Applying fine-tuned or locally-trained speaker diarization pipeline on an Audio file

[Vanargh]

I performed hyper-parameter optimization for the pre-trained speaker diarization pipeline on my own dataset
as per the instruction provided in the tutorial.

https://github.com/pyannote/pyannote-audio/tree/master/tutorials/pipelines/speaker_diarization

Also, I have some fine-tuned or locally-trained components such as SAD, SCD etc.

For pre-trained speaker diarization pipeline, applying on an audio file of our choice is really simple with the following instructions:

import torch
pipeline = torch.hub.load('pyannote/pyannote-audio', 'dia')
test_file = {'uri': 'filename', 'audio': '/path/to/your/filename.wav'}
diarization = pipeline(test_file)

But how can we apply the optimized Speaker Dairization pipeline with locally-trained or fine-tuned components (mentioned above) to an audio file of our choice? (especially in python-script, instead of shell-based commands)