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  1. 26 Apr, 2022 1 commit
  3. 21 Apr, 2022 1 commit
    • hwangjeff's avatar
      Change underlying implementation of RNN-T hypothesis to tuple (#2339) · 6b242c29
      hwangjeff authored 
      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
      6b242c29
  5. 18 Apr, 2022 1 commit
  7. 12 Apr, 2022 1 commit
    • hwangjeff's avatar
      Add Conformer RNN-T model prototype (#2322) · b0c8e239
      hwangjeff authored 
      Summary:
Adds Conformer RNN-T model as prototype feature, by way of factory functions `conformer_rnnt_model` and `conformer_rnnt_base`, which instantiates a baseline version of the model. Also includes the following:
- Modifies `Conformer` to accept arguments `use_group_norm` and `convolution_first` to pass to each of its `ConformerLayer` instances.
- Makes `_Predictor` an abstract class and introduces `_EmformerEncoder` and `_ConformerEncoder`.
- Introduces tests for `conformer_rnnt_model`.
- Adds docs.

Pull Request resolved: https://github.com/pytorch/audio/pull/2322

Reviewed By: xiaohui-zhang

Differential Revision: D35565987

Pulled By: hwangjeff

fbshipit-source-id: cb37bb0477ae3d5fcf0b7124f334f4cbb89b5789
      b0c8e239
  9. 08 Apr, 2022 1 commit
    • moto's avatar
      Add devices/properties badges (#2321) · 72ae755a
      moto authored 
      Summary:
Add badges of supported properties and devices to functionals and transforms.

This commit adds `.. devices::` and `.. properties::` directives to sphinx.

APIs with these directives will have badges (based off of shields.io) which link to the
page with description of these features.

Continuation of https://github.com/pytorch/audio/issues/2316
Excluded dtypes for further improvement, and actually added badges to most of functional/transforms.

Pull Request resolved: https://github.com/pytorch/audio/pull/2321

Reviewed By: hwangjeff

Differential Revision: D35489063

Pulled By: mthrok

fbshipit-source-id: f68a70ebb22df29d5e9bd171273bd19007a81762
      72ae755a
  11. 26 Mar, 2022 1 commit
  13. 24 Mar, 2022 1 commit
  15. 26 Feb, 2022 2 commits
    • Zhaoheng Ni's avatar
      Add apply_beamforming to torchaudio.functional (#2232) · 9c56ffb4
      Zhaoheng Ni authored 
      Summary:
This PR adds ``apply_beamforming`` method to ``torchaudio.functional``.
The method employs the beamforming weight to the multi-channel noisy spectrum to obtain the single-channel enhanced spectrum.
The input arguments are the complex-valued beamforming weight Tensor and the multi-channel noisy spectrum.

Pull Request resolved: https://github.com/pytorch/audio/pull/2232

Reviewed By: mthrok

Differential Revision: D34474561

Pulled By: nateanl

fbshipit-source-id: 2910251a8f111e65375dfb50495b6a415113f06d
      9c56ffb4
    • moto's avatar
      Improve device streaming (#2202) · 365313ed
      moto authored 
      Summary:
This commit adds tutorial for device ASR, and update API for device streaming.

The changes for the interface are
1. Add `timeout` and `backoff` parameters to `process_packet` and `stream` methods.
2. Move `fill_buffer` method to private.

When dealing with device stream, there are situations where the device buffer is not
ready and the system returns `EAGAIN`. In such case, the previous implementation of
`process_packet` method raised an exception in Python layer , but for device ASR,
this is inefficient. A better approach is to retry within C++ layer in blocking manner.
The new `timeout` parameter serves this purpose.

Pull Request resolved: https://github.com/pytorch/audio/pull/2202

Reviewed By: nateanl

Differential Revision: D34475829

Pulled By: mthrok

fbshipit-source-id: bb6d0b125d800f87d189db40815af06fbd4cab59
      365313ed
  17. 25 Feb, 2022 5 commits
  19. 16 Feb, 2022 1 commit
    • Zhaoheng Ni's avatar
      Add EMFORMER_RNNT_BASE_MUSTC bundle to torchaudio.prototype (#2241) · 99b5ef5c
      Zhaoheng Ni authored 
      Summary:
This PR provides a RNNTBundle that is pre-trained on the MuST-C release v2.0 dataset.
The model preserves the casing and punctuations of the transcripts when training the SentencePiece model.

Here is the model performance on the dev and test sets of MuST-C 2.0:
|                   |          WER |
|:-----------------:|-------------:|
| dev               |       0.190  |
| tst-COMMON        |       0.213  |
| tst-HE            |       0.186  |

Pull Request resolved: https://github.com/pytorch/audio/pull/2241

Reviewed By: mthrok

Differential Revision: D34267792

Pulled By: nateanl

fbshipit-source-id: 67bca9f277e66d41a4530d01615f249b3cec7167
      99b5ef5c
  21. 04 Feb, 2022 1 commit
  23. 03 Feb, 2022 1 commit
  25. 02 Feb, 2022 1 commit
  27. 01 Feb, 2022 3 commits
  29. 27 Jan, 2022 1 commit
    • Caroline Chen's avatar
      Add no lm support for CTC decoder (#2174) · 4c3fa875
      Caroline Chen authored 
      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
      4c3fa875
  31. 14 Jan, 2022 1 commit
    • moto's avatar
      Tweak documentation (#2152) · 7f859111
      moto authored 
      Summary:
- Change the version of nightly build to `Nightly Build (VERSION)`.
- Use `BUILD_VERSION` env var for release.
- Automatically change copyright year.
- Update the link to nightly in README so that the main branch directs to the corresponding document.

Because of the way CI job is setup, the resulting documentation says 0.8.0. This is fixed by https://github.com/pytorch/audio/issues/2151.

Pull Request resolved: https://github.com/pytorch/audio/pull/2152

Reviewed By: carolineechen, nateanl

Differential Revision: D33585053

Pulled By: mthrok

fbshipit-source-id: 3c2bf9fc3214c89f989f5ac65b74bc1e276a7161
      7f859111
  33. 06 Jan, 2022 1 commit
  35. 04 Jan, 2022 1 commit
  37. 31 Dec, 2021 1 commit
  39. 30 Dec, 2021 1 commit
  41. 29 Dec, 2021 3 commits
  43. 28 Dec, 2021 4 commits
  45. 23 Dec, 2021 3 commits
  47. 24 Nov, 2021 1 commit
    • hwangjeff's avatar
      Add RNN-T beam search decoder (#2028) · 60a85b50
      hwangjeff authored 
      Summary:
Adds beam search decoder for RNN-T implementation ``torchaudio.prototype.RNNT`` that is TorchScript-able and supports both streaming and non-streaming inference.

Pull Request resolved: https://github.com/pytorch/audio/pull/2028

Reviewed By: mthrok

Differential Revision: D32627919

Pulled By: hwangjeff

fbshipit-source-id: aab99e346d6514a3207a9fb69d4b42978b4cdbbd
      60a85b50
  49. 23 Nov, 2021 1 commit
  51. 18 Nov, 2021 1 commit
    • hwangjeff's avatar
      Add Emformer RNN-T model (#2003) · 78ce7010
      hwangjeff authored 
      Summary:
Adds streaming-capable recurrent neural network transducer (RNN-T) model that uses Emformer for its transcription network. Includes two factory functions — one that allows for building a custom model, and one that builds a preconfigured base model.

Pull Request resolved: https://github.com/pytorch/audio/pull/2003

Reviewed By: nateanl

Differential Revision: D32440879

Pulled By: hwangjeff

fbshipit-source-id: 601cb1de368427f25e3b7d120e185960595d2360
      78ce7010