MEASUREMENT GUIDE

The Best AI Dubbing in the World: How to Measure It

FAMILIAR RESEARCHUPDATED ALL ARTICLES
READ THIS INEnglishEspañolPortuguês中文日本語한국어FrançaisDeutschالعربيةBahasa IndonesiaРусскийItalianoTiếng ViệtไทยNederlandsΕλληνικάУкраїнська粵語NorskCatalàHrvatskiLietuviųSlovenčinaSvenskaБългарскиСрпскиБеларускаяDanskҚазақшаLatviešu

ABSTRACT

The best AI dubbing is the one that scores highest on six dimensions on the same clips: spoken meaning, voice cloning, the face (lip-sync and beyond), scene audio, vocal events and timing, and workflow and delivery. Full data, confidence intervals, and listening examples: the dubbing benchmarks hub.

"Best" is measurable

TABLE 01 · THE SIX DIMENSIONS OF DUBBING QUALITY

Spoken meaning
HOW IT IS SCOREDDid the dub say what you said? Important translation errors per output.
WHAT A FAILURE LOOKS LIKEThe dub changes or loses important ideas.
Voice cloning
HOW IT IS SCOREDClose your eyes: is this the same person? Resemblance to the real speaker.
WHAT A FAILURE LOOKS LIKEYouTube auto-dubbing replaces the speaker's voice with a stock stranger voice.
The face
HOW IT IS SCOREDMouth, jaw, and expressions matched to the new words? The search term is AI lip sync; the lips are only the floor.
WHAT A FAILURE LOOKS LIKEAudio-only tools skip this dimension.
Scene audio
HOW IT IS SCOREDDoes the noise bed (music, ambience, sound effects) survive the dub? Spectral error between the dub's background and the source's.
WHAT A FAILURE LOOKS LIKEThe music, the ambience or the sound effects drop out.
Vocal events and timing
HOW IT IS SCOREDDo laughs, sighs, and beats land where and how they landed? Event shape correlation; transient-timing F1.
WHAT A FAILURE LOOKS LIKEA laugh, a sigh or a beat lands late or flat.
Workflow and delivery
HOW IT IS SCOREDDoes the tool accept your clip, and what comes back? Length minimums, batch caps, language count, resolution, and audio channels: 4K out, true 5.1 out. Streamers require the dub to match the original mix: 5.1 in, 5.1 out.
WHAT A FAILURE LOOKS LIKEA rejected clip scores zero on that clip.

Dubbing vs subtitles and voice-over: the video translation guide.

The method that makes comparisons honest

  1. 01

    Paired and black-box.

    The same source clip and the same target language go to each system; only the final user-facing audio is evaluated.
  2. 02

    One approved meaning.

    Each dub is transcribed with word timestamps and reviewed against it, valid paraphrases accepted.
  3. 03

    Acoustic measures compare the dub to the source scene.

  4. 04

    Equal weight per source clip.

    95% intervals resample source clips.
  5. 05

    A rejected clip is recorded as a rejection.

    Separate products are separate studies.

The current numbers

We ran this method against ElevenLabs Dubbing twice on August 5, 2026.
STUDY A · DUBBING V1 · 418 PAIRED OUTPUTS · 38 CLIPS · 11 LANGUAGES STUDY B · DUBBING V2 (ALPHA), THEIR NEWEST · 111 PAIRED OUTPUTS · MANDARIN · SPANISH · JAPANESE
FIG. 01 · SPEAKER RESEMBLANCE, DUB VS REAL SPEAKER · STABLE DUBBING V1 · 418 PAIRED OUTPUTS · 11 LANGUAGES
Familiar0.4605
ElevenLabs0.3604
FIG. 02 · REVIEW-FLAGGED SPOKEN-OUTPUT MISTAKES · STABLE DUBBING V1 · 418 PAIRED OUTPUTS
ElevenLabs258
Familiar132
  • Familiar sounds 27.8% more like the real speaker; all 11 languages favored Familiar.
  • ElevenLabs made 95.5% more review-flagged mistakes.
  • Predicted naturalness ran 10.2% higher for Familiar (exploratory).
261%

ElevenLabs produced 261% more background spectral error.

12.27 VS 3.40 DB · DUBBING V1
+243.7%

Familiar scored higher on laughter and reaction shape correlation.

0.759 VS 0.221 · DUBBING V1
+24.3%

Familiar scored higher on sound-effect and beat timing F1.

0.947 VS 0.762 · DUBBING V1

The pattern held on the newest product:

FIG. 03 · IMPORTANT SPOKEN TRANSLATION ERRORS · DUBBING V2 (ALPHA) · 111 PAIRED OUTPUTS
ElevenLabs64
Familiar29
270%

ElevenLabs produced 270% more background-sound error: the laughter, the music, the ambience.

11.92 VS 3.22 DB · DUBBING V2 (ALPHA)
221%

ElevenLabs produced 221% more laughter and reaction loudness error.

3.32 VS 1.04 DB · 18 CLIPS, 54 COMPARISONS · DUBBING V2 (ALPHA)
+28.2%

Familiar scored higher on laughter and reaction shape correlation.

0.960 VS 0.749 · DUBBING V2 (ALPHA)
56.5%

ElevenLabs had 56.5% more flagged dubs overall.

36 VS 23
37/38

ElevenLabs source-clip coverage; it rejected clips under 11 seconds.

FAMILIAR 38/38

Clip-level breakdown: the head-to-head paper.

ElevenLabsElevenLabsElevenLabsTHEIR DOCS, READ OCTOBER 30, 2026

Editing the translation means their old model.

On ElevenLabs' current Dubbing v2, in alpha, editing the translation and regenerating the audio through the API is for their Enterprise plans only. Their full editor, Dubbing Studio, is "only available via the v1 model" and "in maintenance mode", in their words. Dubbing v1 is the model our 418-pair study measured, and its output is mono.

+27.8%

FAMILIAR CLOSER TO THE REAL SPEAKER · ALL 11 LANGUAGES

261%

ELEVENLABS MORE BACKGROUND ERROR · THE MUSIC, THE AMBIENCE

+243.7%

FAMILIAR ON LAUGHTER AND REACTION SHAPE

ELEVENLABS DUBBING V1 · 418 PAIRED OUTPUTS · 11 LANGUAGES · AUGUST 5, 2026Read the study Their docs

Translation text has its own study (August 2026): Familiar against expert professional translators, scored blind against expert reference edits.

FIG. 04 · TRANSLATION QUALITY VS THE FIRST-PASS EXPERT PROFESSIONAL · DASHED LINE = THE PROFESSIONAL'S 100%
French100.0%
Chinese98.2%
Hindi103.3%
Indonesian99.6%
FIRST PASS FOR EXPERT PROFESSIONAL TRANSLATORS
  • 100.3% four-language average: Familiar's production translation matches the first-pass professional's quality.
HeyGenHeyGenElevenLabsElevenLabsHeyGen + ElevenLabsONE-SHOT TOOLS · NO CONTROL

One shot today means giving up the rest.

The one-shot tools today give up the rest: the translation cannot be edited, the music and the room drop out, the voice drifts, and the lips fail on a hand over the face, a microphone, blur, bokeh, a side view or several people in frame. No spatial reasoning, no understanding of the world, no control. ElevenLabs' current Dubbing v2 edits only through an Enterprise API, and its full editor is the older model, in maintenance mode; HeyGen stitches a lip-sync over a dub like that, and the lip-sync breaks on the ordinary shots of any episode. Familiar is one shot and the quality: translation first, reviewed; then the voice, the lips and the scene together, through the hard shots.

54.7%

FEWER TRANSLATION ERRORS · FAMILIAR VS ELEVENLABS DUBBING V2

270%

ELEVENLABS MORE BACKGROUND-SOUND ERROR · THE LAUGHTER, THE MUSIC, THE AMBIENCE

+27.8%

FAMILIAR CLOSER TO THE REAL SPEAKER

FAMILIAR · ONE SHOT AND THE QUALITYFamiliar vs Flawless TrueSync The ElevenLabs study The demos

What nobody does well yet

Run the test yourself

A laugh, background music, and a fast aside: those three things break dubs.

No need to trust anyone's numbers, including ours. For a season or catalog pilot, How to Run an AI Dubbing Pilot (2026); for a multi-speaker movie scene, What Is the Best AI Dubbing for Movies?

  • Pick one clip of a speaker you know with all three.
  • Dub it in two tools: same clip, same target language.
  • Check the words. Have a native speaker, or a back-translation, compare the dub against what was said.
  • Close your eyes and ask who is speaking. If the answer is "a narrator," voice cloning failed.
  • Listen for the room. Is the music still there? The ambience? The laugh, and does it sound like the speaker? And ask what the delivered mix is: 5.1, stereo, or mono.
  • Watch the mouth. Does the lip-sync hold, and does the whole face match the new words?
FIG. 05 · PRICE PER FINISHED MINUTE, PER TARGET LANGUAGE · ELEVENLABS = $3/MIN CHECKOUT QUOTE · AUG 5, 2026 · RANGES AT MIDPOINT
ElevenLabs$3 audio only
Human voice$39–94
Full human dub$70–150
  • Full human dubbing, translation plus voice, runs $70 to $150 per finished minute, so it isn't usable for most of the world. The assistive visual-dubbing tools reach release quality with a team of VFX artists and weeks per project, a new project per language; the one-shot tools today are fast and give up the rest, with no control and a lip-sync that comes apart on a hand over the face, a microphone, blur, bokeh, a side view or several people in frame. Familiar is one shot and the quality: its Multimodal Localization Decision Model (MLDM), the first foundation model for multimodal translation, a new paradigm and the first of its kind, is sold by Studio contract at release spec: one render, with the translation, the speaker's own voice, the scene audio, and the lip-sync, delivered up to 4K with a true 5.1 or stereo mix. Your reviewer picks among its translation renderings, and every decision is recorded with the job.
  • Start with the demos on the landing page.
  • Test it on your own footage: email the team. Contracts are sized to your catalog, with all 30 languages, any to any (pricing).
THE FIRST FOUNDATION MODEL FOR MULTIMODAL TRANSLATION
One more criterion for a catalog: where the dub is processed. Familiar runs every dub on its own eight-figure cluster of NVIDIA B300 GPUs in Toronto, Canada, never on a public cloud like Google Cloud or AWS, and never on a third-party inference provider.
PRIVATE INFERENCE · TORONTO, CANADA

REFERENCES

  1. [1]Familiar vs ElevenLabs benchmark: full results, intervals, and listening examples
  2. [2]ElevenLabs Dubbing documentation
  3. [3]AI vs Human Translation: Tested Against Professionals

Multimodal Translation

Familiar Labs builds generationally better dubbing with a Multimodal Localization Decision Model (MLDM), the first foundation model for multimodal translation: one model decides what to localize across text, audio and visual, and shows you the choices. Your voice and lips, carried into new languages. They're a team of 8 with PhDs, backed by Samsung, RedBird, Gold House. Our mission

See the full benchmark →Talk with the team