def test_save_load():
"""test saving/loading of models that has stft, melspectorgrma, and log frequency."""
src_mono, batch_src, input_shape = get_audio(data_format='channels_last',
n_ch=1)
# test STFT save/load
save_load_compare(STFT(input_shape=input_shape, pad_begin=True), batch_src,
allclose_complex_numbers)
# test melspectrogram save/load
save_load_compare(
get_melspectrogram_layer(input_shape=input_shape, return_decibel=True),
batch_src,
np.testing.assert_allclose,
)
# test log frequency spectrogram save/load
save_load_compare(
get_log_frequency_spectrogram_layer(input_shape=input_shape,
return_decibel=True),
batch_src,
np.testing.assert_allclose,
)
# test stft_mag_phase
save_load_compare(
get_stft_mag_phase(input_shape=input_shape, return_decibel=True),
batch_src,
np.testing.assert_allclose,
)
# test stft mag
save_load_compare(get_stft_magnitude_layer(input_shape=input_shape),
batch_src, np.testing.assert_allclose)
def test_log_spectrogram_fail():
"""test if log spectrogram layer works well"""
src_mono, batch_src, input_shape = get_audio(data_format='channels_last',
n_ch=1)
_ = get_log_frequency_spectrogram_layer(input_shape,
return_decibel=True,
log_n_bins=200)
def test_save_load(save_format):
"""test saving/loading of models that has stft, melspectorgrma, and log frequency."""
src_mono, batch_src, input_shape = get_audio(data_format='channels_last',
n_ch=1)
# test STFT save/load
save_load_compare(
STFT(input_shape=input_shape, pad_begin=True),
batch_src,
allclose_complex_numbers,
save_format,
STFT,
)
# test ConcatenateFrequencyMap
specs_batch = np.random.randn(2, 3, 5, 4).astype(np.float32)
save_load_compare(
ConcatenateFrequencyMap(input_shape=specs_batch.shape[1:]),
specs_batch,
np.testing.assert_allclose,
save_format,
ConcatenateFrequencyMap,
)
if save_format == 'tf':
# test melspectrogram save/load
save_load_compare(
get_melspectrogram_layer(input_shape=input_shape,
return_decibel=True),
batch_src,
np.testing.assert_allclose,
save_format,
)
# test log frequency spectrogram save/load
save_load_compare(
get_log_frequency_spectrogram_layer(input_shape=input_shape,
return_decibel=True),
batch_src,
np.testing.assert_allclose,
save_format,
)
# test stft_mag_phase
save_load_compare(
get_stft_mag_phase(input_shape=input_shape, return_decibel=True),
batch_src,
np.testing.assert_allclose,
save_format,
)
# test stft mag
save_load_compare(
get_stft_magnitude_layer(input_shape=input_shape),
batch_src,
np.testing.assert_allclose,
save_format,
)
def test_save_load():
"""test saving/loading of models that has stft, melspectorgrma, and log frequency."""
def _test(layer, input_batch, allclose_func, atol=1e-4):
"""test a model with `layer` with the given `input_batch`.
The model prediction result is compared using `allclose_func` which may depend on the
data type of the model output (e.g., float or complex).
"""
model = tensorflow.keras.models.Sequential()
model.add(layer)
result_ref = model(input_batch)
os_temp_dir = tempfile.gettempdir()
model_temp_dir = tempfile.TemporaryDirectory(dir=os_temp_dir)
model.save(filepath=model_temp_dir.name)
new_model = tf.keras.models.load_model(model_temp_dir.name)
result_new = new_model(input_batch)
allclose_func(result_ref, result_new, atol)
model_temp_dir.cleanup()
return model
src_mono, batch_src, input_shape = get_audio(data_format='channels_last', n_ch=1)
# test STFT save/load
_test(STFT(input_shape=input_shape), batch_src, allclose_complex_numbers)
# test melspectrogram save/load
_test(
get_melspectrogram_layer(input_shape=input_shape, return_decibel=True),
batch_src,
np.testing.assert_allclose,
)
# test log frequency spectrogram save/load
_test(
get_log_frequency_spectrogram_layer(input_shape=input_shape, return_decibel=True),
batch_src,
np.testing.assert_allclose,
)
def test_log_spectrogram_runnable(data_format):
"""test if log spectrogram layer works well"""
src_mono, batch_src, input_shape = get_audio(data_format=data_format,
n_ch=1)
_ = get_log_frequency_spectrogram_layer(input_shape, return_decibel=True)
_ = get_log_frequency_spectrogram_layer(input_shape, return_decibel=False)