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It is written in Theano and Lasagne. It uses end-to-end trained embeddings of 5 different emotions to generate responses conditioned by a given emotion. The code is flexible and allows to condition a response by an arbitrary categorical variable defined for some samples in the training data. With CakeChat you can, for example, train your own persona-based neural conversational model or create an emotional chatting machine without external memory.
Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build chatbots on Facebook, Slack, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram etc. Rasa's primary purpose is to help you build contextual, layered conversations with lots of back-and-forth. To have a real conversation, you need to have some memory and build on things that were said earlier. Rasa lets you do that in a scalable way.
Licensed under the Apache License, Version 2.0. Copyright 2019 Rasa Technologies GmbH. Copy of the license. A list of the Licenses of the dependencies of the project can be found at the bottom of the Libraries Summary.
This repository is the home for a set of templates and solutions to help build conversational experiences using Azure Bot Service and Bot Framework. Things look a little different around here? Find out more in our Wiki page and here.
Stealth is a Ruby based framework for creating conversational (voice & chat) bots. It's design is inspired by Ruby on Rails's philosophy of convention over configuration. It has an MVC architecture with the slight caveat that views are aptly named replies. Stealth is extensible. All service integrations are split out into separate Ruby Gems. Things like analytics and natural language processing (NLP) can be added in as gems as well.
Voice overlay helps you turn your user's voice into text, providing a polished UX while handling for you the necessary permissions. It uses internally the native SFSpeechRecognizer in order to perform the speech to text conversion.