How accurate is AI transcription?
95-99% on clear audio, and the variables that actually move the number.
Free: 60 min per 30 days, files up to 30 min / 50 MB · Paid plans: 10 to 100 hrs, files up to 2 GB. Big video? Upload just its audio track: same transcript, much faster upload.
Encrypted in transit · audio & video auto-delete in 30 days · never used to train AI
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Last updated 21 July 2026
On clear, single-speaker audio in a major language, modern AI transcription is typically 95–99% accurate, which works out to roughly one error every 20 to 100 words. On difficult audio (background noise, heavy crosstalk, strong dialect, specialist jargon), accuracy can fall to 80–90%, which reads noticeably rougher. No single universal number survives contact with real recordings, because the audio matters more than the engine.
What moves the number most, in order: recording quality (mic distance and noise), overlapping speakers, vocabulary rarity (names, drug names, product jargon), and language/dialect. What barely matters: file format, video vs audio, and recording length.
The practical fix isn't a better promise; it's a better editor. Trustample pairs the transcript with a synced player: click any sentence to hear it, click any word to fix it. Getting from 96% to 100% takes minutes, not a re-listen.
Frequently Asked Questions (FAQs)
What does '95% accurate' actually mean?
Word error rate (WER) of 5%: out of 1,000 words, about 50 are wrong, missing or inserted. That sounds bad but reads fine, because most errors are small words and endings. The errors that hurt (names, numbers) are exactly what a 5-minute editing pass catches.
Is AI transcription more accurate than a human?
On clear audio, top AI models now rival casual human transcribers and are dramatically faster. Professional human transcribers still win on terrible audio, heavy crosstalk and specialized domains, but at $1–2 per audio minute and multi-day turnaround.
How can I improve my transcription accuracy?
Record closer to the speaker, reduce background noise, avoid talking over each other, set the spoken language explicitly instead of auto-detect, and add names/jargon to Trustample's custom vocabulary box before uploading. Those five habits are worth more than any engine switch.
Does accuracy differ by language?
Yes. English, Spanish, French, German, Portuguese and Japanese are strongest (roughly 95–99% on clear audio); Korean, Arabic and most other major languages land around 85–95%; low-resource languages and heavy code-switching are weaker. We publish ranges, not single numbers, because that's the truth.
An honest note: Any tool quoting one universal accuracy percentage is marketing to you. Accuracy is a property of your audio as much as the model. Test with your real recordings; the free plan exists exactly for that.
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