🎧 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. Create a new conda environment:

   conda create -n speech_enhance_new python=3.9
   conda activate speech_enhance_new

2. Install dependencies:

   conda install numpy=1.22.4 scipy=1.7.3 librosa=0.9.2
   pip install torch torchaudio gradio pydub rich

3. Download the MP-SENet model:

   • Place the model file in MP-SENet/best_ckpt/g_best_dns
   • Place the config file in MP-SENet/best_ckpt/config.json

🎮 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.