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pep8
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@@ -38,6 +38,7 @@ TEST_CONFIGURATIONS = list(itertools.product(TEST_AUDIO_DESCRIPTORS, MODELS, BAC
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print("RUNNING TESTS WITH TF VERSION {}".format(tf.__version__))
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@pytest.mark.parametrize('test_file', TEST_AUDIO_DESCRIPTORS)
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def test_separator_backends(test_file):
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adapter = get_default_audio_adapter()
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@@ -48,22 +49,24 @@ def test_separator_backends(test_file):
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# Test the stft and inverse stft provides exact reconstruction
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stft_matrix = separator_lib._stft(waveform)
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reconstructed = separator_lib._stft(stft_matrix, inverse=True, length= waveform.shape[0])
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reconstructed = separator_lib._stft(
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stft_matrix, inverse=True, length=waveform.shape[0])
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assert np.allclose(reconstructed, waveform, atol=1e-2)
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# # now also test that tensorflow and librosa STFT provide same results
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from spleeter.audio.spectrogram import compute_spectrogram_tf
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tf_waveform = tf.convert_to_tensor(waveform, tf.float32)
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spectrogram_tf = compute_spectrogram_tf(tf_waveform,
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separator_tf._params['frame_length'],
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separator_tf._params['frame_step'],)
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separator_tf._params['frame_length'],
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separator_tf._params['frame_step'],)
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with tf.Session() as sess:
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spectrogram_tf_eval = spectrogram_tf.eval()
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# check that stfts are equivalent up to the padding in the librosa case
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assert stft_matrix.shape[0] == spectrogram_tf_eval.shape[0] + 2
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assert stft_matrix.shape[1:] == spectrogram_tf_eval.shape[1:]
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assert np.allclose(np.abs(stft_matrix[1:-1]), spectrogram_tf_eval, atol=1e-2)
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assert np.allclose(
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np.abs(stft_matrix[1:-1]), spectrogram_tf_eval, atol=1e-2)
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# compare both separation, it should be close
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out_tf = separator_tf._separate_tensorflow(waveform, test_file)
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@@ -75,6 +78,7 @@ def test_separator_backends(test_file):
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assert np.sum(np.abs(out_lib[instrument])) > 1000
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assert np.allclose(out_tf[instrument], out_lib[instrument], atol=0.1)
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@pytest.mark.parametrize('test_file, configuration, backend', TEST_CONFIGURATIONS)
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def test_separate(test_file, configuration, backend):
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""" Test separation from raw data. """
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