Let’s be honest: no one wants their private nighttime "soundtrack" (a.k.a. snoring or heavy breathing) being uploaded to a corporate server for "analysis." Yet, monitoring sleep health is crucial, especially for detecting potential Sleep Apnea or respiratory distress.
In this tutorial, we are building Sleep Guardian, a high-performance Edge AI system. We’ll combine the feature extraction power of Whisper-tiny with the sequential modeling of Temporal Convolutional Networks (TCN) to create a real-time, localized monitoring system. By leveraging Raspberry Pi and Docker, we ensure this runs 24/7 without ever needing an internet connection.
This is the ultimate project for anyone interested in Real-time Audio Classification, Edge Computing, and Privacy-focused AI.
The Architecture 🏗️
The system operates in a pipeline: capturing raw audio, extracting high-level latent features using a pre-trained transformer encoder, and then classifying those patterns over time.
graph TD
A[Microphone Input] -->|Raw PCM 16kHz| B(Pre-processing)
B -->|Mel Spectrogram| C[Whisper-tiny Encoder]
C -->|Hidden States| D[TCN Classifier]
D -->|Softmax| E{Threshold Engine}
E -->|Normal| F[Log & Ignore]
E -->|Apnea/Heavy Snore| G[Local Alarm/GPIO Alert]
G -->|Critical| H[Push to HomeAssistant/Local Dashboard]
Why Whisper-tiny + TCN? 🧠
While OpenAI's Whisper is famous for Speech-to-Text, its Encoder is a world-class feature extractor for any audio signal. We use the tiny version to keep the footprint small enough for the Raspberry Pi.
However, audio events like "Sleep Apnea" (long pauses followed by gasping) are temporal. A standard CNN only looks at a snapshot. That’s where the Temporal Convolutional Network (TCN) comes in. TCNs provide a larger receptive field than LSTMs and are significantly faster to execute on edge hardware.
Prerequisites
- Hardware: Raspberry Pi 4 (4GB+) or Pi 5.
- Tech Stack: PyTorch, Whisper-tiny, Docker, Python 3.9+.
Step 1: The TCN Classifier Model
We’ll define a TCN that takes the embeddings from Whisper and looks for patterns over a 5-10 second window.
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class ChainedTCNBlock(nn.Module):
def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding):
super(ChainedTCNBlock, self).__init__()
self.conv = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size,
stride=stride, padding=padding, dilation=dilation))
self.relu = nn.ReLU()
self.net = nn.Sequential(self.conv, self.relu)
def forward(self, x):
return self.net(x)
class SleepTCN(nn.Module):
def __init__(self, input_size, num_channels, kernel_size=3):
super(SleepTCN, self).__init__()
layers = []
num_levels = len(num_channels)
for i in range(num_levels):
dilation_size = 2 ** i
in_channels = input_size if i == 0 else num_channels[i-1]
out_channels = num_channels[i]
layers += [ChainedTCNBlock(in_channels, out_channels, kernel_size, stride=1,
dilation=dilation_size, padding=(kernel_size-1) * dilation_size)]
self.network = nn.Sequential(*layers)
self.classifier = nn.Linear(num_channels[-1], 3) # Classes: Normal, Snore, Apnea
def forward(self, x):
# x shape: (Batch, Hidden_Dim, Seq_Len)
y = self.network(x)
return self.classifier(y[:, :, -1])
Step 2: Feature Extraction with Whisper
Instead of training a model from scratch, we use Whisper’s Mel-spectrogram processing.
import whisper
import numpy as np
# Load the smallest model for the Edge
model_whisper = whisper.load_model("tiny")
def get_audio_features(audio_path):
# Load and pad/trim audio to 30s
audio = whisper.load_audio(audio_path)
audio = whisper.pad_or_trim(audio)
# Generate Log-Mel Spectrogram
mel = whisper.log_mel_spectrogram(audio).to(model_whisper.device)
# Extract hidden features from the Encoder
with torch.no_grad():
features = model_whisper.encoder(mel.unsqueeze(0))
return features # Shape: [1, 1500, 384]
Step 3: Deployment Strategy (The "Official" Way) 🛠️
Deploying deep learning on the edge requires strict resource management. Running this inside a Docker container on the Raspberry Pi is the best way to ensure stability.
For more production-ready examples and advanced optimization patterns for Edge AI (like quantization and pruning), I highly recommend checking out the technical deep-dives at WellAlly Tech Blog. They cover extensively how to scale these localized models for clinical-grade reliability.
Dockerfile snippet:
FROM python:3.9-slim
RUN apt-get update && apt-get install -y ffmpeg portaudio19-dev
RUN pip install torch whisper-openai librosa
WORKDIR /app
COPY . .
# Run with optimized thread count for Pi
CMD ["python", "monitor.py", "--threads", "4"]
Real-time Monitoring Logic ⏱️
The core loop involves a sliding window. We capture 5 seconds of audio, process it, and update our "health score."
def real_time_loop():
while True:
audio_chunk = capture_mic(duration=5)
features = get_audio_features(audio_chunk)
# Predict using our TCN
output = sleep_tcn_model(features.permute(0, 2, 1))
prediction = torch.argmax(output, dim=1)
if prediction == 2: # Apnea Detected
trigger_local_alarm()
print("⚠️ ALERT: Abnormal breathing pattern detected!")
elif prediction == 1:
print("💤 Status: Snoring detected.")
Conclusion & Ethical Considerations 🥑
By building this on a Raspberry Pi, you have created a medical-tech tool that respects the ultimate human right: Privacy. There are no API keys, no data harvesting, and no subscription fees.
Next Steps:
- Quantization: Convert your PyTorch model to
ONNXorOpenVINOto drop latency on the Pi 4. - Dashboard: Connect the local alerts to a HomeAssistant instance via MQTT.
If you found this guide helpful, or if you want to see how to integrate this with wearable sensors, head over to the WellAlly Tech Blog for more advanced multimodal AI content!
What are you building for the edge? Let me know in the comments below! 👇
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