def test_InverseSpectrogram(self):
tensor = common_utils.get_whitenoise(sample_rate=8000)
spectrogram = common_utils.get_spectrogram(tensor,
n_fft=400,
hop_length=100)
self._assert_consistency_complex(
T.InverseSpectrogram(n_fft=400, hop_length=100), spectrogram)
def test_InverseSpectrogram_pseudocomplex(self):
tensor = common_utils.get_whitenoise(sample_rate=8000)
spectrogram = common_utils.get_spectrogram(tensor,
n_fft=400,
hop_length=100)
spectrogram = torch.view_as_real(spectrogram)
self._assert_consistency(
T.InverseSpectrogram(n_fft=400, hop_length=100), spectrogram)
def test_inverse_spectrogram(self):
# create a realistic input:
waveform = get_whitenoise(sample_rate=8000,
duration=0.05,
n_channels=2)
length = waveform.shape[-1]
spectrogram = get_spectrogram(waveform, n_fft=400)
# test
inv_transform = T.InverseSpectrogram(n_fft=400)
self.assert_grad(inv_transform, [spectrogram, length])
def test_roundtrip_spectrogram(self, **args):
"""Test the spectrogram + inverse spectrogram results in approximate identity."""
waveform = get_whitenoise(sample_rate=8000,
duration=0.5,
dtype=self.dtype)
s = T.Spectrogram(**args, power=None)
inv_s = T.InverseSpectrogram(**args)
transformed = s.forward(waveform)
restored = inv_s.forward(transformed, length=waveform.shape[-1])
self.assertEqual(waveform, restored, atol=1e-6, rtol=1e-6)