DeepSpeech VS invoice2data

As I understand it DeepSpeech is no longer actively maintained by Mozilla: https://github.com/mozilla/DeepSpeech/issues/3693

For Text To Speech, I've found Piper TTS useful (for situations where "quality"=="realistic"/"natual"): https://github.com/rhasspy/piper

For Speech to Text (which AIUI DeepSpeech provided), I've had some success with Vosk: https://github.com/alphacep/vosk-api

- [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.

Using regex: to match patterns in text after converting the PDF to plain text. Examples include invoice2data and traprange-invoice. However, this method requires knowledge of the format of the data fields.

It's not really working. Tried 2 English PDF invoices. Normal format. One came back empty, the other only had the amount right.

I'm assuming they only trained on some specific documents (passport of country X, etc) and all others don't work.

If someone processes the same document all the time, then my invoice2data project may work better and is open source. It's based on Regx, rather than machine learning: https://github.com/invoice-x/invoice2data