mlboydaisuke/Qwen3-ASR-1.7B-CoreAI

Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's coreai-torch (LLMs: coreai.llm.export) into .aimodel bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol (apple-silicon-llm-bench, macOS 27 beta 26A5353q, 2026-06-11).

<!-- gen-cards:devicemark begin (managed by scripts/gen-cards + tools/devicemark_row.py — edit cards.json, not this block) --> This model has no row on DeviceMark, the on-device LLM leaderboard. <!-- gen-cards:devicemark end -->

Qwen3-ASR-1.7B — Core AI

Qwen3-ASR-1.7B speech-to-text converted for Apple Core AI, running on-device (iPhone + Mac). The zoo's first ASR model: an AuT audio encoder feeding a Qwen3 decoder on the pipelined engine (audio embeds bound to one static input buffer; {lang}<asr_text>{text} output). ≤30 s clips, 52 languages, automatic language detection.

<!-- gen-cards:use-it begin id=qwen3-asr-1.7b (managed by scripts/gen-cards — edit cards.json / QuickStart.swift, not this block) -->

Use it

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

let text = try await CoreAI.transcribe(audioURL, options: .model("qwen3-asr-1.7b"))

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 https://github.com/john-rocky/coreai-kit
open coreai-kit/Examples/Transcribe/Transcribe.xcodeproj
# → Run, then pick "Qwen3-ASR 1.7B" in the model picker

# agents / headless (macOS):
cd coreai-kit/Examples/Transcribe
swift run transcribe-cli --model qwen3-asr-1.7b --audio sample.wav

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

import CoreAIKit

let transcriber = try await KitTranscriber(catalog: "qwen3-asr-1.7b")
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 (52 languages)

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

• —SPM: https://github.com/john-rocky/coreai-kit → product CoreAIKit
• —Info.plist: NSMicrophoneUsageDescription — only if you record
• —Entitlements: none needed (macOS)
• —First run downloads the model — 3.1 GB (Mac) — then it loads from the local cache (Application Support; progress via the downloadProgress callback)
• —Measure in Release — Debug is ~3× slower on per-token host work <!-- gen-cards:use-it end -->

Driven by CoreAIKit KitASRModel:

let asr = try await KitASRModel(model: .qwen3ASR1_7B)
let r = try await asr.transcribe(samples: pcm16kMono)   // -> (language, text)

Layout: gpu-pipelined/ holds the decoder bundle (*_decode_int8hu_n390_s1, int8) + the paired AuT encoder (*_audio_encoder_fp16_k30, fp16). Same bundles on iOS and macOS.

App: coreai-audio (Transcribe tab — pick Qwen3-ASR or Whisper large-v3-turbo). Card: zoo/qwen3-asr.md.

<!-- funnel:v1 -->

More models in this format: Core AI Model Zoo — 75 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.

<!-- /funnel:v1 -->