GitLab

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2199

Reviewed By: hwangjeff

Differential Revision: D33979923

Pulled By: nateanl

fbshipit-source-id: 566ba1944dd3511fee740ac17fea2dcb0e5810fa

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2195

Reviewed By: hwangjeff

Differential Revision: D33950179

Pulled By: nateanl

fbshipit-source-id: 5fcfa4f433fffdcbb3b8e97f7c90fb8f723a30a2

Summary:
Rather than apply SentencePiece's `decode` to directly convert each hypothesis's token id sequence to an output string, we convert each token id sequence to word pieces and then manually join the word pieces ourselves. This allows us to preserve leading whitespaces on output strings and therefore account for word breaks and continuations across token processor invocations, which is particularly useful when performing streaming ASR.

https://user-images.githubusercontent.com/8345689/152093668-11fb775a-bf7b-4b1d-9516-9f8d5a9b6683.mov

Versus the previous behavior visualized in https://github.com/pytorch/audio/issues/2093, the scheme here properly constructs words comprising multiple pieces.

Pull Request resolved: https://github.com/pytorch/audio/pull/2192

Reviewed By: mthrok

Differential Revision: D33936622

Pulled By: hwangjeff

fbshipit-source-id: e550980c7d4cac9e982315508f793a6b816752e9

Summary:
Missed a couple of spots in https://github.com/pytorch/audio/issues/2187.

Pull Request resolved: https://github.com/pytorch/audio/pull/2189

Reviewed By: carolineechen, nateanl, mthrok

Differential Revision: D33926342

Pulled By: hwangjeff

fbshipit-source-id: e1324c0fe8f9be90ad3143d19cd61c3d53f02b06

Summary:
Moves ASR features out of `torchaudio.prototype`. Specifically, merges contents of `torchaudio.prototype.models` into `torchaudio.models` and contents of `torchaudio.prototype.pipelines` into `torchaudio.pipelines` and updates refs, tests, and docs accordingly.

Pull Request resolved: https://github.com/pytorch/audio/pull/2187

Reviewed By: nateanl, mthrok

Differential Revision: D33918092

Pulled By: hwangjeff

fbshipit-source-id: f003f289a7e5d7d43f85b7c270b58bdf2ed6344c

Summary:
Adds script for generating global feature statistics along with new feature statistics json for LibriSpeech RNN-T training recipe.

Pull Request resolved: https://github.com/pytorch/audio/pull/2183

Reviewed By: mthrok

Differential Revision: D33902377

Pulled By: hwangjeff

fbshipit-source-id: ec347a685ae67aefc485084aac6ed2efd653250f

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:
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

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:
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:
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:
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