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Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast

This is the PyTorch implementation for CMPC, as described in our paper:

Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast

@inproceedings{zhu2022unsupervised,
  title={Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast},
  author={Zhu, Boqing and Xu, Kele and Wang, Changjian and Qin, Zheng and Sun, Tao and Wang, Huaimin and Peng, Yuxing},
  booktitle={Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI-22}},
  pages={3787--3794},
  year={2022},
  month={7}
}

Framework

We also provide the pretrained model and testing resources.

Requirments:

  • torch==1.7.0+cu110
  • matplotlib==3.4.3
  • pykeops==1.5
  • pandas==1.1.3
  • librosa==0.6.2
  • Pillow==9.0.1
  • PyYAML==6.0
  • scikit_learn==1.0.2

Download Pre-trained Models

CID CMPC

Data Pre-processing

In order to speed up the iteration of training, we extract the logmel features of voice data through pre-processing.

>> cd experiments/cmpc
>> python data_transform.py --wav_dir {directory-of-the-wav-file} --logmel_dir {destination-path}

Unsupervised Training

The configurations are written in the CONFIG.yaml file, which can be changed according to your needs, such as the path information. The unsupervised training process can begin as:

>> python train.py CONFIG.yaml

Evalution on our trained model

Experiments on three evalution protocals: matching, verification and retrieval. The '--ckp_path' could be the path of downloaded model or your trained model.

>> python matching.py CONFIG.yaml --ckp_path {checkpoint path}
>> python verification.py CONFIG.yaml --ckp_path {checkpoint path}
>> python retrieval.py CONFIG.yaml --ckp_path {checkpoint path}

Testing data

Matching, verification and retrieval testing data is released at ./data directory.