[Question] How do I infer the speaker recognition model/module at 1.0.0b1.
@uditdixit11, 
If you have finetuned or trained all together with a new speaker recognition model, you could test or infer by two steps as shown similarly in [this script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text_infer.py) . 
1. set up test data and 
2. get log_prob by calling forward loop of trained speaker model. 
If it's confusing let me know, I will add a infer script to current examples.  Thanks @nithinraok

Please add the infer script to the current examples. It will be really helpful to us. @nithinraok  Would appreciate an infer script as well, working on it myself right now. Did you try passing test_ds to yaml file to speaker_reco.py by commenting out trainer.fit with your trained .nemo model?  Anyway will post a script very soon for your reference.  Somehow missed your response @uditdixit11, please find the script requested at this [link](https://github.com/NVIDIA/NeMo/blob/afbe52cb726eeb0c8262779299f4cc4354a9625d/examples/speaker_recognition/speaker_reco_infer.py). Let me how it goes. @ShantanuNair  Hi! It appears in the script setting up test data loader and load from test data is a necessary step, in which process audio data were somehow converted to a tensor and can be passed to the `forward()` function as an argument, I was wondering if there is a way to get around this? For example, I just have a running program that takes input audio streams, might there be a way to process the audio stream to a required tensor form and pass it directly to the `forward()` function without having to write to an intermediate test manifest and load from the test data? Has anyone tried this? Help would be appreciated!  You may look at this discussion to get an idea: https://github.com/NVIDIA/NeMo/discussions/2248  > You may look at this discussion to get an idea: #2248

Thank you! @nithinraok 
I have trained a speaker recognition model and save the checkpoints. Even can restore it using the LightningModule.load_from_checkpoint() class method. But couldn't able to perform the next steps after it. 

Please suggest how do I perform the next steps i.e then predict the speaker on unseen data using trained model at  at 1.0.0b1 version ?
