rhino VS Porcupine  

Download pre-trained models: "Porcupine" from Porcupine Wake Word and Video Player Context from Rhino Speech-to-Intent repositories - You can also train a custom models on Picovoice Console.

In order to initialize the voice AI, we’ll need both Porcupine (.ppn) and Rhino (.rhn) model files. Picovoice has made several pre-trained Porcupine and pre-trained Rhino models available on the Picovoice GitHub repositories. For this Barista app, we’re going to use the trigger phrase Hey Barista and the Coffee Maker context.

Get an AccessKey for free from Picovoice Console. You will need it as part of the init function. Also, get the English Parameter for Rhino from GitHub and save it to the public directory. Rhino uses this file as the basis to understand English context (other languages are also supported).

The only viable option that I found that could reliably infer commands from speech is https://github.com/Picovoice/rhino

Unfortunately it is not open source (the GitHub just has binary blobs) and requires an account to log in to generate and download model files, but the accuracy is great and you can use it to send commands to Home Assistant to turn lights on/off etc.

https://github.com/Picovoice/rhino/blob/master/LICENSE sayt it's Apache 2 license

I call it DaVinci. DaVinci uses Picovoice (https://picovoice.ai/) solutions for wake word and voice activity detection and for converting speech to text, Amazon Polly to convert its responses into a natural sounding voice, and OpenAI’s GPT 3.5 to do the heavy lifting. It’s all contained in about 300 lines of Python code.

Download pre-trained models: "Porcupine" from Porcupine Wake Word and Video Player Context from Rhino Speech-to-Intent repositories - You can also train a custom models on Picovoice Console.

Below are some useful resources: Open-source code Picovoice Platform SDK Picovoice website

Download the Porcupine model and turn the binary model into a base64 string.

Download Porcupine (i.e. Deep Neural Network). Run the following to turn the binary model into a base64 string, from the project folder.

Porcupine SDK Porcupine SDK is on GitHub. Find libraries for supported MCUs on the Porcupine GitHub repository. Arduino libraries are available via a specialized package manager offered by Arduino.

Looking for more? Explore other languages on the Picovoice Console and check out for fully-working demos with Porcupine on GitHub.

Don't forget to visit Porcupine's Wake Word's Github repository to see Python demos. If you want to do something similar to the video above, find the open-source codes here

I sometimes see the term "end-to-end", and it always passes right by my ears as marketing jargon. For example, there was a recent post today that linked to this page: https://picovoice.ai/, and you'll find the statement "... end-to-end platform for adding voice to anything on your terms". I did a quick Google search and it seems like the term is used in many different contexts (e.g., encryption, enterprise software for product development, etc.), but to be honest, I'm just not getting it. Maybe someone can explain here within the realm of embedded software? Could you provide some examples as well?