Popular GitHub Repositories for Song Cover

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

YuE: Open Full-song Music Generation Foundation Model, something similar to Suno.ai but open

Qodo-Cover: An AI-Powered Tool for Automated Test Generation and Code Coverage Enhancement! ????

ai cover generator

A collection of corpora for named entity recognition (NER) and entity recognition tasks. These annotated datasets cover a variety of languages, domains and entity types.

A WebUI to create song covers with any RVC v2 trained AI voice from YouTube videos or audio files.

A curated list of articles that cover the software engineering best practices for building machine learning applications.

A curated list of useful resources that cover Offensive AI.

InspireMusic: A Unified Framework for Music, Song, Audio Generation.

Finding the genre of a song with Deep Learning

All in One Version : Youtube WAV Download, Separating Vocal, Splitting Audio, Training, and Inference Using Google Colab

Rap song writing recurrent neural network trained on Kanye West's entire discography

Reading list of hallucination in LLMs. Check out our new survey paper: "Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models"

Speech to Text to Speech. Song now playing. Sends text as OSC messages to VRChat to display on avatar. (STTTS) (Speech to TTS) (VRC STT System) (VTuber TTS)

Understanding emotions from audio files using neural networks and multiple datasets.

This is the dataset repository for the paper: POP909: A Pop-song Dataset for Music Arrangement Generation

This Repo Is In Development. It will slowly but surely become one of the most potent repos available for open source AI agent code for n8n and other use cases that I cover in my YouTube videos. Star the repo to support and to stay up-to-date. Book a call with my team if you want AI agent DFY.

All in One Version : Youtube WAV Download, Separating Vocal, Splitting Audio, Training, and Inference Using Google Colab

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

M.I.L.E.S, a GPT-4-Turbo voice assistant, self-adapts its prompts and AI model, can play any Spotify song, adjusts system and Spotify volume, performs calculations, browses the web and internet, searches global weather, delivers date and time, autonomously chooses and retains long-term memories. Available for macOS and Windows.