This project is dedicated to building a "Synthetic Human" which is called Kenzy for which we have assigned the female gender pronoun of "she". She has visual face recognition (opencv/opencv), speech transcription (coqui), and speech synthesis (festival or mimic3). Kenzy is written in Python and is targeted primarily at the single board computer (SBC) platforms like the Raspberry Pi.
Visit our main site: https://kenzy.ai/
Read the docs: https://docs.kenzy.ai/
Kenzy's architecture is divided into two main components: Containers and Devices. The containers focus on communication between other containers and devices are designed to control input and output operations. The most important container is the Brain which is a special type of container as it collects data and provides the skill engine for reacting to inputs. While a Brain does support all the methods of a normal container it is recommended to create a separate container to store all your devices.
All options, configurations, and startup parameters are driven by the configuration file saved to the following location:
Python Module Overview
|kenzy.containers.Brain||Main service for processing I/O.||8080|
|kenzy.containers.DeviceContainer||Secondary service for devices.||8081|
Python Device Module Overview
|kenzy.devices.Speaker||Audio output device for text-to-speech conversion|
|kenzy.devices.Listener||Microphone device for speech-to-text conversion|
|kenzy.devices.Watcher||Video/Camera device for object recognition|
|kenzy.devices.KasaDevice||Smart plug device for Kasa devices|
|kenzy.panels.RaspiPanel||Panel device designed for Raspberry Pi 7" screen @ 1024x600|
Using the Installation Script
The quickest and easiest way to install Kenzy is to use our installation script:
wget -q -O install.sh https://kenzy.ai/installer && sh install.sh
Running the script exactly as shown above will install Kenzy and all components. If you want to be more selective you can add options as follows:
-b= Install brain dependencies
-l= Install listener dependencies
-s= Install speaker dependencies
-w= Install watcher dependencies
-p= Install panel dependencies
-v [PATH]= Python virtual environment path (will create new if does not already exist)
Installer script has been tested on Ubuntu 22.04+, Debian Buster, and Raspberry Pi OS (Buster).
Kenzy is available through pip, but to use the built-in devices there are a few extra libraries you may require. Please visit the Basic Install page for more details.
# Install PIP (Python package manager) if not already installed sudo apt-get -y install python3-pip # Install the required system packages sudo apt-get -y install \ python3-fann2 \ python3-pyaudio \ python3-pyqt5 \ python3-dev \ libespeak-ng1 \ festival \ festvox-us-slt-hts \ libportaudio2 \ portaudio19-dev \ libasound2-dev \ libatlas-base-dev \ cmake \ swig # Create your local environment and then activate it sudo apt-get -y install python3-venv mkdir -p ~/kenzy cd ~/kenzy python3 -m venv ./.venv --system-site-packages source ./.venv/bin/activate # Install the required build libraries python3 -m pip install scikit-build # Install core required runtime libraries python3 -m pip install urllib3 \ requests \ netifaces \ padatious # Install libraries for SpeakerDevice (Required only if using ```mimic3``` in place of festival) python3 -m pip install mycroft-mimic3-tts[all] # Install optional libraries for WatcherDevice python3 -m pip install opencv-contrib-python \ Pillow # Install optional libraries for KasaDevice python3 -m pip install asyncio \ python-kasa # Install optional libraries for ListenerDevice python3 -m pip install --upgrade numpy \ webrtcvad \ stt # If you have trouble with pyaudio then you may want try to upgrade it python3 -m pip install --upgrade pyaudio # For listener model management (optional) python3 -m pip install coqui-stt-module-manager # Install the kenzy module python3 -m pip install kenzy
To start execute as follows:
python3 -m kenzy
You can disable one or more of the built-in devices or containers with
--disable-builtin-[speaker, watcher, listener, panels, brain, container]. Use the
--help option for full listing of command line options including specifying a custom configuration file.
NOTE: The program will create/save a version of the configuration to
~/.kenzy/config.json along with any other data elements it requires for operation. The configuration file is fairly powerful and will allow you to add/remove devices and containers for custom configurations including 3rd party devices or custom skills.
Troubleshooting: "Cannot find FANN libs"
If you encounter an error trying to install the kenzy module on the Raspberry Pi then you may need to add a symlink to the library FANN library. This is due to a bug/miss in the "find_fann" function within the Python FANN2 library as it doesn't look for the ARM architecture out-of-the-box. To fix it run the following:
Raspberry Pi (ARM)
sudo ln -s /usr/lib/arm-linux-gnueabihf/libdoublefann.so.2 /usr/local/lib/libdoublefann.so
Ubuntu 22.04 LTS (x86_64)
sudo ln -s /usr/lib/x86_64-linux-gnu/libdoublefann.so.2 /usr/local/lib/libdoublefann.so
In order to enable Speech-to-Text (STT) you need to download a speech model. You can use Coqui's model manager or use Kenzy to download one for you. The easiest solution is likely the following command:
python3 -m kenzy --download-models
Web Control Panel
If everything is working properly you should be able to point your device to the web control panel running on the Brain engine to test it out. The default URL is:
Help & Support
Help and additional details is available at https://kenzy.ai