🎧 Representation Chizzler™

A powerful two-stage audio processing tool that combines Voice Activity Detection (VAD) and Speech Enhancement to clean and denoise audio files.

🌟 Features

1. Two-Stage Processing Pipeline:

   • Stage 1: Uses Silero VAD to detect and extract speech segments
   • Stage 2: Applies MP-SENet deep learning model to remove noise

2. Memory-Efficient Processing:

   • Processes audio in chunks to prevent memory issues
   • Automatically converts audio to the required format (16kHz mono WAV)

3. User-Friendly Interface:

   • Beautiful Gradio web interface
   • Real-time progress reporting
   • Compare original, VAD-processed, and denoised versions

🚀 Installation

1. Clone this repository:

   git clone https://github.com/Reza2kn/RepresentationChizzler.git
   cd RepresentationChizzler

2. Create and activate a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   • Create a .env file in the project root
   • Add your Hugging Face token:

     HF_TOKEN=your_huggingface_token_here
     

5. Download MP-SENet:

   • Clone the MP-SENet repository:

     git clone https://github.com/yxlu-0102/MP-SENet.git

   • Download the model checkpoint and config files:
     • Place g_best_dns in MP-SENet/best_ckpt/
     • Place config.json in MP-SENet/best_ckpt/

🎮 Usage

1. Run the app:

   python run.py

2. Open your web browser and navigate to the provided URL

3. Upload an audio file and adjust the parameters:

   • VAD Threshold: Controls voice detection sensitivity (0.1-0.9)
   • Max Silence Gap: Controls merging of close speech segments (1-10s)

4. Compare the results:

   • Original Audio
   • VAD Processed (Speech Only)
   • Final Denoised

🛠️ Parameters

• VAD Threshold (0.1-0.9):

  • Higher values = stricter voice detection
  • Lower values = more lenient detection
  • Default: 0.5

• Max Silence Gap (1-10s):

  • Maximum silence duration to consider segments as continuous
  • Higher values = fewer segments but may include more silence
  • Default: 4.0s

🙏 Credits

This project combines two powerful models:

• Silero VAD for Voice Activity Detection
• MP-SENet for Speech Enhancement

📝 License

This project is licensed under the terms specified in the MP-SENet repository.