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

We share Net together.

• 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.

pip install git+https://github.com/wenet-e2e/wenet.git

Command-line usage (use -h for parameters):

wenet --language chinese audio.wav

Python programming usage:

import wenet

model = wenet.load_model('chinese')
result = model.transcribe('audio.wav')
print(result['text'])

Please refer python usage for more command line and python programming usage.

• Clone the repo

git clone https://github.com/wenet-e2e/wenet.git

• Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
• Create Conda env:

conda create -n wenet python=3.10
conda activate wenet
conda install conda-forge::sox
pip install -r requirements.txt
pre-commit install  # for clean and tidy code

# If you encounter sox compatibility issues
RuntimeError: set_buffer_size requires sox extension which is not available.
# ubuntu
sudo apt-get install sox libsox-dev
# centos
sudo yum install sox sox-devel
# conda env
conda install  conda-forge::sox

Build for deployment

Optionally, if you want to use x86 runtime or language model(LM), you have to build the runtime as follows. Otherwise, you can just ignore this step.

# runtime build requires cmake 3.14 or above
cd runtime/libtorch
mkdir build && cd build && cmake -DGRAPH_TOOLS=ON .. && cmake --build .

Please see doc for building runtime on more platforms and OS.

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}
}