# Porcupine Wake Word Engine
Made in Vancouver, Canada by [Picovoice](https://picovoice.ai)
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](https://console.picovoice.ai/).
## Compatibility
- Python 3.8+
- Runs on Linux (x86_64), macOS (x86_64 and arm64), Windows (x86_64, arm64), and Raspberry Pi (Zero, 3, 4, 5).
## Installation
```console
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](https://console.picovoice.ai/) to get your `AccessKey`.
## Usage
Create an instance of the engine
```python
import pvporcupine
access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)
handle = pvporcupine.create(access_key=access_key, keywords=['picovoice'])
```
`handle` is an instance of Porcupine that detects utterances of "Picovoice". `keywords` input argument is a shorthand
for accessing default keyword model files shipped with the package. The list of default keywords can be retrieved by
```python
import pvporcupine
print(pvporcupine.KEYWORDS)
```
Porcupine can detect multiple keywords concurrently
```python
import pvporcupine
access_key = "${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
```python
import pvporcupine
access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)
keyword_paths = ['/absolute/path/to/keyword/one', '/absolute/path/to/keyword/two', ...]
handle = pvporcupine.create(access_key=access_key, keyword_paths=keyword_paths)
```
The sensitivity of the engine can be tuned per keyword using the `sensitivities` input argument
```python
import pvporcupine
access_key = "${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.
```python
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
```python
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](https://github.com/Picovoice/porcupine/tree/master/lib/common).
## Demos
[pvporcupinedemo](https://pypi.org/project/pvporcupinedemo/) provides command-line utilities for processing real-time
audio (i.e. microphone) and files using Porcupine.
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