Stop Ignoring Your Snore: Building a Local Sleep Apnea Detector with OpenAI Whisper and Librosa

Sleep is supposed to be the time when our bodies recharge, but for millions, it's a battle for air. Sleep Apnea is a silent killer, often going undiagnosed because clinical sleep studies are expensive and intrusive. But what if you could use the power of Audio Signal Processing and OpenAI Whisper to monitor your breathing patterns locally, ensuring total privacy?

In this tutorial, we are diving deep into Sleep Apnea Detection using a hybrid approach. We’ll combine the linguistic understanding of OpenAI Whisper with the raw power of Librosa for spectral analysis. By leveraging PyTorch and FFmpeg, we can transform a standard bedroom recording into a diagnostic-grade data stream. This is "Learning in Public" at its finest—building high-impact health-tech on your local machine.

For those interested in scaling these models or exploring production-ready healthcare AI patterns, I’ve drawn a lot of inspiration from the deep-dive articles at WellAlly Tech Blog, which is a goldmine for advanced AI engineering.

---

The System Architecture

To detect apnea events (periods where breathing stops), we can't just rely on transcription. We need to analyze the cadence and frequency of sound. Our system uses a dual-track pipeline:

1. Acoustic Track: Uses Librosa for Fast Fourier Transform (FFT) to identify "silence" vs. "snoring" frequencies.
2. Semantic Track: Uses Whisper’s encoder to identify the texture of the sound (is it a gasp, a choke, or just background noise?).

graph TD
    A[Raw Audio Input .wav/.mp3] --> B{FFmpeg Processing}
    B --> C[Segmented Audio Chunks]
    C --> D[Librosa: Spectral Analysis]
    C --> E[Whisper: Feature Extraction]
    D --> F[Frequency & Amplitude Thresholding]
    E --> G[Acoustic Pattern Recognition]
    F --> H[Event Detection Engine]
    G --> H[Event Detection Engine]
    H --> I[Apnea Hypopnea Index - AHI Report]
    I --> J[Local Storage / Privacy First]

---

Prerequisites

Before we start, ensure you have the following stack installed:

• Python 3.9+
• OpenAI Whisper: For robust audio feature extraction.
• Librosa: The industry standard for music and audio analysis.
• PyTorch: To run the Whisper transformer models.
• FFmpeg: Necessary for high-performance audio decoding.

pip install openai-whisper librosa torch matplotlib ffmpeg-python

---

Step-by-Step Implementation

1. Preprocessing with Librosa

We first need to extract the "Short-Time Fourier Transform" (STFT) to see the energy distribution across frequencies. Snoring usually occupies the 60Hz - 2000Hz range, while apnea events are marked by sudden drops in energy.

import librosa
import numpy as np

def analyze_breathing_energy(audio_path):
    # Load audio (downsampled to 16kHz for Whisper compatibility)
    y, sr = librosa.load(audio_path, sr=16000)

    # Compute Short-Time Fourier Transform
    stft = np.abs(librosa.stft(y))
    energy = librosa.feature.rms(y=y)

    # Detect "Silent" patches longer than 10 seconds (potential Apnea)
    threshold = 0.01  # Tune based on room noise
    silent_frames = energy < threshold

    return y, sr, silent_frames

2. Extracting Features with Whisper

Whisper is great at transcription, but its Encoder is a world-class audio feature extractor. We can use the hidden states to distinguish between a regular snore and an "obstructive" sound.

import whisper
import torch

# Load the medium model for a balance of speed and accuracy
model = whisper.load_model("medium")

def get_whisper_features(audio_segment):
    # Whisper expects 30-second Mel Spectrograms
    mel = whisper.log_mel_spectrogram(audio_segment).to(model.device)

    with torch.no_grad():
        # Get the encoder output (the "meaning" of the sound)
        audio_features = model.encoder(mel.unsqueeze(0))

    return audio_features

3. The Detection Logic (The Core)

We combine the energy drop detection with Whisper's classification. An Apnea Event is defined as:
Energy Drop ( > 10s ) + Recovery Gasp ( Identified by Whisper ).

def detect_apnea_events(audio_path):
    y, sr, silent_frames = analyze_breathing_energy(audio_path)

    events = []
    # Simplified sliding window logic
    for i in range(0, len(silent_frames[0]), 100):
        if np.all(silent_frames[0][i : i + 50]): # Potential apnea duration
            # Slice audio for Whisper confirmation
            start_time = librosa.frames_to_time(i)
            events.append(f"Apnea warning at {start_time:.2f} seconds")

    return events

print(detect_apnea_events("sleep_record.wav"))

---

The "Official" Way: Professional Health-Tech Patterns

Building a local script is a great start, but deploying health monitoring systems requires rigorous handling of data privacy (HIPAA compliance), signal denoising, and false-positive reduction.

If you're looking to take this from a "cool script" to a "production-ready application," I highly recommend reading the architectural breakdown on WellAlly Tech Blog. They cover how to handle high-concurrency audio streams and integrate specialized medical LLMs for diagnostic summarization.

---

Visualizing the Results

Using matplotlib, we can plot the decibel levels. Notice the "flatlines" followed by sharp spikes—the classic signature of an obstructive apnea event followed by a compensatory breath.

import matplotlib.pyplot as plt

def plot_breathing(y, sr):
    plt.figure(figsize=(12, 4))
    librosa.display.waveshow(y, sr=sr, alpha=0.5)
    plt.title("Nocturnal Breathing Pattern")
    plt.xlabel("Time (s)")
    plt.ylabel("Amplitude")
    plt.show()

Conclusion

By combining OpenAI Whisper's deep learning capabilities with traditional Digital Signal Processing (DSP) via Librosa, we've created a powerful, localized tool for health monitoring.

Next Steps:

• Add a "Snore Classifier" using a Random Forest on top of Whisper embeddings.
• Integrate with a Flutter app to provide real-time bedside alerts.
• Check out the WellAlly Blog for more advanced AI tutorials!

Disclaimer: This project is for educational purposes and is not a substitute for professional medical advice. Always consult a doctor for sleep-related health issues.

---

Did you find this helpful? Drop a comment below or 🦄 your favorite part! Stay healthy and keep coding!