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Mel scale frequency is in general defined/used on power spectrogram and as far as we know, it is not defined for raw (complex value) spectrogram. When `power=None` (when intermediate spectrogram is complex-valued), `MelSpectrogram` fails because of extra dimension (when using pseudo complex) or type mismatch between `torch.float` and torch.cfloat` (when using native complex).

This commit changes type annotation so that power is only float.

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.

* Use `bibtex` for paper citations.
  * add `override.css` for fixing back reference.
  * wav2vec2
  * wav2letter
  * convtasnet
  * deepspeech
  * rnnt-loss
  * griffinlim
* Fix broken references in `filtering`.
* Fix note in soundfile backends.
* Tweak wav2vec2 example.
* Removes unused `pytorch_theme.css`

* Update compatibility matrix
* Remove some obsolete information

These arguments are deprecated in 0.8 release for legal reasons, and not used anymore. Now we can remove the arguments.

Deprecating `torchaudio.compliance.kaldi.resample_waveform`, which is exactly same as `torchaudio.functional.resample`.

* add rocm binary

use the term cu_version

update pkg_helpers

add docker wheel option

update config yml

add force_cuda option

* update config.yml

* remove conda builds

- TorchScript-able `Wav2Vec2Model` class
- Factory functions for three configurations presented in the paper 
  - `wav2vec2_base`
  - `wav2vec2_large`
  - `wav2vec2_large_lv60k`

Replacing the config.guess with a newer version to support newer hardware, such as Nvidia Jetson.

Obtained from: https://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD

See: https://www.gnu.org/software/gettext/manual/html_node/config_002eguess.html

* Add CUDA binary builds

* Add "cuda_version": "cpu" to doc build jobs

* Add required cu_versions to pkg_helpers

* Regenerate config.yml

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

* Update VAD docstring and check for input shape length

* Update docstring in forward for transform

* Address review feedback: merge tests, update wording

Precomputing and caching the resampling kernel in transforms provides speed improvements for resample, but no longer handles the automatic device and dtype recognition and construction based on input waveform. This is BC-breaking if users do not manually move the transforms object to the correct device and dtype, in which case calls to resample will fail if the input waveform is on gpu, or not of float32 dtype. Precomputing the kernel additionally results in very minor precision differences from previous implementation.