未来的版本将通过此处的分支版本维护模型训练模块: https://GitHub.com/seasalt-ai/snowboy
Dear KITT.AI users,
We are writing this update to let you know that we plan to shut down all KITT.AI products (Snowboy, NLU and Chatflow) by Dec. 31st, 2020.
we launched our first product Snowboy in 2016, and then NLU and Chatflow later that year. Since then, we have served more than 85,000 developers, worldwide, accross all our products. It has been 4 extraordinary years in our life, and we appreciate the opportunity to be able to serve the community.
The field of artificial intelligence is moving rapidly. As much as we like our products, we still see that they are getting outdated and are becoming difficult to maintain. All official websites/APIs for our products will be taken down by Dec. 31st, 2020. Our github repositories will remain open, but only community support will be available from this point beyond.
Thank you all, and goodbye!
The KITT.AI Team
Mar. 18th, 2020
by KITT.AI.
Discussion Group (or send email to [email protected])
Version: 1.3.0 (2/19/2018)
Snowboy now brings hands-free experience to the Alexa AVS sample app on Raspberry Pi! See more info below regarding the performance and how you can use other hotword models. The following instructions currently support AVS sdk Version 1.12.1.
Performance
The performance of hotword detection usually depends on the actual environment, e.g., is it used with a quality microphone, is it used on the street, in a kitchen, or is there any background noise, etc. So we feel it is best for the users to evaluate it in their real environment. For the evaluation purpose, we have prepared an Android app which can be installed and run out of box: SnowboyAlexaDemo.apk (please uninstall any previous versions first if you have installed this app before).
Kittai KWD Engine
Set up Alexa AVS sample app following the official AVS instructions
Apply patch to replace the Sensory KWD engine with Kittai engine
# Copy the patch file to the root directory of Alexa AVS sample app. Please replace $ALEXA_AVS_SAMPLE_APP_PATH with the actual path where you
# cloned the Alexa AVS sample app repository, and replace $SNOWBOY_ROOT_PATH with the actual path where you clone the Snowboy repository
cd $ALEXA_AVS_SAMPLE_APP_PATH
cp $SNOWBOY_PATH/resource/alexa/alexa-avs-sample-app/avs-kittai.patch ./
# Apply the patch, this will modify the scripts setup.sh and pi.sh
patch < avs-kittai.patch
sudo bash setup.sh config.json
sudo bash startsample.sh
Here is a demo video for how to use Snowboy hotword engine in Alexa Voice Service.
Personal model
Create your personal hotword model through our website or hotword API
Put your personal model in snowboy/resources
# Please put YOUR_PERSONAL_MODEL.pmdl in $ALEXA_AVS_SAMPLE_APP_PATH/third-party/snowboy/resources,
# and $ALEXA_AVS_SAMPLE_APP_PATH with the actual path where you put the Alexa AVS sample app repository.
cp YOUR_PERSONAL_MODEL.pmdl $ALEXA_AVS_SAMPLE_APP_PATH/third-party/snowboy/resources/
KITT_AI_SENSITIVITY, set KITT_AI_APPLY_FRONT_END_PROCESSING to false in the Alexa AVS sample app code and re-compile# Modify $ALEXA_AVS_SAMPLE_APP_PATH/avs-device-sdk/blob/master/KWD/KWDProvider/src/KeywordDetectorProvider.cpp:
# Replace the model name 'alexa.umdl' with your personal model name 'YOUR_PERSONAL_MODEL.pmdl' at line 52
# Update `KITT_AI_SENSITIVITY` at line 26
# Set `KITT_AI_APPLY_FRONT_END_PROCESSING` to `false` at line 32
sudo bash setup.sh config.json
kitt_ai!Here is a demo video for how to use a personal model in Alexa Voice Service.
Universal model
# Please put YOUR_UNIVERSAL_MODEL.umdl in $ALEXA_AVS_SAMPLE_APP_PATH/third-party/snowboy/resources,
# and $ALEXA_AVS_SAMPLE_APP_PATH with the actual path where you put the Alexa AVS sample app repository.
cp YOUR_UNIVERSAL_MODEL.umdl $ALEXA_AVS_SAMPLE_APP_PATH/third-party/snowboy/resources/
KITT_AI_SENSITIVITY in the Alexa AVS sample app code and re-compile# Modify $ALEXA_AVS_SAMPLE_APP_PATH/avs-device-sdk/blob/master/KWD/KWDProvider/src/KeywordDetectorProvider.cpp:
# Replace the model name 'alexa.umdl' with your universal model name 'YOUR_UNIVERSAL_MODEL.umdl' at line 52
# Update `KITT_AI_SENSITIVITY` at line 26
sudo bash setup.sh config.json
kitt_ai!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 (look into the lib folder):
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!
Currently we have built wrappers for:
If you want support on other hardware/OS, please send your request to [email protected]
Note: Snowboy does not support Windows yet. Please build Snowboy on *nix platforms.
Hackers: free
Business: please contact us at [email protected]
We provide pretrained universal models for testing purpose. When you test those models, bear in mind that they may not be optimized for your specific device or environment.
Here is the list of the models, and the parameters that you have to use for them:
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.
If you want to compile a version against your own environment/language, read on.
To run the demo you will likely need the following, depending on which demo you use and what platform you are working with:
You can also find the exact commands you need to install the dependencies on Mac OS X, Ubuntu or Raspberry Pi below.
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, pleas
Inquiry for .umdl file creation
Unable to find .so files for OS-level hotword detection in Flutter using Snowboy
snowboy不支持 rsicv 架构
error: can't copy 'swig/Python/_snowboydetect.so': doesn't exist or not a regular file
Providing a patch for more Android targets (x86, x86_64)
"Hey Friday" hotword model 794/500
在linux上显示python无法找到snowboy和snowboydetect模块怎么解决
Makefile:73: recipe for target '_snowboydetect.so' failed
编译库到一半停止了
makefile有问题