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  37. 07 Sep, 2022 1 commit
    • moto's avatar
      Tweak documentation (#2656) · 8a0d7b36
      moto authored 
      Summary:
1. Override class `__module__` attribute in `conf.py` so that no manual override is necessary
2. Fix SourceSeparationBundle member attribute

Pull Request resolved: https://github.com/pytorch/audio/pull/2656

Reviewed By: carolineechen

Differential Revision: D39293053

Pulled By: mthrok

fbshipit-source-id: 2b8d6be1aee517d0e692043c26ac2438a787adc6
      8a0d7b36
  39. 24 Aug, 2022 1 commit
    • moto's avatar
      Add StreamWriter (#2628) · 72404de9
      moto authored 
      Summary:
This commit adds FFmpeg-based encoder StreamWriter class.
StreamWriter is pretty much the opposite of StreamReader class, and
it supports;

* Encoding audio / still image / video
* Exporting to local file / streaming protocol / devices etc...
* File-like object support (in later commit)
* HW video encoding (in later commit)

See also: https://fburl.com/gslide/z85kn5a9 (Meta internal)

Pull Request resolved: https://github.com/pytorch/audio/pull/2628

Reviewed By: nateanl

Differential Revision: D38816650

Pulled By: mthrok

fbshipit-source-id: a9343b0d55755e186971dc96fb86eb52daa003c8
      72404de9
  41. 22 Aug, 2022 1 commit
  43. 18 Aug, 2022 2 commits
  45. 15 Aug, 2022 2 commits
  47. 11 Aug, 2022 1 commit
  49. 05 Aug, 2022 1 commit
    • hwangjeff's avatar
      Add convolution operator (#2602) · b396157d
      hwangjeff authored 
      Summary:
Adds functions `convolve` and `fftconvolve`, which compute the convolution of two tensors along their trailing dimension. The former performs the convolution directly, whereas the latter performs it using FFT.

Pull Request resolved: https://github.com/pytorch/audio/pull/2602

Reviewed By: nateanl, mthrok

Differential Revision: D38450771

Pulled By: hwangjeff

fbshipit-source-id: b2d1e063ba21eafeddf317d60749e7120b14292b
      b396157d
  51. 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
  53. 29 Jul, 2022 1 commit
    • Zhaoheng Ni's avatar
      Improve speech enhancement tutorial (#2527) · d6267031
      Zhaoheng Ni authored 
      Summary:
- The "speech + noise" mixture still has a high SNR, which can't show the effectiveness of MVDR beamforming. To make the task more challenging, amplify the noise waveform to reduce the SNR of mixture speech.
- Show the Si-SNR score of mixture speech when visualizing the mixture spectrogram.
- FIx the figure in `rtf_power` subsection.
    - The description of enhanced spectrogram by `rtf_power` is wrong. Correct it to `rtf_power`.
- Print PESQ, STOI, and SDR metric scores.

Pull Request resolved: https://github.com/pytorch/audio/pull/2527

Reviewed By: mthrok

Differential Revision: D38190218

Pulled By: nateanl

fbshipit-source-id: 39562850a67f58a16e0a2866ed95f78c3f4dc7de
      d6267031