How to adapt myself speaker model into the diarization pipeline?
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Thank you for your contribution for the Nemo framework, it is an awesome work. 
I use the Diarization pipeline, which is vad -> spk_emb -> clustering(nmesc+kmeans). When I use myself speaker model, I found the diarization performance is even worse than the open-source titanet-large model on my own test dataset. 
Note that in the aspect of the speaker recognition performance, my speaker model is better than the provided titanet-large on my own test set. However, the diarization equipped with my speaker model cannot get good performance (better eer can not get better der?). 
Is there any hyper-parameters in clustering need to be tuned for different speaker model and test set (such as the self.nme_mat_size, self.max_rp_thershold, self.sparse_search_volume, etc.) 
Hope for your valuable suggestion, thanks ! 