Awesome

MTDVocaLiST

We proposed a lightweight audio-visual synchronization (AVS)model MTDVocaLiST. MTDVocaLiST reduces the model size of VocaLiST by 83.52%, yet still maintaining similar performance. For more details about VocaLiST, please visit its [project webpage]. Audio-visual synchronization aims to determine whether the mouth movements and speech in the video are synchronized. This repository is the official repository for the paper Multimodal Transformer Distillation for Audio-Visual Synchronization. The paper has been accepted by ICASSP 2024.

Datasets and preprocessing

There are 2 datasets involved in this work: i) The AV speech dataset of LRS2, and ii) the AV singing voice dataset of Acappella. The LRS2 dataset can be requested for download here. All the models in this work operate on audios sampled at 16kHz and videos with 25fps. The preprocessing steps are the same as Wav2Lip. The preprocessing step aims to obtain the cropped RGB face frames of size 3x96x96 in the .jpg format and audios of 16kHz sampling rate for each of the video samples in respective datasets.

Leverage pre-trained MTDVocaLiST only

You can download the pre-trained MTDVocaLiST from here.

import torch
from models.student_thin_200_all import SyncTransformer

cpk = torch.load("pretrained/pure_MTDVocaLiST.pth", map_location='cpu')
model = SyncTransformer(d_model=200)
model.load_state_dict(cpk["state_dict"])

Training (Multimodal Transformer Distillation)

You need to download the pre-trained VocaLiST model first from [Weights].

bash run_train_student.sh

Evaluation (Inference)

python3 test_stu.py --data_root /path/to/lip-sync/LRS2_wav2lip/main/ --checkpoint_path /path/to/Best.pth

Comparison with the SOTA AVS model

Citation

If you find our work useful, please consider cite

@INPROCEEDINGS{10446372,
  author={Chen, Xuanjun and Wu, Haibin and Wang, Chung-Che and Lee, Hung-Yi and Jang, Jyh-Shing Roger},
  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Multimodal Transformer Distillation for Audio-Visual Synchronization}, 
  year={2024},
  volume={},
  number={},
  pages={7755-7759},
  doi={10.1109/ICASSP48485.2024.10446372}
}

@inproceedings{kadandale2022vocalist,
  title={VocaLiST: An Audio-Visual Synchronisation Model for Lips and Voices},
  author={Kadandale, Venkatesh S and Montesinos, Juan F and Haro, Gloria},
  booktitle={Interspeech},
  pages={3128--3132},
  year={2022}
}

Acknowledgement

If you have any questions, please feel free to contact me by email at d12942018@ntu.edu.tw.