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  1. 08 Feb, 2023 1 commit
  3. 07 Feb, 2023 2 commits
  5. 06 Feb, 2023 1 commit
  7. 04 Feb, 2023 2 commits
  9. 03 Feb, 2023 1 commit
    • moto's avatar
      Add Linux GPU unit tests on GHA (#3029) · 6bdd3830
      moto authored 
      Summary:
Add GitHub Action-based GPU test jobs.
- It seems that there is 2 hour upper cap so only running CUDA/GPU tests.
- Since Kaldi related features are not available, they are disabled.

Pull Request resolved: https://github.com/pytorch/audio/pull/3029

Reviewed By: hwangjeff

Differential Revision: D42983800

Pulled By: mthrok

fbshipit-source-id: 47fefe39c635d1c73ad6799ddacefd2666fe5403
      6bdd3830
  11. 02 Feb, 2023 2 commits
  13. 01 Feb, 2023 5 commits
  15. 31 Jan, 2023 1 commit
    • Moto Hira's avatar
      Remove unnecessary AVFrame allocation (#3021) · 0709cadc
      Moto Hira authored 
      Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/3021

When input format and encode format is different in StreamWriter, filter for format conversion is inserted.

A temporary AVFilter (`dst_frame`) is used for this case,
but FilterGraph handles the memory allocation,
so there is no need to perform allocation by ourselves.

This `dst_frame` is otherwise not used, so we do not have to allocate memory at all.
This commit removes the unnecessary memory allocation at all.

Reviewed By: xiaohui-zhang

Differential Revision: D42865042

fbshipit-source-id: 2673b06de1e905dc73a11e2ec1cc6ce7b525d451
      0709cadc
  17. 30 Jan, 2023 2 commits
    • Yan Li's avatar
      Fix hybrid demucs tutorial for CUDA (#3017) · da9d1627
      Yan Li authored 
      Summary:
Currently there will be a few errors when this tutorial is run with a CUDA device.

The reasons being:
- The source audio waveform is not properly moved to the GPU. The `to()` method is not in-place for Tensors, so we need to assign the return value of the method call to the variable (otherwise the Tensor would still be on the CPU).
- When performing further analysis and displaying of the output audio, we need to move them back from the GPU to the CPU. This is because some of the functions we call require the Tensor to be on the CPU (e.g. `stft()` and `bss_eval_sources()`).

Pull Request resolved: https://github.com/pytorch/audio/pull/3017

Reviewed By: mthrok

Differential Revision: D42828526

Pulled By: nateanl

fbshipit-source-id: c28bc855e79e3363a011f4a35a69aae1764e7762
      da9d1627
    • moto's avatar
      Add get_build_config ffmpeg utility function (#3014) · 635d8cff
      moto authored 
      Summary:
We often need to look at which FFmpeg was found and linked when debugging an issue.

Version number is often not enough but there is no easy way to find where the library was found either.

This commit adds utility function that prints the build time configuration.

It helps to distinguish if the linked FFmpeg is the one from binary distribution built in CI or locally built.

Pull Request resolved: https://github.com/pytorch/audio/pull/3014

Reviewed By: hwangjeff

Differential Revision: D42794952

Pulled By: mthrok

fbshipit-source-id: 91ed358fde8cfe9d6d950f34742b1722e729cf4e
      635d8cff
  19. 27 Jan, 2023 3 commits
  21. 26 Jan, 2023 3 commits
  23. 24 Jan, 2023 1 commit
  25. 23 Jan, 2023 3 commits
  27. 22 Jan, 2023 1 commit
    • moto's avatar
      Make StreamReader return PTS (#2975) · 0dd59e0d
      moto authored 
      Summary:
This commit makes `StreamReader` report PTS (presentation time stamp) of the returned chunk as well.

Example

```python
from torchaudio.io import StreamReader

s = StreamReader(...)
s.add_video_stream(...)
for (video_chunk, ) in s.stream():
    # video_chunk is Torch tensor type but has extra attribute of PTS
    print(video_chunk.pts)  # reports the PTS of the first frame of the video chunk.
```

For the backward compatibility, we introduce a `_ChunkTensor`, that is a composition
of Tensor and metadata, but works like a normal tensor in PyTorch operations.

The implementation of `_ChunkTensor` is based on [TrivialTensorViaComposition](https://github.com/albanD/subclass_zoo/blob/0eeb1d68fb59879029c610bc407f2997ae43ba0a/trivial_tensors.py#L83).

It was also suggested to attach metadata directly to Tensor object,
but the possibility to have the collision on torchaudio's metadata and new attributes introduced in
PyTorch cannot be ignored, so we use Tensor subclass implementation.

If any unexpected issue arise from metadata attribute name collision, client code can
fetch the bare Tensor and continue.

Pull Request resolved: https://github.com/pytorch/audio/pull/2975

Reviewed By: hwangjeff

Differential Revision: D42526945

Pulled By: mthrok

fbshipit-source-id: b4e9422e914ff328421b975120460f3001268f35
      0dd59e0d
  29. 20 Jan, 2023 4 commits
  31. 19 Jan, 2023 3 commits
    • Zhaoheng Ni's avatar
      Add modularized SSL training recipe (#2876) · 2eaefe27
      Zhaoheng Ni authored 
      Summary:
TorchAudio currently has one training recipe for HuBET + LibriSpeech pre-training. It may not suit well when users want to use customized dataset, or use a new training objective (such as contrastive loss in Wav2Vec2). The PR addresses the issue by providing a modularized training recipe for audio self-supervised learning. Users can inject customized model module, loss function, optimizer, lr scheduler, and datamodule for training a SSL model.

Pull Request resolved: https://github.com/pytorch/audio/pull/2876

Reviewed By: hwangjeff

Differential Revision: D42617414

Pulled By: nateanl

fbshipit-source-id: 6413df45a9d106ed1d5ff830bf628c54368c5792
      2eaefe27
    • hwangjeff's avatar
      Simplify train step in Conformer RNN-T LibriSpeech recipe (#2981) · c6a52355
      hwangjeff authored 
      Summary:
In the Conformer RNN-T LibriSpeech recipe, there's no need to perform manual optimization. This PR modifies the recipe to use automatic optimization instead.

Pull Request resolved: https://github.com/pytorch/audio/pull/2981

Reviewed By: mthrok

Differential Revision: D42507228

Pulled By: hwangjeff

fbshipit-source-id: 9712add951eba356e39f7e8c8dc3bf584ba48309
      c6a52355
    • hwangjeff's avatar
      Make lengths optional for additive noise operators (#2977) · bb077284
      hwangjeff authored 
      Summary:
For greater flexibility, this PR makes argument `lengths` optional for `add_noise` and `AddNoise`.

Pull Request resolved: https://github.com/pytorch/audio/pull/2977

Reviewed By: nateanl

Differential Revision: D42484211

Pulled By: hwangjeff

fbshipit-source-id: 54757dcc73df194bb98c1d9d42a2f43f3027b190
      bb077284
  33. 17 Jan, 2023 2 commits
    • Moto Hira's avatar
      Fix buffer flushing mechanism · 51731bf9
      Moto Hira authored 
      Summary:
When buffered data are cleared from ChunkedBuffer,
the `num_buffered_frames` variable was not updated.

This commit fixes that.

Reviewed By: xiaohui-zhang

Differential Revision: D42538519

fbshipit-source-id: a24a9afcebebd8956d977f05e9c2f0b603d060d1
      51731bf9
    • Zhaoheng Ni's avatar
      Fix mel spectrogram visualization in TTS tutorial (#2989) · b983c665
      Zhaoheng Ni authored 
      Summary:
The mel spectrograms in the TTS tutorial are upside down. The PR fixes it by using `origin="lower"` in imshow.

Pull Request resolved: https://github.com/pytorch/audio/pull/2989

Reviewed By: mthrok

Differential Revision: D42538349

Pulled By: nateanl

fbshipit-source-id: 4388103a49bdfabf1705c1f979d44ecedd5c910a
      b983c665
  35. 16 Jan, 2023 3 commits
    • moto's avatar
      Refactor buffer common utils (#2988) · e259f156
      moto authored 
      Summary:
Split `convert_video` into memory allocation function and write function.

Also put all the buffer implementations into detail namespace.

Pull Request resolved: https://github.com/pytorch/audio/pull/2988

Reviewed By: xiaohui-zhang

Differential Revision: D42536769

Pulled By: mthrok

fbshipit-source-id: 36fbf437d4bfd521322846161ae08a48c782c540
      e259f156
    • Robin Scheibler's avatar
      Fixes examples/source_separation for WSJ0_2mix dataset (#2987) · f9d38796
      Robin Scheibler authored 
      Summary:
The `examples/source_separation` scripts use inconsistent keyword to indicate the WSJ0_2mix dataset. This PR does the following.

1. Use `wsj0mix` consistently as keyword indicating the WSJ0_2mix dataset
2. Corrects `args.data_dir` to `args.root_dir` in eval.py
3. Modify the parameters of `pytorch_lightning.Trainer` according to latest version (use `accelerator="gpu"` and `devices=args.num_devices`, instead of just `gpus=args.num_devices`)

Pull Request resolved: https://github.com/pytorch/audio/pull/2987

Reviewed By: xiaohui-zhang

Differential Revision: D42536992

Pulled By: nateanl

fbshipit-source-id: 10a80263ad7054b1629d8fa023676b607e633d76
      f9d38796
    • moto's avatar
      Refactor chunked buffer implementation (#2984) · 52b6bc3b
      moto authored 
      Summary:
So that the number of Tensor frames stored in buffers is always a multiple of frames_per_chunk.

This makes it easy to store PTS values in aligned manner.

Pull Request resolved: https://github.com/pytorch/audio/pull/2984

Reviewed By: nateanl

Differential Revision: D42526670

Pulled By: mthrok

fbshipit-source-id: d83ee914b7e50de3b51758069b0e0b6b3ebe2e54
      52b6bc3b