What does HackerNews think of NeMo?

With how hard something like this is, we should be looking at the positive side.

Picovoice here solves both the issues of hotword/wake-word detection and intent extraction. This looks like something you could build on top of ARM's [keyword spotting program](https://github.com/ARM-software/ML-KWS-for-MCU) and the wake word services listed in [Rhasspy's docs](https://rhasspy.readthedocs.io/en/latest/wake-word/#raven)

But, to implement something like this from scratch would take a good while.

Also, here are some Automatic Speech Recognition toolkits (which won't run offline on a microcontroller) out there. These are useful to pipe the data into a program that deals with intents (something like [RASA](https://rasa.com)

(Require Internet) * [Deepgram](https://deepgram.com) - I believe they build upon OpenAI's Whisper model and have their own custom models too * Google Cloud / Microsoft Azure / AWS / IBM Watson

(Can be run Offline) * [OpenAI's Whisper](https://github.com/openai/whisper) * [nVidia's NEMO](https://github.com/NVIDIA/NeMo) * [PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech)

When you see how complicated the space is and how many ways you can actually shoot yourself in the foot. This post starts to look a wee bit better.

I worked with this a bit not that long ago. For cloud services, quality of Google and Azure "neural" voices are tough to beat. Interestingly I experienced significant latency for all of the Azure services regardless of region, configuration, etc. Never dug deep enough to figure out what was going on there. Also of note, Azure will let you run their implementation on a local container with the usual "contact us" stuff. Not sure of the terms and pricing on that.

For local, Mozilla TTS was best from a quality standpoint but the GPU inference support was a bit dicey and (possibly) not really supported at all.

For more complex and bespoke applications the Nvidia (I know, I know) NeMO toolkit [0] is very powerful but requires more effort than most to get up and running. However, it provides the ability to do very interesting things with additional training and all things speech.

In the Nvidia world there's also their Riva [1] (formerly Jarvis) solution that works with Triton [2] to build out an architecture for extremely performant and high-scale speech applications with things like model management, revision control, deployment, etc.

[0] https://github.com/NVIDIA/NeMo

[1] https://developer.nvidia.com/riva

[2] https://developer.nvidia.com/nvidia-triton-inference-server