edgevox 0.1.2

Sub-second local voice AI for robots and edge devices.

EdgeVox

Sub-second local voice AI for robots and edge devices.

No cloud APIs. No internet after setup. Fully private. Powered by Gemma 4.

    ______    __         _    __
   / ____/___/ /___ ____| |  / /___  _  __
  / __/ / __  / __ `/ _ \ | / / __ \| |/_/
 / /___/ /_/ / /_/ /  __/ |/ / /_/ />  <
/_____/\__,_/\__, /\___/|___/\____/_/|_|
            /____/

Stack: Silero VAD -> faster-whisper (STT) -> Gemma 4 E2B IT via llama.cpp (LLM) -> Kokoro 82M (TTS)

Tested latency: 0.80s end-to-end on RTX 3080 (STT 0.40s + LLM 0.33s + TTS 0.08s)

Features

• Streaming pipeline — speaks first sentence while LLM generates the rest
• Interrupt support — speak while bot is talking to cut it off
• Wake word detection — "Hey Jarvis" / "Lily" (optional, via OpenWakeWord)
• Beautiful TUI — ASCII logo, sparkline waveform, latency history, GPU/RAM monitor, model info panel
• ROS2 bridge — publishes STT/TTS/state to ROS2 topics for robotics integration
• Slash commands — /reset, /lang, /voice, /say, /mictest, /model in the TUI
• Chat export — Ctrl+S to save conversation as markdown
• 15 languages — English, Vietnamese, French, Spanish, Hindi, Italian, Portuguese, Japanese, Chinese, Korean, German, Thai, Russian, Arabic, Indonesian
• Auto-detects hardware — GPU layers, model size, STT model

Hardware Requirements

Device	RAM	GPU	Expected Latency
PC (i9 + RTX 3080 16GB)	64GB	CUDA	~0.8s
Jetson Orin Nano	8GB	CUDA	~1.5-2s
MacBook Air M1	8GB	Metal	~2-3s
Any modern laptop	16GB+	CPU only	~2-4s

Quick Start

# 1. Install uv (fast Python package manager)
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Create virtual environment
uv venv --python 3.12
source .venv/bin/activate

# 3. Install llama-cpp-python with CUDA (prebuilt wheels)
uv pip install llama-cpp-python \
    --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124

# For Apple Silicon (Metal):
# CMAKE_ARGS="-DGGML_METAL=on" uv pip install llama-cpp-python

# For CPU only:
# uv pip install llama-cpp-python

# 4. Install EdgeVox
uv pip install -e .

# 5. Download all models (~3GB total)
edgevox-setup

# 6. Run!
edgevox

Usage

# TUI mode (default, recommended)
edgevox

# With wake word
edgevox --wakeword "hey jarvis"

# With ROS2 bridge (for robotics)
edgevox --ros2

# CLI mode (simpler, no TUI)
edgevox-cli

# Text mode (no microphone)
edgevox-cli --text-mode

# Custom options
edgevox \
    --whisper-model large-v3-turbo \
    --voice am_adam \
    --language en

TUI Controls

Key	Action
Q	Quit
R	Reset conversation
M	Mute/Unmute mic
/	Open command input
Ctrl+S	Export chat to markdown

Slash Commands

Command	Action
/reset	Reset conversation
/lang XX	Switch language (en, vi, fr, ko, ...)
/langs	List all supported languages
/say TEXT	TTS preview — speak text directly
/mictest	Record 3s + playback to test audio
/model SIZE	Switch Whisper model (small/medium/large-v3-turbo)
/voice XX	Switch TTS voice
/voices	List available voices
/export	Export chat to markdown
/mute	Mute microphone
/unmute	Unmute microphone
/help	Show all commands

ROS2 Integration

EdgeVox can publish voice pipeline events to ROS2 topics, making it easy to add voice interaction to any robot.

# Install with ROS2 support
uv pip install -e ".[ros2]"

# Run with ROS2 bridge
edgevox --ros2

Published Topics

Topic	Type	Description
/edgevox/transcription	std_msgs/String	User's speech (STT output)
/edgevox/response	std_msgs/String	Bot's response text
/edgevox/state	std_msgs/String	Pipeline state (listening, thinking, speaking)
/edgevox/audio_level	std_msgs/Float32	Mic level (0.0-1.0)
/edgevox/metrics	std_msgs/String	JSON latency metrics

Subscribed Topics

Topic	Type	Description
/edgevox/tts_request	std_msgs/String	Send text for the bot to speak
/edgevox/command	std_msgs/String	Commands: reset, mute, unmute

Example: Robot Integration

import rclpy
from std_msgs.msg import String

# Listen to what the user says
node.create_subscription(String, '/edgevox/transcription', on_user_speech, 10)

# Make the robot say something
pub = node.create_publisher(String, '/edgevox/tts_request', 10)
msg = String()
msg.data = "I detected an obstacle ahead."
pub.publish(msg)

Architecture

                        EdgeVox Pipeline
 +-----------+     +------------+     +----------------+
 | Microphone|---->| Silero VAD |---->| faster-whisper  |
 |           |     | (32ms)     |     | (STT)          |
 +-----------+     +------------+     +--------+-------+
                                               |
                                               v
                                      +----------------+
                                      | Gemma 4 E2B IT |
                                      | (streaming)    |
                                      +--------+-------+
                                               | sentence by sentence
                                               v
 +-----------+     +------------+     +----------------+
 |  Speaker  |<----| Kokoro 82M |<----| Sentence       |
 |           |     | (TTS)      |     | Splitter       |
 +-----------+     +------------+     +----------------+
                         |
                         v (optional)
                   +------------+
                   | ROS2 Bridge|----> /edgevox/* topics
                   +------------+

Model Sizes

Component	Model	Size	RAM
VAD	Silero VAD v6	~2MB	~10MB
STT	whisper-small	500MB	~600MB
STT	whisper-large-v3-turbo	1.5GB	~2GB
LLM	Gemma 4 E2B IT Q4_K_M	1.8GB	~2.5GB
TTS	Kokoro 82M	200MB	~300MB
Wake	OpenWakeWord	~2MB	~10MB

M1 Air (8GB): whisper-small + Q4_K_M = 3.4GB PC with GPU: whisper-large-v3-turbo + Q4_K_M = 5.8GB

Documentation

Full docs: EdgeVox Docs (built with VitePress)

cd website && npm run dev

License

MIT