shirayu / whispering

Streaming transcriber with whisper
MIT License
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Bump torchaudio from 0.12.1 to 0.13.1 #70

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Bumps torchaudio from 0.12.1 to 0.13.1.

Release notes

Sourced from torchaudio's releases.

TorchAudio 0.13.1 Release Note

This is a minor release, which is compatible with PyTorch 1.13.1 and includes bug fixes, improvements and documentation updates. There is no new feature added.

Bug Fix

IO

  • Make buffer size configurable in ffmpeg file object operations and set size in backend (#2810)
  • Fix issue with the missing video frame in StreamWriter (#2789)
  • Fix decimal FPS handling StreamWriter (#2831)
  • Fix wrong frame allocation in StreamWriter (#2905)
  • Fix duplicated memory allocation in StreamWriter (#2906)

Model

Recipe

  • Fix issues in HuBERT fine-tuning recipe (#2851)
  • Fix automatic mixed precision in HuBERT pre-training recipe (#2854)

torchaudio 0.13.0 Release Note

Highlights

TorchAudio 0.13.0 release includes:

  • Source separation models and pre-trained bundles (Hybrid Demucs, ConvTasNet)
  • New datasets and metadata mode for the SUPERB benchmark
  • Custom language model support for CTC beam search decoding
  • StreamWriter for audio and video encoding

[Beta] Source Separation Models and Bundles

Hybrid Demucs is a music source separation model that uses both spectrogram and time domain features. It has demonstrated state-of-the-art performance in the Sony Music DeMixing Challenge. (citation: https://arxiv.org/abs/2111.03600)

The TorchAudio v0.13 release includes the following features

  • MUSDB_HQ Dataset, which is used in Hybrid Demucs training (docs)
  • Hybrid Demucs model architecture (docs)
  • Three factory functions suitable for different sample rate ranges
  • Pre-trained pipelines (docs) and tutorial

SDR Results of pre-trained pipelines on MUSDB-HQ test set

Pipeline All Drums Bass Other Vocals
HDEMUCS_HIGH_MUSDB* 6.42 7.76 6.51 4.47 6.93
HDEMUCS_HIGH_MUSDB_PLUS** 9.37 11.38 10.53 7.24 8.32

* Trained on the training data of MUSDB-HQ dataset. ** Trained on both training and test sets of MUSDB-HQ and 150 extra songs from an internal database that were specifically produced for Meta.

Special thanks to @​adefossez for the guidance.

ConvTasNet model architecture was added in TorchAudio 0.7.0. It is the first source separation model that outperforms the oracle ideal ratio mask. In this release, TorchAudio adds the pre-trained pipeline that is trained within TorchAudio on the Libri2Mix dataset. The pipeline achieves 15.6dB SDR improvement and 15.3dB Si-SNR improvement on the Libri2Mix test set.

[Beta] Datasets and Metadata Mode for SUPERB Benchmarks

With the addition of four new audio-related datasets, there is now support for all downstream tasks in version 1 of the SUPERB benchmark. Furthermore, these datasets support metadata mode through a get_metadata function, which enables faster dataset iteration or preprocessing without the need to load or store waveforms.

Datasets with metadata functionality:

... (truncated)

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