{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/wesep-a-scalable-and-flexible-toolkit-towards","title":"WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction","arxiv_id":"2409.15799","date":"2024-09-24","proceeding":null,"authors":["Shuai Wang","Ke Zhang","Shaoxiong Lin","Junjie Li","Xuefei Wang","Meng Ge","Jianwei Yu","Yanmin Qian","Haizhou Li"],"abstract":"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.}","url_abs":"https://arxiv.org/abs/2409.15799v1","url_pdf":"https://arxiv.org/pdf/2409.15799v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"wesep-a-scalable-and-flexible-toolkit-towards","repo_url":"https://github.com/wenet-e2e/wesep","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"target-speaker-extraction","task_name":"Target Speaker Extraction"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}