[Wav2Vec2] SpecAugment Fast
Author: patrickvonplaten
Pull request: yes

# What does this PR do?

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This PR refactores the SpecAugement implementation for Wav2Vec2 by fully relying on PyTorch instead of numpy. 
1) The code is made more readable 
- `attention_mask` is dropped since it's not required to treat masked batch indices differently
- Previously every batch_idx was forced to have the same number of masked indices (overlapping masked indices can lead to some batch_indices to have fewer masked indices). This is also not enforced here since it would make the function very dependend on the batch_size which is not good IMO. I don't see a reason why different batch_idx cannot have different # of masked indices. It was verified via training that the change does not lead to a performance drop.
2) Replacing a for loop with tensorized code lead to a 1% speed-up in training (not really noticeable thb)


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Models:

- albert, bert, xlm: @LysandreJik
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Comments:
Noticed a small speed-up when training (1-2%) only though, and even slighly improved results. More importantly I think the code is much more readable now.