# WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction

arXiv: https://arxiv.org/abs/2409.15799 · HTML: https://mlsanity.com/paper/2409.15799/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2024.

## Built on by (most cited first, 8 of 8)

- [AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement](https://mlsanity.com/paper/2501.15417/) (2025)
- [SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline](https://mlsanity.com/paper/2505.19314/) (2025)
- [LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models](https://mlsanity.com/paper/2504.07402/) (2025)
- [UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement](https://mlsanity.com/paper/2510.20441/) (2025)
- [Target speaker anonymization in multi-speaker recordings](https://mlsanity.com/paper/2510.09307/) (2025)
- [StarTSE: Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model](https://mlsanity.com/paper/2604.19635/) (2026)
- [MoMuSE: Momentum Multi-modal Target Speaker Extraction for Real-time Scenarios with Impaired Visual Cues](https://mlsanity.com/paper/2412.08247/) (2024)
- [Breaking the Quality--Intelligibility Trade-off in Streaming Target Speaker Extraction via Deep-Feature-Anchored Preference Optimization](https://mlsanity.com/paper/2607.10191/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/wenet-e2e/WeSep.