I'm a former professional translator, subtitler and interpreter. After about 15 years away from development, I started building a subtitle pipeline around the workflow I wanted to use. I called it Subtitle Foundry. I'm not making money from it, and I'll keep maintaining it. The purpose is to make subtitles for films, documentaries and other material that would otherwise fail to reach a global audience. Repo: https://github.com/mrtdlgc/subtitle-foundry The app combines state-of-the-art (SOTA) speech recognition and language tools with separate stages for transcription, alignment, context-aware translation and review. You can choose providers and supported processing options, reuse existing subtitles, and inspect the intermediate work. You can run it locally or deploy it to a VPS, start with a local video or YouTube URL, or translate an existing SRT. My current workflow uses ElevenLabs Scribe v2 and Codex; I've tested Turkish-to-English and Ukrainian-to-English. Other transcription and translation connections are available, including personal subscription connections for Claude Code and Grok Build. After generation, there's a subtitle editor with video playback and an audio waveform, so you can check wording and timing, save corrections, and export SRT or ASS. Context helps with difficult dialogue, but I still want a person to be able to listen and correct the result. Building this taught me a lot about model hallucinations and audio processing, and brought me back to JavaScript/TypeScript and Python. One practical priority became keeping completed provider responses and checkpoints, so a review or an interrupted job doesn't mean starting the whole process again. The source is AGPL-3.0 and has setup guides. The app is free; chosen model providers can charge you or use your subscription allowance. The SOTA description refers to the tools used in the workflow; I haven't published a benchmark ranking the whole pipeline. I'd appreciate feedback on the first-run experience and the subtitle editor, especially from people working with language pairs that get little attention.   submitted by   /u/mrtdlgc [link]   [comments]