Update torchaudio requirement from <=0.7.2 to <0.13.1

[dependabot]

Updates the requirements on torchaudio to permit the latest version.

Release notes

Sourced from torchaudio's releases.

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:

• LIBRISPEECH (docs)
• LibriMix (docs)
• QUESST14 (docs)
• SPEECHCOMMANDS (docs)
• (new) FluentSpeechCommands (docs)
• (new) Snips (docs)
• (new) IEMOCAP (docs)
• (new) VoxCeleb1 (Identification, Verification)

[Beta] Custom Language Model support in CTC Beam Search Decoding

In release 0.12, TorchAudio released a CTC beam search decoder with KenLM language model support. This release, there is added functionality for creating custom Python language models that are compatible with the decoder, using the torchaudio.models.decoder.CTCDecoderLM wrapper.

[Beta] StreamWriter

torchaudio.io.StreamWriter is a class for encoding media including audio and video. This can handle a wide variety of codecs, chunk-by-chunk encoding and GPU encoding.

Backward-incompatible changes

• [BC-breaking] Fix momentum in transforms.GriffinLim (#2568)

... (truncated)

Commits

• bc8640b Fix doc in torchaudio.backend (#2781)
• 1b444d8 Add iemocap variants (#2778)
• ee68a98 Update download path for speechcommands (#2777)
• 3b1d85d Add notes on file structure in Voxceleb1 based datasets (#2776)
• 9a013fd Add file_name to the returned item in Snips dataset (#2775)
• 88a8dd4 Update description of HDemucs pipelines (#2774)
• b703a63 Update resampling tutorial (#2773)
• fc6090e Fix leaking matplotlib figure (#2771)
• 55c695b Fix fading in hybrid demucs tutorial (#2769)
• bd37611 Fix CTCDecoder doc (#2766)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #526.