def create_generator(input_shape):
inp = keras.Input(input_shape)
x = MelSpec()(inp)
x = layers.Conv1D(512, 7, padding="same")(x)
x = layers.LeakyReLU()(x)
x = conv_block(x, 256, 8)
x = conv_block(x, 128, 8)
x = conv_block(x, 64, 2)
x = conv_block(x, 32, 2)
x = addon_layers.WeightNormalization(
layers.Conv1D(1, 7, padding="same", activation="tanh"))(x)
return keras.Model(inp, x)
def conv_block(input, conv_dim, upsampling_factor):
# Dilated convolutional block with weight normalizaton.
conv_t = addon_layers.WeightNormalization(
layers.Conv1DTranspose(conv_dim,
16,
upsampling_factor,
padding="same"),
data_init=False,
)(input)
lrelu1 = layers.LeakyReLU()(conv_t)
res_stack = residual_stack(lrelu1, conv_dim)
lrelu2 = layers.LeakyReLU()(res_stack)
return lrelu2
def residual_stack(input, filters):
"""Convolutional residual stack with weight normalization.
Args:
filter: int, determines filter size for the residual stack.
Returns:
Residual stack output.
"""
c1 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=1,
padding="same"),
data_init=False)(input)
lrelu1 = layers.LeakyReLU()(c1)
c2 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=1,
padding="same"),
data_init=False)(lrelu1)
add1 = layers.Add()([c2, input])
lrelu2 = layers.LeakyReLU()(add1)
c3 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=3,
padding="same"),
data_init=False)(lrelu2)
lrelu3 = layers.LeakyReLU()(c3)
c4 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=1,
padding="same"),
data_init=False)(lrelu3)
add2 = layers.Add()([add1, c4])
lrelu4 = layers.LeakyReLU()(add2)
c5 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=9,
padding="same"),
data_init=False)(lrelu4)
lrelu5 = layers.LeakyReLU()(c5)
c6 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=1,
padding="same"),
data_init=False)(lrelu5)
add3 = layers.Add()([c6, add2])
return add3
def conv_block(input, conv_dim, upsampling_factor):
"""Dilated Convolutional Block with weight normalization.
Args:
conv_dim: int, determines filter size for the block.
upsampling_factor: int, scale for upsampling.
Returns:
Dilated convolution block.
"""
conv_t = addon_layers.WeightNormalization(
layers.Conv1DTranspose(conv_dim, 16, upsampling_factor,
padding="same"),
data_init=False,
)(input)
lrelu1 = layers.LeakyReLU()(conv_t)
res_stack = residual_stack(lrelu1, conv_dim)
lrelu2 = layers.LeakyReLU()(res_stack)
return lrelu2
def discriminator_block(input):
conv1 = addon_layers.WeightNormalization(layers.Conv1D(
16, 15, 1, "same"),
data_init=False)(input)
lrelu1 = layers.LeakyReLU()(conv1)
conv2 = addon_layers.WeightNormalization(layers.Conv1D(64,
41,
4,
"same",
groups=4),
data_init=False)(lrelu1)
lrelu2 = layers.LeakyReLU()(conv2)
conv3 = addon_layers.WeightNormalization(layers.Conv1D(256,
41,
4,
"same",
groups=16),
data_init=False)(lrelu2)
lrelu3 = layers.LeakyReLU()(conv3)
conv4 = addon_layers.WeightNormalization(layers.Conv1D(1024,
41,
4,
"same",
groups=64),
data_init=False)(lrelu3)
lrelu4 = layers.LeakyReLU()(conv4)
conv5 = addon_layers.WeightNormalization(layers.Conv1D(1024,
41,
4,
"same",
groups=256),
data_init=False)(lrelu4)
lrelu5 = layers.LeakyReLU()(conv5)
conv6 = addon_layers.WeightNormalization(layers.Conv1D(
1024, 5, 1, "same"),
data_init=False)(lrelu5)
lrelu6 = layers.LeakyReLU()(conv6)
conv7 = addon_layers.WeightNormalization(layers.Conv1D(
1, 3, 1, "same"),
data_init=False)(lrelu6)
return [lrelu1, lrelu2, lrelu3, lrelu4, lrelu5, lrelu6, conv7]
def residual_stack(input, filters):
# Convolutional residual stack with weight normalization.
c1 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=1,
padding="same"),
data_init=False)(input)
lrelu1 = layers.LeakyReLU()(c1)
c2 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=1,
padding="same"),
data_init=False)(lrelu1)
add1 = layers.Add()([c2, input])
lrelu2 = layers.LeakyReLU()(add1)
c3 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=3,
padding="same"),
data_init=False)(lrelu2)
lrelu3 = layers.LeakyReLU()(c3)
c4 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=1,
padding="same"),
data_init=False)(lrelu3)
add2 = layers.Add()([c4, add1])
relu4 = layers.LeakyReLU()(add2)
c5 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=9,
padding="same"),
data_init=False)(relu4)
lrelu5 = layers.LeakyReLU()(c5)
c6 = addon_layers.WeightNormalization(layers.Conv1D(filters,
3,
dilation_rate=1,
padding="same"),
data_init=False)(lrelu5)
add3 = layers.Add()([c6, add2])
return add3