Code for EFT: Expert Fusion Transformer for Voice-Face Representation Learning,ICME,2023

Requirements

pytorch==1.8.1
wandb==0.12.10

Dataset

Download dataset.zip from GoogleDrive (4GB) and unzip it to the project root. The folder structure is shown below:

dataset/
├── evals
│   ├── test_matching_1N.pkl
│   ├── test_matching_g.pkl
│   ├── test_matching.pkl
│   ├── test_retrieval.pkl
│   ├── test_verification_gn.pkl
│   ├── test_verification_g.pkl
│   ├── test_verification_n.pkl
│   ├── test_verification.pkl
│   └── valid_verification.pkl
├── features
└── info
    ├── name2movies.pkl
    ├── name2tracks.pkl
    └── train_valid_test_names.pkl

The dataset/features folder contains vast number of small files. We suggest placing the project on an SSD disk to prevent IO bottlenecks.

Train

python main.py for the default setting in our paper

python main.py --face_features=f_2plus1D_512 for only use the R2+1D expert

python main.py --bc_mode=sbc_3.0 for change the SBC std threshold to 3.0

---

use wandb to view the training process:

1. Create .wb_config.json file in the project root, using the following content:

   {
     "WB_KEY": "Your wandb auth key"
   }
   

2. add --dryrun=False to the training command, for example: python main.py --dryrun=False

Model Checkpoint

You can get the final model checkpoint in eft_checkpoint.zip.