Add kaiser window support to resampling #1509
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carolineechen
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torchaudio/functional/functional.py
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| rolloff: float): | ||
| rolloff: float, | ||
| resampling_method: str, | ||
| beta: float = 6.): |
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According to https://numpy.org/doc/stable/reference/generated/numpy.kaiser.html, a value of 6 is similar to a Hanning window shape (used for sinc_interpolation), but I'm open to other default values as well. Resampy uses ~8.55 for their kaiser_fast and ~14.77 for kaiser_best.
carolineechen
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| # There is probably a way to evaluate those filters more efficiently, but this is kept for | ||
| # future work. | ||
| idx = torch.arange(-width, width + orig_freq) | ||
| idx = torch.arange(-width, width + orig_freq, dtype=torch.float64) |
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through running separate scripts, I realized that #1499 introduced rounding errors if waveform dtype was greater than float32. adding dtype=float64 here to retain implementation accuracy from the prior version, and this implementation will likely be improved after additional discussion on transforms kernel dtype/device computation
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| new_freq (float, optional): The desired frequency. (Default: ``16000``) | ||
| resampling_method (str, optional): The resampling method. (Default: ``'sinc_interpolation'``) | ||
| resampling_method (str, optional): The resampling method. | ||
| Options: [``sinc_interpolation``, ``kaiser_window``] (Default: ``'sinc_interpolation'``) |
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followup: This docstring can be improved.
Co-authored-by: Holly Sweeney <[email protected]>
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Add kaiser window as an option for resampling
Jupyter notebook of some results kaiser_resampling__1_.pdf
cc #1487