Can I generate stereo music with this skill?

Yes, the skill includes specific instructions for using MusicGen-Stereo models to generate audio with wide spatial panning and professional stereo output.

Which AudioCraft models are supported by this skill?

This skill provides implementation patterns for MusicGen (text-to-music), AudioGen (text-to-sound), and EnCodec (audio compression), covering model sizes from 300M to 3.3B parameters.

What are the primary dependencies for running AudioCraft?

To use this skill effectively, you need a Python environment with PyTorch 2.0+, the audiocraft library, and the HuggingFace Transformers library.

How do I use a reference melody to guide generation?

The skill demonstrates how to use the 'musicgen-melody' model to perform melody-conditioned generation, allowing you to use a reference audio file to influence the structure of the AI-generated track.

AudioCraft AI Generation

Name: AudioCraft AI Generation
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Generates high-fidelity music and sound effects from text descriptions using Meta's AudioCraft framework.

About

This skill enables Claude to implement and orchestrate Meta's AudioCraft suite, including MusicGen for text-to-music and AudioGen for sound effect creation. It provides comprehensive patterns for building audio-centric applications, configuring model variants from small (300M) to large (3.3B), and utilizing advanced features like melody-conditioned generation, stereo output, and neural audio compression with EnCodec. This is an essential toolkit for developers and researchers building creative tools, game sound engines, or multimedia processing pipelines that require controllable, state-of-the-art audio synthesis.

Key Features

Style-based conditioning and reference-based music synthesis
Environmental sound effect synthesis through AudioGen
High-fidelity neural audio compression and reconstruction via EnCodec
Text-to-music generation using MusicGen with custom duration and sampling
Melody-conditioned generation for rhythmic and pitch-based control
384 GitHub stars

Use Cases

Generating custom, royalty-free background music for video production and games
Building AI-powered music production tools with style transfer and melody guidance
Creating realistic environmental foley and sound effects for immersive applications

About

Key Features

Style-based conditioning and reference-based music synthesis
Environmental sound effect synthesis through AudioGen
High-fidelity neural audio compression and reconstruction via EnCodec
Text-to-music generation using MusicGen with custom duration and sampling
Melody-conditioned generation for rhythmic and pitch-based control
384 GitHub stars

Use Cases

Generating custom, royalty-free background music for video production and games
Building AI-powered music production tools with style transfer and melody guidance
Creating realistic environmental foley and sound effects for immersive applications