Awesome Open Source
Awesome Open Source

SUSI.AI on Linux

Codacy Badge Build Status Join the chat at https://gitter.im/fossasia/susi_hardware Twitter Follow

This repository contains components to run SUSI.AI on the desktop or a headless smart speaker together with the SUSI.AI Server. Functionalities implemented here include using the microphone to collect voice commands, converting speech to text (STT) using components such as Deep Speech, Flite, Pocket Sphinx, IBM Watson or others, controlling the volume with voice commands and providing a simple GTK interface. In order to use the JSON output of the SUSI.AI Server (written in Java) we use a SUSI.AI API Python Wrapper. The ultimate goal of the project to enable users to install SUSI.AI anywhere, apart from desktops and smart speakers on IoT devices, car systems, washing machines and more.

The functionalities of the project are provided as follows:

  • Hotword detection works for hotword "Susi"
  • Voice detection for Speech to Text (STT) using with Google Speech API, IBM Watson Speech to Text API
  • Voice output for Text to Speech (TTS) working with Google Voice, IBM Watson TTS, Flite TTS
  • SUSI.AI response working through SUSI.AI API Python Wrapper

Project Overview

The SUSI.AI ecosystem consists of the following parts:

 * Web Client and Content Management System for the SUSI.AI Skills - Home of the SUSI.AI community
 |_ susi.ai   (React Application, User Account Management for the CMS, a client for the susi_server at https://api.susi.ai the content management system for susi skills)
 
 * server back-end
 |_ susi_server        (the brain of the infrastructure, a server which computes answers from queries)
 |_ susi_skill_data    (the knowledge of the brain, a large collection of skills provided by the SUSI.AI community)
 
 * android front-end
 |_ susi_android       (Android application which is a client for the susi_server at https://api.susi.ai)
 
 * iOS front-end
 |_ susi_iOS           (iOS application which is a client for the susi_server at https://api.susi.ai)
 
 * Smart Speaker - Software to turn a Raspberry Pi into a Personal Assistant
 | Several sub-projects come together in this device
 |_ susi_installer     (Framework which can install all parts on a RPi and Desktops, and also is able to create SUSIbian disk images)
 |_ susi_python        (Python API for the susi_server at https://api.susi.ai or local instance)
 |_ susi_server        (The same server as on api.susi.ai, hosted locally for maximum privacy. No cloud needed)
 |_ susi_skill_data    (The skills as provided by susi_server on api.susi.ai; pulled from the git repository automatically)
 |_ susi_linux         (a state machine in python which uses susi_python, Speech-to-text and Text-to-speech functions)
 |_ susi.ai            (React Application, the local web front-end with User Account Management, a client for the local deployment of the susi_server, the content management system for susi skills)

Installation

susi_linux is normally installed via the SUSI Installer. In this case there are binaries for configuration and starting and others available in $HOME/SUSI.AI/bin (under default installation settings).

In case of manual installations, the wrappers in wrapper directory need to be configured to point to the respective installation directories and location of the config.json file.

Setting up and configuring Susi on Linux / RaspberryPi

Configuration is done via the file config.json which normally resides in $HOME/.config/SUSI.AI/config.json.

The script $HOME/SUSI.AI/bin/susi-config is best used to query, set, and change configuration of susi_linux. There is also a GUI interface to the configuration in $HOME/SUSI.AI/bin/susi-linux-configure.

The possible keys and values are given by running $HOME/SUSI.AI/bin/susi-config keys

Some important keys and possible values:

- `stt` is the speech to text service, one of the following choices:
    - `google` - use Google STT service
    - `watson` - IBM/Watson STT
    - `bing` - MS Bing STT
    - `pocketsphinx` - PocketSphinx STT system, working offline
    - `deepspeech-local` - DeepSpeech STT system, offline, WORK IN PROGRESS
- `tts` is the text to speech service, one of the following choices:
    - `google` - use Google TTS
    - `watson` - IBM/Watson TTS (login credential necessary)
    - `flite` - flite TTS service working offline
- `hotword.engine` is the choice if you want to use snowboy detector as the hotword detection or not
    - `Snowboy` to use snowboy
    - `PocketSphinx` to use Pocket Sphinx
- `wakebutton` is the choice if you want to use an external wake button or not
    - `enabled` to use an external wake button
    - `disabled` to disable the external wake button
    - `not available` for systems without dedicated wake button

Other interfaces for configuration are available for Android and iOS.

Manual configuration is possible, the allowed keys in [`config.json`](config.json) are currently
- `device`: the name of the current device
- `wakebutton`: whether a wake button is available or not
- `stt`: see above for possible settings
- `tts`: see above for possible settings
- `language': language for STT and TTS processing
- `path.base`: directory where support files are installed
- `path.sound.detection`: sound file that is played when detection starts, relative to `data_base_dir`
- `path.sound.problem`: sound file that is played on general errors, relative to `data_base_dir`
- `path.sound.error.recognition`: sound file that is played on detection errors, relative to `data_base_dir`
- `path.sound.error.timeout`: sound file that is played when timing out waiting for spoken commands
- `path.flite_speech`: flitevox speech file, relative to `data_base_dir`
- `hotword.engine`: see above for possible settings
- `hotword.model`: (if hotword.engine = Snowboy) selects the model file for the hotword
- `susi.mode`: access mode to `accounts.susi.ai`, either `anonymous` or `authenticated`
- `susi.user`: (if susi.mode = authenticated) the user name (email) to be used
- `susi.pass`: (if susi.mode = authenticated) the password to be used
- `roomname`: free form description of the room
- `watson.stt.user`, `watson.stt.pass`, `watson.tts.user`, `watson.tts.pass`: credentials for IBM/Watson server for TTS and STT
- `watson.tts.voice`: voice name selected for IBM/Watson TTS
- `bing.api`: Bing STT API key

For details concerning installation, setup, and operation on RaspberryPi, see
the documentation at [SUSI Installer](https://github.com/fossasia/susi_installer).



## Information for developers

This section is intended for developer.

### **Important:** Tests before making a new release

1. The hotword detection should have a decent accuracy
2. SUSI Linux shouldn't crash when switching from online to offline and vice versa (failing as of now)
3. SUSI Linux should be able to boot offline when no internet connection available (failing as of now)

### Roadmap

- Offline Voice Detection (if possible with satisfactory results)

### General working of SUSI

- SUSI.AI follows a finite state system for the code architecture.
- Google TTS and STT services are used as default services but if the internet fails, a switch to offline services PocketSphinx (STT) and Flite (TTS) is made automatically


### Run SUSI Linux for development purposes

If installed via the SUSI Installer, systemd unit files are installed:
- `ss-susi-linux.service` for the user bus, use as user with `systemctl --user start/enable ss-susi-linux`
- `[email protected]` for the system bus, use as `root` user to start a job for a specific user, 
  independent from whether the user is logged in or not: `sudo systemctl start/enable [email protected]`

By default, it is ran in _production_ mode, where log messages are limited to _error_ and _warning_ only.
In development, you may want to see more logs, to help debugging. You can switch it to "verbose" mode by 2 ways:

1. Run it manually

- Stop systemd service by `sudo systemctl stop ss-susi-linux`
- Use Terminal, _cd_ to `susi_linux` directory and run

python3 -m susi_linux -v

or repeat `v` to increase verbosity:

python3 -m susi_linux -vv


2. Change command run by `systemd`

- Edit the _/lib/systemd/system/ss-susi-linux.service_ and change the command in `ExecStart` parameter:

```ini
ExecStart=/usr/bin/python3 -m susi_linux -v --short-log
  • Reload systemd daemon: sudo systemctl daemon-reload

  • Restart the servive: sudo systemctl restart ss-susi-linux

  • Now you can read the log via journalctl:

    • journalctl -u ss-susi-linux
    • or journalctl -fu ss-susi-linux to get updated when the log is continuously produced.

The -v option is actually the same as the 1st method. The --short-log option is to exclude some info which is already provided by journalctl. For more info about logging feature, see this GitHub issue.


Get A Weekly Email With Trending Projects For These Topics
No Spam. Unsubscribe easily at any time.
python (55,261
artificial-intelligence (661
raspberry-pi (656
embedded (277