What is the exact syntax difference between SRT and WebVTT output from Whisper JSON?

WebVTT starts with a mandatory WEBVTT header line and separates seconds from milliseconds with a dot (00:00:01.250 --> 00:00:03.800), whereas SRT has no header line and separates milliseconds with a comma (00:00:01,250 --> 00:00:03,800). HTML5 elements reject comma-formatted timestamps.

How do I attach the downloaded .vtt file to an HTML5 video element?

Place a element inside your tag and ensure your static host serves .vtt files with the text/vtt MIME type and CORS headers if hosted on another origin.

Can I also convert WhisperX or Deepgram JSON to WebVTT on this page?

Yes. Although this preset defaults to WebVTT output with an OpenAI Whisper sample, the input box and file uploader accept all five supported ASR JSON dialects and export any of them as .vtt.

Zero server upload / No 2 MB file cap / Instant Blob download

OpenAI Whisper JSON to WebVTT Converter

Convert Whisper JSON transcripts directly into standards-compliant .vtt caption tracks for HTML5 <video>, Plyr, Video.js, HLS.js, and Cloudflare Stream without server round-trips.

Quick Workflow Mode One click sets format, line width, line count, and speaker tags

Built-in ASR Sample Dialect

Drop Local .json Transcript Here or Choose File No 2 MB cap / Zero upload

Output Format

Max Chars / Line

Max Lines / Cue

Sync Offset (ms)

Include speaker prefix

Detected: OpenAI Whisper verbose_json | 3 cues | Duration: 00:09.4 (9.4s) | Size: 3.1 KB

Line-Fit (<=42c) 100% (3/3)

Reading Speed 21.6 CPS (2 >20 CPS)

Words / Cues 25 words / 3 cues

Track Duration 00:09.4 (9.4s)

ASR Transcript JSON Input Auto-converts on edit or file drop

{
  "task": "transcribe",
  "language": "english",
  "duration": 9.42,
  "text": "Welcome back to the engineering podcast, today we convert raw Whisper JSON directly inside your browser. Word timestamps keep fast short-form captions crisp and readable.",
  "segments": [
    {
      "id": 0,
      "seek": 0,
      "start": 0.18,
      "end": 5.12,
      "text": " Welcome back to the engineering podcast, today we convert raw Whisper JSON directly inside your browser.",
      "words": [
        {
          "word": "Welcome",
          "start": 0.18,
          "end": 0.54
        },
        {
          "word": "back",
          "start": 0.56,
          "end": 0.78
        },
        {
          "word": "to",
          "start": 0.8,
          "end": 0.92
        },
        {
          "word": "the",
          "start": 0.94,
          "end": 1.06
        },
        {
          "word": "engineering",
          "start": 1.08,
          "end": 1.62
        },
        {
          "word": "podcast,",
          "start": 1.64,
          "end": 2.12
        },
        {
          "word": "today",
          "start": 2.94,
          "end": 3.24
        },
        {
          "word": "we",
          "start": 3.26,
          "end": 3.38
        },
        {
          "word": "convert",
          "start": 3.4,
          "end": 3.78
        },
        {
          "word": "raw",
          "start": 3.8,
          "end": 3.98
        },
        {
          "word": "Whisper",
          "start": 4,
          "end": 4.34
        },
        {
          "word": "JSON",
          "start": 4.36,
          "end": 4.62
        },
        {
          "word": "directly",
          "start": 4.64,
          "end": 4.86
        },
        {
          "word": "inside",
          "start": 4.88,
          "end": 4.98
        },
        {
          "word": "your",
          "start": 4.99,
          "end": 5.04
        },
        {
          "word": "browser.",
          "start": 5.05,
          "end": 5.12
        }
      ]
    },
    {
      "id": 1,
      "seek": 512,
      "start": 5.84,
      "end": 9.42,
      "text": " Word timestamps keep fast short-form captions crisp and readable.",
      "words": [
        {
          "word": "Word",
          "start": 5.84,
          "end": 6.12
        },
        {
          "word": "timestamps",
          "start": 6.16,
          "end": 6.74
        },
        {
          "word": "keep",
          "start": 6.78,
          "end": 7.04
        },
        {
          "word": "fast",
          "start": 7.08,
          "end": 7.38
        },
        {
          "word": "short-form",
          "start": 7.42,
          "end": 7.98
        },
        {
          "word": "captions",
          "start": 8.02,
          "end": 8.48
        },
        {
          "word": "crisp",
          "start": 8.52,
          "end": 8.84
        },
        {
          "word": "and",
          "start": 8.88,
          "end": 9.02
        },
        {
          "word": "readable.",
          "start": 9.06,
          "end": 9.42
        }
      ]
    }
  ]
}

Converted Subtitle Output

WEBVTT

00:00:00.180 --> 00:00:02.120
Welcome back to the engineering podcast,

00:00:02.940 --> 00:00:05.120
today we convert raw Whisper JSON directly
inside your browser.

00:00:05.840 --> 00:00:09.420
Word timestamps keep fast short-form
captions crisp and readable.

Cue Timing, Reading Speed & Line-Width Preview (First 25 Cues)

Showing 3 of 3 cues

#	Start	End	Max Chars	Speed	Cue Lines
1	00:00:00.180	00:00:02.120	40	20.6 CPS	Welcome back to the engineering podcast,
2	00:00:02.940	00:00:05.120	42	28.4 CPS	today we convert raw Whisper JSON directly inside your browser.
3	00:00:05.840	00:00:09.420	36	17.9 CPS	Word timestamps keep fast short-form captions crisp and readable.

Browser-Local Pipeline

How It Works in 3 Steps

Pre-tuned for Whisper JSON to VTT with instant client-side conversion and zero file size caps.

01
Paste or Drop Local ASR JSON

Drop a .json file onto the highlighted upload box, paste raw API output, or pick one of five built-in ASR dialect samples. Multi-megabyte multi-hour files parse in local browser RAM with zero server upload.
02
Select a Layout Pill & Check CPS Health

Tap a one-click mode pill (Broadcast 42c x 2, TikTok/Shorts 16c x 1, Podcast Speaker Tags, or WebVTT Player) and watch the live Cue Health Summary flag >20 CPS fast cues and line-fit compliance.
03
Verify Timings & Download Subtitles

Inspect exact timestamps and per-cue character counts in the preview table, apply a positive or negative millisecond sync offset if needed, then copy or download .srt, .vtt, .txt, or .csv immediately.

Preset Specification & Field Mapping

JSON Schema Notes & Format Field Reference for Whisper JSON to VTT

Detailed parser behavior for this workflow alongside field references for all five supported speech-to-text JSON dialects.

Active preset rules (/whisper-json-to-vtt/)

Prepends the mandatory UTF-8 WEBVTT file header followed by a blank line as required by the W3C WebVTT specification and browser <track kind="captions"> parsers.
Formats cue timings with a period before milliseconds (HH:MM:SS.mmm --> HH:MM:SS.mmm) rather than SubRip commas, preventing silent caption load failures in Safari, Chrome, and Firefox.
Outputs clean cue timing blocks ready for browser <track> elements, HLS playlists, and web player caption menus.
Supports optional speaker prefixes and millisecond sync offsets when embedding diarized or trimmed video clips on the web.

verbose_json Float seconds (1.62)

OpenAI Whisper & faster-whisper

segments[].start / end / text
words[].word / start / end
segments[].words[]

Uses exact word timings when present and falls back to proportional word-boundary splitting on segment-only payloads.

Diarized JSON Float seconds (4.36)

WhisperX + pyannote.audio

segments[].speaker
segments[].words[].speaker
word_segments[]

Splits cues on speaker turn changes and interpolates missing timestamps on unaligned digits or currency symbols.

-oj / -ojf Integer ms (4220)

whisper.cpp Local CLI

transcription[].offsets.from / to
transcription[].timestamps.from / to
transcription[].text

Prioritizes integer millisecond offsets for drift-free timing and falls back to HH:MM:SS,mmm string parsing.

REST / SDK JSON Float seconds (5.44)

Deepgram Nova-2 & Nova-3

results.channels[0].alternatives[0].words[]
results.utterances[].speaker
words[].punctuated_word

Prefers punctuated_word capitalization and maps integer speaker indices into clean Speaker N prefixes.

Utterances JSON Integer ms (6120)

AssemblyAI Transcripts

utterances[].start / end / speaker
utterances[].words[]
words[].text / start / end

Auto-scales integer millisecond word and utterance timings to seconds without colliding with float Whisper schemas.

4 Export Targets Comma vs dot ms

SRT, WebVTT, CSV & TXT Output

SRT: HH:MM:SS,mmm
VTT: WEBVTT + HH:MM:SS.mmm
CSV: index,start,end,speaker,text

Clamps negative sync offsets safely at 00:00:00,000 and guarantees minimum 50 ms cue duration for NLE timelines.

Internal Subtitle Tool Matrix

Switch between all six specialized Whisper and ASR subtitle converters with pre-tuned line widths, speaker diarization labels, and export formats.

All 5 ASR Dialects

Whisper & Multi-ASR JSON to SRT Master Converter

Universal local workbench that auto-detects OpenAI Whisper, WhisperX, whisper.cpp, Deepgram, and AssemblyAI JSON.

.srt | 42 chars x 2 lines Open tool ->

OpenAI Audio API

OpenAI Whisper verbose_json to 42-Char SRT Converter

Tuned for /v1/audio/transcriptions verbose_json responses and faster-whisper CLI exports with nested words arrays.

.srt | word + segment timings Open tool ->

HTML5 Video Track Current view

OpenAI Whisper JSON to W3C WebVTT (.vtt) Exporter

Generates compliant WebVTT subtitle tracks with period-millisecond timestamps for HTML5 <video>, Video.js, and HLS.

.vtt | WEBVTT dot-ms header Open tool ->

Pyannote Diarization

WhisperX Diarized JSON to Speaker-Labeled SRT Tool

Preserves SPEAKER_00 tags, interpolates unaligned numeric tokens, and breaks cues cleanly on every speaker change.

.srt | [SPEAKER_XX] prefixes on Open tool ->

GGML / Apple Silicon

whisper.cpp -oj and -ojf JSON to SRT Online Parser

Reads local whisper.cpp transcription[] integer millisecond offsets and rewraps long decoder segments cleanly.

.srt | ms offsets[] parser Open tool ->

9:16 Vertical Safe Zone

16-Character Single-Line Short Video Caption Splitter

Splits word-level Whisper JSON into punchy single-line captions for TikTok, Instagram Reels, and YouTube Shorts.

.srt | 16 chars x 1 line Open tool ->

Technical FAQ for openai whisper json to vtt

What is the exact syntax difference between SRT and WebVTT output from Whisper JSON?: WebVTT starts with a mandatory WEBVTT header line and separates seconds from milliseconds with a dot (00:00:01.250 --> 00:00:03.800), whereas SRT has no header line and separates milliseconds with a comma (00:00:01,250 --> 00:00:03,800). HTML5 <track> elements reject comma-formatted timestamps.
How do I attach the downloaded .vtt file to an HTML5 video element?: Place a <track kind="captions" src="/captions.vtt" srclang="en" label="English" default> element inside your <video> tag and ensure your static host serves .vtt files with the text/vtt MIME type and CORS headers if hosted on another origin.
Can I also convert WhisperX or Deepgram JSON to WebVTT on this page?: Yes. Although this preset defaults to WebVTT output with an OpenAI Whisper sample, the input box and file uploader accept all five supported ASR JSON dialects and export any of them as .vtt.

Why browser-local parsing beats 2 MB paywalled cloud converters

An hour-long Whisper transcript with word-level timestamps routinely reaches 3 MB to 8 MB of JSON. Cloud upload tools meter that file size behind subscriptions while exposing private audio transcripts to remote storage.

Capability	JSON to SRT Local Engine	Paywalled Cloud Converters
JSON file size limit	No artificial cap; parses >5 MB multi-hour transcripts in milliseconds	Blocks uploads over 1 MB or 2 MB without a paid plan
Data privacy & NDA compliance	Zero bytes leave your browser tab; safe for client, legal, and medical audio	Uploads raw transcript JSON to remote servers and third-party buckets
ASR dialect coverage	Auto-detects OpenAI verbose_json, WhisperX, whisper.cpp, Deepgram, AssemblyAI	Breaks when segments[] is missing or timestamps use integer milliseconds
Cue line-width wrapping	Live 8 to 84 char wrapping, 1 or 2 lines per cue, word-level timing split	Exports raw 100-character decoder blocks that overflow video frames
Speaker labels & sync offset	Instant [SPEAKER_XX] prefixes, turn-boundary breaks, and millisecond offset	Drops pyannote speaker fields or requires manual desktop retiming