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Edge0/Audio8-ASR-0.1B-iOS-ANE

sourceHugging Facecc-by-nc-4.0updated 2mo agoView on Hugging Face

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Model Card

Audio8-ASR-0.1B-iOS-ANE

![GitHub](https://github.com/AutoArk/open-audio-opd) ![arXiv](https://arxiv.org/abs/2605.28139) ![License](https://creativecommons.org/licenses/by-nc/4.0/)

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This repository packages an iPhone-ready, ASR-only build of Audio8-ASR-0.1B. It includes a Swift SDK, a minimal iOS demo app, an optional ANE benchmark app, and a prebuilt model asset bundle.

The ASR model is multilingual, with support for languages including English, Chinese, Cantonese, French, German, Japanese, and Korean.

The on-device pipeline uses Core ML on Apple Neural Engine for the audio tower and ONNX Runtime for the int4 language-model decoder. Audio is transcribed locally; no network request is required by the SDK or demo app.

Path	Description
`SpeechKit/`	Swift Package exposing `SpeechKit`, `ASRKit`, and `SpeechCore`
`dist/ASRModels.bundle`	Prebuilt model assets: Core ML audio tower, ONNX decoder, tokenizer tables, and integrity manifest
`ASRDemo/`	Minimal iOS app for microphone recording and one-shot transcription
`ANEBench/`	Optional iOS app for Core ML / ANE latency and sustained-load checks
`assets/`	Screenshots and model-card media
`config.json`	Machine-readable package metadata and Hugging Face download-stat query file
`GETTING_STARTED.md`	Reproducible setup, build, signing, and device-testing guide
`LICENSE`	Creative Commons Attribution-NonCommercial 4.0 International

Related Repositories

—Audio8-ASR-0.1B: base model checkpoint.
—Audio8-ASR-0.1B-onnx-runtime: ONNX Runtime package.

Packaged Model Variant and Footprint

This release packages the iPhone ANE-oriented variant of Audio8-ASR-0.1B:

—Audio tower/head: compiled Core ML mlmodelc with mixed Float16/Int8 storage, Float16 compute/output tensors, and ANE execution.
—Decoder: ONNX Runtime CPU decoder with int4 shared language-model weights (lm_shared_int4.data) and int4 prefill/decode ONNX graphs.
—Token embedding table: Float16 (token_embedding_fp16.bin).

On a physical iPhone with the Core ML audio tower running on ANE, the demo is designed to keep runtime memory footprint around 200 MB. The example below shows a 183 MB app footprint during a microphone transcription run, with sampled peak footprint varying by device, iOS version, cold/warm start state, and measurement window. We position this package as one of the smallest usable ASR model stacks for on-device iPhone transcription.

Quick Start

bash

brew install xcodegen

cd SpeechKit
swift package resolve
swift build
swift run dev-check
cd ..

cd ASRDemo
xcodegen generate
open ASRDemo.xcodeproj

In Xcode, select the ASRDemo target, choose your signing team, change the bundle identifier to a unique value, then run on an iPhone or iOS Simulator.

The demo uses microphone input. If you want to test a local file without changing the app, use asrkit-cli --file /path/to/audio.wav.

What Runs on ANE

The key acceleration path is the audio tower:

text

audio -> log-mel -> Core ML audio tower on ANE -> projected audio embeddings
      -> ONNX Runtime int4 decoder on CPU -> transcript

The decoder intentionally stays on CPU. Its per-token workload is small enough that ANE dispatch overhead is not beneficial for this build.

Requirements

—macOS on Apple Silicon is recommended.
—Full Xcode, not only Command Line Tools.
—iOS 18+ / macOS 15+ for the Swift package.
—XcodeGen for regenerating the demo projects.
—An Apple Developer account for physical-device signing. A free Personal Team is enough for local device testing.

Validation

bash

cd SpeechKit
swift run dev-check
swift test

# Transcribe a local file with the bundled model assets:
swift run -c release asrkit-cli .. --file /path/to/audio.wav

# Repeat one local file to watch stability and footprint:
swift run -c release asrkit-cli .. --file /path/to/audio.wav --repeat 10

dev-check is the fastest smoke test and does not require model inference. asrkit-cli --file loads dist/ASRModels.bundle and runs end-to-end transcription on macOS.

Hugging Face counts model downloads through query files such as config.json. If you automate downloads with snapshot_download or hf_hub_download, include the root config.json in the request path so repository downloads are counted.

For memory footprint and thermal checks, run ASRDemo on a physical iPhone, record one utterance, then tap Repeat Last 10x while watching the in-app System panel. Simulator memory is useful for trends only; it is not equivalent to iPhone memory pressure or Jetsam behavior.

Repository Status

This staging copy is intended for review before publishing to https://huggingface.co/AutoArk-AI/Audio8-ASR-0.1B-iOS-ANE.

The repository is distributed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).