This repo provides the link of the data and the code of DenseNet3D Model in LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild. Our paper can be found here.
Download LRW1000 Dataset and place LRW1000_Public in the root of this repository. Instead, you can create symbolic links to this project:
ln -s LRW1000_Public Lipreading-DenseNet3D/LRW1000_Public
You can train or test the model by running:
python main.py options_lip.toml
Model architecture details and data annotation items are configured in options_lip.toml. Please pay attention that you may need modify the code in options_lip.toml and change the parameters to make the scripts work just as expected.
- PyTorch 1.0+
- toml
- tensorboardX
- imageio
If this repository was useful for your research, please cite our work:
@inproceedings{yang2019lrw,
title={LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild},
author={Yang, Shuang and Zhang, Yuanhang and Feng, Dalu and Yang, Mingmin and Wang, Chenhao and Xiao, Jingyun and Long, Keyu and Shan, Shiguang and Chen, Xilin},
booktitle={2019 14th IEEE International Conference on Automatic Face \& Gesture Recognition (FG 2019)},
pages={1--8},
year={2019},
organization={IEEE},
url={https://github.com/Fengdalu/Lipreading-DenseNet3D}
}
Another implmentation Of DenseNet-3D
Learn an Effective Lip Reading Model without Pains (Strong Recommended)
LipNet-PyTorch (The state-of-the-art PyTorch Version)
CAS-VSR-S68: A sentence-level audio-visual speech dataset for speaker-adaptive lip-reading
CAS-VSR-S101: A wild sentence-level audio-visual speech dataset
CAS-VSR-MOV20: A challenging lip-reading dataset with various visual conditions