Lectures
Students read or listen in their language as the lecturer speaks.
Universities
The lecturer speaks as usual. Students scan a QR code and read or listen on their own phones, with no app to download.
How far behind the speaker?About 2.4 seconds with Baian. Lower is better.
How often Baian was preferredOver professional translations · English into each language
The line marks 50%: an even split.
On campus
Students read or listen in their language as the lecturer speaks.
Students scan a QR code and follow in their phone’s browser.
Translated captions on the hall screen through Projector view.
Past sessions stay on your page to read again.
What students see
They pick one of the languages you turn on, then read as the lecturer speaks, or listen through their own headphones where audio is available.
Setup
Three steps before the lecture. No receivers, booths or student accounts.
Plug the sound desk into a laptop by USB, or use an audio interface.
Choose your languages and press start.
Attendees scan it and follow on their own phones.
Questions
No. They scan a QR code or open a link and follow in their phone’s browser. There’s no app and no account.
Yes. The lecturer speaks as usual. Students scan a QR code and read the translation, or listen with headphones, on their own phones.
A laptop or tablet connected to the lectern microphone or the room’s sound system, by USB or an audio interface, and internet for the laptop and students’ phones. We can walk your campus AV team through setup.
Yes. Ended sessions appear under Previous Sessions on your page, with the translation in each language.
Yes. Put translated captions on the hall screen through Projector view while families follow on their phones.
Bring a short recording, your languages and your audience size. We’ll show you the listener experience.
Arrange a demo WhatsApp1st in Arabic (5.1%) and Korean (2.9%), 2nd in Russian and Danish. Fewer errors than all three OpenAI real-time models in 13 of the 14 languages we publish.
FLEURS: short Wikipedia sentences read aloud. Measures how many words come out wrong.
AssemblyAI published scores for 14 real-time engines. We ran Baian’s live interpreter on the same test set.
We joined clips into 15-minute recordings with 1.5-second gaps and played them into Baian’s live interpreter in real time, like a microphone, with automatic language detection. AssemblyAI streamed each clip separately with the language set in advance. Scoring ignores punctuation, capital letters, Arabic vowel marks and how numbers are written. Our charts show 13 of AssemblyAI’s 14 engines; the full table is on AssemblyAI’s site.
Caption Tool scored above the translators in Hausa, Swahili, Tamil and Urdu (preferred on 67% to 75% of sentences). Baian’s live interpreter had about the same overall quality in Arabic, Hindi and Korean.
148 English sentences that also have professional human translations. Measures translation quality with Google’s MetricX-24 automatic judge.
Baian ran the test. The human translations come from the public FLoRes-200 set.
Baian worked from the spoken audio; the translators worked from text. We compared scores sentence by sentence and counted a result only when the 95% range did not cross zero and two runs agreed. “About the same” means the test found no clear difference; it does not prove the translations were identical.
Median 2.1 seconds across 53 recordings in 15 languages (1.3 to 2.7 seconds per recording).
How long after a sentence ends its finished translation appears.
Baian, on its production Live service.
Measured on Baian’s production Live service from 13 to 15 October 2026, from the end of each spoken sentence to the finished English line in our captured listener feed. Earlier words in a long sentence wait longer than this. Spoken audio arrives a little after the text and was not measured.
Estimated medians: the speaker’s words appear in their own language about 1.0 seconds after they are spoken, and the first translation covering a word about 2.4 seconds after it.
Estimated from our captured listener feed for the same 53 recordings. The capture is incomplete, so some early previews are missing, and previews can still change before the sentence is finished. Times are measured to the feed, not to a phone screen.
Median 2.69 seconds behind the speaker, across 180 speeches.