Media & Entertainment · Subtitle localization automation
Banao builds subtitle localization pipelines that run speech-to-text, machine translation, and frame-accurate timing correction in sequence — cutting the per-language turnaround from days to hours without removing your linguists from the loop.
The pipeline wires into your MAM or CMS and delivers reviewer-ready subtitle files in your target formats. Linguists approve and correct; the model learns from every correction.
A national broadcast network— subtitle drafting pipeline deployed across regional language feeds, with a linguist review step before air.
Subtitle automation is not a single model. It is a chain — transcript, translation, timing, review, delivery — and the weakest link determines throughput.
Transcription models tuned to your accent, genre, and terminology — sports commentary, legal proceedings, news — not a generic model applied to every feed regardless of content type.
Translation tuned to your style guide and prohibited term list, so the output lands inside your brand register and needs a linguist review, not a full rewrite.
Subtitle blocks segmented to reading-speed rules and timed to the frame, so the reviewer spends time on language quality, not on re-timing every card.
A structured approval step where human reviewers correct and confirm — not an optional add-on. Corrections feed back into the translation memory for every subsequent title.
Deliverables in SRT, VTT, TTML, or your broadcast format, pushed directly into your asset management or content system — no manual file transfer between pipeline steps.
Every subtitle file is scored against confidence thresholds. Low-confidence segments are flagged to the review queue first, so linguists spend time where the model is least certain.
Numbers shown dotted (··) are being finalised in our case-study pack. The work is live; we publish metrics only once verified.
A national broadcast network
Every regional feed required separate manual transcription, translation, and timing correction. Banao deployed a pipeline that generates reviewer-ready subtitle files per language, with a structured approval step before air — reducing the volume the review team processes each week.
Banao runs a ~300-person engineering company on its own AI products. InterviewGod screens our own hires before any client's candidate goes through it. Vikaas runs our own demand-gen pipeline end to end.
A localization pipeline that has to survive week-on-week internal use — tight deadlines, mixed source quality, multiple reviewers — is already hardened before it handles your catalogue.
We would rather tell you before the build than after the invoice:
Subtitle automation has a measurable cost at every stage: transcription time, translation cost per word, timing hours per episode. We quantify yours before quoting a build.
We audit a sample of your content — mixed quality, your hardest language pairs — and hand back baseline accuracy numbers, a cost-per-title estimate, and a go/no-go on each component. Yours to keep either way. If you proceed, the Sprint cost is credited against the build.
Data pipeline first: ingest, format normalisation, and rights handling. Then the transcription and translation models, timing logic, and review workflow — wired into your MAM or CMS.
Deployed pipeline with linguist dashboard, quality scoring, and exception routing. Linguist corrections feed back into translation memory every week, so quality improves with catalogue volume.
Which languages can the pipeline support?
Coverage depends on your source language and target pairs. For mainstream European and South Asian languages, off-the-shelf models provide a strong starting point that domain tuning improves. For lower-resource languages, the Discovery Sprint establishes whether the available base is sufficient or whether additional training data is needed first.
Does AI replace our linguists?
No. The pipeline generates a reviewer-ready draft — transcript, translation, and timed cards — and routes it to a linguist for approval and correction. The model handles the repetitive first pass; the linguist handles the language quality call. Reviewer throughput goes up; the editorial standard stays with people.
How does it handle strong accents or background noise?
Domain-tuned speech-to-text handles accent variation better than a generic model, but heavy background noise, overlapping dialogue, or no clean audio mix reduces accuracy significantly. The Discovery Sprint tests your actual source audio to establish a realistic baseline — if the audio needs remediation, we say so before any model work begins.
What subtitle formats does it deliver?
SRT, VTT, TTML, and broadcast-specific formats including EBU STL and PAC. Files are delivered directly into your MAM or CMS via API, not as a manual file drop. If your system requires a format not listed, the integration stage covers it.
How long does a full pipeline deployment take?
From Discovery Sprint through production for a single language pair: typically eight to twelve weeks, depending on MAM complexity and how many approval steps your workflow requires. Multi-language deployments run in parallel once the core pipeline is stable.
Bring a sample of your hardest content — mixed accents, noisy audio, technical vocabulary. In 45 minutes we will tell you what the pipeline can do with it and what it would cost to build.
Book a 45-min scoping call
