Captions cut for every screen shape, from one file.

Live demo · Quick start · CLI · Player · Format spec · Benchmark

_{One .overtone.json file in a 16:9 and a 9:16 player (social style pack), on the Blender open movie Tears of Steel.
Film: (CC) Blender Foundation | mango.blender.org, CC BY 3.0.}

Overtone stores each spoken word once, with its time and speaker, and cuts captions separately for each picture shape: 16:9 and 9:16 by default, or any aspect you name. A CLI makes the file from your subtitles, audio or video. A video.js plugin shows the cut that fits the player.

Re-cut subtitles you already have. Offline, no API key, no ffmpeg (Node.js 22.12 or newer):

curl -O https://download.blender.org/demo/movies/ToS/subtitles/TOS-en.srt
npx overtone-captions recut TOS-en.srt

You get TOS-en.overtone.json, .horizontal and .vertical .vtt and .srt files, and a transcript. Word times are estimated from the cues.

Caption a video from its audio. This needs ffmpeg and provider API keys (OpenAI and Azure AI Speech by default; overtone providers lists the variables). --dry-run shows where the audio goes and the estimated cost, and sends nothing:

npm install -g overtone-captions
overtone transcribe talk.mp4 --language en --context "Panel about urban cycling" --dry-run
overtone transcribe talk.mp4 --language en --context "Panel about urban cycling"

--local runs offline instead, with whisper.cpp and Ollama.

Show the captions in video.js:

import videojs from 'video.js';
import 'video.js/dist/video-js.css';
import 'videojs-overtone';
import 'videojs-overtone/themes/base.css';
import 'videojs-overtone/themes/clean.css';

videojs('my-video').overtone({ src: 'talk.overtone.json' });

Most captions are cut once, for one screen, by filling each line to a length limit. The 16:9 cut is too wide for a 9:16 phone video, lines end on "the" or "of", and two speakers share a caption.

Overtone keeps a cut per screen shape in one file. The cuts follow reading rules: lines avoid ending on weak words, names and numbers stay together, each speaker gets their own caption, and pauses become line or caption breaks, so viewers who cannot hear a hesitation can read it.

The same words and times from a NASA interview, cut for 9:16 (22 characters × 2 lines):

the 2004 class. Jim,
thank you for joining
us. It's great to be
here, Allard. Thanks.

Jim, thank you
for joining us.
It's great to be here,
Allard. Thanks.

through you? Okay. A
little about me: I
grew up in Eugene,
Oregon. I joined the

Okay.
A little about me:
I grew up
in Eugene, Oregon.
I joined
the Astronaut Corps

Why a new format instead of WebVTT or TTML: those store finished cues, so a player can only rewrap them. .overtone.json stores each word once, plus any number of cuts that point to those words. Export WebVTT or SRT per cut when you need a standard file.

Why not WhisperX, stable-ts or a hosted speech API: they give good words and times, often cheaper. Overtone adds the caption layout on top. overtone recut re-cuts their SRT or WebVTT output, so you can keep your engine.

• Hearings. Several engines hear the audio. The text hearing (gpt-transcribe) gives the words; the timing hearing (Azure AI Speech) gives word times and speakers. With ELEVENLABS_API_KEY set, ElevenLabs Scribe adds a second hearing.
• Text vote. Where the hearings disagree, the text model re-listens to the snippet and an LLM chooser picks the words.
• Editor. An LLM corrects misheard words, names and terms using your context, your glossary and its own world knowledge (it fixes "Telecom Cell" to "Telkomsel" without being told), labels speakers and marks key words. Edits it is unsure about go on a review list for you to check by ear.
• Line cuts. A versioned rule set cuts the lines for each profile. Same words and rule set, same captions.

The LLM never sets timestamps: each word takes its time from the engines. Code checks only the structure of the LLM's changes. How the pipeline works has every step.

See every style pack in the style gallery.

Small and public. Method, full tables and caveats: bench/RESULTS.md and bench/captions/RESULTS.md.

• Line breaks (three English clips, 20.8 minutes, 16:9, rule set 1): 0.3% of Overtone's breaks end on a word like "the", "of" or "and" with no pause after it. Filling lines to the limit does that at 28.8% of its breaks, ElevenLabs' SRT export at 16.1%. Overtone shows all 47 of the speakers' 0.5–1.5 s pauses as a break; the other two show 28% and 72%.
• Text (six clips in four languages, 20 minutes, 3 runs, 2026-10-23): on the 5 clips where every engine returned text, word error rate 5.3%, against 6.2% for gpt-transcribe alone and 7.7% for Azure Speech alone.
• Silent failures: gpt-transcribe returned nothing for a quiet meeting in every run. Overtone retried with the language the other engines heard and scored 32.8%, against 42.9% for Azure Speech alone.
• Cost: $0.023–0.025 per audio minute in those runs, $0.029 with ElevenLabs Scribe (list prices).

• Full line-cutting and number rules exist for English and Dutch. German, French, Spanish, Italian and Portuguese have language detection; other languages get neutral rules. See Language.
• In scripts without spaces (Chinese, Japanese, Thai), a "word" is a whole phrase: one time per phrase, and line breaks only between phrases.
• No translation, no sound tags such as [music], no GUI editor. Media up to 2 hours.

MIT, see LICENSE. Made by Lont.