Description Existing audio-visual datasets for human speech are either captured in a clean, controlled environment but contain only a small amount of speech data without natural conversations, or are collected in-the-wild with unreliable audio quality, interfering sounds, low face resolution, and unreliable or occluded lip motion. The Facestar dataset aims to enable research on audio-visual modeling in a large-scale and high-quality setting. Core dataset features: 10 hours of high-quality audio-visual speech data audio recordings in a quiet environment at 16kHz […]

Info Usage Extract metadata The dataset provided metadata in the form of an XML file train_label.xml which can be hard to processed. We first convert this into a more accessible JSON file. The result will be saved as list/train_image_metadata.json. Split data Since we use the data above for training, evaluation, and testing, we split it into corresponding CSV files. The results are stored in the list folder as CSVs file of tuples of (image_id, vehicle_id, cam_id): reid_train.csv: contains the training […]

Project Page | Paper This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by Matching Training Trajectories paper (CVPR 2022). Please see our project page for more results. Dataset Distillation by Matching Training Trajectories George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu CMU, MIT, UC Berkeley CVPR 2022 The task of “Dataset Distillation” is to learn a small number of synthetic images such that a model trained on this set […]

A 🤗 Datasets extension package that provides support for executing arbitrary SQL queries on HF datasets. It uses DuckDB as a SQL engine and follows its query syntax. Installation Quick Start

Chord-Conditioned Melody Choralization with Controllable Harmonicity and Polyphonicity This is the source code of DeepChoir, a melody choralization system, which can generate a four-part chorale for a given melody conditioned on a chord progression, trained/validated on Chordified JSB Chorales Dataset. The evaluation data we used in our experiments in the outputs folder, and the musical discrimination test is available at https://sander-wood.github.io/deepchoir/test. The generated samples (chorales, folk songs and a symphony) are in the samples folder, you can also listening them […]

This repository contains the code for our paper “ZeroGen: Efficient Zero-shot Learning via Dataset Generation”.Our implementation is built on the source code from dino. Thanks for their work. If you use this code, please cite our paper: @article{ye2022zerogen, title={ZeroGen: Efficient Zero-shot Learning via Dataset Generation}, author={Jiacheng Ye and Jiahui Gao and Qintong Li and Hang Xu and Jiangtao Feng and Zhiyong Wu and Tao Yu and Lingpeng Kong}, year={2022}, eprint={2202.07922}, archivePrefix={arXiv}, primaryClass={cs.CL} } Setup All requirements for ZEROGEN can be […]

Introduction This repository contains PyTorch image dataloaders and utility functions to load datasets for supervised continual learning. Currently supported datasets: MNIST Pairwise-MNIST Fashion-MNIST not-MNIST (letters version of MNIST, see EMNIST for more detail) CIFAR-10 CIFAR-100 German Traffic Signs Street View House Numbers (SVHN) Incremental CIFAR-100 Incremental TinyImageNet Features The provided interface simplifies typical data loading for supervised continual learning scenarios. Dataset order, additional training data (for replay buffers) and test data (for global metrics computation) can all be specified. A […]

This repository contains Sofcatalà web site content (articles and programs descriptions). Dataset are available in the dataset directory. Dataset size: articles.json contains 623 articles with 366915 words programes.json contains 330 program descripctions with 49110 words The license of the data is Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) GitHub View Github    

Project setup run command to setup assets(dataset from UD) It uses project.yml file and download the data from UD GitHub repository. Download vectors Download fasttext vectors wget https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.ur.300.vec.gz Use these vectors to prune it so that model size is reduced. I’m currently using 100000 vectors for training the model. mkdir vectors python -m spacy init vectors ur cc.ur.300.vec.gz ./vectors –truncate 100000 –name ur_model.vectors Train the model Now run the command to train the tagger and parser for Urdu language.

How to cite If you use these data please cite Description This dataset is licensed under a CC-BY-4.0 license Statistics Varieties: 101 Concepts: 254 Lexemes: 26,224 Sources: 0 Synonymy: 1.08 Cognacy: 26,224 cognates in 3,173 cognate sets (812 singletons) Cognate Diversity: 0.11 Invalid lexemes: 0 Tokens: 115,799 Segments: 367 (0 BIPA errors, 0 CTLS sound class errors, 369 CLTS modified) Inventory