SleepSound-DeepFilter: Fine-Tuning Whisper for Real-time Sleep Apnea and Snoring Detection

Is your snoring just "loud breathing," or is it something more serious? Sleep apnea affects nearly a billion people worldwide, yet most remain undiagnosed. As developers, we have the tools to change this.

In this tutorial, we are building SleepSound-DeepFilter, an end-to-end pipeline designed for real-time respiratory monitoring and Sleep Apnea detection. By leveraging Audio Signal Processing and fine-tuning the Whisper architecture, we’ll transform raw nocturnal sounds into actionable health insights. If you've been looking to master Whisper model fine-tuning or advanced PyTorch audio workflows, you're in the right place!

The Architecture: From Soundwaves to Diagnosis

Before we dive into the code, let’s look at the data flow. We aren't just transcribing text; we are extracting the rhythmic and spectral signatures of breathing.

graph TD
    A[Raw Audio Input] --> B[Librosa Preprocessing]
    B --> C[STFT / Mel-Spectrogram Conversion]
    C --> D[Whisper Encoder - Feature Extraction]
    D --> E[Custom PyTorch Classification Head]
    E --> F{Event Classification}
    F --> |Normal| G[Continuous Monitoring]
    F --> |Apnea/Snoring| H[FastAPI Alert System]
    H --> I[Health Dashboard]

Prerequisites

To follow this advanced guide, you’ll need:

• Tech Stack: Python 3.9+, PyTorch, Librosa, OpenAI Whisper API (or local weights), and FastAPI.
• Hardware: A GPU (NVIDIA T4 or better) is highly recommended for the training phase.

Step 1: Preprocessing Audio with Librosa

Sleep audio is noisy. We need to isolate respiratory sounds from background hums. We'll use librosa to slice the audio and convert it into Mel-spectrograms, which are essentially "images" of sound that Whisper's encoder can understand.

import librosa
import numpy as np

def preprocess_audio(file_path, duration=5, sr=16000):
    # Load audio file (standardizing to 16kHz for Whisper)
    audio, _ = librosa.load(file_path, sr=sr, duration=duration)

    # Normalize volume to handle different mic distances
    audio = librosa.util.normalize(audio)

    # Generate Mel-spectrogram
    spectrogram = librosa.feature.melspectrogram(y=audio, sr=sr, n_mels=80)
    log_spectrogram = librosa.power_to_db(spectrogram, ref=np.max)

    return log_spectrogram

# Example usage
# spec = preprocess_audio("nocturnal_clip_001.wav")

Step 2: Fine-Tuning the Whisper Backbone

While Whisper is famous for speech-to-text, its Encoder is a world-class feature extractor for any audio. We will freeze the transformer layers and attach a custom classification head to detect "Apnea Events" vs. "Normal Breathing."

import torch
import torch.nn as nn
from transformers import WhisperModel

class SleepApneaDetector(nn.Module):
    def __init__(self):
        super(SleepApneaDetector, self).__init__()
        # Load the pre-trained Whisper encoder
        self.whisper = WhisperModel.from_pretrained("openai/whisper-tiny").encoder

        # Custom classification head
        self.classifier = nn.Sequential(
            nn.Linear(384, 128), # Whisper-tiny hidden size is 384
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(128, 3) # Classes: 0: Normal, 1: Snore, 2: Apnea
        )

    def forward(self, mel_spec):
        # Extract features using Whisper
        with torch.no_grad():
            features = self.whisper(mel_spec).last_hidden_state

        # Mean pooling of the temporal dimension
        pooled_features = torch.mean(features, dim=1)
        return self.classifier(pooled_features)

model = SleepApneaDetector().to("cuda")

Step 3: Deploying with FastAPI

To make this an "Edge Monitoring" model, we need a lightweight API to receive audio chunks from a mobile device or a bedside IoT microphone.

from fastapi import FastAPI, UploadFile, File
import torch

app = FastAPI()

@app.post("/analyze-breathing")
async def analyze_breathing(file: UploadFile = File(...)):
    # 1. Save and Preprocess
    with open("temp.wav", "wb") as buffer:
        buffer.write(await file.read())

    features = preprocess_audio("temp.wav")
    features_tensor = torch.tensor(features).unsqueeze(0).to("cuda")

    # 2. Inference
    with torch.no_grad():
        prediction = model(features_tensor)
        label = torch.argmax(prediction, dim=1).item()

    classes = ["Normal", "Snoring", "Apnea Alert"]
    return {"status": "success", "detection": classes[label]}

Advanced Patterns and Production Scaling

When moving from a notebook to a production-grade healthcare app, you need to consider signal-to-noise ratios (SNR), patient data privacy (HIPAA), and model quantization for edge deployment.

For a deeper dive into production-ready AI patterns, including how to optimize these models for low-latency inference on mobile devices, check out the official WellAlly Tech Blog. They cover advanced architectural patterns that go far beyond basic tutorials, specifically focusing on healthcare and signal processing scalability.

Conclusion

Building a SleepSound-DeepFilter isn't just a coding exercise—it’s a peek into the future of preventative medicine. By combining the power of Whisper API for feature extraction and FastAPI for deployment, we’ve created a system that can monitor respiratory health without invasive sensors.

What's next?

1. Try augmenting your data with "White Noise" to make the model more robust.
2. Experiment with Whisper-Medium if you have the VRAM to spare.
3. Don't forget to star the repo and let me know your results in the comments! 👇