  Speaker modeling is essential for many related tasks, such as speaker
recognition and speaker diarization. The dominant modeling approach is
fixed-dimensional vector representation, i.e., speaker embedding. This paper
introduces a research and production oriented speaker embedding learning
toolkit, Wespeaker. Wespeaker contains the implementation of scalable data
management, state-of-the-art speaker embedding models, loss functions, and
scoring back-ends, with highly competitive results achieved by structured
recipes which were adopted in the winning systems in several speaker
verification challenges. The application to other downstream tasks such as
speaker diarization is also exhibited in the related recipe. Moreover, CPU- and
GPU-compatible deployment codes are integrated for production-oriented
development. The toolkit is publicly available at
https://github.com/wenet-e2e/wespeaker.
