This is a Python re-implementation of the spectral clustering and constrained spectral clustering algorithms in these two papers:
- Speaker Diarization with LSTM
- Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection
We recently added new functionalities to this library to include algorithms in a new paper. We updated the APIs as well.
If you depend on our old API, please use an older version of this library:
pip3 install spectralcluster==0.1.0
This is not a Google product.
This is not the original C++ implementation used by the paper.
- 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. The example below should be closest to the original C++
implemention used our
ICASSP 2018 paper.
from spectralcluster import configs
labels = configs.icassp2018_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,)
.
You can also create your own clusterer like this:
from spectralcluster import SpectralClusterer
clusterer = SpectralClusterer(
min_clusters=2,
max_clusters=7,
autotune=None,
laplacian_type=None,
refinement_options=None,
custom_dist="cosine")
labels = clusterer.predict(X)
For the complete list of parameters of SpectralClusterer
, see
spectralcluster/spectral_clusterer.py
.
In our ICASSP 2018 paper, we apply a sequence of refinment operations on the affinity matrix, which is critical to the performance on the speaker diarization results.
You can specify your refinment operations like this:
from spectralcluster import RefinementOptions
from spectralcluster import ThresholdType
from spectralcluster import ICASSP2018_REFINEMENT_SEQUENCE
refinement_options = RefinementOptions(
gaussian_blur_sigma=1,
p_percentile=0.95,
thresholding_soft_multiplier=0.01,
thresholding_type=ThresholdType.RowMax,
refinement_sequence=ICASSP2018_REFINEMENT_SEQUENCE)
Then you can pass the refinement_options
as an argument when initializing your
SpectralClusterer
object.
For the complete list of RefinementOptions
, see
spectralcluster/refinement.py
.
In our ICASSP 2018 paper,
we apply a refinement operation CropDiagonal
on the affinity matrix, which replaces each diagonal element of the affinity matrix by the max non-diagonal value of the row. After this operation, the matrix has similar properties to a standard Laplacian matrix, and it is also less sensitive (thus more robust) to the Gaussian blur operation than a standard Laplacian matrix.
In the new version of this library, we support different types of Laplacian matrix now, including:
- None Laplacian (affinity matrix):
W
- Unnormalized Laplacian:
L = D - W
- Graph cut Laplacian:
L' = D^{-1/2} * L * D^{-1/2}
- Random walk Laplacian:
L' = D^{-1} * L
You can specify the Laplacian matrix type with the laplacian_type
argument of the SpectralClusterer
class.
Note: Refinement operations are applied to the affinity matrix before computing the Laplacian matrix.
In our ICASSP 2018 paper, the K-Means is based on Cosine distance.
You can set custom_dist="cosine"
when initializing your SpectralClusterer
object.
You can also use other distances supported by scipy.spatial.distance, such as "euclidean"
or "mahalanobis"
.
In our ICASSP 2018 paper,
the affinity between two embeddings is defined as (cos(x,y)+1)/2
.
You can also use other affinity functions by setting affinity_function
when initializing your SpectralClusterer
object.
We also support auto-tuning the p_percentile
parameter of the RowWiseThreshold
refinement operation, which was original proposed in this paper.
You can enable this by passing in an AutoTune
object to the autotune
argument when initializing your SpectralClusterer
object.
Example:
from spectralcluster import AutoTune
autotune = AutoTune(
p_percentile_min=0.60,
p_percentile_max=0.95,
init_search_step=0.01,
search_level=3)
For the complete list of parameters of AutoTune
, see
spectralcluster/autotune.py
.
In the Turn-to-Diarize paper, the spectral clustering is constrained by speaker turns. We implemented two constrained spectral clustering methods:
If you pass in a ConstraintOptions
object when initializing your SpectralClusterer
object, you can call the predict
function with a constraint_matrix
.
Example usage:
from spectralcluster import constraint
ConstraintName = constraint.ConstraintName
constraint_options = constraint.ConstraintOptions(
constraint_name=ConstraintName.ConstraintPropagation,
apply_before_refinement=True,
constraint_propagation_alpha=0.6)
clusterer = spectral_clusterer.SpectralClusterer(
max_clusters=2,
refinement_options=refinement_options,
constraint_options=constraint_options,
laplacian_type=LaplacianType.GraphCut,
row_wise_renorm=True)
labels = clusterer.predict(matrix, constraint_matrix)
The constraint matrix can be constructed from a speaker_turn_scores
list:
from spectralcluster import constraint
constraint_matrix = constraint.ConstraintMatrix(
speaker_turn_scores, threshold=1).compute_diagonals()
Our papers are 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}
}
@inproceedings{xia2022turn,
title={{Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection}},
author={Wei Xia and Han Lu and Quan Wang and Anshuman Tripathi and Yiling Huang and Ignacio Lopez Moreno and Hasim Sak},
booktitle={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2022},
organization={IEEE}
}
We also have fully supervised speaker diarization systems, powered by uis-rnn. Check this Google AI Blog.
To learn more about speaker diarization, here is a curated list of resources: awesome-diarization.