Efficient Batch Generation Methodology
It is recommended when there is a need to deal with super 2000 chapters of content:
- Hardware-level acceleration::
- pass (a bill or inspection etc)
--threadsParameter Enable Multi-Threading (20 threads per machine recommended) - Accelerating TTS reasoning with GPUs (needs modification)
requirements.txt(Add CUDA version of speech library)
- pass (a bill or inspection etc)
- distributed architecture::
- Configure multiple cloud servers (4 cores and 8G or more recommended)
- Assigning tasks with Redis queues:
python app/distribute_tasks.py - Generate results via rsync synchronization
- Preprocessing Optimization::
- run ahead
python app/preprocess.pyHarmonized Text Encoding (HTE) - Disable GUI Logging Output (Settings)
logging.level=ERROR)
- run ahead
Test data shows that: five 20-thread servers process 2000 chapters in only 5 hours, 15 times more efficient than a single machine.
This answer comes from the articleTool to automatically crawl novels and generate multi-character audiobooksThe
May not be reproduced without permission:AI productivity tools " How to optimize the efficiency of large-scale audio generation for Auto-Audio-Book?
