Roadmap | Docs | Papers | Runtime | Pretrained Models | HuggingFace

We share Net together.

• 2022.12: Horizon X3 pi BPU, see wenet-e2e#1597, Kunlun Core XPU, see wenet-e2e#1455, Raspberry Pi, see wenet-e2e#1477, IOS, see wenet-e2e#1549.
• 2022.11: TrimTail paper released, see https://arxiv.org/pdf/2211.00522.pdf

• Production first and production ready: The core design principle, WeNet provides full stack production solutions for speech recognition.
• Accurate: WeNet achieves SOTA results on a lot of public speech datasets.
• Light weight: WeNet is easy to install, easy to use, well designed, and well documented.

please refer doc for install.

You can directly discuss on Github Issues.

For Chinese users, you can aslo scan the QR code on the left to follow our offical account of WeNet. We created a WeChat group for better discussion and quicker response. Please scan the personal QR code on the right, and the guy is responsible for inviting you to the chat group.

1. We borrowed a lot of code from ESPnet for transformer based modeling.
2. We borrowed a lot of code from Kaldi for WFST based decoding for LM integration.
3. We referred EESEN for building TLG based graph for LM integration.
4. We referred to OpenTransformer for python batch inference of e2e models.

@inproceedings{yao2021wenet,
  title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
  author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
  booktitle={Proc. Interspeech},
  year={2021},
  address={Brno, Czech Republic },
  organization={IEEE}
}

@article{zhang2022wenet,
  title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
  author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
  journal={arXiv preprint arXiv:2203.15455},
  year={2022}
}