Instructions to use JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Asteroid
How to use JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k with Asteroid:
from asteroid.models import BaseModel model = BaseModel.from_pretrained("JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k") - Notebooks
- Google Colab
- Kaggle
Speed up inference time locally (Asteroid)
#1
by huks - opened
Hi, when I use the inference on hugging face it takes only about 0.5s for a short audio (3-5s) and it states that the inference was performed on CPU.
Running it locally on my CPU or even on the GPU the inference takes quite longer (+1.8s even with ONNX runtime).
Do you have any idea how hugging face achieves this result on CPU? Any possibility to tune it locally?
Thanks in advance
Hi,
I guess it just depends on your CPU speed. I just tested locally on my laptop for audio a wav file sampled at 16kHz and 4 seconds long, I have an average of 0.75s over 100 try.
If you want to speed up the inference locally here are some steps you can take :
- Disable gradient computation for inference using "with torch.no_grad():"
- Use JIT to trace your model and run inference on the traced model