Core AI model zoo

Whisper large-v3-turbo — Core AI

openai/whisper-large-v3-turbo (809M, MIT) — OpenAI’s multilingual speech-to-text: 100 languages, automatic language detection, ≤30 s window per decode. Runs fully on device on the stock Core AI runtime (no engine patch), token-for-token identical to the PyTorch greedy reference.

Bundle: 🤗 mlboydaisuke/whisper-large-v3-turbo-CoreAI-official — macos/ and ios/ each hold the fp16 JIT .aimodel (~1.6 GB; every iPhone generation specializes it on its first load: iPhone 18 Pro 4.37 s, first call 2.11 s, 0.26 s on relaunch, 2026-09-26), ios-h18p/ the bundle compiled for the iPhone 17 Pro (~3.2 GB, that phone only; it sat in ios/ until Hub revision 171153c6, 2026-09-26, and comes from a separate export of 2026-06-28). Catalog id: whisper-large-v3-turbo (its pin still points at the previous layout until the next kit release).

Whisper large-v3-turbo demo Whisper large-v3-turbo on iPhone 17 Pro — the zoo’s coreai-audio app, real speed.

Use it

New to Core AI? Start with CoreAIKit 0.7.3. Follow its requirements and first-run steps for qwen3-0.6b, then open the same release’s ChatDemo. The README records the tested OS/SDK and download size; model and device coverage is stated per example.

⚡ 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 text = try await CoreAI.transcribe(audioURL)

Every op, one shape — Cookbook.

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

git clone --branch 0.7.3 --depth 1 https://github.com/john-rocky/coreai-kit
export DEVELOPER_DIR=/Applications/Xcode-27.0.0-RC.app/Contents/Developer
open -a /Applications/Xcode-27.0.0-RC.app coreai-kit/Examples/Transcribe/Transcribe.xcodeproj
# → Run, then pick "Whisper large-v3-turbo" in the model picker

# agents / headless (macOS):
cd coreai-kit/Examples/Transcribe
swift run -c release transcribe-cli --model whisper-large-v3-turbo --audio sample.wav

Use Xcode build 27A266a from the release’s .xcode-pin; adjust the app path if your installation is named differently.

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

import CoreAIKit

let transcriber = try await KitTranscriber(catalog: "whisper-large-v3-turbo")
let samples = try AudioFile.pcm16kMono(url)  // any wav/m4a/mp3 → 16 kHz mono Float
let result = try await transcriber.transcribe(samples: samples)
// result.text, result.language ("en", "ja", … auto-detected)

The take-home is Examples/Transcribe/Sources/QuickStart.swift — this exact code as one typed function, no UI; both the runner’s GUI and its CLI call it. Recording? MicRecorder (kit API) captures mic audio as 16 kHz mono [Float] — the record button and permission prompt are your app’s own chrome.

Integration checklist

Measured (M4 Max, GPU)

Greedy decode vs the HF PyTorch reference (generate, greedy):

Metric Value
Transcript token-for-token identical to PyTorch greedy
First step (compile + warmup) 0.68 s
Per token (steady state) 0.18 s

The fixed window caps one decode at 128 tokens — enough for a 30 s window; chunk longer audio into 30 s segments (the runner and KitWhisperModel do this for you).

How the bundle works (why it’s not the stock recipe verbatim)

Apple’s official models/whisper/export.py traces a single decode step (decoder_input_ids [1,1], no KV cache) — that graph can’t be driven autoregressively. This bundle is the same recipe traced at a fixed 128-token decoder window: pad the buffer, read the logits at the real last position (causal attention ignores the padding, so the read is exact), constant shape so MPSGraph compiles once (a dynamic-length export recompiles every step → ~15 s/token; this is 0.18 s/token). Full write-up: knowledge/whisper-asr-fixed-decode.md · recipe: conversion/export_whisper_fixed.py · hashes + export environment: the HF card.

Driving the raw graph yourself (no kit)? apps/CoreAITranscribe implements this exact decode loop + the log-mel frontend from scratch (macOS + iOS, file or mic) — the reference if you’re porting to another language or runtime.

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