| # 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`. |
|
|
