Repository for the paper Evaluating Self-Supervised Speech Representations for Speech Emotion Recognition

What's New

• Nov 2021: Introduce S3PRL-VC, see Any-to-one and Any-to-any docs for more info.
• Oct 2021: Support DistilHuBERT, 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: We now have a tutorial that introduces our toolkit, you can 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.
• June 2021: Support extracting multiple hidden states from the SSL pretrained models
• 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.

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

1. Python >= 3.6
2. Install sox on your OS
3. Install s3prl

sh pip install -e ./

1. Install the specific fairseq

sh pip install fairseq@git+https://github.com//pytorch/fairseq.git@f2146bdc7abf293186de9449bfa2272775e39e1d#egg=fairseq

1. 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

Development pattern for contributors

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

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 CC-BY-NC, however portions of the project are available under separate license terms: S3PRL is licensed under the MIT license.

Used by

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} }

  SSL SER (this repository)

  @article{atmaja2022evaluating, title={Evaluating Self-Supervised Speech Representations for Speech Emotion Recognition}, author={Atmaja, Bagus Tris and Sasou, Akira}, journal={IEEE Access}, volume={10}, pages={124396--124407}, year={2022}, publisher={IEEE} }