def build(self, inputs):
"""Build the graph for this configuration.
Args:
inputs: A dict of inputs. For training, should contain 'wav'.
Returns:
A dict of outputs that includes the 'predictions',
'init_ops', the 'push_ops', and the 'quantized_input'.
"""
num_stages = 10
num_layers = 30
filter_length = 3
width = 512
skip_width = 256
num_z = 16
# Encode the source with 8-bit Mu-Law.
x = inputs['wav']
batch_size = self.batch_size
x_quantized = utils.mu_law(x)
x_scaled = tf.cast(x_quantized, tf.float32) / 128.0
x_scaled = tf.expand_dims(x_scaled, 2)
encoding = tf.placeholder(
name='encoding', shape=[batch_size, num_z], dtype=tf.float32)
en = tf.expand_dims(encoding, 1)
init_ops, push_ops = [], []
###
# The WaveNet Decoder.
###
l = x_scaled
l, inits, pushs = utils.causal_linear(
x=l,
n_inputs=1,
n_outputs=width,
name='startconv',
rate=1,
batch_size=batch_size,
filter_length=filter_length)
for init in inits:
init_ops.append(init)
for push in pushs:
push_ops.append(push)
# Set up skip connections.
s = utils.linear(l, width, skip_width, name='skip_start')
# Residual blocks with skip connections.
for i in range(num_layers):
dilation = 2**(i % num_stages)
# dilated masked cnn
d, inits, pushs = utils.causal_linear(
x=l,
n_inputs=width,
n_outputs=width * 2,
name='dilatedconv_%d' % (i + 1),
rate=dilation,
batch_size=batch_size,
filter_length=filter_length)
for init in inits:
init_ops.append(init)
for push in pushs:
push_ops.append(push)
# local conditioning
d += utils.linear(en, num_z, width * 2, name='cond_map_%d' % (i + 1))
# gated cnn
assert d.get_shape().as_list()[2] % 2 == 0
m = d.get_shape().as_list()[2] // 2
d = tf.sigmoid(d[:, :, :m]) * tf.tanh(d[:, :, m:])
# residuals
l += utils.linear(d, width, width, name='res_%d' % (i + 1))
# skips
s += utils.linear(d, width, skip_width, name='skip_%d' % (i + 1))
s = tf.nn.relu(s)
s = (utils.linear(s, skip_width, skip_width, name='out1') + utils.linear(
en, num_z, skip_width, name='cond_map_out1'))
s = tf.nn.relu(s)
###
# Compute the logits and get the loss.
###
logits = utils.linear(s, skip_width, 256, name='logits')
logits = tf.reshape(logits, [-1, 256])
probs = tf.nn.softmax(logits, name='softmax')
return {
'init_ops': init_ops,
'push_ops': push_ops,
'predictions': probs,
'encoding': encoding,
'quantized_input': x_quantized,
}
def build(self, inputs):
"""Build the graph for this configuration.
Args:
inputs: A dict of inputs. For training, should contain 'wav'.
Returns:
A dict of outputs that includes the 'predictions',
'init_ops', the 'push_ops', and the 'quantized_input'.
"""
num_stages = 10
num_layers = 30
filter_length = 3
width = 512
skip_width = 256
num_z = 16
# Encode the source with 8-bit Mu-Law.
x = inputs['wav']
batch_size = self.batch_size
x_quantized = utils.mu_law(x)
x_scaled = tf.cast(x_quantized, tf.float32) / 128.0
x_scaled = tf.expand_dims(x_scaled, 2)
encoding = tf.placeholder(name='encoding',
shape=[batch_size, num_z],
dtype=tf.float32)
en = tf.expand_dims(encoding, 1)
init_ops, push_ops = [], []
###
# The WaveNet Decoder.
###
l = x_scaled
l, inits, pushs = utils.causal_linear(x=l,
n_inputs=1,
n_outputs=width,
name='startconv',
rate=1,
batch_size=batch_size,
filter_length=filter_length)
for init in inits:
init_ops.append(init)
for push in pushs:
push_ops.append(push)
# Set up skip connections.
s = utils.linear(l, width, skip_width, name='skip_start')
# Residual blocks with skip connections.
for i in range(num_layers):
dilation = 2**(i % num_stages)
# dilated masked cnn
d, inits, pushs = utils.causal_linear(x=l,
n_inputs=width,
n_outputs=width * 2,
name='dilatedconv_%d' %
(i + 1),
rate=dilation,
batch_size=batch_size,
filter_length=filter_length)
for init in inits:
init_ops.append(init)
for push in pushs:
push_ops.append(push)
# local conditioning
d += utils.linear(en,
num_z,
width * 2,
name='cond_map_%d' % (i + 1))
# gated cnn
assert d.get_shape().as_list()[2] % 2 == 0
m = d.get_shape().as_list()[2] // 2
d = tf.sigmoid(d[:, :, :m]) * tf.tanh(d[:, :, m:])
# residuals
l += utils.linear(d, width, width, name='res_%d' % (i + 1))
# skips
s += utils.linear(d, width, skip_width, name='skip_%d' % (i + 1))
s = tf.nn.relu(s)
s = (utils.linear(s, skip_width, skip_width, name='out1') +
utils.linear(en, num_z, skip_width, name='cond_map_out1'))
s = tf.nn.relu(s)
###
# Compute the logits and get the loss.
###
logits = utils.linear(s, skip_width, 256, name='logits')
logits = tf.reshape(logits, [-1, 256])
probs = tf.nn.softmax(logits, name='softmax')
return {
'init_ops': init_ops,
'push_ops': push_ops,
'predictions': probs,
'encoding': encoding,
'quantized_input': x_quantized,
}