Speaker Diarization Inference: cuda=False, using CPU for eigen decomposition when creating model from NeuralDiarizer.from_pretrained()
I'm having this same issue! The environment you are deploying the NeMo diarization seems like CUDA is not enabled.
If your CUDA setting is not properly set, it will run it on CPU then it will get very slow.
Check `torch.cuda.is_available()` is True in your container environment on the instance you are using. This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.
Hi, im having the same problem as above, 

I tried to create the model with:
```
        model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic', map_location='cuda')
```
```
        model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic', map_location='cuda:0')
```
```
        model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic', map_location='cuda').to('cuda')
```
```
        model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic', map_location='cuda:0').to('cuda:0')
```
and even
```
        model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic').to('cuda:0')
```

On a certain machine (the jupyter notebook on AWS Sagemaker, running on Data Science 3.0, ml.g4dn.xlarge machine), all of these option worked with GPU flawlessly, this only takes 6 seconds for my audio file.

But when i deploy it on AWS Sagemaker Endpoint(with the container [PyTorch 2.0.0 with HuggingFace transformers](763104351884.dkr.ecr.ap-southeast-1.amazonaws.com/huggingface-pytorch-inference:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04), running on ml.g4dn.xlarge), Nemo keeps telling me:
```
... speaker_utils:464] cuda=False, using CPU for eigen decomposition. This might slow down the clustering process.
``` 
just as above. This took 112 seconds to complete with the same audio file.

i dont understand why with the same audio file, same code, it could result in two completely different outcomes?

I appreciate any help, thank you in advanced