Migrate kaldi spectrogram (#687)

* Migrate spectrogram

* Update spectrogram in kaldi.py to support device and dtype

* Remove failing tests
Signed-off-by: Bhargav Kathivarapu <bhargavkathivarapu31@gmail.com>

test/kaldi_compatibility_impl.py

@@ -86,6 +86,17 @@ class Kaldi(common_utils.TestBaseMixin):
         kaldi_result = _run_kaldi(command, 'scp', wave_file)
         self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8)
 
+    @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_spectrogram_args.json')))
+    @unittest.skipIf(_not_available('compute-spectrogram-feats'), '`compute-spectrogram-feats` not available')
+    def test_spectrogram(self, kwargs):
+        """spectrogram should be numerically compatible with compute-spectrogram-feats"""
+        wave_file = common_utils.get_asset_path('kaldi_file.wav')
+        waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device)
+        result = torchaudio.compliance.kaldi.spectrogram(waveform, **kwargs)
+        command = ['compute-spectrogram-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-']
+        kaldi_result = _run_kaldi(command, 'scp', wave_file)
+        self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8)
+
     @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_mfcc_args.json')))
     @unittest.skipIf(_not_available('compute-mfcc-feats'), '`compute-mfcc-feats` not available')
     def test_mfcc(self, kwargs):

torchaudio/compliance/kaldi.py

@@ -272,6 +272,9 @@ def spectrogram(waveform: Tensor,
         Tensor: A spectrogram identical to what Kaldi would output. The shape is
         (m, ``padded_window_size // 2 + 1``) where m is calculated in _get_strided
     """
+    device, dtype = waveform.device, waveform.dtype
+    epsilon = _get_epsilon(device, dtype)
+
     waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
         waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient)
 
@@ -287,7 +290,7 @@ def spectrogram(waveform: Tensor,
     fft = torch.rfft(strided_input, 1, normalized=False, onesided=True)
 
     # Convert the FFT into a power spectrum
-    power_spectrum = torch.max(fft.pow(2).sum(2), EPSILON).log()  # size (m, padded_window_size // 2 + 1)
+    power_spectrum = torch.max(fft.pow(2).sum(2), epsilon).log()  # size (m, padded_window_size // 2 + 1)
     power_spectrum[:, 0] = signal_log_energy
 
     power_spectrum = _subtract_column_mean(power_spectrum, subtract_mean)