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Porcupine wake word engine.

Project description

Porcupine Wake Word Engine

Made in Vancouver, Canada by Picovoice

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. Raspberry Pi, BeagleBone, Android, iOS, Linux (x86_64), macOS (x86_64), Windows (x86_64), and web browsers are supported. Additionally, enterprise customers have access to ARM Cortex-M SDK.
  • 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.


  • Python 3
  • Runs on Linux (x86_64), macOS (x86_64), Windows (x86_64), Raspberry Pi, NVIDIA Jetson (Nano), and BeagleBone.


pip3 install pvporcupine


Create an instance of the engine

import pvporcupine

handle = pvporcupine.create(keywords=['picovoice'])

handle is an instance of Porcupine that detects utterances of "Picovoice". keywords input argument is a shorthand for accessing default keyword model files shipped with the package. The list of default keywords can be retrieved by

import pvporcupine


Porcupine can detect multiple keywords concurrently

import pvporcupine

handle = pvporcupine.create(keywords=['bumblebee', 'picovoice'])

To detect non-default keywords use keyword_paths input argument instead

import pvporcupine

keyword_paths = ['/absolute/path/to/keyword/one', '/absolute/path/to/keyword/two', ...]

handle = pvporcupine.create(keyword_paths=keyword_paths)

The sensitivity of the engine can be tuned per keyword using the sensitivities input argument

import pvporcupine

handle = pvporcupine.create(
        keywords=['grapefruit', 'porcupine'],
        sensitivities=[0.6, 0.35])

Sensitivity is the parameter that enables trading miss rate for the false alarm rate. It is a floating point number within [0, 1]. A higher sensitivity reduces the miss rate at the cost of increased false alarm rate.

When initialized, the valid sample rate is given by handle.sample_rate. Expected frame length (number of audio samples in an input array) is handle.frame_length. The engine accepts 16-bit linearly-encoded PCM and operates on single-channel audio.

def get_next_audio_frame():

while True:
    keyword_index = handle.process(get_next_audio_frame())
    if keyword_index >= 0:
        # detection event logic/callback

When done resources have to be released explicitly



pvporcupinedemo provides command-line utilities for processing real-time audio (i.e. microphone) and files using Porcupine.

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