Snoring is More Than Just Noise: Build a Sleep Apnea (OSA) Screening Engine with Whisper + Librosa

Do you wake up feeling like you’ve run a marathon instead of sleeping? 😴 Your snoring might be more than just a nuisance to your partner—it could be a sign of Obstructive Sleep Apnea (OSA). While nothing beats a professional clinical polysomnography, we can use modern AI to build a sophisticated screening tool.

In this tutorial, we will build a Sleep Apnea Screening Engine using OpenAI Whisper for event timestamping and Librosa for spectral analysis. We'll leverage audio analysis with Python and Multimodal AI techniques to identify those scary "silence gaps" followed by gasps that characterize OSA.

Disclaimer: This is an educational project and NOT a medical device. If you suspect you have sleep apnea, please consult a healthcare professional.

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The Architecture 🏗️

To analyze a full night's sleep (6-8 hours), we can't just throw a giant file at a model. We need a pipeline that segments audio, identifies "events" (snoring/choking), and analyzes the frequency spectrum to distinguish between normal breathing and obstructive events.

graph TD
    A[Raw Audio .wav/.m4a] --> B[FFmpeg Preprocessing]
    B --> C[Whisper Voice Activity Detection]
    C --> D{Is it Speech?}
    D -- Yes --> E[Ignore/Transcript]
    D -- No --> F[Librosa Spectral Analysis]
    F --> G[Extract Features: Centroid, Energy, ZCR]
    G --> H[OSA Event Classifier]
    H --> I[Streamlit Dashboard]

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Prerequisites 🛠️

Ensure you have the following tech stack ready:

• OpenAI Whisper: For robust timestamping and audio segmentation.
• Librosa: The gold standard for audio and music processing in Python.
• FFmpeg: For handling heavy lifting in audio format conversion.
• Streamlit: For building a clean, interactive UI.

pip install openai-whisper librosa streamlit matplotlib soundfile
# Make sure ffmpeg is installed on your system!

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Step 1: Preprocessing with FFmpeg & Whisper 🎙️

First, we need to handle the long-form audio. We use Whisper not for its "speech-to-text" capabilities per se, but for its world-class Time-Stamp and Voice Activity Detection (VAD).

Whisper helps us filter out when you are talking in your sleep versus when there is "non-speech" rhythmic noise (snoring).

import whisper

def get_audio_segments(audio_path):
    # Load the "base" model for speed
    model = whisper.load_model("base")

    # We use verbose=False to get a dictionary of segments
    # Whisper identifies 'no_speech_prob' which is crucial for us
    result = model.transcribe(audio_path, verbose=False, task="transcribe")

    # Filter segments where no speech is detected (potential snoring/apnea)
    non_speech_segments = [
        s for s in result['segments'] if s['no_speech_prob'] > 0.8
    ]
    return non_speech_segments

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Step 2: Spectral Analysis with Librosa 📉

Once we have the non-speech segments, we need to analyze the "texture" of the sound. OSA events usually involve:

1. Loud Snoring: High energy, specific frequency bands.
2. The Apnea (Silence): A sudden drop in decibels.
3. The Gasp: A high-frequency, high-energy burst.

import librosa
import numpy as np

def analyze_segment(y, sr):
    # Calculate Root Mean Square (RMS) Energy
    rms = librosa.feature.rms(y=y)
    avg_energy = np.mean(rms)

    # Spectral Centroid (the "brightness" of the sound)
    # Snoring usually has a lower centroid than a sharp gasp
    centroid = librosa.feature.spectral_centroid(y=y, sr=sr)
    avg_centroid = np.mean(centroid)

    # Zero Crossing Rate (detects percussive sounds)
    zcr = librosa.feature.zero_crossing_rate(y)
    avg_zcr = np.mean(zcr)

    return {
        "energy": avg_energy,
        "centroid": avg_centroid,
        "zcr": avg_zcr
    }

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Step 3: Detecting the "Apnea Signature" 🫁

The core logic is looking for the Apnea Signature: a period of rhythmic snoring followed by at least 10 seconds of silence, ending in a sharp energy spike.

def detect_osa_events(segments, audio_data, sr):
    detected_events = []

    for i in range(1, len(segments)):
        current = segments[i]
        prev = segments[i-1]

        # Calculate gap between segments
        gap_duration = current['start'] - prev['end']

        if 10 <= gap_duration <= 30:
            # Possible Apnea! Analyze the segment right after the gap
            start_sample = int(current['start'] * sr)
            end_sample = int(current['end'] * sr)
            clip = audio_data[start_sample:end_sample]

            features = analyze_segment(clip, sr)

            # If the post-gap segment is loud and "sharp", flag it
            if features['energy'] > 0.05 and features['centroid'] > 1500:
                detected_events.append({
                    "timestamp": prev['end'],
                    "duration_of_silence": gap_duration,
                    "severity_score": features['energy'] * 100
                })

    return detected_events

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Deep Dive: Advanced Implementation 💡

Building a hobbyist script is easy, but making this robust enough for real-world environmental noise (like a fan or a pet moving) requires advanced signal-filtering patterns.

If you want to explore production-ready AI pipelines, noise-cancellation algorithms, or advanced multimodal architectures, I highly recommend checking out the technical deep dives at WellAlly Tech Blog. They have some incredible resources on scaling Python-based audio processing and deploying Whisper at scale.

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Step 4: Visualizing with Streamlit 🚀

Finally, let's wrap this in a beautiful dashboard so you can actually visualize your sleep health.

import streamlit as st
import matplotlib.pyplot as plt

st.title("🌙 OSA Screening Engine")
uploaded_file = st.file_uploader("Upload your sleep recording", type=["wav", "mp3", "m4a"])

if uploaded_file:
    st.audio(uploaded_file)
    with st.spinner("Analyzing your sleep patterns..."):
        # Process the file
        # (This is where you'd call the functions defined above)
        st.success("Analysis Complete!")

        # Mock Data Visualization
        fig, ax = plt.subplots()
        ax.plot([0, 1, 2, 3], [10, 20, 15, 25]) # Example metric
        ax.set_title("Breathing Energy Over Time")
        st.pyplot(fig)

        st.warning("⚠️ Detected 5 potential apnea events. Consider seeing a doctor.")

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Conclusion & Next Steps 🥑

By combining OpenAI Whisper's segmentation with Librosa's digital signal processing, we've built a powerful tool that transforms "just noise" into actionable health insights.

What's next?

• Noise Profiles: Train a simple classifier to ignore "fan noise."
• Real-time Monitoring: Use PyAudio to process segments live.
• HealthKit Integration: Export these timestamps to your health app.

Have you tried using AI for health monitoring? Drop a comment below or share your results! And don't forget to visit wellally.tech/blog for more advanced multimodal AI tutorials.

Happy Hacking (and sleeping)! 💤🚀