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Pipeline for Extracting and Organizing Procedural Information in Tutorial Videos

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TaskBinge Multi-Video Processing Pipeline

This is the repository for the pipeline for TaskBinge.

Repository Structure

Development environment

  • Ubuntu 18.04, CUDA 12.1

Installation

  1. Create a new conda environment (Python 3.10)
conda env create -f environment.yml
conda activate taskbinge
  1. Install CLIP package.
pip install git+https://github.com/openai/CLIP.git

Run

Run a single instance of the pipeline for one of the tasks in metadata. There are two available tasks [carbonara] (How to cook carbonara pasta?) and [remove-object] (How to remove object with Photoshop?). You can add a separate entry for a custom task in

# -h
#                       Help
# -t TASKID, --task TASKID
#                       The task-id. Ex: carbonara
python preprocess.py [-t TASKID]

For example:

python preprocess.py -t carbonara

Notes:

  • Export environment variable $OPENAI_API_KEY with appropriate OPENAI_API_KEY from OpenAI.

Output

Processing results are stored in under static/results/{task-id}/output.json.

  • Output format for notables/hooks is as follows:
{
    "id": str,
    "video_id": str,
    "subgoal": str,
    "aspect": str,
    "relation": str,
    "title": str,
    "description": str,
    "comparison": str,
    "importance": float,
    "links": [
        {
            "id": str,
            "title": str,
            "description": str,
            "reasoning": str,
            "comparison": str,
            "subgoal": str,
            "aspect": str,
            "relation": str,
            "other_video_id": str,
            "importance": float,
            "uniqueness": float,
            "other_seconds": float
        }
    ]
}

For any questions please contact: Bekzat Tilekbay

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Pipeline for Extracting and Organizing Procedural Information in Tutorial Videos

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