AI Transcription Helper
Ryna AI does not process audio files directly, but it cleans up the raw transcript you get from Whisper or Otter.ai, labels speakers, pulls summaries, and turns the text into usable meeting notes.
Ryna AI Editorial Team · Updated 28.04.2026
A professional transcriber spends about 4 hours of work per 1 hour of audio, and full verbatim takes longer still. Automatic tools cut that time, but the raw output isn't clean: even on good audio, speech recognition lands around 85-90% word accuracy, and it drops fast with accents, crosstalk, jargon, and proper nouns. What you get is usually a wall of text with no punctuation, no paragraphs, jumbled speakers, and a steady stream of 'um', 'uh', 'like', 'you know' — something that needs fixing, not reading.
Ryna AI is built for exactly that second step. You paste the raw transcript; Ryna strips filler words and needless repetition, adds punctuation and paragraphs, corrects mis-heard words from context, labels speakers (Speaker A/B, by name, or as a clean Q&A for interviews), and — if you ask — pulls a summary, a decision/action table, or quotable lines from the same text. Like a real assistant, it asks for context first: how many speakers, the topic, and which names and terms must be spelled correctly. You tell it which cleanup level you want — verbatim, intelligent verbatim, or clean read — and it formats accordingly.
The output is always a draft. Ryna can't hear the audio, only see the text; it guesses an unclear word from surrounding context and can sometimes guess wrong — so checking names, numbers, and critical phrases against the original recording is on you. Ryna doesn't do the audio-to-text step (use Whisper, Otter.ai, or Word/Google dictation for that) and doesn't generate images. The free plan gives near-unlimited daily messages for text you paste; Plus (399.99 TRY/month) lets you upload long transcripts as .txt/.docx/PDF for analysis, read a photo of a handwritten note, and switch on deep thinking for messy multi-speaker cleanup.
Why use Ryna AI for this
Removes filler words and repetition ('um', 'uh', 'like', 'you know') — turns a raw dump into a readable 'clean read' without changing the meaning.
Corrects speech-recognition errors from context: mis-heard terms, run-on spellings, missing punctuation and paragraphing — turns a wall of text into a document that breathes.
Speaker labeling and format options: Speaker A/B, real names, or a Q&A layout for interviews; choose between verbatim / intelligent verbatim / clean read.
Keeps or simplifies timestamps: collapses per-sentence [00:03:14] stamps into topic-based section headers ([00:00] Intro, [12:30] Budget).
Multiple outputs from one transcript: clean transcript + 5-bullet summary + decision/owner/due-date table + quotable lines — all in one chat.
Term and name glossary support: give a 'spell these brands and names this way' list, and it fixes the proper nouns speech recognition keeps mangling, consistently across the whole text.
Example prompts
Copy any prompt below and paste into chat.rynaai.com. Each prompt is tuned for a different scenario — try them all to see how Ryna AI adapts.
How it works — step by step
- 1
Convert audio to text first, then bring it to chat.rynaai.com
Ryna doesn't process audio files, so do the audio-to-text step with Whisper, Otter.ai, or Word/Google dictation and save the output as .txt/.docx. Then open a new chat at chat.rynaai.com and paste the raw text.
- 2
Give context and a name list
State how many speakers there are, the topic, the language, and a list of names/terms that must be spelled correctly. E.g. 'This is a 3-person product meeting; names: Ayşegül, Kerem, Ryna AI, KOSGEB.'
- 3
Choose the output type and format
Be explicit about what you want: clean read, word-for-word verbatim, a Q&A for an interview, or a summary. Also say whether speakers should be labeled by name (Ayşegül) or by letter (Speaker A).
Example: input → output
You ask
This is Whisper output, two people: interviewer and candidate. Remove filler words, add punctuation, label the speakers, don't change the meaning. Spell this name correctly: Ryna AI. Text: um hi welcome uh could you like tell us a bit about yourself sure yeah so um ive been at rina ai for uh three years on the product side you know before that i was a uh pijem i mean product manager at a startup for like two years
Ryna AI returns
Clean read (Q&A), filler removed, meaning preserved: **Interviewer:** Hi, welcome. Could you tell us a bit about yourself? **Candidate:** Sure. I've been at Ryna AI for three years on the product side; before that I was a product manager (PM) at a startup for about two years. — Note: Context-based corrections: 'rina ai' → 'Ryna AI', 'pijem' → 'PM (product manager)'. These are best guesses; check them against the original recording. Paste the rest and I'll continue in the same format.
Pro tips
- Keep the original raw transcript and place it side by side with Ryna's output. Because Ryna can't hear the audio, it guesses unclear words from context and sometimes guesses wrong — this comparison is essential for numbers, dates, and proper nouns.
- For multi-speaker, overlapping recordings, turn on Whisper's speaker separation (diarization) at the transcription stage. If the text has no speaker cues, Ryna's labeling is a guess and won't be perfect at the handoffs.
- Put your term-and-name glossary at the very top of the prompt: list the brands, people, and technical terms speech recognition keeps mangling as 'spell these this way'; Ryna applies them consistently across the whole text in one pass.
- Explicitly add 'don't change the meaning, just clean it up.' Otherwise Ryna will sometimes 'improve' sentences into phrasing the speaker never actually said; if fidelity matters in transcription, this instruction is critical.
- Split a long transcript into 15-20 minute blocks. It holds context better, and if one block has an error you won't reprocess everything; end each block with 'continue from the last speaker' to keep continuity.
- For sensitive content (therapy sessions, legal statements, HR interviews), anonymize names and organizations before pasting. Use Ryna as a formatting and summary aid, not a permanent record-keeper.
Common mistakes to avoid
- ✕Pasting the raw transcript with no context and just saying 'fix it' — without the speaker count, topic, and name list, Ryna can't label correctly and misses proper nouns.
- ✕Publishing the output without comparing it to the original recording — especially numbers, dates, names, and quotes; since Ryna can't hear the audio, errors can slip through unnoticed.
- ✕Saying 'I want verbatim' and then being surprised that filler words and repetition were removed — not stating the cleanup level (word-for-word / fluent / summary) up front.
- ✕Trying to paste a 60-minute transcript in one go and having it cut off midway — split long text into blocks or upload it as a file with Plus.
- ✕Expecting Ryna to transcribe the audio — Ryna doesn't process audio files; the audio-to-text step happens in tools like Whisper or Otter.ai, and Ryna handles everything after.
Who this is for
Managers who keep meeting notes, journalists running interviews, podcast teams, and students cleaning up recorded lecture transcripts.
FAQ
Can Ryna transcribe an audio file directly?
No. Ryna is text-based; do the audio-to-text step with Whisper, Otter.ai, or Word/Google dictation, then bring the text to Ryna. Ryna cleans up that raw text, labels speakers, formats it, and summarizes it if you want.
Can Ryna correctly recover unclear or garbled words?
Ryna can't hear the audio, only see the text; it guesses an unclear word from surrounding context and can sometimes guess wrong. For legal, medical, or official transcripts, treat the output as a draft, not a finished document — always verify names, numbers, and critical phrases against the original recording, and use a certified/professional transcription service where required.
How many speakers can it distinguish?
If the text has speaker cues (Whisper diarization output, 'Speaker 1:' labels, etc.), it labels them all consistently. Without cues it guesses from sentence transitions and won't be perfect on overlapping speech — the most reliable approach is to enable speaker separation at the transcription stage.
How do I hand it a long meeting transcript?
Paste a 5-15 minute transcript directly. For 30+ minute transcripts, split them into 15-20 minute blocks, or use Plus (399.99 TRY/mo) to upload them as .txt/.docx/PDF; Plus also unlocks file analysis, deep thinking, and web research.
Is it good at Turkish transcripts?
Yes, Turkish is its primary language; it naturally fixes Turkish punctuation, suffixes, and inverted sentences and doesn't read like a translation from English. Still, supporting it with a term list for the run-on/split-spelling and proper-noun errors speech recognition commonly makes in Turkish noticeably improves the result.
Related use cases
Free — near-unlimited daily messages
No credit card. Plus at $12/mo (399.99 TRY) unlocks image analysis, file analysis (PDF/Word/Excel), deep thinking, web research, and assistants.