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aigc-audio/audiogpt

10,188PythonAudience · researcherComplexity · 4/5Setup · hard

TLDR

A research project that lets you control multiple audio and speech AI models using plain-text instructions, covering text-to-speech, transcription, sound generation, and talking-face video synthesis.

Mindmap

mindmap
  root((AudioGPT))
    What it does
      LLM controls audio models
      Plain-text instructions
    Speech Tasks
      Text to speech
      Transcription
      Audio enhancement
      Speaker separation
    Creative Tasks
      Singing synthesis
      Sound effects
      Talking-face video
    Setup
      Research prototype
      Multiple pretrained models
    Audience
      Researchers
      Audio developers
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Things people build with this

USE CASE 1

Generate realistic spoken audio from text using a plain-English prompt to select and run the right synthesizer automatically.

USE CASE 2

Transcribe or enhance audio recordings by describing what you want in natural language rather than calling individual model APIs.

USE CASE 3

Create sound effects from text descriptions or generate audio that matches the mood of an input image.

USE CASE 4

Produce a talking-head video of a face speaking from an audio clip using a single text instruction.

Tech stack

Python

Getting it running

Difficulty · hard Time to first run · 1day+

Requires downloading and installing multiple large pre-trained models, many components are still marked as works in progress.

No license information is mentioned in the explanation.

In plain English

AudioGPT is a research project that connects a large language model (a ChatGPT-style AI) with a collection of specialized audio and speech models. The idea is that you can describe what you want in plain text, and the system figures out which underlying model to use and runs it for you, whether that means generating speech from text, transcribing spoken audio, separating voices in a recording, or synthesizing a talking face video from an audio clip. The project covers four broad categories of audio tasks. In the speech category, it can convert text to spoken audio using several different synthesizer models, recognize and transcribe speech, enhance audio quality, separate overlapping speakers, and convert mono audio to spatial (binaural) audio. In the singing category, it can generate sung vocal performances from text lyrics. In the general audio category, it can create sound effects from text descriptions, fill in missing portions of audio, generate sounds based on an input image, and detect or extract specific sounds from a recording. It also includes a talking head task, which means generating a video of a face speaking from an audio input. This is a research implementation, not a polished commercial product. Many of the individual models listed in the README are marked as works in progress, meaning they are included but not fully functional yet. Getting it running requires following a separate setup guide and installing multiple pre-trained models. The project is accompanied by a published research paper on arXiv and a live demo on Hugging Face for those who want to try the concept without setting up the code locally. The README for this repository is brief and points mostly to other documentation rather than explaining everything in one place.

Copy-paste prompts

Prompt 1
I want to use AudioGPT to generate a sound effect from a text description like 'rain falling on a metal roof'. Walk me through the setup and the exact prompt to run.
Prompt 2
Using AudioGPT, how do I transcribe an audio file and then enhance its quality in a single pipeline? Show me the steps and any config I need.
Prompt 3
I want to separate two overlapping speakers in a recording using AudioGPT. Which underlying model does it use for speaker separation and how do I trigger that task?
Prompt 4
Help me set up AudioGPT locally, what pre-trained models do I need to download, where do they go, and what does the setup guide say to run first?
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