Add Wav2Vec2Conformer
# What does this PR do?

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Fixes #16640

This PR adds Fairseq's Wav2Vec2Conformer checkpoints. 

Wav2Vec2Conformer outperforms Wav2Vec2 on Librispeech by some margin (1.8% WER instead of 2.2% WER).
Compared to Wav2Vec2 the "attention" block is enhanced by a "Conformer"-block as described in https://arxiv.org/abs/2005.08100. Thus, the "large" Wav2Vec2Conformer checkpoints have roughly twice as many parameters as the "large" Wav2Vec2 checkpoints.

All checkpoints can be found 

### **Final ToDos before merging**
- [x] Add all checkpoints - see here: https://huggingface.co/models?other=wav2vec2-conformer
- [x] Evaluate checkpoints on Librispeech yields following results:
   - Without LM:
     [rel-pos-960h](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large-960h-ft):  **1.85 WER** (clean) | **3.82 WER** (other)
     [rope-960h](https://huggingface.co/facebook/wav2vec2-conformer-rope-large-960h-ft): **1.96 WER** (clean) | **3.98 WER** (other)
  - With 4-gram LM:
    [rel-pos-960h](https://huggingface.co/patrickvonplaten/wav2vec2-conformer-rel-pos-large-960h-ft-4-gram): **1.94  WER** (clean) |  **3.58 WER** (other)
     [rope-960h](https://huggingface.co/patrickvonplaten/wav2vec2-conformer-rope-large-960h-ft-4-gram): **1.88 WER** (clean) | **3.57 WER** (other)
- [ ] Discuss with @sravyapopuri388 about how to release model in Transformers. Will a paper be published ? A short article?

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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- [ ] Did you write any new necessary tests?


## Who can review?

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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @LysandreJik

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

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Currently blocked by https://github.com/pytorch/fairseq/issues/4356<|||||>_The documentation is not available anymore as the PR was closed or merged._<|||||>@sgugger would be great if you could do a quick 2nd round of review, notably:

1.) UniSpeechSatBaseModelOutput and others don't exist anymore and are replaced by Wav2Vec2ModelOutput. Logically this makes sense since those models are all trained using the Wav2Vec2 loss and thus need to return both `extract_features` and `last_hidden_states`. Also those model outputs were never in the public init so I think that's fine from a bcp point of view
2.) There is no official paper for this model (yet) - I'm in contact with the authors and we're discussing how to best promote it. Would like to merge this model in a somewhat silent way until the authors come back to me. => thus it's not mentioned in the README.md .

Let me know if that's fine for you :-) <|||||>Test failure is unrelated