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  1. 12 May, 2022 1 commit
  3. 11 May, 2022 1 commit
    • hwangjeff's avatar
      Refactor LibriSpeech Conformer RNN-T recipe (#2366) · 69467ea5
      hwangjeff authored 
      Summary:
Modifies the example LibriSpeech Conformer RNN-T recipe as follows:
- Moves data loading and transforms logic from lightning module to data module (improves generalizability and reusability of lightning module and data module).
- Moves transforms logic from dataloader collator function to dataset (resolves dataloader multiprocessing issues on certain platforms).
- Replaces lambda functions with `partial` equivalents (resolves pickling issues in certain runtime environments).
- Modifies training script to allow for specifying path model checkpoint to restart training from.

Pull Request resolved: https://github.com/pytorch/audio/pull/2366

Reviewed By: mthrok

Differential Revision: D36305028

Pulled By: hwangjeff

fbshipit-source-id: 0b768da5d5909136c55418bf0a3c2ddd0c5683ba
      69467ea5
  5. 28 Apr, 2022 1 commit
  7. 26 Apr, 2022 1 commit
  9. 22 Apr, 2022 1 commit
    • Zhaoheng Ni's avatar
      Introduce DistributedBatchSampler (#2299) · 6411c9ad
      Zhaoheng Ni authored 
      Summary:
When using customized `batch_sampler`, pytorch_lightning can't wrap the distributed sampler onto it. Hence we provide a `DistributedBatchSampler` that supports `BucketizeBatchSampler` in `ddp` mode.

The `DistributedBatchSampler` assumes `BucketizeBatchSampler.iter_list` is a list of lists, where each sub-list contains a batch of indices. Setting `shuffle` to `True` will shuffle the lists based on `seed` and current `epoch`.

The `shuffle` only happens in the initialization, and won't be changed if user don't reset it. The reason is shuffling `BucketizeBatchSampler` may have a different length than before, do shuffling in ``__iter__`` may result in mismatch between ``__len__`` and the real length value.
Hence users need to set `reload_dataloaders_every_n_epochs=1` in pytorch_lightning's Trainer. Then the value of ``__len__``  and the real length is the same.

Pull Request resolved: https://github.com/pytorch/audio/pull/2299

Reviewed By: hwangjeff

Differential Revision: D35781538

Pulled By: nateanl

fbshipit-source-id: 6e8396615497f1aeddab1ee5678830c0445c2b2a
      6411c9ad
  11. 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
  13. 13 Apr, 2022 2 commits
    • hwangjeff's avatar
      Add Conformer RNN-T LibriSpeech training recipe (#2329) · c262758b
      hwangjeff authored 
      Summary:
Adds Conformer RNN-T LibriSpeech training recipe to examples directory.

Produces 30M-parameter model that achieves the following WER:

|                     |          WER |
|:-------------------:|-------------:|
| test-clean          |       0.0310 |
| test-other          |       0.0805 |
| dev-clean           |       0.0314 |
| dev-other           |       0.0827 |

Pull Request resolved: https://github.com/pytorch/audio/pull/2329

Reviewed By: xiaohui-zhang

Differential Revision: D35578727

Pulled By: hwangjeff

fbshipit-source-id: afa9146c5b647727b8605d104d928110a1d3976d
      c262758b
    • hwangjeff's avatar
      Add nightly build installation code snippet to prototype feature tutorials (#2325) · fb51cecc
      hwangjeff authored 
      Summary:
Tutorial notebooks that leverage TorchAudio prototype features don't run as-is on Google Colab due to its runtime's not having nightly builds pre-installed. To make it easier for users to run said notebooks in Colab, this PR adds a code block that installs nightly Pytorch and TorchAudio builds as a comment that users can copy and run locally.

Pull Request resolved: https://github.com/pytorch/audio/pull/2325

Reviewed By: xiaohui-zhang

Differential Revision: D35597753

Pulled By: hwangjeff

fbshipit-source-id: 59914e492ad72e31c0136a48cd88d697e8ea5f6c
      fb51cecc
  15. 05 Apr, 2022 1 commit
  17. 04 Apr, 2022 2 commits
  19. 01 Apr, 2022 1 commit
  21. 25 Mar, 2022 1 commit
  23. 24 Mar, 2022 2 commits
  25. 22 Mar, 2022 1 commit
    • Hagen Wierstorf's avatar
      Fix calculation of SNR value in tutorial (#2285) · 8395fe65
      Hagen Wierstorf authored 
      Summary:
The calculation of the SNR in tha data augmentation examples seems to be wrong to me:

![image](https://user-images.githubusercontent.com/173624/159487032-c60470c6-ef8e-48a0-ad5e-a117fcb8d606.png)

If we start from the definition of the signal-to-noise ratio using the root mean square value we get:

```
SNR = 20 log10 ( rms(scale * speech) / rms(noise) )
```
this can be transformed to
```
scale = 10^(SNR/20) rms(noise) / rms(speech)
```
In the example not `rms` is used but `lambda x: x.norm(p=2)`, but as we have the same length of the speech and noise signal, we have
```
rms(noise) / rms(speech) = noise.norm(p=2) / speech.norm(p=2)
```
this would lead us to:
```
10^(SNR/20) = e^(SNR / 10)
```
which is not true.

Hence I changed `e^(SNR / 10)` to `10^(SNR/20)`.

For the proposed SNR values of 20 dB, 10 dB, 3 dB the value of the scale would change from 7.39, 2.72, 1.35 to 10.0, 3.16, 1.41.

Pull Request resolved: https://github.com/pytorch/audio/pull/2285

Reviewed By: nateanl

Differential Revision: D35047737

Pulled By: mthrok

fbshipit-source-id: ac24c8fd48ef06b4b611e35163084644330a3ef3
      8395fe65
  27. 17 Mar, 2022 1 commit
  29. 10 Mar, 2022 1 commit
  31. 08 Mar, 2022 1 commit
  33. 26 Feb, 2022 1 commit
    • 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
  35. 24 Feb, 2022 1 commit
  37. 23 Feb, 2022 1 commit
  39. 17 Feb, 2022 2 commits
  41. 16 Feb, 2022 6 commits
  43. 15 Feb, 2022 1 commit
  45. 11 Feb, 2022 5 commits
  47. 10 Feb, 2022 1 commit
  49. 09 Feb, 2022 1 commit
    • hwangjeff's avatar
      Fix librosa calls (#2208) · e5d567c9
      hwangjeff authored 
      Summary:
Yesterday's release of librosa 0.9.0 made args keyword-only and changed default padding from "reflect" to "zero" for some functions. This PR adjusts callsites in our tutorials and tests accordingly.

Pull Request resolved: https://github.com/pytorch/audio/pull/2208

Reviewed By: mthrok

Differential Revision: D34099793

Pulled By: hwangjeff

fbshipit-source-id: 4e2642cdda8aae6d0a928befaf1bbb3873d229bc
      e5d567c9
  51. 04 Feb, 2022 1 commit
  53. 03 Feb, 2022 1 commit