alexa/alexa-avs-sample-app

Name: alexa-avs-sample-app

Owner: Alexa

Description: This project demonstrates how to access and test the Alexa Voice Service using a Java client (running on a Raspberry Pi), and a Node.js server.

Created: 2016-03-22 06:03:32.0

Updated: 2018-01-18 19:28:58.0

Pushed: 2017-12-22 19:44:24.0

Homepage: https://developer.amazon.com/avs

Size: 20422

Language: Shell

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README

:warning: Starting January 25, 2018, the AVS Java Sample App will be put into maintenance mode. To leverage the latest Alexa features, please use the AVS Device SDK C++ Sample App, which you can find here. To discuss any specific dependencies on the AVS Java Sample App, feel free to reach out to us here.

About the project

This project provides a step-by-step walkthrough to help you build a hands-free Alexa Voice Service (AVS) prototype in 60 minutes, using wake word engines from Sensory or KITT.AI. Now, in addition to pushing a button to “start listening”, you can now also just say the wake word “Alexa”, much like the Amazon Echo. You can find step-by-step instructions to set up the hands-free prototype on Raspberry Pi, or follow the instructions to set up the push-to-talk only prototype on Linux, Mac, or Windows.


What is AVS?

Alexa Voice Service (AVS) is Amazon?s intelligent voice recognition and natural language understanding service that allows you as a developer to voice-enable any connected device that has a microphone and speaker.


Get started

You can set up this project on the following platforms -

Or you can prototype with these third-party dev kits -


What's new?

January 31, 2018:

Updates

January 25, 2018:

Important

December 3, 2017:

Updates

Known Issues

October 11, 2017:

Updates

Known Issues

July 6, 2017:

Updates

June 21, 2017:

Updates

May 31, 2017:

Updates

May 4, 2017:

Updates

April 27, 2017:

Updates

April 20, 2017:

Updates

Maintenance

Bug Fixes

Known Issues


Important considerations

Contribute

This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.