In the normal (non-continuous) inference mode when classifying data you sample data until you have a full window of data (e.g. 1 second for a keyword spotting model, see the Create impulse tab in the studio), you then classify this window (using the
run_classifier function), and a prediction is returned. Then you empty the buffer, sample new data, and run the inferencing again. Naturally this has some caveats when deploying your model in the real world: 1) you have a delay between windows, as classifying the window takes some time and you're not sampling then, making it possible to miss events. 2) there's no overlap between windows, thus if an event is at the very end of the window, not the full event might be captured - leading to a wrong classification.