This repository contains the demo for the audio-to-video synchronisation network (SyncNet). This network can be used for audio-visual synchronisation tasks including:
- Removing temporal lags between the audio and visual streams in a video;
- Determining who is speaking amongst multiple faces in a video.
Please cite the paper below if you make use of the software.
pip install -r requirements.txt
In addition, ffmpeg is required.
SyncNet demo:
python demo_syncnet.py --videofile data/example.avi --tmp_dir /path/to/temp/directory
Check that this script returns:
AV offset: 3
Min dist: 5.353
Confidence: 10.021
Full pipeline:
sh download_model.sh
python run_pipeline.py --videofile /path/to/video.mp4 --reference name_of_video --data_dir /path/to/output
python run_syncnet.py --videofile /path/to/video.mp4 --reference name_of_video --data_dir /path/to/output
python run_visualise.py --videofile /path/to/video.mp4 --reference name_of_video --data_dir /path/to/output
Outputs:
$DATA_DIR/pycrop/$REFERENCE/*.avi - cropped face tracks
$DATA_DIR/pywork/$REFERENCE/offsets.txt - audio-video offset values
$DATA_DIR/pyavi/$REFERENCE/video_out.avi - output video (as shown below)
This implementation supports both CUDA GPU and CPU execution:
- CUDA GPU: Automatically detected and used if available for faster processing
- CPU: Used as fallback when CUDA is not available, or can be forced for compatibility
The code automatically detects and uses the best available device:
- If CUDA is available → Uses GPU for acceleration
- If CUDA is not available → Falls back to CPU
To force CPU-only execution (e.g., for compatibility or debugging), you can set:
import os os.environ['CUDA_VISIBLE_DEVICES'] = ''
Or modify the device selection in the scripts directly.
@InProceedings{Chung16a,
author = "Chung, J.~S. and Zisserman, A.",
title = "Out of time: automated lip sync in the wild",
booktitle = "Workshop on Multi-view Lip-reading, ACCV",
year = "2016",
}