The tool left the burned-in fitness captions in English instead of localizing them; the review cites unchanged lines such as "This is a famous," "For every question," and "I don't know."

✗ Failed🧾 artifact-verifiedinput + output shownTest date not recordedHeyGen
What was measured
Translation Accuracy

Is the translated output semantically correct, and does it preserve technical terms, tone, and meaning?

decisive for this rankingtransformation

If the translation changes meaning, tone, or technical terms, the tool fails the core job of translating the video. (3 of 3 judges)

What was given, what came back

Test input: Fitness instructor short (English → Hindi) · video · group: video-translation-voice-clone-lip-sync
Input — what we sent

A short fitness/coaching video with energetic single-speaker English speech, used to test whether tools can translate into Hindi while preserving fast delivery, motivational tone, and original-face lip sync.

Why this input is hard
  • · Fast, energetic speech transcription
  • · Tone preservation for motivational fitness content
  • · English-to-Hindi translation quality
  • · Lip-sync accuracy on a moving face
  • · End-to-end dubbing workflow automation
Provenance
Observation
7b2219e2-7642-4ec4-88cd-d0d4ad20c414
Evidence run
ec1dd100-6af8-4f49-8f19-b78492702b14
Study
Translate Videos with Voice Cloning and Lip Sync Using AI
Research task
86b96dfpf
Tested at
not recorded
Source
first-party
Evidence state
verified
Proof shown
input + output shown
Cost / latency
not captured
Repeat run
not captured
Tester
not captured

The last three rows are honest blanks, not placeholders — our capture has no field for them yet.

Query this
get_evidence({
  tool: "heygen",
  scenario: "video-translation-voice-clone-lip-sync"
})
MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 9 other tools
measured on Translation Accuracy
From the same study (page rebuilt from a later run)
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com