Metadata-Version: 2.1
Name: spectralcluster
Version: 0.0.7
Summary: Spectral Clustering
Home-page: https://github.com/wq2012/SpectralCluster
Author: Quan Wang
Author-email: quanw@google.com
License: UNKNOWN
Description: # Spectral Clustering [![Build Status](https://travis-ci.org/wq2012/SpectralCluster.svg?branch=master)](https://travis-ci.org/wq2012/SpectralCluster) [![PyPI Version](https://img.shields.io/pypi/v/spectralcluster.svg)](https://pypi.python.org/pypi/spectralcluster) [![Python Versions](https://img.shields.io/pypi/pyversions/spectralcluster.svg)](https://pypi.org/project/spectralcluster) [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://wq2012.github.io/SpectralCluster)
        
        ## Overview
        
        This is a Python re-implementation of the spectral clustering algorithm in the
        paper [Speaker Diarization with LSTM](https://google.github.io/speaker-id/publications/LstmDiarization/).
        
        ![refinement](https://raw.githubusercontent.com/wq2012/SpectralCluster/master/resources/refinement.png)
        
        ## Disclaimer
        
        **This is not the original implementation used by the paper.**
        
        Specifically, in this implementation, we use the K-Means from
        [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html),
        which does NOT support customized distance measure like cosine distance.
        
        ## Dependencies
        
        * numpy
        * scipy
        * scikit-learn
        
        ## Installation
        
        Install the [package](https://pypi.org/project/spectralcluster/) by:
        
        ```bash
        pip3 install spectralcluster
        ```
        
        or
        
        ```bash
        python3 -m pip install spectralcluster
        ```
        
        ## Tutorial
        
        Simply use the `predict()` method of class `SpectralClusterer` to perform
        spectral clustering:
        
        ```python
        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](https://github.com/google/uis-rnn).
        Check this [Google AI Blog](https://ai.googleblog.com/2018/11/accurate-online-speaker-diarization.html).
        
        To learn more about speaker diarization, here is a curated list of resources:
        [awesome-diarization](https://github.com/wq2012/awesome-diarization).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
