AI video translation, explained: what you get and who it's for

A working definition of AI video translation

The term covers any workflow in which software, rather than a person, does the core language work of making a video understandable in another language. Three kinds of model usually do the heavy lifting: a speech recognition model that writes down what was said, a translation model that renders that text in the target language, and a text-to-speech model that reads the translation aloud. A person may still review the result, but the first draft of every output is machine-made.

It is not the same as translating the video file itself. The pictures, on-screen text, music and graphics are untouched by most tools; what changes is the language channel, meaning what viewers hear or read. That matters when you plan a project, because slides, lower thirds and interface screenshots stay in the original language unless you edit them separately (see translating on-screen text).

The three outputs and what each one is for

Nearly every tool in this category produces some combination of the following. They solve different problems, so decide which one you need before you start.

Translated subtitles

A timed text file, usually SRT or VTT, in the target language. The player shows it over the original picture, and the speaker's own voice stays audible.

Translated voice track

A synthetic voice reading the translation, timed to the original lines, delivered as an audio file or a new video. Viewers listen instead of reading.

Translated transcript

The full text of what was said, in the target language, often with timestamps. Used for reading, searching, quoting and review, with no playback needed.

Subtitles preserve the original performance and are cheap to produce. Voice tracks reach people who can't or won't read along, including young children and anyone watching while their eyes are on a task. Transcripts are the working material for reviewers, researchers and anyone repurposing the content into articles or notes. If you are torn between the first two, the subtitles vs dubbing guide lays out the trade-offs.

Who uses it, and the problem it solves for them

• Creators who want an existing library to reach viewers in other languages without re-recording anything.
• Course authors and lecturers whose students study in a second language, a case covered in more depth under online course translation.
• Companies with multilingual staff translating training, onboarding and internal announcements.
• Researchers, journalists and analysts who need to understand footage in a language they don't speak, where a translated transcript is the entire goal.
• Marketing teams testing whether a market responds before paying for full localization.

The common thread is volume relative to budget. When there is more video than anyone could afford to send to a studio, automation changes the question from which single video can be translated to which videos deserve careful human review.

How it differs from a traditional studio workflow

A conventional translation of a video passes through several hands. A transcriber produces a source script and a translator adapts it. For subtitles, a subtitler spots the cues and condenses lines to readable lengths. For dubbing, an adapter rewrites the script to fit timing and mouth movements, a casting director chooses voice actors, an engineer and director run recording sessions, and a mixer rebuilds the soundtrack with the music and effects. Quality control happens at each handoff.

AI video translation collapses most of those roles into models running one after another. That removes scheduling, handoffs and per-session fees, which is where the speed and price difference comes from. It also removes the adaptation step: a human dubbing adapter rewrites for meaning, timing and culture at once, while a machine translates what was said and then fits the audio into the time available.

Studio workflow

Several specialists, booked sessions, multiple review rounds, music and effects remixed, lip-sync possible.

AI workflow

One person submits a file, models run in sequence, outputs arrive in one pass, and review is optional and up to you.

For a fuller decision framework, read AI vs human video translation.

A worked example: one video, three outputs

The point of the example is the shape of the work, not the numbers: the expensive part is no longer producing the translation, it is the half hour a fluent colleague spends checking it.

Where AI video translation works well

• Clear speech from one main speaker, with everyday or instructional vocabulary.
• Content where speed matters more than polish, such as internal updates, time-sensitive announcements and social clips.
• Large back catalogs, where a per-minute price decides what is feasible at all.
• Understanding foreign-language footage for yourself, where a translated transcript may be all you need.
• Drafting, where the machine output is a starting point that a human translator edits rather than writes from scratch.

Limitations to understand before relying on it

Errors compound through the chain. If the recognizer mishears a word, the translator faithfully translates the wrong word, and the voice reads it with full confidence. How often that happens depends on audio quality, vocabulary and the language pair; the article on AI video translation accuracy explains how to test it on your own material.

• Idioms, jokes and wordplay tend to come through literally.
• Names, brands and jargon can be misheard, or translated when they should have stayed as they were.
• Many tools read every speaker with the same synthetic voice.
• Lip movements won't match unless a tool re-renders faces, which brings its own artifacts and ethical questions.
• Background music and effects may be replaced rather than preserved, depending on the tool.

What AI video translation looks like in mydubly

mydubly is a browser-based video translator. You choose a video file from your device (MP4, MOV, WebM, MKV or M4V, up to 2 hours), pick one of 21 target languages, and the spoken language is detected automatically. The video file stays on your device; your browser extracts the audio and uploads only that.

With full translation output on, you receive a translated MP4 with a new AI voice track, the translated audio on its own, SRT and VTT subtitles, and transcripts in both languages, for 50 credits per minute. Transcript mode skips the voice and returns a timestamped transcript and subtitles, optionally translated, for 1 credit per minute. One of 8 stock voices reads the whole video. There is no voice cloning and no lip-sync, and subtitles arrive as separate files rather than burned into the picture. The original speech is removed by AI vocal separation rather than left underneath, while your music and effects stay in the mix and dip automatically while the AI voice speaks; separation isn't perfect, so faint traces of the original voice can remain in dense mixes. For audio-only recordings, the audio translator does the same job without a picture.

Where to go next

If you want to see what happens between upload and download, read how AI video translation works. If you would rather try it, run a short clip through the video translator in transcript mode first, read the translated text, and then decide whether a voice track is worth adding.