How accurate is Whisper large-v3 for meetings?
Whisper large-v3 is OpenAI’s openly released speech-recognition model and remains a benchmark standard for transcription accuracy across accents, domains, and dozens of languages. For clear meeting audio it produces reliably quotable transcripts; accuracy degrades in the ways all ASR does — crosstalk, poor microphones, heavy jargon.
Why do self-hosted tools standardize on Whisper?
Because it is open, inspectable, and independently benchmarked — no vendor mystery about what handles your audio. Donna calls Whisper large-v3 through the Groq API using your own key: you get the model’s accuracy at low metered cost, with a direct provider relationship instead of a notetaker middleman, and long recordings handled by chunking on your server before upload.
What actually determines transcript quality in meetings?
Audio in, accuracy out. The strongest factors are microphone quality, one-speaker-at-a-time discipline, and vocabulary predictability. A meeting bot has one structural advantage here: it records the meeting’s mixed audio feed directly from the platform rather than a laptop mic across the room — Donna captures the clean feed from her own virtual audio device on the server.
What about names, jargon, and code-switching?
Large-v3 handles multilingual speech and code-switching notably well, which matters for teams that drift between languages mid-sentence. Proper nouns and deep internal jargon remain ASR’s hardest ground — Donna’s reports mitigate this by citing timestamps with every claim, so any load-bearing quote is one click from the recording that proves it.
Related questions
Is Whisper better than the proprietary engines cloud notetakers use?
Cloud vendors rarely disclose their stacks, which makes comparison one-sided: Whisper’s performance is public and independently tested, theirs is a trust exercise. Practically, large-v3 is at or near the standard those engines are measured against.
Does Donna transcribe non-English meetings?
Yes — Whisper large-v3 is multilingual, so non-English and mixed-language meetings transcribe well. Donna’s analysis report itself is written in English today.
How fast is transcription after a meeting?
Donna chunks the audio and transcribes via Groq immediately after the call ends; Groq’s inference is fast enough that the report, including the DeepSeek analysis passes, is typically ready within minutes of leaving the meeting.
Put Donna in your next meeting
Donna deploys onto your own VPS in an afternoon: nginx, pm2, PostgreSQL, your API keys. Early access is open — tell us about your team and we’ll get her a seat at your table.
Request early access