Comments (9)
Could you describe how you get the int8 models?
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Fangjun will get back to you about it, but: hi, martin shkreli!
We might need more hardware info and details about what differed between those two runs.
from sherpa-onnx.
Hi guys, thanks again for the wonderful repo. I followed this link to download the model:
https://k2-fsa.github.io/sherpa/onnx/tts/pretrained_models/vits.html#download-the-model
Then, I used that file (vits-ljs.int8.onnx) for inference in the python script (offline-tts.py). This was on an 8xA100 instance.
from sherpa-onnx.
Could you describe how you get the int8 models?
hi Fangjun, i just wanted to try and get your attention one more time, sorry if I am being annoying!
from sherpa-onnx.
The int8 model is obtained via the following code
sherpa-onnx/scripts/vits/export-onnx-ljs.py
Lines 204 to 208 in d771762
Note that it uses
sherpa-onnx/scripts/vits/export-onnx-ljs.py
Line 207 in d771762
It is a known issue about onnxruntime that quint8 is slower.
For instance, if you search with google, you can find similar issues:
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int8 model mentioned in this issue is about 4x less in file size than that of float32.
If memory matters, then int8 model is preferred.
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hi @csukuangfj do you know how to optimize speed of an int8 model? I was experimenting several months ago with it, but i was not able to convert to qint8 and quint8 is really slow on cpu.
from sherpa-onnx.
You don't need to optimize speed, you need to pick MB-iSTFT VITS model, they are order of magnitude faster than raw VITS with the same quality.
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