def func(tensor):
cmn_window = 600
min_cmn_window = 100
center = False
norm_vars = False
a = torch.tensor([[-1.915875792503357, 1.147700309753418],
[1.8242558240890503, 1.3869990110397339]],
device=tensor.device,
dtype=tensor.dtype)
return F.sliding_window_cmn(a, cmn_window, min_cmn_window, center,
norm_vars)
def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
Returns:
Tensor: Tensor of audio of dimension (..., time).
"""
cmn_waveform = F.sliding_window_cmn(
waveform, self.cmn_window, self.min_cmn_window, self.center, self.norm_vars)
return cmn_waveform
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)
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)
result = F.sliding_window_cmn(tensor, **kwargs)
command = ['apply-cmvn-sliding'] + _convert_args(**kwargs) + ['ark:-', 'ark:-']
kaldi_result = _run_kaldi(command, 'ark', tensor)
torch.testing.assert_allclose(result, kaldi_result)