ASR Inference from Tensor instead of file[Question]
All NeMo models are pytorch modules, so it should be easy enough to look at their validation/test step code and replicate for tensors. 

The easiest possible way might be to simply copy paste the validation step code and modify it use individual tensors instead of a batch from a data loader. Make sure to use no_grad()! Thank you, I can handle that! Closing the issue but I'll share the code if I implement it. 
I would like to do inference on a tensor of audio samples instead of a file as I am inferring on audio passed via microphone and saving/loading adds extra latency. The only inference method I've been able to find for quartznet is `transcribe` which takes a list of files. Is there an easy way to infer on tensors? Or is my best bet to look at `nemo.collections.asr.models.ctc_models.transcribe` and replicate for tensors instead of files? 
