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Spectral Clustering

Project description

Spectral Clustering

Build Status Python application PyPI Version Python Versions Downloads codecov Documentation


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



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.


  • numpy
  • scipy
  • scikit-learn


Install the package by:

pip3 install spectralcluster


python3 -m pip install spectralcluster


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

from spectralcluster import SpectralClusterer

clusterer = SpectralClusterer(

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/



Our paper is cited as:

  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)},


Laplacian matrix

Question: Why are you performing eigen-decomposition directly on the similarity matrix instead of its Laplacian matrix? (source)

Answer: No, we are not performing eigen-decomposition directly on the similarity matrix. In the sequence of refinement operations, the first operation is CropDiagonal, which replaces each diagonal element of the similarity matrix by the max non-diagonal value of the row. After this operation, the matrix has similar properties to a standard Laplacian matrix.

Question: Why don't you just use the standard Laplacian matrix?

Answer: Our Laplacian matrix is less sensitive (thus more robust) to the Gaussian blur operation.

Cosine vs. Euclidean distance

Question: Your paper says the K-Means should be based on Cosine distance, but this repository is using Euclidean distance. Do you have a Cosine distance version?

Answer: You can find a variant of this repository using Cosine distance for K-means instead of Euclidean distance here: FlorianKrey/DNC


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

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

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