The linguists I know are not necessarily a representative sample... but they're mostly interested in just that: maximally faithful IPA representation. They want to know if the speaker switches back and forth between aspirated p and unaspirated p on the same word, because that tells them something about the language — that aspirated consonants are not meaningful.

Linguists studying the sounds of a language, its phonology, often want to find "minimal pairs", words that differ by only a single sound. For example, din and tin in English. You record a native speaker saying both words, and telling you their meaning, and then you play back either recording A or recording B to other native speakers and ask them which word it is. If they can identify the word every time, then you've found two sounds that are meaningfully distinct in this language. (Some languages don't distinguish the d and t sounds, but English does). But if the native speakers go 50/50 on which word it is, or ask to hear it in a sentence for clarification because it could be two or three different words, then you've found a pair of sounds that this language does not distinguish. (Note that you're playing the words in isolation, because sentence context might make it obvious which one it is, e.g. you can't tell if an English speaker is saying their or there until you hear more words of the sentence).

So yes, the linguists that I know (who, again, are not necessarily a representative sample) are interested in as faithful an IPA representation as they can get, because that inconsistent transcription will give them many clues about the language. It still all has to be checked, because that switching back and forth between aspirated and unaspirated p (for example) could have been an artifact of a poor-quality microphone not picking up the aspiration, or a windy day causing aspiration sounds that the speaker never said, or the speech-to-text model making a mistake. But I watched my wife listen to the same two-second recording on loop over and over, trying to be certain of which sound she was hearing in the middle of the word. Double-checking the output of the model would (in most cases) only require listening to the audio once or twice, not half-a-dozen times like she typically did while researching her thesis. At the time, LLMs were not really a thing yet, but if she were doing her thesis today I bet a speech-to-IPA model would have saved her quite a lot of time — but only if it output every distinction, even the ones not meaningful in the target language. The "maximally faithful" representation, as you put it.