Optimized solutions for multilingual support
Although VideoRAG is primarily oriented towards English-speaking environments, multi-language support can be extended in the following ways:
- Speech recognition layer optimization::
- Replace WhisperModel in asr.py with multilingual version
- Configuration of language detection pre-module
- Add domain adaptive fine-tuning process
- Text processing layer modification::
- Integrated Multilingual Transformer Model
- Setting language tags when working with mixed-language documents
- Configuration of a specialized dictionary
- visual semantic alignment::
- Mitigating Language Dependencies with ImageBind's Cross-Modal Features
- Increase the library of culturally relevant visual concepts
- Building language-independent feature representations
- Implementation steps::
- Limit the number of supported languages during the testing phase
- Building multilingual assessment datasets
- Progressive expansion of language coverage
Alternative: An intermediary language approach can be considered, whereby all content is uniformly translated into English for processing, and then the results are translated back into the target language.
This answer comes from the articleVideoRAG: A RAG framework for understanding ultra-long videos with support for multimodal retrieval and knowledge graph constructionThe
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