Realtime-AI-Translator hits latency, audio wall

This is a strong local-first prototype that proves the stack works, but it also shows where real-time translation pipelines still break in practice on consumer hardware.

• –The repo documents a clear async queue architecture and multiple stabilization passes, which makes it useful for other builders even before performance is “production-grade.”
• –Latency appears to be an architecture tradeoff: shorter chunks improve responsiveness but hurt semantic coherence, while clause-level buffering preserves quality but adds delay.
• –Community feedback points toward specialized translation models (for example NLLB-class models) over general LLMs for lower-latency translation quality on-device.
• –macOS audio routing remains a frequent failure point in local voice apps; channel mapping and multi-output device setup are often as hard as model tuning.
• –Early traction is small (new repo, minimal stars/comments), but the implementation notes make it a practical reference for offline speech+LLM workflows.