def test_build_model(self, gin_file):
"""Tests if Model builds properly and produces audio of correct shape.
Args:
gin_file: Name of gin_file to use.
"""
with gin.unlock_config():
gin.clear_config()
gin.parse_config_file(gin_file)
model = models.Autoencoder()
controls = model.get_controls(self.inputs)
self.assertIsInstance(controls, dict)
# Confirm that model generates correctly sized audio.
audio_gen_shape = controls['processor_group']['signal'].shape.as_list()
self.assertEqual(audio_gen_shape, list(self.inputs['audio'].shape))
(add, ['noise/signal', 'harmonic/signal'])]
processor_group = ddsp.processors.ProcessorGroup(dag=dag,
name='processor_group')
# Loss_functions
spectral_loss = ddsp.losses.SpectralLoss(loss_type='L1',
mag_weight=1.0,
logmag_weight=1.0)
with strategy.scope():
# Put it together in a model.
model = models.Autoencoder(preprocessor=preprocessor,
encoder=None,
decoder=decoder,
processor_group=processor_group,
losses=[spectral_loss])
trainer = trainers.Trainer(model, strategy, learning_rate=1e-3)
"""## or [`gin`](https://github.com/google/gin-config)"""
gin_string = """
import ddsp
import ddsp.training
# Preprocessor
models.Autoencoder.preprocessor = @preprocessing.F0LoudnessPreprocessor()
preprocessing.F0LoudnessPreprocessor.time_steps = 1000