Building a Privacy-First Sleep Monitoring System with Whisper-tiny and TCN on Raspberry Pi

Short summary

This tutorial builds a privacy-first Edge AI sleep monitoring system on Raspberry Pi using Whisper-tiny's encoder for audio feature extraction and a Temporal Convolutional Network for classifying snoring and sleep apnea events. The pipeline captures raw audio, extracts Mel-spectrogram features via Whisper, and classifies temporal patterns locally without any cloud upload. Includes full PyTorch code for the TCN model and Whisper feature extraction, deployable via Docker for 24/7 offline operation.

• •Build a fully local sleep monitoring system on Raspberry Pi using Whisper-tiny encoder and TCN classifier
• •Pipeline extracts Mel-spectrogram features via Whisper, then classifies Normal/Snore/Apnea patterns over time using TCN
• •Complete PyTorch code provided for the TCN architecture and Whisper feature extraction, deployable via Docker offline

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