GitLab

Summary:
Modifies the example LibriSpeech Conformer RNN-T recipe as follows:
- Moves data loading and transforms logic from lightning module to data module (improves generalizability and reusability of lightning module and data module).
- Moves transforms logic from dataloader collator function to dataset (resolves dataloader multiprocessing issues on certain platforms).
- Replaces lambda functions with `partial` equivalents (resolves pickling issues in certain runtime environments).
- Modifies training script to allow for specifying path model checkpoint to restart training from.

Pull Request resolved: https://github.com/pytorch/audio/pull/2366

Reviewed By: mthrok

Differential Revision: D36305028

Pulled By: hwangjeff

fbshipit-source-id: 0b768da5d5909136c55418bf0a3c2ddd0c5683ba

Summary:
Adds Conformer RNN-T LibriSpeech training recipe to examples directory.

Produces 30M-parameter model that achieves the following WER:

|                     |          WER |
|:-------------------:|-------------:|
| test-clean          |       0.0310 |
| test-other          |       0.0805 |
| dev-clean           |       0.0314 |
| dev-other           |       0.0827 |

Pull Request resolved: https://github.com/pytorch/audio/pull/2329

Reviewed By: xiaohui-zhang

Differential Revision: D35578727

Pulled By: hwangjeff

fbshipit-source-id: afa9146c5b647727b8605d104d928110a1d3976d