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Complete and autonomous NLU solution leveraging opensource libs, such as OpenNLP, Stanford, Duckling and more. Web, mobile, social networks, smart speakers and more. Create your bot once, connect it progressively to multiple channels as you need them. Simple graphical interfaces to build stories and models, manage multilingual and multichannel bots, better understand users with analytics. Program complex stories using Kotlin, Python or Node.js provided components, or integrate with any language by leveraging Tock APIs. Try Tock online, deploy in minutes using Docker running configurations, or setup your own platform to the Cloud, On-Premise, even embedded. ...

mirai is a high-efficiency robot library that runs on all platforms and provides QQ Android protocol support. mirai is completely free and open-source software for learning and entertainment use only. mirai will not impose fees or impose material conditions on users in any way. mirai is maintained by the entire open source community and is not the work of an individual. All contributors enjoy the copyright of their work. If citing a package published by mirai without modifying mirai, derived...

...It includes a built-in Microsoft TTS demo interface and lets users configure custom HTTP requests, making it possible to route TTS through various cloud providers or local servers. The app can import other local TTS engines, giving Android devices a unified interface to multiple voices and providers, and it features simple narration/dialogue detection based on Chinese quotation marks so it can read text with different styles for narration and dialogue. It is built with Kotlin and Jetpack Compose, and the project is structured into multiple libraries (lib-tts, lib-server, lib-compose, lib-database, etc.) to separate UI, server logic, and TTS handling. ...

KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning models in production environments.