- Latest version (2021-08-17)
- Statistics and reference performances of the dataset are changed, as the annotation are updated.
- INTERSPEECH proceeding (2021-06-15)
pip install -r requirements.txt
In addition to the Python dependencies, ffmpeg must be installed on the system.
- Download the videos to
$DATA_DIR/originalusign yt-dlp.
yt-dlp --verbose -f best/best -o {download_path} https://www.youtube.com/watch?v={vid}
- Run the following to convert the videos and visualise the annotation.
python3 run_convert.py --data_dir $DATA_DIR
python3 run_visualize.py --data_dir $DATA_DIR
- About the meaning of 'eval' in jsons
-> "label" is the annotation of each frame, and has 1 (active) or 0 (non-active). On the other hand, "eval" indicates whether each frame is evaluated or not. The start and end frames of each track are excluded from evaluation, beacause it is nearly impossible to get accurate timestamps of boundary frames.
- About the unavailable youtube videos
-> YouTube videos might be removed by the uploader. In that case, conventionally, evaluate the model performance without them. (I would like to share the dataset, but due to the copyright issue, I cannot.)
- About box coordinates
-> In json, elements of bbox list represent [x1, y1, x2, y2], where (x1, y1) is the top-left coordinate and (x2, y2) is the bottom-right coordinate. The coordinates are normalized with respect to the video frame size, where (0.0, 0.0) corresponds to the top-left, and (1.0, 1.0) corresponds to bottom-right.
-> You can find some negative coordinates in json. The reason is that some of the faces are on the edges of the video frame, and we draw bboxes over the video frame with zero-padded edges.
Please cite the following if you make use of the code.
@inproceedings{kim2021you,
title={Look Who's Talking: Active Speaker Detection in the Wild},
author={Kim, You Jin and Heo, Hee-Soo and Choe, Soyeon and Chung, Soo-Whan and Kwon, Yoohwan and Lee, Bong-Jin and Kwon, Youngki and Chung, Joon Son},
booktitle={Interspeech},
year={2021}
}
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