def write_feature_files(models, n_samples, feature_dir, lab_dir):
# Constants that should not make any difference
samp_period = 0.01
max_file_samples = 2000
fea_prefix = 'test'
htk_time_const = 1e7
fea_type = htkmfc.USER
model_names = []
for m in models:
model_names.append(m.name)
n_dims = models[0].means.shape[1]
n_fea_files = int(math.ceil(n_samples / float(max_file_samples)))
samples_estimated = 0
file_names = []
for counter in range(n_fea_files):
fea_name = fea_prefix + str(counter)
file_names.append(fea_name + '.fea')
fea_file_name = os.path.join(feature_dir, fea_name + '.fea')
lab_file_name = os.path.join(lab_dir, fea_name + '.lab')
n_samples_to_generate = min(max_file_samples,
n_samples - samples_estimated)
samples, ind_vector = SailGMM.sample_gmms(models,
n_samples_to_generate)
samples_estimated += n_samples_to_generate
htk_lab_writer = htklab.HTKlab_write(filename=lab_file_name,
samp_period=round(samp_period *
htk_time_const),
labels=model_names)
htk_lab_writer.write(ind_vector)
htk_fea_writer = htkmfc.HTKFeat_write(filename=fea_file_name,
sampPeriod=int(samp_period *
htk_time_const),
veclen=n_dims,
paramKind=fea_type)
htk_fea_writer.writeall(samples)
return (file_names)
def write_feature_files(models, n_samples, feature_dir, lab_dir):
# Constants that should not make any difference
samp_period = 0.01
max_file_samples = 2000
fea_prefix = 'test'
htk_time_const = 1e7
fea_type = htkmfc.USER
model_names = []
for m in models:
model_names.append(m.name)
n_dims = models[0].means.shape[1]
n_fea_files = int(math.ceil(n_samples / float(max_file_samples)))
samples_estimated = 0
file_names=[]
for counter in range(n_fea_files):
fea_name = fea_prefix+str(counter)
file_names.append(fea_name+'.fea')
fea_file_name = os.path.join(feature_dir,fea_name+'.fea')
lab_file_name = os.path.join(lab_dir,fea_name+'.lab')
n_samples_to_generate = min(max_file_samples, n_samples-samples_estimated)
samples, ind_vector = SailGMM.sample_gmms(models, n_samples_to_generate)
samples_estimated += n_samples_to_generate
htk_lab_writer = htklab.HTKlab_write(filename=lab_file_name,
samp_period=round(samp_period*htk_time_const),
labels=model_names)
htk_lab_writer.write(ind_vector)
htk_fea_writer = htkmfc.HTKFeat_write(filename=fea_file_name,
sampPeriod=int(samp_period*htk_time_const),
veclen=n_dims,
paramKind=fea_type)
htk_fea_writer.writeall(samples)
return(file_names)
if not os.path.exists(working_dir):
try:
os.makedirs(working_dir)
except:
logging.exception("Cannot create working directory: {}".format(working_dir))
# create file handler which logs even debug messages
fh = logging.FileHandler(os.path.join(working_dir,'experiment.log'))
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
if model_file != None and os.path.exists(model_file):
mod_f = open(model_file,'r')
model_definition_string = mod_f.read()
models = SailGMM.gmm_list_from_string(model_definition_string)
test_gmm_classification(models=models, n_samples=n_samples, working_dir=args.working_dir,
apply_hlda=args.apply_hlda, hlda_nuisance_dims=args.hlda_nuisance_dims,
do_not_generate_features=args.do_not_generate_features)
except:
logging.exception(
"Cannot create working directory: {}".format(working_dir))
# create file handler which logs even debug messages
fh = logging.FileHandler(os.path.join(working_dir, 'experiment.log'))
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
if model_file != None and os.path.exists(model_file):
mod_f = open(model_file, 'r')
model_definition_string = mod_f.read()
models = SailGMM.gmm_list_from_string(model_definition_string)
test_gmm_classification(
models=models,
n_samples=n_samples,
working_dir=args.working_dir,
apply_hlda=args.apply_hlda,
hlda_nuisance_dims=args.hlda_nuisance_dims,
do_not_generate_features=args.do_not_generate_features)
