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* Simplify trainining step in av-asr recipe

* Run pre-commit

Summary:
This PR is to include few changes in the AV-ASR recipe. The changes include better results, a faster face detector (Mediapipe), renamed variable names, a streamlined dataloader, and a few illustrated examples. These changes were made to improve the usability of the recipe.

Pull Request resolved: https://github.com/pytorch/audio/pull/3493

Reviewed By: mthrok

Differential Revision: D47758072

Pulled By: mpc001

fbshipit-source-id: 4533587776f3a7a74f3f11b0ece773a0934bacdc

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/3489

Reviewed By: mthrok

Differential Revision: D47726448

Pulled By: mpc001

fbshipit-source-id: 3d5aa7646c6bb816dcbbf70c61e98404bb148841

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/3410

Differential Revision: D46496786

Pulled By: mthrok

fbshipit-source-id: e517b273c40b340f39ce7db7ab1be1c3eb5f2059

Summary:
This PR adds AV-ASR recipe which contains sample implementations of training and evaluation pipelines for RNNT based automatic, visual, and audio-visual (ASR, VSR, AV-ASR) models on LRS3. This repository includes both streaming/non-streaming modes.

CC stavros99 xiaohui-zhang YumengTao mthrok nateanl hwangjeff

Pull Request resolved: https://github.com/pytorch/audio/pull/3278

Reviewed By: nateanl

Differential Revision: D46121550

Pulled By: mpc001

fbshipit-source-id: bb44b97ae25e87df2a73a707008be46af4ad0fc6

Summary:
In the Conformer RNN-T LibriSpeech recipe, there's no need to perform manual optimization. This PR modifies the recipe to use automatic optimization instead.

Pull Request resolved: https://github.com/pytorch/audio/pull/2981

Reviewed By: mthrok

Differential Revision: D42507228

Pulled By: hwangjeff

fbshipit-source-id: 9712add951eba356e39f7e8c8dc3bf584ba48309

Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/2535

Modifies LibriSpeech Conformer RNN-T example recipe to make the Lightning module and datamodule more generic and reusable.

Reviewed By: mthrok

Differential Revision: D36731576

fbshipit-source-id: 4643e86fac78f3c2bacc15f5d385bc7b10f410a2

Summary:
Applies new import merging and sorting from µsort v1.0.

When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.

Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.

For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting

Reviewed By: lisroach

Differential Revision: D36402214

fbshipit-source-id: b641bfa9d46242188524d4ae2c44998922a62b4c

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:
PyTorch Lite, which is becoming a standard for mobile PyTorch usage, does not support containers containing custom classes. Consequently, because TorchAudio's RNN-T decoder currently returns and accepts lists of `Hypothesis` namedtuples, it is not compatible with PyTorch Lite. This PR resolves said incompatibility by changing the underlying implementation of `Hypothesis` to tuple.

Pull Request resolved: https://github.com/pytorch/audio/pull/2339

Reviewed By: nateanl

Differential Revision: D35806529

Pulled By: hwangjeff

fbshipit-source-id: 9cbae5504722390511d35e7f9966af2519ccede5

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