Technical article
Bringing voice models into one local runtime
The questions behind a shared path from speech to text and back again.
Draft preview. Placeholder content for review; this work has not been published.
The question
Speech recognition and speech synthesis sit on opposite sides of a conversation. Recognition turns sound into text; synthesis turns text into sound. A local runtime can give both a shared place to load models, select an execution backend and manage resources, while keeping their different input and output requirements explicit.
What we want to examine
A fast model is only one part of a responsive voice workflow. Model loading, audio buffering and the handoff between stages also take time. Keeping a model ready reduces startup delay but uses memory. Choosing a backend therefore depends on the machine and the task, rather than a single speed ranking.
What needs to be measured
A useful comparison should identify the model, hardware, backend and audio conditions. Startup time and steady-state latency should be reported separately. This draft sets out the questions; it does not report measured Takokit performance.