/
project_ivectors_semaine.py
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/
project_ivectors_semaine.py
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import os
import sys
import bob
import numpy
import shutil
import cPickle
import scipy.sparse
import facereclib.utils as utils
from sklearn import preprocessing
if len(sys.argv)!=2:
print '\nUsage: python project_ivectors_semaine.py <number_of_gaussians> '
sys.exit()
# parameters
training_threshold = 5e-4
variance_threshold = 5e-4
max_iterations = 25
relevance_factor = 4 # Relevance factor as described in Reynolds paper
gmm_enroll_iterations = 1 # Number of iterations for the enrollment phase
subspace_dimension_of_t = 100
INIT_SEED = 5489
# parameters for the GMM
gaussians = sys.argv[1]
# TIMIT with FAU no label UBM 39D
if (gaussians == '64'):
ubm_file = '/mnt/alderaan/mlteam3/Assignment3/data/giantubm_model/gmm_ubm/39D/gmm_giantubm_64G.hdf5'
if (gaussians == '128'):
ubm_file = '/mnt/alderaan/mlteam3/Assignment3/data/giantubm_model/gmm_ubm/39D/gmm_giantubm_128G.hdf5'
if (gaussians == '256'):
ubm_file = '/mnt/alderaan/mlteam3/Assignment3/data/giantubm_model/gmm_ubm/39D/gmm_giantubm_256G.hdf5'
if (gaussians == '512'):
ubm_file = '/mnt/alderaan/mlteam3/Assignment3/data/giantubm_model/gmm_ubm/39D/gmm_giantubm_512G.hdf5'
input_ubm_features = '/mnt/alderaan/mlteam3/Assignment2/data/Noisy_TIMIT_buckeye/ubm_noisy_mfcc_39D'
'''
input_emotion_train_features = '/mnt/alderaan/mlteam3/Assignment3/data/Semaine/train_test_data_vaded/train/mfcc/39D/'
input_emotion_test_features = '/mnt/alderaan/mlteam3/Assignment3/data/Semaine/train_test_data_vaded/test/mfcc/39D'
'''
input_emotion_train_features = '/mnt/alderaan/mlteam3/Assignment3/data/Semaine/emotionwise_data_copy/train/'
input_emotion_test_features = '/mnt/alderaan/mlteam3/Assignment3/data/Semaine/emotionwise_data_copy/test/'
def normalize(data):
return preprocessing.normalize(data,norm='l2')
def read_mfcc_features(input_features):
with open(input_features, 'rb') as file:
features = cPickle.load(file)
features = scipy.sparse.coo_matrix((features),dtype=numpy.float64).toarray()
if features.shape[1] != 0:
features = normalize(features)
return features
def load_training_gmmstats(input_features):
gmm_stats_list = []
for root, dir, files in os.walk(input_features):
for file in files:
features_path = os.path.join(root, str(file))
features = read_mfcc_features(features_path)
stats = bob.machine.GMMStats(ubm.dim_c, ubm.dim_d)
if features.shape[1] == 39:
ubm.acc_statistics(features, stats)
gmm_stats_list.append(stats)
return gmm_stats_list
def train_enroller(input_features):
# load GMM stats from UBM training files
gmm_stats = load_training_gmmstats(input_features)
# Training IVector enroller
output_file = 'data/tv_enroller_512G_39D_25i_100.hdf5'
print "training enroller (total variability matrix) ", max_iterations, 'max_iterations'
# Perform IVector initialization with the UBM
ivector_machine = bob.machine.IVectorMachine(ubm, subspace_dimension_of_t)
ivector_machine.variance_threshold = variance_threshold
# Creates the IVectorTrainer and trains the ivector machine
ivector_trainer = bob.trainer.IVectorTrainer(update_sigma=True, convergence_threshold=variance_threshold, max_iterations=max_iterations)
# An trainer to extract i-vector (i.e. for training the Total Variability matrix)
ivector_trainer.train(ivector_machine, gmm_stats)
ivector_machine.save(bob.io.HDF5File(output_file, 'w'))
print "IVector training: saved enroller's IVector machine base to '%s'" % output_file
return ivector_machine
def lnorm_ivector(ivector):
norm = numpy.linalg.norm(ivector)
if norm != 0:
return ivector/numpy.linalg.norm(ivector)
else:
return ivector
def save_ivectors(data, feature_file):
hdf5file = bob.io.HDF5File(feature_file, "w")
hdf5file.set('ivec', data)
def project_ivectors(input_features):
"""Extract the ivectors for all files of the database"""
print "projecting ivetors"
tv_enroller = bob.machine.IVectorMachine(ubm, subspace_dimension_of_t)
tv_enroller.load(bob.io.HDF5File("/mnt/alderaan/mlteam3/Assignment3/data/tv_enroller_512G_39D_25i_100.hdf5"))
#print input_features
for root, dir, files in os.walk(input_features):
ivectors = []
for file in files:
features_path = os.path.join(root, str(file))
features = read_mfcc_features(features_path)
stats = bob.machine.GMMStats(ubm.dim_c, ubm.dim_d)
if features.shape[1] == 39:
ubm.acc_statistics(features, stats)
ivector = tv_enroller.forward(stats)
lnorm_ivector(ivector)
ivectors.append(ivector)
ivectors_path = '/mnt/alderaan/mlteam3/Assignment3/data/Semaine/ivectors_100_emotionwise/test/'+ gaussians + '/' + input_features.split('/')[-1]
if not os.path.exists(ivectors_path):
os.makedirs(ivectors_path)
ivectors_path = ivectors_path + '/' + os.path.split(root)[1] + '_' + gaussians + '_100.ivec'
save_ivectors(ivectors, ivectors_path)
print "saved ivetors to '%s' " % ivectors_path
#############################################
ubm_hdf5 = bob.io.HDF5File(ubm_file)
ubm = bob.machine.GMMMachine(ubm_hdf5)
ubm.set_variance_thresholds(variance_threshold)
#train_enroller(input_ubm_features)
#project_ivectors(input_ubm_features)
#project_ivectors(input_emotion_train_features)
project_ivectors(input_emotion_test_features)