LipLang-AI

LipLang AI represents a groundbreaking advancement in the field of silent communication, specifically tailored for individuals with speech impairments. By harnessing the power of deep learning, this model deciphers spoken words from the subtle movements of lips captured in video sequences. Our approach combines 3D Convolutional Neural Networks (3D CNN) and Gated Recurrent Units (GRU) to achieve accurate and efficient lip reading.Silent communication has long been a challenge for those who cannot rely on spoken language. LipLang AI aims to bridge this gap by transforming lip movements into meaningful text. Our model processes video frames, extracting spatial and temporal features using 3D CNNs.These networks learn intricate patterns from volumetric data, capturing both the visual context and the dynamic evolution of lip motion.Additionally, Gated Recurrent Units (GRUs) handle sequential dependencies, ensuring that the model adapts to varying speaking rates and nuanced lip gestures.To train our model, we utilize the GRID dataset, a valuable resource for lip reading research. Multiple video streams are preprocessed, aligned and augmented to enhance robustness. The neural network architecture incorporates 3D convolutions and LSTM layers, optimizing the balance between memory capacity and computational efficiency. Loss functions and callbacks are carefully chosen for effective training.Our web application, built using Streamlit, Python and TensorFlow,serves as a practical interface for users. Beyond the initial scope, LipLang AI can be extended to various use cases, such as webcam integration or deployment on edge devices. This flexibility opens up a wide range of possibilities for lip reading applications.LipLang AI is not only a technical achievement but also a step toward a more inclusive and accessible world. By decoding silent cues, it empowers individuals with speech difficulties to express themselves effectively, fostering independence and connection.