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LoCoNet: Long-Short Context Network for Active Speaker Detection

Dependencies

Start from building the environment

conda env create -f requirements.yml
conda activate loconet

export PYTHONPATH=project_dir/dlhammer:$PYTHONPATH and replace project_dir with your code base location

Data preparation

We follow TalkNet's data preparation script to download and prepare the AVA dataset.

python train.py --dataPathAVA AVADataPath --download

AVADataPath is the folder you want to save the AVA dataset and its preprocessing outputs, the details can be found in here . Please read them carefully.

After AVA dataset is downloaded, please change the DATA.dataPathAVA entry in the config file.

Training script

python -W ignore::UserWarning train.py --cfg configs/multi.yaml OUTPUT_DIR <output directory>

Pretrained model

Please download the LoCoNet trained weights on AVA dataset here.

python -W ignore::UserWarning test_multicard.py --cfg configs/multi.yaml  RESUME_PATH {model download path}

Citation

Please cite the following if our paper or code is helpful to your research.

@article{wang2023loconet,
  title={LoCoNet: Long-Short Context Network for Active Speaker Detection},
  author={Wang, Xizi and Cheng, Feng and Bertasius, Gedas and Crandall, David},
  journal={arXiv preprint arXiv:2301.08237},
  year={2023}
}

Acknowledge

The code base of this project is studied from TalkNet which is a very easy-to-use ASD pipeline.