def call(self, audio_data, sample_rate=None):
"""
Caculate plp features 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, (plp_order + 1)) containing plp features of every frame in speech.
"""
p = self.config
with tf.name_scope('plp'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(
tf.constant(p.sample_rate), tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
sample_rate = tf.cast(sample_rate, dtype=float)
plp = py_x_ops.plp(
audio_data,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length,
plp_order=p.plp_order)
return plp
def test_plp(self):
''' test plp op'''
with self.session():
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
output = py_x_ops.plp(input_data, sample_rate)
output_true = np.array(
[[-0.209490, -0.326126, 0.010536, -0.027167, -0.117118],
[-0.020293, -0.454695, -0.104243, 0.001560, -0.234854],
[-0.015118, -0.444044, -0.156695, -0.086221, -0.319310],
[-0.031856, -0.130708, 0.047435, -0.089916, -0.160247],
[0.052763, -0.271487, 0.011329, 0.025320, 0.012851]])
self.assertEqual(tf.rank(output).eval(), 2)
logging.info('Shape of PLP: {}'.format(output.shape))
self.assertAllClose(output.eval()[50:55, 5:10], output_true)
def test_plp(self):
''' test plp op'''
with self.session():
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
output = py_x_ops.plp(input_data, sample_rate)
output_true = np.array([
-0.000000, -0.959257, -0.095592, -0.219479, -0.104977,
-0.185207, -0.153651, -0.081711, -0.156977, -0.072177,
0.077400, 0.027594, 0.040156, -0.000000, -0.956464, -0.086729,
-0.211084, -0.062403, -0.212304, -0.240348, -0.081032,
-0.036527, -0.071906, 0.025969, 0.004119, 0.003473, -0.000000,
-0.952486, -0.094521, -0.143834, -0.133079, -0.244882,
-0.175419, -0.040801, -0.071001, -0.134758, 0.061415, 0.085666,
0.012909, -0.000000, -0.928211, -0.108592, -0.249340,
-0.141225, -0.199109, -0.081247, -0.044329, -0.140386,
-0.174557, -0.045552
])
self.assertEqual(tf.rank(output).eval(), 1)
self.assertAllClose(output.eval().flatten()[:50], output_true)