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

# 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)

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

from transformers import AutoProcessor, MusicgenForConditionalGeneration
import scipy