WIT (Wikipedia-based Image Text) Dataset is a large multimodal multilingual dataset comprising 37M+ image-text sets with 11M+ unique images across 100+ languages.
WIT (Wikipedia-based Image Text) Dataset is a large multimodal multilingual dataset comprising 37M+ image-text sets with 11M+ unique images across 100+ languages.
Unofficial Implementation of Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration
Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration This repo contains only model Implementation of Zero-Shot Text-to-Speech for Text
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
textgenrnn Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly tr
Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"
T5: Text-To-Text Transfer Transformer The t5 library serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Lear
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
textgenrnn Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly tr
Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"
T5: Text-To-Text Transfer Transformer The t5 library serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Lear
Official PyTorch code for ClipBERT, an efficient framework for end-to-end learning on image-text and video-text tasks
Official PyTorch code for ClipBERT, an efficient framework for end-to-end learning on image-text and video-text tasks. It takes raw videos/images + text as inputs, and outputs task predictions. ClipBERT is designed based on 2D CNNs and transformers, and uses a sparse sampling strategy to enable efficient end-to-end video-and-language learning.