📘 Lecture Summarizer

Lecture Summarizer is a streamlined tool designed to automatically convert lecture audio into concise, structured summaries. By leveraging state-of-the-art speech-to-text and natural language processing models, this tool enhances productivity for students, educators, and professionals who rely on spoken content.

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🔍 Overview

This tool allows users to:

• Convert audio recordings of lectures into accurate transcripts using models such as OpenAI Whisper or Google Gemini.
• Automatically generate clean, coherent summaries of those transcripts.
• Extract key points, bullet lists, and highlight important insights for easy reference.

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🧠 Core Technologies

• Audio-to-Text Conversion:

  • OpenAI Whisper: Robust and open-source ASR model for multilingual speech recognition.
  • Google Gemini: State-of-the-art model offering multimodal capabilities, including transcription.

• Text Summarization:

  • Custom summarization pipeline using LLMs or fine-tuned transformer-based models (e.g., BART, T5).
  • Optionally includes keyword extraction and topic segmentation.

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🛠️ Installation & Setup

1. Clone the Repository

git clone https://github.com/deep-div/Lecture-Summarize.git
cd lecture-summarizer

2. Install Dependencies

pip install -r requirements.txt

3. Add API Keys (if using OpenAI or Gemini)

Create a .env file:

OPENAI_API_KEY=your_openai_key
GOOGLE_API_KEY=your_google_key

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📚 Use Cases

• Students: Review key points from long lectures in minutes.
• Teachers: Create notes or study material from recorded sessions.
• Researchers: Summarize interviews, presentations, or conference talks.

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Let me know if you’d like a version tailored for a specific framework (e.g., Streamlit, Flask) or platform (web, desktop, mobile).