def test_mfcc(self, kwargs): """mfcc should be numerically compatible with compute-mfcc-feats""" wave_file = get_asset_path('kaldi_file.wav') waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) result = torchaudio.compliance.kaldi.mfcc(waveform, **kwargs) command = ['compute-mfcc-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)
def test_pitch_feats(self, kwargs): """compute_kaldi_pitch produces numerically compatible result with compute-kaldi-pitch-feats""" sample_rate = kwargs['sample_rate'] waveform = get_sinusoid(dtype='float32', sample_rate=sample_rate) result = F.compute_kaldi_pitch(waveform[0], **kwargs) waveform = get_sinusoid(dtype='int16', sample_rate=sample_rate) wave_file = self.get_temp_path('test.wav') save_wav(wave_file, waveform, sample_rate) command = ['compute-kaldi-pitch-feats'] + convert_args(**kwargs) + ['scp:-', 'ark:-'] kaldi_result = run_kaldi(command, 'scp', wave_file) self.assert_equal(result, expected=kaldi_result)
def test_sliding_window_cmn(self): """sliding_window_cmn should be numerically compatible with apply-cmvn-sliding""" kwargs = { 'cmn_window': 600, 'min_cmn_window': 100, 'center': False, 'norm_vars': False, } tensor = torch.randn(40, 10, dtype=self.dtype, device=self.device) result = F.sliding_window_cmn(tensor, **kwargs) command = ['apply-cmvn-sliding'] + convert_args(**kwargs) + ['ark:-', 'ark:-'] kaldi_result = run_kaldi(command, 'ark', tensor) self.assert_equal(result, expected=kaldi_result)