Whisper

Whisper is OpenAI’s speech recognition model. It is open source and can be self-hosted, and there are newer hosted transcription models available through the API. It turns meetings, calls and field recordings into text you can search and summarise.

Very good on clear speech and respectable in messy conditions, and it improves with domain vocabulary. We measure accuracy on your actual recordings during the pilot rather than quoting a brochure number.

Yes, dozens, including translation to English. We test the languages that matter to you before anything goes live.

That is the deciding factor in deployment. Self hosted Whisper keeps audio entirely on your infrastructure, and the hosted API does not train on your business data. We match the deployment to the sensitivity.

Self hosted costs are hardware and upkeep; hosted is priced per minute of audio. At volume the comparison gets interesting, and we model both honestly.

Data sovereignty, usually. If recordings contain health information, legal matters or anything that cannot leave your environment, running it on your own hardware answers that cleanly. Otherwise the hosted models are less work.

Accents are generally handled well. Local place names, organisation names and industry terms are where errors show up, and that is fixable by giving it the vocabulary rather than accepting the default output.

Search them, summarise them, and pull actions out of them. The value is rarely the transcript itself. It is that six months of meetings and calls become something you can ask questions of.