Enterprise-level batch processing implementation
For scenarios where a large number of corporate documents need to be handled, the base functionality can be extended with the following options:
- Clustered deployment: Modify the worker_count parameter in app.py to enable multi-processing, with Docker containerized deployment can linearly increase the processing power. Measurement shows that 8-core server can parallel processing 5 average 50 pages of the PDF
- automatic assembly line: Use watchdog library to monitor the specified folder to realize the workflow of "Drag and Drop PDF→Automatically Generate MP3″. Sample scripts are available in the EnterpriseSolutions section of the project wiki.
- Quality calibration systemIntegration of third-party ASR systems (e.g. Deepgram) for speech-to-text verification of the output audio, automatically triggering re-generation when the error rate exceeds the threshold.
Advanced Solution: For sensitive enterprise documents, it is recommended to 1) privatize the deployment of the Llama model 2) disable the project's Internet connection 3) add pyArmor in requirements.txt to achieve code obfuscation. Documents in the financial/medical industry need to pay special attention to the accuracy of digit reading during speech synthesis.
This answer comes from the articleOpen NotebookLM: convert PDF to podcasts of open source toolsThe
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