Real-Time Speaker Recognition on Raspberry Pi Using SpeechBrain

This guide demonstrates how to deploy a real-time speaker recognition system on a Raspberry Pi using SpeechBrain’s ECAPA-TDNN model. We’ll cover model selection, setup, optimization, and deployment for efficient identification.

🧠 Why ECAPA-TDNN Over x-vector?

Conclusion: ECAPA-TDNN is more robust and suitable even for embedded systems like Raspberry Pi when optimized properly.

🧰 Raspberry Pi Setup

Hardware

• Raspberry Pi 4 Model B (4GB+ recommended)
• USB Microphone
• MicroSD Card (16GB+)
• Internet Connectivity

Update System

sudo apt update && sudo apt upgrade -y

🔧 Install Dependencies

Python & Pip

sudo apt install -y python3 python3-pip

PyTorch for ARM

pip3 install torch torchvision torchaudio

Additional Libraries

sudo apt install -y libatlas-base-dev sox
pip3 install numpy scipy

🎤 Install SpeechBrain

Clone & Install

git clone https://github.com/speechbrain/speechbrain.git
cd speechbrain
pip3 install -r requirements.txt
pip3 install .

📥 Download ECAPA-TDNN Model

wget https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb/resolve/main/embedding_model.ckpt

🧪 Speaker Inference Script

speaker_recognition.py

import torchaudio
from speechbrain.pretrained import EncoderClassifier

# Load model
classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb")

def get_embedding(audio_path):
    signal, fs = torchaudio.load(audio_path)
    embeddings = classifier.encode_batch(signal)
    return embeddings.squeeze().detach().cpu().numpy()

if __name__ == "__main__":
    emb = get_embedding("test.wav")
    print(emb)

Compare With Enrolled Embeddings

from scipy.spatial.distance import cosine

def identify_speaker(test_emb, enrolled_embs, threshold=0.5):
    min_dist = float('inf')
    identified = "Unknown"
    for spk, emb in enrolled_embs.items():
        dist = cosine(test_emb, emb)
        if dist < min_dist:
            min_dist = dist
            identified = spk
    return identified if min_dist < threshold else "Unknown"

📦 Enroll Speaker Embeddings

Before real-time recognition, collect samples from known users and compute their embeddings:

enrolled = {
    "Alice": get_embedding("samples/alice.wav"),
    "Bob": get_embedding("samples/bob.wav"),
    "Charlie": get_embedding("samples/charlie.wav")
}

⚙️ Optimize for Real-Time

Audio Tips

• Use 16kHz mono WAV input
• Clip audio to 1–2 seconds

Performance Profiling

sudo apt install -y htop
htop

Code Optimization

• Cache model in memory
• Use numpy arrays efficiently
• Avoid unnecessary reloads

Optional: Hardware Acceleration

• Consider converting model to ONNX and using onnxruntime
• Use NEON/SIMD (enabled by default on RPi4)
• Avoid float64 operations, stick to float32

✅ Final Thoughts

You now have a lightweight, high-performing speaker recognition system on your Raspberry Pi. With ECAPA-TDNN and optimizations, you can enable smart, voice-driven experiences anywhere. This setup is perfect for offline, real-time applications like smart homes, security, or personalized assistants.

Feel free to extend this with:

• Live microphone streaming with sounddevice
• Web UI using Flask
• Visual dashboards with matplotlib or dash

Got questions or want to go further? Drop a comment or reach out!