To turn your LipNET machine learning model into an app, you will need to write some code using HTML tags. In this tutorial, we will walk you through the steps to create a simple web app that utilizes your LipNET model for lip reading.
Step 1: Set Up Your Project
First, create a new folder on your computer for your project. Inside this folder, create an HTML file and name it index.html. This will be the main file for your app.
Step 2: Implement LipNET Model
Next, you will need to include your LipNET model in your project. You can either upload the model to a cloud storage service (such as Google Drive or Dropbox) or host it on a server. For this tutorial, we will assume you have hosted the model on a server.
To load the model in your HTML file, you can use the following code:
<script src="https://cdn.jsdelivr.net/gh/yourusername/yourrepository/yourmodel.js"></script>Replace "yourusername", "yourrepository", and "yourmodel.js" with the appropriate values for your model.
Step 3: Create User Interface
Now, let’s create a simple user interface for our app. Add the following code to your index.html file:
<!DOCTYPE html>
<html>
<head>
<title>Lip Reading App</title>
</head>
<body>
<h1>Lip Reading App</h1>
<video id="video" width="320" height="240" controls autoplay></video>
<div id="result"></div>
</body>
</html>In this code snippet, we have added a title and a video element to display the lip reading video stream. We have also added a div element with an id of "result" to display the lip reading result.
Step 4: Add Script for Lip Reading
Next, we will add a script tag to our HTML file to handle the lip reading functionality. Add the following code to your index.html file:
<script>
const video = document.getElementById('video');
const result = document.getElementById('result');
navigator.mediaDevices.getUserMedia({ video: true })
.then(stream => {
video.srcObject = stream;
})
.catch(error => {
console.error('Error accessing camera:', error);
});
video.addEventListener('play', async () => {
const lipNet = new LipNet();
setInterval(async () => {
const predictions = await lipNet.predict(video);
result.innerText = predictions[0].label;
}, 1000); // Update result every second
});
</script>This script sets up the video element to display the camera feed, creates a new instance of your LipNET model, and continuously makes predictions on the video stream. The predicted label is then displayed in the result div.
Step 5: Test Your App
Finally, open your index.html file in a web browser to test your lip reading app. You should see a video feed from your camera and the predicted lip reading result displayed in the app.
Congratulations! You have successfully turned your LipNET machine learning model into an app using HTML tags. Feel free to customize the app further by adding more features and improving the user interface.
but it only works for that specific data set cause they contain a align file, what if i wanted the model to decode a seperate mpg/mp4 file that's not a part of that dataset
So there any public app???)
I hate to say this but we sadly live in a world where if you were to make a set of lip reading AIs, the AI that does bad lip reading would probably be more profitable.
why don't you use gradio instead of streamlit?
Help us to load our own videos!
Amazing
Nick please make a detailed video on building this app…
The most weried thing i made is not a ml app,instead is a system resource monitoring app.😂
How generalizable would this be seeing as we don't all have the same pronunciation and diction?
What framework you used instead of Tkinter?
Hey Nick what keyboard is that? Is that the apple keyboard?
Impressive, and for sure we HAVE to see this implementation, problems and possible solutions.
I guess that only work for english, what about accents? does it has any problem with those which english is not mother tonge?
Is there any synergy with pairing it up with something like whisper? I guess you could determine if the person in the picture is the one talking or someone else off screen.
Great 👍, how i can get LipNet model
Hey Nicolas..i did something similar for this as my final year project under the topic of Lip Reading for the hearing impaired…is it okay if i can share my thesis of the paper that I did…I used the same Grid Corpus dataset but in terms of image processing to crop the mouth I used the Haar Cascade classifier…but i created 4 different models of 4 different English categories such as verbs, pronouns, digits and adverbs…but the highest accuracy I got was 57% but i think it was due to overfitting because i got 96% on the training data
Bro too good
Abeg Big Bros
Detailed tutorial of this would really be appreciated.
Much love
💪💪💪👏👏👏👏
That ending though 😅
It's just phenomenal. Great work.