build(deps): bump torchaudio from 0.10.2 to 2.0.2 in /requirements

[dependabot[bot]]

Bumps torchaudio from 0.10.2 to 2.0.2.

Release notes

Sourced from torchaudio's releases.

v2.0.2

TorchAudio 2.0.2 Release Note

This is a minor release, which is compatible with PyTorch 2.0.1 and includes bug fixes, improvements and documentation updates. There is no new feature added.

Bug fix

• #3239 Properly set #samples passed to encoder (#3204)
• #3238 Fix virtual function issue with CTC decoder (#3230)
• #3245 Fix path-like object support in FFmpeg dispatcher (#3243, #3248)
• #3261 Use scaled_dot_product_attention in Wav2vec2/HuBERT's SelfAttention (#3253)
• #3264 Use scaled_dot_product_attention in WavLM attention (#3252, #3265)

Full Changelog: https://github.com/pytorch/audio/compare/v2.0.1...v2.0.2

Torchaudio 2.0 Release Note

Highlights

TorchAudio 2.0 release includes:

• Data augmentation operators, e.g. convolution, additive noise, speed perturbation
• WavLM and XLS-R models and pre-trained pipelines
• Backend dispatcher powering revised info, load, save functions
• Dropped support of Python 3.7
• Added Python 3.11 support

[Beta] Data augmentation operators

The release adds several data augmentation operators under torchaudio.functional and torchaudio.transforms:

• torchaudio.functional.add_noise
• torchaudio.functional.convolve
• torchaudio.functional.deemphasis
• torchaudio.functional.fftconvolve
• torchaudio.functional.preemphasis
• torchaudio.functional.speed
• torchaudio.transforms.AddNoise
• torchaudio.transforms.Convolve
• torchaudio.transforms.Deemphasis
• torchaudio.transforms.FFTConvolve
• torchaudio.transforms.Preemphasis
• torchaudio.transforms.Speed
• torchaudio.transforms.SpeedPerturbation

The operators can be used to synthetically diversify training data to improve the generalizability of downstream models.

For usage details, please refer to the documentation for torchaudio.functional and torchaudio.transforms, and tutorial “Audio Data Augmentation”.

[Beta] WavLM and XLS-R models and pre-trained pipelines

The release adds two self-supervised learning models for speech and audio.

• WavLM that is robust to noise and reverberation.
• XLS-R that is trained on cross-lingual datasets.

Besides the model architectures, torchaudio also supports corresponding pre-trained pipelines:

• torchaudio.pipelines.WAVLM_BASE

... (truncated)

Commits

• 31de77d Add older mkl build contraint only (#3302) (#3308)
• bc77e2b Add mkl dependency to torchaudio MacOS x86 builds (#3300) (#3301)
• 701239f Enhance UX on TorchAudio pages to improve awareness of doc versioning (#3167)...
• f5a3a3d Remove temp channel for python 3.11, simplify logic around cuda (#3250) (#3274)
• b114e52 [Rlease only change] Advance version for release 2.0.2 (#3273)
• 54f6c1f [Cherry-pick] Use scaled_dot_product_attention in WavLM attention (#3252, #32...
• e99de15 Use scaled_dot_product_attention in Wav2vec2/HuBERT's SelfAttention (#3253) (...
• d92216d [Cherry-pick] Fix path-like object support in FFmpeg dispatcher (#3243, #3248...
• a4ea69e [Cherry-pick] Fix virtual function issue with CTC decoder (#3230) (#3238)
• 9df28ff [Cherry-pick] Properly set #samples passed to encoder (#3204) (#3239)
• Additional commits viewable in compare view

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[codecov[bot]]

Codecov Report

Merging #1660 (01bfd64) into master (fc6c97a) will decrease coverage by 11%. The diff coverage is n/a.

Additional details and impacted files

@@           Coverage Diff            @@
##           master   #1660     +/-   ##
========================================
- Coverage      85%     75%    -11%     
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  Files         291     291             
  Lines       12856   12856             
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- Hits        10985    9610   -1375     
- Misses       1871    3246   +1375