Spectral Clustering

Overview

This is a Python re-implementation of the spectral clustering algorithm in the paper Speaker Diarization with LSTM.

Disclaimer

This is not the original implementation used by the paper.

Specifically, in this implementation, we use the K-Means from scikit-learn, which does NOT support customized distance measure like cosine distance.

Dependencies

• numpy
• scipy
• scikit-learn

Installation

Install the package by:

pip3 install spectralcluster

or

python3 -m pip install spectralcluster

Tutorial

Simply use the predict() method of class SpectralClusterer to perform spectral clustering:

from spectralcluster import SpectralClusterer

clusterer = SpectralClusterer(
    min_clusters=2,
    max_clusters=100,
    p_percentile=0.95,
    gaussian_blur_sigma=1)

labels = clusterer.predict(X)

The input X is a numpy array of shape (n_samples, n_features), and the returned labels is a numpy array of shape (n_samples,).

For the complete list of parameters of the clusterer, see spectralcluster/spectral_clusterer.py.

Citations

Our paper is cited as:

@inproceedings{wang2018speaker,
  title={Speaker diarization with lstm},
  author={Wang, Quan and Downey, Carlton and Wan, Li and Mansfield, Philip Andrew and Moreno, Ignacio Lopz},
  booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={5239--5243},
  year={2018},
  organization={IEEE}
}

Misc

Our new speaker diarization systems are now fully supervised, powered by uis-rnn. Check this Google AI Blog.

A variant of this repository using Cosine distance for K-means instead of Euclidean distance is available at FlorianKrey/DNC.

To learn more about speaker diarization, here is a curated list of resources: awesome-diarization.