def test_batch_TimeStretch(self): waveform, sample_rate = torchaudio.load(self.test_filepath) kwargs = { 'n_fft': 2048, 'hop_length': 512, 'win_length': 2048, 'window': torch.hann_window(2048), 'center': True, 'pad_mode': 'reflect', 'normalized': True, 'onesided': True, } rate = 2 complex_specgrams = torch.stft(waveform, **kwargs) # Single then transform then batch expected = transforms.TimeStretch(fixed_rate=rate, n_freq=1025, hop_length=512)(complex_specgrams).repeat(3, 1, 1, 1, 1) # Batch then transform computed = transforms.TimeStretch(fixed_rate=rate, n_freq=1025, hop_length=512)(complex_specgrams.repeat(3, 1, 1, 1, 1)) self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape)) self.assertTrue(torch.allclose(computed, expected, atol=1e-5))
def test_timestretch_non_zero(self, rate, test_pseudo_complex): """Verify that ``T.TimeStretch`` does not fail if it's not close to 0 ``T.TimeStrech`` is not differentiable around 0, so this test checks the differentiability for cases where input is not zero. As tested above, when spectrogram contains values close to zero, the gradients are unstable and gradcheck fails. In this test, we generate spectrogram from random signal, then we push the points around zero away from the origin. This process does not reflect the real use-case, and it is not practical for users, but this helps us understand to what degree the function is differentiable and when not. """ n_fft = 16 transform = T.TimeStretch(n_freq=n_fft // 2 + 1, fixed_rate=rate) waveform = get_whitenoise(sample_rate=40, duration=1, n_channels=2) spectrogram = get_spectrogram(waveform, n_fft=n_fft, power=None) # 1e-3 is too small (on CPU) epsilon = 1e-2 too_close = spectrogram.abs() < epsilon spectrogram[too_close] = epsilon * spectrogram[too_close] / spectrogram[too_close].abs() if test_pseudo_complex: spectrogram = torch.view_as_real(spectrogram) self.assert_grad(transform, [spectrogram])
def test_TimeStretch(self, test_pseudo_complex): n_freq = 400 hop_length = 512 fixed_rate = 1.3 tensor = torch.view_as_complex(torch.rand((10, 2, n_freq, 10, 2))) self._assert_consistency_complex( T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate), tensor, test_pseudo_complex)
def test_TimeStretch(self): n_freq = 400 hop_length = 512 fixed_rate = 1.3 tensor = torch.rand((10, 2, n_freq, 10, 2)) self._assert_consistency( T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate), tensor, )
def test_timestretch_zeros_fail(self): """Test that ``T.TimeStretch`` fails gradcheck at 0 This is because ``F.phase_vocoder`` converts data from cartesian to polar coordinate, which performs ``atan2(img, real)``, and gradient is not defined at 0. """ n_fft = 16 transform = T.TimeStretch(n_freq=n_fft // 2 + 1, fixed_rate=0.99) waveform = torch.zeros(2, 40) spectrogram = get_spectrogram(waveform, n_fft=n_fft, power=None) self.assert_grad(transform, [spectrogram])
def test_TimeStretch(self): n_fft = 1025 n_freq = n_fft // 2 + 1 hop_length = 512 fixed_rate = 1.3 tensor = torch.rand((10, 2, n_freq, 10), dtype=torch.cfloat) batch = 10 num_channels = 2 waveform = common_utils.get_whitenoise(sample_rate=8000, n_channels=batch * num_channels) tensor = common_utils.get_spectrogram(waveform, n_fft=n_fft) tensor = tensor.reshape(batch, num_channels, n_freq, -1) self._assert_consistency_complex( T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate), tensor, )
###################################################################### # SpecAugment # ----------- # # `SpecAugment <https://ai.googleblog.com/2019/04/specaugment-new-data-augmentation.html>`__ # is a popular spectrogram augmentation technique. # # ``torchaudio`` implements ``TimeStretch``, ``TimeMasking`` and # ``FrequencyMasking``. # # TimeStretch # ~~~~~~~~~~ # spec = get_spectrogram(power=None) stretch = T.TimeStretch() rate = 1.2 spec_ = stretch(spec, rate) plot_spectrogram(torch.abs(spec_[0]), title=f"Stretched x{rate}", aspect='equal', xmax=304) plot_spectrogram(torch.abs(spec[0]), title="Original", aspect='equal', xmax=304) rate = 0.9 spec_ = stretch(spec, rate) plot_spectrogram(torch.abs(spec_[0]), title=f"Stretched x{rate}", aspect='equal', xmax=304) ###################################################################### # TimeMasking # ~~~~~~~~~~~ #