Docs | Tutorial | Papers | Runtime (x86) | Runtime (android)
We share neural Net together.
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.
We release various pretrained models. Please see examples/$dataset/s0/README.md
for model download links and WeNet benchmark on different speech datasets.
git clone https://github.com/wenet-e2e/wenet.git
# [option 1]
conda create -n wenet python=3.8
conda activate wenet
pip install -r requirements.txt
conda install pytorch==1.6.0 cudatoolkit=10.1 torchaudio=0.6.0 -c pytorch
# [option 2: working on machine with GPU 3090]
conda create -n wenet python=3.8
conda activate wenet
pip install -r requirements.txt
conda install pytorch torchvision torchaudio=0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
# runtime build requires cmake 3.14 or above
cd runtime/server/x86
mkdir build && cd build && cmake .. && cmake --build .
Please scan the QR code on the left to follow the offical account of WeNet.
In addition to discussing in Github Issues, 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.
If you can not access the QR image, please access it on gitee.
@article{zhang2021wenet,
title={WeNet: Production First and Production Ready End-to-End Speech Recognition Toolkit},
author={Zhang, Binbin and Wu, Di and Yang, Chao and Chen, Xiaoyu and Peng, Zhendong and Wang, Xiangming and Yao, Zhuoyuan and Wang, Xiong and Yu, Fan and Xie, Lei and others},
journal={arXiv preprint arXiv:2102.01547},
year={2021}
}
@article{zhang2020unified,
title={Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition},
author={Zhang, Binbin and Wu, Di and Yao, Zhuoyuan and Wang, Xiong and Yu, Fan and Yang, Chao and Guo, Liyong and Hu, Yaguang and Xie, Lei and Lei, Xin},
journal={arXiv preprint arXiv:2012.05481},
year={2020}
}
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