Audio8 / 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, 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.

Contents

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 Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).