dhapin

Muhammad Dhafin Al Khairy's starred repositories

BotsApp

Your personal assistant, on WhatsApp! The most user friendly userbot for the platform!

Language:TypeScriptGPL-3.0550300

face_recognition

The world's simplest facial recognition api for Python and the command line

Language:PythonMIT5261800

A modern, web-based photo management server. Run it on your home server and it will let you find the right photo from your collection on any device. Smart filtering is made possible by object recognition, face recognition, location awareness, color analysis and other ML algorithms.

Language:PythonAGPL-3.0182800

photoprism

AI-Powered Photos App for the Decentralized Web 🌈💎✨

Language:GoNOASSERTION3412900

immich

High performance self-hosted photo and video management solution.

Language:TypeScriptAGPL-3.04220800

deepface

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

Language:PythonMIT1136400

insightface

State-of-the-art 2D and 3D Face Analysis Project

Language:Python2246400

openface

Face recognition with deep neural networks.

Language:LuaApache-2.01507600

facenet

Face recognition using Tensorflow

Language:PythonMIT1368200

photoview

Photo gallery for self-hosted personal servers

Language:TypeScriptAGPL-3.0490800

librephotos

A self-hosted open source photo management service. This is the repository of the backend.

Language:PythonMIT675300

Mangio-RVC-Fork

*CREPE+HYBRID TRAINING* A very experimental fork of the Retrieval-based-Voice-Conversion-WebUI repo that incorporates a variety of other f0 methods, along with a hybrid f0 nanmedian method.

Language:PythonMIT96000

Applio

VITS-based Voice Conversion focused on simplicity, quality and performance.

Language:PythonMIT141300

manim_editor

Web Presenter for Mathematical Animations using Manim.

Language:PythonMIT25100

manim

A community-maintained Python framework for creating mathematical animations.

Language:PythonMIT2036000

svoice

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.

Language:PythonNOASSERTION119800