Source code for svm_scoring

# -*- coding: utf-8 -*-
#
# This file is part of SIDEKIT.
#
# SIDEKIT is a python package for speaker verification.
# Home page: http://www-lium.univ-lemans.fr/sidekit/
#
# SIDEKIT is a python package for speaker verification.
# Home page: http://www-lium.univ-lemans.fr/sidekit/
#    
# SIDEKIT is free software: you can redistribute it and/or modify
# it under the terms of the GNU LLesser General Public License as 
# published by the Free Software Foundation, either version 3 of the License, 
# or (at your option) any later version.
#
# SIDEKIT is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with SIDEKIT.  If not, see <http://www.gnu.org/licenses/>.

"""
Copyright 2014-2015 Anthony Larcher

:mod:`svm_scoring` provides functions to perform speaker verification 
by using Support Vector Machines.
"""

__license__ = "LGPL"
__author__ = "Anthony Larcher"
__copyright__ = "Copyright 2014-2015 Anthony Larcher"
__license__ = "LGPL"
__version__ = "1.0"
__maintainer__ = "Anthony Larcher"
__email__ = "anthony.larcher@univ-lemans.fr"
__status__ = "Production"
__docformat__ = 'reStructuredText'

import os
import numpy as np
import multiprocessing
import ctypes
import logging
import sidekit.sv_utils
from sidekit.bosaris import Ndx
from sidekit.bosaris import Scores
from sidekit.statserver import StatServer


[docs]def svm_scoring_singleThread(svmDir, test_sv, ndx, scoreMat, segIdx=[]): """Compute scores for SVM verification on a single thread (two classes only as implementeed at the moment) :param svmDir: directory where to load the SVM models :param test_sv: StatServer object of super-vectors. stat0 are set to 1 and stat1 are the super-vector to classify :param ndx: Ndx object of the trials to perform :param scoreMat: ndarray of scores to fill :param segIdx: list of segments to classify. Classify all if the list is empty. """ assert os.path.isdir(svmDir), \ 'First parameter should be a directory' assert isinstance(test_sv, StatServer),\ 'Second parameter should be a StatServer' assert isinstance(ndx, Ndx),\ 'Third parameter should be an Ndx' if segIdx == []: segIdx = range(ndx.segset.shape[0]) # Load SVM models Msvm = np.zeros((ndx.modelset.shape[0], test_sv.stat1.shape[1])) bsvm = np.zeros(ndx.modelset.shape[0]) for m in range(ndx.modelset.shape[0]): svmFileName = os.path.join(svmDir, ndx.modelset[m] + '.svm') w, b = sidekit.sv_utils.read_svm(svmFileName) Msvm[m, :] = w bsvm[m] = b # Compute scores against all test segments for ts in segIdx: logging.info('Compute trials involving test segment %d/%d', ts + 1, ndx.segset.shape[0]) #Select the models to test with the current segment models = ndx.modelset[ndx.trialmask[:, ts]] ind_dict = dict((k,i) for i,k in enumerate(ndx.modelset)) inter = set( ind_dict.keys() ).intersection(models) idx_ndx = np.array([ ind_dict[x] for x in inter ]) scores = np.dot(Msvm[idx_ndx, :], test_sv.stat1[ts, :]) \ + bsvm[idx_ndx] # Fill the score matrix scoreMat[idx_ndx, ts] = scores
[docs]def svm_scoring(svmDir, test_sv, ndx, numThread=1): """Compute scores for SVM verification on multiple threads (two classes only as implementeed at the moment) :param svmDir: directory where to load the SVM models :param test_sv: StatServer object of super-vectors. stat0 are set to 1 and stat1 are the super-vector to classify :param ndx: Ndx object of the trials to perform :param numThread: number of thread to launch in parallel :return: a Score object. """ # Remove missing models and test segments existingModels, modelIdx = sidekit.sv_utils.check_file_list(ndx.modelset, svmDir, '.svm') clean_ndx = ndx.filter(existingModels, test_sv.segset, True) S = np.zeros(clean_ndx.trialmask.shape) dims = S.shape tmp_stat1 = multiprocessing.Array(ctypes.c_double, S.size) S = np.ctypeslib.as_array(tmp_stat1.get_obj()) S = S.reshape(dims) # Split the list of segment to process for multi-threading los = np.array_split(np.arange(clean_ndx.segset.shape[0]), numThread) jobs = [] for idx in los: p = multiprocessing.Process(target=svm_scoring_singleThread, args=(svmDir, test_sv, ndx, S, idx)) jobs.append(p) p.start() for p in jobs: p.join() Score = Scores() Score.scoremat = S Score.modelset = clean_ndx.modelset Score.segset = clean_ndx.segset Score.scoremask = clean_ndx.trialmask return Score