Snoring or Sleep Apnea? Building an AI Sleep Guardian with Whisper and Librosa 😴💤

Have you ever woken up feeling like you’ve run a marathon in your sleep? You might be one of the millions suffering from Sleep Apnea, a condition where breathing repeatedly stops and starts. While a clinical polysomnography is the gold standard, we can build a sophisticated "Learning in Public" project to analyze sleep audio.

In this tutorial, we are diving deep into Audio Signal Processing and Machine Learning to distinguish between harmless snoring and dangerous apnea events. By leveraging OpenAI Whisper for event tagging and Librosa for time-domain feature extraction, we'll transform raw noise into actionable health insights. If you've been looking to master Digital Signal Processing (DSP) and PyTorch-based audio analysis, you're in the right place! 🚀

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The Architecture 🏗️

To detect sleep anomalies, we don't just "listen"—we decompose the signal. We use FFmpeg for normalization, Whisper to identify ambient "non-speech" events, and Librosa to calculate the physical properties of the sound.

graph TD
    A[Raw Sleep Audio] --> B[FFmpeg Preprocessing]
    B --> C{Feature Pipeline}
    C --> D[OpenAI Whisper]
    C --> E[Librosa DSP Engine]
    D --> F[Temporal Text Tokens: 'Snore', 'Silence']
    E --> G[RMS Energy & Zero-Crossing Rate]
    F & G --> H[Anomaly Detection Logic]
    H --> I[Health Dashboard/Report]

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Prerequisites 🛠️

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

• Python 3.9+
• FFmpeg: The Swiss Army knife for audio files.
• OpenAI Whisper: For high-accuracy audio transcription and timestamping.
• Librosa: The industry standard for music and audio analysis.
• PyTorch: To power the Whisper inference.

pip install openai-whisper librosa torch matplotlib ffmpeg-python

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Step 1: Preprocessing with FFmpeg 🎧

Sleep audio is often recorded in messy environments. We need to normalize the volume and convert the sample rate to 16kHz, which is the optimal frequency for Whisper and most DSP algorithms.

import ffmpeg

def preprocess_audio(input_file, output_file):
    """Normalize and resample audio to 16kHz."""
    try:
        (
            ffmpeg
            .input(input_file)
            .output(output_file, ar='16000', ac=1, format='wav')
            .overwrite_output()
            .run(quiet=True)
        )
        print(f"✅ Processed: {output_file}")
    except ffmpeg.Error as e:
        print(f"❌ Error during preprocessing: {e}")

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Step 2: Temporal Event Tagging with Whisper 🗣️

Whisper isn't just for transcribing meetings. It’s incredibly good at identifying "silence" or "non-verbal" cues. We’ll use it to create a timeline of when "sounds" actually occur.

import whisper

def get_audio_segments(file_path):
    # Use 'base' or 'small' for faster processing on local machines
    model = whisper.load_model("base")

    # We use a specific prompt to bias the model towards sound event detection
    result = model.transcribe(file_path, verbose=False)

    segments = []
    for segment in result['segments']:
        segments.append({
            "start": segment['start'],
            "end": segment['end'],
            "text": segment['text'].strip().lower()
        })
    return segments

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Step 3: Deep Feature Extraction with Librosa 📊

Now for the heavy lifting. Sleep Apnea is often characterized by a "crescendo" of snoring followed by a "sudden silence" (the apnea). We use Librosa to extract RMS Energy and the Zero-Crossing Rate (ZCR). High energy = Snoring; Near-zero energy = Potential Apnea.

import librosa
import numpy as np

def analyze_signal_features(y, sr):
    # Calculate Root Mean Square (RMS) energy
    rms = librosa.feature.rms(y=y)[0]

    # Calculate Zero-Crossing Rate (detecting noise vs tonal sounds)
    zcr = librosa.feature.zero_crossing_rate(y)[0]

    # Times for each frame
    times = librosa.frames_to_time(np.arange(len(rms)), sr=sr)

    return times, rms, zcr

# Load and analyze
y, sr = librosa.load("processed_audio.wav")
times, rms, zcr = analyze_signal_features(y, sr)

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Step 4: Connecting the Dots (The Logic) 🧠

We look for patterns where Whisper detects "silence" or "heavy breathing" while the Librosa RMS energy drops below a threshold for more than 10 seconds.

def detect_apnea_events(times, rms, threshold=0.01, min_duration=10):
    apnea_candidates = []
    silence_start = None

    for i, energy in enumerate(rms):
        if energy < threshold:
            if silence_start is None:
                silence_start = times[i]
        else:
            if silence_start is not None:
                duration = times[i] - silence_start
                if duration >= min_duration:
                    apnea_candidates.append((silence_start, times[i]))
                silence_start = None

    return apnea_candidates

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The "Official" Way to Scale 🥑

While this script works for local analysis, building a production-ready medical-adjacent AI involves complex data pipelines and rigorous validation. If you're looking for more production-ready examples and advanced DSP patterns, I highly recommend checking out the WellAlly Tech Blog. They cover deep dives into deploying Python-based audio models at scale and optimizing PyTorch inference for real-time monitoring.

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Conclusion: Take Action! 🚀

We’ve successfully combined the transcription power of Whisper with the surgical precision of Librosa. By mapping signal energy against temporal segments, we can provide a much clearer picture of what happens during the night.

Next Steps:

1. Visualize: Use matplotlib to overlay the Whisper segments on the RMS energy graph.
2. Fine-tune: Train a custom PyTorch classifier on top of these features to distinguish between "Heavy Snoring" and "Obstruction."
3. Stay Safe: Remember, this is an experimental tool—not a medical device!

Are you working on audio-based health tech? Drop a comment below or share your results! Let’s keep learning in public. 💻✨