DNN based hotword and wake word detection toolkit
C++ Makefile TypeScript Shell Python
Latest commit dc0799b Dec 30, 2016 @chenguoguo chenguoguo committed on GitHub Merge pull request #96 from Kitt-AI/devel


Snowboy Hotword Detection


Home Page

Full Documentation

Discussion Group (or send email to snowboy-discussion@kitt.ai)

(The discussion group is new since September 2016 as we are getting many messages every day. Please send general questions there. For bugs, use Github issues.)

Version: 1.1.0 (9/20/2016)


Alexa support

Snowboy now brings hands-free experience to the Alexa AVS sample app on Raspberry Pi! Here is how you can use other Snowboy models

Personal model

Universal model

  • Replace the hotword model in Alexa AVS sample app (after installation) with your universal model
  • Run the wake word agent with engine set to kitt_ai!

Hotword as a Service

Snowboy now offers Hotword as a Service through the https://snowboy.kitt.ai/api/v1/train/ endpoint. Check out the Full Documentation and example Python/Bash script (other language contributions are very welcome).

As a quick start, POST to https://snowboy.kitt.ai/api/v1/train:

    "name": "a word",
    "language": "en",
    "age_group": "10_19",
    "gender": "F",
    "microphone": "mic type",
    "token": "<your auth token>",
    "voice_samples": [
        {wave: "<base64 encoded wave data>"},
        {wave: "<base64 encoded wave data>"},
        {wave: "<base64 encoded wave data>"}

then you'll get a trained personal model in return!


Snowboy is a customizable hotword detection engine for you to create your own hotword like "OK Google" or "Alexa". It is powered by deep neural networks and has the following properties:

  • highly customizable: you can freely define your own magic phrase here – let it be “open sesame”, “garage door open”, or “hello dreamhouse”, you name it.

  • always listening but protects your privacy: Snowboy does not use Internet and does not stream your voice to the cloud.

  • light-weight and embedded: it even runs on a Raspberry Pi and consumes less than 10% CPU on the weakest Pi (single-core 700MHz ARMv6).

  • Apache licensed!

Currently Snowboy supports:

  • all versions of Raspberry Pi (with Raspbian based on Debian Jessie 8.0)
  • 64bit Mac OS X
  • 64bit Ubuntu (12.04 and 14.04)
  • iOS
  • Android

It ships in the form of a C++ library with language-dependent wrappers generated by SWIG. We welcome wrappers for new languages -- feel free to send a pull request!

If you want support on other hardware/OS, please send your request to snowboy@kitt.ai

Pricing for Snowboy models

Hackers: free

  • Personal use
  • Community support

Business: please

us at snowboy@kitt.ai

  • Personal use
  • Commercial license
  • Technical support

Precompiled node module

Snowboy is available in the form of a native node module precompiled for: 64 bit Ubuntu, MacOS X, and the Raspberry Pi (Raspbian 8.0+). For quick installation run:

npm install --save snowboy

For sample usage see the examples/Node folder. You may have to install dependencies like fs, wav or node-record-lpcm16 depending on which script you use.

Precompiled Binaries with Python Demo

If you want to compile a version against your own environment/language, read on.


Snowboy's Python wrapper uses PortAudio to access your device's microphone.

Mac OS X

brew install swig, sox, portaudio and its Python binding pyaudio:

brew install swig portaudio sox
pip install pyaudio

If you don't have Homebrew installed, please download it here. If you don't have pip, you can install it here.

Make sure that you can record audio with your microphone:

rec t.wav

Ubuntu/Raspberry Pi

First apt-get install swig, sox, portaudio and its Python binding pyaudio:

sudo apt-get install swig3.0 python-pyaudio python3-pyaudio sox
pip install pyaudio

Then install the atlas matrix computing library:

sudo apt-get install libatlas-base-dev

Make sure that you can record audio with your microphone:

rec t.wav

If you need extra setup on your audio (especially on a Raspberry Pi), please see the full documentation.

Compile a Node addon

Compiling a node addon for Linux and the Raspberry Pi requires the installation of the following dependencies:

sudo apt-get install libmagic-dev libatlas-base-dev

Then to compile the addon run the following from the root of the snowboy repository:

node-pre-gyp clean configure build

Compile a Java Wrapper

# Make sure you have JDK installed.
cd swig/Java

SWIG will generate a directory called java which contains converted Java wrappers and a directory called jniLibs which contains the JNI library.

To run the Java example script:

cd examples/Java
make run

Compile a Python Wrapper

cd swig/Python

SWIG will generate a _snowboydetect.so file and a simple (but hard-to-read) python wrapper snowboydetect.py. We have provided a higher level python wrapper snowboydecoder.py on top of that.

Feel free to adapt the Makefile in swig/Python to your own system's setting if you cannot make it.

Compile an iOS Wrapper

Using Snowboy library in Objective-C does not really require a wrapper. It is basically the same as using C++ library in Objective-C. We have compiled a "fat" static library for iOS devices, see the library here lib/ios/libsnowboy-detect.a.

To initialize Snowboy detector in Objective-C:

snowboy::SnowboyDetect* snowboyDetector = new snowboy::SnowboyDetect(
    std::string([[[NSBundle mainBundle]pathForResource:@"common" ofType:@"res"] UTF8String]),
    std::string([[[NSBundle mainBundle]pathForResource:@"snowboy" ofType:@"umdl"] UTF8String]));
snowboyDetector->SetSensitivity("0.45");        // Sensitivity for each hotword
snowboyDetector->SetAudioGain(2.0);             // Audio gain for detection

To run hotword detection in Objective-C:

int result = snowboyDetector->RunDetection(buffer[0], bufferSize);  // buffer[0] is a float array

You may want to play with the frequency of the calls to RunDetection(), which controls the CPU usage and the detection latency.

Compile an Android Wrapper

cd swig/Android
# Make sure you set up the NDKROOT variable in Makefile before you run.
# We have only tested with NDK version r11c.

Using Snowboy library on Android devices is a little bit tricky. We have only tested with NDK version r11c. We do not support r12 yet because of the removal of armeabi-v7a-hard ABI in r12. We have compiled Snowboy using Android's cross-compilation toolchain for ARMV7 architecture, see the library here lib/android/armv7a/libsnowboy-detect.a. We then use SWIG to generate the Java wrapper, and use Android's cross-compilation toolchain to generate the corresponding JNI libraries. After running make, two directories will be created: java and jniLibs. Copy these two directories to your Android app directory (e.g., app/src/main/) and you should be able to call Snowboy funcitons within Java.

To initialize Snowboy detector in Java:

# Assume you put the model related files under /sdcard/snowboy/
SnowboyDetect snowboyDetector = new SnowboyDetect("/sdcard/snowboy/common.res",
snowboyDetector.SetSensitivity("0.45");         // Sensitivity for each hotword
snowboyDetector.SetAudioGain(2.0);              // Audio gain for detection

To run hotword detection in Java:

int result = snowboyDetector.RunDetection(buffer, buffer.length);   // buffer is a short array.

You may want to play with the frequency of the calls to RunDetection(), which controls the CPU usage and the detection latency.

Quick Start for Python Demo

Go to the examples/Python folder and open your python console:

In [1]: import snowboydecoder

In [2]: def detected_callback():
   ....:     print "hotword detected"

In [3]: detector = snowboydecoder.HotwordDetector("resources/snowboy.umdl", sensitivity=0.5, audio_gain=1)

In [4]: detector.start(detected_callback)

Then speak "snowboy" to your microphone to see whetheer Snowboy detects you.

The snowboy.umdl file is a "universal" model that detect different people speaking "snowboy". If you want other hotwords, please go to snowboy.kitt.ai to record, train and downloand your own personal model (a .pmdl file).

When sensitiviy is higher, the hotword gets more easily triggered. But you might get more false alarms.

audio_gain controls whether to increase (>1) or decrease (<1) input volume.

Two demo files demo.py and demo2.py are provided to show more usages.

Note: if you see the following error:

TypeError: __init__() got an unexpected keyword argument 'model_str'

You are probably using an old version of SWIG. Please upgrade. We have tested with SWIG version 3.0.7 and 3.0.8.

Advanced Usages & Demos

See Full Documentation.

Change Log


  • Offering Hotword as a Service through /api/v1/train endpoint.
  • No version bump since decoder is not changed.

v1.1.0, 9/20/2016

  • Added library for Node.
  • Added support for Python3.
  • Added universal model alexa.umdl
  • Updated universal model snowboy.umdl so that it works in noisy environment.

v1.0.4, 7/13/2016

  • Updated universal snowboy.umdl model to make it more robust.
  • Various improvements to speed up the detection.
  • Bug fixes.

v1.0.3, 6/4/2016

  • Updated universal snowboy.umdl model to make it more robust in non-speech environment.
  • Fixed bug when using float as input data.
  • Added library support for Android ARMV7 architecture.
  • Added library for iOS.

v1.0.2, 5/24/2016

  • Updated universal snowboy.umdl model
  • added C++ examples, docs will come in next release.

v1.0.1, 5/16/2016

  • VAD now returns -2 on silence, -1 on error, 0 on voice and >0 on triggered models
  • added static library for Raspberry Pi in case people want to compile themselves instead of using the binary version

v1.0.0, 5/10/2016

  • initial release