Build a Private AI Voice Assistant: Fully Offline, No Cloud Required

Every time you say “Hey Siri” or “OK Google,” your voice recording travels to a data center, gets processed, and a response comes back. Your question, your voice, and the context of your request are all logged on someone else’s server. In 2026, there is an alternative: build your own voice assistant that runs entirely on hardware you own, with no internet connection required.

This project combines local speech recognition, a local large language model, and text-to-speech — all running on a Raspberry Pi 5 with the AI HAT+ 2 (see our Raspberry Pi 5 AI HAT+ 2 guide) or a desktop computer. The result is a voice assistant that is completely private, always available, and surprisingly capable.

Why Build Your Own?

According to XDA Developers’ 2026 assessment, privacy-focused makers are increasingly building local alternatives to commercial assistants. The reasons go beyond privacy:

No subscription fees — Amazon, Google, and Apple increasingly monetize assistant interactions
No internet dependency — works during outages, travel, and in areas with poor connectivity
Full customization — define exactly how your assistant behaves, what it knows, and what it can control
No eavesdropping — your voice data never leaves your home

Hardware Options

Option A: Raspberry Pi 5 Setup (~$180)

Component	Cost
Raspberry Pi 5 (8GB)	$80
AI HAT+ 2 (Hailo-10H)	$70
ReSpeaker Lite 2-Mic Array	$15
3.5mm powered speaker	$10
64GB MicroSD card	$10

This setup runs small LLMs (1-3B parameters) with acceptable response times of 2-4 seconds. Good for smart home control and simple Q&A.

Option B: Mini PC Setup (~$300-500)

Component	Cost
Mini PC (N100/Ryzen 5) with 16GB RAM	$200-400
USB microphone	$15
Speakers	$10

A mini PC with 16GB RAM runs 7-9B parameter models fluently, providing GPT-3.5-level responses locally. For about $500, you get something genuinely competitive with commercial assistants.

Option C: Desktop/Laptop (~$0 additional)

If you already have a desktop or laptop with 16GB+ RAM, you need only a microphone. This is the cheapest way to get started.

Software Stack

The entire stack is open source and free:

1. Wake Word Detection: OpenWakeWord

OpenWakeWord listens for a custom activation phrase (you choose your own wake word) using a tiny neural network that runs constantly with minimal CPU usage. No audio leaves the device until the wake word is detected.

2. Speech-to-Text: Whisper.cpp

OpenAI’s Whisper model, compiled for efficient local execution via whisper.cpp. According to Seeed Studio’s maker guide, the small Whisper model runs in near-real-time on a Raspberry Pi 5, converting your spoken question to text in 1-2 seconds.

3. Language Model: Ollama + Llama 3.3

Ollama serves a local LLM that processes your question and generates a response. On a Raspberry Pi 5 with AI HAT+ 2, a 3B-parameter model responds in 2-3 seconds. On a desktop with a decent GPU, an 8B model responds in under a second.

4. Text-to-Speech: Piper TTS

Piper is an open-source text-to-speech engine with natural-sounding voices. It runs locally and can generate speech in real-time on a Raspberry Pi.

5. Smart Home Integration: Home Assistant

If you want your assistant to control lights, thermostats, and other devices, Home Assistant provides the automation layer. Your voice assistant connects to Home Assistant via its local API.

Setup Guide (Pi 5 Version)

Step 1: Install the OS

Flash Raspberry Pi OS (64-bit) to your MicroSD card using the Raspberry Pi Imager. Boot the Pi and complete initial setup.

Step 2: Install Ollama

curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.2:3b

Step 3: Install Whisper.cpp

git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp && make
./models/download-ggml-model.sh small

Step 4: Install Piper TTS

pip install piper-tts

Step 5: Install Home Assistant (Optional)

Follow the Home Assistant installation guide for Raspberry Pi. This enables voice control of smart home devices.

Step 6: Connect the Pipeline

A Python script ties everything together: OpenWakeWord listens continuously, Whisper transcribes on activation, the transcript goes to Ollama, and the response is spoken through Piper.

The full pipeline script is about 100 lines of Python. The ESP32 sensors from our smart home automation guide can feed data into this assistant, letting you ask “What’s the temperature in the bedroom?” and get a real answer from your own sensors.

Performance Expectations

Hardware	Model Size	Response Time	Quality
Pi 5 + AI HAT+ 2	3B	3-5 seconds	Basic Q&A, smart home
Mini PC (16GB)	8B	1-3 seconds	Good conversations
Desktop + GPU	13B+	< 1 second	Near-commercial quality

The Pi 5 setup is genuinely usable for smart home control and simple questions. For extended conversations and complex reasoning, a mini PC or desktop provides a much better experience.

Compared to Commercial Assistants

Feature	DIY Assistant	Alexa/Google/Siri
Privacy	Complete — no data leaves home	Voice recordings sent to cloud
Internet required	No	Yes
Monthly cost	$0	$0 (ad-supported)
Smart home control	Via Home Assistant	Native integrations
General knowledge	Good (with local LLM)	Excellent (with internet)
Music/streaming	Limited	Excellent
Customization	Complete	Limited

The DIY approach wins on privacy and customization. Commercial assistants win on convenience and streaming media integration. For a maker who values understanding and controlling their own systems — the same philosophy behind fixing cars and repairing plumbing — building your own assistant is deeply satisfying.

Sources

5 Raspberry Pi 5 projects that actually push the hardware — XDA Developers — accessed March 26, 2026
Top Raspberry Pi AI Projects in 2026 — Seeed Studio — accessed March 26, 2026
18 Practical Smart Home Projects Using Arduino, ESP32, and Raspberry Pi — Seeed Studio — accessed March 26, 2026