Am I the only one who remembers when that was the stuff of science fiction? It was not so long ago an open question if machines would ever be able to transcribe speech in a useful way. How quickly we become numb to the magic.
For example, for automatic speech recognition (ASR), see: https://huggingface.co/spaces/hf-audio/open_asr_leaderboard
The current best ASR model has 600M params (tiny compared to LLMs, and way faster than any LLM: 3386.02 RTFx vs 62.12 RTFx, much cheaper) and was trained on 120,000h of speech. In comparison, the next best speech LLM (quite close in WER, but slightly worse) has 5.6B params and was trained on 5T tokens, 2.3M speech hours. It has been always like this: With a fraction of the cost, you will get a pure ASR model which still beats every speech LLM.
The same is true for translation models, at least when you have enough training data, so for popular translation pairs.
However, LLMs are obviously more powerful in what they can do despite just speech recognition or translation.
The problem with Google-Translate-type models is the interface is completely wrong. Translation is not sentence->translation, it's (sentence,context)->translation (or even (sentence,context)->(translation,commentary)). You absolutely have to be able to input contextual information, instructions about how certain terms are to be translated, etc. This is trivial with an LLM.
Just whatever small LLM I have installed as the default for the `llm` command line tool at the time. Currently that's gemma3:4b-it-q8_0 though it's generally been some version of llama in the past. And then this fish shell function (basically a bash alias)
function trans
llm "Translate \"$argv\" from French to English please"
end