How to Code a Machine Learning Lip Reading App with Python Tensorflow and Streamlit

Machine learning has revolutionized the way we interact with technology, and one area where it has made significant advancements is in the field of lip reading. In this article, we will walk you through how to code a machine learning lip reading app using Python, Tensorflow, and Streamlit.

Step 1: Setting Up Your Environment

First, make sure you have Python installed on your system. You can download and install Python from the official website. Once Python is installed, you can use pip, Python’s package installer, to install the required libraries:

    
    pip install tensorflow==2.5
    pip install streamlit
    
    

Step 2: Collecting and Preprocessing Data

The next step is to gather a dataset of videos containing people speaking. You can use publicly available datasets or create your own. Preprocess the videos to extract the frames and convert them to grayscale. You can then label each frame with the corresponding word being spoken.

Step 3: Building the Lip Reading Model

Now, it’s time to build the machine learning model using Tensorflow. You can use a pre-trained model like LipNet or train your own using a convolutional neural network (CNN) or a recurrent neural network (RNN).

Step 4: Creating the User Interface with Streamlit

Streamlit is a popular Python library for creating web apps for data science and machine learning projects. You can use Streamlit to build a simple and intuitive user interface for your lip reading app. Here’s a basic example of a Streamlit app:

    
    import streamlit as st
    import tensorflow as tf
    import cv2

    st.title('Lip Reading App')

    uploaded_file = st.file_uploader("Choose a video file", type=["mp4"])

    if uploaded_file is not None:
        # Process the video and use the lip reading model to extract the spoken words
        ...
    
    

Step 5: Deploying the App

Once you have built and tested your lip reading app, you can deploy it to a web server or cloud platform for others to use. You can use services like Heroku, AWS, or Google Cloud Platform to host your app.

Conclusion

With the power of Python, Tensorflow, and Streamlit, you can create a machine learning lip reading app that can recognize and interpret spoken words from lip movements. This technology has the potential to assist people with hearing impairments and improve communication in noisy environments. We hope this article has provided you with the knowledge and inspiration to start building your own lip reading app.