xla VS snowboy

> The PyTorch/XLA Team at Google

Meanwhile you have an issue from 5 years ago with 0 support

https://github.com/pytorch/xla/issues/202

PyTorch has had an XLA backend for years. I don't know how performant it is though. https://pytorch.org/xla

It's curtains for XLA, to be precise. And PyTorch officially supports XLA backend nowadays too ([1]), which kind of makes JAX and PyTorch standing on the same foundation.

1. https://github.com/pytorch/xla

Pytorch supports other kinds of accelerators (e.g. FPGA, and https://github.com/pytorch/glow), but unless you want to become a ML systems engineer and have money and time to throw away, or a business case to fund it, it is not worth it. In general, both pytorch and tensorflow have hardware abstractions that will compile down to device code. (XLA, https://github.com/pytorch/xla, https://github.com/pytorch/glow). TPUs and GPUs have very different strengths; so getting top performance requires a lot of manual optimizations. Considering the the cost of training LLM, it is time well spent.

While apparently TPUs can theoretically achieve great speedups, getting to the point where they beat a single GPU requires a lot of fiddling around and debugging. A specific setup is required to make it work properly. E.g., here it says that to exploit TPUs you might need a better CPU to keep the TPU busy, than the one in colab. The tutorials I looked at oversimplified the whole matter, the same goes for pytorch-lightning which implies switching to TPU is as easy as changing a single parameter. Furthermore, none of the tutorials I saw (even after specifically searching for that) went into detail about why and how to set up a GCS bucket for data loading.

XLA on TPUs via pytorch/xla.

I don't know of any such trick except for using TensorFlow. In fact, I benchmarked PyTorch XLA vs TensorFlow and found that the former's performance was quite abysmal: PyTorch XLA is very slow on Google Colab. The developers' explanation, as I understood it, was that TF was using features not available to the PyTorch XLA developers and that they therefore could not compete on performance. The situation may be different today, I don't know really.

Great question. This is technically referred to as "Wake Word Detection". You run a really small model locally that is just processing 500ms (for example) of audio at a time through a light weight CNN or RNN. The idea here is that it's just binary classification (vs actual speech recognition).

There are some open source libraries that make this relatively easy:

- https://github.com/Kitt-AI/snowboy (looks to be shutdown now)

Leon uses Snowboy for its hotword detection. Unfortunately the project has been discontinued and is suffering from the lack of maintainability.

Voice recognition: Snowboy Hotword Detection - closed 2020-12-31

For step 6, setting up the wake word, the post recommends KITT.AI or Sensory, but Kitt is closing down and Sensory is not free. Do you know of any other services that are still around and well maintained?

Looks really nice and clean! Have you by chance tested out the snowboy hotword detector https://github.com/Kitt-AI/snowboy? (Just noticed that they are actually shutting down)

hi, i just checked it and really loved the plugin, but saw that the site that used to analyze the voice to make a new hotword (https://github.com/Kitt-AI/snowboy) got shut down at 31/12/2020, and i havent find any other place that can do that :(