论文标题
WEKWS：生产第一小脚印端到端关键字点斑点工具包
WeKws: A production first small-footprint end-to-end Keyword Spotting Toolkit
论文作者
论文摘要
关键字斑点（KWS）启用基于语音的用户交互，并逐渐成为智能设备的必不可少的组件。最近，端到端（E2E）方法已成为开发KWS任务的最受欢迎的方法。但是，E2E KWS方法的研究和部署之间仍然存在差距。在本文中，我们介绍了WEKWS，一种生产质量，易于构建且可容纳的E2E KWS工具包。 WEKW包含了几个最先进的骨干网络的实现，从而使其在三个公开可用数据集上获得了高度竞争的结果。为了使WEKW成为纯净的E2E工具包，我们利用精致的最大损失来使模型本身学习关键字的结尾位置，这大大简化了训练管道，并使WEKW非常有效地在现实世界中应用。该工具包可在https://github.com/wenet-e2e/wekws上公开获得。
Keyword spotting (KWS) enables speech-based user interaction and gradually becomes an indispensable component of smart devices. Recently, end-to-end (E2E) methods have become the most popular approach for on-device KWS tasks. However, there is still a gap between the research and deployment of E2E KWS methods. In this paper, we introduce WeKws, a production-quality, easy-to-build, and convenient-to-be-applied E2E KWS toolkit. WeKws contains the implementations of several state-of-the-art backbone networks, making it achieve highly competitive results on three publicly available datasets. To make WeKws a pure E2E toolkit, we utilize a refined max-pooling loss to make the model learn the ending position of the keyword by itself, which significantly simplifies the training pipeline and makes WeKws very efficient to be applied in real-world scenarios. The toolkit is publicly available at https://github.com/wenet-e2e/wekws.
