Open Source Voice Assistants: Control Your Privacy and Customize Your Experience

OpenVoiceOS

1# Example OpenVoiceOS plugin
2
3from ovos_core.skill_interface import OVOSSkill
4from ovos_utils.intents import IntentBuilder
5
6class ExampleSkill(OVOSSkill):
7    def __init__(self, *args, **kwargs):
8        super().__init__(*args, **kwargs)
9
10    def initialize(self):
11        intent = IntentBuilder("ExampleIntent")\
12            .require("ExampleKeyword", "Example")\
13            .build()
14        self.register_intent(intent, self.handle_example_intent)
15
16    def handle_example_intent(self, message):
17        self.speak_dialog("example.response")
18
19    def stop(self):
20        pass
21

Leon

1// Example Leon skill
2
3module.exports = {
4  load: function () {
5    console.log('Example skill loaded');
6  },
7  run: function (params) {
8    if (params.intent === 'say_hello') {
9      return 'Hello!';
10    }
11  },
12  unload: function () {
13    console.log('Example skill unloaded');
14  }
15};
16

Rhasspy

Technical Aspects of Building an Open Source Voice Assistant

Speech Recognition (STT)

• Kaldi: A powerful and widely used speech recognition toolkit.
• DeepSpeech: Mozilla's open-source speech recognition engine.
• VosK: Lightweight offline STT with support for many languages

Text-to-Speech (TTS)

• eSpeak: A compact and versatile TTS engine.
• MaryTTS: A Java-based TTS system with a focus on high quality and naturalness.
• Mimic: A fast, local TTS engine.

Natural Language Processing (NLP)

• Intent Recognition: Identifying the user's goal (e.g., setting a timer, playing music).
• Entity Extraction: Identifying key pieces of information within the text (e.g., the duration of the timer, the name of the song).

• spaCy: A fast and accurate NLP library for Python.
• NLTK: A comprehensive toolkit for NLP research and development.
• Rasa: An open-source framework for building conversational AI assistants.

1import spacy
2
3nlp = spacy.load("en_core_web_sm")
4doc = nlp("Set a timer for 30 minutes.")
5
6for token in doc:
7    print(token.text, token.pos_, token.dep_)
8
9for ent in doc.ents:
10    print(ent.text, ent.label_)
11

Setting up your Development Environment

• Installing Python and Node.js (depending on the chosen platform).
• Installing the necessary NLP libraries (e.g., spaCy, NLTK).
• Setting up a virtual environment to manage dependencies.
• Configuring your audio input and output devices.

Deploying and Customizing Your Open Source Voice Assistant

Hardware Options

• Raspberry Pi: A low-cost and versatile single-board computer, perfect for DIY projects. This makes it a good fit for Raspberry Pi voice assistant projects.
• Desktop Computer: A more powerful option for demanding applications.
• Custom Hardware: You can even design and build your own hardware specifically tailored to your voice assistant.

Software Setup and Configuration

Creating Custom Skills/Plugins

1# Example custom command (Mycroft skill)
2
3from mycroft import MycroftSkill, intent_handler
4
5class ControlLights(MycroftSkill):
6    def __init__(self, name, skill_id):
7        super().__init__(name, skill_id)
8
9    @intent_handler('turn_on_lights.intent')
10    def handle_turn_on_lights(self, message):
11        # Code to turn on lights goes here
12        self.speak_dialog('lights.on')
13
14def create_skill(name, skill_id):
15    return ControlLights(name, skill_id)
16

Security and Privacy Considerations

Future of Open Source Voice Assistants