Sleep Hacking: Build a Local Sleep Apnea & Snore Monitor with Whisper and FFT 🌙💤

Have you ever woken up feeling like you’ve been hit by a truck, even after eight hours of sleep? You might be part of the millions dealing with sleep apnea or chronic snoring. While there are plenty of apps for this, most of them ship your bedroom audio to the cloud. Creepy, right? In this tutorial, we are building a privacy-first, local sleep apnea detection system. We’ll combine FFT spectrum analysis for frequency detection and Faster-Whisper for intelligent pattern recognition. By leveraging audio signal processing in Python, we can identify breathing irregularities without a single byte of data leaving your machine. If you're interested in more production-ready health-tech implementations, definitely check out WellAlly Tech Blog for advanced patterns in medical AI. The Architecture: From Soundwaves to Insights Our system works in a three-stage pipeline: filtering, feature extraction, and classification. We don't want to run heavy AI models on 8 hours of silence, so we use Fast Four