forked from mrq/tortoise-tts
(QoL improvements for) a multi-voice TTS system trained with an emphasis on quality
e650800447
Noticed that the autoregressive batch size was being set off of VRAM size. Adjusted to scale for the VRAM capacity of 90 series GPUs. In this case, 16 -> 32 batches. Using the standard pre-set with ChungusVGAN, I went from 16 steps to 8. Over an average of 3 runs, I achieved an average of 294 seconds with 16 batches, to 234 seconds with 32. Can't complain at a 1.2x speed increase with functionally 2 lines of code. Can't complain. I restarted tortoise each run, and executing ```torch.cuda.empty_cache()``` just before loading the autoregressive model to clean the memory cache each time. |
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config | ||
convert | ||
scripts | ||
tortoise | ||
.gitignore | ||
CITATION.cff | ||
LICENSE | ||
MANIFEST.in | ||
README.md | ||
requirements_legacy.txt | ||
requirements.txt | ||
setup.py |
(QoL improvements for) TorToiSe
This repo is for my modifications to neonbjb/tortoise-tts. If you need the original README, refer to the original repo.
> w-where'd everything go?
Please migrate to mrq/ai-voice-cloning, as that repo is the more cohesive package for voice cloning.