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- 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>

- 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

* [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.

* Remove `class FunctionalComplex` header accidentally re-introduced in #1490 

It was reported in #1478 that spectrogram masking operations were done in-place and modified the original input tensors. This PR fixes this behavior and adds tests to ensure that the input tensor is not changed.

- Put functional test logic into one place, `functional_impl.py`
- Tidy imports

* Fixed floor_divide deprecation warnings seen in pytest output

* Fixed warning in test_flanger_triangle_linear

The VAD function trims the input tensor to the first instance of voice
activity on any channel or item. Trimming batches this way may be
undesirable as the item with earliest activity will dominate. Either
way, the batch behaviour does not match the itemwise behaviour.

The VAD batch consistency tests currently pass out of coincidence, but
they specify incorrect behaviour. This commit removes them.

Use shorter input sequences to avoid time out error on CI

Merges lfilter and spectrogram classes together in the common implementation and modifies the cpu and gpu test definitions accordingly

1. `F.phase_vocoder` accepts Tensor with complex dtype.
    * The implementation path has been updated from #758 so that they share the same code path by internally converting the input Tensor to complex dtype and performing all the operation on top of it.
    * Adopted `torch.polar` for simpler Tensor generation from magnitude and angle.
2. Updated tests
    * librosa compatibility test for complex dtype and pseudo complex dtype
        * Extracted the output shape check test and moved it to functional so that it will be tested on all the combination of `{CPU | CUDA} x {complex64 | complex128}`
    * TorchScript compatibility test for `F.phase_vocoder` and `T.TimeStretch`.
    * batch consistency test for `T.TimeStretch`.

Co-authored-by: krishnakalyan3 <skalyan@cloudera.com>

This PR additionally adds batching to kaldi compliance resample interface.

* Refactor Kaldi compatibility tests
Co-authored-by: Jeff Hwang <jeffhwang@fb.com>

* Parameterize `test_sliding_window_cmn`

* Extract test naming function

* Pass a spectrogram to `F.sliding_window_cmn`

* Set manual seed for remaining rand calls in suite

Parameterize `test_create_fb` so each set of values are tested
independently. Also explicitly skip on older versions of librosa (< 0.7.2) when
`norm="slaney"`.