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
| 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
mlmodelcwith 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).