Subtitles that sync on the first try:
transcribed, timed, rendered
Burning accurate subtitles onto a video by hand means transcribing, timing every word, and nudging captions until they stop drifting from the voice. An agent transcribes locally with word-level timestamps and renders the captions straight into the final cut.
The process today
Captioning a video by hand means transcribing the audio, splitting it into caption-length chunks, timing each chunk against the voice, and then nudging that timing again once a cut or a cross-fade shifts the audio by half a second. Doing this for one video is tedious but manageable. Doing it for the volume of short-form content a brand needs to stay visible is where it breaks down, because the timing work does not get faster with practice the way writing does.
Teams usually find that captions are either the first thing cut under deadline pressure, which costs reach on platforms that favor watch time with sound off, or they ship with timing drift that makes the video look unpolished even when the content underneath is strong. A karaoke-style caption, where each word highlights as it is spoken, raises the bar further: that effect is close to impossible to time by hand at any volume, which is why most teams that want it either pay for an expensive third-party tool per video, outsource the timing to a freelancer, or skip the effect entirely and settle for plain static captions instead.
A pipeline built for exactly this problem shows what the automated version looks like end to end: a Telegram bot collects the photos and video, speech gets transcribed locally with word-level timestamps rather than sent to an external transcription API, a vision model detects scene boundaries, an AI director writes a JSON timeline, and ffmpeg renders the final cut with subtitles and music ducking already applied.
What the agent does
The agent takes raw footage, transcribes the speech locally with word-level timestamps so every word has its own precise start and end time, not just the sentence it belongs to. That level of precision is what makes karaoke-style captions, where each word highlights as it is spoken, possible to render automatically instead of hand-timed.
Captions are placed scene-aware, so the rendering step checks where the subject or product is in frame and keeps text out of the way rather than overlaying it in a fixed position regardless of what is happening on screen. The same transcript that drives the captions exports as a plain text transcript, which can be repurposed directly into a blog post or podcast show notes without retranscribing anything.
Rendering happens through ffmpeg as part of the same pipeline that handles scene detection and timeline assembly, so subtitles are burned into the final export alongside music ducking and any other audio treatment, rather than added as a separate pass after editing is already done.
What stays with humans
A human still reviews caption accuracy before a video with burned-in text goes live, since a misheard word or name is easier to catch by ear than to catch automatically. Style choices, karaoke highlighting versus plain captions, font and placement, are set by the team, not defaulted by the model.
Guards
New footage types run a dry run against 5-10 past videos before go-live, so the team can check transcription accuracy on their actual speakers and audio conditions. Transcription runs locally rather than through an external API, every rendered file is logged against its source footage, and a kill switch stops the pipeline without losing any already-transcribed audio.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $450 | One caption style and one language, transcription through render | 3 to 5 days |
| Department package | from $2,500 | Subtitles plus video script drafting plus podcast show notes for one content team | 2 to 4 weeks |
Running cost is usually $15 to $50 a month in compute depending on footage volume, with a budget cap set before launch.
Related
Subtitle generation sits right after video script drafting in most content pipelines, and the same transcript output feeds directly into podcast show notes for teams running both formats. See automation everything and AI agents for the broader pipeline approach. The full transcription-to-render pipeline is detailed in AI Reels editor on Telegram.
If subtitles are the step that keeps getting skipped under deadline, get in touch and we will scope a pipeline for your footage.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does subtitle and transcript automation cost?
From $450 for one caption style and one language, live in 3 to 5 days. Multi-language pipelines or custom caption animation styles usually run $1,000 to $2,500.
How long before it is live?
3 to 5 days once we have sample footage and your preferred caption style, karaoke-word highlighting or standard line captions. Most of the time goes into tuning placement so captions never cover a face or a key product shot.
Which tools does it connect to?
The transcription and rendering run through a local pipeline using ffmpeg, so there is no dependency on a third-party transcription API for the core flow. Output drops into Telegram, a shared drive or directly into your editing tool's folder.
What happens if the transcription gets a word wrong?
Transcripts and captions are generated with word-level timestamps attached, so a human reviewer can spot and fix a misheard word in seconds rather than re-timing a whole line. Review happens before any video with burned-in captions goes live.
Is our footage and audio data safe?
Speech is transcribed locally rather than sent to a third-party transcription service, raw footage stays in your own storage, and every processed file is logged so you know what ran and when.