WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction (2409.15799v1)

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.}

• The paper introduces the WeSep toolkit, which provides a novel open-source solution for isolating speakers in multi-talker environments.
• The paper details a Unified I/O system and on-the-fly data simulation that enhance robust training with dynamic mono-speaker audio integration.
• The paper demonstrates enhanced cross-domain performance with state-of-the-art separation models and offers easy export options for diverse deployment scenarios.

The paper "WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction" presents WeSep, a novel toolkit developed to advance the field of target speaker extraction (TSE). This area of research focuses on isolating specific speaker signals from overlapping multi-talker environments, an endeavor often referred to as the cocktail party problem. The primary contribution of this paper is the development of an open-source toolkit that addresses the current scarcity of resources in the TSE domain.

Key Features of WeSep

1. Flexible Target Speaker Modeling: WeSep integrates versatile approaches for speaker modeling, allowing seamless integration with existing mainstream models. This flexibility paves the way for future integration of more advanced pre-trained models, enhancing the toolkit's adaptability.
2. Scalable Data Management: At the heart of WeSep's data handling capabilities is the Unified I/O (UIO) system, which is crucial for managing vast datasets efficiently. This system is vital for scaling TSE systems to production-level applications.
3. On-the-Fly Data Simulation: This feature enables dynamic data incorporation, leveraging pre-prepared mono-speaker audios without the need for pre-mixing. This is crucial for robust training of models, as it enhances the variability and diversity of training data.
4. Integration and Deployment: Models developed using WeSep can be exported with ease using formats such as PyTorch JIT or ONNX. The toolkit provides deployment codes in C++, which cater to a wide array of deployment scenarios.

Architectural and Methodological Implications

WeSep's architecture supports multiple speaker modeling approaches, including audio-based embeddings and visual cues, which can be combined through various fusion techniques such as concatenation, addition, multiplication, and Feature-wise Linear Modulation (FiLM). It incorporates several state-of-the-art separation models like ConvTasNet and offers options for joint training of speaker models to enhance flexibility.

The on-the-fly simulation mechanism and dynamic speaker mixing bolster dataset variability, fostering a more generalized learning environment. The emphasis on minimizing preprocessed data requirements shifts focus toward real-time data enhancement methods like adaptive noise integration.

Experimental Evaluation and Results

The paper evaluates WeSep's performance using standard datasets such as Libri2Mix and VoxCeleb1. The findings show that WeSep's models, particularly those employing FiLM for embedding integration, achieve commendable results on in-domain datasets. They also exhibit superior generalization capabilities on out-of-domain datasets. Notably, systems trained with WeSep on the VoxCeleb1 dataset demonstrated competitive results on Libri2Mix, highlighting its robust cross-domain capabilities.

Implications and Future Directions

WeSep's scalability and versatility have significant implications for both academic and practical applications, particularly in domains requiring robust speaker extraction like user-customized interfaces, hearing aid technology, and front-end speech processing systems for complex environments. Future iterations of WeSep are envisioned to integrate cutting-edge models and embrace visual modalities, expanding its utility beyond purely audio-based cues. Additionally, the potential introduction of blind source separation within its framework could further extend its applicability across a range of speech processing tasks.

In conclusion, WeSep emerges as an essential toolkit in the TSE field, offering remarkable flexibility, scalability, and integration capabilities crucial for advancing both research and practical implementations in speaker extraction.