Speech-to-Intent Engine - macOS Quick Start

  • Speech-to-Intent
  • Offline NLU
  • Local Voice Recognition
  • Voice Activation
  • Voice Commands
  • macOS

Requirements

  • Git
  • Python 3
  • Command Line Tools for Xcode
  • Homebrew

Cloning the Repository

Clone the repository using

git clone --recursive https://github.com/Picovoice/rhino.git

Installing Dependencies

Navigate to the root of the repository and install common Python dependencies

pip3 install -r requirements.txt

Install PortAudio

brew install portaudio

Install Python dependencies needed for real-time demo

pip3 install -r demo/python/requirements.txt

Runing the Unit Tests

Test the validity of installation by running Python binding's unit tests

python3 binding/python/test_rhino.py

Running Real-Time Demo

Run the microphone demo application. It opens an input audio stream, monitors it using Picovoice wake word engine, and when the wake phrase "Picovoice" is detected it will infer the intent within the follow-up spoken command using Speech-to-Intent engine.

python3 demo/python/rhino_demo_mic.py --rhino_context_file_path \
resources/contexts/mac/smart_lighting_mac.rhn

Now you can say something like "Picovoice, turn on the lights in the kitchen" and it outputs the result of inference into terminal

detected wake phrase
intent: turnLight
---
state: on
location: kitchen

Creating Custom Models

Enterprises who are commercially engaged with Picovoice can create custom NLU models using Picovoice Console.