Four moves from raw audio to a global release.
No tool-juggling, no manual handoffs. Every step picks up exactly where the last one left off — and every stage inherits the timecodes from the one before it, so nothing drifts between languages.
Drop your video — any format, any source.
Camera footage, podcast feed, or edit-suite export: everything works. The file is the only thing the pipeline needs to start.
If you already have a transcript or a translation, upload it alongside your media. The pipeline skips the stage you've already done and the credit cost drops automatically, so you only pay for the work that's actually left. This is the single biggest lever on your bill — uploading both a transcript and a translation cuts the dubbing rate by up to 65%.
Audio becomes structured, editable text.
Expect 90–95% accuracy in primary languages — English, Spanish, French, German, Portuguese — with speaker identification built in. Other languages vary more, but still give you a working starting point rather than a blank page.
This is the stage that makes the rest of the pipeline hold together. The transcript becomes your master timeline, and every downstream step inherits its timecodes automatically. That's why dubs land in sync without anyone re-aligning them by hand. Before anything moves forward you can edit segments, split them, adjust timecodes, and assign or create speakers.
Your tone, across 80+ languages.
Pick a Translation Style and a Content Type — Cinema versus YouTube, Legal versus Dynamic — and the engine adapts voice, rhythm and terminology, not just words. A documentary and a gaming channel should not sound the same in Japanese, and they don't.
Context-aware adaptation reaches up to 90%, which mostly matters because it makes proofreading fast: you're correcting nuance, not rewriting meaning. Export as DOC, PDF, SRT or CSV to drop straight into any production workflow.
AI dubbing, frame-perfect delivery.
Generate dubs in Standard or Deluxe — Standard for high-volume work where reliability matters most, Deluxe for audiences where premium voice quality isn't optional. Deliver final audio stems, or export video for QA review first.
Need the dub to sound like a specific person? Voice Cloning is available as an add-on at 8 credits per minute: upload an authorized voice sample once and every dub matches that tone across every language in the project.
What each stage costs
Credits per minute of content. One balance covers every service — you're not buying four subscriptions. Uploading your own source files lowers the rate on the stages that come after them.
| Service | Full package | With your files |
|---|---|---|
| Transcription | 8 | — |
| Translation Deluxe | 18 | 14−22% |
| AI Dubbing · Standard | 25 | 10−60% |
| AI Dubbing · Deluxe | 37 | 20−46% |
| Voice Cloning add-on | 8 | — |
Where the money actually goes
Every stage is priced per minute out of one balance, and two of them get cheaper the moment you bring your own work:
| Stage | From a raw file | With your own files |
|---|---|---|
| Transcription | 7 cr/min | — |
| Translation Deluxe | 18 cr/min | 14 with your transcript |
| AI Dubbing Standard | 25 cr/min | 10 with transcript + translation |
| AI Dubbing Deluxe | 37 cr/min | 20 on the same basis |
| Voice cloning | adds 8 cr/min | |
Uploading a transcript and a translation as a CSV is the single biggest lever on the bill — it cuts the dubbing rate by 60%. The platform charges less for the work you already did.
And the second language is not a second project. Transcription and the master timeline are done; another market costs the dubbing pass again, over the same timecodes.
Why the pipeline stops between stages
The expensive mistake in dubbing is not the rate per minute. It is the regeneration.
An error in the transcript propagates into the translation and hardens into the voice. Catch it at the end and you pay to generate all three stages again. It was free to fix at step one and expensive at step three.
So the pipeline stops. After transcription you review the segments — text, speakers, timecodes — and correct them while correcting them costs nothing. Only then does translation run, on a transcript you approved. The translation stops the same way, before a single credit goes on audio.
It is also why overlapping speech is kept as separate lines rather than flattened into a queue: repairing cross-talk after the fact is the other way projects lose an afternoon.
Where to go from here
Every language and accent in the library is listed in the language index — nine Spanish accents, thirteen Arabic dialects, and Indian, Australian, Canadian, South African and Yorkshire inside English. The case-study data covers what getting the accent right does to watch time, and the YouTube guide covers multi-language audio specifically.
Comparing platforms? The side-by-sides are honest about where the other one wins: vs HeyGen, vs ElevenLabs, vs DittoDub, vs Maestra, vs Dubverse, vs CAMB.AI and vs Rask AI — or all of them at once.
What the platform needs from you, and what it does not
The shortest version: a video or audio file. No script, no transcript, no glossary, no prepared timings. Everything downstream is derived from the file itself.
What you can hand over changes the price rather than the outcome. A transcript you already have drops translation from 18 credits a minute to 14. A transcript and a translation drop Standard dubbing from 25 to 10 — a 60% cut, because you did the two stages the platform would otherwise have to. Uploading a CSV with both is the single biggest lever on a bill.
What the platform does not need is a clean recording studio. Background music, room tone, overlapping speakers and mixed languages in one file are the normal case, not the exception — which is why speaker identification and the music-and-effects split happen before anything is translated rather than after.
The artefacts, and which ones you can take elsewhere
A finished job is not one file. It is a set, and all of it is yours to export:
- The dubbed audio, mastered to a broadcast loudness target rather than left at whatever level the generator produced.
- The mixed video, with your original music and effects underneath the new voice instead of replaced by it.
- The transcript, segmented with speakers and timecodes.
- The translation, aligned to the same segments.
- Subtitle files in both languages, carrying the timing and reading-speed conventions captions are supposed to follow — ready to upload to YouTube without re-timing.
That last one matters more than it looks. Because the transcript and translation already exist by the time audio is generated, subtitles are a by-product rather than a second project — and they serve the viewer who never turns the sound on, plus the search index, which reads text and not audio.
Three decisions the platform will not make for you
Which variety of the language. Spanish is not one choice and neither is Arabic, Portuguese, Chinese or English. A Castilian track in Bogotá and a Mandarin track in Hong Kong are the same category of mistake, and no amount of audio quality repairs it. The language index lays out what each language splits into.
Formal or informal. Most languages encode the relationship between speaker and listener in the grammar — tu or vous, du or Sie, tum or aap. Left undecided, the translation picks one and keeps it for the whole video.
How much English stays. In South Asian languages especially, everyday speech mixes English constantly. Translating every term out produces something correct on paper and stilted in the ear.
All three are glossary decisions, and all three are free to make at the translation review and expensive to discover after the voices exist. That is the whole reason the pipeline stops between stages instead of running end to end and presenting a bill.
Run the whole pipeline in one session.
Transcription, translation and dubbing on a single timeline, with one credit balance across all of them.
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