This respository is implementation of the proposed method in LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild. Our paper can be found here.
- python 3.6.7
- pytorch 1.0.0.dev20181103
- scipy 1.1.0
This model is pretrained on LRW with RGB lip images(112×112), and then tranfer to LRW-1000 with the same size. We train the model end-to-end.
You can train the model as follow:
python main.py --data_root "data path" --index_root "index root"
Where the data_root and index_root specifys the "LRW-1000 data path" and "label path" correspondly.
All the parameters we use is set as default value in args.py.You can also pass parameters through console just like:
python main.py --gpus 0,1 --batch_size XXX --lr 1e-4 --data_root "data path" --index_root "index root" ...
Note:
Please pay attention that you may need modify the code in dataset.py and change the parameters data_root and index_root to make the scripts work just as expected.
If this repository was useful for your research, please cite our work:
@article{shuang18LRW1000,
title={LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild},
author={Shuang Yang, Yuanhang Zhang, Dalu Feng, Mingmin Yang, Chenhao Wang, Jingyun Xiao, Keyu Long, Shiguang Shan, Xilin Chen},
booktitle={arXiv},
year={2018}
}