Audio Analyser

Audio Analyser is a powerful tool for analyzing, transcribing, and translating audio content from YouTube videos. It provides detailed insights into audio characteristics, speech patterns, and emotional content, along with accurate transcription and translation capabilities.

Features

• Audio Analysis

  • Pitch analysis (average, variability, range)
  • Signal quality metrics (RMS, noise floor, SNR)
  • Voice quality assessment (jitter, shimmer, quality score)
  • Emotion detection and intensity analysis
  • Speech pattern analysis (pause count, duration, word rate)

• Transcription & Translation

  • Accurate YouTube video transcription
  • Multi-language translation support
  • Real-time translation streaming
  • Timestamped transcript segments

• Visualization

  • Interactive charts and graphs
  • Detailed audio metrics visualization
  • Comparative analysis views
  • Raw data and visual toggle

Technology Stack

Backend

• Python 3.12
• FastAPI
• yt-dlp
• SciPy
• NumPy
• NLTK

Frontend

• React.js
• Tailwind CSS
• Chart.js
• Axios

Installation

Backend Setup

1. Clone the repository:

   git clone https://github.com/yourusername/audio-analyser.git
   cd audio-analyser/backend

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Start the server:

   uvicorn main:app --reload

Frontend Setup

1. Navigate to the frontend directory:

   cd ../frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm start

Usage

1. Open the application in your browser (default: http://localhost:3000)
2. Enter a YouTube video URL
3. View the transcription and analysis results
4. Use the translation feature to translate the transcript
5. Explore the detailed audio analysis visualizations

Screenshots

API Documentation

The backend API is documented using Swagger UI. After starting the server, visit:

http://localhost:8000/docs

Configuration

Create a .env file in the backend directory with the following variables:

API_KEY=your_api_key_here

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/YourFeatureName)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/YourFeatureName)
5. Open a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• FastAPI for the backend framework
• React for the frontend framework
• Chart.js for data visualization
• yt-dlp for YouTube video processing

I have used Locally running LLM on my System. Feel free to use whatever you want.

Contact

For any inquiries, please contact aman1024soni@gmail.com