Visual Speech Recognition for Multiple Languages

The state-of-art PyTorch implementation of the method described in the paper "LipNet: End-to-End Sentence-level Lipreading" (https://arxiv.org/abs/1611.01599)

Auto-AVSR: Lip-Reading Sentences Project

The PyTorch Code and Model In "Learn an Effective Lip Reading Model without Pains", (https://arxiv.org/abs/2011.07557), which reaches the state-of-art performance in LRW-1000 dataset.

DenseNet3D Model In "LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild", https://arxiv.org/abs/1810.06990

Audio-Visual Speech Recognition using Sequence to Sequence Models

A pipeline to read lips and generate speech for the read content, i.e Lip to Speech Synthesis.

"LipNet: End-to-End Sentence-level Lipreading" in PyTorch

End-to-end pipeline for lip reading at the word level using a tensorflow CNN implementation.

A Keras implementation of LipNet

LipNet with gluon

SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token Synchronization (Interspeech 2024)

Visual Speech Recognition using deep learing methods

PyTorch models for lipreading words and sentences

This project aims to develop and test different lip reading algorithms on words and on sentences, using the GRID Corpus Dataset.

Implementation of "Combining Residual Networks with LSTMs for Lipreading" in Keras and Tensorflow2.0

Speaker-Independent Speech Recognition using Visual Features

A video demo of IEEE International Conference on Acoustics, Speech and Signal Processing submitted paper titled "Lip-to-Speech Synthesis in the Wild with Multi-task Learning"

The concurrent lipreader for the smart masses (DC27 AI Village)