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Description:

pvporcupine 3.0.3

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:

Arm Cortex-M, STM32, Arduino, and i.MX RT
Raspberry Pi (Zero, 3, 4, 5)
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.

Compatibility

Python 3.8+
Runs on Linux (x86_64), macOS (x86_64 and arm64), Windows (x86_64), and Raspberry Pi (Zero, 3, 4, 5).

Installation
pip3 install pvporcupine

AccessKey
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.
Usage
Create an instance of the engine
import pvporcupine

access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)

handle = pvporcupine.create(access_key=access_key, 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

print(pvporcupine.KEYWORDS)

Porcupine can detect multiple keywords concurrently
import pvporcupine

access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)

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

To detect non-default keywords use keyword_paths input argument instead
import pvporcupine

access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)
keyword_paths = ['/absolute/path/to/keyword/one', '/absolute/path/to/keyword/two', ...]

handle = pvporcupine.create(access_key=access_key, keyword_paths=keyword_paths)

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

access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)

handle = pvporcupine.create(
access_key=access_key,
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():
pass

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

When done resources have to be released explicitly
handle.delete()

Non-English Wake Words
In order to detect non-English wake words you need to use the corresponding model file. The model files for all supported languages are available here.
Demos
pvporcupinedemo provides command-line utilities for processing real-time
audio (i.e. microphone) and files using Porcupine.