def do_GET(s):
s.send_response(200)
s.send_header("Content-type", "text/html")
s.end_headers()
path = s.path
if '-' in path:
acao, nome, pessoa = path.split('-')
if acao.replace('/', '') == 'gravar':
#Adicionando pessoa ao banco
db.add_model((caminhoVoz + nome), pessoa)
v = Voiceid(db, (caminhoVoz + nome + ".wav"))
s.wfile.write("Gravado")
if acao.replace('/', '') == 'biometria':
#Verificação de arquivo de voz
v = Voiceid(db, (caminhoVoz + nome + ".wav"))
usuario = v.extract_speakers()
s.wfile.write("%s" % usuario)
if not "S0 (unknown)" in str(usuario):
ser.write(ledLigado)
for the_file in os.listdir(caminhoVoz):
file_path = os.path.join(caminhoVoz, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception, e:
print e
def set_name(device_id, voice_path, new_name):
print "set " + voice_path + " to: " + new_name
new_name = new_name.replace(' ', '')
if not new_name.isalnum():
print 'error: SPEAKER_ID must be alphanumeric'
return
if new_name in db.get_speakers()['U'] or new_name in db.get_speakers(
)['M'] or new_name in db.get_speakers()['F']:
voice = Voiceid(db, voice_path, single=True)
voice.extract_speakers(quiet=True, thrd_n=3)
cluster = voice.get_cluster('S0')
cluster.set_speaker(new_name)
voice.update_db()
return
try:
# assume only one speaker in one sample
ww = fm.file2wav(voice_path)
file_basename, extension = os.path.splitext(ww)
db.add_model(file_basename, new_name)
os.remove(file_basename + ".seg")
os.remove(file_basename + ".ident.seg")
os.remove(file_basename + ".init.gmm")
except IOError:
print "voice file doesn't exist"
except OSError:
print "WARNING: error deleting some intermediate files"
except TypeError:
print "Type error"
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 recognize(device_id, voice_path):
# voice_db_lock.acquire()
print db.get_speakers()
# assume only one speaker in one sample, To Do: multiple speakers in one sample
# set to True to force to avoid diarization, in case a single speaker in the file
try:
voice = Voiceid(db, voice_path, single=True)
# extract_speakers(interactive=False, quiet=False, thrd_n=1)
voice.extract_speakers(quiet=True, thrd_n=3)
# clusters = voice.get_clusters()
cluster = voice.get_cluster('S0')
# speaker = cluster.get_best_speaker()
speaker = "unknown"
speakers = cluster.get_best_five()
if len(speakers) > 0:
value = speakers[0][1]
if value > -33.0:
speaker = speakers[0][0]
# speaker = cluster.get_speaker()
print speaker
payload = {
'audio': 'http://52.24.205.33/voice/' + voice_path,
'userName': speaker,
'time': '1'
}
requests.post('http://129.236.234.21:8080/message',
data=json.dumps(payload),
headers=HEADERS)
client.publish("ais/recognize/result/" + device_id + "/" + voice_path,
speaker)
os.remove(voice.get_file_basename() + '.seg')
os.remove(voice.get_file_basename() + '.g.seg')
os.remove(voice.get_file_basename() + '.s.seg')
w = voice.get_file_basename() + '.wav'
if voice.get_filename() != w:
os.remove(w)
shutil.rmtree(voice.get_file_basename())
except IOError:
print "voice file doesn't exist"
from voiceid.sr import Voiceid
from voiceid.db import GMMVoiceDB
# create voice db
db = GMMVoiceDB("voice")
# add models to db: params the basename of
# 'giov_wave.wav' and the speaker, Giovanni
# db.add_model("rec", "test")
# db.add_model("irina", "irina")
# print db.get_speakers()
# process a video/audio file containing various speakers
v = Voiceid(db, "rec.wav")
# extract the speakers from file and search into db
v.extract_speakers()
bestUsers = v.get_user()
print "Dict: " + str(bestUsers)
print "Max: " + max(bestUsers, key=bestUsers.get)
"""
for c in v.get_clusters():
cluster = v.get_cluster(c)
print cluster.get_best_speaker()
"""
def checkAudio(self, audio):
""" Check voices inside audio file """
v = Voiceid(self._db, audio)
v.extract_speakers(quiet=True)
return v
def save_callback(self, file_=None):
""" Adds a file to the queue after it's been saved """
if self.test_mode == True:
vid = Voiceid(self.db, file_, single=True)
self.queue_processes.append((vid, None))
#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()
