},
padding_values={
'audio': tf.constant(0, dtype=tf.float32),
'mel': tf.constant(0, dtype=tf.float32),
},
)
features = dataset.make_one_shot_iterator().get_next()
melgan_config = malaya_speech.config.melgan_config
generator = melgan.Generator(
melgan.GeneratorConfig(**melgan_config['melgan_generator_params']),
name='melgan-generator',
)
discriminator = melgan.MultiScaleDiscriminator(
melgan.DiscriminatorConfig(**melgan_config['melgan_discriminator_params']),
name='melgan-discriminator',
)
mels_loss = melgan.loss.TFMelSpectrogram()
mse_loss = tf.keras.losses.MeanSquaredError()
mae_loss = tf.keras.losses.MeanAbsoluteError()
def compute_per_example_generator_losses(audios, outputs):
y_hat = outputs
p_hat = discriminator(y_hat)
p = discriminator(tf.expand_dims(audios, 2))
adv_loss = 0.0
for i in range(len(p_hat)):
import malaya_speech.config
from malaya_speech.train.loss import calculate_2d_loss, calculate_3d_loss
hifigan_config = malaya_speech.config.hifigan_config
generator = hifigan.Generator(
hifigan.GeneratorConfig(**hifigan_config['hifigan_generator_params']),
name='hifigan_generator',
)
multiperiod_discriminator = hifigan.MultiPeriodDiscriminator(
hifigan.DiscriminatorConfig(
**hifigan_config['hifigan_discriminator_params']),
name='hifigan_multiperiod_discriminator',
)
multiscale_discriminator = melgan.MultiScaleDiscriminator(
melgan.DiscriminatorConfig(
**hifigan_config['melgan_discriminator_params'],
name='melgan_multiscale_discriminator',
))
discriminator = hifigan.Discriminator(multiperiod_discriminator,
multiscale_discriminator)
stft_loss = stft.loss.MultiResolutionSTFT(**hifigan_config['stft_loss_params'])
mels_loss = melgan.loss.TFMelSpectrogram()
mse_loss = tf.keras.losses.MeanSquaredError()
mae_loss = tf.keras.losses.MeanAbsoluteError()
def compute_per_example_generator_losses(features):
y_hat = generator(features['mel'], training=True)
audios = features['audio']
sc_loss, mag_loss = calculate_2d_loss(audios, tf.squeeze(y_hat, -1),