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cwawegan_architecture.py
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cwawegan_architecture.py
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from tensorflow.keras.layers import Input, Conv1D, Flatten, Dense, Conv2DTranspose, Reshape, Lambda, LeakyReLU, ReLU, Embedding, Concatenate, BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
from tensorflow import pad, maximum, random, int32
#Original WaveGAN: https://github.com/chrisdonahue/wavegan
#Label embeding using the method in https://machinelearningmastery.com/how-to-develop-a-conditional-generative-adversarial-network-from-scratch/
#phase shuffle [directly from the original waveGAN implementation]
def apply_phaseshuffle(args):
x, rad = args
pad_type = 'reflect'
b, x_len, nch = x.get_shape().as_list()
phase = random.uniform([], minval=-rad, maxval=rad + 1, dtype=int32)
pad_l = maximum(phase, 0)
pad_r = maximum(-phase, 0)
phase_start = pad_r
x = pad(x, [[0, 0], [pad_l, pad_r], [0, 0]], mode=pad_type)
x = x[:, phase_start:phase_start+x_len]
x.set_shape([b, x_len, nch])
return x
#TODO: clean/redo this
def Conv1DTranspose(input_tensor, filters, kernel_size, strides=2, padding='same'
, name = '1DTConv', activation = 'relu'):
x = Conv2DTranspose(filters=filters, kernel_size=(1, kernel_size), strides=(1, strides), padding=padding,
name = name, activation = activation)(K.expand_dims(input_tensor, axis=1))
x = K.squeeze(x, axis=1)
return x
def generator(z_dim = 100,
architecture_size = 'large',
use_batch_norm = False,
n_classes = 5):
generator_filters = [1024, 512, 256, 128, 64]
label_input = Input(shape=(1,), dtype='int32', name='generator_label_input')
label_em = Embedding(n_classes, n_classes * 20, name = 'label_embedding')(label_input)
label_em = Dense(16, name = 'label_dense')(label_em)
label_em = Reshape((16, 1), name = 'label_respahe')(label_em)
generator_input = Input(shape=(z_dim,), name='generator_input')
x = generator_input
if architecture_size == 'small':
x = Dense(16384, name='generator_input_dense')(x)
x = Reshape((16, 1024), name='generator_input_reshape')(x)
if use_batch_norm == True:
x = BatchNormalization()(x)
if architecture_size == 'medium' or architecture_size == 'large':
x = Dense(32768, name='generator_input_dense')(x)
x = Reshape((16, 2048), name='generator_input_reshape')(x)
if use_batch_norm == True:
x = BatchNormalization()(x)
x = ReLU()(x)
x = Concatenate()([x, label_em])
if architecture_size == 'small':
for i in range(4):
x = Conv1DTranspose(
input_tensor = x
, filters = generator_filters[i+1]
, kernel_size = 25
, strides = 4
, padding='same'
, name = f'generator_Tconv_{i}'
, activation = 'relu'
)
if use_batch_norm == True:
x = BatchNormalization()(x)
x = Conv1DTranspose(
input_tensor = x
, filters = 1
, kernel_size = 25
, strides = 4
, padding='same'
, name = 'generator_Tconv_4'
, activation = 'tanh'
)
if architecture_size == 'medium':
#layer 0 to 4
for i in range(5):
x = Conv1DTranspose(
input_tensor = x
, filters = generator_filters[i]
, kernel_size = 25
, strides = 4
, padding='same'
, name = f'generator_Tconv_{i}'
, activation = 'relu'
)
if use_batch_norm == True:
x = BatchNormalization()(x)
#layer 5
x = Conv1DTranspose(
input_tensor = x
, filters = 1
, kernel_size = 25
, strides = 2
, padding='same'
, name = 'generator_Tconv_5'
, activation = 'tanh'
)
if architecture_size == 'large':
#layer 0 to 4
for i in range(5):
x = Conv1DTranspose(
input_tensor = x
, filters = generator_filters[i]
, kernel_size = 25
, strides = 4
, padding='same'
, name = f'generator_Tconv_{i}'
, activation = 'relu'
)
if use_batch_norm == True:
x = BatchNormalization()(x)
#layer 5
x = Conv1DTranspose(
input_tensor = x
, filters = 1
, kernel_size = 25
, strides = 4
, padding='same'
, name = 'generator_Tconv_5'
, activation = 'tanh'
)
generator_output = x
generator = Model([generator_input, label_input], generator_output, name = 'Generator')
return generator
def discriminator(architecture_size='small',
phaseshuffle_samples = 0,
n_classes = 5):
discriminator_filters = [64, 128, 256, 512, 1024, 2048]
if architecture_size == 'large':
audio_input_dim = 65536
elif architecture_size == 'medium':
audio_input_dim = 32768
elif architecture_size == 'small':
audio_input_dim = 16384
label_input = Input(shape=(1,), dtype='int32', name='discriminator_label_input')
label_em = Embedding(n_classes, n_classes * 20)(label_input)
label_em = Dense(audio_input_dim)(label_em)
label_em = Reshape((audio_input_dim, 1))(label_em)
discriminator_input = Input(shape=(audio_input_dim,1), name='discriminator_input')
x = Concatenate()([discriminator_input, label_em])
if architecture_size == 'small':
# layers 0 to 3
for i in range(4):
x = Conv1D(
filters = discriminator_filters[i]
, kernel_size = 25
, strides = 4
, padding = 'same'
, name = f'discriminator_conv_{i}'
)(x)
x = LeakyReLU(alpha = 0.2)(x)
if phaseshuffle_samples > 0:
x = Lambda(apply_phaseshuffle)([x, phaseshuffle_samples])
#layer 4, no phase shuffle
x = Conv1D(
filters = discriminator_filters[4]
, kernel_size = 25
, strides = 4
, padding = 'same'
, name = f'discriminator_conv_4'
)(x)
x = Flatten()(x)
if architecture_size == 'medium':
# layers
for i in range(4):
x = Conv1D(
filters = discriminator_filters[i]
, kernel_size = 25
, strides = 4
, padding = 'same'
, name = f'discriminator_conv_{i}'
)(x)
x = LeakyReLU(alpha = 0.2)(x)
if phaseshuffle_samples > 0:
x = Lambda(apply_phaseshuffle)([x, phaseshuffle_samples])
x = Conv1D(
filters = discriminator_filters[4]
, kernel_size = 25
, strides = 4
, padding = 'same'
, name = 'discriminator_conv_4'
)(x)
x = LeakyReLU(alpha = 0.2)(x)
x = Conv1D(
filters = discriminator_filters[5]
, kernel_size = 25
, strides = 2
, padding = 'same'
, name = 'discriminator_conv_5'
)(x)
x = LeakyReLU(alpha = 0.2)(x)
x = Flatten()(x)
if architecture_size == 'large':
# layers
for i in range(4):
x = Conv1D(
filters = discriminator_filters[i]
, kernel_size = 25
, strides = 4
, padding = 'same'
, name = f'discriminator_conv_{i}'
)(x)
x = LeakyReLU(alpha = 0.2)(x)
if phaseshuffle_samples > 0:
x = Lambda(apply_phaseshuffle)([x, phaseshuffle_samples])
#last 2 layers without phase shuffle
x = Conv1D(
filters = discriminator_filters[4]
, kernel_size = 25
, strides = 4
, padding = 'same'
, name = 'discriminator_conv_4'
)(x)
x = LeakyReLU(alpha = 0.2)(x)
x = Conv1D(
filters = discriminator_filters[5]
, kernel_size = 25
, strides = 4
, padding = 'same'
, name = 'discriminator_conv_5'
)(x)
x = LeakyReLU(alpha = 0.2)(x)
x = Flatten()(x)
discriminator_output = Dense(1)(x)
discriminator = Model([discriminator_input, label_input], discriminator_output, name = 'Discriminator')
return discriminator