WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910618036994048 |
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| author | Wang, Shuai Zhang, Ke Lin, Shaoxiong Li, Junjie Wang, Xuefei Ge, Meng Yu, Jianwei Qian, Yanmin Li, Haizhou |
| author_facet | Wang, Shuai Zhang, Ke Lin, Shaoxiong Li, Junjie Wang, Xuefei Ge, Meng Yu, Jianwei Qian, Yanmin Li, Haizhou |
| contents | 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.} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15799 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction Wang, Shuai Zhang, Ke Lin, Shaoxiong Li, Junjie Wang, Xuefei Ge, Meng Yu, Jianwei Qian, Yanmin Li, Haizhou Audio and Speech Processing Sound 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.} |
| title | WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2409.15799 |