# Audiocraft Audio Generation

PyTorch library for audio generation including text-to-music (MusicGen) and...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Media
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill audiocraft-audio-generation -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill audiocraft-audio-generation -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill audiocraft-audio-generation -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill audiocraft-audio-generation -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# AudioCraft: Audio Generation

Comprehensive guide to using Meta's AudioCraft for text-to-music and text-to-audio generation with MusicGen, AudioGen, and EnCodec.

## When to use AudioCraft

**Use AudioCraft when:**
- Need to generate music from text descriptions
- Creating sound effects and environmental audio
- Building music generation applications
- Need melody-conditioned music generation
- Want stereo audio output
- Require controllable music generation with style transfer

**Key features:**
- **MusicGen**: Text-to-music generation with melody conditioning
- **AudioGen**: Text-to-sound effects generation
- **EnCodec**: High-fidelity neural audio codec
- **Multiple model sizes**: Small (300M) to Large (3.3B)
- **Stereo support**: Full stereo audio generation
- **Style conditioning**: MusicGen-Style for reference-based generation

**Use alternatives instead:**
- **Stable Audio**: For longer commercial music generation
- **Bark**: For text-to-speech with music/sound effects
- **Riffusion**: For spectogram-based music generation
- **OpenAI Jukebox**: For raw audio generation with lyrics

## Quick start

### Installation

```bash
# From PyPI
pip install audiocraft

# From GitHub (latest)
pip install git+https://github.com/facebookresearch/audiocraft.git

# Or use HuggingFace Transformers
pip install transformers torch torchaudio
```

### Basic text-to-music (AudioCraft)

```python
import torchaudio
from audiocraft.models import MusicGen

# Load model
model = MusicGen.get_pretrained('facebook/musicgen-small')

# Set generation parameters
model.set_generation_params(
    duration=8,  # seconds
    top_k=250,
    temperature=1.0
)

# Generate from text
descriptions = ["happy upbeat electronic dance music with synths"]
wav = model.generate(descriptions)

# Save audio
torchaudio.save("output.wav", wav[0].cpu(), sample_rate=32000)
```

### Using HuggingFace Transformers

```python
from transformers import AutoProcessor, MusicgenForConditionalGeneration
import scipy

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/18-multimodal/audiocraft/SKILL.md