AudioMuse-AI is an Open Source Dockerized environment that brings automatic playlist generation to Jellyfin, Navidrome, LMS, Lyrion and Emby. Using powerful tools like Librosa and ONNX, it performs sonic analysis on your audio files locally, allowing you to curate the perfect playlist for any mood or occasion without relying on external APIs.

Template to build an iPlug2 project with CI/CD and publish a WAM to github pages

Simple clap rhythm detection on Raspberry Pi using Csound and Python. Toy example.

Audio-Visual Generalized Zero-Shot Learning using Large Pre-Trained Models

Code for ICASSP 2024 Paper: RECAP: Retrieval-Augmented Audio Captioning

Code for the paper: MACE: Leveraging Audio for Evaluating Audio Captioning Systems

Offline AI music recommender & recogniser. Local song ID + smart playlists (CLAP + FAISS).

Code generated C++11/Python/Rust/Go/Bash command line arguments parser, based on protobuf configuration

An MCP server for controlling the Carla audio plugin host

A user-friendly interface for interacting with my FFmpeg Fork with new CLIP and CLAP Classification functionallity, along with an analysis script for in-depth video comprehension.

Audio Plugin Uninstaller (currently only windows)

AudioMuse-AI-DCLAP is a lightweight, high-speed distilled version of LAION CLAP, designed for fast and efficient text-to-music search

Self-hosted semantic audio search engine powered by CLAP + FastAPI + Qdrant + Gradio. Search and tag sound effects or music by meaning — not just filenames.

Audio Diffusion

A simple tool designed to simplify the clapping process on Medium.

Local Music Recommendation System

This reporsitory code form Weakly Supervised Automaed Audio Captioning via Text Only Training

Plex collection creator and recommendator using LAION-CLAP

多种音频评估方法复现

Pytorch implementation of CLAP : LEARNING AUDIO CONCEPTS FROM NATURAL LANGUAGE SUPERVISION