Questions regarding VAD fine-tuning.
Hello,

I would like to fine-tune the vad multilingual marblenet model using my custom speaker diarization dataset. At the moment, I read the following tutorials:
1. https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/06_Voice_Activiy_Detection.ipynb#scrollTo=Z7FxGpSE_FyM
2. https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/01_ASR_with_NeMo.ipynb
3. https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/01_ASR_with_NeMo.ipynb

However, I am not sure yet how to adapt these tutorials to the marblenet model. Most of my questions are related to the data preparation and preprocessing step:
1. I read in the marblenet paper that the nn takes a fixed length of 64 dimensional melfilterbank features from 0.63s length segments. Does this mean that I should extract speech and non speech segments of length 0.63s?
2. Should the labels be: "speech" and "non-speech"?
3. Is there any script that takes care of this data preparation and preprocessing part? Or is there any new tutorial similar to the ASR tutorial?
4. Can I find the preprocessing tools such as SpecAugment or SpecCutOut in the nemo toolkit?

Thank you in advance!