def call(self, audio_data, sample_rate=None):
"""
Caculate cepstrum of audio data.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return:A float tensor of size (num_frames, ceps_subband_num) containing normalized cepstrum
(tag_ceps_mean_norm = True) or cepstrum (tag_ceps_mean_norm = False) of every frame in speech.
"""
p = self.config
with tf.name_scope('cepstrum'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=float)
assert_op = tf.assert_equal(
tf.constant(p.sample_rate), tf.cast(sample_rate, dtype=float))
with tf.control_dependencies([assert_op]):
cepstrum = py_x_ops.cepstrum(
audio_data,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length,
ceps_subband_num=p.ceps_subband_num,
tag_ceps_mean_norm=p.tag_ceps_mean_norm)
return cepstrum
def test_cepstrum(self):
''' test cepstrum op'''
with self.session():
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
output = py_x_ops.cepstrum(input_data, sample_rate)
output_true = np.array(
[[0.525808, 0.579537, 0.159656, 0.014726, -0.1866810],
[0.225988, 1.557304, 3.381828, 0.132935, 0.7128600],
[-1.832759, -1.045178, 0.753158, 0.116107, -0.9307780],
[-0.696277, 1.333355, 1.590942, 2.041829, -0.0805630],
[-0.377375, 2.984320, 0.036302, 3.676640, 1.1709290]])
self.assertEqual(tf.rank(output).eval(), 2)
logging.info('Shape of cepstrum: {}'.format(output.shape))
self.assertAllClose(output.eval()[15:20, 7:12], output_true)