %.3f %%' %
(metric_dev_other_epoch * 100))
wer_test_clean, _ = do_eval_wer(
models=[model],
dataset=test_clean_data,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_WORD,
eval_batch_size=1)
logger.info(' WER (test-clean): %.3f %%' %
(wer_test_clean * 100))
wer_test_other, _ = do_eval_wer(
models=[model],
dataset=test_other_data,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_WORD,
eval_batch_size=1)
logger.info(' WER (test-other): %.3f %%' %
(wer_test_other * 100))
logger.info(
' WER (test-mean): %.3f %%' %
((wer_test_clean + wer_test_other) * 100 / 2))
else:
metric_dev_other_epoch, wer_dev_other_epoch, _ = do_eval_cer(
models=[model],
dataset=dev_other_data,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_CHAR,
eval_batch_size=1)
logger.info(
' CER / WER (dev-other): %.3f %% / %.3f %%' %
((metric_dev_other_epoch * 100),
(wer_dev_other_epoch * 100)))
cer_test_clean, wer_test_clean, _ = do_eval_cer(
models=[model],
dataset=test_clean_data,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_CHAR,
eval_batch_size=1)
logger.info(
' CER / WER (test-clean): %.3f %% / %.3f %%' %
((cer_test_clean * 100), (wer_test_clean * 100)))
cer_test_other, wer_test_other, _ = do_eval_cer(
models=[model],
dataset=test_other_data,
beam_width=1,
max_decode_len=MAX_DECODE_LEN_CHAR,
eval_batch_size=1)
logger.info(
' CER / WER (test-other): %.3f %% / %.3f %%' %
((cer_test_other * 100), (wer_test_other * 100)))
logger.info(
' CER / WER (test-mean): %.3f %% / %.3f %%' %
(((cer_test_clean + cer_test_other) * 100 / 2),
((wer_test_clean + wer_test_other) * 100 / 2)))
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=metric_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
# TODO: evaluate the best model by beam search here
duration_train = time.time() - start_time_train
logger.info('Total time: %.3f hour' % (duration_train / 3600))
if params['backend'] == 'pytorch':
tf_writer.close()
# Training was finished correctly
with open(os.path.join(model.save_path, 'COMPLETE'), 'w') as f:
f.write('')
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()
# Load a config file (.yml)
params = load_config(join(args.model_path, 'config.yml'), is_eval=True)
logger = set_logger(args.model_path)
for i, data_type in enumerate(
['dev_clean', 'dev_other', 'test_clean', 'test_other']):
# 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,
data_size=params['data_size'],
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))
if params['label_type'] == 'word':
wer, df = eval_word(models=[model],
dataset=dataset,
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_WORD,
min_decode_len=MIN_DECODE_LEN_WORD,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty,
progressbar=True)
logger.info(' WER (%s): %.3f %%' % (dataset.label_type,
(wer * 100)))
logger.info(df)
else:
wer, cer, df = eval_char(models=[model],
dataset=dataset,
eval_batch_size=args.eval_batch_size,
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_CHAR,
min_decode_len=MIN_DECODE_LEN_CHAR,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty,
progressbar=True)
logger.info(' WER / CER (%s): %.3f / %.3f %%' %
(dataset.label_type, (wer * 100), (cer * 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
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='eval1',
# data_type='eval2',
# data_type='eval3',
data_size=params['data_size'],
label_type=params['label_type'],
label_type_sub=params['label_type_sub'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
reverse=False,
tool=params['tool'])
params['num_classes'] = dataset.num_classes
params['num_classes_sub'] = dataset.num_classes_sub
# 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)
a2c_oracle = False
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:
batch_size = len(batch['xs'])
if a2c_oracle:
if dataset.is_test:
max_label_num = 0
for b in range(batch_size):
if max_label_num < len(list(batch['ys_sub'][b][0])):
max_label_num = len(list(batch['ys_sub'][b][0]))
ys_sub = np.zeros((batch_size, max_label_num), dtype=np.int32)
ys_sub -= 1 # pad with -1
y_lens_sub = np.zeros((batch_size, ), dtype=np.int32)
for b in range(batch_size):
indices = dataset.char2idx(batch['ys_sub'][b][0])
ys_sub[b, :len(indices)] = indices
y_lens_sub[b] = len(indices)
# NOTE: transcript is seperated by space('_')
else:
ys_sub = batch['ys_sub']
y_lens_sub = batch['y_lens_sub']
else:
ys_sub = None
y_lens_sub = None
best_hyps, aw, best_hyps_sub, aw_sub, aw_dec, _ = model.decode(
batch['xs'],
batch['x_lens'],
beam_width=args.beam_width,
max_decode_len=MAX_DECODE_LEN_WORD,
min_decode_len=MIN_DECODE_LEN_WORD,
beam_width_sub=args.beam_width_sub,
max_decode_len_sub=MAX_DECODE_LEN_CHAR,
min_decode_len_sub=MIN_DECODE_LEN_CHAR,
length_penalty=args.length_penalty,
coverage_penalty=args.coverage_penalty,
teacher_forcing=a2c_oracle,
ys_sub=ys_sub,
y_lens_sub=y_lens_sub)
for b in range(len(batch['xs'])):
word_list = dataset.idx2word(best_hyps[b], return_list=True)
if dataset.label_type_sub == 'word':
char_list = dataset.idx2word(best_hyps_sub[b],
return_list=True)
else:
char_list = dataset.idx2char(best_hyps_sub[b],
return_list=True)
speaker = batch['input_names'][b].split('_')[0]
# word to acoustic & character to acoustic
plot_hierarchical_attention_weights(
aw[b][:len(word_list), :batch['x_lens'][b]],
aw_sub[b][:len(char_list), :batch['x_lens'][b]],
label_list=word_list,
label_list_sub=char_list,
spectrogram=batch['xs'][b, :, :dataset.input_freq],
save_path=mkdir_join(save_path, speaker,
batch['input_names'][b] + '.png'),
figsize=(40, 8))
# word to characater
plot_nested_attention_weights(
aw_dec[b][:len(word_list), :len(char_list)],
label_list=word_list,
label_list_sub=char_list,
save_path=mkdir_join(
save_path, speaker,
batch['input_names'][b] + '_word2char.png'),
figsize=(40, 8))
# with open(join(save_path, speaker, batch['input_names'][b] + '.txt'), 'w') as f:
# f.write(batch['ys'][b][0])
if is_new_epoch:
break
