Timestamps and subtitle codes
Remove SRT and VTT timing lines, sequence numbers, cue settings, and WebVTT headers.
Paste a rough transcript or upload TXT, SRT, or VTT. Remove timestamps, filler words, repeated phrases, broken caption lines, and messy formatting without losing the original meaning.
Raw transcripts usually contain structural noise and speech habits that make them difficult to publish, share, or review. The cleaner handles the repetitive cleanup first and preserves the substance.
Remove SRT and VTT timing lines, sequence numbers, cue settings, and WebVTT headers.
Reduce unnecessary “um,” “uh,” repeated starts, and accidental adjacent word repetition.
Merge short caption fragments into readable sentences while keeping speaker changes separate.
Use AI Polish to restore sentence boundaries, capitalization, punctuation, and basic grammar.
Keep speaker attribution for interviews and meetings or remove labels when plain prose is required.
Turn a wall of transcript text into readable paragraphs without converting it into a summary.
The tool removes transcription clutter while retaining names, numbers, claims, and the speaker’s original point.
1\n00:00:01,000 –> 00:00:05,000\nSPEAKER 1: um so today today we’re going to talk about how to how to clean transcripts\n\n2\n00:00:05,100 –> 00:00:09,000\nand make them easier to read for the team
SPEAKER 1: Today we’re going to talk about how to clean transcripts and make them easier to read for the team.
The workflow is deliberately short because a transcript utility should not require its own training seminar.
Add plain text, TXT, SRT, or VTT content. The original transcript remains visible throughout the process.
Use Quick Clean for structure, Clean Verbatim for speech noise, or AI Polish for contextual editing.
Edit the result, compare word counts, copy it to another application, or download a TXT file.
The same cleanup engine works across captions, meetings, interviews, lectures, podcasts, and ordinary speech-to-text output.
Paste copied YouTube captions and remove timestamps, repeated caption fragments, and broken lines.
Preserve speaker labels while removing filler words, stutters, and repetitive speech patterns.
Format long conversations into readable speaker turns and paragraphs without summarizing the discussion.
Remove subtitle sequence numbers, timing cues, cue settings, and WebVTT metadata from subtitle files.
Quick Clean and Clean Verbatim use deterministic browser logic. AI Polish requires a configured secure VoiceDash endpoint because production API credentials must not be placed inside page JavaScript.
Answers about transcript formats, cleanup behavior, privacy, and the difference between this tool and VoiceDash.
An AI transcript cleaner turns rough speech-to-text output into more readable text by removing transcription artifacts and correcting punctuation, capitalization, grammar, paragraph structure, and speaker formatting.
Yes. Quick Clean removes common SRT, VTT, and copied-caption timestamp formats locally in the browser.
Yes. Upload TXT, SRT, or VTT files. The local parser removes subtitle sequence numbers, timestamps, cue settings, and WebVTT headers.
The local presets do not summarize. The AI endpoint must also be instructed to preserve meaning, names, numbers, URLs, and speaker attribution while refusing to invent or summarize content.
Yes. Preserve speaker labels for meetings, podcasts, and interviews, or disable that option to produce plain continuous text.
Local cleanup runs in the browser. AI Polish sends text to the configured secure backend endpoint. The production page must state the actual retention and logging policy of that endpoint.
This tool cleans text after transcription. VoiceDash turns natural speech into polished writing directly at the cursor, reducing the need to export, clean, and paste transcripts afterward.