def split_data_by_s5(src_dir,
des_dir,
keywords=['train_si284', 'test_eval92', 'test_dev93']):
count = 0
for key in keywords:
wav_file_list = os.path.join(src_dir, key + '.flist')
label_file_list = os.path.join(src_dir, key + '.txt')
new_path = check_path_exists(os.path.join(des_dir, key))
with open(wav_file_list, 'r') as wfl:
wfl_contents = wfl.readlines()
for line in wfl_contents:
line = line.strip()
if os.path.isfile(line):
shutil.copyfile(
line, os.path.join(des_dir, key,
line.split('/')[-1]))
print(line)
else:
tmp = '/'.join(
line.split('/')[:-1] + [line.split('/')[-1].upper()])
shutil.copyfile(
tmp,
os.path.join(des_dir, key,
line.split('/')[-1].replace('WV1',
'wv1')))
print(tmp)
with open(label_file_list, 'r') as lfl:
lfl_contents = lfl.readlines()
for line in lfl_contents:
label = ' '.join(line.strip().split(' ')[1:])
with open(
os.path.join(des_dir, key,
line.strip().split(' ')[0] + '.label'),
'w') as lf:
lf.writelines(label)
print(key, label)
optimizer_fn = optimizer_functions_dict[FLAGS.optimizer]
batch_size = FLAGS.batch_size
num_hidden = FLAGS.num_hidden
num_feature = FLAGS.num_feature
num_classes = get_num_classes(level)
num_epochs = FLAGS.num_epochs
lr = FLAGS.lr
grad_clip = FLAGS.grad_clip
datadir = FLAGS.datadir
logdir = FLAGS.logdir
savedir = os.path.join(logdir, level, 'save')
resultdir = os.path.join(logdir, level, 'result')
loggingdir = os.path.join(logdir, level, 'logging')
check_path_exists([logdir, savedir, resultdir, loggingdir])
mode = FLAGS.mode
keep = FLAGS.keep
keep_prob = 1 - FLAGS.dropout_prob
print('%s mode...' % str(mode))
if mode == 'test':
batch_size = 100
num_epochs = 1
train_mfcc_dir = os.path.join(datadir, level, 'train', 'mfcc')
train_label_dir = os.path.join(datadir, level, 'train', 'label')
test_mfcc_dir = os.path.join(datadir, level, 'test', 'mfcc')
test_label_dir = os.path.join(datadir, level, 'test', 'label')
logfile = os.path.join(