Multi-dimensional accelerated processing
The current generation process involves three stages of PDF parsing, dialogue generation and audio synthesis, which can be optimized in the following ways:
- preprocessing splitting: Split long papers into multiple PDFs by chapter for separate processing (requires modification of paper_to_podcast.py's batch logic)
- Modeling Alternatives: Add ollama support in requirements.txt to replace some OpenAI calls with local models (requires 8GB+ GPU video memory)
- parallelization: Modify Discussion Chain to generate asynchronous execution of conversations for three actors (requires Python asyncio modification)
real comparison: Developer tests show that processing time for a 20-page paper can be reduced from 35 minutes to 12 minutes (using Ollama+ chapter splitting). Be careful to balance speed and quality, and it is recommended to keep the Enhancement Chain to ensure dialog coherence.
This answer comes from the articlePaper to Podcast: Converting Academic Papers to Multi-Person Conversation PodcastsThe
May not be reproduced without permission:AI productivity tools " How to optimize the technical bottleneck of long podcast generation time?
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