Add ASR task for SUPERB
Wait until #2620 is merged before pushing the README tags in this PR > Thanks!
> 
> One question: aren't you adding `task_templates` to the `_info` method (and to the `dataset_infos.json`?

great catch! i've now added the asr task template (along with a mapping from superb task -> template) and updated the `dataset_infos.json` :)  > Good!
> 
> I have a suggested refactoring... Tell me what you think! :)

your approach is much more elegant - i've included your suggestions 🙏 
This PR starts building up the SUPERB benchmark by including the ASR task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/v0.2.0/downstream#asr-automatic-speech-recognition).

Usage:

```python
from datasets import load_dataset 

asr = load_dataset("superb", "asr")
# DatasetDict({
#     train: Dataset({
#         features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
#         num_rows: 28539
#     })
#     validation: Dataset({
#         features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
#         num_rows: 2703
#     })
#     test: Dataset({
#         features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
#         num_rows: 2620
#     })
# })
```

I've used the GLUE benchmark as a guide for filling out the README.

To move fast during the evaluation PoC I propose to merge one task at a time, so we can continue building the training / evaluation framework in parallel.

Note: codewise this PR is ready for review - I'll add the missing YAML tags once #2620 is merged :)