In this article, we will discuss using existing Machine Learning models, with a focus on a model trained for lip reading in English, to demonstrate how simple and accessible it is today to start developing tools that utilize pre-trained models. Let’s explore the fascinating world of AI-powered lip reading and the potential it holds.
Using existing Machine Learning models, specifically one trained for English lip reading, to showcase how simple and accessible it is today to start developing tools that utilize pre-trained models.
In recent years, the ability to utilize Machine Learning models was reserved for a select few. Not because it was a secret, but due to the technical complexity and in-depth knowledge required to employ and train such models. However, times have changed, and now, anyone can access and utilize these powerful tools with relative ease. While training models still demands extensive knowledge and expertise, using pre-trained models is becoming increasingly accessible, thanks to the advancements in AI technology and the availability of user-friendly tools.
Today, I want to focus on a fascinating application of AI: lip reading. We’ve all seen it in movies and TV shows, but how does it work in real life? Is it accurate? In this article, I will guide you through the process of developing an AI-powered lip-reading system using existing Machine Learning models. We will also discuss the potential benefits and limitations of this technology.
Let’s start with a quick demonstration. In the GIF below, you can see a person speaking, and an AI model is able to read their lips and display the words on the screen, even though it has never heard the audio. This is an impressive display of the power of Machine Learning and lip reading:
[Insert GIF: https://yuv.ai/content/images/2024/07/lipreading–1-.gif]
How is this possible? How can AI (or more specifically, Machine Learning) be used to read lips and display the words on a screen? The key lies in training data and advanced algorithms. Lip reading models are trained on vast amounts of video data, where people are recorded speaking different words and sentences. The model learns to associate specific lip movements and patterns with corresponding words, enabling it to predict what is being said based solely on visual input.
To build such a lip-reading system, one might think that you need to be an AI expert or have access to complex code. But thanks to pre-trained models and user-friendly tools, it’s becoming surprisingly simple. For this example, I turned to ChatGPT, and here’s the prompt I used:
“Code for machine learning vision that uses my computer camera and types to the screen the words it identifies from my lips. I mean lip reading and printing it to the screen.”
And here’s the response I received, along with the code to make it work:
“For building a lip-reading application that can detect words from lip movements and display them on the screen with a typing effect, you could consider using the LipNet model. LipNet is known for its ability to perform end-to-end sentence-level lip reading from video input. It processes sequences of video frames and outputs text sequences, using a combination of spatiotemporal convolutional neural networks (STCNNs) and gated recurrent units (GRUs), trained with connectionist temporal classification (CTC) loss to handle sequence prediction without the need for pre-aligned data (ar5iv).
Here’s the code to make it work:
“`python
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
import time
# Load the LipNet model
model = load_model(‘lipnet_model.h5’)
# Function to preprocess the frame (implement according to your model’s requirement)
def preprocess_frame(frame):
# Example: Resize frame to 100×50, convert to grayscale
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame = cv2.resize(frame, (100, 50))
frame = np.expand_dims(frame, axis=-1)
return np.expand_dims(frame, axis=0)
# Function to extract lip region (you may need a more accurate method)
def extract_lips(frame):
# Placeholder function: return the same frame passed
return frame
# Initialize webcam
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print(“Cannot open camera”)
exit()
predicted_text = “”
—
#automation #ai #yuvai #magen #magenai #yuvalavidani #hackit #avidani
—
Magen AI Automations Magazine (by YUV.AI)
Yuval Avidani
@yuvalav