def test_splice(self):
''' test batch splice frame '''
with self.session():
feat = tf.ones([1, 3, 2], dtype=tf.float32)
for l_ctx in range(0, 4):
for r_ctx in range(0, 4):
ctx = l_ctx + 1 + r_ctx
out = tffeat.splice(feat,
left_context=l_ctx,
right_context=r_ctx)
self.assertTupleEqual(out.eval().shape, (1, 3, 2 * ctx))
self.assertAllEqual(out, tf.ones([1, 3, 2 * ctx]))
with self.assertRaises(ValueError):
out = tffeat.splice(feat, left_context=-2,
right_context=-2).eval()
with self.assertRaises(ValueError):
out = tffeat.splice(feat, left_context=2,
right_context=-2).eval()
with self.assertRaises(ValueError):
out = tffeat.splice(feat, left_context=-2,
right_context=2).eval()
def tdnn(x,
name,
in_dim,
context,
out_dim,
has_bias=True,
method='splice_layer'):
'''
TDNN implementation.
Args:
context:
a int of left and right context, or
a list of context indexes, e.g. (-2, 0, 2).
method:
splice_layer: use column-first patch-based copy.
splice_op: use row-first while_loop copy.
conv1d: use conv1d as TDNN equivalence.
'''
if hasattr(context, '__iter__'):
context_size = len(context)
if method in ('splice_op', 'conv1d'):
msg = 'Method splice_op and conv1d does not support context list.'
raise ValueError(msg)
context_list = context
else:
context_size = context * 2 + 1
context_list = range(-context, context + 1)
with tf.variable_scope(name):
if method == 'splice_layer':
x = splice_layer(x, 'splice', context_list)
x = linear(x,
'linear', [in_dim * context_size, out_dim],
has_bias=has_bias)
elif method == 'splice_op':
x = speech_ops.splice(x, context, context)
x = linear(x,
'linear', [in_dim * context_size, out_dim],
has_bias=has_bias)
elif method == 'conv1d':
kernel = tf.get_variable(
name='DW',
shape=[context, in_dim, out_dim],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
x = tf.nn.conv1d(x, kernel, stride=1, padding='SAME')
if has_bias:
b = tf.get_variable(name='bias',
shape=[out_dim],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0))
x = tf.nn.bias_add(x, b)
else:
raise ValueError('Unsupported method: %s.' % (method))
return x
def test_splice(self):
''' test batch splice frame '''
with self.cached_session(use_gpu=False, force_gpu=False):
feat = tf.ones([1, 3, 2], dtype=tf.float32)
for l_ctx in range(0, 4):
for r_ctx in range(0, 4):
ctx = l_ctx + 1 + r_ctx
out = tffeat.splice(feat, left_context=l_ctx, right_context=r_ctx)
self.assertTupleEqual(out.eval().shape, (1, 3, 2 * ctx))
self.assertAllEqual(out, tf.ones([1, 3, 2 * ctx]))
with self.assertRaises(tf.errors.InvalidArgumentError):
out = tffeat.splice(feat, left_context=-2, right_context=-2).eval()
with self.assertRaises(tf.errors.InvalidArgumentError):
out = tffeat.splice(feat, left_context=2, right_context=-2).eval()
with self.assertRaises(tf.errors.InvalidArgumentError):
out = tffeat.splice(feat, left_context=-2, right_context=2).eval()