Speaker diarization based on python implementation from http://voicebiometry.org/
re numpy scipy pickle sklearn subprocess multiprocessing
Run script in root directory - get_models.sh to download and prepare models
convert models from .txt.gz to .npy
extract i-vectors from wav audio files
extract multiple i-vectors used for diarization from wav audio files
run diarization on previously extracted i-vector using PLDA model