def test_output_shape_is_correct(self): loss_obj = losses.PretrainedCREPEEmbeddingLoss() input_audio = tf.random.uniform((3, 16000), dtype=tf.float32) target_audio = tf.random.uniform((3, 16000), dtype=tf.float32) loss = loss_obj(input_audio, target_audio) self.assertListEqual([], loss.shape.as_list())
def test_output_shape_is_correct(self): loss_obj = losses.PretrainedCREPEEmbeddingLoss() input_audio = tf.ones((3, 16000), dtype=tf.float32) target_audio = tf.ones((3, 16000), dtype=tf.float32) loss = loss_obj(input_audio, target_audio) self.assertListEqual([], loss.shape.as_list()) self.assertTrue(np.isfinite(loss))
def setUp(self): """Create some dummy input data for the chain.""" super().setUp() # Create a network output dictionary. self.nn_outputs = { 'audio': tf.ones((3, 8000), dtype=tf.float32), 'audio_synth': tf.ones((3, 8000), dtype=tf.float32), 'magnitudes': tf.ones((3, 200, 2), dtype=tf.float32), 'f0_hz': 200 + tf.ones((3, 200, 1), dtype=tf.float32), } # Create Processors. spectral_loss = losses.SpectralLoss() crepe_loss = losses.PretrainedCREPEEmbeddingLoss(name='crepe_loss') # Create DAG for testing. self.dag = [ (spectral_loss, ['audio', 'audio_synth']), (crepe_loss, ['audio', 'audio_synth']), ] self.expected_outputs = ['spectral_loss', 'crepe_loss']