def main():
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
print('Num GPUs Available: ', len(tf.config.experimental.list_physical_devices('GPU')))
raw_dataset = waveform_dataset.get_stft_dataset(
DATASET_PATH, frame_length=FFT_FRAME_LENGTH, frame_step=FFT_FRAME_STEP
)
generator = spec_gan.Generator(channels=2, in_shape=Z_IN_SHAPE)
discriminator = spec_gan.Discriminator(input_shape=SPECTOGRAM_IMAGE_SHAPE)
generator_optimizer = tf.keras.optimizers.Adam(1e-4, beta_1=0.5, beta_2=0.9)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4, beta_1=0.5, beta_2=0.9)
get_waveform = lambda stft:\
spectral.stft_2_waveform(
stft, FFT_FRAME_LENGTH, FFT_FRAME_STEP
)[0]
save_examples = lambda epoch, real, generated:\
save_helper.save_wav_data(
epoch, real, generated, SAMPLING_RATE, RESULT_DIR, get_waveform
)
stft_gan_model = wgan.WGAN(
raw_dataset, generator, [discriminator], Z_DIM,
generator_optimizer, discriminator_optimizer, discriminator_training_ratio=D_UPDATES_PER_G,
batch_size=BATCH_SIZE, epochs=EPOCHS, checkpoint_dir=CHECKPOINT_DIR,
fn_save_examples=save_examples
)
stft_gan_model.restore('ckpt-100', 1000)
stft_gan_model.train()
def main():
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
print('Num GPUs Available: ', len(tf.config.experimental.list_physical_devices('GPU')))
raw_dataset, magnitude_stats, phase_stats =\
waveform_dataset.get_magnitude_phase_dataset(
DATASET_PATH, FFT_FRAME_LENGTH, FFT_FRAME_STEP, LOG_MAGNITUDE,
INSTANTANEOUS_FREQUENCY
)
normalized_raw_dataset = []
pb_i = utils.Progbar(len(raw_dataset))
for spectogram in raw_dataset:
norm_mag = waveform_dataset.normalize(spectogram[:, :, 0], *magnitude_stats)
norm_phase = waveform_dataset.normalize(spectogram[:, :, 1], *phase_stats)
norm = np.concatenate([np.expand_dims(norm_mag, axis=2),
np.expand_dims(norm_phase, axis=2)], axis=-1)
normalized_raw_dataset.append(norm)
pb_i.add(1)
normalized_raw_dataset = np.array(normalized_raw_dataset)
generator = spec_gan.Generator(channels=2, activation=activations.tanh, in_shape=Z_IN_SHAPE)
discriminator = spec_gan.Discriminator(input_shape=SPECTOGRAM_IMAGE_SHAPE)
generator_optimizer = tf.keras.optimizers.Adam(1e-4, beta_1=0.5, beta_2=0.9)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4, beta_1=0.5, beta_2=0.9)
get_waveform = lambda spectogram:\
save_helper.get_waveform_from_normalized_spectogram(
spectogram, [magnitude_stats, phase_stats], FFT_FRAME_LENGTH,
FFT_FRAME_STEP, LOG_MAGNITUDE, INSTANTANEOUS_FREQUENCY
)
save_examples = lambda epoch, real, generated:\
save_helper.save_wav_data(
epoch, real, generated, SAMPLING_RATE, RESULT_DIR, get_waveform
)
spec_phase_gan_model = wgan.WGAN(
normalized_raw_dataset, generator, [discriminator], Z_DIM,
generator_optimizer, discriminator_optimizer, discriminator_training_ratio=D_UPDATES_PER_G,
batch_size=BATCH_SIZE, epochs=EPOCHS, checkpoint_dir=CHECKPOINT_DIR,
fn_save_examples=save_examples
)
spec_phase_gan_model.train()
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
print("Num GPUs Available: ",
len(tf.config.experimental.list_physical_devices('GPU')))
raw_dataset = waveform_dataset.get_waveform_dataset(DATASET_PATH)
generator = wave_gan.Generator()
discriminator = wave_gan.Discriminator(input_shape=WAVEFORM_SHAPE,
weighting=CRITIC_WEIGHTINGS[0])
spec_discriminator = spec_gan.Discriminator(
input_shape=MAGNITUDE_IMAGE_SHAPE, weighting=CRITIC_WEIGHTINGS[1])
generator_optimizer = tf.keras.optimizers.Adam(1e-4,
beta_1=0.5,
beta_2=0.9)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4,
beta_1=0.5,
beta_2=0.9)
get_waveform = lambda waveform: waveform
save_examples = lambda epoch, real, generated:\
save_helper.save_wav_data(
epoch, real, generated, SAMPLING_RATE,
RESULT_DIR, get_waveform
)
wave_gan_model = wgan.WGAN(raw_dataset,
generator, [discriminator, spec_discriminator],
Z_DIM,
generator_optimizer,
discriminator_optimizer,
discriminator_training_ratio=D_UPDATES_PER_G,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
checkpoint_dir=CHECKPOINT_DIR,
fn_save_examples=save_examples,
fn_get_discriminator_input_representations=
_get_discriminator_input_representations)
wave_gan_model.train()
def main():
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
print('Num GPUs Available: ',
len(tf.config.experimental.list_physical_devices('GPU')))
raw_dataset = waveform_dataset.get_waveform_dataset(DATASET_PATH)
generator = wave_gan.Generator()
discriminator = wave_gan.Discriminator(input_shape=WAVEFORM_SHAPE)
generator_optimizer = tf.keras.optimizers.Adam(1e-4,
beta_1=0.5,
beta_2=0.9)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4,
beta_1=0.5,
beta_2=0.9)
get_waveform = lambda waveform: waveform
save_examples = lambda epoch, real, generated:\
save_helper.save_wav_data(
epoch, real, generated, SAMPLING_RATE, RESULT_DIR, get_waveform
)
wave_gan_model = wgan.WGAN(raw_dataset,
generator, [discriminator],
Z_DIM,
generator_optimizer,
discriminator_optimizer,
discriminator_training_ratio=D_UPDATES_PER_G,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
checkpoint_dir=CHECKPOINT_DIR,
fn_save_examples=save_examples)
wave_gan_model.restore('ckpt-180', 1800)
wave_gan_model.train()