  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.
