Building a Local Voice AI Stack on Apple Silicon
This guide documents a production-tested architecture for fully local voice AI on Apple Silicon, using Whisper.cpp for speech-to-text, Ollama for language inference, and Kokoro ONNX for text-to-speech, all running on-device without internet or API keys.
Why it matters
This architecture demonstrates how to build a fully local, high-performance voice AI stack that avoids cloud APIs, internet, and per-usage charges.
Key Points
- 1Leverages Whisper.cpp with Metal GPU acceleration for fast speech-to-text
- 2Uses Ollama for local language model inference and Kokoro ONNX for text-to-speech
- 3Targets low latency (under 3 seconds total) for real-time voice conversation
- 4Avoids cloud APIs, internet, and per-usage charges by running everything locally
Details
The architecture combines Whisper.cpp for speech recognition, Ollama for language understanding, and Kokoro ONNX for text-to-speech, all running on Apple Silicon hardware like the M3 Pro. Whisper.cpp provides fast, GPU-accelerated speech-to-text, with model options ranging from tiny (75MB) to large (3GB) to balance speed and accuracy. The system uses ffmpeg's built-in silence detection to trigger the speech processing pipeline. To avoid the cold-start latency of Python-based text-to-speech, it employs a persistent Kokoro ONNX server. The target latency budget is under 3 seconds total, making it suitable for real-time voice conversation applications without cloud API dependencies.
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