DeepAI AI Chat
Log In Sign Up

Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit

10/31/2022
by   Hongji Wang, et al.
0

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.

READ FULL TEXT
10/30/2022

WeKws: A production first small-footprint end-to-end Keyword Spotting Toolkit

Keyword spotting (KWS) enables speech-based user interaction and gradual...
06/23/2022

The SJTU X-LANCE Lab System for CNSRC 2022

This technical report describes the SJTU X-LANCE Lab system for the thre...
09/30/2021

Fine-tuning wav2vec2 for speaker recognition

This paper explores applying the wav2vec2 framework to speaker recogniti...
04/07/2019

VAE-based regularization for deep speaker embedding

Deep speaker embedding has achieved state-of-the-art performance in spea...
11/04/2022

SPEAKER VGG CCT: Cross-corpus Speech Emotion Recognition with Speaker Embedding and Vision Transformers

In recent years, Speech Emotion Recognition (SER) has been investigated ...
05/25/2023

Ordered and Binary Speaker Embedding

Modern speaker recognition systems represent utterances by embedding vec...