def __init__(self,
is_training,
cbhg_out_units=256,
conv_channels=128,
max_filter_width=16,
projection1_out_channels=128,
projection2_out_channels=128,
num_highway=4,
prenet_out_units=(256, 128),
drop_rate=0.5,
trainable=True,
name=None,
**kwargs):
super(EncoderV1, self).__init__(name=name,
trainable=trainable,
**kwargs)
self.prenet_out_units = prenet_out_units
self.prenets = [
PreNet(out_unit, is_training, drop_rate)
for out_unit in prenet_out_units
]
self.cbhg = CBHG(cbhg_out_units, conv_channels, max_filter_width,
projection1_out_channels, projection2_out_channels,
num_highway, is_training)
class EncoderV1(tf.layers.Layer):
def __init__(self, is_training,
cbhg_out_units=256, conv_channels=128, max_filter_width=16,
projection1_out_channels=128,
projection2_out_channels=128,
num_highway=4,
prenet_out_units=(256, 128), drop_rate=0.5,
trainable=True, name=None, **kwargs):
super(EncoderV1, self).__init__(name=name, trainable=trainable, **kwargs)
self.prenet_out_units = prenet_out_units
self.prenets = [PreNet(out_unit, is_training, drop_rate) for out_unit in prenet_out_units]
self.cbhg = CBHG(cbhg_out_units,
conv_channels,
max_filter_width,
projection1_out_channels,
projection2_out_channels,
num_highway,
is_training)
def build(self, input_shape):
embed_dim = input_shape[2].value
with tf.control_dependencies([tf.assert_equal(self.prenet_out_units[0], embed_dim)]):
self.built = True
def call(self, inputs, input_lengths=None, **kwargs):
prenet_output = reduce(lambda acc, pn: pn(acc), self.prenets, inputs)
cbhg_output = self.cbhg(prenet_output, input_lengths=input_lengths)
return cbhg_output
def compute_output_shape(self, input_shape):
return self.cbhg.compute_output_shape(input_shape)
def __init__(self,
is_training,
num_freq,
cbhg_out_units=256,
conv_channels=128,
max_filter_width=8,
projection1_out_channels=256,
projection2_out_channels=80,
num_highway=4,
trainable=True,
name=None,
dtype=None,
**kwargs):
super(PostNet, self).__init__(trainable=trainable,
name=name,
dtype=dtype,
**kwargs)
self.cbhg = CBHG(cbhg_out_units,
conv_channels,
max_filter_width,
projection1_out_channels,
projection2_out_channels,
num_highway,
is_training,
dtype=dtype)
self.linear = tf.layers.Dense(num_freq, dtype=dtype)