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Add pretrained weights from https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#pre-trained-models
- Wav2Vec 2.0 Base / Large / Large (LV-60)
- XLSR-53

Adds an implementation of Emformer, a memory-efficient transformer architecture 
introduced in https://ieeexplore.ieee.org/document/9414560 that targets low-latency 
streaming speech recognition applications.

This commit adds
- HUBERT_LARGE
- HUBERT_XLARGE
- HUBERT_ASR_XLARGE

* Rename factory functions `wav2vec2_asr_ARCH` to `wav2vec2_ft_ARCH`

In #1783, we split the factory functions of wav2vec2 into ones for pretraining models
and ones for fine-tuning models (pretraining model + extra Linear module).

I picked the name scheme `wav2vec2_asr_ARCH` for factory functions of fine-tuning models,
but did not feel right, because the architecture code is more generic.
Even though the resulting model architecture was used for  ASR fine-tuning in the paper, 
it does not have to be ASR.
This became more evident as we add pre-trained parameters support, such as #1799.
It matters more for the weight files that for which task and on which dataset it was
trained on. For factory function, ASR task is not relevant.

Therefore renaming the functions by replacing `_asr_` to `_ft_` fine-tuning.

Note: Since the new functions are not release yet, this PR itself is not BC-breaking.

This commit adds the following HuBERT model architectures

 - `base` (pre-training)
 - `large` (pre-training / fine-tuning)
 - `xlarge` (pre-training / fine-tuning)

Since the internal components are same as `Wav2Vec2Model`, it reuses the existing modules..
With these models, it is possible to 
- import the pre-trained model published by `fairseq` and TorchScript it.
- fine-tune the existing model for downstream task.

* [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.