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.
Why hand-timed captions do not scale
Captioning a video by hand means transcribing the audio, splitting it into caption-length chunks, and timing each chunk against the voice. Then a cut or a cross-fade shifts the audio by half a second, and the timing needs nudging again. Doing this once is tedious but manageable. A brand posting short-form content at real volume is where it breaks down, because the timing work does not get faster with practice the way writing does.
Captions end up as 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 an otherwise strong video look unpolished. A karaoke-style caption, each word highlighting as it is spoken, raises the bar further. That effect is close to impossible to time by hand at any volume. Most teams that want it pay for an expensive third-party tool per video, outsource the timing to a freelancer, or settle for plain static captions instead.
We built a pipeline for exactly this problem, and it 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 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 transcribes and renders
The agent takes raw footage and transcribes the speech locally with word-level timestamps. Every word gets its own precise start and end time, not just the sentence it belongs to. That precision is what makes karaoke-style captions possible to render automatically instead of hand-timed.
Captions are placed scene-aware. The rendering step checks where the subject or product sits in frame and keeps text out of the way. It never just overlays text in a fixed spot no matter what is on screen. The same transcript that drives the captions also exports as a plain text file. It is ready to drop straight into a blog post or podcast show notes, no retranscribing needed.
Rendering happens through ffmpeg, as part of the same pipeline that handles scene detection and timeline assembly. Subtitles get burned into the final export alongside music ducking and any other audio treatment, instead of arriving 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. 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. That lets the team 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 the AI Reels editor on Telegram case study.
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 only 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?
Transcription and rendering run through a local pipeline using ffmpeg, so the core flow has no dependency on a third-party transcription API. 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 carry word-level timestamps, so a human reviewer can spot and fix a misheard word in seconds instead of 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.