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Hoping this will quickly make into someone's home grown self-hosted version of Alexa. Alexa, turn the lights on in the kitchen. Alexa, turn on the kitchen light. Alexa, light up the kitchen. Should all accomplish the same task using this framework.
I see your point, but the tasks that you have listed (and more difficult variants) can be easily handled using rule-based systems.
[1] https://github.com/tensorflow/models/tree/master/syntaxnet
"Python 3 support is not available yet." [1]. It's only supported in Python 2.7, Why? [1] https://github.com/tensorflow/models/tree/master/syntaxnet
"Python 3 support is not available yet." [1]. It's only supported in Python 2.7, Why? [1] https://github.com/tensorflow/models/tree/master/syntaxnet
http://www.nvidia.com/object/gpu-accelerated-applications-te...
But it is a little bit out of date when it comes to version numbers.
If you're going to try TensorBox (https://github.com/TensorBox/TensorBox) it will get a bit harder still because of conflicts and build issues with specific versions of TensorFlow.
There has to be an easier way to distribute a package.
That said, all this is super interesting and Google really moved the needle by opensourcing TensorFlow and other ML packages.
Hoping this will quickly make into someone's home grown self-hosted version of Alexa. Alexa, turn the lights on in the kitchen. Alexa, turn on the kitchen light. Alexa, light up the kitchen. Should all accomplish the same task using this framework.
Just simply treating the sentence as a bag of words and looking for "on" or "off" or "change" (and their synonyms) and the presence of known smart objects works extremely well. I could say "Hey Marvin, turn on the lights and TV", or "Hey Marvin, turn the lights and TV on", or even "Hey Marvin, on make lights and TV."
(It's named Marvin it after the android from The Hitchhiker's Guide, my eventual goal is to have it reply with snarky/depressed remarks).
Adding 30 seconds of "memory" of the last state requested also made it seem a million times smarter and turns requests into a conversation rather than a string of commands. If it finds a mentioned smart object with no state mentioned, it assume the previous one.
"Hey Marvin, turn on the lights." lights turn on "The TV too." tv turns on
The downside to this approach is I would be showing it off to friends, and it could mis trigger. "Marvin turn off the lights." lights turn off "That's so cool, so it controls your TV, too?" TV turns off But it was mostly not an issue in real usage.
Ultimately I've got the project on hold for now because I can't find a decent, non-commercial way of converting voice to text. I'd really rather not send my audio out to Amazon/Google/MS/IBM. Not just because of privacy, but cost and "coolness" factor (I want as much as possible processed locally and open-source).
CMUSphinx's detection was mostly very bad. I couldn't even do complex NLP if I wanted because it picks up broken/garbled sentences. I currently build a "most likely" sentence by looping through sphinx's 20 best interpretations of the sentence and grabbing all the words that are likely to be commands or smart objects. I tried setting up Kaldi, but didn't get it working after a weekend and haven't tried again since. I don't really know any other options to use aside from CMUSphone, Kaldi, or a butt SaaS.
I've wanted to add a text messaging UI layer to it. Maybe I'll use that as an excuse to try playing with ParseySaurus.
I've been fighting Tensorflow in the last couple of days to try an application on it, never before have I seen such a convoluted build process and a maze of dependencies. The best manual on getting tensorflow with CUDA support up and running is here: http://www.nvidia.com/object/gpu-accelerated-applications-te... But it is a little bit out of date when it comes to version numbers. If you're going to try TensorBox ( h…
"Python 3 support is not available yet." [1]. It's only supported in Python 2.7, Why? [1] https://github.com/tensorflow/models/tree/master/syntaxnet