def test_detla_delta(self):
''' test delta delta'''
with self.session():
feat = tf.constant(self.data[None, :], dtype=tf.float32)
output = py_x_ops.delta_delta(feat, order=self.order, window=self.window)
self.assertEqual(tf.rank(output).eval(), tf.rank(feat).eval())
self.assertEqual(output.shape, (1, self.feat_dim * (self.order + 1)))
self.assertAllClose(output.eval(), self.output_true[None, :])
def call(self, feat, order, window):
"""
Caculate delta of feats.
:param feat: a float tensor of size (num_frames, dim_feat).
:param order: an int.
:param window: an int.
:return: A tensor with shape (num_frames, (dim_feat * (order + 1))),
containing delta of features of every frame in speech.
"""
p = self.config
with tf.name_scope('delta_delta'):
delta_delta = py_x_ops.delta_delta(feat, order, window)
return delta_delta
def delta_delta(feat, order=2):
'''
params:
feat: a tensor of shape [nframe, nfbank] or [nframe, nfbank, 1]
return: [nframe, nfbank, 3]
'''
feat = tf.cond(tf.equal(tf.rank(feat), 3),
true_fn=lambda: feat[:, :, 0],
false_fn=lambda: feat)
shape = tf.shape(feat)
# [nframe nfbank*3]
nframe = shape[0]
nfbank = shape[1]
delta = py_x_ops.delta_delta(feat, order=order)
feat_with_delta_delta = tf.reshape(delta, (nframe, nfbank, (order + 1)))
return feat_with_delta_delta