LipNet Implementation with Streamlit UI

This project reimplements LipNet, a lipreading model, and integrates it with a user-friendly interface using Streamlit.

Overview

LipNet is a lipreading model designed to interpret human speech by analyzing lip movements. This project provides a streamlined implementation of LipNet and enhances it with a web-based UI powered by Streamlit.

Features

Lipreading Model: Implements the LipNet architecture for accurate lipreading from video data.
Streamlit UI: Provides an intuitive interface for users to interact with the lipreading model.
Data Selection: Allows users to choose input data sources (videos) for lipreading analysis.
Real-Time Lipreading: Capable of processing lip movements in near real-time for quick feedback.

Technologies Used

Python: Core programming language.
TensorFlow / PyTorch: Deep learning frameworks for model implementation.
Streamlit: Web application framework for creating the user interface.
HTML/CSS: Basic styling and layout for the Streamlit app.

Setup

Clone the repository:

git clone https://github.com/your_username/lipnet-streamlit.git
cd lipnet-streamlit

Install the dependencies:
```
pip install -r requirements.txt
```
Run the Streamlit app:
```
streamlit run app.py 
```

Usage

Select a video file or webcam input for lipreading.
Wait for the model to process the video and display the interpreted text.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.

License

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

Acknowledgments

The original LipNet research paper and authors.link
Streamlit community for their excellent documentation and support.

yatin-kundra/LipNet-A-Lip-Reading-Model