Test accuracy of 0 with high loss on test set after Finetuning Titanet-large model for Speaker Identification
Hello Team.

I have following three issues:
1. I tried to finetune the titanet-large model using the German dataset with 274 speakers and the total data size is small. THe dataset is around 30h - 40h in total. I tried with few different configurations like - adamw optimizer with 0.0001 learning rate and angularsoftmax loss, adamw optimizer with 0.0001 learning rate and crossentropy loss, novograd optimizer with 0.0001 learning rate and crossentropy loss, sgd optimizer with 0.0001 learning rate and crossentropy loss, adamw optimizer with 0.008 learning rate and crossentropy loss, and so on. 
Issue: I have a small test set with 14 speakers who are common with training set, the total test size is 14minutes. The test accuracy is 0 with high loss (around 30 - 60). Please do provide some inputs.

2. I had earlier tried to finetune the TitaNet-large model with VoxCeleb German subset data. The test accuracy was around 96%. I updated the nemo-toolkit with newer version or from the main branch like two days ago and used the model from earlier to finetune the model with another German dataset with 274 speakers. I am having the following issue:
'''
Error executing job with overrides: [] 
Error in call to target 'nemo.collections.common.losses.cross_entropy.CrossEntropyLoss':                                                                     TypeError("__init__() got an unexpected keyword argument 'scale'")
full_key: loss  
'''
3. After updating the nemo-toolkit to a newer version, I am not able to load the models that were finetuned using the earlier versions of the nemo-toolkit. Receiving the following erroe message. please provide inputs:
'''
RuntimeError: Error(s) in loading state_dict for EncDecSpeakerLabelModel:
Unexpected key(s) in state_dict: "decoder.final.bias".
'''

Regards,
Sreeni...