AI Video & Voice Production: ElevenLabs, Veo, Runway and MoreAI voice: text-to-speech, cloning and dubbing · Lesson 3 of 16
AI dubbing and translation for new markets
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AI dubbing and translation for new markets
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0:00 Dubbing and translation
You've made a great video in English. Now imagine the same video, in your presenter's voice, speaking Arabic for Riyadh and Urdu for Lahore, ready by tomorrow. That's what AI dubbing promises. In this lecture you'll learn how dubbing works step by step, where it shines and where it breaks, and the one workflow change that makes the difference between a dub that embarrasses you and one that sells.
0:30 Why it matters
Why should you care? Because language is often the biggest barrier between your content and a new market. For creators and brands serving Pakistan, the Gulf, the UK, the US and diaspora audiences, dubbing used to mean hiring voice actors per language. That was too expensive for most small teams. Now the cost of a first draft is close to zero. But a bad dub can cost you more in trust than it saves in money, so the process matters.
1:05 The dubbing assembly line
Here's how dubbing works under the hood. Think of it as an assembly line with five stations. First, transcription turns speech into text with timings. Second, separation pulls voices apart from music and effects and identifies each speaker. Third, translation. Fourth, voice generation, often in a voice matching the original speaker. Fifth, timing and mixing puts the new audio back under the video. Some tools add a sixth station, lip-sync. Every station can introduce an error, and errors compound down the line.
1:41 Translation versus localization
And here's the key idea of this lecture. Translation changes the words. Localization adapts the message. A literal translation of a Punjabi joke will not land in Gulf Arabic. Ounces need to become grams. A reference to popping to the supermarket needs local phrasing. And Arabic isn't one market: Gulf, Egyptian, Levantine and Modern Standard Arabic suit different audiences. So the rule is: adapt the script first, with a native reviewer, then generate the audio. Fixing text is cheap. Fixing audio is not.
2:17 Automatic versus studio
In ElevenLabs, these stations come in two flavors. Automatic dubbing runs the whole line and hands you a finished file. Dubbing Studio stops before the end and gives you an editor. You can correct the transcript and the translation line by line, reassign voices when the speaker detection gets confused, and regenerate a single segment instead of the whole video. For anything customer-facing, the studio is where quality comes from. Automatic is fine for a quick internal preview.
2:51 Example 1: a coach's tip video
First example, simple. A fitness coach dubs a two-minute English tip video into Spanish using the automatic mode. The draft is decent, but it translates her catchphrase literally and it sounds odd. In the editor she marks the catchphrase as keep in English, regenerates just that segment, and asks a Spanish-speaking client to watch it once. Total extra time, maybe fifteen minutes. The lesson: always use the step where you can edit the translated transcript before or after generation.
3:25 Example 2: UK halal food brand
Second example, a real business case. A UK halal food brand wants its recipe videos in Arabic for Saudi Arabia and the UAE, and in Urdu for Pakistan. AI drafts the adapted scripts. Native reviewers swap ingredient names for local ones, convert measurements, and fix phrasing. The presenter has signed consent covering these languages, so the dub uses her voice. Lip-sync is applied only to close-up talking shots. Reviewers catch two mispronounced dish names, fixed with the pronunciation tool. The description says: AI-dubbed from English with the presenter's permission.
4:04 Measuring a dub
How did they know it worked? They didn't stop at publishing. They compared completion rates for the Arabic and Urdu versions against the English original, and read comments for complaints about wording or pronunciation. Illustratively, if the Urdu version holds viewers about as well as English, the localization is doing its job. If viewers drop off at the same point in every dubbed version, it's probably a script problem, not a language one. That comparison turns dubbing from a gamble into a measured experiment.
4:41 Watch me do it
Watch me do it with the API, for a weekly series. In the lesson's script, I open the video file and call the dubbing create function with a project name, source language English, target language Arabic, and speakers set to zero so it detects them automatically. The important flag is dubbing studio set to true. That means the result lands in an editable project, not a finished file. Then I poll the status every twenty seconds until it says dubbed. Finally, I send the project link to my reviewer, not to the client.
5:21 Review in the studio
Once the job finishes, the human part begins. My reviewer opens the studio project, watches it through once without stopping, and notes timestamps where anything sounds off. Then they fix the words: a product name that should stay in English, a phrase that's too formal. I regenerate only those segments, and export. Finally, I transcribe the Arabic audio for captions, so the subtitles match the dub, not the English script. Total human time is a fraction of the video length times two. That's the right place to spend it.
6:00 Common mistakes
Common mistakes. Publishing without a native-speaker review. Assuming one Arabic version fits every Arabic-speaking market. Forgetting that on-screen text, captions and thumbnails are still in the original language. And dubbing someone else's video, or a person in their own voice, without consent. A dub makes a real person appear to speak a language they may not speak, so disclosure is wise, and on some platforms it's required.
6:29 Recap
Recap. Dubbing is a chain of transcription, separation, translation, voice generation and mixing, so errors compound. Localize the script with a native reviewer before generating audio. Use the editable studio step, fix segments, not whole videos. Get consent for voices you dub, and disclose. Caption from the dubbed audio, not the original script. And measure it. Compare completion rates across language versions, so you learn whether the localization is working or whether the script itself needs fixing.
7:02 Try this now
Try this now. Pick one of your videos and one target market. Fill in the localization brief from the lesson: dialect, units, references to swap, terms to keep in English, and who reviews. Only after that, run a dub, and time how long the review takes. Write down every change your reviewer makes. After three videos you'll have a keep-in-English list and a style note for that market, and each dub after that gets faster and better. That's how a one-off experiment becomes a repeatable localization workflow.
One video, many languages
AI dubbing lets you take a video in one language and produce versions in others, often keeping the original speaker's voice characteristics. Some tools also adjust lip movements to match the new language (lip-sync). For creators and brands serving Pakistan, the Gulf, the UK, the US and diaspora audiences, this can open markets that were previously too expensive to reach.
How AI dubbing works (conceptually)
- Transcription: speech is converted into text with timings.
- Speaker separation: voices are separated from music and effects, and individual speakers are identified.
- Translation: text is translated into the target language.
- Voice generation: the translated text is spoken, often in a voice matching the original speaker.
- Timing and mixing: new audio is aligned to the video and mixed with the original background audio.
- Optional lip-sync: video tools such as HeyGen's video translation can adjust mouth movements to match the new audio.
In ElevenLabs these steps are packaged as Dubbing (automatic) and Dubbing Studio (an editor where you can correct the transcript and translation line by line, reassign voices and regenerate individual segments before export).
Each step can introduce errors, which is why human review matters.
Where AI dubbing works well
- Educational and explainer content with a clear single speaker.
- Product demos and tutorials.
- Talking-head content with clean audio.
- Internal training and onboarding.
Where it struggles
- Humor, idioms and wordplay: a literal translation of a Punjabi joke will not land in Gulf Arabic.
- Cultural references: festivals, sports and local brands may need adapting, not translating.
- Overlapping speakers and noisy audio.
- Dialects: "Arabic" is not one market. Gulf, Egyptian, Levantine and Modern Standard Arabic suit different audiences and contexts. The same applies to Urdu versus Hindi, and British versus American English.
- Length differences: some languages need more words for the same idea, so speech may be sped up or timings may drift.
Translation versus localization
Translation changes the words. Localization adapts the message for a market: examples, measurements, currency, humor, formality, visuals and calls to action. The best workflow is:
- Adapt the script for the target market first (with AI help and a native reviewer).
- Then generate the dubbed audio.
- Then review the full video with a native speaker.
Many dubbing tools let you edit the translated transcript before generating audio. Use that step; it is far cheaper than fixing audio afterwards.
Rights and consent
- Dubbing someone else's video requires the rights to that content, and dubbing a person in their own voice into another language also involves their likeness and voice. Get consent.
- Your own content: you may still need to consider music licenses, which can be territory-specific, and on-screen text.
- Disclosure: if a video makes a real person appear to speak a language they do not speak, consider telling the audience it has been AI-dubbed. Some platforms require labeling of realistic synthetic content (see Module 5).
Worked example
A UK-based halal food brand wants its English recipe videos in Arabic for KSA and UAE and in Urdu for Pakistan.
- Script adaptation: AI drafts Arabic and Urdu versions; native reviewers adjust ingredient names to local ones, swap measurements from ounces to grams, and change "pop to your local supermarket" to phrasing that fits each market.
- Dubbing: audio is generated in the presenter's voice (with her written consent covering these languages).
- Lip-sync: applied for close-up talking segments.
- Review: native speakers watch the full videos, flag two mispronounced dish names, and these are fixed via the pronunciation tool.
- Label: description notes "AI-dubbed from English with the presenter's permission".
Hands-on: automate a dub, then review it
For a recurring series, the API saves clicks. This script submits a video for dubbing into Arabic and polls until it finishes. It uses the official SDK; parameter names are from the current reference, so re-check them if the SDK changes.
import os, time
from elevenlabs.client import ElevenLabs
client = ElevenLabs(api_key=os.environ["ELEVENLABS_API_KEY"])
with open("recipe_ep12_en.mp4", "rb") as video:
job = client.dubbing.create(
file=video,
name="Recipe EP12 - AR",
source_lang="en",
target_lang="ar",
num_speakers=0, # 0 = detect automatically
dubbing_studio=True, # keep it editable for human review
)
dub_id = job.dubbing_id
while True:
meta = client.dubbing.get(dubbing_id=dub_id)
if meta.status in ("dubbed", "failed"):
break
time.sleep(20)
print("status:", meta.status)
# Next: open the project in Dubbing Studio, have a native reviewer fix terms,
# regenerate changed segments, then export.Notice dubbing_studio=True: the job lands in an editable project instead of a finished file, which keeps the native-speaker review in the loop.
Localization brief template
Market / dialect: KSA (Gulf Arabic, formal-friendly)
Units and currency: grams, SAR
References to swap: "pop to the supermarket" -> local phrasing; UK brands -> generic
Terms to keep in English: brand name, product names
Tone: warm, respectful; avoid slang
On-screen text: re-typeset in Arabic (right-to-left)
Reviewer: Name, deadline
Disclosure line: "AI-dubbed from English with the presenter's permission."Second worked example: a SaaS onboarding series
A Lahore-based SaaS company dubs twelve onboarding videos from English into Urdu and Arabic. The first automated pass mistranslates two feature names and uses formal Arabic that feels stiff for small-business owners. The team adds the feature names to a keep-in-English list, asks the reviewer for a friendlier register, regenerates only the affected segments in Dubbing Studio, and tracks completion rates per language afterwards to see whether the localized versions are actually watched.
Pitfalls
- Publishing without a native-speaker review.
- Assuming a single "Arabic" version fits all Arabic-speaking markets.
- Forgetting on-screen text, captions and thumbnails still in the original language.
Key takeaways
- AI dubbing chains transcription, translation, voice generation and mixing, with optional lip-sync.
- It works best for clear single-speaker explainers and struggles with humor, dialects and noisy audio.
- Localize the script with a native reviewer before generating audio.
- Dubbing a person in their own voice needs their consent, and disclosure is often wise or required.
Check your understanding
Quick questions to lock in the lesson. They don’t count towards your certificate.
Put it into practice
Pick one of your videos, write a localization checklist for one target market (language, dialect, references, units, CTA), and adapt the script before any dubbing.
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