AI TRANSCRIPTION
AI transcription for the hours of audio nobody has time to type
Machine transcription turns hours of recording into searchable text in minutes, at a fraction of what it costs to have a person type it. It is not perfect — accented speech, crosstalk, background noise and specialist terminology are where it slips. For interview archives, meeting recordings, research material and media logging, that trade is usually worth making. Where it is not, a human pass can be added. Across 48 languages and 63 regional versions.
48 languages · Timecodes and speaker labels · SRT, VTT, DOCX, JSON · Human check optional
OVERVIEW
The alternative to machine transcription is usually not a person — it is nothing
Most recorded audio is never transcribed at all. Not because nobody wants it in text, but because paying a person to type a hundred hours of interviews is impossible to justify — so the recordings sit in a folder and everything said inside them stays unfindable. Machine transcription changes that arithmetic. The whole archive becomes searchable text in an afternoon.
What comes back is timecoded text with speakers separated, in the format your workflow already uses: SRT and VTT for video, DOCX for reading and editing, JSON for indexing and search. What does not come back is a perfect script. Clear single-speaker audio transcribes very well. Three people talking over each other in a room with an air conditioner does not, in any language.
Where accuracy matters more than speed, a linguist reviews the output against the audio — correcting names, terminology and the passages the machine guessed at. This can be applied to the whole set or only to the files that need it, which is how most projects use it
WHAT WE DO
The recordings that are worth having in text
01 —Interviews and research recordings
Field interviews, user research sessions and qualitative studies transcribed with speakers separated, so a quote can be found and cited without listening through the whole recording again.
02 — Meetings, calls and internal recordings
Board meetings, client calls and internal sessions turned into searchable text, with timecodes, so a decision can be traced back to the moment it was made.
03 — Media and broadcast archives
Existing footage and audio archives logged at volume, so material that was never catalogued becomes findable by what was actually said in it.
04 — Lectures, training and conference recordings
Course recordings, conference sessions and training material transcribed in full, ready to be turned into notes, summaries or subtitles.
05— Subtitle and caption source text
Where the transcript is the first step towards subtitles, it comes back timecoded and segmented in SRT or VTT — ready to move into subtitle work rather than be retyped.
PROCESS
From a folder of recordings to text you can search
01
Audio intake and assessment
We take the files as they are — MP3, WAV, MP4, MOV, or a link to where they already live. Before anything runs, we check the audio and flag the recordings where the result will be weaker, so you know in advance rather than after you have paid for them.
02
Machine transcription
Each file is transcribed in its source language, with settings matched to the recording rather than one setting applied to everything. Accented speech, several speakers and technical subject matter each behave differently.
03
Speaker separation and timecoding
Speakers are separated and labelled, and timecodes applied at the interval your workflow needs — per sentence for subtitle work, per paragraph for reading, per word where the transcript feeds a search index.
04
Human review, where you want it
You choose which files get a linguist pass. Names, terminology, figures and the passages the machine was unsure about are corrected against the audio — across everything, or only on the recordings that will be quoted or published.
05
Delivery
Files come back in the format your workflow uses — SRT, VTT, DOCX, TXT or JSON — named to match the source files, so nothing has to be matched up by hand at the other end.
KNOW THE DIFFERENCE
AI transcription compared: DIGI MEDIA vs alternatives
Three ways to get a recording into text, and what each one actually gives you back.
| Criteria | Consumer AI tool | Human transcriptionist | DIGI MEDIA |
|---|---|---|---|
| Speed | Fast | Hours per hour of audio | Fast, and parallel across the whole set |
| Cost at volume | Free or near-free | Adds up quickly across an archive | Machine rate, human pass only where needed |
| Speaker separation | Basic or none | Yes | Labelled and consistent across files |
| Timecodes | One fixed format | If requested | Per sentence, paragraph or word |
| Languages | Strong in English, weaker elsewhere | One per transcriptionist | 48 languages, one supplier |
| Accented or noisy audio | Degrades silently | Handles it | Flagged before processing, human pass available |
| Human review | Not available | It is the service | Optional, chosen per file |
| Data handling | Uploaded to a consumer service | Varies | NDA · EU infrastructure · under contract |
DECISION SUPPORT
Is AI transcription right for your recordings?
A quick self-check to confirm this is the right fit — and an honest pointer elsewhere if what you need is something else.
✓ This service is right for you if
- You have hours of recorded audio that has never been transcribed at all
- You need the content findable and searchable more than you need it word-perfect
- The recordings are clear enough for a machine to follow — one or two speakers, reasonable audio
- You want speakers separated and timecodes you can jump straight to
- You need several languages handled by one supplier rather than several
- Your recordings are confidential and cannot be uploaded to a free service
Typical projects we handle
- A research team transcribing an interview archive so quotes can be found and cited
- A broadcaster logging existing footage so material is searchable by what was said in it
- A company transcribing board and client calls for the internal record
- A university transcribing lecture and conference recordings
- A production team generating subtitle source text from finished video
Not sure this is the right service? If accuracy matters more than speed — legal recordings, medical dictation, anything that will be quoted as evidence — that is Transcription, done by a person. If what you actually need is subtitles on screen, with reading speed and line breaks handled, that is Subtitles. Send us a sample file and we will tell you which one your material needs.
WHO WE HELP
Built for teams sitting on more audio than they can listen to
From research archives to broadcast libraries to lecture halls — our AI transcription services serve every team whose recordings have outgrown the time available to play them back.
Research Teams & Market Research
Interview archives, focus groups and user research sessions transcribed so findings can be searched and quoted without replaying hours of audio.
Media & Broadcast Archives
Existing footage logged at volume, so a library catalogued by title becomes searchable by what was actually said inside it.
Universities & Education
Lectures, seminars and conference recordings transcribed in full, ready to become notes, study material, or subtitles for students who need them.
Corporate Teams
Board meetings, client calls and internal sessions turned into a searchable record, with timecodes back to the moment a decision was made.
Production & Post-Production
Rushes, interviews and finished cuts transcribed as subtitle source text — timecoded and segmented, so the subtitle stage starts from text rather than from audio.
WHY US
Searchable beats perfect, for most of what you record
Nobody listens to an archive twice. They search it, or they forget it exists.
48
languages · one supplier
Flagged first
weak audio identified before processing
Your format
SRT · VTT · DOCX · JSON
Human pass
OPTIONAL, PER FILE
The expensive part is not the transcription. It is the hour someone spends scrubbing through a recording to find thirty seconds of it — repeated every time anyone needs something from that archive. Text costs once. Searching audio costs every time. Across 48 languages and 63 regional versions.
LANGUAGES
AI transcription in 48 languages
We transcribe recordings in 48 languages and 63 regional versions — in the language they were spoken in, not translated. Where you also need the transcript in another language, that runs as a second step through translation.
SEE IT IN ACTION
Inside a DIGI MEDIA transcription project
What comes back, and what a human pass changes when you ask for one.
Timecoded, speaker-labelled, and honest about what it guessed
Speakers are separated, timecodes run at the interval your workflow needs, and the file arrives named to match the source. Where a linguist reviews the output, the corrections are almost always the same three things: names, specialist terms, and the passages where the audio was unclear. Everything else the machine gets right.
CREDENTIALS
DIGI MEDIA AI transcription — timecoded, speaker-labelled transcripts across 48 languages
A recording usually carries more than the document that comes out of it — a board meeting with the arguments still in it, an interview under embargo, research audio that identifies the people speaking. Free transcription services upload that audio to a third party and keep a copy. Ours is processed under contract on EU infrastructure, and the files stay yours.
Native Linguists
Target-market speakers for the review pass
No Consumer Tools
Processed under contract, not uploaded
NDA-Backed Handling
Unreleased campaigns under NDA
FAQ
AI transcription frequently asked questions
What researchers, producers and archive teams ask before sending a folder of recordings.
How accurate is it?
It depends on the recording, not on us. Clear single-speaker audio in a widely spoken language transcribes very well. Heavy accents, crosstalk, background noise and specialist vocabulary all reduce it. We do not publish an accuracy percentage, because the same number would be true and false depending on the file. We check your material first and tell you which recordings will need a human pass.
What kind of audio works well, and what does not?
Works well: interviews with one or two speakers, meetings recorded on a decent microphone, lectures, studio audio. Struggles: several people talking over each other, phone recordings on a poor line, heavy background noise, and very specialist terminology. If you are not sure, send one file and we will tell you before you commit the rest.
Which formats do you deliver?
SRT and VTT for video and subtitle work, DOCX and TXT for reading and editing, JSON where the transcript feeds a search index or a database. Files come back named to match your source files, so nothing has to be matched up by hand.
Can you handle more than one language?
Yes — 48 languages, and different languages within the same project. Each file is transcribed in the language it was spoken in. If you also need the text in another language afterwards, that is a translation step and it is quoted separately.
What happens to my recordings?
They are processed under contract on EU infrastructure, not uploaded to a consumer service. Files are held only for as long as the project requires, and we confirm the retention period in writing before you send anything sensitive. NDA on request.
RELATED SERVICES
Explore our full audio and subtitle ecosystem
Transcription is usually the first step towards something else. These are the services it feeds.
READY WHEN YOU ARE
Sitting on recordings you have never had time to transcribe?
Send us one file. We will transcribe it, tell you honestly how the rest of your material is likely to perform, and quote the archive from there.
✓ One file first · ✓ Weak audio flagged before processing · ✓ 48 languages · ✓ Your format · ✓ NDA on request
