GitLab

Summary:
following pr https://github.com/pytorch/audio/issues/2716
- For preprocessing
  - The HuBERT feature takes lots of memory which may not fit some machines. Enable to use a subset of feature for training a k-means model.

- For pre-training
  - Normalize the loss based on the total number of masked frames across all GPUs.
  - Use mixed precision training. fp16 is not well supported in pytorch_lightning.
  - Log accuracies of masked/unmasked frames during training.
  - Clip the gradients with norm `10.0`.

- For ASR fine-tuning
  - Normalize the loss based on the total number of batches across all GPUs, same as in the conformer recipe of TorchAudio.
  - Use mixed precision training.
  - Add "|" after the end of transcription to capture the silence/word termination, same as in fairseq recipe.

- Update the WER results on LibriSpeech dev and test sets.

|                   | WER% (Viterbi)|  WER% (KenLM) |
|:-----------------:|--------------:|--------------:|
| dev-clean         |       10.9    |       4.2     |
| dev-other         |       17.5    |       9.4     |
| test-clean        |       10.9    |       4.4     |
| test-other        |       17.8    |       9.5     |

Pull Request resolved: https://github.com/pytorch/audio/pull/2744

Reviewed By: carolineechen

Differential Revision: D40282322

Pulled By: nateanl

fbshipit-source-id: 4723584c912e70e8970149fe09de005385eaab90