WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction
<|reference_start|>WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction: Target speaker extraction (TSE) focuses on isolating the speech of a specific target speaker from overlapped multi-talker speech, which is a typical setup in the cocktail party problem. In recent years, TSE draws increasing attention due to its potential for various applications such as user-customized interfaces and hearing aids, or as a crutial front-end processing technologies for subsequential tasks such as speech recognition and speaker recongtion. However, there are currently few open-source toolkits or available pre-trained models for off-the-shelf usage. In this work, we introduce WeSep, a toolkit designed for research and practical applications in TSE. WeSep is featured with flexible target speaker modeling, scalable data management, effective on-the-fly data simulation, structured recipes and deployment support. The toolkit is publicly avaliable at \url{https://github.com/wenet-e2e/WeSep.}<|reference_end|>
@article{wang2024wesep:,
    title={WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target
  Speaker Extraction},
    author={Shuai Wang, Ke Zhang, Shaoxiong Lin, Junjie Li, Xuefei Wang, Meng Ge,
  Jianwei Yu, Yanmin Qian, Haizhou Li},
    journal={arXiv preprint arXiv:2409.15799},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15799},
    primaryClass={eess.AS cs.SD}
}