[Question] Low accuracy in VAD when inference?
I trained VAD model using my own dataset follow instructions in [tutorial 6](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/06_Voice_Activiy_Detection.ipynb)
My dataset is Vietnamese telephony conversation, 15 hours with 8 hours of speech and 7 hours of noise. I got accuracy 95% on my test set.
The format of dataset quite similar with google speech dataset. Each sample of speech is one statement and have duration 2s to 5s.

When i follow instruction in [tutorial 7](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/07_Online_Offline_Microphone_VAD_Demo.ipynb) to inference sample in test set what contain only one statement, the prediction is good. The problem is when i inference long audio has duration 20s of speech and noise, the prediction is really bad.

This is predict sample of speech has only one statement
![foo](https://user-images.githubusercontent.com/83386486/121889898-f44b2380-cd43-11eb-9a6f-6b5abf18b1e9.png)

And long audio, 0 to 5s of audio is noise.
![bar](https://user-images.githubusercontent.com/83386486/121889893-f2816000-cd43-11eb-8dc0-929b4f2af029.png)

1. Any idea to deal with this problem
2. I want to tune threshold but it required RTTM ground truth, what is the format of RTTM file and how to generate this file from dataset like google speech.
3. How to tune best window size with my own dataset
