A minimum unofficial implementation of the "A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement" (CRN) using PyTorch
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Updated
Sep 5, 2020 - Python
A minimum unofficial implementation of the "A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement" (CRN) using PyTorch
This repositary contain all my exercises and projects of Udacity Computer Vision Nanodegree Program
Sequence-to-sequence model implementations including RNN, CNN, Attention, and Transformers using PyTorch
Contains additional materials for two keras.io blog posts.
16 projects in the framework of Computer Vision algorithms: 16 projects in the framework of Computer Vision algorithms: CNN, RNN, LSTM, Facial KeyPoints, Image Captioning, SLAM, Edge Detectors, Day Night Classifier, etc.
Caption generation through a CNN-RNN model further to be converted to speech using a text to speech library for a visually challenged person for understanding the content of an image in the form of speech.
This project aims to assist visually impaired individuals by providing a solution to convert images into spoken language. Leveraging deep learning and natural language processing, the system processes images, generates descriptive captions, and converts these captions into audio output.
A Deep Learning-based approach to classify human gestures for smart appliances.
This project builds a video classification model using CNNs for spatial feature extraction and RNNs for temporal sequence modeling. Utilizing the UCF101 dataset, it covers data preprocessing, feature extraction, model training, and evaluation, providing a comprehensive approach to action recognition in videos.
Image Captioning using CNN-RNN architecture made with ❤️ in Pytorch. Do 🌟 he repo if you find it useful 🚀
Built a CNN-RNN neural network architecture to automatically generate captions from images describing that image.
A WebApp that Generates Caption for Images using CNN-RNN Architecture
To develop gesture recognition feature for smart TV which help user control the TV without using remote.
scene text detection
Tuning, training, and transfer learning CRNN models for handwritten text words.
A neural network architecture that automatically generate captions from images.
Develop a cool feature in the smart-TV that can recognise five different gestures performed by the user which will help users control the TV without using a remote.
This GitHub repository contains the implementation of a deep learning model capable of generating captions for images in the form of speech.
Image Captioning Generator Keras
Image to text to speech generation
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