README.md

metadata

tags:
  - automatic-speech-recognition
  - ios
  - swift
  - coreml
  - onnxruntime
  - apple-neural-engine
  - on-device
language:
  - en
  - zh
  - fr
  - ja
  - yue
library_name: coreml
license: apache-2.0
repository: https://github.com/AutoArk/open-audio-opd

Audio8-ASR-0.1B-iOS-ANE

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, and Japanese.

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.

Contents

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	Apache License 2.0

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

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:

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

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 Apache License 2.0. Confirm separately that the model weights and tokenizer assets are intended to be released under the same license before publishing.