History

Rudrabha Mukhopadhyay 43419e6135 Added acknowledgements		2020-10-03 10:13:30 +05:30
..
scores_LSE	Changed names of folders	2020-10-03 10:04:46 +05:30
test_filelists	Initial commit	2020-08-17 22:38:19 +05:30
gen_videos_from_filelist.py	Initial commit	2020-08-17 22:38:19 +05:30
README.md	Added acknowledgements	2020-10-03 10:13:30 +05:30
real_videos_inference.py	Initial commit	2020-08-17 22:38:19 +05:30

README.md

Evaluation of Lip-sync using LSE-D and LSE-C metric.

We use the pre-trained syncnet model available in this repository.

Steps to set-up the evaluation repository for LSE-D and LSE-C metric:

Clone the SyncNet repository.

git clone https://github.com/joonson/syncnet_python.git

Follow the procedure given in the above linked repository to download the pretrained models and set up the dependencies.
- Note: Please install a separate virtual environment for the evaluation scripts. The versions used by Wav2Lip and the publicly released code of SyncNet is different and can cause version mis-match issues. To avoid this, we suggest the users to install a separate virtual environment for the evaluation scripts

cd syncnet_python
pip install -r requirements.txt
sh download_model.sh

The above step should ensure that all the dependencies required by the repository is installed and the pre-trained models are downloaded.

Running the evaluation scripts:

Copy our evaluation scripts given in this folder to the cloned repository.

    cd WaveLip/evaluation/scores_LSE/
    cp *.py syncnet_python/
    cp *.sh syncnet_python/

Note: We will release the files test filelists for LRW, LRS2 and LRS3 shortly once we receive permission from the dataset creators. We will also release the Real World Dataset we have collected shortly.

Our evaluation technique does not require ground-truth of any sorts. Given lip-synced videos we can directly calculate the scores from only the generated videos. Please store the generated videos (from our test sets or your own generated videos) in the following folder structure.

video data root (Folder containing all videos)
├── All .mp4 files

To run evaluation on the LRW, LRS2 and LRS3 test files, please run the following command:

python calculate_scores_LRS.py --data_root /path/to/video/data/root --tmp_dir tmp_dir/

Evaluation of image quality using FID metric.

We will update the scripts for the FID metric shortly

Please open the issues with the "Evaluation" label if you face any issues in the evaluation scripts.

Acknowledgements