# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import sys
from optparse import OptionParser
try:
import yaafelib as yaafe
except ImportError, e:
print 'ERROR: cannot load yaafe packages: ', e
sys.exit()
if (yaafe.loadComponentLibrary('yaafe-io') != 0):
print 'WARNING: cannot load yaafe-io component library !'
output_format_choices = ['csv']
if yaafe.isComponentAvailable('H5DatasetWriter'):
output_format_choices.append('h5')
def listFeatures():
features = [feat.__name__
for feat in yaafe.AudioFeatureFactory.get_all_features()
if not feat.TRANSFORM]
transforms = [feat.__name__
for feat in yaafe.AudioFeatureFactory.get_all_features()
if feat.TRANSFORM]
if len(features) == 0:
print 'No features available ! Please check that YAAFE_PATH env var is set correctly.'
return
from __future__ import absolute_import, print_function
import sys
from optparse import OptionParser
try:
import yaafelib as yaafe
except ImportError as e:
print('ERROR: cannot load yaafe packages: ', e)
sys.exit()
from yaafelib._compat import iteritems
if (yaafe.loadComponentLibrary('yaafe-io') != 0):
print('WARNING: cannot load yaafe-io component library !')
output_format_choices = ['csv']
if yaafe.isComponentAvailable('H5DatasetWriter'):
output_format_choices.append('h5')
def listFeatures():
features = [feat.__name__
for feat in yaafe.AudioFeatureFactory.get_all_features()
if not feat.TRANSFORM]
transforms = [feat.__name__
for feat in yaafe.AudioFeatureFactory.get_all_features()
if feat.TRANSFORM]
if len(features) == 0:
print('No features available ! Please check that YAAFE_PATH env var is set correctly.')
return
import csv
import sys
import numpy as np
from numpy import sin, linspace, pi
from pylab import plot, show, title, xlabel, ylabel, subplot, figure
from scipy import fft, arange
from copy import copy
from random import randint
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from scipy.io import wavfile
import random
import yaafelib as yaafe
yaafe.loadComponentLibrary("yaafe-io")
# old_settings = np.seterr(all='raise')
def getdata(directory, sessions):
data = []
offsets = []
annotations = []
files = ["gaccel.csv", "waccel.csv"]
device = ["gaccel", "waccel"]
print "Offsets"
with open(directory + "/offsets.txt") as offsets_file:
readfile = csv.reader(offsets_file, delimiter=" ")
rowi = 0
for row in readfile:
offsets.append({})
def main():
if (yaafe.loadComponentLibrary('yaafe-io') != 0):
print 'WARNING: cannot load yaafe-io component library !'
output_format_choices = ['csv']
if yaafe.isComponentAvailable('H5DatasetWriter'):
output_format_choices.append('h5')
parser = OptionParser(version='yaafe.py, Yaafe v%s' %
yaafe.getYaafeVersion())
parser.add_option('-v',
'--verbose',
dest='verbose',
action='store_true',
default=False,
help='display more output')
parser.add_option('-l',
'--list',
dest='listFeatures',
action='store_true',
default=False,
help='list all available features and output formats')
parser.add_option('-d',
'--describe',
dest='describe',
default=None,
help='describe a feature or an output format')
parser.add_option('-f',
'--feature',
action='append',
dest='feature',
metavar='FEATUREDEFINITION',
help='feature to extract')
parser.add_option('-c',
'--config-file',
dest='configFile',
default=None,
help='feature extraction plan')
parser.add_option(
'-r',
'--rate',
dest='sample_rate',
type='int',
default=None,
help=
'working samplerate in Hz. If not set, use input file sample rate. ')
parser.add_option('',
'--resample',
dest='resample',
action='store_true',
default=False,
help='Resample input signal to the analysis sample rate')
parser.add_option(
'-n',
'--normalize',
dest='normalize',
action='store_true',
default=False,
help=
'normalize input signal by removing mean and scale maximum absolute value to 0.98 (or other value given with --normalize-max)'
)
parser.add_option(
'',
'--normalize-max',
dest='normalize_max',
type='float',
default=0.98,
help=
'Normalize input signal so that maximum absolute value reached given value (see -n, --normalize)'
)
parser.add_option('-i',
'--input',
dest='input_list',
default=None,
help='text file, each line is an audio file to process')
parser.add_option('-b',
'--base-dir',
dest='out_dir',
default='',
help='output directory base')
parser.add_option('-o',
'--output-format',
dest='format',
default='csv',
choices=output_format_choices,
help='Features output format: %s' %
'|'.join(output_format_choices))
parser.add_option(
'-p',
'--output-params',
dest='formatparams',
action='append',
default=[],
metavar='key=value',
help=
'add an output format parameter (can be used multiple times, use -l options to list output formats and parameters)'
)
parser.add_option(
'',
'--dump-dataflow',
dest='dumpDataflow',
default='',
metavar='FILE',
help='output dataflow plan (suitable for process with yaafe-engine)')
parser.add_option(
'',
'--dump-graph',
dest='dumpGraph',
default='',
metavar='FILE',
help="output dataflow in dot format (suitable for display with graphviz"
)
parser.add_option('-s',
'--data-block-size',
dest='buffer_size',
type='int',
default=None,
help='Prefered size for data blocks.')
parser.add_option('',
'--show',
dest='showFeatures',
default=None,
help='Show all features in a H5 file')
(options, audiofiles) = parser.parse_args()
if options.listFeatures:
listFeatures()
return
if options.describe:
if options.describe in yaafe.getOutputFormatList():
describeOutputFormat(options.describe)
else:
describeFeature(options.describe)
return
if options.showFeatures:
showFeatures(options.showFeatures)
return
if not options.sample_rate:
print "ERROR: please specify sample rate !"
return
if options.buffer_size:
yaafe.setPreferedDataBlockSize(options.buffer_size)
if options.verbose:
yaafe.setVerbose(True)
# initialize feature plan
fp = yaafe.FeaturePlan(
sample_rate=options.sample_rate,
normalize=(options.normalize_max if options.normalize else None),
resample=options.resample)
if options.configFile:
if not fp.loadFeaturePlan(options.configFile):
return
elif options.feature:
for feat in options.feature:
if not fp.addFeature(feat):
return
if options.dumpDataflow:
fp.getDataFlow().save(options.dumpDataflow)
if options.dumpGraph:
fp.getDataFlow().save(options.dumpGraph)
# read audio file list
if options.input_list:
fin = open(options.input_list, 'r')
for line in fin:
audiofiles.append(line.strip())
fin.close()
if audiofiles:
# initialize engine
engine = yaafe.Engine()
if not engine.load(fp.getDataFlow()):
return
# initialize file processor
afp = yaafe.AudioFileProcessor()
oparams = dict()
for pstr in options.formatparams:
pstrdata = pstr.split('=')
if len(pstrdata) != 2:
print 'ERROR: invalid parameter syntax in "%s" (should be "key=value")' % pstr
return
oparams[pstrdata[0]] = pstrdata[1]
afp.setOutputFormat(options.format, options.out_dir, oparams)
# process audio files
for audiofile in audiofiles:
afp.processFile(engine, audiofile)
def main():
if (yaafe.loadComponentLibrary('yaafe-io')!=0):
print 'WARNING: cannot load yaafe-io component library !'
output_format_choices = ['csv']
if yaafe.isComponentAvailable('H5DatasetWriter'):
output_format_choices.append('h5')
parser = OptionParser(version='yaafe.py, Yaafe v%s'%yaafe.getYaafeVersion())
parser.add_option('-v','--verbose',dest='verbose',action='store_true',default=False,
help='display more output')
parser.add_option('-l','--list',dest='listFeatures',action='store_true',default=False,
help='list all available features and output formats')
parser.add_option('-d','--describe',dest='describe',default=None,
help='describe a feature or an output format')
parser.add_option('-f','--feature',action='append',dest='feature',
metavar='FEATUREDEFINITION', help='feature to extract')
parser.add_option('-c','--config-file',dest='configFile',default=None,
help='feature extraction plan')
parser.add_option('-r','--rate',dest='sample_rate',type='int', default=None,
help='working samplerate in Hz. If not set, use input file sample rate. ')
parser.add_option('','--resample',dest='resample',action='store_true',default=False,
help='Resample input signal to the analysis sample rate')
parser.add_option('-n','--normalize',dest='normalize',action='store_true',default=False,
help='normalize input signal by removing mean and scale maximum absolute value to 0.98 (or other value given with --normalize-max)');
parser.add_option('','--normalize-max',dest='normalize_max',type='float',default=0.98,
help='Normalize input signal so that maximum absolute value reached given value (see -n, --normalize)')
parser.add_option('-i','--input',dest='input_list',default=None,
help='text file, each line is an audio file to process')
parser.add_option('-b','--base-dir',dest='out_dir',default='',
help='output directory base')
parser.add_option('-o','--output-format',dest='format',default='csv',
choices=output_format_choices, help='Features output format: %s'%'|'.join(output_format_choices))
parser.add_option('-p','--output-params',dest='formatparams', action='append', default=[],
metavar='key=value',
help='add an output format parameter (can be used multiple times, use -l options to list output formats and parameters)')
parser.add_option('','--dump-dataflow',dest='dumpDataflow',default='', metavar='FILE',
help='output dataflow plan (suitable for process with yaafe-engine)')
parser.add_option('','--dump-graph',dest='dumpGraph',default='', metavar='FILE',
help="output dataflow in dot format (suitable for display with graphviz")
parser.add_option('-s','--data-block-size',dest='buffer_size',type='int',default=None,
help='Prefered size for data blocks.')
parser.add_option('','--show',dest='showFeatures',default=None,
help='Show all features in a H5 file')
(options,audiofiles) = parser.parse_args()
if options.listFeatures:
listFeatures()
return
if options.describe:
if options.describe in yaafe.getOutputFormatList():
describeOutputFormat(options.describe)
else:
describeFeature(options.describe)
return
if options.showFeatures:
showFeatures(options.showFeatures)
return
if not options.sample_rate:
print "ERROR: please specify sample rate !"
return
if options.buffer_size:
yaafe.setPreferedDataBlockSize(options.buffer_size)
if options.verbose:
yaafe.setVerbose(True)
# initialize feature plan
fp = yaafe.FeaturePlan(sample_rate=options.sample_rate,
normalize=(options.normalize_max if options.normalize else None),
resample=options.resample)
if options.configFile:
if not fp.loadFeaturePlan(options.configFile):
return
elif options.feature:
for feat in options.feature:
if not fp.addFeature(feat):
return
if options.dumpDataflow:
fp.getDataFlow().save(options.dumpDataflow)
if options.dumpGraph:
fp.getDataFlow().save(options.dumpGraph)
# read audio file list
if options.input_list:
fin = open(options.input_list,'r')
for line in fin:
audiofiles.append(line.strip())
fin.close()
if audiofiles:
# initialize engine
engine = yaafe.Engine()
if not engine.load(fp.getDataFlow()):
return
# initialize file processor
afp = yaafe.AudioFileProcessor()
oparams = dict()
for pstr in options.formatparams:
pstrdata = pstr.split('=')
if len(pstrdata)!=2:
print 'ERROR: invalid parameter syntax in "%s" (should be "key=value")'%pstr
return
oparams[pstrdata[0]] = pstrdata[1]
afp.setOutputFormat(options.format,options.out_dir,oparams)
# process audio files
for audiofile in audiofiles:
afp.processFile(engine,audiofile)
import numpy
from os import walk
import yaafelib as yaafe
from sklearn import neighbors
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.svm import NuSVC
from sklearn import linear_model
from sklearn.linear_model import SGDClassifier
from sklearn import tree
from sklearn.neighbors.nearest_centroid import NearestCentroid
yaafe.loadComponentLibrary('yaafe-io')
fp = yaafe.FeaturePlan(sample_rate=8000)
fp.loadFeaturePlan('./featureplan')
engine = yaafe.Engine()
engine.load(fp.getDataFlow())
afp = yaafe.AudioFileProcessor()
afp.setOutputFormat('csv', './outputs', {
'Metadata': 'false',
'Precision': '2'
})
emotions = ['angry', 'happy', 'neutral', 'unhappy']
feats = ['eng', 'lpc', 'lsf', 'ldd', 'mfc']
def getProperties(audiofile):
props = ""
for feat in feats:
import numpy
from os import walk
import yaafelib as yaafe
from sklearn import neighbors
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.svm import NuSVC
from sklearn import linear_model
from sklearn.linear_model import SGDClassifier
from sklearn import tree
from sklearn.neighbors.nearest_centroid import NearestCentroid
yaafe.loadComponentLibrary('yaafe-io')
fp = yaafe.FeaturePlan(sample_rate=8000)
fp.loadFeaturePlan('./featureplan')
engine = yaafe.Engine()
engine.load(fp.getDataFlow())
afp = yaafe.AudioFileProcessor()
afp.setOutputFormat('csv', './outputs', {'Metadata': 'false', 'Precision': '2'})
emotions = ['angry', 'happy', 'neutral', 'unhappy']
feats = ['eng', 'lpc', 'lsf', 'ldd', 'mfc']
def getProperties(audiofile):
props = ""
for feat in feats:
lines = []
outfile = "./outputs" + audiofile[1:] + "." + feat + ".csv"
with open(outfile, 'r') as f:
for line in f.readlines():
