1/8/2024 0 Comments Google speech to text api pi![]() ![]() Configure the config.yaml with your diagram ID, version ID, and list of wakeword(s).Install system dependencies and python dependencies.Install the audio driver(s) and set up the ~/.asoundrc file.Record your conversational application's diagramId and versionId.Create an API key for the project/workspace and keep it in a safe place."Train Assistant" on the "Run" page to train the natural language processing language model.Create your desired "Voice Assistant" conversational application. ![]() This demo implements a simplified Python3 Voiceflow client for demonstration purposes. Voiceflow SDK clients are currently a work in progress and not production-ready. On the Raspberry Pi, we will make use of the following technologies: The language model and logic of your conversational agent can be designed and tested on the Voiceflow Creator tool. The Raspberry Pi is a thin client, in this application, since it is only responsible for calling Voiceflow's dialog manager API with the user input transcript, and playing back the audio generated from Voiceflow. Someone telling you they'd like a "large" soda is simpler to understand and process than someone telling you they'd like a "750mL cup" of soda. "550mL") into several broad categories like "large", "medium", and "small". This process is akin to a familiar subconscious process in our every day conversation: simplifying the amount of something from concrete numbers (eg. Instead of training machine-learning models to directly ingest the input transcript and determine the appropriate response, the input transcript can be parsed by a specialized model to classify what the user "intends" to do in other words, an "intent". Intent classification and entity extraction are two natural language processing techniques used to further simplify the problem space for the dialog manager. Intent Classification → Natural Language Processing Voiceflow approaches this problem from a different perspective: allow the designer to specify an observable and explainable logical transition graph that can be triggered by various different user intents. Many competing implementations for this black-box exist in many cases, they are quite literally black-box machine learning models. Now that inputs and output formats are specified, the dialog manager problem is simplified to a "black box" application that ingests an input transcript from the user and outputs the response transcript to the user. To ensure maximum flexibility, Voiceflow's dialog manager API expects a text transcript input and produces both a text and an audio output based on a preconfigured voice persona. Similarly, on the output-side, dialog managers usually produce a text output so that different implementations of text-to-speech can be used to produce the desired voice(s) based on branding and context. ![]() Most dialog manager implementations that accept audio inputs usually implement some variation of speech-to-text functionality to rectify the input audio (large parameter space) into a text transcript (smaller parameter space) in order to reduce the computational cost and duration of the downstream processes. Inputs and outputs can be divided into 2 broad categories: audio and transcript. ![]() Though definitions vary, at a high level, all dialog managers determine the most fitting output given some (set of) user input(s). At the heart of modern conversational agents is the "dialog manager". ![]()
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