In this tutorial, we’re going to use Spokestack’s AutoML training tool to create a keyword recognizer model and use it, along with the spokestack-python library, to make a digital swear jar. The full code is on GitHub if you’d like to run it for yourself.
The concept of a swear jar is simple: you have a list of words or short phrases that you’d like to stop saying, or stop someone else from saying. Every time one of those words does slip out, you drop a coin in a jar. When the jar’s full, you take it to the bank and open a savings account to spend when you’re better behaved.
We should note that if swearing’s not your thing, this concept also lets you reward yourself for saying nice things—call it a “compliment jar”. We wouldn’t want to limit this tutorial to us reprobates.
Anyway, the first thing that might come to mind for this use case is a wake word detector: you don’t want your app running ASR all day, continuously streaming a pipe of data to wherever; you just want it to notice when you’ve said certain things. But there’s a wrinkle: if movie ratings have taught us anything, it’s that certain words are just … ickier than others. We’d like to make it more expensive to say <insert epithet here> than, say, <insert milder epithet here>. (You didn’t think we were actually going to suggest profanity for your list, did you? This is a family-friendly tech site we’re running here.)