Measuring what local AI gets wrong in translated dialogue

说明与用途

Python 工具包检查翻译对话的时间、重叠、漂移和术语一致性，发现需要人工审核的错误。

问题

Local open-weight models produce translated dialogue that may not fit timing constraints or preserve meaning, making automated dubbing unreliable.

结果

The toolkit identified that 4 of 12 translations fit timing, 2 needed stretching, 2 were too short, and 4 were too long. Shorter variants from Gemma improved fit for 3 lines, but timing alone did not guarantee meaning preservation.

工作流

• Translate dialogue with Opus‑MT (English→French) or generate shorter variants with Gemma 3 4B via Ollama
• Run localisation‑measurements verifier to check dialogue fit, timing, overlap, drift, terminology
• Review failed lines and adjust or reject them
• Optionally regenerate shorter variants with Gemma
• Repeat verification until all checks pass

证据

Localisation measurements is a Python toolkit with four command-line checks: Estimated dialogue fit inside a time window. Subtitle timing, overlap and reading speed. Latency and accumulated drift budgets. Terminology consistency and figures.

来源

The core tools use only the Python standard library. A separate example uses Opus-MT for English-to-French translation and Gemma 3 4B through local Ollama for shorter variants.

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The verifier passes 78 cases checking exit codes and hand-calculated results. The example input can deliberately produce a failing quality-check exit code: that means a measurement found a problem.

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Line 008 changes from past to present tense while passing its timing check. All candidates require human review.

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