I’ve got a recording in another language that I need to understand, and I’m kinda stuck.
I know a few words but nowhere near enough to follow the whole thing. It’s a longer recording too, so manually going through it and translating bits one by one sounds like a nightmare.I mainly just want to get an English version of what’s being said, then I can go back to the audio if something doesn’t sound right. Anyone dealt with this before? What did you end up using? tnx guys

Every other aligner (MFA , CTC) , the scores are calculated against human annotated datasets. But , are human annotated dataset the ground truth? how are these things taken care of ? How do I even build a top notch aligner , and even if I build one , how do I know if its top notch . this is frustrating

I am curious to understand from NLP practitioners what the current state of the art is for implementing language models within the following type of pipeline, especially when optimizing for speed

Raw/unstructured text -> NER/multi word phrase extraction -> normalize/link to canonical entities/wordforms