Comments (2)
nisqa = nisqaModel(args)
# Print the device of the model
print(" The parameters Device of the NISQA MODEL: ",next(nisqa.model.parameters()).device)
# Execute the prediction directly
nisqa.predict()
The code gives me "cuda:0" result of the print statement, but model inference goes to CPU. Thanks for your guidence.
from nisqa.
it looks like it does go through the GPU, it's just that the model is relatively small so that the utilization is quite low. The CPU usage you are seeing is probably from the data preprocessing, such as computing Mel-specs
from nisqa.
Related Issues (20)
- Can this model be modified into a speech quality classification model? HOT 1
- RuntimeError: Could not infer dtype of numpy.float32 HOT 10
- Using NISQA as a loss function HOT 2
- NISQA Corpus download issue HOT 1
- Continuous metrics? HOT 4
- TTS naturalness prediction based on which model
- Could you tell me if the MOS rating is objective or subjective? HOT 4
- Full Reference or No Reference When Subjectively Rating a Speech HOT 1
- upper bound and larger bound inconsistent with step sign HOT 1
- Audio input requirements HOT 2
- pip package HOT 4
- Interpertation of Different metrics HOT 1
- The predict result seems not reliable HOT 3
- License
- upper bound and larger bound inconsistent with step sign
- It seams slowly because of some functions running on CPU
- max window length error for most audio files HOT 1
- Utilizing finetuned weights
- mos_pred预测分数有些奇怪
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from nisqa.