High latency and privacy concerns plague cloud-based voice assistants. The objective of Knik was to develop a local-first voice console utilizing the cutting-edge Kokoro-82M TTS model to achieve sub-second response times without sending private audio to the cloud.
AI & REAL-TIME COMMUNICATIONS
Knik Local-First Inference.
High latency and privacy concerns plague cloud-based voice assistants. The objective of Knik was to develop a local-first voice console utilizing the cutting-edge Kokoro-82M TTS model to achieve sub-second response times without sending private audio to the cloud.
EXECUTIVE SUMMARY
KEY CONTRIBUTION
"Architected a continuous audio streaming pipeline over WebSockets, decoupling the STT and TTS engines to allow mid-sentence interruption and playback synchronization."
SYSTEM ARCHITECTURE
High-level overview of the control and data plane components.
CONTROL PLANE / ORCHESTRATION
FastAPI Orchestrator
Async Python backend managing conversational state
MCP Router
Intelligent routing system for tool execution
DATA PLANE / INFERENCE
Kokoro TTS
Local 82M parameter text-to-speech engine
Local Tools
Native integrations and tools payload
ENGINEERING STACK
Locally-hosted AI processing and reactive UI.
FRONTEND
ReactTypeScriptViteTailwindCSS
BACKEND
PythonFastAPIUvicornWebSockets
AI ENGINE
Kokoro-82MONNXRuntimePyAudioWhisper
TECHNICAL DECISIONS
Documenting the trade-offs and architectural shifts during development.
Communication Protocol
Rationale: Real-time conversational AI requires full-duplex communication. WebSockets allowed us to stream audio chunks continuously as they were generated by the model, reducing perceived latency by over 800ms.
MEASURABLE IMPACT
Performance metrics for the local AI pipeline.
TTS LATENCY
350 ms
⚡ 3x faster than cloud APIsMEMORY FOOTPRINT
1.2 GB
⚡ Edge deployment readyPOSTMORTEM & LEARNINGS
Reflections on building local AI.
The Kokoro model's parameter efficiency allowed for incredibly fast CPU inference. Decoupling the audio buffering from the inference thread completely eliminated UI stuttering during playback.
Migrating the inference engine to WebAssembly (WASM) or WebGPU to run entirely in the browser. This will completely eliminate the need for a local Python backend and drastically simplify installation for end-users.