def test_feature_extractor(feature_type, exception_expectation):
# For now, just test that it runs
# TODO: test that the output is similar to Kaldi
with exception_expectation:
fe = FeatureExtractor(type=feature_type)
samples, sr = torchaudio.load(
'test/fixtures/libri/libri-1088-134315-0000.wav')
fe.extract(samples=samples, sampling_rate=sr)
def test_overlay_fbank():
# Treat it more like a test of "it runs" rather than "it works"
t = np.linspace(0, 1, 8000, dtype=np.float32)
x1 = np.sin(440.0 * t).reshape(1, -1)
x2 = np.sin(55.0 * t).reshape(1, -1)
feature_extractor = FeatureExtractor(type='fbank')
f1 = feature_extractor.extract(x1, 8000).numpy()
f2 = feature_extractor.extract(x2, 8000).numpy()
mixer = FbankMixer(
base_feats=f1,
frame_shift=feature_extractor.spectrogram_config.frame_shift,
)
mixer.add_to_mix(f2)
fmix_feat = mixer.mixed_feats
fmix_time = feature_extractor.extract(x1 + x2, 8000).numpy()
np.testing.assert_almost_equal(fmix_feat, fmix_time, decimal=0)