Multilingual Processing Optimization Solution
For the conversion needs of 13 languages, Open NotebookLM provides the following optimized paths:
- pre-language detection: The system analyzes the default language through PDF metadata, or the user can manually specify it in the interface. Non-Latin documents (such as Chinese / Japanese) is recommended to confirm the encoding format in advance
- Layered processing mechanism: ① Verify the original text using the LangDetect library ② Match the LLM fine-tuning version of the corresponding language ③ Call the TTS voice library of the corresponding language (e.g., MeloTTS for Korean-specific voices)
- Thesaurus grafting: Add a glossary to the lang_packs folder in the project directory to significantly improve the conversion accuracy of technical documents.
Troubleshooting: If you encounter mixed-language documents, it is recommended to 1) use PDF editor to split different language chapters 2) enable experimental_code_switching=True parameter in app.py. German and other languages with more compound words, you need to appropriately increase the value of the processing_timeout parameter.
This answer comes from the articleOpen NotebookLM: convert PDF to podcasts of open source toolsThe
May not be reproduced without permission:AI productivity tools " How to improve the accuracy of podcast conversion for multilingual PDFs?
