3 ms·

I actually made this exact thing Saturday afternoon. Great job! First prototype was on an Arduino it and eventually ran out of firmware space. So then I upgrad

I actually made this exact thing Saturday afternoon. Great job! First prototype was on an Arduino it and eventually ran out of firmware space. So then I upgraded to the RPi and from there it was a breeze. Suggestions: (1) Use Wit.ai for NLP. There is some added latency but the capabilities far out reach Sphinx in the long run. It's free. Less code to maintain. Easier to deploy and distribute. (2) Try to find a small mic so that you can put everything in a sleek package. (3) Add support for bluetooth speakers (you're on a RPi, it's basically done for you) (4) 3D print a custom case, throw some 3M tape on it and it's ready to be wall-mounted!

fixedd 13y ago
And use festival, not espeak!
crm416 13y ago
Great suggestions. I've been meaning to take a deeper dive into Wit.ai for a while now. It seems like their intents-entities architecture would actually fit in pretty cleanly with Jasper. As an aside: I don't think it'd be difficult to developer Jasper modules that use Wit without modifying much of the original source (as long as the speech-to-text systems pick up on the text you'd need to pass to Wit).
- mfreeman451 13y ago
  It would also be nice to have a gateway that bridges the gap between your desktops/workstations/mobile devices and jasper, so jasper can fire off commands.
hardwaresofton 13y ago
I also did a project like this last year -- I only got around to asking for date & time working, and was happy enough to stop going there... I disagree with (1), however: In the interest of making WAN-independent software (as in, I don't want my home automation to stop working if I can't call out to wit.ai), I actually don't agree with that point. I think that using CMUSphinx can be made extremely accurate with continuous training (something I'm angling to put in my version). Then again, I'm planning on making a competing product so differentiation is good for me. I think there are a lot of ways they can improve on the idea
ar7hur 13y ago
Hi Kyle, Wit.AI team here -- thanks for suggestion (1) :-) Actually our typical latency is less than 0.5s if you stream audio to the API (instead of waiting until a silence, then sending a WAV file). Also, we are working on an embeddable client (you would still use Wit.AI online to train your model, but then they can run locally on your Raspberry Pi).

fixedd 13y ago

And use festival, not espeak!

crm416 13y ago

Great suggestions. I've been meaning to take a deeper dive into Wit.ai for a while now. It seems like their intents-entities architecture would actually fit in pretty cleanly with Jasper. As an aside: I don't think it'd be difficult to developer Jasper modules that use Wit without modifying much of the original source (as long as the speech-to-text systems pick up on the text you'd need to pass to Wit).

mfreeman451 13y ago
It would also be nice to have a gateway that bridges the gap between your desktops/workstations/mobile devices and jasper, so jasper can fire off commands.

hardwaresofton 13y ago

I also did a project like this last year -- I only got around to asking for date & time working, and was happy enough to stop going there... I disagree with (1), however: In the interest of making WAN-independent software (as in, I don't want my home automation to stop working if I can't call out to wit.ai), I actually don't agree with that point. I think that using CMUSphinx can be made extremely accurate with continuous training (something I'm angling to put in my version). Then again, I'm planning on making a competing product so differentiation is good for me. I think there are a lot of ways they can improve on the idea

ar7hur 13y ago

Hi Kyle, Wit.AI team here -- thanks for suggestion (1) :-) Actually our typical latency is less than 0.5s if you stream audio to the API (instead of waiting until a silence, then sending a WAV file). Also, we are working on an embeddable client (you would still use Wit.AI online to train your model, but then they can run locally on your Raspberry Pi).