PolySub : AI-Powered Multilingual Subtitle and Dubbing with GenAI
Bibliographic record
Abstract
Language barriers go beyond communication gaps; they can restrict access to knowledge, limit collaboration, and reduce the global reach of digital content. PolySub addresses this challenge by providing an AI-powered platform for multi- lingual subtitling and dubbing, enabling seamless cross-language video accessibility. The system integrates OpenAI Whisper for transcription, Meta NLLB for translation and Meta MMS (TTS) model for audio generation, producing SRT files, dubbed videos with subtitles and original videos with embedded subtitles as outputs. Designed for scale, PolySub supports more than 150 languages, ensuring accessibility for diverse audiences. Evalua- tion shows strong performance, achieving a BLEU score of 37.0 and a BERTScore of 85.8, reflecting accurate, fluent, and se- mantically consistent output. This paper presents the conceptual framework of PolySub, outlines its architecture, and highlights how automated multilingual pipelines can enhance accessibility, scalability, and global communication across education, media, and professional domains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".