/Light-ASD

The repository for IEEE CVPR 2023 (A Light Weight Model for Active Speaker Detection)

Primary LanguagePythonMIT LicenseMIT

A Light Weight Model for Active Speaker Detection

PWC

This repository contains the code and model weights for our paper (CVPR 2023):

A Light Weight Model for Active Speaker Detection
Junhua Liao, Haihan Duan, Kanghui Feng, Wanbing Zhao, Yanbing Yang, Liangyin Chen

Evaluate on AVA-ActiveSpeaker dataset

Data preparation

Use the following code to download and preprocess the AVA dataset.

python train.py --dataPathAVA AVADataPath --download

The AVA dataset and the labels will be downloaded into AVADataPath.

Training

You can train the model on the AVA dataset by using:

python train.py --dataPathAVA AVADataPath

exps/exps1/score.txt: output score file, exps/exp1/model/model_00xx.model: trained model, exps/exps1/val_res.csv: prediction for val set.

Testing

Our model weights have been placed in the weight folder. It performs mAP: 94.06% in the validation set. You can check it by using:

python train.py --dataPathAVA AVADataPath --evaluation

Evaluate on Columbia ASD dataset

Testing

The model weights trained on the AVA dataset have been placed in the weight folder. Then run the following code.

python Columbia_test.py --evalCol --colSavePath colDataPath

The Columbia ASD dataset and the labels will be downloaded into colDataPath. And you can get the following F1 result.

Name Bell Boll Lieb Long Sick Avg.
F1 82.7% 75.7% 87.0% 74.5% 85.4% 81.1%

We have also provided the model weights fine-tuned on the TalkSet dataset. Due to space limitations, we did not exhibit it in the paper. Run the following code.

python Columbia_test.py --evalCol --pretrainModel weight/finetuning_TalkSet.model --colSavePath colDataPath

And you can get the following F1 result.

Name Bell Boll Lieb Long Sick Avg.
F1 97.7% 86.3% 98.2% 99.0% 96.3% 95.5%

An ASD Demo with pretrained Light-ASD model

You can put the raw video (.mp4 and .avi are both fine) into the demo folder, such as 0001.mp4.

python Columbia_test.py --videoName 0001 --videoFolder demo

By default, the model loads weights trained on the AVA-ActiveSpeaker dataset. If you want to load weights fine-tuned on TalkSet, you can execute the following code.

python Columbia_test.py --videoName 0001 --videoFolder demo --pretrainModel weight/finetuning_TalkSet.model

You can obtain the output video demo/0001/pyavi/video_out.avi, where the active speaker is marked by a green box and the non-active speaker by a red box.

Citation

Please cite our paper if you use this code or model weights.

@InProceedings{Liao_2023_CVPR,
    author    = {Liao, Junhua and Duan, Haihan and Feng, Kanghui and Zhao, Wanbing and Yang, Yanbing and Chen, Liangyin},
    title     = {A Light Weight Model for Active Speaker Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {22932-22941}
}

Acknowledgments

Thanks for the support of TaoRuijie's open source repository for this research.