AI dubbing and translation tools for reaching an audience that doesn't speak your language

I published a video dubbed into Spanish about a year ago, and a Spanish-speaking friend messaged me within an hour to say it was understandable, clearly machine-made, and that the word I’d used for “clip” meant something closer to “paperclip” in the context I’d used it.

That’s roughly the state of AI dubbing. Genuinely usable, occasionally embarrassing, and improving fast enough that the version of this post from two years ago would be useless now.

Why bother at all

The argument for translation is simple arithmetic. Most of the world doesn’t speak English, and the competition for attention in other languages is a fraction of what it is in English.

The specific opportunity: a topic that’s saturated in English is often barely covered in Portuguese or Indonesian. The same video, dubbed, can find an audience that has almost no alternatives. I’ve watched creators with modest English followings build much larger audiences in a second language purely because they got there first.

The argument against is also simple. A badly dubbed video makes you look careless to exactly the audience you’re trying to win, and you won’t be able to tell it’s bad.

Start with subtitles, not dubbing

Translated subtitles are the cheapest experiment in content and almost nobody runs it properly.

They cost close to nothing, carry very little risk, and a large fraction of viewers watch with sound off regardless. More importantly, they generate the data you need. Add subtitles in four languages, wait a month, and your analytics will tell you where the demand actually is instead of where you guessed it would be.

Generating them is a solved problem. Most caption tools handle translation as a checkbox now, and the best AI caption and subtitle tools for short-form video covers the options. Do this first. Dub only where the numbers justify it.

What AI dubbing actually does now

The modern pipeline has three stages, and each can fail differently.

Transcription. Your original audio to text. Reliable now, unless your audio is poor or you use a lot of jargon.

Translation. Text to target language. Good for general speech, weak on idiom, humour and technical terms. This is where my paperclip incident happened.

Voice synthesis. Translated text spoken in a cloned version of your voice. This is the part that’s improved most dramatically and it’s genuinely impressive.

ElevenLabs is where I do this, because the voice cloning holds up across languages better than anything else I’ve tried. My ElevenLabs review covers the wider tool, and ElevenLabs vs Murf compares it against the obvious alternative if you’re deciding.

Where it still falls apart

Worth knowing before you publish to an audience you can’t evaluate.

Idiom and humour. Jokes rarely survive. A pun in English becomes a confusing literal statement. If your style is playful, expect the dubbed version to be noticeably flatter.

Technical terms and brand names. Product names get translated when they shouldn’t, or pronounced as if they were words in the target language. Most tools let you supply a glossary, and it’s the single highest-value ten minutes you’ll spend.

Timing. Languages have different lengths. German is longer than English, and dubbed audio can drift out of sync with what’s on screen. Tools handle this by speeding up speech, which sounds rushed.

Cultural register. Formality levels don’t map. Getting the informal-versus-formal “you” wrong in several languages reads as either rude or oddly stiff, and you will not hear it.

Numbers, dates and currency. Frequently left in the original format. “$49” spoken in a Spanish dub to a European audience is a small friction that adds up.

The workflow I’d recommend

If you decide to go for it, this is the sequence that keeps the risk manageable.

One language first. Pick the one your analytics point at. Resist doing five at once.

Build a glossary before you start. Product names, technical terms, anything that must not be translated. Ten minutes now, saves a re-record later.

Get a native speaker to check video one. Not a fluent speaker, a native one. Pay them if you have to. This single step catches the class of error that’s invisible to you and obvious to your new audience.

Publish to a separate channel or track. Most platforms now support multi-language audio on a single video, which is better than fragmenting your channel. Where they don’t, a dedicated channel per language works and is easier to measure.

Read the comments, or have them translated. Your new audience will tell you what’s wrong, if you’re paying attention.

Is it worth the money?

Depends entirely on whether you’re monetising in a way that scales with audience.

If you earn from ads or affiliates, more viewers means more revenue, and dubbing has a clear payback. If you sell a service in English to English speakers, a large Portuguese audience is lovely and financially irrelevant.

Be honest about which you are before you subscribe. I’ve watched people dub diligently into six languages while selling a consulting service only available in one.

For the wider picture of scaling output without scaling effort, how to turn one blog post into 10 pieces of content with AI covers the repurposing mindset this belongs to, and the best AI tools for TikTok growth is where a lot of the second-language opportunity actually lives right now.

My take

Do subtitles now, in four languages, this week. It costs almost nothing and the data tells you what to do next. That’s the actionable part of this whole post.

Dubbing is genuinely good enough to publish in the major languages, and I’d use ElevenLabs for it without hesitation. But treat the first video in any language as a test that a human reviews, not as a publish. The failure mode isn’t that it sounds robotic, it’s that it says something slightly wrong in a way you are structurally unable to detect.

The creators getting real results from this aren’t the ones dubbing everything. They’re the ones who found one language with genuine demand and served it properly.