licence plate detection and recognition
🗃 Open source self-hosted web archiving. Takes URLs/browser history/bookmarks/Pocket/Pinboard/etc., saves HTML, JS, PDFs, media, and more...
The PyTorch-based audio source separation toolkit for researchers
Background Matting: The World is Your Green Screen
Model Serving Made Easy
Prevent cloud misconfigurations during build-time for Terraform, Cloudformation, Kubernetes, Serverless framework and other infrastructure-as-code-languages with Checkov by Bridgecrew.
VS Code in the browser
📊 Cube — Headless Business Intelligence for Building Data Applications
:art: Diagram as Code for prototyping cloud system architectures
End-to-End Speech Processing Toolkit
Simple and rapid application development framework, built on top of Flask. includes detailed security, auto CRUD generation for your models, google charts and much more. Demo (login with guest/welcome) - http://flaskappbuilder.pythonanywhere.com/
Apache ECharts (incubating) is a powerful, interactive charting and data visualization library for browser
Apache Superset is a Data Visualization and Data Exploration Platform
Predictive AI layer for existing databases.
🤗nlp – Datasets and evaluation metrics for Natural Language Processing in NumPy, Pandas, PyTorch and TensorFlow
A list of useful payloads and bypass for Web Application Security and Pentest/CTF
AWS Quick Start Team
Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
Making it easy to query APIs via SQL
Apache Superset UI packages
We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.
A repository to show off to the community methods of testing NestJS including Unit Tests, Integration Tests, E2E Tests, pipes, filters, interceptors, GraphQL, Mongo, TypeORM, and more!