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Speaker Diarization is the problem of separating speakers in an audio. There could be any number of speakers and final result should state when speaker starts and ends. In this project, we analyze given audio file with 2 channels and 2 speakers (on separate channels).

  • Updated Jun 11, 2020
  • Python
ClashLuke
ClashLuke commented Nov 24, 2019

Hello there,
I recently stumbled upon this repository and was interested in trying out your code. However, using single-threaded sklearn doesn't seem to be efficient to me, compared to using GPU-optimized PyTorch or TF.
Do you have any plans of moving to those frameworks, or would you accept a pullrequest implementing these?
Regards,
Luke

nazariiixa
nazariiixa commented Aug 3, 2019

I have 10 bit input data like this
const double inputs[110][8] = {
{540,131,48,3,0,0,0,0},
{624,167,63,15,0,0,0,0},
{736,224,96,31,0,0,0,0},...
but after learning output is the same for exemple
0.8215888
0.8215888
0.8215888
...
after i divide for 1024 i have data like this
const double inputs[110][8] = {
{0.52734375,0.1279296875,0.046875,0.0029296875,0,0,0,0},
{0.609375,0.1630859375,

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