def decode():
"""Decoding the inputs using current model."""
tfrecords_lst, num_batches = read_list_file('tt_tf', FLAGS.batch_size)
with tf.Graph().as_default():
with tf.device('/cpu:0'):
with tf.name_scope('input'):
tt_mixed, tt_labels, tt_genders, tt_lengths = get_padded_batch(
tfrecords_lst,
FLAGS.batch_size,
FLAGS.input_size * 2,
FLAGS.output_size * 2,
num_enqueuing_threads=1,
num_epochs=1,
shuffle=False)
tt_inputs = tf.slice(tt_mixed, [0, 0, 0],
[-1, -1, FLAGS.input_size])
tt_angles = tf.slice(tt_mixed, [0, 0, FLAGS.input_size],
[-1, -1, -1])
# Create two models with train_input and val_input individually.
with tf.name_scope('model'):
model = LSTM(FLAGS,
tt_inputs,
tt_labels,
tt_lengths,
tt_genders,
infer=True)
init = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess = tf.Session()
sess.run(init)
ckpt = tf.train.get_checkpoint_state(FLAGS.save_dir + '/nnet')
if ckpt and ckpt.model_checkpoint_path:
tf.logging.info("Restore from " + ckpt.model_checkpoint_path)
model.saver.restore(sess, ckpt.model_checkpoint_path)
else:
tf.logging.fatal("checkpoint not fou1nd.")
sys.exit(-1)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
#cmvn_filename = os.path.join(FLAGS.date_dir, "/train_cmvn.npz")
#if os.path.isfile(cmvn_filename):
# cmvn = np.load(cmvn_filename)
#else:
# tf.logging.fatal("%s not exist, exit now." % cmvn_filename)
# sys.exit(-1)
data_dir = FLAGS.data_dir
if not os.path.exists(data_dir):
os.makedirs(data_dir)
processed = 0
try:
for batch in xrange(num_batches):
if coord.should_stop():
break
if FLAGS.assign == 'def':
cleaned1, cleaned2, angles, lengths = sess.run(
[model._cleaned1, model._cleaned2, tt_angles, tt_lengths])
else:
x1, x2 = model.get_opt_output()
cleaned1, cleaned2 = sess.run([x1, x2])
spec1 = cleaned1 * np.exp(angles * 1j)
spec2 = cleaned2 * np.exp(angles * 1j)
#sequence = activations * cmvn['stddev_labels'] + \
# cmvn['mean_labels']
for i in range(0, FLAGS.batch_size):
tffilename = tfrecords_lst[i + processed]
(_, name) = os.path.split(tffilename)
(partname, _) = os.path.splitext(name)
wav_name1 = data_dir + '/' + partname + '_1.wav'
wav_name2 = data_dir + '/' + partname + '_2.wav'
wav1 = istft(spec1[i, 0:lengths[i], :], size=256, shift=128)
wav2 = istft(spec2[i, 0:lengths[i], :], size=256, shift=128)
audiowrite(wav1, wav_name1, 8000, True, True)
audiowrite(wav2, wav_name2, 8000, True, True)
processed = processed + FLAGS.batch_size
if batch % 50 == 0:
print(batch)
except Exception, e:
# Report exceptions to the coordinator.
coord.request_stop(e)
def decode():
"""Decoding the inputs using current model."""
tfrecords_lst, num_batches = read_list_file('tt', FLAGS.batch_size)
with tf.Graph().as_default():
with tf.device('/cpu:0'):
with tf.name_scope('input'):
tt_mixed, tt_inputs, tt_labels1, tt_labels2, tt_lengths = get_padded_batch_v2(
tfrecords_lst,
1,
FLAGS.input_size,
FLAGS.output_size,
num_enqueuing_threads=1,
num_epochs=1,
shuffle=False)
# Create two models with train_input and val_input individually.
with tf.name_scope('model'):
model = LSTM(FLAGS,
tt_inputs,
tt_mixed,
tt_labels1,
tt_labels2,
tt_lengths,
infer=True)
init = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess = tf.Session()
sess.run(init)
ckpt = tf.train.get_checkpoint_state(FLAGS.save_dir + '/nnet')
if ckpt and ckpt.model_checkpoint_path:
tf.logging.info("Restore from " + ckpt.model_checkpoint_path)
model.saver.restore(sess, ckpt.model_checkpoint_path)
else:
tf.logging.fatal("checkpoint not fou1nd.")
sys.exit(-1)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
#cmvn_filename = os.path.join(FLAGS.date_dir, "/train_cmvn.npz")
#if os.path.isfile(cmvn_filename):
# cmvn = np.load(cmvn_filename)
#else:
# tf.logging.fatal("%s not exist, exit now." % cmvn_filename)
# sys.exit(-1)
data_dir = FLAGS.data_dir
if not os.path.exists(data_dir):
os.makedirs(data_dir)
try:
for batch in xrange(num_batches):
if coord.should_stop():
break
if FLAGS.assign == 'def':
cleaned1, cleaned2 = sess.run(
[model._cleaned1, model._cleaned2])
else:
x1, x2 = model.get_opt_output()
cleaned1, cleaned2 = sess.run([x1, x2])
#sequence = activations * cmvn['stddev_labels'] + \
# cmvn['mean_labels']
tffilename = tfrecords_lst[batch]
(_, name) = os.path.split(tffilename)
(uttid, _) = os.path.splitext(name)
(partname, _) = os.path.splitext(uttid)
#np.savetxt('data/mask/'+partname + '_1.mask', m1)
#np.savetxt('data/mask/'+partname + '_2.mask', m2)
kaldi_writer1 = kio.ArkWriter(data_dir + '/' + partname +
'_1.wav.scp')
kaldi_writer2 = kio.ArkWriter(data_dir + '/' + partname +
'_2.wav.scp')
kaldi_writer1.write_next_utt(
data_dir + '/' + partname + '_1.wav.ark', uttid,
cleaned1[0, :, :])
kaldi_writer2.write_next_utt(
data_dir + '/' + partname + '_2.wav.ark', uttid,
cleaned2[0, :, :])
kaldi_writer1.close()
kaldi_writer2.close()
if batch % 500 == 0:
print(batch)
except Exception, e:
# Report exceptions to the coordinator.
coord.request_stop(e)