ABSTRACT
01.What ElevenLabs Dubbing is
ElevenLabs ships two dubbing products. The stable Dubbing v1 API is the programmatic path most integrations use; the newer Website Dubbing V2 Alpha is its next-generation product, currently offered through the website. Both take a video or audio file, transcribe it, translate the script, and generate new speech intended to resemble the original speaker.
Both products are audio only. The output is a new soundtrack: the video's face is untouched, so the mouth on screen keeps speaking the original language. Whether that matters depends on the job, and this review treats audio-only as a scope decision, not a defect. The measurements below are audio against audio.
02.What it does well
A fair review starts with credit, and ElevenLabs has earned real credit:
- A mature, well-documented API. The dubbing endpoints are stable, the documentation is clear, and integration is straightforward. Few AI audio products are this pleasant to build against.
- A very wide language list. Its coverage is among the broadest available, useful when the target language is far outside the mainstream.
- Strong standalone text-to-speech. The voice library and speech quality that built the brand are genuinely good; for narration and voice-over from a script, it remains a leading tool.
- The category's biggest brand. It is the product most people find first when they search voice cloning or AI dubbing, and the ecosystem around it (tutorials, integrations, plugins) reflects that.
03.What we measured
Two paired, black-box studies, both run on August 5, 2026, and reported separately, never pooled. The same source clips and target languages went to each system; only the final user-facing audio was evaluated. Against the stable v1 API: 418 paired outputs, 38 clips, 11 languages.
| Measure | Result | Raw scores |
|---|---|---|
| Review-flagged spoken-output mistakes | ElevenLabs had 95.5% more | 258 vs 132 · Familiar made 48.8% fewer |
| Background spectral error | ElevenLabs produced 261% more | 12.27 vs 3.40 dB |
| Laughter/reaction shape correlation | Familiar 243.7% higher | 0.759 vs 0.221 |
| Speaker resemblance | Familiar 27.8% higher | 0.461 vs 0.360 · all 11 languages favored Familiar |
| Sound-effect and beat timing | Familiar 24.3% higher F1 | 0.947 vs 0.762 |
| Predicted naturalness | Familiar 10.2% higher | 2.51 vs 2.28, exploratory |
Against the Website Dubbing V2 Alpha: 111 paired outputs across Mandarin, Spanish, and Japanese. V2 Alpha is an improvement over v1 in places, and where it was inconclusive we say so.
| Measure | Result | Raw scores |
|---|---|---|
| Important spoken-meaning mistakes | ElevenLabs made 121% more | 64 vs 29 · Familiar made 54.7% fewer |
| Dubs with any important meaning problem | ElevenLabs had 56.5% more | 36/111 vs 23/111 |
| Background-sound error | ElevenLabs produced 270% more | 11.92 vs 3.22 dB |
| Laughter/reaction loudness error | ElevenLabs produced 221% more | 3.32 vs 1.04 dB |
| Laughter/reaction shape correlation | Familiar 28.2% higher | 0.960 vs 0.749 · 18 qualifying clips |
| Speaker resemblance | Inconclusive | +3.5% point estimate for Familiar; the interval crossed zero |
| Predicted naturalness | Inconclusive | No reportable difference at this sample size |
| Source-clip coverage | Familiar 38/38 · V2 Alpha 37/38 | Rejected the 10.94-second clip under the 11-second minimum; counted as coverage, no quality score assigned |
A separate qualitative audit on the same clips, run the same day, catalogued the failure classes behind the numbers: ElevenLabs dropped or invented words 45 times, broke laughs and sound effects 8 times, lost the speaker's voice 6 times, collided overlapping speakers 6 times, flattened the delivery 4 times, and stripped the scene ambience 4 times. Confidence intervals, per-language splits, and listening examples for both studies are at /benchmark; the full head-to-head write-up is Familiar vs ElevenLabs Dubbing: Two Paired Studies.
04.Workflow limits at catalogue scale
Quality aside, three product limits shape what a real dubbing job feels like, all observed August 5, 2026:
- Clips under 11 seconds are rejected. Shorts, cold opens, and reaction cuts below the floor cannot be dubbed at all; our 10.94-second clip was refused.
- 20 items per batch. Four videos into six languages is 24 outputs, already more than one batch holds.
- 30 concurrent requests. Ten videos into ten languages is 100 requests: at least four waves of batching and waiting, which at catalogue scale turns one job into hours.
None of this matters for a single video into one language. It matters a great deal for a back catalogue or a multi-language channel. If those limits are the constraint, the survey in Best Alternatives to ElevenLabs for Dubbing compares the options.
05.Price
| Option | Per minute | What it covers |
|---|---|---|
| Familiar | $2.50 | All-in: translation, your voice, scene audio preserved, the face re-rendered |
| ElevenLabs Dubbing | $3 (checkout quote, Aug 5, 2026) | Audio only |
At the observed checkout quote, ElevenLabs is $3 per dubbed minute per language for a new soundtrack. Familiar is $2.50 per finished minute per target language with the face included. The wider price landscape, including human dubbing rates, is in How Much Does Dubbing Cost in 2026?
06.Verdict
For standalone voice generation, ElevenLabs remains a leading tool, and for audio-only dubbing into a long-tail language it may be the only practical option. For dubbing video, where your voice, your scene, and your face have to survive the translation, the measurements point elsewhere: fewer meaning mistakes, lower scene-audio error, and higher speaker resemblance in both studies' conclusive results, plus a face that speaks the new language. That is the line we publish: "Translation quality and voice: tested more accurate than ElevenLabs."
Familiar dubs across 25 languages, any to any, at $2.50 per finished minute all-in, and is the only voice + face translation in real-time. The free tier is 4 minutes of video a month into 1 language, no card. Paired before/after examples are at /demos.
REFERENCES
Dubbing is finally good. See the measurements, then try it on your own video.