add-on beat

README.rst

-Diarization lib
-+++++++++++++++
-
-
-=============================
-S4D - Sidekit For Diarization
-=============================
-
-| **S4D** is an open source package for speaker diarization.
-| The aim of **S4D** is to provide an educational and efficient toolkit 
-| for speaker diarization including the whole chain of treatment 
-| that goes from the audio data to the analysis of the system performance.
-
-
-PREREQUISITES
-=============
-
-*Sidekit for Diarization* requires the following software installed for your platform:
-
-1. [Python](http://www.python.org)
-2. [NumPy](http://www.numpy.org/)
-3. [Scipy](http://http://www.scipy.org/)
-4. [Pandas](http://http://www.pandas.org/)
-5. [GLPK](https://www.gnu.org/software/glpk/)
-6. [Sphinx 1.1.0 or newer](http://http://sphinx-doc.org/) to build the documentation
-7. [SIDEKIT](https://lium.univ-lemans.fr/sidekit/)
-
-INSTALLATION
-============
-
-We recommend the use of a virtual environment (e.g. [Miniconda](https://conda.io/miniconda.html) or [Virtualenv](https://virtualenv.readthedocs.io/en/latest/)).
-
-TUTORIALS
-=========
-
-Once your installation is complete, you can take a look at the [tutorials](https://git-lium.univ-lemans.fr/Meignier/s4d/tree/master/tutorials).
-
-
-:Authors: 
-	Sylvain Meignier \&
-    Anthony Larcher \&
-    Pierre-Alexandre Broux \&
-    Florent Desnous
-    
-
-
-:Version: 0.1.0 of 2020/01/23
\ No newline at end of file

s4d/__init__.py

@@ -57,4 +57,4 @@ __maintainer__ = "Sylvain Meignier"
 __email__ = "sylvain.meignierr@univ-lemans.fr"
 __status__ = "Production"
 __docformat__ = 'reStructuredText'
-__version__ = "0.1.0"
+__version__ = "0.1.4.1"

s4d/model_iv.py

@@ -105,6 +105,25 @@ class ModelIV:
 
         return self.ivectors
 
+    def train_per_segment(self, feature_server, idmap, normalization=True):
+        """
+
+        :param feature_server:
+        :param idmap:
+        :param normalization:
+        :return:
+        """
+        stat = StatServer(idmap, distrib_nb=self.ubm.distrib_nb(), feature_size=self.ubm.dim())
+        stat.accumulate_stat(ubm=self.ubm, feature_server=feature_server, seg_indices=range(stat.segset.shape[0]),
+                             num_thread=self.nb_thread)
+        fa = FactorAnalyser(mean=self.tv_mean, Sigma=self.tv_sigma, F=self.tv)
+        self.ivectors = fa.extract_ivectors_single(self.ubm, stat)
+
+        if normalization:
+            self.ivectors.spectral_norm_stat1(self.norm_mean[:1], self.norm_cov[:1])
+
+        return self.ivectors
+
     def score_cosine(self, use_wccn=True):
         """
 

s4d/requirements.txt

@@ -5,8 +5,8 @@ scipy>=0.19.0
 matplotlib>=2.0.2
 bottleneck>=1.3.1
 setuptools>=38.5.2
-sidekit>=1.3
+sidekit>=1.3.>6.4
 six>=1.11.0
 scikit_learn>=0.19.1
 sortedcontainers>=1.5.9
-h5py>=2.5.0
\ No newline at end of file
+h5py>=2.5.0

setup.py

@@ -33,7 +33,7 @@ setup(
     license='LGPL',
     platforms=['Linux, Windows', 'MacOS'],
     description='S4D: SIDEKIT for Diarization',
-    long_description=open('README.rst').read(),
+    long_description=open('README.txt').read(),
     install_requires=[
         "mock>=1.0.1",
         "nose>=1.3.4",
@@ -48,12 +48,12 @@ setup(
         "PyYAML>=3.11",
         "h5py>=2.5.0",
         "pandas>=0.21.1",
-        "PyAudio==0.2.11",
+        "PyAudio>=0.2.11",
         "bottleneck>=1.3.1",
         "setuptools>=38.5.2",
-        "sidekit>1.3",
-        "scikit_learn==0.19.1",
-        "sortedcontainers==1.5.9"
+        "sidekit>=1.3.6.4",
+        "scikit_learn>=0.22",
+        "sortedcontainers>=1.5.9"
     ],
     package_data={'s4d': ['docs/*']},
     classifiers=[        "Programming Language :: Python :: 3",