def bestSpeaker(file):
"""Returns best matching speaker: String in voice db for file: String. """
v = Voiceid(db, file, True)
v.extract_speakers()
clu = v.get_clusters().values()[0]
if len(clu.get_best_five()) <= 0:
return None;
return clu.get_best_five()[0][0]
def segment_input(wavfile, dbpath='./voicedb'):
db = GMMVoiceDB(dbpath)
v = Voiceid(db, wavfile)
v.extract_speakers()
speaker_clusters={}
for c in v.get_clusters():
cluster = v.get_cluster(c)
print cluster
cluster.print_segments()
print
def voicerec(fname):
db = GMMVoiceDB('models')
v = Voiceid(db,fname)
v.extract_speakers()
txt=''
for c in v.get_clusters():
cl= v.get_cluster(c)
cluster=str(cl)
cluster=cluster.split(' ')
cluster=cluster[1]
cluster=cluster.strip('(')
cluster=cluster.strip(')')
txt=txt+'Speaker : '+cluster
seg=str(cl.print_segments())
txt=txt+'\n'+seg
out.insertPlainText(txt+"\n")
def voicerec(fname):
db = GMMVoiceDB('models')
v = Voiceid(db, fname)
v.extract_speakers()
txt = ''
for c in v.get_clusters():
cl = v.get_cluster(c)
cluster = str(cl)
cluster = cluster.split(' ')
cluster = cluster[1]
cluster = cluster.strip('(')
cluster = cluster.strip(')')
txt = txt + 'Speaker : ' + cluster
seg = str(cl.print_segments())
txt = txt + '\n' + seg
out.insertPlainText(txt + "\n")
def GetSpeakers(voicedb, audiofile):
from voiceid.sr import Voiceid
from voiceid.db import GMMVoiceDB
import logger
try:
v = Voiceid(voicedb, audiofile)
except:
logger.write("e", "File: voicehandler.py | Function: GetSpeakers | Error: Could not call Voiceid constructor!")
return False
try:
v.extract_speakers()
except:
logger.write("e", "File: voicehandler.py | Function: GetSpeakers | Error: Could not extract speakers from file!")
return False
try:
for c in v.get_clusters():
cluster = v.get_cluster(c)
voicedb.set_maxthreads(5)
except:
logger.write("e", "File: voicehandler.py | Function: GetSpeakers | Error: Could not get clusters!")
return False
return cluster
def recognition():
global audio, db, label
if audio != None:
print 'Comparing...'
#dic = db.get_speakers()
v = Voiceid(db, audio+'.wav')
#v.extract_speakers()
v.diarization()
dic = db.get_speakers()
dist = []
d2u = {}
for gender in dic:
users = dic[gender]
print('Gender: ',gender)
for u in users:
print(db.match_voice(audio+'.wav', u, gender))
dist.append(db.match_voice(audio+'.wav', u, gender)[u])
d2u[dist[-1]]=u
print('--------------')
print("Best Speaker (Own)")
dist.sort(None, None, True)
print( (dist[0]-dist[1]+CEIL),EPS,dist )
if (dist[0]-dist[1]+CEIL)>EPS :
print(d2u[dist[0]])
#print(dic)
print('--------------')
print("Best Speaker")
print("Audio: ",audio.split('/')[-1])
v.extract_speakers(False, True)
for c in v.get_clusters():
cluster = v.get_cluster(c)
print(cluster.get_best_speaker() )
print('------------')
'''
print 'Comparing...'
v = Voiceid(db, audio+'.wav')
#s = StringIO.StringIO()
#with s as sys.stdout:
v.extract_speakers(False, True)
#with open('output.txt','w') as sys.stdout:
for c in v.get_clusters():
cluster = v.get_cluster(c)
user = cluster.get_best_speaker()
print('------------')
if user != "unknown":
print("Best Speaker: %s" % (user) )
print("Speaker: %s" % (cluster.get_speaker()) )
print("Gender: %s" % (cluster.get_gender()) )
print("Distance: %s" % (cluster.get_distance()) )
print("Mean: %s" % (cluster.get_mean()) )
print("Dist. from mean: %s" % (cluster.get_m_distance()) )
print("Best five speakers: " % (cluster.get_best_five()) )
else:
print("User Unknown")
print("Gender: %s" % (cluster.get_gender()) )
print("Distance: %s" % (cluster.get_distance()) )
print("Best five speakers: " % (cluster.get_best_five()) )
print('------------')
#sys.stdout = sys.__stdout__
#labelB["text"] = "Users: " + str(listName)'''
print "Done!"
else :
print 'Record audio first!'
#v = Voiceid(db, '../Test_set_all/GianniVattimoPostmodernit-Lyotard.mp3')
#v = Voiceid(db, '../Test_set_all/Monica-Bellucci-interview.mp3')
#v = Voiceid(db, '../Test_set_all/Pasolini1971.mp3')
#v = Voiceid(db, '/home/felix/Desktop/u/facciamoiconti.mp3')
#v = Voiceid(db, '/home/felix/Desktop/u/facciamoiconti/S32.wav')
#v = Voiceid(db, '/Users/labcontenuti/Desktop/voiceid/adaltavoce/voci/FabrizioGifuni-promessisposi-p1.wav')
#v = Voiceid(db, '/Users/labcontenuti/Desktop/voiceid/adaltavoce/test/Piero_Baldini##mobyDick01_1sec.wav')
#v = Voiceid(db, '/Users/labcontenuti/Desktop/voiceid/adaltavoce/test/Piero_Baldini##mobyDick01_3sec.wav')
#v = Voiceid(db, '/Users/labcontenuti/Desktop/voiceid/DataSetVideolinaVoci/Giacomo_Mameli/2/Giacomo_Mameli##1-1.wav')
v = Voiceid(db, '/Users/labcontenuti/Documents/workspace/activevoice/audio_test/2sec.wav')
v.extract_speakers()
for c in v.get_clusters():
#print "c ", c
cluster = v.get_cluster(c)
"""
print "cluster.get_name ", cluster.get_name()
print "cluster.get_best_speaker ", cluster.get_best_speaker()
print "cluster.get_duration ", cluster.get_duration()
print "cluster.to_dict ", cluster.to_dict()
print "cluster.get_segments ", cluster.get_segments()
cluster.print_segments()
#cluster.print_segments()
"""
print "cluster.wave ", cluster.wave
print "cluster.get_name ", cluster.get_name()
print "\n\n\n\n\n\n"
list_seg=cluster.get_segments()
def test_dir():
all_wav = []
for f in os.listdir(DIR_PATH):
if fnmatch.fnmatch(f, "*.wav") and f.find("##") > -1:
all_wav.append(f)
all_wav = verify_goodness(all_wav)
print all_wav
print "verifica ok... procediamo"
logging.basicConfig(filename=DIR_PATH + "/result.log", level=logging.INFO)
db = GMMVoiceDB("/Users/labcontenuti/.voiceid/gmm_db")
# filter_criteria="Fabrizio_Gifuni##02Pasticciaccio.wav"
for wav in all_wav:
# if wav.find(filter_criteria)>-1:
print "--------------------" + wav + "------------------------"
v = Voiceid(db, DIR_PATH + wav)
v.extract_speakers()
logging.info("\n\n\n---------------------" + wav + "------------------")
wav_tostring = str(wav) + "; "
for c in v.get_clusters():
cluster = v.get_cluster(c)
for seg in cluster.get_segments():
print "\n seg: ", seg
print seg.get_start()
print seg.get_end()
print humanize_time(seg.get_duration())
print "cluster.get_duration ", cluster.get_duration()
logging.info("duration: " + str(cluster.get_duration()))
wav_tostring = wav_tostring + ": " + "duration:" + str(cluster.get_duration())
print "cluster.get_name ", cluster.get_name()
print "cluster.get_best_speaker ", cluster.get_best_speaker(), " correct?", verify_speaker(
wav, cluster.get_best_speaker()
)
logging.info("best_speaker " + str(cluster.get_best_speaker()))
wav_tostring = wav_tostring + "; " + "best_speaker:" + str(cluster.get_best_speaker())
print "v._get_time() ", v._get_time(), " ", humanize_time(v._get_time())
logging.info("get_time: " + str(humanize_time(v._get_time())))
wav_tostring = wav_tostring + "; " + "get_time:" + str(humanize_time(v._get_time()))
# if cluster.get_best_speaker()=="unknown":
print "cluster.get_best_five ", cluster.get_best_five(), cluster.get_best_five()[0]
logging.info("best of five: " + str(cluster.get_best_five()[0]))
wav_tostring = wav_tostring + "; " + "best of five:" + str(cluster.get_best_five()[0])
print "cluster.get_distance() ", str(cluster.get_distance())
logging.info("distance best-closest " + str(cluster.get_distance()))
wav_tostring = wav_tostring + "; " + "distance best-closest:" + str(cluster.get_distance())
print
if not cluster.get_best_speaker() == "unknown":
if verify_speaker(wav, cluster.get_best_speaker()):
find_ok.append(wav_tostring)
else:
find_errata.append(wav_tostring)
else:
not_find.append(wav_tostring)
if verify_speaker(wav, cluster.get_best_five()[0][0]):
unknown_ok.append(wav_tostring)
else:
unknown_errata.append(wav_tostring)
print "Number TEST--" + str(len(find_ok) + len(find_errata) + len(not_find))
print "OK--------- " + str(len(find_ok))
print "ERRATA-------" + str(len(find_errata))
print "UNKNOWN------" + str(len(not_find))
print "UNKNOWN ERR--" + str(len(unknown_errata))
print "UNKNOWN OK--" + str(len(unknown_ok))
# resutl_file=file(DIR_PATH+"result.txt", "a+")
logging.info("\n\n") # for wav in unknown_errata:
logging.info("Number TEST " + str(len(find_ok) + len(find_errata) + len(not_find)))
logging.info("ok " + str(len(find_ok)))
logging.info("ERRATA" + str(len(find_errata)))
logging.info("UNKNOWN" + str(len(not_find)))
logging.info("UNKNOWN ERR " + str(len(unknown_errata)))
logging.info("UNKNOWN OK " + str(len(unknown_ok)))
logging.info("\n\n\n----UNKNOWN OK ---")
for f in unknown_ok:
logging.info(f)
logging.info("\n\n\n----UNKNOWN ERRATA ---")
for f in unknown_errata:
logging.info(f)
logging.info("\n\n\n----ERRATA---")
for f in find_errata:
logging.info(f)
if DIR_PATH:
for f in os.listdir(DIR_PATH):
if fnmatch.fnmatch(f, '*.wav') and f.find("##")>-1:
all_wav.append(DIR_PATH+"/"+f)
if FILE_PATH:
if fnmatch.fnmatch(FILE_PATH, '*.wav') and FILE_PATH.find("##")>-1:
all_wav.append(FILE_PATH)
print "all_wav ", all_wav
verify_goodness(all_wav)
print "verifica ok... procediamo"
db = GMMVoiceDB(DB_PATH)
for wav in all_wav:
OK=0
OK_best5=0
print "--------------------"+ wav+ "------------------------"
v = Voiceid(db, wav)
v.extract_speakers()
for c in v.get_clusters():
cluster = v.get_cluster(c)
print "cluster.get_best_speaker ", str(cluster.get_best_five()[0]) , " correct?", verify_speaker(wav, cluster.get_best_speaker())
if verify_identity(PID, str(cluster.get_best_five()[0])):
OK=OK+1
if verify_speaker(wav, cluster.get_best_five()[0] ):
OK_best5=OK_best5+1
if 2*OK>= len(v.get_clusters()):
print "------------------VERIFICA RIUSCITA------------------Doveva riuscire? ", 2*OK_best5>= len(v.get_clusters())
else:
print "+++++++++++++++++VERIFICA FALLITA+++++++++++++++++++++Doveva riuscire? ", 2*OK_best5>= len(v.get_clusters())
print "RND"
#Using voiceid(google opensource project) for voice recognisation using python
from voiceid.sr import Voiceid
from voiceid.db import GMMVoiceDB
db = GMMVoiceDB('mydir') #creating database/directory of registered voices
db.add_model('C:\Users\raman\Documents\raman.wma', 'Raman','M')
db.add_model('C:\Users\raman\Documents\lalit.wma', 'Lalit','M')
db.add_model('C:\Users\raman\Documents\aneesha.wma', 'Aneesha','F')
db.add_model('C:\Users\raman\Documents\priyanka.wma', 'Priyanka','F')
db.get_speakers() # this will get all the speakers in the database/directory
if(db.matchvoice('C:\Users\raman\Documents\lalit.wma', 'Lalit')) #the matchvoice function will compare the voices
{
print "Lalit is present" }
else print "Lalit is absent"
v = Voiceid(db)
v.extract_speakers()
for c in v.get_clusters():
cluster = v.get_cluster(c)
print cluster
cluster.print_segments()
#v = Voiceid(db, '/home/felix/Desktop/u/facciamoiconti/S32.wav')
#v = Voiceid(db, '/Users/labcontenuti/Desktop/voiceid/adaltavoce/voci/FabrizioGifuni-promessisposi-p1.wav')
#v = Voiceid(db, '/Users/labcontenuti/Desktop/voiceid/adaltavoce/test/Piero_Baldini##mobyDick01_1sec.wav')
#v = Voiceid(db, '/Users/labcontenuti/Desktop/voiceid/adaltavoce/test/Piero_Baldini##mobyDick01_3sec.wav')
#v = Voiceid(db, '/Users/labcontenuti/Desktop/voiceid/DataSetVideolinaVoci/Giacomo_Mameli/2/Giacomo_Mameli##1-1.wav')
v = Voiceid(db, '/Users/labcontenuti/Documents/workspace/activevoice/audio_test/2sec.wav')
#v = Voiceid(db, '/Users/labcontenuti/Desktop/voiceid/DataSetVideolina_FIC02/Fadda_Paolo/Paolo_Fadda##6-30s.wav')
#v = Voiceid(db, '//Users/labcontenuti/Music/facciamo_i_conti_internet.wav')
v.diarization()
print v
v.extract_cluster()
print v.get_clusters()
exit()
v.extract_speakers_without_diarization()
#v.extract_speakers()
for c in v.get_clusters():
#print "c ", c
cluster = v.get_cluster(c)
print "cluster.wave ", cluster.wave
print "cluster.get_name ", cluster.get_name()
print "\n\n\n\n\n\n"
list_seg=cluster.get_segments()
for seg in list_seg:
print "start %s stop %s" % (humanize_time(float(seg.get_start()) / 100), humanize_time(float(seg.get_end()) / 100))
print "best speaker ", cluster.get_best_speaker()
print "get_basename ",seg.get_basename()
