A Keras implementation of LipNet
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Updated
Oct 30, 2018 - Python
A Keras implementation of LipNet
Chainer code for using Residual Networks with LSTMs for Lipreading
"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.
Audio-Visual Speech Recognition using Sequence to Sequence Models
DenseNet3D Model In "LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild", https://arxiv.org/abs/1810.06990
Speaker-Independent Speech Recognition using Visual Features
PyTorch models for lipreading words and sentences
The concurrent lipreader for the smart masses (DC27 AI Village)
This project aims to develop and test different lip reading algorithms on words and on sentences, using the GRID Corpus Dataset.
A pipeline to read lips and generate speech for the read content, i.e Lip to Speech Synthesis.
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)
Implementation of "Combining Residual Networks with LSTMs for Lipreading" in Keras and Tensorflow2.0
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 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.
Implementation of a method to lipreading using landmark from 3D talking head
Visual Speech Recognition for Multiple Languages
Replication of the state-of-the-art LIPNET model for end-to-end sentence-level lipreading.
Курсовой проект по теме "Анализ эффективности архитектур визуального распознавания речи"
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