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Package Health Score

69 / 100

security
Security review needed
popularity
Recognized
maintenance
Sustainable
community
Sustainable

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Popularity

Recognized

GitHub Stars: 2.16K
Forks: 478
Contributors: 50

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package s3prl, we found that it has been starred 2,157 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Mar 18, 2024
Direct Vulnerabilities: 0
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0
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Indirect Vulnerabilities: 0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 50
Open PR: 3
Last Release: 4 months ago
Last Commit: 1 month ago

Further analysis of the maintenance status of s3prl based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that s3prl demonstrates a positive version release cadence with at least one new version released in the past 12 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 50
Funding: No

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Package

Python Versions Compatibility: >=3.6, <4

Age: 3 years
Latest Release: 4 months ago
Dependencies: 3 Direct / 30 Total
Versions: 24
Maintainers: 1
Wheels: No

Readme

Contact

We prefer to have discussions directly on Github issue page, so that all the information is transparent to all the contributors and is auto-archived on the Github. If you wish to use email, please contact:

Leo Yang (leo19941227@gmail.com)
Andy T. Liu (liuandyt@gmail.com)

Please refer to the legacy citation of S3PRL and the timeline below justifying our ownership on this project. This information is only used to protect us from half-truths. We encourage to cite individual papers most related to the function you are using to give fair credit to the developer of the function. Please find the names in the Change Log. Finally, we thank our advisor Prof. Hung-yi Lee for his advice and support.

If you have any question (e.g., about who developed which functions or how the project started), feel free to engage in an open and responsible conversation on the GitHub issue page, and we'll be happy to help!

Star History

Contribution (pull request)

Guidelines:

If you want to add new features (e.g. new upstream), please discuss with us on the Github issue page first before implementing the actual pull request. So we can discuss about whether we want the feauture, how to achieve the feature, and whether the feature is maintainable. (As S3PRL is growing too large, we might not consider new tasks or complex new features.) Thanks!
Reporting bugs or the PR fixing the bugs is always welcome!

Steps:

Create a personal fork of the main S3PRL repository in GitHub.
Make your changes in a named branch different from master, e.g. you create a branch new-awesome-feature.
Contact us if you have any questions during development.
Generate a pull request through the Web interface of GitHub.
Please verify that your code is free of basic mistakes, we appreciate your contribution!

Environment compatibilities

We support the following environments. The test cases are ran with tox locally and on github action:

Env	versions
os	`ubuntu-18.04`, `ubuntu-20.04`
python	`3.7`, `3.8`, `3.9`, `3.10`
pytorch	`1.8.1`, `1.9.1`, `1.10.2`, `1.11.0`, `1.12.1` , `1.13.1` , `2.0.1` , `2.1.0`

Change Log

Dec 2023: Support Multi-resolution HuBERT (MR-HuBERT, see Multiresolution HuBERT)
Oct 2023: Support ESPnet pre-trained upstream models (see ESPnet HuBERT and WavLabLM)
Sep 2022: We upgrade the codebase to support testing, documentation and a new S3PRL PyPI package for easy installation and usage for upstream models. See our online doc for more information.
Mar 2022: Introduce SUPERB-SG, see Speech Translation by Hsiang-Sheng Tsai (NTU), Out-of-domain ASR by Heng-Jui Chang (NTU), Voice Conversion by Wen-Chin Huang (Nagoya), Speech Separation and Speech Enhancement by Zili Huang (JHU) for more info.
Nov 2021: Introduce S3PRL-VC by Wen-Chin Huang (Nagoya), see Any-to-one for more info. We highly recommend to consider the newly released official repo of S3PRL-VC which is developed and actively maintained by Wen-Chin Huang. The standalone repo contains much more recepies for the VC experiments. In S3PRL we only include the Any-to-one recipe for reproducing the SUPERB results.
Oct 2021: Support DistilHuBERT by Heng-Jui Chang (NTU), see docs for more info
Sep 2021: We host a challenge in AAAI workshop: The 2nd Self-supervised Learning for Audio and Speech Processing! See SUPERB official site for the challenge details and the SUPERB documentation in this toolkit!
Aug 2021: Andy T. Liu (NTU) and Shu-wen Yang (NTU) introduces the S3PRL toolkit in MLSS 2021, you can also watch it on Youtube!
July 2021: We are now working on packaging s3prl and reorganizing the file structure in v0.3. Please consider using the stable v0.2.0 for now. We will test and release v0.3 before August.
June 2021: Support SUPERB: Speech processing Universal PERformance Benchmark, submitted to Interspeech 2021. Use the tag superb-interspeech2021 or v0.2.0. The individual task contributors include: Shu-wen Yang (NTU), Pohan Chi (NTU), Yist Lin (NTU), Yung-Sung Chuang (NTU), Jiatong Shi (CMU), Xuankai Chang (CMU), Wei-Cheng Tseng (NTU) and Tzu-Hsien Huang (NTU).
June 2021: Support extracting multiple hidden states for all the SSL pretrained models by Shu-wen Yang (NTU).
Jan 2021: Readme updated with detailed instructions on how to use our latest version!
Dec 2020: We are migrating to a newer version for a more general, flexible, and scalable code. See the introduction below for more information! The legacy version can be accessed the tag v0.1.0.
Oct 2020: Shu-wen Yang (NTU) and Andy T. Liu (NTU) added varioius classic upstream models, including PASE+, APC, VQ-APC, NPC, wav2vec, vq-wav2vec ...etc.
Oct 2019: Andy T. Liu (NTU), Shu-wen Yang (NTU) and Pohan Chi (NTU) implemented the Mockingjay pre-training and several simple downstream evaluation tasks. This work was the very start of the S3PRL project which established lots of foundamental modules and coding styles. Feel free to checkout to the oldest commits to explore our legacy codebase!

Introduction and Usages

This is an open source toolkit called s3prl, which stands for Self-Supervised Speech Pre-training and Representation Learning. Self-supervised speech pre-trained models are called upstream in this toolkit, and are utilized in various downstream tasks.

The toolkit has three major usages:

Pretrain

Pretrain upstream models, including Mockingjay, Audio ALBERT and TERA.
Document: pretrain/README.md

Upstream

Easily load most of the existing upstream models with pretrained weights in a unified I/O interface.
Pretrained models are registered through torch.hub, which means you can use these models in your own project by one-line plug-and-play without depending on this toolkit's coding style.
Document: upstream/README.md

Downstream

Utilize upstream models in lots of downstream tasks
Benchmark upstream models with SUPERB Benchmark
Document: downstream/README.md

Below is an intuitive illustration on how this toolkit may help you:

Feel free to use or modify our toolkit in your research. Here is a list of papers using our toolkit. Any question, bug report or improvement suggestion is welcome through opening up a new issue.

If you find this toolkit helpful to your research, please do consider citing our papers, thanks!

Installation

Python >= 3.6
Install sox on your OS
Install s3prl: Read doc or pip install -e ".[all]"
(Optional) Some upstream models require special dependencies. If you encounter error with a specific upstream model, you can look into the README.md under each upstream folder. E.g., upstream/pase/README.md=

Reference Repositories

Pytorch, Pytorch.
Audio, Pytorch.
Kaldi, Kaldi-ASR.
Transformers, Hugging Face.
PyTorch-Kaldi, Mirco Ravanelli.
fairseq, Facebook AI Research.
CPC, Facebook AI Research.
APC, Yu-An Chung.
VQ-APC, Yu-An Chung.
NPC, Alexander-H-Liu.
End-to-end-ASR-Pytorch, Alexander-H-Liu
Mockingjay, Andy T. Liu.
ESPnet, Shinji Watanabe
speech-representations, aws lab
PASE, Santiago Pascual and Mirco Ravanelli
LibriMix, Joris Cosentino and Manuel Pariente

License

The majority of S3PRL Toolkit is licensed under the Apache License version 2.0, however all the files authored by Facebook, Inc. (which have explicit copyright statement on the top) are licensed under CC-BY-NC.

Used by

List of papers that used our toolkit (Feel free to add your own paper by making a pull request)

Self-Supervised Pretraining

Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders (Liu et al., 2020)

@article{mockingjay,
   title={Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders},
   ISBN={9781509066315},
   url={http://dx.doi.org/10.1109/ICASSP40776.2020.9054458},
   DOI={10.1109/icassp40776.2020.9054458},
   journal={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
   publisher={IEEE},
   author={Liu, Andy T. and Yang, Shu-wen and Chi, Po-Han and Hsu, Po-chun and Lee, Hung-yi},
   year={2020},
   month={May}
}

TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech (Liu et al., 2020)

@misc{tera,
    title={TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech},
    author={Andy T. Liu and Shang-Wen Li and Hung-yi Lee},
    year={2020},
    eprint={2007.06028},
    archivePrefix={arXiv},
    primaryClass={eess.AS}
}

Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation (Chi et al., 2020)

@inproceedings{audio_albert,
    title={Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation},
    author={Po-Han Chi and Pei-Hung Chung and Tsung-Han Wu and Chun-Cheng Hsieh and Shang-Wen Li and Hung-yi Lee},
    year={2020},
    booktitle={SLT 2020},
}

Explanability

Understanding Self-Attention of Self-Supervised Audio Transformers (Yang et al., 2020)

@inproceedings{understanding_sat,
    author={Shu-wen Yang and Andy T. Liu and Hung-yi Lee},
    title={{Understanding Self-Attention of Self-Supervised Audio Transformers}},
    year=2020,
    booktitle={Proc. Interspeech 2020},
    pages={3785--3789},
    doi={10.21437/Interspeech.2020-2231},
    url={http://dx.doi.org/10.21437/Interspeech.2020-2231}
}

Adversarial Attack

Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning (Wu et al., 2020), code for computing LNSR: utility/observe_lnsr.py

@inproceedings{mockingjay_defense,
    author={Haibin Wu and Andy T. Liu and Hung-yi Lee},
    title={{Defense for Black-Box Attacks on Anti-Spoofing Models by Self-Supervised Learning}},
    year=2020,
    booktitle={Proc. Interspeech 2020},
    pages={3780--3784},
    doi={10.21437/Interspeech.2020-2026},
    url={http://dx.doi.org/10.21437/Interspeech.2020-2026}
}

Adversarial Defense for Automatic Speaker Verification by Cascaded Self-Supervised Learning Models (Wu et al., 2021)

@misc{asv_ssl,
    title={Adversarial defense for automatic speaker verification by cascaded self-supervised learning models},
    author={Haibin Wu and Xu Li and Andy T. Liu and Zhiyong Wu and Helen Meng and Hung-yi Lee},
    year={2021},
    eprint={2102.07047},
    archivePrefix={arXiv},
    primaryClass={eess.AS}

Voice Conversion

S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations (Lin et al., 2021)

@misc{s2vc,
      title={S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations},
      author={Jheng-hao Lin and Yist Y. Lin and Chung-Ming Chien and Hung-yi Lee},
      year={2021},
      eprint={2104.02901},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}

Benchmark and Evaluation

SUPERB: Speech processing Universal PERformance Benchmark (Yang et al., 2021)

@misc{superb,
      title={SUPERB: Speech processing Universal PERformance Benchmark},
      author={Shu-wen Yang and Po-Han Chi and Yung-Sung Chuang and Cheng-I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan-Ting Lin and Tzu-Hsien Huang and Wei-Cheng Tseng and Ko-tik Lee and Da-Rong Liu and Zili Huang and Shuyan Dong and Shang-Wen Li and Shinji Watanabe and Abdelrahman Mohamed and Hung-yi Lee},
      year={2021},
      eprint={2105.01051},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Utilizing Self-supervised Representations for MOS Prediction (Tseng et al., 2021)

@misc{ssr_mos,
    title={Utilizing Self-supervised Representations for MOS Prediction},
    author={Wei-Cheng Tseng and Chien-yu Huang and Wei-Tsung Kao and Yist Y. Lin and Hung-yi Lee},
    year={2021},
    eprint={2104.03017},
    archivePrefix={arXiv},
    primaryClass={eess.AS}
}

}

Citation

If you find this toolkit useful, please consider citing following papers.

If you use our pre-training scripts, or the downstream tasks considered in TERA and Mockingjay, please consider citing the following:

@misc{tera,
  title={TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech},
  author={Andy T. Liu and Shang-Wen Li and Hung-yi Lee},
  year={2020},
  eprint={2007.06028},
  archivePrefix={arXiv},
  primaryClass={eess.AS}
}

@article{mockingjay,
   title={Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders},
   ISBN={9781509066315},
   url={http://dx.doi.org/10.1109/ICASSP40776.2020.9054458},
   DOI={10.1109/icassp40776.2020.9054458},
   journal={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
   publisher={IEEE},
   author={Liu, Andy T. and Yang, Shu-wen and Chi, Po-Han and Hsu, Po-chun and Lee, Hung-yi},
   year={2020},
   month={May}
}

If you use our organized upstream interface and features, or the SUPERB downstream benchmark, please consider citing the following:

@inproceedings{yang21c_interspeech,
  author={Shu-wen Yang and Po-Han Chi and Yung-Sung Chuang and Cheng-I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan-Ting Lin and Tzu-Hsien Huang and Wei-Cheng Tseng and Ko-tik Lee and Da-Rong Liu and Zili Huang and Shuyan Dong and Shang-Wen Li and Shinji Watanabe and Abdelrahman Mohamed and Hung-yi Lee},
  title={{SUPERB: Speech Processing Universal PERformance Benchmark}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={1194--1198},
  doi={10.21437/Interspeech.2021-1775}
}

s3prl dependencies

filelock requests transformers

FAQs

What is s3prl?

Self-Supervised Speech Pre-training and Representation Learning Toolkit. Visit Snyk Advisor to see a full health score report for s3prl, including popularity, security, maintenance & community analysis.

Is s3prl popular?

The python package s3prl receives a total of 4,807 weekly downloads. As such, s3prl popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is s3prl well maintained?

We found indications that s3prl maintenance is sustainable demonstrating some project activity. We saw a total of 50 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is s3prl safe to use?

While scanning the latest version of s3prl, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 25 July-2024, at 17:51 (UTC).

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Star History

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Change Log

Introduction and Usages

Pretrain

Upstream

Downstream

Installation

Reference Repositories

License

Used by

Self-Supervised Pretraining

Explanability

Adversarial Attack

Voice Conversion

Benchmark and Evaluation

Citation

s3prl dependencies

FAQs

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