FacialMMT

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A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations

Wenjie Zheng, Jianfei Yu, Rui Xia, and Shijin Wang

📄 Paper | 🎥 Slides

This repository contains the data and code for FacialMMT, a framework that uses facial sequences of real speaker to help multimodal emotion recognition.

• Facial sequences of real speaker in MELD dataset: In this part, we will provide the data of extracted real speaker's facial sequences using our first-stage approach.
• A multimodal facial expression-aware multi-task learning model: In this part, we will provide the source code and pre-trained models for ease of both direct evaluation and training from scratch.

Demo :computer:

Dependencies

Start from building the environment

conda create -n FacialMMT python=3.8.15
conda activate FacialMMT
pip install -r requirements.txt

Start from the existing environment

pip install -r requirements.txt

Data and pretrained models preparation

• For ease of reproducibility, you can download the corresponding data and models from the link below and organize it as follows. Afterwards, you should modify the FacialMMT/preprocess_data/T+A+V/*_facseqs_160_paths_final.json file to your own path.

Download link: BaiduNetdisk, HuggingFace

FacialMMT
├── preprocess_data
│   ├── T+A+V
│   │   ├── meld_train_vision_utt.pkl
│   │   ├── meld_val_vision_utt.pkl
│   │   ├── meld_test_vision_utt.pkl
│   │   ├── meld_train_audio_utt.pkl
│   │   ├── meld_val_audio_utt.pkl
│   │   ├── meld_test_audio_utt.pkl
│   │   ├── test_utt_profile.json
│   │   ├── val_utt_profile.json
│   │   ├── train_utt_profile.json
│   │   ├── test_facseqs_160_paths_final.json
│   │   ├── val_facseqs_160_paths_final.json
│   │   ├── train_facseqs_160_paths_final.json 
│   ├── V
│   │   ├── meld_train_vision_utt.pkl
│   │   ├── meld_val_vision_utt.pkl
│   │   ├── meld_test_vision_utt.pkl
├── pretrained_model
│   ├── roberta-large
│   │   ├── xxx
│   │   ├── xxx
│   ├── bert-large
│   │   ├── xxx
│   │   ├── xxx
│   ├── Swin_tiny_Ms-Celeb-1M.pt
│   ├── FacialMMT-unimodal
│   │   ├── unimodal_model_V.pt
│   ├── FacialMMT-RoBERTa
│   │   ├── multimodal_T+A+V_RoBERTa.pt
│   │   ├── best_swin_RoBERTa.pt
│   ├── FacialMMT-BERT
│   │   ├── multimodal_T+A+V_BERT.pt
│   │   ├── best_swin_BERT.pt

Evaluating FacialMMT on the test set of MELD

(These results are different from that in our paper because we train the model again)

Multimodal Emotion Recognition

you can check our FacialMMT-RoBERTa by running the script below.

python main.py --choice_modality T+A+V --plm_name roberta-large --load_multimodal_path FacialMMT-RoBERTa/multimodal_T+A+V_RoBERTa.pt --load_swin_path FacialMMT-RoBERTa/best_swin_RoBERTa.pt --doEval 1

you can get the following W-F1 result: 66.73

To evaluate the performance of FacialMMT-BERT, run the script below.

python main.py --choice_modality T+A+V --plm_name bert-large --load_multimodal_path FacialMMT-BERT/multimodal_T+A+V_BERT.pt --load_swin_path FacialMMT-BERT/best_swin_BERT.pt --doEval 1

you can get the following W-F1 result: 64.87

Visual Emotion Recognition

Furthermore, we provide a uni-model that uses only the visual modality to evaluate the effectiveness of our own extracted visual data (facial sequences of real speaker).

python main.py --choice_modality V --load_unimodal_path FacialMMT-unimodal/unimodal_model_V.pt --doEval 1

you can get the following W-F1 result: 36.53

Training from scratch

1. First, you need to personally obtain the auxiliary dataset Aff-Wild2. You can find the dataset here.

2. Visual representations at the utterance level need to be obtained. We used InceptionResNetV1.

3. Audio representations at the utterance level need to be obtained. We used wav2vec2.0.

4. You can run the script below to train the model from scratch.

python main.py --choice_modality T+A+V --plm_name roberta-large

(We trained on a single 3090Ti with batch_size=1. If your hardware allows, we recommend setting a larger batch_size to speed up training.)

Citation

Please consider citing the following if this repo is helpful to your research.

@inproceedings{zheng2023facial,
  title={A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations},
  author={Zheng, Wenjie and Yu, Jianfei and Xia, Rui and Wang, Shijin},
  booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={15445--15459},
  year={2023}
}

Please let me know if I can future improve in this repositories or there is anything wrong in our work. You can ask questions via issues in Github or contact me via email [email protected]. Thanks for your support!