yiwa-develop伊瓦，家庭语音控制"假机器人"

# Snowboy Hotword Detection by [KITT.AI](http://kitt.ai). [Home Page](https://snowboy.kitt.ai) [Full Documentation](http://docs.kitt.ai/snowboy) and [FAQ](http://docs.kitt.ai/snowboy#faq) [Discussion Group](https://groups.google.com/a/kitt.ai/forum/#!forum/snowboy-discussion) (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: 1.1.1 (3/24/2017) ## Alexa support Snowboy now brings hands-free experience to the [Alexa AVS sample app](https://github.com/alexa/alexa-avs-sample-app) on Raspberry Pi! Here is how you can use other Snowboy models **Personal model** * Create your personal hotword model through our [website](https://snowboy.kitt.ai) or [hotword API](https://snowboy.kitt.ai/api/v1/train/) * Replace the hotword model in [Alexa AVS sample app](https://github.com/alexa/alexa-avs-sample-app) (after installation) with your personal model ``` # Please replace YOUR_PERSONAL_MODEL.pmdl with the personal model you just # created, and $ALEXA_AVS_SAMPLE_APP_PATH with the actual path where you # cloned the Alexa AVS sample app repository. cp YOUR_PERSONAL_MODEL.pmdl $ALEXA_AVS_SAMPLE_APP_PATH/samples/wakeWordAgent/ext/resources/alexa.umdl ``` * Set `APPLY_FRONTEND` to `false` and update `SENSITIVITY` in the [Alexa AVS sample app code](https://github.com/alexa/alexa-avs-sample-app/blob/master/samples/wakeWordAgent/src/KittAiSnowboyWakeWordEngine.cpp) and re-compile ``` # Please replace $ALEXA_AVS_SAMPLE_APP_PATH with the actual path where you # cloned the Alexa AVS sample app repository. cd $ALEXA_AVS_SAMPLE_APP_PATH/samples/wakeWordAgent/src/ # Modify KittAiSnowboyWakeWordEngine.cpp and update SENSITIVITY at line 28. # Modify KittAiSnowboyWakeWordEngine.cpp and set APPLY_FRONTEND to false at # line 30. make ``` * Run the wake word agent with engine set to `kitt_ai`! **Universal model** * Replace the hotword model in [Alexa AVS sample app](https://github.com/alexa/alexa-avs-sample-app) (after installation) with your universal model ``` # Please replace YOUR_UNIVERSAL_MODEL.umdl with the personal model you just # created, and $ALEXA_AVS_SAMPLE_APP_PATH with the actual path where you # cloned the Alexa AVS sample app repository. cp YOUR_UNIVERSAL_MODEL.umdl $ALEXA_AVS_SAMPLE_APP_PATH/samples/wakeWordAgent/ext/resources/alexa.umdl ``` * Update `SENSITIVITY` in the [Alexa AVS sample app code](https://github.com/alexa/alexa-avs-sample-app/blob/master/samples/wakeWordAgent/src/KittAiSnowboyWakeWordEngine.cpp) and re-compile ``` # Please replace $ALEXA_AVS_SAMPLE_APP_PATH with the actual path where you # cloned the Alexa AVS sample app repository. cd $ALEXA_AVS_SAMPLE_APP_PATH/samples/wakeWordAgent/src/ # Modify KittAiSnowboyWakeWordEngine.cpp and update SENSITIVITY at line 28. make ``` * 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](http://docs.kitt.ai/snowboy) and example [Python/Bash script](examples/REST_API) (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! ## Introduction 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 * Pine64 (Debian Jessie 8.5, 3.10.102 BSP2) * Intel Edison (Ubilinux based on Debian Wheezy 7.8) * Samsung Artik (built with Fedora 25 for ARMv7) 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: * Java/Android * Go (thanks to @brentnd) * Node (thanks to @evancohen) * Perl (thanks to @iboguslavsky) * Python * iOS/Swift3 (thanks to @grimlockrocks) * iOS/Object-C (thanks to @patrickjquinn) If you want support on other hardware/OS, please send your request to [snowboy@kitt.ai](mailto:snowboy.kitt.ai) Note: **Snowboy does not support Windows** yet. Please build Snowboy on *nix platforms. ## Pricing for Snowboy models Hackers: free * Personal use * Community support Business: please contact us at [snowboy@kitt.ai](mailto:snowboy@kitt.ai) * Personal use * Commercial license * Technical support ## Pretrained universal models 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: * **resources/snowboy.umdl**: Universal model for the hotword "Snowboy". Set SetSensitivity to 0.5 for better performance. * **resources/alexa.umdl**: Universal model for the hotword "Alexa". Set SetSensitivity to 0.5, and preferably set ApplyFrontend to true. This model is depressed. * **resources/alexa_02092017.umdl**: Universal model for the hotword "Alexa". This is still work in progress. Set SetSensitivity to 0.15. ## 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 * 64 bit Ubuntu [12.04](https://s3-us-west-2.amazonaws.com/snowboy/snowboy-releases/ubuntu1204-x86_64-1.1.1.tar.bz2) / [14.04](https://s3-us-west-2.amazonaws.com/snowboy/snowboy-releases/ubuntu1404-x86_64-1.1.1.tar.bz2) * [MacOS X](https://s3-us-west-2.amazonaws.com/snowboy/snowboy-releases/osx-x86_64-1.1.1.tar.bz2) * Raspberry Pi with Raspbian 8.0, all versions ([1/2/3/Zero](https://s3-us-west-2.amazonaws.com/snowboy/snowboy-releases/rpi-arm-raspbian-8.0-1.1.1.tar.bz2)) * Pine64 (Debian Jessie 8.5 (3.10.102)) ([download](https://s3-us-west-2.amazonaws.com/snowboy/snowboy-releases/pine64-debian-jessie-1.1.1.tar.bz2)) * Intel Edison (Ubilinux based on Debian Wheezy 7.8) ([download](https://s3-us-west-2.amazonaws.com/snowboy/snowboy-releases/edison-ubilinux-1.1.1.tar.bz2)) If you want to compile a version against your own environment/language, read on. ## Dependencies To run the demo you will likely need the following, depending on which demo you use and what platform you are working with: * SoX (audio conversion) * PortAudio or PyAudio (audio capturing) * SWIG 3.0.10 or above (compiling Snowboy for different languages/platforms) * ATLAS or OpenBLAS (matrix computation) You can also find the exact commands you need to install the dependencies on Mac OS X, Ubuntu or Raspberry Pi below. ### Mac OS X `br