Stop Snoring, Start Analyzing: Building a Real-time Sleep Monitor with OpenAI Whisper & Silero VAD
Ever woken up feeling like a truck hit you, despite spending eight hours in bed? You might be a "heavy breather," or worse, suffering from undiagnosed sleep apnea. While wearable rings and watches are cool, they often miss the acoustic nuances of what’s actually happening in your room. In this tutorial, we’re going to build a high-performance real-time sleep analysis system. By leveraging OpenAI…
Sleep apnea often goes undiagnosed, but wearable devices can miss important acoustic clues. This tutorial explains how to build a real-time sleep analysis system using OpenAI Whisper for classification and Silero VAD for voice activity detection. The process begins with filtering out silence and ambient noise from the microphone stream using Silero VAD, which then triggers Whisper for further analysis.
Whisper identifies different sleep patterns such as rhythmic breathing, snoring, and potential apnea events. Once classified, these events are displayed in a time-series dashboard. To optimize performance, the system uses WebRTC for audio streaming and Librosa for audio manipulation. The tutorial provides step-by-step instructions to set up these components, ensuring listeners get accurate and actionable insights about their sleep health.
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