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64f7370 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | # SpeechKit ASR
On-device speech recognition SDK for iOS 18+ / macOS 15+. Runs
Audio8-ASR-0.1B locally: Apple Neural Engine for the audio encoder, CPU int4
for the language-model decoder. No network request is required by the SDK.
The public demo also exposes process memory footprint, peak footprint, CPU,
thermal state, battery state, and a rough whole-device power estimate for
device-side validation.
## Installation
### 1. Add the package
```swift
dependencies: [
.package(path: "../SpeechKit"), // or the git URL + version once hosted
]
targets: [
.target(name: "YourApp", dependencies: [
.product(name: "SpeechKit", package: "SpeechKit"),
]),
]
```
### 2. Add the model bundle
Build it once (`python3 Scripts/make_asset_bundle.py`) -> `dist/ASRModels.bundle`
(~437 MB, models pre-compiled to `.mlmodelc`). Drag the bundle into your app
target as a **folder reference** (blue icon), or download it on first launch
and pass its URL to `ASRTranscriber(bundleURL:)`.
### 3. Info.plist
Only needed if you capture the microphone yourself:
`NSMicrophoneUsageDescription`.
## Usage
### One-shot transcription
```swift
import SpeechKit
let transcriber = try ASRTranscriber() // finds ASRModels.bundle in main bundle
transcriber.warmUp() // optional; off main thread
let result = try transcriber.transcribe(samples) // [Float], 16 kHz mono
print(result.text, result.timings.total)
// Async, with cancellation:
let token = ASRTranscriber.CancellationToken()
let result = try await transcriber.transcribe(samples, cancellation: token)
// Any audio file:
let result = try transcriber.transcribeFile(url)
```
Streaming and VAD are not part of this ASR-only build. Capture an utterance,
convert it to 16 kHz mono Float32, then call `transcribe`.
## Error handling
Every API throws `SpeechError` (SpeechCore). Cases cover asset validation
(`assetCorrupted`, `assetSchemaUnsupported`), audio input (`audioTooShort`),
inference (`inferenceFailed`, `promptTooLong`), and `cancelled`.
Pass `Options.verifyAssets = true` on the first launch after install/update to
SHA-256-check every asset (~1-2 s), then persist a flag and skip it.
## Threading contract
- `ASRTranscriber` methods are thread-safe; inference is serialized internally.
- Synchronous `transcribe` blocks; never call from the main thread.
- Async callbacks resume from an internal queue; dispatch to main for UI.
## Constraints
| | |
|---|---|
| Language | Model-defined |
| Audio window | 30 s max per call |
| First launch | ANE compilation ~30-60 s (system-cached afterwards); `warmUp()` recommended |
| Memory | ~600 MB resident with models loaded |
| Min OS | iOS 18 / macOS 15 (multifunction Core ML models) |
## Regression testing
```bash
swift run dev-check # lightweight local smoke check
swift test # full Xcode recommended
swift run -c release asrkit-cli <workspace-dir> # numeric regression vs Python refs
swift run -c release asrkit-cli <workspace-dir> --stress # 100-pass memory check
swift run -c release asrkit-cli <workspace-dir> --file /path/to/audio.wav --repeat 10
```
`dev-check` does not require model assets and is the recommended first check on
a fresh machine. Some Command Line Tools-only installations cannot expose the
Apple Testing/XCTest frameworks cleanly; install and select full Xcode before
using `swift test`.
|