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

or

python3 -m pip install spectralcluster

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.

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}
}

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.