Add Wav2Vec2 & Hubert ForSequenceClassification
# What does this PR do?

This adds a Hubert extension for sequence classification.
Ultimately this classification head should be compatible with s3prl `UtteranceLevel` [implementation](https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/model.py#L35) to support classification tasks from SUPERB, such as [Keyword Spotting](https://huggingface.co/datasets/superb#ks) and transfer their pretrained models.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

@patrickvonplaten @patil-suraj 

Accuracy evaluation on SUPERB tasks:

- **KS** has uniform-length samples, so no padding
- **ER** has non-uniform padded batches 
- **SID** is evaluated with batch_size=1 as in `s3prl`

| Task | Model         | normalize=True | normalize=False | Paper  |
| ---- | ------------- | -------------- | --------------- | ------ |
| **KS**   | Wav2Vec2-base | 0.9627         | 0.9643          | 0.9623 |
|      | Hubert-base   | 0.9669         | 0.9672          | 0.9630  |
| **ER**   | Wav2Vec2-base | 0.5281         | 0.6258          | 0.6343 |
|      | Hubert-base   | 0.5502         | 0.6359          | 0.6492 |
| **SID**  | Wav2Vec2-base | 0.7360         | 0.7518          | 0.7518 |
|      | Hubert-base   | 0.8071         | 0.8071          | 0.8142 |

So far `normalize=False` is always better, as expected (`s3prl` never used normalization during eval).
There's also some slight variation with the official results, but it's of the same magnitude as `s3prl` vs `paper` results. <|||||>- [x] Passed integration test for all 4 tasks on both models
- [x] Added `Copied from` where possible (the script just inserts a full copy of `W2V2.forward()` before `End copy`, so I didn't use it there)
- [x] Added dummy examples to `forward()` docs
- [x] Moved the models to `https://huggingface.co/superb`

@patrickvonplaten everything should be ready to merge now :) <|||||>Awesome job @anton-l ! Feel free to merge the PR whenever you want