baselineMethods

Defined in baselineMethods.py

Index

• func diarizationOracleNumSpkrs
• func diarizationEigenGapNumSpkrs

def diarizationOracleNumSpkrs()

def diarizationOracleNumSpkrs(audio_dataset, method="KMeans"):

Defined in baselineMethods.py

Predict the diarization labels using the oracle number of speakers for all the audio files in audio_dataset with KMeans/ Spectral clustering algorithm.

Parameters:

Argument	Detail
audio_dataset:	utils.DiarizationDataset, Diarization dataset
method:	str, Name of the method to be used for clustering part. Supports: "KMeans" or "Spectral"

Returns:

Variable	Detail
hypothesis_dir:	str, Directory where all the predicted RTTM diarization files are saved

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def diarizationEigenGapNumSpkrs()

def diarizationEigenGapNumSpkrs(audio_dataset):

Defined in baselineMethods.py

Predict the diarization labels using for all the audio files in audio_dataset with Spectral clustering algorithm. It uses Eigen principle to predict the optimal number of speakers. The module uses already implented spectral algorithm from here: https://github.com/wq2012/SpectralCluster

Parameters:

Argument	Detail
audio_dataset:	utils.DiarizationDataset, Diarization dataset

Returns:

Variable	Detail
hypothesis_dir:	str, Directory where all the predicted RTTM diarization files are saved

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