Transcription for educators and the researchers buried in audio
From lecture recordings to a summer's worth of qualitative interviews — turned into searchable, codeable text by the end of the afternoon.
Most lecture audio is never listened to twice
Lecture halls produce a staggering amount of audio that nobody ever listens to twice. Students record class on their phones with the best intentions, then never go back. Researchers spend an entire summer running 30 interviews and another six months turning them into something analysable.
Transcrify cuts the second half of that loop down to an afternoon. Drop in a 90-minute lecture and you get a searchable, paragraph-broken transcript that can be skimmed in five minutes — or grep'd for "midterm" or "definition of homeostasis" in one. For qualitative research, every interview comes back with speakers labelled, ready to import into NVivo, Atlas.ti, or your favourite coding workflow.
It's also one of the simplest accessibility wins a department can make. Captions on recorded lectures, transcripts attached to course materials — both are now a few clicks instead of a budget line.
Built for the way academic work actually flows
Whether you're teaching 200 students or coding 60 interviews, the workflow is the same: get the audio out of the way so the thinking can start.
Lecture capture, no setup
Record the lectern mic, drop the file in, get a transcript ready for the LMS by lunch.
Built for qualitative research
Speaker labels, verbatim or cleaned mode, direct export to NVivo and Atlas.ti.
50+ languages
Strong on European languages, Mandarin, Japanese, Hindi — plus most major regional dialects.
Accessibility-ready captions
SRT and VTT export means lecture recordings meet WCAG and ADA standards without a separate captioning workflow.
Search across a whole semester
Students can search across every lecture for a term and jump straight to the minute it was discussed.
Bulk-process a whole study
Drag a folder of 40 interviews in. Walk away. Come back to a coded-ready dataset.
From lecture hall to study notes by lunch
- 1
Record
Press record on the lectern mic at 9am Monday. Stop at 10:30.
- 2
Upload
Walk to your office. Drop the file in. Head to your 11am meeting.
- 3
Auto-generated material
By 11:15 you have a transcript with timestamps — exportable as captions for the LMS or as PDF notes for students.
- 4
Search across the semester
At exam time, students search "Krebs cycle" across all 12 lectures and jump straight to the relevant minute.
How lectures and interviews become text
Upload the recording
Lecture, seminar, focus group, one-on-one interview — any audio or video format works.
AI transcribes
Speaker diarization handles multi-participant sessions. 50+ languages supported.
Export to where the work happens
Push to NVivo, Atlas.ti, your LMS captions, or a PDF for course materials.
Where it's already part of the workflow
Teaching, research, accessibility, archives — wherever academic work meets audio.
Lecture notes
Recorded lectures turned into searchable, reviewable text — distributed as part of the course pack.
Qualitative research interviews
Speaker-labelled transcripts ready for thematic coding in NVivo, Atlas.ti, or Dovetail.
Seminars and guest lectures
One-off events captured and archived alongside the rest of the curriculum.
E-learning captions
WCAG-compliant SRT/VTT for online courses, generated in minutes per video.
Thesis defences
Recorded committee discussions transcribed for the candidate's records.
Conference proceedings
Panel discussions and keynote talks turned into archival text and accessible video.
How a sociology PhD finished her thesis 12 weeks early
"I used to budget eight weeks for transcribing interviews. This semester it took me an afternoon. I'm not joking — an afternoon."
Priya was working on her doctoral thesis: a qualitative study of immigrant labour markets that involved 42 hour-long interviews. Her budget allocated €80 per interview hour for professional transcription — roughly €3,400 — and turnaround was seven to ten business days per batch.
A colleague suggested Transcrify. She uploaded all 42 interviews on a Friday afternoon. By the time she finished her coffee, the first half were ready. By the end of the day, all of them were in her NVivo project, speaker-labelled, ready for coding.
The €3,200 she'd budgeted for transcription went into a research assistant for the analysis phase instead. Her thesis hit first-draft about 12 weeks ahead of schedule. "Transcription wasn't just the slow part of the project," she says. "It was the part that made the project feel impossible."
Common questions from departments and students
Will it handle multiple speakers in a focus group?
Does it support languages other than English?
Can I export to NVivo or Atlas.ti?
Are transcripts good enough for ADA / WCAG compliance?
Are there discounts for educational institutions?
Stop budgeting weeks for transcription
Upload one of last semester's lectures or a sample interview. See how much of your project timeline you've been spending on the wrong thing.