SpeechAnalyzer Framework Expert

Real-time on-device speech transcription requires creating a SpeechTranscriber, downloading language models via AssetInventory, allocating the locale, and then instantiating a SpeechAnalyzer to begin processing live audio input. This approach ensures privacy-preserving transcription directly on the device.

An unallocated locale error occurs when you attempt to instantiate transcription before the required language model finishes downloading. You must download and install models using AssetInventory first, then explicitly allocate the locale to ensure the framework recognizes the requested language.

Yes, you can migrate from SFSpeechRecognizer or WhisperKit to SpeechAnalyzer for live audio processing. The framework provides migration insights and practical code patterns for transitioning to AsyncStream-based input feeding and robust error handling for modern macOS and iOS environments.

Yes, the Speech framework supports offline transcription across 10+ languages. You must manage locale settings and handle model downloads through AssetInventory before initiating the transcription session to ensure offline capabilities function correctly without requiring a network connection.

The best way to handle real-time audio input buffers is using AsyncStream-based input feeding with AVAudioInput. This method provides best-practice sequencing for feeding audio data to SpeechTranscriber, allowing you to process both volatile and final transcription results robustly.

You should use volatile transcription results for real-time, interim text updates during live speech, and final results for completed, stable text output. Choosing between them depends on whether your application requires immediate feedback or accurate, finished transcription segments for processing.