Your as-broadcast recordings become text you can search by word, by speaker and by hour. Type a name and see every time it was said across months of output, each hit a timestamp you click to hear the audio in context.
Recordings you send us · not a live listener · no alerting · speaker labels · up to 10 hours per file
Whipscribe is asynchronous. You send us recordings — the hourly as-broadcast files your logger already writes, a show export, a rendered clip — and text comes back once the job has run. We do not connect to a stream, there is no always-on capture, and nothing here alerts you on audio as it airs.
So this does not replace whatever captures your output. It sits behind it: your recordings stay the authoritative material, and the transcript is the index that makes them findable. If you need a word flagged the moment it is spoken, that is a live monitoring product and this isn't one.
Broadcasters keep what went out. Almost nobody can search it. One 18-hour day is 1,080 minutes of speech; a month of those is 32,400; a year is roughly 6,500 hours sitting as audio files named after timestamps. When a question arrives — did we say that, when did we cover it, who was on air — the only tool is a person with headphones and a guess about which hour to start.
Upload files, submit them through the API one call each, or give us read-only access to the bucket or Drive folder your logger writes into. Up to 10 hours per file, so an hourly logger file is one job and a whole day is 18 or 24 of them.
Diarization splits the audio into labelled turns, so the presenter, the guest and the caller are separated in the record rather than merged into one voice. Timestamps run the whole way through the file.
Type a person, a brand, a place, a claim. Search returns every line containing it with its timestamp, so you read the surrounding lines and judge relevance before you listen to anything. Accents and case are ignored.
Ask in plain language and get an answer grounded only in that transcript, with citations. Every citation is a link: click it and the transcript jumps to that line while the audio plays from it — so the answer is a route to the evidence, not a substitute for it.
“When did we discuss the roadworks, and who raised it?”Export TXT, DOCX, JSON, SRT or VTT with timings intact. Attach the passage and its timecode to an internal note, a response, or a report — so whoever reads it next can go and hear it themselves.
All of these are the same operation underneath: find the words, then hear them.
A name, a company, a claim, a piece of music. Search across every hour you've transcribed instead of guessing which show it was on and scrubbing from there.
A complaint or an internal query usually paraphrases. The transcript gives you the verbatim line and the audio behind it, which is what settles the disagreement.
Speaker labels separate presenter from guest from caller, so attribution comes out of the record rather than out of somebody's memory of the shift.
Count occurrences across a week or a month of output — coverage of a topic, mentions of a sponsor, how a story travelled across dayparts — from text rather than sampling by ear.
A searchable text record makes it much faster for a person to locate a segment, read what was said around it, and hear the audio in context. That is genuinely useful when you have to answer a question about your own output under time pressure. That is the whole claim.
We do not claim that using Whipscribe satisfies any regulator's rules, licence condition, code, or retention requirement, and we make no statement about any specific regulatory regime. Your recording, retention and reporting obligations remain yours; your own recordings remain the authoritative material; machine transcription mishears names, figures and technical terms, so verify the audio before you rely on a line. Nothing here is legal advice. For jurisdiction-by-jurisdiction notes written up separately, see the broadcast hub.
An API key, one submit per file, then poll. A day of hourly logger files is a loop; a back catalogue is the same loop pointed at a longer list.
# submit every hour of yesterday's as-broadcast audio for f in 2026-08-05-*.mp3; do curl -s https://whipscribe.com/api/v1/transcribe \ -H "X-API-Key: $WHIPSCRIBE_KEY" \ -F "file=@$f" -F "diarize=true" -F "source=api" done # each call returns {"job_id":"…","status":"queued"} # list what you've submitted, then pull a finished transcript curl -H "X-API-Key: $WHIPSCRIBE_KEY" "https://whipscribe.com/api/v1/jobs?limit=100" curl -H "X-API-Key: $WHIPSCRIBE_KEY" "https://whipscribe.com/api/v1/jobs/<job_id>/result?format=json"
Prefix-scoped access to your own S3 (or S3-compatible) bucket or a Google Drive folder, so whole folders of logger output get transcribed without anyone opening a browser.
Whipscribe ships an MCP server, so an assistant can submit files, search transcripts and pull quoted passages as part of a review workflow rather than a separate tool.
For output that isn't in your own archive — a catch-up player, another broadcaster's clip, an embedded recording — the Whipscribe extension sends browser audio into the queue.
Every field, status and export format, including how jobs are scoped to your key and how results are fetched. For whoever wires this to the logger.
Credit packs are one-time and don't expire: $2 / 60 min, $8 / 1,000 min, $12 / 2,000 min, $24 / 5,000 min. At the 5,000-minute pack that's just under half a cent a minute — so the decision stops being “which hours can we afford to transcribe” and becomes “how much of the schedule do we want indexed”.
| What you index | Minutes | Cheapest way to buy it | Cost |
|---|---|---|---|
| One 18-hour broadcast day | 1,080 | 1,080 min drawn from a $24 / 5,000-min pack | ~$5.18 |
| One flagged hour a day, for a month | 1,800 | one $12 / 2,000-min pack, 200 min spare | $12 |
| 18-hour days, every day for a month | 32,400 | 6.5 × the $24 / 5,000-min pack | ~$156 |
| Round-the-clock, 24 h a day for a month | 43,200 | 8.7 × the $24 / 5,000-min pack — talk to us | ~$207 |
There is no free tier and no daily free allowance — you buy minutes, not seats, and unused credits stay yours. Backfilling a year of archive is a volume conversation: contact sales → · full pricing →
Pick a logger file where you know something was said, put it through, and search for it. That single test tells you whether a searchable record would have saved you the afternoon you spent last time.
No. Whipscribe works on recordings you send us — the hourly as-broadcast files your logger writes, or a show export. There is no live listener, no always-on capture, and no alerting on audio as it airs. What you get is the record afterwards, in text you can search.
No, and we won't claim it does. This is a transcription tool that makes recordings you already keep searchable, so a person can find a segment fast and hear it in context. Your obligations remain yours, your recordings remain the authoritative material, and nothing here is legal advice.
Search returns every line containing your phrase with its timestamp, so you read the context before you listen. Or ask the transcript a question and get an answer grounded only in that transcript, with citations you click to play the audio from that exact moment.
Yes — diarization splits the audio into labelled turns, so presenter, guest and caller are distinguished. Labels mark speakers as distinct rather than naming them; you rename once and it carries through.
Strong on clean studio speech, weaker on phone lines, crosstalk, music beds and unfamiliar proper nouns — which are exactly the words a query often turns on. Treat the transcript as the index and the audio as the record: click the timestamp and listen before you act on a line.
On private infrastructure running faster-whisper and whisperX. Your recordings are never handed to a third-party transcription service and are never used to train a model.