How to Dub Videos with AI

What AI Video Dubbing Actually Does

Translating a spoken track and re-recording it by hand means booking a voice actor, scheduling a session, matching takes to picture, and mixing. AI dubbing compresses that into a repeatable pipeline that runs on your own schedule.

The important thing to understand is that this is a text-first process. The system does not interpret your video directly. It converts the audio into text, works on the text, converts the text back into speech, and places the result back on the timeline. Every strength and weakness follows from that design.

It does not give you lip-sync, and it does not reinterpret performance. If a video depends on the original speaker's exact facial timing, or on singing, or on layered overlapping speech, treat those as separate problems. The video dubbing page covers the product surface itself.

The Pipeline, Step by Step

Five stages, each with a different failure mode. Knowing which stage is failing is most of the work of debugging a dub.

1. Extract the audio track with FFmpeg. The video's existing audio is pulled out as a standalone file. This is where container or codec problems surface first, and where a missing or silent track becomes obvious.
2. Transcribe with the engine's native ASR. The audio is converted to text with genuine time information attached. ASR supports 20 languages: zh, yue, en, ja, ko, th, vi, id, ms, fil, ar, tr, fr, de, es, it, pt, pl, ru, and uk.
3. Translate. The transcript becomes the target language. Sentences come back as text, ready to be checked before synthesis.
4. Synthesize new speech with TTS. The translated text is spoken by the selected voice. Output format, sample rate, and emotion are set here.
5. Replace the audio track and generate SRT subtitles. The new audio is muxed back onto the original video, and an SRT subtitle file is produced from the same transcript.

Because translation happens on text rather than audio, you get something a purely audio-based approach cannot offer: a review point between steps two and four. Reading the transcript and correcting it before synthesis is the single highest-value step in the whole process, and it is the one most often skipped.

A useful side effect: because the subtitle file comes from the same transcript, subtitles and dubbed audio cannot drift apart. That consistency is hard to achieve when the two are produced separately.

Why Timestamps Decide Sync

The distinguishing technical detail of this approach is that transcription produces real timestamps from the audio itself, rather than positions inferred from character counts.

This matters more than it sounds. A common shortcut in cheaper pipelines is to divide the duration of a video by its word count and distribute lines evenly. That produces audio that is broadly the right length but drifts steadily out of alignment with the picture, and it is unusable for anything where a speaker is visibly talking. Real per-segment timing keeps speech starting when the original speech started.

Even with true timestamps, one honest caveat: translated speech and source speech rarely have identical durations. German and Finnish typically run longer than English; Japanese and Korean often run shorter. Timing derived from the original therefore represents the correct start, not a guarantee of the correct length.

In practice you close that gap with modest per-segment adjustments in an editor, rather than trying to force the translation into the original timing by compressing words until they are unnatural.

Language Support and Its Real Limit

Two different language counts appear in this product, and confusing them leads to the most common wrong assumption about video dubbing.

Cantonese (yue) and Thai are included in recognition, which is not always expected. Regional variants are supported as first-class entries.

If your source language falls outside the list, the workaround is to produce the transcript elsewhere and bring it in as text, then synthesize the new language from that transcript. You lose automatic timestamp extraction, so alignment becomes manual.

Preparing Your Source Material

Most dubbing problems are present in the source before the pipeline starts. A short preparation pass removes most of them.

Audio conditions

• Confirm the track is not silent and is not offset from the video.
• Reduce background music where possible. Music under speech is transcribed unreliably, and the error compounds at every later step.
• Watch for overlapping speakers. Two people talking at once produces a transcript that is not fixable by editing.
• Normalize levels so quiet passages are audible to the recognizer.

Text conditions

• Build a list of proper nouns: brand names, product names, people, places. These are where recognition fails first, and a wrong brand name in a subtitle is worse than a slightly late line.
• Decide what happens to on-screen text that assumes the original language.
• Note any wordplay, puns, or idiom that will not survive literal translation, and rewrite rather than translate.

Scope

Longer videos take proportionally longer, and every segment is a place a translation can land badly. Start with one representative video rather than a queue.

Upload, Process, Download

With the source prepared, the sequence in the product is short. The value is in knowing what to check between each step rather than in the steps themselves.

Seven output formats are available: mp3, pcm, flac, wav, pcmu_raw, pcmu_wav, and opus. For video work, wav is the sensible choice for an editing master. Sample rates available are 8000, 16000, 22050, 24000, 32000, and 44100 Hz, with the engine's ceiling at 32 kHz, which is the default and matches measured output of 32 kHz mono. Detail on both is in the voice selection guide.

If you also want a TTS voice for your own script rather than a dub of existing footage, that is a different job. The text-to-speech workspace handles it directly, with a 10,000 character limit per generation.

Common Problems and What Causes Them

The pattern in that table is worth naming: nearly every bad output traces to a text or source problem two stages upstream, not to the synthesis stage. When a dub sounds wrong, check the transcript before you change the voice.

If you need the new language in many segments at once rather than as one dub, the batch text-to-speech workflow is the more direct route.

Honest Limits of the Process

Anyone claiming AI dubbing handles every video perfectly is describing something that does not exist. Here is what to expect.

• Source language must be within the 20 recognized languages. This is the hard gate. Outside it, there is no automatic transcript.
• No lip-sync. The new audio is placed correctly in time, but mouth movements from the original footage are unchanged. Dubbing works best for narration, interviews with off-camera speakers, screen recordings, and animation, and least well for tight close-up dialogue.
• Duration differences remain. Real timestamps give correct starts, not correct lengths, for every language pair.
• Overlapping speech is unreliable. Recognition assumes separable speakers.
• Music and sound effects are not localized. The audio track is replaced; embedded non-speech elements are a separate mix decision.
• Context is limited to the audio. Transcription sees sound, not picture, so sarcasm, tone-dependent meaning, and on-screen information are not available to it.
• Long videos take time. Processing scales with duration, and review time grows faster than the video does.

None of this makes the approach impractical. It makes it predictable, which is more useful. You can assess a candidate video against the language list and the shot types in about a minute and know what you are getting into.

Volume limits apply per plan. The free tier includes 120 characters per generation and 600 seconds of audio per month; Pro at $24.99 includes 1,000 characters per generation and 3,000 seconds per month; Enterprise at $99.99 includes 4,000 characters per generation and 12,000 seconds per month. Full details are on the pricing page, and common questions are answered in the FAQ.