NLU, dialogue management and voice processing components cover the backend of the voice assistant so what about the frontend? Well, this is where the biggest problem lies - if you search for the open source voice interface widgets, you will very likely end up with no results. Mozilla TTS takes care of the opposite - it takes the input (in our case - the response of the assistant produced by a dialogue system) in a text format and uses machine learning to create an audio representation of it. What is Mozilla DeepSpeech and Mozilla TTS? Mozilla DeepSpeech is a speech-to-text framework which takes user input in an audio format and uses machine learning to convert it into a text format which later can be processed by NLU and dialogue system.

It has a big and active open source community ready to help out with technical questions.Both tools are written in Python which makes it slightly easier to integrate with Rasa.In comparison to alternatives, Mozilla tools seem to be the most OS agnostic.This allows you to implement things quickly, but also gives you all the freedom to build custom components. Mozilla tools come with a set or pre-trained models, but you can also train your own using custom data.After exploring the currently available options: CMUSphinx, Mozilla DeepSpeech, Mozilla TTS, Kaldi, we decided to go with Mozilla tools - Mozilla DeepSpeech and Mozilla TTS. While open source Rasa is a rather obvious choice for NLU and dialogue management, deciding on STT and TTS is a more difficult task simply because there aren't that many open source frameworks to choose from. Text-to-speech (TTS) - a component which takes the response of the assistant in a text format and produces a voice representation of it which is then sent back to the user.Dialogue management - a component which determines how an assistant should respond at specific state of the conversation and generates that response in a text format.NLU - a component which takes user input in text format and extract structured data (intents and entities) which helps and assistant to understand what the user wants.Speech-to-text (STT) - a voice processing component which takes user input in an audio format and produces a text representation of it.Voice interface - a frontend which users use to communicate with the assistant (web or mobile app, smart speaker, etc).In general, there are five main components which are necessary to build a voice assistant: The goal of this post is to show you how you can build your own voice assistant using only open source tools. Implementing the text-to-speech component.Implementing the speech-to-text component.In this post, you will learn how you can build a voice assistant using only open source tools - from backend, all the way to the frontend.

So, what options do you have if you want to build a voice assistant that runs locally and ensures the security of your data? Well, let's find out. Leveraging platforms like Google Assistant removes the hurdle of implementing the voice processing and frontend components, but it forces you to compromise on the security of your data and the flexibility of the tools you use. We touched on the voice topic some time ago when we experimented with building a Rasa-powered Google Assistant. However, unless you use hosted of-the-shelf solutions, development of voice assistants come with a whole new set of challenges that go beyond NLU and dialogue management - in addition to those, you need to take care of speech-to-text, text-to-speech components as well as the frontend. With platforms like Google Assistant and Alexa becoming more and more popular, voice-first assistants are destined to be the next big thing for customer interactions across various industries. But what if you wanted to build and assistant that runs locally and ensures the privacy of your data? You can do it using open source Rasa, Mozilla DeepSpeech and Mozilla TTS tools. Platforms like Google Assistant makes it easy to build custom voice assistants.

Mozilla speech to text trial