GitLab

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