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NotebookLM-Lite (Sci-Fi Edition)

NotebookLM-Lite is a note-taking application inspired by Google's NotebookLM, aiming to fully replicate features such as document Q&A, podcast generation, and presentation creation. It is currently under development. For a simple Chinese podcast generation feature, please refer to the v0.1 branch.

✨ Features

  • Unified LLM Interface: Seamlessly switch between OpenAI and Anthropic models.
  • Neural Audio Synthesis: High-quality, multi-speaker podcast generation using CosyVoice (via Dashscope).
  • PDF Intelligence: Robust parsing and semantic understanding of uploaded documents.
  • Interactive Player: Integrated audio player with playback controls and download capability.

🛠️ Tech Stack

  • Frontend: React, Vite, TailwindCSS, Framer Motion (animations)
  • Backend: Python, FastAPI, Uvicorn
  • AI/ML: OpenAI API, Anthropic API, Dashscope (TTS)

🚀 Getting Started

Prerequisites

  • Python 3.10+
  • Node.js 18+
  • FFmpeg (Must be installed and added to system PATH)

Installation

1. Backend Setup

Navigate to the project root:

# Install Python dependencies
pip install -r requirements.txt

# Start the API server
python backend/main.py

The backend will run at http://localhost:8000.

2. Frontend Setup

Open a new terminal and navigate to the frontend directory:

cd frontend

# Install Node dependencies
npm install

# Start the development server
npm run dev

The UI will be available at http://localhost:5173.

📖 Usage Guide

  1. Launch: Open http://localhost:5173 in your browser.
  2. Configure:
    • Select your LLM Provider (OpenAI Compatible or Anthropic).
    • Enter your API Key and Model Name (e.g., gpt-4o).
    • Enter your Dashscope API Key for TTS service.
  3. Upload: Drag & drop a PDF file into the "Data Source" area.
  4. Generate: Click INITIALIZE SEQUENCE. The system will process the text and synthesize audio.
  5. Listen: Use the built-in player to listen to your podcast or download it.

📄 License

Apache 2.0

About

Implement the AI podcast feature of NotebookLM to generate Chinese podcasts in a clear and concise manner.

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