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)