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Porcupine wake word engine demo for ReSpeaker 4-mic hat.

Project description

Porcupine ReSpeaker Demo

Made in Vancouver, Canada by Picovoice

This package contains a commandline demo for controlling ReSpeaker 4-mic microphone array LEDs using Porcupine.


Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications. It is

  • using deep neural networks trained in real-world environments.
  • compact and computationally-efficient. It is perfect for IoT.
  • cross-platform:
    • Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT
    • Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone
    • Android and iOS
    • Chrome, Safari, Firefox, and Edge
    • Linux (x86_64), macOS (x86_64, arm64), and Windows (x86_64)
  • scalable. It can detect multiple always-listening voice commands with no added runtime footprint.
  • self-service. Developers can train custom wake word models using Picovoice Console.


Follow the instructions on Seeed Studio to install and set up the microphone array.

Then install the demo:

sudo pip3 install ppnrespeakerdemo


Porcupine requires a valid Picovoice AccessKey at initialization. AccessKey acts as your credentials when using Porcupine SDKs. You can get your AccessKey for free. Make sure to keep your AccessKey secret. Signup or Login to Picovoice Console to get your AccessKey.


Below are the colors associated with supported wake words for this demo:

  • #ffff33 Alexa
  • #ff8000 Bumblebee
  • #ffffff Computer
  • #ff0000 Hey Google
  • #800080 Hey Siri
  • #ff3399 Jarvis
  • #00ff00 Picovoice
  • #0000ff Porcupine
  • #000000 Terminator

Run the demo:

porcupine_respeaker_demo --access_key ${ACCESS_KEY}

Wait for the demo to initialize and print [Listening] in the terminal. Say:


The demo outputs:

detected 'Picovoice'

The lights are now set to green. Say:


The lights are set to yellow now. Say:


to turn off the lights.

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ppnrespeakerdemo-2.1.2.tar.gz (13.1 kB view hashes)

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ppnrespeakerdemo-2.1.2-py3-none-any.whl (12.7 kB view hashes)

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