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SMILE: A Multimodal Dataset for Understanding Laughter

This is the repository of SMILE: A Multimodal Dataset for Understanding Laughter. It comprises SMILE dataset, and codes involving the description of the dataset and evaluation for laughter reasoning.

teaser_v3(1)-1

Installation

$ conda create -n SMILE python==3.10.11
$ conda activate SMILE

# move to FastChat/ directory
$ cd FastChat

$ pip3 install --upgrade pip  
$ pip3 install -e .
$ pip3 install openai
$ pip3 install scikit-image
$ pip3 install evaluate
$ pip3 install bert-score

Download the SMILE Dataset

  1. Now, we are updating SMILE dataset v.2. After the update, we will update the laugh reasoning benchmark.

  2. Download SMILE dataset v.2 in here

  3. unzip the dataset.

    
├── annotations
|    ├── data_split.json
|    ├── GT_laughter_reason.json
|    └── multimodal_textual_representation.json
|
└── videos
     └── SMILE_videos.zip
            ├── video_clips
            └── video_segments

  4. Details about each file

    • data_split.json: key index for train, validation, test split
    • GT_laughter_reason.json: Ground-Truth laughter reason for video clip
    • multimodal_textual_representation.json: multimodal textual representation encoded from video clip
    • video_clips: 887 video clips from sitcom and TED, Note: sitcom has an underbar in the key index, while TED does not. You can use this information for splitting our dataset by video types.
    • video_segments: 4482 video segments trimmed from video clip by utterances.

  5. SMILE dataset v.1 for evaluation

    • We provide v.1 dataset for evaluation download in hear
    • Note that sitcom_reasoning_{train/val}.json and ted_reasoning_{train/val}.json are subset of smile_reasoning_{train/val}.json.
    
├── SMILE_v1_evaluation
     ├── smile_reasoning_train.json
     ├── smile_reasoning_val.json
     ├── sitcom_reasoning_train.json
     ├── sitcom_reasoning_val.json
     ├── ted_reasoning_train.json
     └── ted_reasoning_val.json

Evaluation

Laugh reasoning

We provide the inference code for in-context and zero-shot experiment using GPT3.

As the fine-tuneded GPT3 requires a certain openai api-key which the model was fine-tuned on, we instead provide the inferecne code for fine-tuned model using LLaMA.

Please evaluate the models with the provided v.1. dataset.

In-context and Zero-shot experiment (GPT3)

Note that running GPT3 requires your own openai api-key and it also charges for running the model.

Replace the { } with your own information.

$ python gpt3_inferece.py -openai_key {your openai api key} -engine {name of gpt3 model} -shot {fewshot or zeroshot} -val_data {path/for/validation_data} -train_data {path/for/train_data} -random_seed {any integer number}

Fine-tuned experiment (LLaMA)

We provide the pre-trained weights of the LLaMA for the research purpose only.

Training dataLink
SMILESMILE_checkpoint
SMILE_SitcomSitcom_checkpoint
SMILE_TedTed_checkpoint
 ├── SMILE
      ├── checkpoint
 ├── SMILE_SITCOM
      ├── checkpoint
 ├── SMILE_TED
      ├── checkpoint

Replace the { } with your own information.

You should direct the checkpoint directory for the model_path, e.g., "SMILE/checkpoint".

$ python FastChat/fastchat/serve/inference.py -model_path {path/for/fine-tuned model} -val_data {path/for/validation_data} -train_data {path/for/train_data} -random_seed {any integer number}

Acknowledgement

We are grateful for the following awesome projects, our SMILE arising from: