AI Transcription
AI Transcription turns spoken audio and video into clear, editable text for captions, notes, interviews, and production workflows.
This page is the workspace, not a teaser. Upload a recording, start AI Transcription in the editor above, then review a plain-text draft and an SRT file with timestamps. You stay on the same URL from file drop to copy or download. Use the result as a first pass for subtitles, show notes, quotes, or search inside a long clip — then correct names and terms before you publish.
Drop your audio or video here
or click to browse — MP3, MP4, WAV, M4A, and more
MP3 · MP4 · WAV · M4A · AAC · MOV · MKV · WEBM · OGG · FLAC (max 200 MB)
How to use AI Transcription
Upload audio or video
Drop an MP3, WAV, M4A, MP4, MOV, WebM, or similar file into the editor. Clear speech and limited background noise help the model map words more reliably. A built-in sample is available if you want to test the pipeline before using your own media.
Start speech-to-text
Optionally set a language, or leave auto-detect on when the recording is mixed or unknown. Submit the file and wait while speech is converted into text. Longer clips take more time; a short, well-mic’d take usually finishes faster than a noisy hour-long file.
Review, copy, or export
Open the plain-text tab for notes and quotes, or the SRT tab for timed captions. Check names, numbers, and brand terms, then copy the transcript or download .txt / .srt. Treat the result as a strong first draft, not a finished subtitle file.
What this AI Transcription workspace does
Speech-to-text from real media
The editor reads spoken words from the file you upload instead of asking you to type from a blank page. Interviews, meetings, lessons, podcasts, and social clips are typical sources. The output is text you can search, quote, and edit.
Subtitle-ready SRT alongside plain text
The same pass can give you a readable transcript and a timed SRT. Use the text tab for notes and the SRT tab when you plan captions. Timing still needs a human pass for line length, reading speed, and speaker changes.
Built for media production
Editors use the transcript to jump to a quote, draft show notes, or seed a caption file before burning subtitles. You are not generating a new video here. You are turning speech you already recorded into text you can work with.
Browser workflow, one URL
Open the tool in a modern browser, drop the file, and stay on this page through processing. There is no separate landing page to bounce through. Sign in if the product asks you to, then export when the transcript looks right.
A practical AI Transcription guide
AI Transcription is speech-to-text for a file you already have. You are not writing a prompt to invent dialogue. You upload audio or video, convert speech into words, and leave with text plus optional timestamps. The notes below cover what to record, how to read a draft, how captions differ from a transcript, and where this page should stop so a human editor can finish the job.
What to record before you upload
A usable source has one or two speakers close to the microphone, limited room echo, and music or crowd noise that stays in the background. Phone voice memos, lav mics on a jacket, and camera audio from a quiet room all work. Problems usually come from overlapping talk, a speaker far from the mic, wind, or a soundtrack louder than the voice. A messy file can still return words, but you will spend longer cleaning names and dropped phrases. Keep files within the 200 MB limit. Supported audio includes MP3, WAV, M4A, AAC, OGG, and FLAC; video includes MP4, MOV, AVI, MKV, WebM, and MPEG-TS. If you only want to see the pipeline, use a sample on this page first, then swap in your own recording.
Language, speakers, and what the model hears
When you know the spoken language, set it so the decoder is not guessing among similar sounds. Auto-detect is reasonable for a single clear language and weaker when speakers switch mid-sentence. This workspace is not a live meeting bot and does not promise speaker labels for every turn. If two people talk over each other, the text may merge lines. Accents, proper nouns, and domain jargon (drug names, product SKUs, legal citations) are the first things to check. A glossary in your head — how you spell the brand, the guest, the city — is faster than hoping the model invented the right spelling.
How to judge a transcript before you publish
Read the opening minute against the waveform in your head: did the greeting, the title of the show, and the first proper noun land? Skim for repeated words, missing negations (not / never), and numbers that change meaning if they are off by a digit. For SRT, watch cue length. A caption that fills the screen for two seconds is hard to read; a cue that lasts ten seconds with one word is equally wrong. You get a timed draft. You still split, merge, and rephrase for the platform you publish on. If a stretch is silence or music, empty output is honest — do not force captions onto a beat drop unless you meant to.
Captions, notes, quotes, and search
A transcript is a document. Captions are a timed reading experience. Use plain text when you need searchable notes, an interview pull quote, or a first draft of a blog post from a recorded talk. Use SRT when you will burn subtitles, upload soft captions, or hand a file to Add Subtitles to Video. This page does not burn captions into pixels; it stops at text and SRT. That split is useful: you can edit wording without re-encoding video, then take the SRT into a caption tool when the lines are short enough to read.
Limits, privacy, and what this page is not
Processing can take several minutes on a long file, and a six-minute ceiling applies if the job stalls. Very large uploads should stay within 200 MB. Files are sent to the speech-to-text backend so the model can hear them; they are not turned into a public gallery or used to train a public model from this UI. This editor will not translate a transcript into another language, will not clean studio noise, and will not write a screenplay from silence. For lyrics from a song, use the lyrics tool. For burned-in captions, use Add Subtitles to Video after you export SRT. Related links on this page stay inside transcription and caption tools so the next click matches the job you started.
Background reading: Speech recognition (Wikipedia)
Who uses AI Transcription
Podcasts, interviews, and show notes
Hosts drop an episode to pull quotes, chapter ideas, and a searchable archive. Speech-to-text is the first pass; the producer still spells guest names and sponsor reads before the notes go live.
Social clips and subtitle drafts
Short-form editors need timed lines for Reels, Shorts, and TikTok. Export SRT, shorten cues, then burn or upload captions. The transcript on this page is the draft, not the final burned MP4.
Meetings, lessons, and research
Students and teams turn a lecture or standup into notes they can search. Confirm action items and numbers against the recording. Do not treat an automated transcript as an official legal record without a human review.
Internal reviews and accessibility drafts
Producers share a text file so a colleague can comment without scrubbing a long timeline. Accessibility still needs a caption pass for reading speed and speaker ID. A machine draft gets you to that pass faster.
AI Transcription FAQ
Related caption and audio tools
Run AI Transcription on this page
Upload audio or video in the editor above, review the text and SRT, then copy or download when the draft looks right.