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  1. 03 Aug, 2022 2 commits
    • Sean Kim's avatar
      Add HDEMUCS_HIGH_MUSDB (#2601) · 6ecc11c2
      Sean Kim authored 
      Summary:
Add new model pretrained weights and tests

Pull Request resolved: https://github.com/pytorch/audio/pull/2601

Reviewed By: carolineechen, nateanl

Differential Revision: D38396673

Pulled By: skim0514

fbshipit-source-id: e06f97d28508543bc18e671344386a947bc870c1
      6ecc11c2
    • bshall's avatar
      An implemenation of the ITU-R BS.1770-4 loudness recommendation (#2472) · 946b180a
      bshall authored 
      Summary:
I took a stab at implementing the ITU-R BS.1770-4 loudness recommendation (closes https://github.com/pytorch/audio/issues/1205). To give some more details:
- I've implemented K-weighting following csteinmetz1 instead of BrechtDeMan since it fit well with torchaudio's already implemented filters (`treble_biquad` and `highpass_biquad`).
- I've added four audio files to test compliance with the recommendation. These are linked in [this pdf](https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-BS.2217-2-2016-PDF-E.pdf). There are many more test files there but I didn't want to bog down the assets directory with too many files. Let me know if I should add or remove anything.
- I've kept many of the constant internal to the function (e.g. the block duration, overlap, and the absolute threshold gamma). I'm not sure if these should be exposed in the signature.
- I've implemented support for up to 5 channels (following both csteinmetz1 and BrechtDeMan). The recommendation includes weights for up to 24 channels. Is there any convention for how many channels to support?

I hope this is helpful! looking forward to hearing from you.

Pull Request resolved: https://github.com/pytorch/audio/pull/2472

Reviewed By: hwangjeff

Differential Revision: D38389155

Pulled By: carolineechen

fbshipit-source-id: fcc86d864c04ab2bedaa9acd941ebc4478ca6904
      946b180a
  3. 28 Jul, 2022 2 commits
  5. 26 Jul, 2022 1 commit
  7. 25 Jul, 2022 1 commit
  9. 22 Jul, 2022 1 commit
    • Zhaoheng Ni's avatar
      Add documents for SourceSeparationBundle (#2559) · 6cee56ab
      Zhaoheng Ni authored 
      Summary:
- Add documentation page for `SourceSeparationBundle` and `CONVTASNET_BASE_LIBRI2MIX`.
- Add citation of Libri2Mix dataset in the bundle documentation.
- url in integration test should use slash instead of `os.path.join` as it will fail on Windows. Change it to f-string.

Pull Request resolved: https://github.com/pytorch/audio/pull/2559

Reviewed By: carolineechen

Differential Revision: D38036116

Pulled By: nateanl

fbshipit-source-id: 736732805191113955badfec3955e2e24e8f4836
      6cee56ab
  11. 21 Jul, 2022 1 commit
    • Zhaoheng Ni's avatar
      Add SourceSeparationBundle to prototype (#2440) · 83362580
      Zhaoheng Ni authored 
      Summary:
- Add SourceSeparationBundle class for source separation pipeline
- Add `CONVTASNET_BASE_LIBRI2MIX` that is trained on Libri2Mix dataset.
- Add integration test with example mixture audio and expected scale-invariant signal-to-distortion ratio (Si-SDR) score. The test computes the Si-SDR score with permutation-invariant training (PIT) criterion for all permutations of sources and use the highest value as the final output. The test verifies if the score is equal to or larger than the expected value.

Pull Request resolved: https://github.com/pytorch/audio/pull/2440

Reviewed By: mthrok

Differential Revision: D37997646

Pulled By: nateanl

fbshipit-source-id: c951bcbbe8b7ed9553cb8793d6dc1ef90d5a29fe
      83362580
  13. 19 Jul, 2022 1 commit
  15. 12 Jul, 2022 1 commit
  17. 07 Jul, 2022 1 commit
  19. 06 Jul, 2022 1 commit
    • Caroline Chen's avatar
      Fix fluent test for windows (#2510) · 09daa438
      Caroline Chen authored 
      Summary:
fluent dataset test currently fails on windows, due to new line generation in csv writer in testing and incorrect path parsing in dataset impl.

Pull Request resolved: https://github.com/pytorch/audio/pull/2510

Reviewed By: carolineechen

Differential Revision: D37573203

Pulled By: mthrok

fbshipit-source-id: 4868bc649690c7e596b002686c6128ce735d3564
      09daa438
  21. 28 Jun, 2022 1 commit
    • moto's avatar
      Refactor AVDictionary clean up (#2507) · 0ad03adf
      moto authored 
      Summary:
Small clean up in ffmpeg binding code.

1. Make `get_option_dict` and `clean_up_dict` public utility
2. Merge the exception into `clean_up_dict`
3. Get rid of custom string join function and use `c10::Join`.

Pull Request resolved: https://github.com/pytorch/audio/pull/2507

Reviewed By: hwangjeff

Differential Revision: D37466022

Pulled By: mthrok

fbshipit-source-id: 44b769ac6ff1ab20e6d6ae086cd1447deacb5969
      0ad03adf
  23. 27 Jun, 2022 4 commits
  25. 23 Jun, 2022 1 commit
  27. 21 Jun, 2022 1 commit
    • Sean Kim's avatar
      Create musdb handler and tests (#2484) · b92a8a09
      Sean Kim authored 
      Summary:
Create dataset handler and tests for new dataset. Manually tested and unit tested to test validity. Pre-commit ran for style checks.

Pull Request resolved: https://github.com/pytorch/audio/pull/2484

Reviewed By: carolineechen, nateanl

Differential Revision: D37250556

Pulled By: skim0514

fbshipit-source-id: d2c8d73d22fd9d7282026265676f3eab1e178d51
      b92a8a09
  29. 20 Jun, 2022 1 commit
  31. 13 Jun, 2022 1 commit
  33. 10 Jun, 2022 1 commit
  35. 08 Jun, 2022 2 commits
  37. 07 Jun, 2022 1 commit
  39. 04 Jun, 2022 1 commit
    • moto's avatar
      Make FFmpeg log level configurable (#2439) · 877a88c5
      moto authored 
      Summary:
Undesired logs are one of the loudest UX complains we get.
Yet, loading media files involves uncertainty which is
difficult to debug without debug log.

This commit introduces utility functions to configure logging level
so that we can ask users to enable it when they encounter an issue,
while defaulting to non-verbose option.

Pull Request resolved: https://github.com/pytorch/audio/pull/2439

Reviewed By: hwangjeff, xiaohui-zhang

Differential Revision: D36903763

Pulled By: mthrok

fbshipit-source-id: f4ddd9915b13197c2a2eb97e965005b8b5b8d987
      877a88c5
  41. 03 Jun, 2022 1 commit
  43. 02 Jun, 2022 3 commits
  45. 01 Jun, 2022 3 commits
  47. 31 May, 2022 1 commit
  49. 29 May, 2022 1 commit
    • moto's avatar
      Update source info (#2418) · bb77cbeb
      moto authored 
      Summary:
Add num_frames and bits_per_sample to match with the current
`torchaudio.info` capability.

Pull Request resolved: https://github.com/pytorch/audio/pull/2418

Reviewed By: carolineechen

Differential Revision: D36749077

Pulled By: mthrok

fbshipit-source-id: 7b368ee993cf5ed63ff2f53c9e3b1f50fcce7713
      bb77cbeb
  51. 23 May, 2022 2 commits
    • Zhaoheng Ni's avatar
      Add assertion checks to multi-channel functions (#2401) · 38e530d7
      Zhaoheng Ni authored 
      Summary:
- The multi-channel functions only support complex-valued tensors for spectrogram and PSD matrices.
- The mask can be real-valued or complex-valued, hence there is no explicit assertion for mask.
- The shape of input Tensors need to be verified before the computation. For example, the shape of PSD matrix must be `(..., freq, channel, channel)`, the shape of the mask must be `(..., freq, time)`, etc.
- The autograd unittest of `apply_beamforming` has wrong dimensions for beamform_weights detected by the assertion check. FIx it in this PR.

Pull Request resolved: https://github.com/pytorch/audio/pull/2401

Reviewed By: carolineechen

Differential Revision: D36597689

Pulled By: nateanl

fbshipit-source-id: 6ad1adebe3726851cc1d865650bdf177a98985f6
      38e530d7
    • Zhaoheng Ni's avatar
      Add LibriLightLimited dataset (#2302) · af9cab3b
      Zhaoheng Ni authored 
      Summary:
The `LibriLightLimited` dataset is created for fine-tuning SSL models, such as Wav2Vec2 and HuBERT. It is a supervised subset of [Libri-Light](https://github.com/facebookresearch/libri-light) dataset. To distinguish the unsupervised subset and the supervised one, it's clearer to put it in a separate dataset class for fine-tuning purpose.
It contains "10 min", "1 hour", "10 hour" splits.

Pull Request resolved: https://github.com/pytorch/audio/pull/2302

Reviewed By: mthrok

Differential Revision: D36388188

Pulled By: nateanl

fbshipit-source-id: ba49f1c9996be17db5db41127d8ca96224c94249
      af9cab3b
  53. 21 May, 2022 1 commit
    • moto's avatar
      Add file-like object support to Streaming API (#2400) · a984872d
      moto authored 
      Summary:
This commit adds file-like object support to Streaming API.

## Features
- File-like objects are expected to implement `read(self, n)`.
- Additionally `seek(self, offset, whence)` is used if available.
- Without `seek` method, some formats cannot be decoded properly.
  - To work around this, one can use the existing `decoder` option to tell what decoder it should use.
  - The set of `decoder` and `decoder_option` arguments were added to `add_basic_[audio|video]_stream` method, similar to `add_[audio|video]_stream`.
  - So as to have the arguments common to both audio and video in front of the rest of the arguments, the order of the arguments are changed.
  - Also `dtype` and `format` arguments were changed to make them consistent across audio/video methods.

## Code structure

The approach is very similar to how file-like object is supported in sox-based I/O.
In Streaming API if the input src is string, it is passed to the implementation bound with TorchBind,
if the src has `read` attribute, it is passed to the same implementation bound via PyBind 11.

![Untitled drawing](https://user-images.githubusercontent.com/855818/169098391-6116afee-7b29-460d-b50d-1037bb8a359d.png)

## Refactoring involved
- Extracted to https://github.com/pytorch/audio/issues/2402
  - Some implementation in the original TorchBind surface layer is converted to Wrapper class so that they can be re-used from PyBind11 bindings. The wrapper class serves to simplify the binding.
  - `add_basic_[audio|video]_stream` methods were removed from C++ layer as it was just constructing string and passing it to `add_[audio|video]_stream` method, which is simpler to do in Python.
  - The original core Streamer implementation kept the use of types in `c10` namespace minimum. All the `c10::optional` and `c10::Dict` were converted to the equivalents of `std` at binding layer. But since they work fine with PyBind11, Streamer core methods deal them directly.

## TODO:
- [x] Check if it is possible to stream MP4 (yuv420p) from S3 and directly decode (with/without HW decoding).

Pull Request resolved: https://github.com/pytorch/audio/pull/2400

Reviewed By: carolineechen

Differential Revision: D36520073

Pulled By: mthrok

fbshipit-source-id: a11d981bbe99b1ff0cc356e46264ac8e76614bc6
      a984872d
  55. 20 May, 2022 1 commit
  57. 19 May, 2022 1 commit
    • moto's avatar
      Refactor Streamer implementation (#2402) · eed57534
      moto authored 
      Summary:
* Move the helper wrapping code in TorchBind layer to proper wrapper class for so that it will be re-used in PyBind11.
* Move `add_basic_[audio|video]_stream` methods from C++ to Python, as they are just string manipulation. This will make PyBind11-based binding simpler as it needs not to deal with dtype.
* Move `add_[audio|video]_stream` wrapper signature to Streamer core, so that Streamer directly deals with `c10::optional`.†

† Related to this, there is a slight change in how the empty filter expression is stored. Originally, if an empty filter expression was given to `add_[audio|video]_stream` method, the `StreamReaderOutputStream` was showing it as empty string `""`, even though internally it was using `"anull"` or `"null"`. Now `StreamReaderOutputStream` shows the corresponding filter expression that is actually being used.

Ref https://github.com/pytorch/audio/issues/2400

Pull Request resolved: https://github.com/pytorch/audio/pull/2402

Reviewed By: nateanl

Differential Revision: D36488808

Pulled By: mthrok

fbshipit-source-id: 877ca731364d10fc0cb9d97e75d55df9180f2047
      eed57534