Video Summarization Project

This project allows users to upload videos or provide video URLs, which are then processed to generate a textual summary. The algorithmic approach involves video preprocessing, feature extraction, temporal summarization, content clustering, summary length control, and summary generation.

Table of Contents

• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Uploading Videos
  • Providing Video URLs
• Algorithmic Approach
• File Structure
• Dependencies
• Contributing
• License

Getting Started

Prerequisites

Make sure you have the following prerequisites installed:

• Python (version 3.x)
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/Nehul-Krushna/Video_Summmarization_Project.git
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. run the script:

   python3 run.py
   

Usage

Uploading Videos

Users can upload videos directly through the website. Supported video formats include MP4.

Providing Video URLs

Alternatively, users can provide video URLs (YouTube links) to generate summaries.

Algorithmic Approach

Video Processing and Summarization:

1. Input Handling:

• Users can upload a video file directly or provide a URL to a video hosted online.
• The application checks the validity of the input file or URL.
• If a valid video file is uploaded, it is saved in the designated upload directory. If a valid URL is provided, the video is downloaded and saved locally.

2. Video Processing:

• The uploaded video file undergoes audio extraction to convert it into a suitable format for text processing.
• Audio extraction is performed using the moviepy library, which extracts audio from video files.
• The extracted audio is saved as a separate file in the upload directory.

3. Speech-to-Text Conversion:

• The extracted audio file is then processed using the AssemblyAI service for speech-to-text conversion.
• AssemblyAI provides accurate transcription of audio content into text format.
• The converted text data is used for further processing to generate summaries.

4. Text Summarization:

• The converted text is divided into smaller chunks to improve processing efficiency.
• Each chunk of text is passed through a pre-trained summarization model from the transformers library.
• The summarization model generates concise summaries for each chunk, capturing the key information.
• Summaries from all chunks are aggregated to create a comprehensive summary of the entire video content.

5. Output Presentation:

• The generated summary is displayed to the user on the web interface.
• Users can view the summarized content and download it for reference.
• Additionally, a progress bar is shown to indicate the processing status, providing real-time feedback to the user.

6. Error Handling:

• The application includes error handling mechanisms to address various scenarios, such as invalid input files, unavailable URLs, or exceeding processing limits.
• Users are provided with informative error messages to guide them through the usage of the application effectively.

7. Performance Optimization:

• To reduce execution time and enhance user experience, optimizations such as text chunking and parallel processing are implemented.
• Text chunking divides the text into manageable portions, enabling efficient processing and summarization.
• Parallel processing techniques leverage the computing resources efficiently, enabling faster execution of tasks.

File Structure

• app/: Contains the Flask web application files.
  • static/: Static files such as CSS and JavaScript.
  • templates/: HTML templates for the web application.
  • routes.py: Handles the application routes.
• requirements.txt: List of Python dependencies.
• run.py: Main script to run the Flask application.