def test_batch_mulaw(self):
waveform, sample_rate = torchaudio.load(self.test_filepath) # (2, 278756), 44100
# Single then transform then batch
waveform_encoded = transforms.MuLawEncoding()(waveform)
expected = waveform_encoded.unsqueeze(0).repeat(3, 1, 1)
# Batch then transform
waveform_batched = waveform.unsqueeze(0).repeat(3, 1, 1)
computed = transforms.MuLawEncoding()(waveform_batched)
# shape = (3, 2, 201, 1394)
self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
self.assertTrue(torch.allclose(computed, expected))
# Single then transform then batch
waveform_decoded = transforms.MuLawDecoding()(waveform_encoded)
expected = waveform_decoded.unsqueeze(0).repeat(3, 1, 1)
# Batch then transform
computed = transforms.MuLawDecoding()(computed)
# shape = (3, 2, 201, 1394)
self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
self.assertTrue(torch.allclose(computed, expected))
def test_mu_law_companding(self):
quantization_channels = 256
waveform = self.waveform.clone()
waveform /= torch.abs(waveform).max()
self.assertTrue(waveform.min() >= -1. and waveform.max() <= 1.)
waveform_mu = transforms.MuLawEncoding(quantization_channels)(waveform)
self.assertTrue(waveform_mu.min() >= 0. and waveform_mu.max() <= quantization_channels)
waveform_exp = transforms.MuLawDecoding(quantization_channels)(waveform_mu)
self.assertTrue(waveform_exp.min() >= -1. and waveform_exp.max() <= 1.)
def test_mu_law_companding(self):
quantization_channels = 256
waveform = self.waveform.clone()
if not waveform.is_floating_point():
waveform = waveform.to(torch.get_default_dtype())
waveform /= torch.abs(waveform).max()
self.assertTrue(waveform.min() >= -1. and waveform.max() <= 1.)
waveform_mu = transforms.MuLawEncoding(quantization_channels)(waveform)
self.assertTrue(waveform_mu.min() >= 0. and waveform_mu.max() <= quantization_channels)
waveform_exp = transforms.MuLawDecoding(quantization_channels)(waveform_mu)
self.assertTrue(waveform_exp.min() >= -1. and waveform_exp.max() <= 1.)
def test_MuLawDecoding(self):
tensor = torch.rand((1, 10))
self._assert_consistency(T.MuLawDecoding(), tensor)