🎙️ Audio Diarization Library

🚧 Repository is under development! Major improvements are on the way. 🚧

🚀 Demo

Try the early version on Colab: https://colab.research.google.com/drive/1YIy4nhF3b6bgrD8rdtWPWimKyKTofBmh?authuser=1

📖 Overview

This library enables audio diarization, the process of segmenting and labeling speakers in an audio file. It supports a variety of algorithms and models, while being flexible for further extensions.

🛠️ Current Capabilities

- 🎧 Speaker Identification: Assigns names to different speakers based on provided samples.
- 🔧 Customizable Algorithms: Choose between various diarization methods.
- ⚙️ Base Functionality: A robust starting point for diarization tasks, with room for optimization.
 

🛤️ Planned Improvements

• 📊 Visualization: Add graphs and interactive visualizations for audio data and diarization results.
• 🧠 Advanced AI: Integrate machine learning (ML) and neural network (NN) features for superior accuracy.
• ☁️ Cloud Deployment: Deploy the library on cloud platforms (e.g., AWS, Google Cloud) with Docker containerization.
• 🔧 Optimize code, use faster asr, remove silence faster (potentially remove ai from here)

🛡️ Technologies to Be Added

• Data Visualization: Tools like Plotly or Matplotlib for creating intuitive graphs.
• ML/NN: Advanced ML and NN techniques for state-of-the-art diarization.
• Scalable Deployment: Cloud-hosted solutions using Docker and Kubernetes.

🔨 Technologies Used (So Far)

• 🐍 Python: Core programming language.
• 🗣️ Audio Processing: Libraries like Whisper and PyTorch for speaker recognition and segmentation.
• 🛠️ Custom Algorithms: Implementation of base-level diarization logic.

📌 Notes

This repository is in its early stages. Contributions and suggestions are welcome! See the demo above to explore the current functionality.

📜 License

This project is licensed under the MIT License.