Datasets:
HenryAI
/
KerasBERTv1-Data

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)(lrelu5)
add3 = layers.Add()([c6, add2])
return add3
Each convolutional block uses the dilations offered by the residual stack and upsamples the input data by the upsampling_factor.
# Dilated convolutional block consisting of the Residual stack
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
The discriminator block consists of convolutions and downsampling layers. This block is essential for the implementation of the feature matching technique.
Each discriminator outputs a list of feature maps that will be compared during training to compute the feature matching loss.
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]
Create the generator
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)
# We use a dynamic input shape for the generator since the model is fully convolutional
generator = create_generator((None, 1))
generator.summary()
Model: \"model\"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, None, 1)] 0
__________________________________________________________________________________________________
mel_spec (MelSpec) (None, None, 80) 0 input_1[0][0]
__________________________________________________________________________________________________
conv1d (Conv1D) (None, None, 512) 287232 mel_spec[0][0]
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU) (None, None, 512) 0 conv1d[0][0]
__________________________________________________________________________________________________
weight_normalization (WeightNor (None, None, 256) 2097921 leaky_re_lu[0][0]
__________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, None, 256) 0 weight_normalization[0][0]
__________________________________________________________________________________________________
weight_normalization_1 (WeightN (None, None, 256) 197121 leaky_re_lu_1[0][0]
__________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, None, 256) 0 weight_normalization_1[0][0]
__________________________________________________________________________________________________
weight_normalization_2 (WeightN (None, None, 256) 197121 leaky_re_lu_2[0][0]