def test_save_load(data_format):
src_mono, batch_src, input_shape = get_audio(data_format='channels_last', n_ch=1)
# test Frame save/load
save_load_compare(
Frame(frame_length=128, hop_length=64, input_shape=input_shape),
batch_src,
np.testing.assert_allclose,
)
# test Energy save/load
save_load_compare(
Energy(frame_length=128, hop_length=64, input_shape=input_shape),
batch_src,
np.testing.assert_allclose,
)
# test mu law layers
save_load_compare(
MuLawEncoding(quantization_channels=128),
batch_src,
np.testing.assert_allclose,
)
save_load_compare(
MuLawDecoding(quantization_channels=128),
np.arange(0, 256, 1).reshape((1, 256, 1)),
np.testing.assert_allclose,
)
# test mfcc layer
expand_dim = (0, 3) if data_format in (_CH_LAST_STR, _CH_DEFAULT_STR) else (0, 1)
save_load_compare(
LogmelToMFCC(n_mfccs=10),
np.expand_dims(librosa.power_to_db(librosa.feature.melspectrogram(src_mono).T), expand_dim),
np.testing.assert_allclose,
)
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_save_load_channel_swap(data_format, save_format):
src_mono, batch_src, input_shape = get_audio(data_format='channels_last', n_ch=1)
save_load_compare(
ChannelSwap(input_shape=input_shape),
batch_src,
np.testing.assert_allclose,
save_format=save_format,
layer_class=ChannelSwap,
training=None,
)
def test_save_load_spec_augment(data_format, save_format):
batch_src, input_shape = get_spectrogram(data_format=data_format)
spec_augment = SpecAugment(
input_shape=input_shape,
freq_mask_param=5,
time_mask_param=10,
n_freq_masks=4,
n_time_masks=3,
mask_value=0.0,
data_format=data_format,
)
save_load_compare(
spec_augment,
batch_src,
np.testing.assert_allclose,
save_format=save_format,
layer_class=SpecAugment,
training=None,
)
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,
)