#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Elie Khoury <Elie.Khoury@idiap.ch>
# Tue 9 Jun 23:10:43 CEST 2015
#
# Copyright (C) 2012-2015 Idiap Research Institute, Martigny, Switzerland
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# This program 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 General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""Cepstral Features for speaker recognition"""
import numpy
import bob
from .. import utils
import struct
import logging
logger = logging.getLogger("bob.bio.spear")
from bob.bio.base.extractor import Extractor
class SPROFeatures(Extractor):
""" Extracts the Cepstral features """
def __init__(
self,
features_mask = numpy.arange(0,60),
normalize_flag = True,
**kwargs
):
# call base class constructor with its set of parameters
Extractor.__init__(
self,
features_mask = features_mask,
normalize_flag = normalize_flag,
)
# copy parameters
self.features_mask = features_mask
self.normalize_flag = normalize_flag
# TODO: remove redundent code by creating base class
[docs] def normalize_features(self, params):
normalized_vector = [ [ 0 for i in range(params.shape[1]) ] for j in range(params.shape[0]) ]
for index in range(params.shape[1]):
vector = numpy.array([row[index] for row in params])
n_samples = len(vector)
norm_vector = utils.normalize_std_array(vector)
for i in range(n_samples):
normalized_vector[i][index]=numpy.asscalar(norm_vector[i])
data = numpy.array(normalized_vector)
return data
[docs] def SPRORead(self, input_file):
with open(input_file, "rb") as fid:
vect_size = struct.unpack("h", fid.read(2))[0]
flag = struct.unpack('i', fid.read(4))[0]
rate = struct.unpack('f', fid.read(4))[0]
header_size = 2 + 4 + 4
list_features=[]
while 1:
chunk = fid.read(4)
if not chunk: break
var = struct.unpack('f', chunk)[0]
feat = numpy.zeros((1, vect_size))
feat[0, 0] = var
for l in range(1, vect_size):
chunk = fid.read(4)
if not chunk: break
var = struct.unpack('f', chunk)[0]
feat[0,l] = var
list_features.append(feat)
features = numpy.vstack(list_features)
return features
nb = (int) (file.len() - header_size) / (getFeatureSize * 4)
def __call__(self, data):
"""Read the SPRO feature file and (optionally) returns normalized cepstral features for the given VAD labels """
spro_file = data[0]
vad_labels = data[1]
# Read SPRO features
cepstral_features=self.SPRORead(spro_file)
features_mask = self.m_config.features_mask
filtered_features = numpy.ndarray(shape=((vad_labels == 1).sum(),len(features_mask)), dtype=numpy.float64)
i=0
cur_i=0
for row in cepstral_features:
if vad_labels[i]==1:
for k in range(len(features_mask)):
filtered_features[cur_i,k] = row[features_mask[k]]
cur_i = cur_i + 1
i = i+1
if self.m_config.normalizeFeatures:
normalized_features = self.normalize_features(filtered_features)
else:
normalized_features = filtered_features
if normalized_features.shape[0] == 0:
logger.warn("No speech found in: %s", input_file)
# But do not keep it empty!!! This avoids errors in next steps
normalized_features=numpy.array([numpy.zeros(len(features_mask))])
return normalized_features