Home› Projects› audio-ai-hub
audio-ai-hub
Python

audio-ai-hub

The hub for audio AI research: papers, open models, benchmarks & datasets across audio LLMs, speech recognition, TTS, music & audio generation.

by BinWang28 · GitHub
Stars
Forks
Created
Last commit
Category
Language
audio-aiaudio-generationaudio-llmPython
View on GitHub
In plain words

Access a hub of research papers, models, and datasets focused on audio AI.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
audio-ai-hub — GitHub preview card
📈 Star history
945944
2026-07-202026-08-31
📈 Track audio-ai-hub

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

The hub for audio AI research: papers, open models, benchmarks & datasets across audio LLMs, speech recognition, TTS, music & audio generation.

audio-ai-hub has 944 stars on GitHub. It has been forked 48 times. audio-ai-hub is written mainly in Python. It has been in active development since 2024. Its main topics are audio-ai, audio-generation, audio-llm, music-generation.

❓ Frequently asked questions

What is audio-ai-hub?

The hub for audio AI research: papers, open models, benchmarks & datasets across audio LLMs, speech recognition, TTS, music & audio generation.

Is audio-ai-hub open source?

audio-ai-hub is an open-source project.

Is audio-ai-hub free?

Yes. audio-ai-hub is free and open source — you can use, modify and self-host it.

What language is audio-ai-hub written in?

audio-ai-hub is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — audio-ai-hub

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=binwang28-audio-ai-hub)](https://olud.ai/project/binwang28-audio-ai-hub.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.