11–20 of 78 posts
I wonder how this compares to snips[1]. I recently connected snips to my lights and it works pretty flawlessly. Picovoice does look like its easier to integrate into an app though. [1] http://snips.ai/
Can I use this for my home automation setup? It seems the github link is only part of the product. Do you license this for individual users?
Can I use this for my home automation setup? It seems the github link is only part of the product. Do you license this for individual users?
This is my question too. I looked at Snips but they had no timeline on Windows support and were more interested in talking about their crypto coin than answering basic questions about their software.
Earlier quoted context omitted.
This is my question too. I looked at Snips but they had no timeline on Windows support and were more interested in talking about their crypto coin than answering basic questions about their software.
Where were you enquiring on? I've had a lot of help from their discord channel. You could give that a try.
I went to the website, and had questions, so I tried to email them. I got a reply to the email telling me to join the Discord. I did, and asked in the Discord, and the only person who bothered to reply told me to read the website. ...I wouldn't have asked the questions if they were answered on the website.
"You can turn off your internet connection and it will keep working."
That's nice!
Link to the github repo: https://github.com/picovoice/porcupine The keyword generating application: https://github.com/Picovoice/Porcupine/tree/master/tools/opt... The keyword generating application is free for personal use only and requires a license for commercial use, but there's no pricing available and it only provides an email address for contacting about purchasing a license. If anyone from the team is reading…
Thanks again for sharing
The demo is pretty cool - the thing I liked best was this line: "You can turn off your internet connection and it will keep working." That's nice!
We will add another demo for speech-to-intent module fairly soon (over this weekend). Stay tuned!
I wonder how this compares to snips[1]. I recently connected snips to my lights and it works pretty flawlessly. Picovoice does look like its easier to integrate into an app though. [1] http://snips.ai/
With Picovoice you can use the voice control engine to accomplish this. Maybe something similar to this demo?
https://picovoice.ai/#voice-control-demo
The cool thing about this engine is that it is tiny. It uses less than 8% CPU on RPi3 and altogether it is less than 2MB (code, model, etc). Technically you can run it on something much smaller and cheaper than RPi.
Alternatively, the speech-to-intent engine could be a good candidate. More information on this along with an interactive demo will be released this weekend.
> Picovoice is a team of applied scientists and engineers who strive to build a future where our lives are enhanced with ambient voice AIs, while respecting your privacy. Oh wow, thank you! I also saw a reference that this is all open-source but https://github.com/Picovoice/ does not have a Picovoice repository. Is it https://github.com/Picovoice/Porcupine ?
Porcupine is just wake word detection, which in itself is very useful indeed. Not sure what the plans for open sourcing the rest of the components are though.
1- Speech-to-Intent: It allows you to issue complex voice commands in a specific domain and in turn returns the intent. For example, in the case of a coffee maker you can say "Please may I have a single shot espresso with no milk and two sugars". The engine returns a JSON-like object with {"product": "espresso", "milk": "no", "sugar", "two", "# shots": "2"}. It is a tightly coupled domain-specific speech recognition and NLU. It is small (less than 3MB and 8% CPU usage on RPi3) and ideal for home automation, industrial application, service industry, etc.
2- Speech-to-Text: It is large vocabulary speech recognition software that runs locally. It will support all platforms currently being supported. It allows you to do large vocabulary transcription with high accuracy locally on an embedded platform.