audiocraft-audio-generation_skill

This skill helps you generate music and sound effects from text descriptions using AudioCraft, enabling melody conditioning, style transfer, and stereo output.
  • Python

3

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill vadimcomanescu/codex-skills --skill audiocraft-audio-generation

  • LICENSE.txt1.1 KB
  • SKILL.md15.7 KB

Overview

This skill provides a concise guide to using AudioCraft for neural audio generation, covering MusicGen (text-to-music), AudioGen (text-to-sound), and EnCodec (neural audio codec). It explains model choices, core workflows, and common generation parameters so you can quickly produce music, sound effects, stereo audio, and melody-conditioned outputs. The content focuses on practical examples, installation, and performance tips for real projects.

How this skill works

AudioCraft encodes text prompts with a text encoder (T5) and autoregressively generates discrete audio tokens with a transformer decoder. An EnCodec decoder converts tokens back to high-fidelity waveforms, supporting stereo and configurable durations. Models come in multiple sizes and specializations (melody, style, stereo) and expose generation parameters such as duration, top_k, temperature, and classifier-free guidance (cfg_coef).

When to use it

  • Generate music from text descriptions (ambient, pop, orchestral, etc.)
  • Create sound effects and environmental audio for games or media
  • Produce melody-conditioned music using an input audio motif
  • Generate stereo outputs or perform style-conditioned transfers from reference audio
  • Quick prototyping with small models, higher quality with medium/large models

Best practices

  • Choose model size based on budget and latency: small for quick tests, medium/large for production quality
  • Set duration and sampling rate correctly (MusicGen 32 kHz, AudioGen 16 kHz) and resample inputs when needed
  • Use batch generation for efficiency when creating multiple clips in one run
  • Tune top_k, temperature, and cfg_coef for the balance between creativity and prompt adherence
  • Clear GPU cache and consider half precision or shorter durations to reduce memory usage

Example use cases

  • Text-to-music demo app: generate short tracks from user prompts with MusicGen-small for low latency
  • Sound design pipeline: batch-create SFX (thunder, footsteps, doors) with AudioGen and save 16 kHz WAVs
  • Melody-conditioned composition: feed a melody WAV to musicgen-melody to produce arranged tracks
  • Style transfer: use musicgen-style with a reference clip to generate tracks in a matching style
  • Audio continuation: provide an intro audio file to continue or expand musical ideas programmatically

FAQ

Start with musicgen-small (300M) for fast iterations; switch to medium/large for higher quality once parameters are tuned.

How do I condition generation on a melody or style?

Use musicgen-melody and generate_with_chroma for melody conditioning, or musicgen-style and generate_with_style for reference-based style transfer.

What generation params control creativity vs. prompt fidelity?

Temperature increases creativity; top_k/top_p control sampling diversity; cfg_coef (classifier-free guidance) increases adherence to the text prompt.

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