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* [BC-Breaking] Split pretraining and finetuning factory functions

Previously, factory functions of wav2vec2 only generated the architecture
for the fine-tuning architecture used in wav2ve2 paper for ASR task.
That is, pre-training architecture + Linear module, and it did not
provide a straightforward way to generate architectures for pre-training.

The goal of the original implementation was to allow the inference of
wav2vec2 in non-Python environment via TorchScript. Now we would like to
expand it to pre-training/fine-tuning and HuBERT model as well.

Therefore, we need to have factory functions for both pre-training and
fine-tuning. This commit introduces new factory functions and separate
functions for pre-training and fine-tuning.

1. New functions for ASR fine-tuning.

We introdcue `wav2vec2_asr_XXX` functions which generates the architecture
used for the fine-tuning task in wav2vec2 paper. *1

2. Re-purpse the old functions

The existing functions, `wav2vec2_XXX`, now generates the architecture with
pre-trainig module only. (no Linear module)

Note
*1 This architecture is just one way to define architecture for fine-tuning
and it is not universal definition. The new `wav2vec2_asr_XXX` functions are
designed to provide these specific fine-tuning configuration and they are not
meant to support generic architecture for downstream task.

* Add basic filtfilt implementation

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

- 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

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

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

* 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`

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

Co-authored-by: Vincent Quenneville-Belair <vincentqb@gmail.com>

Replace the prototype RNNT implementation (using warp-transducer) with one without external library dependencies

This PR additionally adds batching to kaldi compliance resample interface.