AI experts/developers - your valuable opinion please !!!
I am a beginner in the world of AI – just been going through some basic material so far. I am well experienced in C/C++ development. I am going to start implementing something as a Proof of concept. Experts, your valuable inputs can save months off my time – please help! I have ample time available in the holiday weeks, and ideally would like something working in a month (~100-150 hours). Here is the basic idea: 1) Input will primarily be voice : Voice commands (statements / phrases) : Not more than ~1000 commands. 2) My hardware with probably Raspberry PI and in-build AI will analyze the words spoken, It will respond with answers among a set of limited (~1000) answers. The model will store newer questions/answers, and keep learning from the growing set. It’s an offline setup for now, not connected online! 3) It’s essentially a conversational model of information exchange. The volume and possibilities of the commands as well as the answers will be limited. (< ~1000) 4) At some point, I do plan to put in a visual recognition aspect too, but probably not now. I was thinking of using something like Raspberry PI with TensorFlow lite AI. Will need some voice recognition software (perhaps can be picked up from some open source project ?). Can someone please give me their thoughts / ideas / best and quickest ways to utilize proper tools and implement this prototype ? Thanks!!
Raspberry is too constrained a system for any serious work with ML. But because you have already planned for it, here is one way of doing this: 1) Get raspberrypi with mic and speaker . 2) Build your speech model using tensorflow on big enough server/laptop to train . https://www.tensorflow.org/tutorials/sequences/audio_recognition is a great start . 3) Freeze the trained model and make it compatible with Tensorflow lite. 4) Use it on raspberry pi. At this stage you would be able to identify words. To build a question/answer system, you would need to understand sequence modelling . RNNs, LSTMs and Seq2Seq are abstractions. To get faster results, you can start from Seq2seq directly . One way of using this is following this blog https://blog.kovalevskyi.com/how-to-create-a-chatbot-with-tf-seq2seq-for-free-e876ea99063c You can of course go deeper and work with LSTMs or even RNNs which are lower level primitive but you would effectively be doing research when working at this level and results are not guaranteed. Assuming you are confident with python and can pickup basic tf workings in week, A basic toy model can be built within 2 weeks from fairly ground up.
Thank you for the detailed answer ! I thought of Raspberry only because of small form factor, because it’s cheap, because I need it to run in offline mode, and because it’s been known to run (probably trained TF) beforehand. Are there any alternatives which are similar ?
Here, I'll save you some time. https://dialogflow.com Voice recognition is tricky to get right. From your post, it sounds like you want to build things from scratch. If that's the case, here are a couple things you should read about: Phoneme detection Recurrent neural networks Hidden Markov models LSTM and DTNN Language modeling
Thank you for the inputs appreciate it a lot ! will explore
FYI, you're not going to build a whole state of the art voice recognition by yourself in 100 hours. If you're trying to make a startup, figure out where you add value and outsource the parts that don't.