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  1. 12 Oct, 2022 1 commit
    • Zhaoheng Ni's avatar
      Improve hubert recipe for pre-training and fine-tuning (#2744) · 928248d7
      Zhaoheng Ni authored 
      Summary:
following pr https://github.com/pytorch/audio/issues/2716
- For preprocessing
  - The HuBERT feature takes lots of memory which may not fit some machines. Enable to use a subset of feature for training a k-means model.

- For pre-training
  - Normalize the loss based on the total number of masked frames across all GPUs.
  - Use mixed precision training. fp16 is not well supported in pytorch_lightning.
  - Log accuracies of masked/unmasked frames during training.
  - Clip the gradients with norm `10.0`.

- For ASR fine-tuning
  - Normalize the loss based on the total number of batches across all GPUs, same as in the conformer recipe of TorchAudio.
  - Use mixed precision training.
  - Add "|" after the end of transcription to capture the silence/word termination, same as in fairseq recipe.

- Update the WER results on LibriSpeech dev and test sets.

|                   | WER% (Viterbi)|  WER% (KenLM) |
|:-----------------:|--------------:|--------------:|
| dev-clean         |       10.9    |       4.2     |
| dev-other         |       17.5    |       9.4     |
| test-clean        |       10.9    |       4.4     |
| test-other        |       17.8    |       9.5     |

Pull Request resolved: https://github.com/pytorch/audio/pull/2744

Reviewed By: carolineechen

Differential Revision: D40282322

Pulled By: nateanl

fbshipit-source-id: 4723584c912e70e8970149fe09de005385eaab90
      928248d7
  3. 28 Jul, 2022 1 commit
  5. 23 Jun, 2022 1 commit
  7. 17 Jun, 2022 1 commit
  9. 07 Jun, 2022 1 commit
    • Zhaoheng Ni's avatar
      Add HuBERT fine-tuning recipe (#2352) · ab5edfcd
      Zhaoheng Ni authored 
      Summary:
The PR contains the CTC fine-tuning recipe of HuBERT Base model.
The files include:
- lightning module
- training script
- README and the result table
- evaluation scripts

Pull Request resolved: https://github.com/pytorch/audio/pull/2352

Reviewed By: hwangjeff

Differential Revision: D36915712

Pulled By: nateanl

fbshipit-source-id: 0249635ad5e81a8aa2d228c1d5fe84d78b62a15b
      ab5edfcd
  11. 26 May, 2022 1 commit
  13. 23 May, 2022 1 commit
  15. 15 May, 2022 1 commit
    • John Reese's avatar
      [codemod][usort] apply import merging for fbcode (8 of 11) · d62875cc
      John Reese authored 
      Summary:
Applies new import merging and sorting from µsort v1.0.

When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.

Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.

For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting

Reviewed By: lisroach

Differential Revision: D36402214

fbshipit-source-id: b641bfa9d46242188524d4ae2c44998922a62b4c
      d62875cc
  17. 12 May, 2022 2 commits
    • Zhaoheng Ni's avatar
      Fix CollateFn in HuBERT pre-training recipe (#2296) · 09639680
      Zhaoheng Ni authored 
      Summary:
- When cropping the waveform and corresponding label, we use the formula `torch.div(audio_start - kernel_size * sample_rate, stride * sample_rate, rounding_mode="floor")` to align the audio start and label start indices. However, sometimes the value can be negative, which result in an empty label. The training example will hurt the performance after zero-padding (i.e., the labels are all zero for the input waveform).
This PR fixes the bug by checking if `label_start` is negative, and change it to zero if so.
- If `pad` is True, the `length` should be the length of each waveform instead of the max length. Fix it to make the model ignore the padding component in pre-training.

Pull Request resolved: https://github.com/pytorch/audio/pull/2296

Reviewed By: mthrok

Differential Revision: D36323217

Pulled By: nateanl

fbshipit-source-id: 1ffa71e39bbc0e8dee55c3b829911bc2e785b423
      09639680
    • John Reese's avatar
      [black][codemod] formatting changes from black 22.3.0 · 595dc5d3
      John Reese authored 
      Summary:
Applies the black-fbsource codemod with the new build of pyfmt.

paintitblack

Reviewed By: lisroach

Differential Revision: D36324783

fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc
      595dc5d3
  19. 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
  21. 05 Apr, 2022 1 commit
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