GitLab

Summary:
update example ASR pipeline to use the recently added pretrained LM API for decoding

Pull Request resolved: https://github.com/pytorch/audio/pull/2317

Reviewed By: mthrok

Differential Revision: D35361354

Pulled By: carolineechen

fbshipit-source-id: cac7cf55bd9f86417f319191c1405819fe2a7b46

Summary:
Some arguments in `ArgumentParser` are not used in the `lexicon_decoder`. Fix them to use the ones in the parser.

Pull Request resolved: https://github.com/pytorch/audio/pull/2315

Reviewed By: carolineechen

Differential Revision: D35357678

Pulled By: nateanl

fbshipit-source-id: 4e70418cf03708b82bc158cafd9999a80ad08f92

Summary:
- Refactor the current `LibriSpeechRNNTModule`'s unit test.
- Add unit tests for `TEDLIUM3RNNTModule` and `MuSTCRNNTModule`
- Replace the lambda with partial in `TEDLIUM3RNNTModule` to pass the lightning unit test.

Pull Request resolved: https://github.com/pytorch/audio/pull/2240

Reviewed By: mthrok

Differential Revision: D34285195

Pulled By: nateanl

fbshipit-source-id: 4f20749c85ddd25cbb0eafc1733c64212542338f

Summary:
This PR adds ``EMFORMER_RNNT_BASE_MUSTC`` support in `pipeline_demo.py`. The bundle is trained on MuST-C release 2.0 dataset. The model  preserves the casing and punctuations in the transcript.

Here is a screen recording of how it works in streaming and non-streaming modes:

https://user-images.githubusercontent.com/8653221/154356521-fe84bdc1-fb0c-41bd-8729-9edbb3224a07.mov

Pull Request resolved: https://github.com/pytorch/audio/pull/2248

Reviewed By: hwangjeff

Differential Revision: D34282598

Pulled By: nateanl

fbshipit-source-id: 42ed7e2623031dfebd176ef0c6bfd70da3c897d4

Summary:
- Use dictionary to select the `RNNTBundle` and the corresponding dataset.
- Use the dictionary's keys as choices in ArgumentParser

Pull Request resolved: https://github.com/pytorch/audio/pull/2239

Reviewed By: mthrok

Differential Revision: D34267070

Pulled By: nateanl

fbshipit-source-id: 99c7942d5c7c1518694e1ae02a55a7decd87c220

Summary:
- Add docstring to `eval.py` and `pipeline_demo.py` under `emformer_rnnt` directory.
- Refactor logger and ArgumentParser

Pull Request resolved: https://github.com/pytorch/audio/pull/2238

Reviewed By: mthrok

Differential Revision: D34267059

Pulled By: nateanl

fbshipit-source-id: 4b8d3d183ee7bc0ad71ce305cab87bfa90208b2e

Summary:
LM in example script was unintentionally changed to None when adding no LM support previously. this changes it back and is consistent with the WERs listed in the readme

Pull Request resolved: https://github.com/pytorch/audio/pull/2235

Reviewed By: nateanl

Differential Revision: D34273042

Pulled By: carolineechen

fbshipit-source-id: 824b1ce18195e39dc534b2ec9c5312bbe3bb1812

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2237

Reviewed By: mthrok

Differential Revision: D34267000

Pulled By: nateanl

fbshipit-source-id: 4c264aea6cf3fba5d8728d5fe60f9f471815852d

Summary:
Replace underscore with dash in ArgumentParser's arguments.

Pull Request resolved: https://github.com/pytorch/audio/pull/2236

Reviewed By: mthrok

Differential Revision: D34266977

Pulled By: nateanl

fbshipit-source-id: ceacac12c04016a8dbf2a1a7d6bbcf65d4d53d21

Summary:
- Add a MUSTC dataset under examples
- Add a lightning module for MuST-C dataset
- Refactor `train.py`, `eval.py`, and `global_stats.py` scripts

Pull Request resolved: https://github.com/pytorch/audio/pull/2219

Reviewed By: hwangjeff

Differential Revision: D34180466

Pulled By: nateanl

fbshipit-source-id: 9fc74ce7527da1a81dd0738e124428f9d516d164

Summary:
Adds SentencePiece model training script for LibriSpeech Emformer RNN-T example recipe; updates readme with references.

Pull Request resolved: https://github.com/pytorch/audio/pull/2218

Reviewed By: nateanl

Differential Revision: D34177295

Pulled By: hwangjeff

fbshipit-source-id: 9f32805af792fb8c6f834f2812e20104177a6c43

Summary:
We refactored the demo script that can apply RNNT decoding using both `torchaudio.pipelines.EMFORMER_RNNT_BASE_LIBRISPEECH` and `torchaudio.prototype.pipelines.EMFORMER_RNNT_BASE_TEDLIUM3` in both streaming and non-streaming mode. (The first hypothesis prediction is streaming and the second one is non-streaming).

We convert each token id sequence to word pieces and then manually join the word pieces. 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/8653221/153627956-f0806f18-3c1c-44df-ac07-ec2def58a0cf.mov

Pull Request resolved: https://github.com/pytorch/audio/pull/2203

Reviewed By: carolineechen

Differential Revision: D34006388

Pulled By: nateanl

fbshipit-source-id: 3d31173ee10cdab8a2f5802570e22b50fcce5632

Summary:
Adds unit tests for Emformer RNN-T LibriSpeech recipe. Also makes changes to recipe to resolve errors with pickling lambda functions in Windows.

Pull Request resolved: https://github.com/pytorch/audio/pull/2216

Reviewed By: nateanl

Differential Revision: D34171480

Pulled By: hwangjeff

fbshipit-source-id: 5fcebb457051f3041766324863728411180f5e1e

Summary:
- Removes 100-batch truncation in TEDLIUM3 recipe.
- Reinstates `train_spm.py` for TEDLIUM3.

Pull Request resolved: https://github.com/pytorch/audio/pull/2217

Reviewed By: nateanl

Differential Revision: D34171525

Pulled By: hwangjeff

fbshipit-source-id: 54698e5e1b094c26c28eec9b8b1722223077876c

Summary:
Consolidates LibriSpeech and TED-LIUM Release 3 Emformer RNN-T training recipes in a single directory.

Pull Request resolved: https://github.com/pytorch/audio/pull/2212

Reviewed By: mthrok

Differential Revision: D34120104

Pulled By: hwangjeff

fbshipit-source-id: 29c6e27195d5998f76d67c35b718110e73529456

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2177

Reviewed By: hwangjeff

Differential Revision: D33893052

Pulled By: nateanl

fbshipit-source-id: 00ff011eb96662b162c0327196a9564721e9c8f7

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