I’m a former professional translator/subtitler/interpreter and the developer of Subtitle Foundry. One design decision in the project is to keep transcription, translation and timing separately inspectable. Here’s why that matters even when the pipeline uses state-of-the-art (SOTA) speech and language tools. A fluent final sentence can hide where an error entered. For a hypothetical example, suppose the spoken line is “he didn’t go,” but ASR drops the negation. A translation model might use the surrounding dialogue to recover the intended meaning, or it might confidently translate the wrong statement. Reading only the final subtitle won’t establish which happened. This is an illustrative example, not a measured result from my films. That leads to three different checks: Transcription: compare the recognized source words against the audio. Keep raw ASR output available even if a later stage improves the displayed text. Translation: compare the target meaning against the source audio/corrected transcript and dialogue context. A fluent rewrite is not sufficient evidence that an apparent ASR correction was justified. Timing and readability: listen while watching the cue boundaries, speaker changes, line breaks and display duration. Correct words can still make poor subtitles when shown at the wrong time. Timing evidence also needs a label. My default route uses ElevenLabs Scribe v2’s native word timestamps on the complete recording. Other supported workflows use chunks and optional speech-activity alignment. Speech activity tells you where speech occurs; it does not by itself locate each word. Treating estimated timing as equivalent to provider word timing makes later review harder. Keep source evidence when changing the target-language cues. The app has an exact-timing mode for existing subtitles and an editorial mode that forms target-language cues from source words and speaker turns. This allows different sentence structure in the translation while retaining evidence for where the dialogue belongs. Both workflows end in video/waveform review, with saved manual corrections. Evaluate stages before comparing a final file. For a useful comparison, I would keep the same audio excerpts, corrected source reference and source offsets across providers; record model/settings; then assess source transcription, translated meaning and cue timing separately. Aggregate word-error rates alone don’t measure translation quality or subtitle readability. Intermediate artifacts and cached provider responses also make it easier to inspect one stage without paying to regenerate every other stage. My film testing so far has mainly been Turkish-to-English and Ukrainian-to-English with ElevenLabs plus Codex. I haven’t published a controlled end-to-end benchmark, so these are implementation choices and an evaluation approach, not a performance ranking. Source and workflow documentation: https://github.com/mrtdlgc/subtitle-foundry I’m not making money from this AGPL-3.0 project, and I’ll keep maintaining it. The purpose is to help material that lacks usable subtitles reach a global audience. I used Codex during development, and this write-up was prepared with Codex assistance. submitted by /u/mrtdlgc
Originally posted by u/mrtdlgc on r/ArtificialInteligence