Replace a video’s spoken audio with natural AI voiceover in 20+ languages. UnmarkAI first removes burned-in hardsubs and on-screen text, then helps transcribe, translate, generate approved voice clones or narrator voices, and align lip-sync timing so every localized export starts from a clean master.
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Use this page for voiceover, voice cloning, and speaker review. For the broader subtitle cleanup, translation, dubbing, and localization workflow, start from the AI Video Translation hub.
AI video dubbing replaces or layers over a video’s original spoken audio with a translated voice track generated by AI. It pairs naturally with broader dubbing and localization: detect the speakers, transcribe the dialogue, translate for spoken delivery, synthesize multilingual voiceover, and align the new audio to scene timing and lip movement so the localized video sounds native.
Voiceover in the viewer’s own language lets audiences hear the content instead of reading every line. Dubbing works best from a clean visual base: if burned-in source captions remain, the viewer sees one language while hearing another, which weakens trust and makes the localized cut feel unfinished.
AI voice dubbing transcribes the original audio, translates and adapts the transcript with a shared term glossary, generates target-language voiceover using neural text-to-speech or approved voice cloning, then aligns the dubbed track to scene cuts, speaker turns, and lip movement. The localized video keeps the original pacing and emotion without re-recording on set.
Voice cloning is legal for content you own or have clear rights to dub, and when you have permission to use the speaker’s voice where required. UnmarkAI must not be used to clone someone’s voice without consent, impersonate others, or dub content you lack the rights to — for third-party talent or licensed footage, confirm the dubbing and voice rights in your agreement first.
Most AI dubbing tools start with the audio track. UnmarkAI starts one step earlier: remove burned-in subtitles and on-screen text from authorized footage so the translated voice, captions, and market-specific overlays do not conflict with the old frame.
Use UnmarkAI when the goal is a repeatable AI dubbing pipeline for global audiences, not a one-off robotic voiceover pasted over the original audio. The workflow accounts for clean frames, speaker identity, script review, target-language timing, and final export QA.
Remove burned-in hardsubs, source-language captions, title labels, and on-screen text from videos you own or are authorized to dub. A clean master gives every target language room for new captions, voice cues, and market-specific edits without stale text fighting the new audio.
Turn the original audio into a speaker-aware transcript, then translate it with a shared glossary. Review names, product terms, jokes, calls to action, and cultural references before voice generation so the dub sounds intentional instead of literal.
Create AI voiceover that matches the original emotion, pacing, and character tone. Clone an approved speaker voice for continuity, assign different voices to multiple speakers, or choose a narrator voice when a neutral localized delivery is better for the channel.
Align dubbed audio to scene cuts, visible speech, caption timing, and lip movement per language. QA each export for drift, pronunciation, speaker assignment, clean frames, and file naming before the localized version ships.
Match the dubbing mode to each market by content type, consent, budget, and viewing habit. Clone approved original speakers, preserve multiple speakers, pick a neutral narrator, or ship dubbing plus subtitles for maximum reach.
Reproduce the original speaker’s voice in another language so the same person seems to speak every target language — ideal when vocal identity is part of the performance, for content you own or are authorized to dub.
Detect and review who is speaking, then keep each speaker distinct in the target language. This matters for interviews, podcasts, courses, testimonials, and short dramas where audience trust depends on clear speaker identity.
Pick a natural, cast-style narrator voice for each market instead of cloning a source speaker. This is often fastest for explainers, software demos, training, and product videos where clarity matters more than performer identity.
Maximum reach and accessibility: dubbed audio for native feel plus translated subtitles for sound-off autoplay, noisy feeds, and viewers who prefer captions on the localized export.
The workflow is built for videos you own or are authorized to dub — clean the frame, transcribe and translate, generate voiceover, then sync and QA each localized export before publishing.
Start AI video dubbingStrip burned-in hardsubs, source-language captions, timestamps, and lower-third text from authorized videos so the dubbed voiceover and translated captions sit on a clean visual base.
Review the speaker transcript, adapt the translation for spoken delivery, and keep names, product terms, jokes, and calls to action consistent across every target locale.
Clone approved source voices, assign separate voices to multiple speakers, or choose market-specific narrator voices before generating the dubbed track for each language.
Align dubbed audio to scene timing and lip movement, check caption sync and pronunciation, then ship multilingual exports in one batch instead of re-recording in a dubbing studio.
From YouTube channels to e-learning, short dramas, customer support, and marketing — clean your master, generate natural voiceover, and deliver batch dubbed videos for every market.
Generate AI voiceover in the languages global audiences actually listen in. Each target locale gets consistent terminology, natural voice, and synced timing.
AI dubbing quality depends on the transcript and translation as much as the voice model. Review the script, glossary, speaker assignments, and timing before export so the final video sounds localized instead of machine-translated.
Fix recognition errors, adapt literal translations for spoken delivery, and keep key phrases consistent before generating the dub.
Use approved cloned voices or narrator voices per speaker, content type, and market instead of forcing one generic voice across the whole video.
Check scene cuts, line length, caption placement, pronunciation, and export naming before publishing each localized version.
| Workflow need | Subtitles only | Generic TTS dubbing | UnmarkAI clean-master dubbing |
|---|---|---|---|
| Remove burned-in subtitles first | ✗ No | ✗ Usually skipped | ✓ Clean master first |
| Let audiences hear the target language | ✗ Reading required | ✓ Voice track | ✓ Natural AI voiceover |
| Keep speaker identity | ✗ No audio change | ~ Generic voices | ✓ Approved cloning or narrator voices |
| Handle multiple speakers | ~ Names in text only | ~ Often manual | ✓ Speaker-aware review |
| Sync timing and lip movement | ~ Caption timing only | ~ Audio timing only | ✓ Scene, caption, and lip timing |
| Prepare batch market exports | ~ Per subtitle file | ~ One job at a time | ✓ Batch localized exports |
Run through these checks before publishing each dubbed localized export.
Lock character names, product terms, pronunciation, calls to action, and approved adaptations so every locale uses consistent wording across voiceover and subtitles.
Remove burned-in hardsubs, source-language captions, timestamps, and lower-third text first so new voiceover and captions never overlap stale on-screen text.
Confirm the dubbed voiceover matches the original speaker energy, pacing, and tone across every target language.
For interviews, podcasts, testimonials, and dramas, review speaker assignment so each person remains distinct in the target language.
Keep the dubbed audio aligned to on-screen timing, scene changes, and where supported, lip movement so the new voice never drifts.
Adjusted translations often run longer or shorter than the source; trim or rephrase so the dubbed line lands inside the scene window.
For cloned voices or third-party talent, verify you have permission to dub and use the speaker’s voice before generating each localized export.
Use a stable language-and-version naming scheme across every dubbed export so downstream channels ingest them without manual cleanup.
Check audio sync, caption alignment, clean frames, and file format per market, then publish the reviewed dubbed video to each channel.
AI video dubbing is the process of replacing a video’s original spoken audio with a translated voice track generated by artificial intelligence. Instead of only adding subtitles, AI dubbing transcribes the dialogue, translates it for spoken delivery, synthesizes multilingual voiceover, optionally preserves approved speaker voices, and aligns the new audio to scene timing and lip movement. UnmarkAI is built for videos you own or are authorized to dub: it first removes burned-in hardsubs and on-screen text, then creates a clean master for synced dubbed exports in each target language.
AI voice dubbing starts by detecting speech and creating a transcript. The transcript is translated and adapted for spoken timing, then reviewed with a shared glossary so names, product terms, and phrases stay consistent. The system generates target-language audio with neural text-to-speech, approved voice cloning, or selected narrator voices, then aligns the audio to scene cuts, speaker turns, and lip movement. The result is a localized video that sounds natural without booking a dubbing studio or re-recording on set for every market.
Voice cloning is legal when applied to content you own or have clear rights to dub, and when you have permission to use the speaker’s voice where required. UnmarkAI is built for videos you own or are authorized to edit — it must not be used to clone someone’s voice without consent, impersonate others, or dub content you do not have the rights to. For third-party talent or licensed footage, confirm the dubbing and voice rights in your agreement before generating a cloned-voice dub.
Yes. Upload a video you own or are authorized to dub, choose the target languages, and the workflow produces a dubbed, synced version for each one. Freezing a shared glossary first means character names, product terms, pronunciation, calls to action, and tone stay consistent across every localized export. Teams can ship multilingual versions in one batch instead of running a separate voiceover session for each language.
Modern AI dubbing aligns the dubbed voice track to scene cuts, on-screen timing, and — where the source supports it — lip movement, so the new audio matches the visible speech instead of drifting over long takes. Timing alignment is checked per language during QA, together with caption sync and clean frames, before each localized export is published.
Subtitles translate written text and leave the original audio untouched, which is useful for sound-off viewing but still requires reading. AI dubbing replaces or layers over the spoken audio with a translated voice track, so audiences hear the content in their own language. The two are complementary: many teams ship dubbed audio plus translated subtitles for accessibility, noisy feeds, bilingual viewers, and platforms where captions remain visible by default.
Burned-in subtitles are part of the video pixels, so they cannot be turned off like an SRT or VTT caption track. If they stay in the frame, the localized version can show one language on screen while another language plays in the voiceover. Removing source-language hardsubs first creates a clean master that can receive new translated captions, market-specific legal text, and dubbed audio without visual conflict.
AI dubbing can support multiple speakers when the workflow includes speaker detection, transcript review, and per-speaker voice assignment. For interviews, podcasts, courses, testimonials, and short dramas, each speaker should be checked before export so the target-language voices remain distinct and the viewer can still tell who is talking.
Upload a video you own or are authorized to dub, choose target languages, and start the clean-master dubbing workflow in the UnmarkAI dashboard.
Last updated: July 2026 · Reviewed by the UnmarkAI localization team
