facebookresearch/audiocraft

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

23586 stars · 2688 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch library from Meta for audio processing and generation with deep learning, featuring the EnCodec neural audio codec and generative models like MusicGen, AudioGen, MAGNeT, and JASCO. It includes both inference and training code for state-of-the-art text-to-music and text-to-sound models.

Use cases

• generate music from a text prompt
• create sound effects from text descriptions
• compress and tokenize audio with a neural codec
• train custom audio generation models
• watermark generated audio
• condition music generation on melodies or chords

When to choose

• you need state-of-the-art text-to-music or text-to-sound generation in Python
• you want to research or train audio generation models with PyTorch
• you need a high-fidelity neural audio codec like EnCodec

When to avoid

• you only need simple audio playback or format conversion
• you lack a GPU and want fast real-time generation
• you need a production-ready end-user application rather than a research library

Facets

library · maturity active

audio-processing machine-learning deep-learning llm-inference llm-training tts machine-learning deep-learning artificial-intelligence audio python cross-platform text-to-music text-to-audio neural-audio-codec audio-watermarking pytorch generative-models musicgen encodec audio gpu

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markdown · JSON · MCP: product_card(name="facebookresearch/audiocraft")