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* Add basic filtfilt implementation

* Add filtfilt to functional package; add tests
Co-authored-by: V G <vladislav.goncharenko@phystech.edu>

* Reduce length of waveform in pitch_shift batch_consistency test

Summary: To address the test failure in T96406395

Reviewed By: carolineechen

Differential Revision: D30163741

fbshipit-source-id: f88d86b3da7b1ee52518934567b0b0a62700ee58

* Fix batch consistency test in transforms

Summary: The stress test still fails. Add n_fft to address it.

Reviewed By: mthrok

Differential Revision: D30218279

fbshipit-source-id: 7858efd3e5ac0073193a7883fd314486efc73814
Co-authored-by: Zhaoheng Ni <zni@fb.com>

- Provide InverseSpectrogram module that corresponds to Spectrogram module
- Add length parameter to the forward method in transforms
Co-authored-by: dgenzel <dgenzel@fb.com>
Co-authored-by: Zhaoheng Ni <zni@fb.com>

D30080845

- Renamed torchaudio.functional.create_fb_matrix to torchaudio.functional.melscale_fbanks.
- Added interface with a warning for create_fb_matrix

Summary:
- Add linear_fbank method
- Add LFCC in transforms

Rebases #1571; addresses #1569:

"In 0.9.0 we are deprecating the lazy behavior of MelScale because it can make an invalid 
TorchScript object and it does not align with the design of torchaudio. Now in master 
branch, we can remove the implementation."
Co-authored-by: Pankaj Patil <pankaj.patil2099@hotmail.com>
Co-authored-by: moto <855818+mthrok@users.noreply.github.com>
Co-authored-by: hwangjeff <jeffhwang@fb.com>

Porting Tacotron2 from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/model.py

The test was broken from the beginning and for a reason unrelated to
ROCm, but was disabled for ROCm in https://github.com/pytorch/audio/pull/1411.
https://github.com/pytorch/audio/pull/1604 fixed the test so that we are re-enabling it.

Since 0.9.0-RC1, `T.Resample` precomputes and caches resampling kernel for performance improvement. (10x improvement).

The implementation from 0.8.0 computed the kernel on-the-fly on the same `device`/`dtype` as the input Tensor, 
but in the newer version, the kernel is precomputed at the construction time and is cached with `float32` first.
This causes degradation if one wants to perform resampling on `float64`, because `sinc` values computed on `float32`s are not good enough for resampling in `float64`.

The reason why we decided to use `float32` for initial caching is to keep the UX disruption minimum, and there were no way to make it work for `float64`. This PR adds `dtype` argument, that can be used for overwriting the cache precision.

`torchaudio.compliance.kaldi.resample_waveform` has been replaced with `torchaudio.funcitonal.resample`.

* [BC-Breaking] Default to native complex type when returning raw spectrogram

Part of https://github.com/pytorch/audio/issues/1337 .

- This code changes the return type of spectrogram to be native complex dtype,
when (and only when) returning raw (complex-valued) spectrogram.
- Change `return_complex=False` to `return_complex=True` in spectrogram ops.
- `return_complex` is only effective when `power` is `None`. It is ignored for
cases where `power` is not `None`. Because the returned Tensor is power spectrogram,
which is real-valued Tensors.

* autograd test from carolineechen/audio#2

* fix numpy backward: be careful to not modify inplace.