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JevMeter

Score every sentence in a video, then render the results as a broadcast-style data overlay.

What Jev decides

For each configured dimension, whether the current sentence exhibits that quality.

JevMeter separates transcription and alignment from judgment, sends one multi-question Noul request per sentence, aggregates probabilities over time, and renders a 16:9 analytics video.

NOUL
Jev role
Apply the same semantic yes-or-no dimensions to every spoken sentence.
Input
One aligned transcript sentence plus the configured judgment questions.
Output
Multiple Noul probabilities for that sentence.
Decision frequency
Once per transcribed sentence.
Fallback
Transcription, alignment, aggregation, and video rendering are deterministic pipeline stages and can be rerun separately.
Author reported · Source reviewed

How it works

Input
One aligned transcript sentence plus the configured judgment questions.
Jev
NOUL
Output
Multiple Noul probabilities for that sentence.

Reported performance

Reported total Jev cost

0.0497 USD

Evaluate the two example sentence datasets documented in the README.

Author reported · Source reviewed

Captured Sep 21, 2026

Overview

JevMeter turns long-form video into a timeline of sentence-level judgments. Transcription and word alignment run first; each sentence is then evaluated on configured yes-or-no dimensions, and the probabilities are smoothed into charts, gauges, and overlays for a rendered output video.

Despite the visual meter, the current pipeline uses Noul probabilities rather than Jev Score. The judgments are not fact-checks, author evaluation is not independent, and users remain responsible for rights to downloaded source media.

Language
Python
License
MIT
Author
ChetasLua
Sources checked Sep 21, 2026

Sources

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