Whisper Scribe AI

Whisper Scribe AI sits in the pragmatic middle of the transcription market: it does one job — audio and video to clean, speaker-labeled text — and does it without a meeting calendar, a desktop client, or a workflow builder bolted on. The core is OpenAI's Whisper, which the vendor states plainly on the homepage and in the FAQ. That's an honest framing, and it means the accuracy ceiling is roughly what Whisper gives you on clear audio: the vendor claims up to 99% and shows a sample transcript labeled 98.4%. Where it breaks down is predictable — noisy rooms, heavy accents, crosstalk — and the FAQ says as much rather than burying it in an asterisk. What you actually get on day one: drag in files up to 2 GB each, or batch a stack of them, or hit record in the browser. Diarization labels Speaker 1, Speaker 2, and lets you rename or reassign in one click. Word-level timestamps mean you can click to the moment a specific word was said — useful for clipping and fact-checking. AI summaries distill key points, decisions, and action items, and finished transcripts can be translated into another language before export. Exports cover TXT, Word, PDF, SRT/VTT, and HTML, plus clipboard. On the free plan, export is TXT only. Pricing is where it earns its keep. Free is 60 minutes a month with no card — enough to genuinely trial it. Starter runs $5.75/mo billed annually ($69/yr) or $9.90/mo month-to-month for 300 minutes; Pro is $8.25/mo billed annually ($99/yr) or $14.99/mo monthly for 600 minutes; Unlimited is $16.58/mo billed annually ($199/yr) or $24.99/mo monthly with no cap. Annual billing saves between 34% and 45% depending on tier, which is a real gap — the monthly price is roughly what you'd pay a competitor, so the discount is the pitch. The limits are structural, not cosmetic. Files cap at 2 GB. The free tier's 60 minutes goes fast if you process long interviews. There's no calendaring, no live-stream transcription, no on-prem, no offline. And the vendor's documentation and developer pages weren't reachable during this run, so we can't say anything about API availability either way — if piping transcripts into another system is a requirement, verify that directly before committing. Where it fits: journalists transcribing interviews, researchers working through focus groups or lectures, podcasters producing show notes and SRT captions, and business teams who want meeting recordings turned into action items. Where it doesn't: teams that need transcription wired into their own stack, anyone who needs real-time streaming, and anyone with consistently poor source audio.