LabelBuddy: An Open Source Music and Audio Language Annotation Tagging Tool Using AI Assistance

TL;DR

LabelBuddy is introduced, an open-source collaborative auto-tagging audio annotation tool designed to bridge the gap between human intent and machine understanding, which supports multi-user consensus, containerized model isolation, and a roadmap for extending agents and LALMs.

Abstract

The advancement of Machine learning (ML), Large Audio Language Models (LALMs), and autonomous AI agents in Music Information Retrieval (MIR) necessitates a shift from static tagging to rich, human-aligned representation learning. However, the scarcity of open-source infrastructure capable of capturing the subjective nuances of audio annotation remains a critical bottleneck. This paper introduces \textbf{LabelBuddy}, an open-source collaborative auto-tagging audio annotation tool designed to bridge the gap between human intent and machine understanding. Unlike static tools, it decouples the interface from inference via containerized backends, allowing users to plug in custom models for AI-assisted pre-annotation. We describe the system architecture, which supports multi-user consensus, containerized model isolation, and a roadmap for extending agents and LALMs. Code available at https://github.com/GiannisProkopiou/gsoc2022-Label-buddy.

Figures (2)

• Figure 1: System Architecture Overview: The architecture decouples the Django web server from Dockerized ML inference.
• Figure 2: The annotation interface displaying AI-generated predictions as editable waveform regions.