Hi Sir , as of now I have used pytorch resnet32 network. I have used librosa mel spectogram datasets to train my model. As of now i have written a program which continuously monitors for loudness above certain decibel (like above 70dB for cough or sneeze) with a microphone using pyaudio. And if there are any sound occuring above that value , I have linked it to another program which records the audio for 5 seconds as a .wav file and passes it into resnet32 to detect whether it is cough or sneeze or not. But the problem is the process of recording and detecting the sound takes 10 seconds. So i want to reduce the latency involved and thats why I wanted to pass live mel spectogram into resnet32 model , i mean live batches from mic changed to mel spectogram and into resnet32 to detect without any delay.