SpeechAnalyzer Framework Expert

This skill provides comprehensive guidance on implementing Apple's modern Speech framework introduced for macOS 26+ and iOS 26+. The framework features SpeechAnalyzer and SpeechTranscriber for on-device speech-to-text transcription, offering 2.2x faster performance than Whisper Large V3 Turbo with out-of-process execution and automatic system updates.

Best Practices

1. Always allocate locale after model download: Call transcriber.allocate(locale:) only after downloading assets via AssetInventory.assetInstallationRequest() to avoid "Cannot use modules with unallocated locales" errors.

2. Use proper audio format conversion: Convert audio buffers to the analyzer's required format using AVAudioConverter with primeMethod = .none to prevent timestamp drift.

3. Follow the complete setup sequence: Instantiate transcriber → download models → allocate locale → create analyzer → get best format → create AsyncStream → start analyzer → consume results. Skipping steps causes runtime errors.

4. Leverage out-of-process execution: The framework runs transcription in a separate process, eliminating memory limits in your app and providing automatic system updates without app redeployment.

5. Handle both volatile and final results: Process result.isFinal to distinguish between live transcription updates (volatile) and completed segments (final) for proper UI updates.

6. Check locale support before use: Use SpeechTranscriber.supportedLocales and installedLocales to verify language availability before creating transcriber instances.

7. Implement proper AsyncStream lifecycle management: Create input streams with AsyncStream<AnalyzerInput>.makeStream() and properly yield converted buffers to the continuation for reliable transcription.