Reading
A high-performance bidirectional AI voice and visual pipeline enabling real-time interaction between Android endpoints and locally GPU-accelerated PCs, an integrated full local AI companion solution combining STT, LLM, and TTS.
Section 01
CivicBot is a fully localized end-to-end AI voice companion system developed by Mouhamed and Nader from Tunisia's Bizerte Higher Institute. It avoids cloud dependency, integrating STT (Faster-Whisper), LLM (via Ollama like Phi-3/Llama3.2), TTS (Kokoro-82M) in an end-edge architecture (Android + local GPU PC). Key use cases include infrastructure repair, tourism guidance, and elderly assistance, embodying tech-for-good values.
Section 02
Most existing voice assistants rely on cloud services, posing privacy risks and network limitations. CivicBot aims to solve these issues with a local-first approach. As a civic-tech solution, it targets real social problems (infrastructure repair requests, tourism, elderly support) to combine cutting-edge tech with people's livelihood needs, reflecting tech for good.
Section 03
End-edge Collaboration: Android mobile (portability) + local GPU server (computing power) via bidirectional pipeline. Voice Pipeline:
Section 04
Android End: Jetpack Compose (UI), CameraX (image capture), WebSocket (real-time communication), 16kHz PCM audio, R8/Proguard optimization. PC Backend: Asyncio/websockets (high concurrency), ctranslate2 (GPU acceleration for Whisper), multiphase resampling (audio conversion), thread pool (non-blocking LLM execution). Network & Security: Tailscale integration (end-to-end encryption, zero-config network, private IPs).
Section 05
Hardware: Windows/Linux OS, NVIDIA RTX3050+ (6GB CUDA12.x), Python3.9+, Android Studio. Deployment Steps: Clone repo → create virtual env → install dependencies → start Ollama → build Android app → configure Tailscale.
Section 06
Infrastructure Repair: Citizens submit reports via voice (lower participation barrier). Tourism: Localized service for tourists (stable even with poor network). Elderly Assistance: Help with crossing roads, emergency calls; large audio output and simple interaction for tech-unfamiliar users.
Section 07
Highlights: Local-first (privacy/availability), low latency (streaming, quantization, async, hardware acceleration), modular design (easy component replacement). Future: As edge models improve and hardware costs drop, local AI will expand to smart homes, industrial inspection, education, healthcare. Decentralized AI deployment may be a key future direction.
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