def test_spectral_centroid(self):
sample_rate = 8000
transform = T.SpectralCentroid(sample_rate=sample_rate)
waveform = get_whitenoise(sample_rate=sample_rate,
duration=0.05,
n_channels=2)
self.assert_grad(transform, [waveform], nondet_tol=1e-10)
def test_spectral_centroid(self, n_fft, hop_length):
sample_rate = 16000
waveform = get_whitenoise(sample_rate=sample_rate,
n_channels=1).to(self.device, self.dtype)
result = T.SpectralCentroid(
sample_rate=sample_rate,
n_fft=n_fft,
hop_length=hop_length,
).to(self.device, self.dtype)(waveform)
expected = librosa.feature.spectral_centroid(
y=waveform[0].cpu().numpy(),
sr=sample_rate,
n_fft=n_fft,
hop_length=hop_length)
self.assertEqual(result,
torch.from_numpy(expected),
atol=5e-4,
rtol=1e-5)
def test_SpectralCentroid(self):
sample_rate = 44100
waveform = common_utils.get_whitenoise(sample_rate=sample_rate)
self._assert_consistency(T.SpectralCentroid(sample_rate=sample_rate),
waveform)