We have 1,045 hours of overnight bedroom audio. For training a sleep-phase model, about 53 of those hours are usable.

That ratio is the least glamorous number in this project and probably the most important one. People argue about model architecture. Almost nobody talks about the fact that, for a long time, every additional hour of audio adds almost nothing you can actually learn from.

Recording a night is easy. Having something to learn from is expensive

Capturing audio overnight is a solved problem: a ring buffer, a phone on the charger, a file in the morning. We had thousands of hours before the model was worth training.

The hard part is the label. To say "this 30-second window is deep sleep" you need a reference you trust. Ours comes from wearable staging, but only on nights where the phone recording and the wearable recording overlapped cleanly.

That word, cleanly, is doing a lot of work.

Two devices mean two clocks, two ideas of when the night started, and two chances to fail independently.…