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Pricing
English
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Turn your recording into
a clean transcript you can actually share.

audien·to is an AI audio tool that turns any recording into a clean, readable transcript — filler removed, punctuation fixed, brand names spelled right — in seconds. Optionally translates into another of 67 languages in the same pass. Unlike free transcribers that hand you a raw word stream, it gives you publishable copy. Free tier, no signup.

Drop any audio in. We'll strip the “ums,” fix punctuation, get the brand names right, and hand back either a flowing article or a cued subtitle file. Translation? Pick the output language and we'll do it in the same pass.

● Record
Drop audio here
or click to choose a file · up to 2h · auto-deleted in 72h
LANGUAGE
required · 67 supported
What you'll get
  • Cleaned transcript
  • Speaker tags
  • Punctuation fixed
Speakers auto-tagged. Tweak any section after upload from the Options panel — no setup needed up front.
The guide

Why a clean transcript matters

A raw transcript reads like the audio sounds: every “um,” every false start, every misheard brand name. It's fine for keyword search, but unreadable to anyone who wasn't in the room.

A clean transcript fixes that without changing what was said. Filler is removed, sentences land complete, the brand names are spelled right. Same words your speakers used — just in a form your readers can actually skim.

What “clean” actually means

  • Filler removed — “um,” “like,” “you know” trimmed; meaning preserved.
  • Punctuation in — periods, commas, question marks where speakers paused.
  • Names right — brand names, product names, people — spelled the way the speaker meant.
  • Speakers tagged — who said what, no more guessing from voice quirks.
  • Optional translation — same content, in any of 67 languages.

How audien·to cleans yours

What lands in the doc

  • The transcriptspeaker-tagged, paragraph-broken, punctuated. Reads like prose, not stenography.
  • Timestampskept inline for the Transcript output; dropped when 'Remove timestamps' is on or for the Subtitles output.
  • Speaker labelsAda / Lorenzo / Guest 3 — auto-detected when reliable, generic when not.
  • Brand namesSubstack, Klaviyo, GPT — caught in context, not phonetically.
  • No invented wordsif the speaker said it, it's there. Filler is the only thing edited out.

Cleanup choices that hold up

  • Edit aggressivenessLight keeps every word. Standard trims filler. Heavy rewrites paragraphs for readability while preserving meaning.
  • Transcript vs. SubtitlesTranscript keeps inline timestamps and speaker labels — best for sharing or repurposing into a doc; flip the 'Remove timestamps' toggle to collapse it into flowing prose. Subtitles produce a cue file with start/end times — pick this if the recording is going on video.
  • Speaker labelskeep when speakers matter (interview, meeting); drop for monologue or solo recording.
  • Filler ≠ paceremoving every “um” sometimes flattens cadence; Standard leaves a few intentional ones.
  • Don't over-editif the speaker repeats a phrase for emphasis, keep it. We're cleaning, not rewriting.

Knobs in the Options panel

  • OutputTranscript (cleaned + timestamped + speaker-tagged) · Subtitles (cued for video).
  • Remove timestampsToggle on to drop [MM:SS] prefixes and produce flowing prose. Transcript only.
  • Edit aggressivenessLight · Standard · Heavy. Transcript only.
  • Subtitle formatSRT · VTT · Plain text. Subtitles only.
  • Subtitle modeSingle language · Bilingual stacked · Bilingual inline. Subtitles only.
  • Speaker labelskeep · drop. Transcript only.
  • Output language23 supported. Set this to translate while cleaning — same pass.
Why this works

Why modern AI hears what older tools missed

When you upload audio, two AIs go to work. The first one listens. It learned from millions of hours of real speech — accented, overlapping, full of “ums” and brand names that didn’t exist five years ago — so it can hear “Klaviyo,” “Substack,” or “the GPT pipeline” without flinching. The words older tools used to silently mangle come back right.

It hears in context. Instead of guessing one sound at a time, it takes in the whole sentence and uses everything around a tricky word to figure out what was actually said. That’s how a brand-new product name still lands correctly: the words around it tell the AI what kind of sentence it’s in.

And it cleans as it goes. Disfluency — “uh, like, I think… yeah” — doesn’t drop the rest of the sentence on the floor. Punctuation and capitalization come built in, so what you read is prose, not a wall of lowercase. By the time it hands off, the transcript already looks like what a careful typist would have given you.

What happens once we have your words

A raw transcript is the floor, not the ceiling. The second AI reads the whole document the way a careful editor would. It groups related discussion into chapters even when nobody says “moving on.” It surfaces the quote you’d actually screenshot — not the longest sentence on the page. It separates a decision from a tangent, an action item from a passing wish.

That’s the jump older tools couldn’t make: they gave you words, we give you shape. A meeting becomes minutes with owners, dates, and resolved questions. A podcast becomes show notes whose chapters track the real narrative, not the nearest five-minute mark. A voice memo becomes a send-ready email in your voice — not a list of fragments to stitch back together.

Each tool on this page is one of those pairings — the same listening AI up front, the same writing AI behind it, shaped for one specific output. You don’t pick. You don’t tweak. A thirty-second upload comes back as the thing you actually wanted, ready to use.