data-generation

A powerful, feature-rich, random test data generator.

List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.

Synthetic Data Generation for tabular, relational and time series data.

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

The Declarative Data Generator

Conditional GAN for generating synthetic tabular data.

Data generation and property-based testing for Elixir. 🔮

Generate strings that match a given regular expression

MockNeat - the modern faker lib.

Deep Convolutional Neural Networks for Musical Source Separation

A library to model multivariate data using copulas.

Random dataframe and database table generator

Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.

Generate random data sets

The DataHelix generator allows you to quickly create data, based on a JSON profile that defines fields and the relationships between them, for the purpose of testing and validation

Generate relevant synthetic data quickly for your projects. The Databricks Labs synthetic data generator (aka `dbldatagen`) may be used to generate large simulated / synthetic data sets for test, POCs, and other uses in Databricks environments including in Delta Live Tables pipelines

Benerator is a leading software solution to generate, obfuscate, pseudonymize and migrate data for development, testing, and training purposes.

A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.

A novel approach for synthesizing tabular data using pretrained large language models

Custom image data generator for TF Keras that supports the modern augmentation module albumentations