rhasspy VS DeepSpeech

I've been working on this for a few months now and I think I have it to a point where I am ready to share. This is definitely a very niche solution but I am creating a new skill handler for Grocy for the Open Source Voice Assistant Rhasspy (https://github.com/rhasspy/rhasspy). My handler is here: https://github.com/MCHellspawn/hermes-app-grocy. It is not complete yet but getting there. With is skill and a working Rhasspy 2.5 setup you can do a lot of tasks in Grocy with your voice. So far you can create and delete shopping lists, create products and add and remove them from shopping lists, list chores, mark them complete or skipped, and more.

It'll run on a cheap Ubuntu box if you can't get a Pi.

And lots of people seem to like Rhasspy too:

  https://github.com/rhasspy/rhasspy

Here is one example https://community.rhasspy.org/

I tried Amazon's Alexa, the top end model with a display. Often it would taunt you about new/interesting things on the screen, but I could never get them to work. I'd had to memorize things to get even the basics working. Ended up unplugging it.

However Google's Assistant in comparison worked great, no memorization, and very useful. Sure time, weather, set timers, and alarms worked great with a very flexible set of natural language queries. Even more complex things like what will be the temperature tomorrow at 10pm, simple calculations and unit conversions. But also things like IMDB like queries about directors, actors, which movies someone was in, etc generally worked well. It seemed to really understand things, not just "A web search returned ...". Even more complex things like the wheelbase of a 2004 WRX would return an answer, not a search result.

With all that said I'm looking for a non-cloud/on site solution, even if it requires more work, most recently noticed https://github.com/rhasspy/rhasspy

I just found this one: https://github.com/rhasspy/rhasspy

- [DeepSpeech](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text

I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest way to implement it using free, open-source software. Here's what he used originally, followed by some open source candidates I'm considering but would love feedback and advice before starting: Original Tools: - YoloV8 does the heavy lifting with the object detection - OpenAI Whisper handles voice - GPT-4 handles the “AI” - Google Custom Search Engine handles web browsing - MacOS/iOS handles streaming the video from my iPhone to my Mac - Python for the rest Open Source Alternatives: - [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection - Replacing GPT-4 is still a challenge as I know there are some good open-source LLms like Llama 2, but I don't know how to apply this in the code perhaps in the form of api - [DeepSpeech](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text - [Coqui TTS](https://github.com/coqui-ai/TTS) instead of Whisper for text-to-speech - Browser automation with [Selenium](https://www.selenium.dev/) instead of Google Custom Search - Stream video from phone via RTSP instead of iOS integration - Python for rest of code I'm new to working with tools like OpenCV, DeepSpeech, etc so would love any advice on the best way to replicate the original project in an open source way before I dive in. Are there any good guides or better resources out there? What are some pitfalls to avoid? Any help is much appreciated!

algo assim? https://github.com/mozilla/DeepSpeech

If you're not afraid to get a little technical, you could take a look at mozilla/DeepSpeech (installation & usage docs here).

There is a lot of TTS and SST development going on (https://github.com/mozilla/TTS; https://github.com/mozilla/DeepSpeech; https://github.com/common-voice/common-voice). That is the only way they work: Contributions from the wild.

Mozilla did release DeepSpeech[0] and Firefox Translation[1] (the latter of which they included in Firefox, to offer client-side webpage translations.)

They definitely have fewer resources than OpenAI, and they do not produce SOTA research (their publications have plummeted to 1/year anyway[2]). So the only way for them to make progress is to seek government grants or make challenges like these.

This challenge is unlikely to be profitable for the winning team: the expected value of winnings are likely around $1K when taking into account the probability that another team gets a better rank, but ML research projects are often more expensive (recently, Alpaca spent upwards of $600 on computation alone; and of course pretraining large models is much more expensive). So the main gain will be publicity.

[0]: https://github.com/mozilla/deepspeech

[1]: https://github.com/mozilla/firefox-translations/

[2]: https://research.mozilla.org/