With OCR the risk is you get another xerox[1] incident where all your data looks plausible but is incorrect. Hope you kept the originals!

(This is why for my personal doc scans, I use OCR only for full text search, but retain the original raw scans forever)

[1] https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...

Seems to not be to difficult in finding or creating training code. So a pretty decent amount of high quality training data should be many hours. And a few hours in high end data enter GPU compute, and many iterations to get it right.

And someone has already converted it to onnx format: https://huggingface.co/eschmidbauer/cohere-transcribe-03-202... - so it can be run on CPU instead of GPU.

It doesn't use an extra model (so it supports every language that works with Whisper out of the box and use less memory), it works by applying Dynamic Time Warping to cross-attention weights.

My experiences with Google’s Chirp have been horrendous, with it sometimes skipping sections of speech entirely, hallucinating speech where the audio contains noise, and unreliable word level timestamps. And this all is even with using their new audio prefiltering feature.

AWS works slightly better, but also has trouble with keeping word level timestamps in sync.

Whisper is nice but hallucinates regularly.

OpenAI’s new transcription models are delivering accurate output but do not support word level timestamps…

A lot of this could be worked around by sending the resulting transcripts through a few layers of post processing, but… I just want to pay for an API that is reliable and saves me from doing all that work.