There’s just not a single one-size-fits-all model/pipeline. You choose the right one for the job, depending on whether you need streaming (i.e., low latency; words output right when they’re spoken), run on device (e.g. phone) or server, what languages/dialects, conversational or more “produced” like a news broadcast or podcast, etc. Best way is to benchmark with data in your target domain.
Sure, you're just going to try lots of things and see what works best, but it's confusing to be comparing things at such different levels of abstraction where a lot of the time you don't even know what you're comparing and it's impossible to do apples-to-apples even on your own test data. If your need is "speaker identification", you're going to end up comparing commercial black boxes like Speechmatics (probably custom) vs commercial translucent boxes like Gladia (some custom blend of whisper + pyannote + etc) vs [asr_api]/[some_specific_sepformer_model]. Like, I can observe that products I know to be built on top of whisper don't seem to handle overlapping speaker diarization that well, but I don't actually have any way of knowing if that's got anything to do with whisper.
