Automates translation, but the setup remains manual, so the workflow is only partially automated.

◐ Mixed🧾 artifact-verifiedinput + output shownTested Jun 24, 2026D-ID
What was measured
Automation Level

How much manual effort is required across the pipeline: transcription → translation → dubbing → lip sync → export.

context, not decisivecapability

How much manual work the pipeline needs matters for ease of use, but it does not determine whether the tool can translate, clone the voice, and sync lips correctly. (3 of 3 judges)

What was given, what came back

Test input: Hindi vlog-style talking head (Hindi → English) · video · group: video-translation-voice-clone-lip-sync
Input — what we sent
D-ID — Input 2 Educational.mp4
Input not captured
This run recorded no prompt or input file for the test, so we cannot show you what produced the result below. Capture gaps are tracked, not hidden.

A casual Hindi/Hinglish talking-head or vlog-style creator video translated into English to stress informal language handling, slang, and natural-sounding voice cloning on real creator content.

Output — unretouched
D-ID — D-ID_EducationalVideo_EN-ES.mp4.mp4
Provenance
Observation
34a6ffbb-1ed4-4266-8746-e58b35117c46
Evidence run
9feca581-b454-4bda-a610-a646cd0092f0
Study
Translate Videos with Voice Cloning and Lip Sync Using AI
Research task
86b96dfpf
Tested at
Jun 24, 2026
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: "d-id",
  scenario: "video-translation-voice-clone-lip-sync"
})
MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 1 other tool
measured on Automation Level
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com