* 2022.12: Horizon X3 pi BPU, see https://github.com/wenet-e2e/wenet/pull/1597, Kunlun Core XPU, see https://github.com/wenet-e2e/wenet/pull/1455, Raspberry Pi, see https://github.com/wenet-e2e/wenet/pull/1477, IOS, see https://github.com/wenet-e2e/wenet/pull/1549.
* 2022.11: TrimTail paper released, see https://arxiv.org/pdf/2211.00522.pdf
* 2022.10: Squeezeformer is supported, see https://github.com/wenet-e2e/wenet/pull/1447.
## 训练及推理
* 2022.07: RNN-T is supported now, see [rnnt](https://github.com/wenet-e2e/wenet/tree/main/examples/aishell/rnnt) for benchmark.
### 环境配置
## Highlights
在光源可拉取训练的docker镜像，本工程推荐的镜像如下：
***Production first and production ready**: The core design principle of WeNet. WeNet provides full stack solutions for speech recognition.
**Unified solution for streaming and non-streaming ASR*: [U2++ framework](https://arxiv.org/pdf/2203.15455.pdf)--develop, train, and deploy only once.
**Runtime solution*: built-in server [x86](https://github.com/wenet-e2e/wenet/tree/main/runtime/libtorch) and on-device [android](https://github.com/wenet-e2e/wenet/tree/main/runtime/android) runtime solution.
**Model exporting solution*: built-in solution to export model to LibTorch/ONNX for inference.
1. We borrowed a lot of code from [ESPnet](https://github.com/espnet/espnet) for transformer based modeling.
2. We borrowed a lot of code from [Kaldi](http://kaldi-asr.org/) for WFST based decoding for LM integration.
3. We referred [EESEN](https://github.com/srvk/eesen) for building TLG based graph for LM integration.
4. We referred to [OpenTransformer](https://github.com/ZhengkunTian/OpenTransformer/) for python batch inference of e2e models.
## Citations
``` bibtex
@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},
The main motivation of WeNet is to close the gap between research and production end-to-end (E2E) speech recognition models,
to reduce the effort of productionizing E2E models, and to explore better E2E models for production.
## :fire: News
* 2022.12: Horizon X3 pi BPU, see https://github.com/wenet-e2e/wenet/pull/1597, Kunlun Core XPU, see https://github.com/wenet-e2e/wenet/pull/1455, Raspberry Pi, see https://github.com/wenet-e2e/wenet/pull/1477, IOS, see https://github.com/wenet-e2e/wenet/pull/1549.
* 2022.11: TrimTail paper released, see https://arxiv.org/pdf/2211.00522.pdf
* 2022.10: Squeezeformer is supported, see https://github.com/wenet-e2e/wenet/pull/1447.
* 2022.07: RNN-T is supported now, see [rnnt](https://github.com/wenet-e2e/wenet/tree/main/examples/aishell/rnnt) for benchmark.
## Highlights
***Production first and production ready**: The core design principle of WeNet. WeNet provides full stack solutions for speech recognition.
**Unified solution for streaming and non-streaming ASR*: [U2++ framework](https://arxiv.org/pdf/2203.15455.pdf)--develop, train, and deploy only once.
**Runtime solution*: built-in server [x86](https://github.com/wenet-e2e/wenet/tree/main/runtime/libtorch) and on-device [android](https://github.com/wenet-e2e/wenet/tree/main/runtime/android) runtime solution.
**Model exporting solution*: built-in solution to export model to LibTorch/ONNX for inference.
1. We borrowed a lot of code from [ESPnet](https://github.com/espnet/espnet) for transformer based modeling.
2. We borrowed a lot of code from [Kaldi](http://kaldi-asr.org/) for WFST based decoding for LM integration.
3. We referred [EESEN](https://github.com/srvk/eesen) for building TLG based graph for LM integration.
4. We referred to [OpenTransformer](https://github.com/ZhengkunTian/OpenTransformer/) for python batch inference of e2e models.
## Citations
``` bibtex
@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},
