def get_mfcc_dtw(path_mapping):
asim = acoustic_similarity_mapping(path_mapping,
rep='mfcc',
match_function='dtw',
use_multi=True,
num_cores=6)
return asim
def calc_asim(path_mapping, rep, match_func, cache=None):
asim, cache = acoustic_similarity_mapping(path_mapping,
rep=rep,
match_function=match_func,
use_multi=True,
num_cores=4,
cache=cache,
return_rep=True)
return asim, cache
def get_mfcc_vowel_mid(path_mapping):
asim = acoustic_similarity_mapping(path_mapping,
rep='mfcc',
match_function=midpoint_distance,
use_multi=True,
num_cores=6,
call_back=callback)
return asim
def run(self):
kwargs = self.kwargs
self.results = list()
if kwargs['type'] == 'one':
try:
asim = analyze_directory(kwargs['query'], **kwargs)
except AcousticSimError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
elif kwargs['type'] == 'two':
try:
asim, output_val = acoustic_similarity_directories(
*kwargs['query'], **kwargs)
except AcousticSimError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
#asim[(kwargs['query'][0],kwargs['query'][1])] = output_val
elif kwargs['type'] == 'file':
try:
asim = acoustic_similarity_mapping(kwargs['query'], **kwargs)
except AcousticSimError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
return
for k, v in asim.items():
if self.stopped:
return
self.results.append(list(k) + [v])
if kwargs['type'] == 'two':
self.results.append([
os.path.basename(kwargs['query'][0]),
os.path.basename(kwargs['query'][1]), output_val
])
else:
self.results.append(['AVG', 'AVG', sum(asim.values()) / len(asim)])
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
kwargs = self.kwargs
self.results = list()
if kwargs['type'] == 'one':
try:
asim = analyze_directory(kwargs['query'], **kwargs)
except AcousticSimError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
elif kwargs['type'] == 'two':
try:
asim, output_val = acoustic_similarity_directories(*kwargs['query'],**kwargs)
except AcousticSimError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
#asim[(kwargs['query'][0],kwargs['query'][1])] = output_val
elif kwargs['type'] == 'file':
try:
asim = acoustic_similarity_mapping(kwargs['query'], **kwargs)
except AcousticSimError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
return
for k,v in asim.items():
if self.stopped:
return
self.results.append(list(k) + [v])
if kwargs['type'] == 'two':
self.results.append([os.path.basename(kwargs['query'][0]),os.path.basename(kwargs['query'][1]), output_val])
else:
self.results.append(['AVG', 'AVG',sum(asim.values())/len(asim)])
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def analyze_config(self,config, use_aic = False, num_cores = 1):
kwarg_dict = config.to_kwargs()
kwarg_dict['num_cores'] = num_cores
asim = acoustic_similarity_mapping(self.mapping, **kwarg_dict)
if use_aic:
with tempfile.NamedTemporaryFile(mode='w',delete=False) as f:
#with open('temp.txt','w') as f:
tempname = f.name
#tempname = 'temp.txt'
header = [x for x in self.listenerResp[0].keys() if x != 'axbtuple'] + ['Independent']
writer = DictWriter(f,header,delimiter='\t')
writer.writerow({x:x for x in header})
for line in self.listenerResp:
try:
line.update({'Independent': asim[line['axbtuple']]})
writer.writerow({k:v for k,v in line.items() if k != 'axbtuple'})
except KeyError:
pass
scriptname = os.path.join(os.path.dirname(os.path.abspath(__file__)),'get_aic.r')
com = ['Rscript',scriptname,tempname]
p = subprocess.Popen(com,stdout=subprocess.PIPE,stderr=subprocess.PIPE,stdin=subprocess.PIPE)
stdout, stderr = p.communicate()
if stderr:
print(com)
print(stderr)
raise(ValueError)
output = str(stdout.decode()).strip()
if output == 'LmerError':
print(com)
raise(ValueError)
aic = float(output)
os.remove(tempname)
return aic
x = []
y = []
for k,v in asim.items():
resps = array([int(l['Dependent']) for l in self.listenerResp if l['axbtuple'] == k])
if len(resps) == 0:
continue
x.append(sum(resps)/len(resps))
y.append(v)
correlation = pearsonr(x,y)
return correlation[0]
def calc_asim(path_mapping, rep, match_func, cache = None):
asim, cache = acoustic_similarity_mapping(path_mapping, rep = rep,
match_function = match_func, use_multi=True,
num_cores = 4, cache = cache, return_rep = True)
return asim, cache
def get_mfcc_vowel_mid(path_mapping):
asim = acoustic_similarity_mapping(path_mapping, rep = 'mfcc',
match_function = midpoint_distance, use_multi=True,
num_cores = 6, call_back = callback)
return asim
def get_mfcc_dtw(path_mapping):
asim = acoustic_similarity_mapping(path_mapping, rep = 'mfcc',
match_function = 'dtw', use_multi=True,
num_cores = 6)
return asim
def analyze_config(self, config, use_aic=False, num_cores=1):
kwarg_dict = config.to_kwargs()
kwarg_dict['num_cores'] = num_cores
asim = acoustic_similarity_mapping(self.mapping, **kwarg_dict)
if use_aic:
with tempfile.NamedTemporaryFile(mode='w', delete=False) as f:
#with open('temp.txt','w') as f:
tempname = f.name
#tempname = 'temp.txt'
header = [
x for x in self.listenerResp[0].keys() if x != 'axbtuple'
] + ['Independent']
writer = DictWriter(f, header, delimiter='\t')
writer.writerow({x: x for x in header})
for line in self.listenerResp:
try:
line.update({'Independent': asim[line['axbtuple']]})
writer.writerow(
{k: v
for k, v in line.items() if k != 'axbtuple'})
except KeyError:
pass
scriptname = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'get_aic.r')
com = ['Rscript', scriptname, tempname]
p = subprocess.Popen(com,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE)
stdout, stderr = p.communicate()
if stderr:
print(com)
print(stderr)
raise (ValueError)
output = str(stdout.decode()).strip()
if output == 'LmerError':
print(com)
raise (ValueError)
aic = float(output)
os.remove(tempname)
return aic
x = []
y = []
for k, v in asim.items():
resps = array([
int(l['Dependent']) for l in self.listenerResp
if l['axbtuple'] == k
])
if len(resps) == 0:
continue
x.append(sum(resps) / len(resps))
y.append(v)
correlation = pearsonr(x, y)
return correlation[0]