DeepL and Whisper Quietly Save Time

DeepL — making English read naturally

We use it for reports and replying to email.

The best part is simply that the English reads naturally. There is little of the stiffness that gives machine translation away, and the output is usable as-is.

What actually helped was less the translation quality than the change in order. Even when writing English feels like a chore, switching to "draft in Japanese, then polish" lowers the barrier.

Whisper — transcription that runs locally

We used it when building an app that handles speech recognition.

We picked it because it runs locally and is accurate. Everything stays on the machine, so no audio goes to the cloud. That is strong both for privacy and for offline operation.

It became the first candidate whenever the requirement is "build transcription into my own app".

Choosing between them

"Where it runs" as a selection criterion

Putting the two side by side shows that accuracy is not the only axis in a selection.

Accuracy was part of why we chose Whisper, but what really mattered was where it runs. "Audio never goes to the cloud" is not something you can add later the way accuracy can be improved. It is an architectural constraint, and it has to be decided first.

Rather than spending time comparing headline features, deciding "where does it run" and "what leaves the machine" first narrows the options on its own.

Originally published at tech.rebounder.jp.