GitLab

Summary:
Add support for CTC lexicon decoder without LM support by adding a non language model `ZeroLM` that returns score 0 for everything. Generalize the decoder class/API a bit to support this, adding it as an option for the kenlm decoder at the moment (will likely be separated out from kenlm when adding support for other kinds of LMs in the future)

Pull Request resolved: https://github.com/pytorch/audio/pull/2174

Reviewed By: hwangjeff, nateanl

Differential Revision: D33798674

Pulled By: carolineechen

fbshipit-source-id: ef8265f1d046011b143597b3b7c691566b08dcde

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2178

Reviewed By: mthrok

Differential Revision: D33797649

Pulled By: nateanl

fbshipit-source-id: 7a8f54294e7b5bd4d343c8e361e747bfd8b5b603

Summary:
following up on https://github.com/pytorch/audio/pull/2141#discussion_r779055465, adding brief beam search description and linking to resources

Pull Request resolved: https://github.com/pytorch/audio/pull/2173

Reviewed By: nateanl

Differential Revision: D33791731

Pulled By: carolineechen

fbshipit-source-id: 603fdd177c9a3c8276a4692fb7bb385bd01b9bfb

Summary:
- Rename `BucketizeSampler` to `BucketizeBatchSampler`
- Fix bugs in `BucketizeBatchSampler`
- Adjust HuBERTDataset based on the latest `BucketizeBatchSampler`.

Pull Request resolved: https://github.com/pytorch/audio/pull/2150

Reviewed By: mthrok

Differential Revision: D33689963

Pulled By: nateanl

fbshipit-source-id: 203764e9af5b7577ba08ebaa30ba5da3b67fb7e7

Summary:
Closes https://github.com/pytorch/audio/issues/2162

Pull Request resolved: https://github.com/pytorch/audio/pull/2163

Reviewed By: nateanl

Differential Revision: D33666354

Pulled By: mthrok

fbshipit-source-id: 3e7a963b9ac85046317df8d5dab91af363e5668b

Summary:
additionally add decoding results for wav2vec2 large and also on the test-clean dataset

Pull Request resolved: https://github.com/pytorch/audio/pull/2161

Reviewed By: mthrok

Differential Revision: D33644670

Pulled By: carolineechen

fbshipit-source-id: a219a15af46f82a6bd90169bb3001dbad8f0a96e

[PyTorchLightning/pytorch-lightning] Add deprecation path for renamed training type plugins (#11227)

Summary:
### New commit log messages
  4eede7c30 Add deprecation path for renamed training type plugins (#11227)

Reviewed By: edward-io, daniellepintz

Differential Revision: D33409991

fbshipit-source-id: 373e48767e992d67db3c85e436648481ad16c9d0

Summary:
Add explanation and demonstration of different beam search decoder parameters.
Additionally use a better sample audio file and load in with token list instead of tokens file.

Pull Request resolved: https://github.com/pytorch/audio/pull/2141

Reviewed By: mthrok

Differential Revision: D33463230

Pulled By: carolineechen

fbshipit-source-id: d3dd6452b03d4fc2e095d778189c66f7161e4c68

Summary:
This PR:

- Replaces the `data_source` with `lengths`
- Adds a `shuffle` argument to decide whether to shuffle the samples in the buckets.
- Add `max_len` and `min_len` to filter out samples that are > max_len or < min_len.

cc nateanl

Pull Request resolved: https://github.com/pytorch/audio/pull/2147

Reviewed By: carolineechen

Differential Revision: D33454369

Pulled By: nateanl

fbshipit-source-id: 3835169ec7f808f8dd9650e7f183f79091efe886

Summary:
### New commit log messages
  b64dea9dc Rename `DDPPlugin` to `DDPStrategy` (#11142)

Reviewed By: jjenniferdai

Differential Revision: D33259306

fbshipit-source-id: b4608c6b96b4a7977eaa4ed3f03c4b824882aef0

Summary:
add script for running CTC beam search decoder on librispeech dataset with torchaudio pretrained wav2vec2 models

Pull Request resolved: https://github.com/pytorch/audio/pull/2130

Reviewed By: mthrok

Differential Revision: D33419436

Pulled By: carolineechen

fbshipit-source-id: 0a0d00f4c17ecdbb497c9eda78673aa939d73c57

Summary:
cc mthrok

Pull Request resolved: https://github.com/pytorch/audio/pull/2116

Reviewed By: mthrok

Differential Revision: D33368453

Pulled By: jdsgomes

fbshipit-source-id: 09cf3fe5ed6f771c2f16505633c0e59b0c27453c

Summary:
Regroup RNN-T components under `torchaudio.prototype.models` and `torchaudio.prototype.pipelines`.

Updated docs: https://492321-90321822-gh.circle-artifacts.com/0/docs/prototype.html

Pull Request resolved: https://github.com/pytorch/audio/pull/2110

Reviewed By: carolineechen, mthrok

Differential Revision: D33354116

Pulled By: hwangjeff

fbshipit-source-id: 9cf4afed548cb173d56211c16d31bcfa25a8e4cb

Reviewed By: zertosh

Differential Revision: D33347867

fbshipit-source-id: 7672f65392e363c0359de2d86e745782a09cf9dc

Summary:
### Change list

* Split the documentation of prototypes
* Add a new API reference section dedicated for prototypes.
* Hide the signature of KenLMLexiconDecoder constructor. (cc carolineechen )
  * https://489516-90321822-gh.circle-artifacts.com/0/docs/prototype.ctc_decoder.html#torchaudio.prototype.ctc_decoder.KenLMLexiconDecoder
* Hide the signature of RNNT constructor. (cc hwangjeff )
  * https://489516-90321822-gh.circle-artifacts.com/0/docs/prototype.rnnt.html#torchaudio.prototype.RNNT
* Tweak CTC tutorial
  * Replace hyperlinks to API reference with backlinks
  * Add `progress=False` to download

### Follow-up

RNNT decoder and CTC decode returns their own `Hypothesis` classes. When I tried to add Hypothesis of CTC decode to the documentation, the build process complains that it's ambiguous.
I think the Hypothesis classes can be put inside of each decoder. (if TorchScript supports it) or make the name different, but in that case the interface of each Hypothesis has to be generic enough.

### Before

https://pytorch.org/audio/main/prototype.html

<img width="1390" alt="Screen Shot 2021-12-28 at 1 05 53 PM" src="https://user-images.githubusercontent.com/855818/147594425-6c7f8126-ab76-4edc-a616-a00901e7e9ef.png">

### After

https://489516-90321822-gh.circle-artifacts.com/0/docs/prototype.html

<img width="1202" alt="Screen Shot 2021-12-28 at 8 37 35 PM" src="https://user-images.githubusercontent.com/855818/147619281-8152b1ae-e127-40b2-a944-dc11b114b629.png">

https://489516-90321822-gh.circle-artifacts.com/0/docs/prototype.rnnt.html

<img width="1415" alt="Screen Shot 2021-12-28 at 8 38 27 PM" src="https://user-images.githubusercontent.com/855818/147619331-077b55b5-c5e9-47ab-bfe6-873e41c738c8.png">

https://489516-90321822-gh.circle-artifacts.com/0/docs/prototype.ctc_decoder.html

<img width="1417" alt="Screen Shot 2021-12-28 at 8 39 04 PM" src="https://user-images.githubusercontent.com/855818/147619364-63df3457-a4b2-4223-973f-f4301bd45280.png">

Pull Request resolved: https://github.com/pytorch/audio/pull/2108

Reviewed By: hwangjeff, carolineechen, nateanl

Differential Revision: D33340816

Pulled By: mthrok

fbshipit-source-id: 870edfadbe41d6f8abaf78fdb7017b3980dfe187

Summary:
Adds pretrained Emformer RNN-T inference pipeline that's capable of performing streaming and non-streaming ASR.

Includes demo script that uses pipeline to alternately perform streaming and non-streaming ASR on LibriSpeech test samples (video below).

https://user-images.githubusercontent.com/8345689/147590753-d5126557-d575-4551-8dfe-5977276cb4ad.mov

Pull Request resolved: https://github.com/pytorch/audio/pull/2093

Reviewed By: mthrok

Differential Revision: D33340776

Pulled By: hwangjeff

fbshipit-source-id: fbb3b1d471b4e9f1b93fa9dea9c464154537a8ac

Summary:
Remove it as it's already introduced in the [gallery](https://github.com/pytorch/audio/blob/main/examples/tutorials/mvdr_tutorial.py).

Pull Request resolved: https://github.com/pytorch/audio/pull/2109

Reviewed By: carolineechen

Differential Revision: D33341574

Pulled By: nateanl

fbshipit-source-id: e5c1c8537063b9453947dc3ecafa70e9b6c74146

Summary:
demonstrate usage of the CTC beam search decoder w/ lexicon constraint and KenLM support, on a LibriSpeech sample and using a pretrained wav2vec2 model

rendered: https://485200-90321822-gh.circle-artifacts.com/0/docs/tutorials/asr_inference_with_ctc_decoder_tutorial.html

follow-ups:
- incorporate `nbest`
- demonstrate customizability of different beam search parameters

Pull Request resolved: https://github.com/pytorch/audio/pull/2106

Reviewed By: mthrok

Differential Revision: D33340946

Pulled By: carolineechen

fbshipit-source-id: 0ab838375d96a035d54ed5b5bd9ab4dc8d19adb7

Summary:
This commit updates the documentation configuration so that if an API (function or class) is used in tutorials, then it automatically add the links to the tutorials.

It also adds `py:func:` so that it's easy to jump from tutorials to API reference.

Note: the use of `py:func:` is not required to be recognized by Shpinx-gallery.

* https://482162-90321822-gh.circle-artifacts.com/0/docs/transforms.html#feature-extractions

<img width="776" alt="Screen Shot 2021-12-24 at 12 41 43 PM" src="https://user-images.githubusercontent.com/855818/147367407-cd86f114-7177-426a-b5ee-a25af17ae476.png">

* https://482162-90321822-gh.circle-artifacts.com/0/docs/transforms.html#mvdr

<img width="769" alt="Screen Shot 2021-12-24 at 12 42 31 PM" src="https://user-images.githubusercontent.com/855818/147367422-01fd245f-2f25-4875-a206-910e17ae0161.png">

Pull Request resolved: https://github.com/pytorch/audio/pull/2101

Reviewed By: hwangjeff

Differential Revision: D33311283

Pulled By: mthrok

fbshipit-source-id: e0c124d2a761e0f8d81c3d14c4ffc836ffffe288

Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/2096

run: `arc lint --apply-patches --paths-cmd 'hg files -I "./**/*.py"'`

Reviewed By: mthrok

Differential Revision: D33297351

fbshipit-source-id: 7bf5956edf0717c5ca90219f72414ff4eeaf5aa8

Summary:
1. Reorder Audio display so that audios are playable from browser in doc
2. Add link to function documentations

https://470342-90321822-gh.circle-artifacts.com/0/docs/tutorials/audio_data_augmentation_tutorial.html

Pull Request resolved: https://github.com/pytorch/audio/pull/2082

Reviewed By: carolineechen

Differential Revision: D33227725

Pulled By: mthrok

fbshipit-source-id: c7ee360b6f9b84c8e0a9b72193b98487d03b57ab

Summary:
The PR adds PyTorch Lightning based training script for HuBERT Base model. There are two iterations of pre-training and 1 iteration of ASR fine-tuning on LibriSpeech dataset.

Pull Request resolved: https://github.com/pytorch/audio/pull/2000

Reviewed By: carolineechen

Differential Revision: D33021467

Pulled By: nateanl

fbshipit-source-id: 77fe5a751943b56b63d5f1fb4e6ef35946e081db

Summary:
Add training recipe for RNN-T Emformer ASR model to examples directory.

Pull Request resolved: https://github.com/pytorch/audio/pull/2052

Reviewed By: nateanl

Differential Revision: D32814096

Pulled By: hwangjeff

fbshipit-source-id: a5153044efc16cb39f0e6413369a6791637af76a

* Refactor tutorial organization

* Merge tutorial subdirectoris under to examples/gallery/tutorials
* Do not use index.rst generated by Sphinx-gallery
* Instead use flat structure so that all the tutorials are listed in left menu
* Use `_assets` dir for artifacts of tutorials

It turned out that generated tutorials can embed the audio if the following conditions are met.
This commit changes how audio samples are shown in tutorials so that they become playable in doc.

1. There is only one `IPython.display.Audio` call in a cell
2. An object of `IPython.display.Audio` is the last object interpreter receives in the cell
3. Audio format is `wav`
   (`flac` can be embedded as well, but browsers (Chrome/Safari) won't play it)

Ref: https://stackoverflow.com/a/33109647

Move the computation of `#classes -> #bits` to the constructor of WaveRNN and attach it to the instance, so that it can be reused elsewhere.