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Joel Acevedo-Aviles
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Release candidate 1 version 0.7.2 (tested)
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‎README.md

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### pySLGR 0.7.2
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A Python tool for Speaker, Language, and Gender Recognition (SLGR) and general meta-data extraction.
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Preliminary release:
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* This release has initial functionality and basic algorithms for running SLGR systems
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* Future releases will have hyper-parameter training, documentation, and example systems
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Requirements:
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* Linux
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* Boost libraries
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* Anaconda Python 2.7.x
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Installation:
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* Download the files from the git release tab
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* Type 'conda install pyslgr-0.7.2-py27_0.tar.bz2' to install pySLGR
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* To run the examples untar the examples and models in the same directory
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* 'cd examples' and then './example_signal.py'
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Building Cython interface only:
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* Type 'make' to build
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* Type 'make clean' to clean directories
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* Type 'make eg' to see some examples -- more examples are available in the examples subdirectory
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Building an Anaconda package:
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* cd pyslgr
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* conda build .
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* conda install pyslgr --use-local
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### pySLGR 0.7.2
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A Python tool for Speaker, Language, and Gender Recognition (SLGR) and general meta-data extraction.
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####Preliminary release:
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* This release has initial functionality and basic algorithms for running SLGR systems
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* Future releases will have hyper-parameter training, documentation, and example systems
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####Requirements:
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* Linux (Ubuntu)
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* Boost libraries
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* Anaconda v4.3.1 for Python 2.7.x
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####Installation:
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* Download the files from the git release tab
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* Type 'conda install pyslgr-0.7.2-py27_0.tar.bz2' to install pySLGR
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* To run the examples untar the examples and models in the same directory
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* 'cd examples' and then run examples with the command
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python <example_name.py>
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For instance to run the example_signal example type:
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python example_signal.py
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####Building pySLGR from Source Code
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#####1. Build Cython Interface:
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* Type 'make' to build
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* Type 'make clean' to clean directories
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* Type 'make eg' to see some examples -- more examples are available in the examples subdirectory
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#####2. Build Anaconda package:
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* Install conda-build package (version 2.1.10) in Anaconda if not installed already. Use the following command:
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conda install conda-build=2.1.10
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* cd <installation_dir>/pyslgr/pyslgr
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* Run the following command:
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conda build .
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####Installing Conda Package After Build
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* Use the following command:
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conda install pyslgr --use-local
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‎examples/example_gsv.py

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#!/usr/bin/env python
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import json
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from pyslgr.GMMModel import GMMSAD
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from pyslgr.GSV import GSV
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from pyslgr.LLSignal import LLSignal
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from pyslgr.MFCCFeatures import MFCCFeatures
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from pyslgr.FeatPipe import FeatPipe
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from pyslgr.sad import XtalkSAD
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if __name__ == "__main__":
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# Load example signal
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fn = 'signals/example.sph'
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x = LLSignal()
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x.load_sph(fn, 0)
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# Initialize feature pipe
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mfcc_pipe_fn = "config/sid_mfcc+gmmsad_pipe.json"
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with open(mfcc_pipe_fn, 'r') as fp:
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pipe_config = json.load(fp)
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fpipe = FeatPipe(pipe_config, MFCCFeatures, GMMSAD)
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# Get MFCCFeatures
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f = fpipe.process(x)
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# Info
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print 'Number of output features: {}'.format(f.num_outfeat())
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# f.save_raw('tmp/gsv_feat.dat')
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# Load in and create GSV
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with open('config/gsv.json', 'r') as fp:
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config = json.load(fp)
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gsv = GSV(config)
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# Now compute a GSV expansion
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v = gsv.process(f)
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print 'A few elements of GSV expansion: {}'.format(v[0:10])
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print 'GSV expansion dimension = {}'.format(len(v))
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#!/usr/bin/env python
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import json
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from pyslgr.GMMModel import GMMSAD
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from pyslgr.GSV import GSV
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from pyslgr.LLSignal import LLSignal
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from pyslgr.MFCCFeatures import MFCCFeatures
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from pyslgr.FeatPipe import FeatPipe
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from pyslgr.sad import XtalkSAD
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if __name__ == "__main__":
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# Load example signal
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fn = 'signals/example.sph'
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x = LLSignal()
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x.load_sph(fn, 0)
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# Initialize feature pipe
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mfcc_pipe_fn = "config/sid_mfcc+gmmsad_pipe.json"
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with open(mfcc_pipe_fn, 'r') as fp:
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pipe_config = json.load(fp)
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fpipe = FeatPipe(pipe_config, MFCCFeatures, GMMSAD)
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# Get MFCCFeatures
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f = fpipe.process(x)
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# Info
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print 'Number of output features: {}'.format(f.num_outfeat())
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# f.save_raw('tmp/gsv_feat.dat')
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# Load in and create GSV
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with open('config/gsv.json', 'r') as fp:
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config = json.load(fp)
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gsv = GSV(config)
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# Now compute a GSV expansion
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v = gsv.process(f)
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print 'A few elements of GSV expansion: {}'.format(v[0:10])
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print 'GSV expansion dimension = {}'.format(len(v))
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print 'Done! Successfully completed GSV tests'

‎examples/example_ivec.py

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#!/usr/bin/env python
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import json
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from pyslgr.GMMModel import GMMSAD
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from pyslgr.iVector import iVector
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from pyslgr.LLSignal import LLSignal
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from pyslgr.MFCCFeatures import MFCCFeatures
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from pyslgr.FeatPipe import FeatPipe
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import sys
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if __name__ == "__main__":
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# Load example signal
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fn = 'signals/example.sph'
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x = LLSignal()
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x.load_sph(fn, 0)
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# Initialize feature pipe
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mfcc_pipe_fn = "config/sid_mfcc+gmmsad_pipe.json"
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with open(mfcc_pipe_fn, 'r') as fp:
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pipe_config = json.load(fp)
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fpipe = FeatPipe(pipe_config, MFCCFeatures, GMMSAD)
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# Get MFCCFeatures
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f = fpipe.process(x)
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# Info
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print 'Number of output features: {}'.format(f.num_outfeat())
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# f.save_raw('tmp/ivec_feat.dat')
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# Load in and create ivector
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with open('config/ivec.json', 'r') as fp:
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config = json.load(fp)
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ivec = iVector(config)
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# Now compute an ivector
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v = ivec.process(f)
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print 'A few elements of iVector: {}'.format(v[0:10])
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print 'iVector dimension = {}'.format(len(v))
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#!/usr/bin/env python
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import json
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from pyslgr.GMMModel import GMMSAD
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from pyslgr.iVector import iVector
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from pyslgr.LLSignal import LLSignal
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from pyslgr.MFCCFeatures import MFCCFeatures
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from pyslgr.FeatPipe import FeatPipe
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import sys
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if __name__ == "__main__":
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# Load example signal
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fn = 'signals/example.sph'
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x = LLSignal()
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x.load_sph(fn, 0)
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# Initialize feature pipe
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mfcc_pipe_fn = "config/sid_mfcc+gmmsad_pipe.json"
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with open(mfcc_pipe_fn, 'r') as fp:
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pipe_config = json.load(fp)
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fpipe = FeatPipe(pipe_config, MFCCFeatures, GMMSAD)
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# Get MFCCFeatures
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f = fpipe.process(x)
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# Info
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print 'Number of output features: {}'.format(f.num_outfeat())
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# f.save_raw('tmp/ivec_feat.dat')
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# Load in and create ivector
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with open('config/ivec.json', 'r') as fp:
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config = json.load(fp)
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ivec = iVector(config)
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# Now compute an ivector
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v = ivec.process(f)
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print 'A few elements of iVector: {}'.format(v[0:10])
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print 'iVector dimension = {}'.format(len(v))
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print 'Done! Successfully completed iVector tests'

‎pyslgr/build.sh

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python conda_setup.py install
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dest_dir=${SP_DIR}/${PKG_NAME}
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echo Copying Extension Modules to $dest_dir ...
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cp -r $PKG_NAME $dest_dir
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cp -rL $PKG_NAME $dest_dir
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# filtersSrc=`pwd`/../../filters
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# filtersDest=${SP_DIR}/$PKG_NAME/filters
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# echo Copying filters to $filtersDest

‎pyslgr/meta.yaml

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package:
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name: pyslgr
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version: "0.7.2"
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source:
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path: ../
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requirements:
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build:
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- python
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- setuptools
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run:
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- python
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package:
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name: pyslgr
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version: "0.7.2"
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source:
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path: ../
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requirements:
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build:
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- python
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- setuptools
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run:
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- python
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build:
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preserve_egg_dir: True
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