Why do Whisper CLI --max_line_width and --max_line_count often fail or error out?

In Python Whisper CLI, passing --max_line_width or --max_line_count throws an error unless --word_timestamps True is also enabled, and changing your target width from 42 chars (YouTube 16:9) to 16 chars (Reels 9:16) otherwise requires re-running GPU transcription. Splitting from saved word-level JSON takes under 10 milliseconds in your browser.

What happens when a single spoken word is longer than the max characters per line setting?

Single Latin words that exceed the configured character threshold stay intact on their own single-word cue rather than hyphenating mid-word, preserving clean readability during fast playback.

What character limit works best for TikTok, Reels, and YouTube Shorts?

For large center-screen bold subtitles (65 to 85 pt on a 1080px canvas), 14 to 18 characters on 1 line keeps every word inside the central safe margin away from right-side action buttons. For standard two-line lower-third captions on horizontal 16:9 video, switch Max chars to 42 and Max lines to 2.

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

Whisper SRT Max Characters Per Line Splitter

Pre-configured to 16 max characters per line and 1 line per cue so vertical 9:16 captions never clip behind TikTok, Instagram Reels, or YouTube Shorts UI overlays.

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 | 12 cues | Duration: 00:09.4 (9.4s) | Size: 3.1 KB

Line-Fit (<=16c) 100% (12/12)

Reading Speed 21.3 CPS (5 >20 CPS)

Words / Cues 25 words / 12 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

1
00:00:00,180 --> 00:00:00,920
Welcome back to

2
00:00:00,940 --> 00:00:01,620
the engineering

3
00:00:01,640 --> 00:00:02,120
podcast,

4
00:00:02,940 --> 00:00:03,780
today we convert

5
00:00:03,800 --> 00:00:04,620
raw Whisper JSON

6
00:00:04,640 --> 00:00:04,980
directly inside

7
00:00:04,990 --> 00:00:05,120
your browser.

8
00:00:05,840 --> 00:00:06,740
Word timestamps

9
00:00:06,780 --> 00:00:07,380
keep fast

10
00:00:07,420 --> 00:00:07,980
short-form

11
00:00:08,020 --> 00:00:08,840
captions crisp

12
00:00:08,880 --> 00:00:09,420
and readable.

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

Showing 12 of 12 cues

#	Start	End	Max Chars	Speed	Cue Lines
1	00:00:00,180	00:00:00,920	15	20.3 CPS	Welcome back to
2	00:00:00,940	00:00:01,620	15	22.1 CPS	the engineering
3	00:00:01,640	00:00:02,120	8	16.7 CPS	podcast,
4	00:00:02,940	00:00:03,780	16	19.0 CPS	today we convert
5	00:00:03,800	00:00:04,620	16	19.5 CPS	raw Whisper JSON
6	00:00:04,640	00:00:04,980	15	44.1 CPS	directly inside
7	00:00:04,990	00:00:05,120	13	100.0 CPS	your browser.
8	00:00:05,840	00:00:06,740	15	16.7 CPS	Word timestamps
9	00:00:06,780	00:00:07,380	9	15.0 CPS	keep fast
10	00:00:07,420	00:00:07,980	10	17.9 CPS	short-form
11	00:00:08,020	00:00:08,840	14	17.1 CPS	captions crisp
12	00:00:08,880	00:00:09,420	13	24.1 CPS	and readable.

Browser-Local Pipeline

How It Works in 3 Steps

Pre-tuned for Short Video Splitter 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 Short Video Splitter

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

Active preset rules (/short-video-caption-splitter/)

Locks default wrapping to maxCharsPerLine of 16 and maxLinesPerCue of 1, producing punchy 2-to-4-word captions tailored for large bold fonts inside 1080x1920 vertical safe zones.
Uses word-level startSec and endSec timings so rapid short-form speech snaps onto screen on the exact spoken syllable rather than drifting across multi-sentence blocks.
Breaks cues early on natural micro-pauses (0.35s gap threshold) and caps cue duration at 2.5 seconds to maintain brisk visual pacing.
Displays the exact maximum character count and reading speed of generated cues so editors can verify safe-zone compliance before importing into CapCut, Premiere Pro, or DaVinci Resolve.

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

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 Current view

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 whisper srt max characters per line

Why do Whisper CLI --max_line_width and --max_line_count often fail or error out?: In Python Whisper CLI, passing --max_line_width or --max_line_count throws an error unless --word_timestamps True is also enabled, and changing your target width from 42 chars (YouTube 16:9) to 16 chars (Reels 9:16) otherwise requires re-running GPU transcription. Splitting from saved word-level JSON takes under 10 milliseconds in your browser.
What happens when a single spoken word is longer than the max characters per line setting?: Single Latin words that exceed the configured character threshold stay intact on their own single-word cue rather than hyphenating mid-word, preserving clean readability during fast playback.
What character limit works best for TikTok, Reels, and YouTube Shorts?: For large center-screen bold subtitles (65 to 85 pt on a 1080px canvas), 14 to 18 characters on 1 line keeps every word inside the central safe margin away from right-side action buttons. For standard two-line lower-third captions on horizontal 16:9 video, switch Max chars to 42 and Max lines to 2.

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