Core AI model zoo

Stable Audio Open Small — Core AI (on-device music generation)

The model zoo’s first MUSIC / AUDIO generation model for Apple Core AI. Type a prompt, get ~11s of 44.1 kHz stereo audio — generated entirely on-device on Apple Silicon. A community port of stabilityai/stable-audio-open-small (Stability AI + Arm) to Core AI.

A latent diffusion text-to-audio model: a T5 text encoder conditions a DiT (diffusion transformer) that denoises a latent over 8 rectified-flow steps, then an Oobleck VAE decodes the latent to a waveform. Distilled (ARC) for few-step generation, so it’s fast.

Stable Audio Open Small demo Stable Audio Open Small on iPhone 17 Pro — the zoo’s coreai-audio app, 12 s of audio in ~1 s.

Use it

⚡ One line — this model is the default behind the kit’s task op (import CoreAIOps; no session, no model plumbing, downloads on first use):

let audio = try await CoreAI.compose(prompt)

Twenty ops, one shape — Cookbook.

▶️ Run it (source) — the Music runner (GUI + CLI, one app for every text-to-music model in the catalog):

git clone https://github.com/john-rocky/coreai-kit
open coreai-kit/Examples/Music/Music.xcodeproj
# → Run, then pick "Stable Audio Open Small" in the model picker

# agents / headless (macOS):
cd coreai-kit/Examples/Music
swift run music-cli --model stable-audio-open-small --prompt "128 BPM tech house drum loop" --output loop.wav

💻 Build with it — complete; the glue is kit API, copy-paste runs:

import CoreAIKit

let musician = try await KitMusician(catalog: "stable-audio-open-small")
let audio = try await musician.generate(prompt)
// audio.samples: 44.1 kHz stereo (planar L/R) — play it or write a WAV

The take-home is Examples/Music/Sources/QuickStart.swift — this exact code as one typed function, no UI; the CLI is an argument shell over it, and the GUI drives the same KitMusician(catalog:) and plays the result. Length? generate(_:seconds:) up to the model’s ~11 s window. The WAV container is your app’s territory (the runner ships a 30-line writer with planar-stereo support).

Integration checklist

What’s in the bundle (macos/)

Three Core AI .aimodel bundles + a tiny host sampler loop:

bundle role I/O
sa_cond_fp16b T5-base encoder + number conditioner input_ids[1,64], attention_mask[1,64], seconds_norm[1] → cross_attn_cond[1,65,768], global_embed[1,768], cond_mask[1,65]
sa_dit_fp16 diffusion transformer (run 8×) x[1,64,256], t[1], cross_attn_cond, global_embed, cross_attn_cond_mask → v[1,64,256]
sa_vae_fp16 Oobleck VAE decoder latent[1,64,256] → audio[1,2,524288]

Host loop (StableAudioRunner): tokenize (T5, t5_tokenizer/) → conditioner → start from Gaussian noise → 8-step rectified-flow euler x = x + (t_next − t)·v over the fixed schedule [1.0, .9944, .9845, .9579, .8909, .7455, .5125, .2739] → 0 → VAE decode → 44.1 kHz stereo wav. No KV cache, no CFG (cfg_scale 1.0 — the model is ARC-distilled).

Performance (M4 Max, GPU)

metric value
8-step DiT ~200 ms (25 ms/step)
VAE decode ~185 ms
total ~0.4 s for ~11.9 s of audio (~30× real-time)
size fp16, ~1.0 GB (DiT 651M + cond 210M + VAE 149M)

Numerics: each bundle engine-gated vs the reference at cos ≥ 0.9999; full pipeline reproduces the reference audio exactly.

Roadmap

Credits & license

A community Core AI conversion — all credit to Stability AI (and Arm) for Stable Audio Open Small; T5 text encoder by Google. This bundle is governed by the Stability AI Community License (free for non-commercial use and for commercial use under $1M annual revenue; review the license before use). No retraining — conversion only.

Part of the Core AI model zoo.

⬇️ Download: 🤗 mlboydaisuke/Stable-Audio-Open-Small-CoreAI — this card and the model page are the same document; scripts/gen-cards keeps the Use it block in sync. Reproduce it: python3 conversion/zoo_convert.py show stable-audio-open-small prints the command and its prerequisites; recipe.toml is the record.

This site is open source. Improve this page.