invoice2data VS silero-models

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

I doubt it's currently actually "the best open source text to speech", but the answer I came up with when throwing a couple of hours at the problem some months ago was "Silero" [0, 1].

Following the "standalone" guide [2], it was pretty trivial to make the model render my sample text in about 100 English "voices" (many of which were similar to each other, and in varying quality). Sampling those, I got about 10 that were pretty "good". And maybe 6 that were the "best ones" (pretty natural, not annoying to listen to).

IIRC the license was free for noncommercial use only. I'm not sure exactly "how open source" they are, but it was simple to install the dependencies and write the basic Python to try it out; I had to write a for loop to try all the voices like I wanted. I ended using something else for the project for other reasons, but this could still be fairly good backup option for some use cases IMO.

  [0] https://github.com/snakers4/silero-models#text-to-speech

Glados was the first experiment. I moved on to silero afterwards: https://github.com/snakers4/silero-models

Some months ago I tried the Silero Models: https://github.com/snakers4/silero-models

With the audio sources I had, in English, the transcription had many mistakes. The good side is that installing and running the software worked as described in their documentation, so maybe it’s worth giving it a try by yourself.

Silero V3: fast high-quality text-to-speech in 20 languages with 173 voices\ (56 comments)