def diarization(file_properties):
cd_go="cd " + get_base_dir() + "/plugins_script/speaker_extractor/;"
java="java -Xmx2048m " #da definire in base alla macchina
java_classpath = " -classpath " + get_base_dir() + "/plugins_script/speaker_extractor/lium_spkdiarization-8.4.1.jar "
commandline=java+java_classpath+" it.crs4.identification.DBScore \""+file_properties + "\""
#commandline = java+java_classpath + " it.crs4.active.diarization.Diarization \"" +file_properties + "\"" # effettua solo lo splitting
print "diarization -- command \n"
print commandline
start_subprocess(commandline)
def _build_model(audio_file,train_wav_file,name_surname,duration, token):
classpath= os.path.join(get_base_dir(), 'plugins_script',"speaker_extractor","lium_spkdiarization-8.4.1.jar" )
ubm= os.path.join(get_media_root(),"models/audio/globals/ubm.gmm")
start=0
audio_file="/tmp/prova.wav"
gmm_path= build_model (classpath, ubm,audio_file, start, duration, train_wav_file, name_surname, token)
return gmm_path
def __build_model(auth_params, func_params):
"""
Funzione utilizzata per costruire il modello di riconoscimento audio
a partire da un insieme di istanze che vengono fornite dall'utente
attraverso l'apposita GUI.
In particolar ela funzione consente di :
- ottenere la lista di istanze specificate dall'utente
- recuperare il path completo associato a ciascuna istanza
- unire le istanze in un unico file audio
- effettuare la costruzione del modello di riconoscimento vocale
- creare la persona e il modello sul database
- associare i file creati alla persona e al modello
- rimuovere tutti i file temporanei creati
"""
try:
token = auth_params.get('token', '1234')
f_name = func_params.get('first_name', 'Pinco')
s_name = func_params.get('last_name', 'Pallino')
inst_ids = func_params.get('inst_list', [])
ubm = os.path.join(get_media_root(), 'models', 'audio', 'globals', 'ubm.gmm')
classpath= os.path.join(get_base_dir(), 'plugins_script', 'speaker_extractor' , 'lium_spkdiarization-8.4.1.jar')
# crea un modello e una persona con i dati forniti
person = create_person(f_name, s_name, token=token)
model = create_model(person['id'], 'audio', f_name + ' ' + s_name, last_update=None, token=token)
#print person, model
# recupera gli oggetti corrispondenti alle istanze
#print inst_ids
inst_paths = []
for inst_id in inst_ids:
inst = get_instance(inst_id, token=token)
inst_paths.append(os.path.join(get_media_root(), inst['features']))
#print inst_paths
# concat all provided feature files
temp = '/tmp/model_' + str(model['id']) + '.wav'
concatena_multi(inst_paths, temp)
#print temp
# calcola e imposta il modello generato nel database
model_path = create_new_model(classpath, ubm, temp, 0, None, None)
set_model_file(model['id'], model_path, token=token)
#print model_path
# remove all created temporary files
#os.remove(model_path)
except Exception as e:
print e
return 'Error during entity model building'
def post_di_esempio(id_item, fp,token):
print "***** PLUGIN SPEAKER RECOGNITION: POST DI ESEMPIO ---> Start"
classpath= os.path.join(get_base_dir(), 'plugins_script',"speaker_extractor","lium_spkdiarization-8.4.1.jar" )
ubm= os.path.join(get_media_root(),"models/audio/globals/ubm.gmm")
id_persona=None
#id_item=3601
#name_p=open(name_file, "r")
#name_p_list=name_p.readlines()
#result=make_name_compact(fp) #result simile a [[nome,start,stop][nome,start,stop]]
result=segfile_compact_name2(fp)
print "result=",result
uniform_tag_ids_arr =[]
for res in result:
try:
name=res[0]
feature_path = None
model = None
p=re.compile('[A-Z]')
print "find name ", name
st=int( float(res[1]))#*1000 )
print "start ", st
dur=int (float(res[2]))#*1000)
print "dur ",dur
#feature_path = split4diarization(fp,st,dur,fp+"_"+str(st)+"_"+str(dur))
feature_path = split4diarization(os.path.join(get_media_root(), 'items', str(id_item), 'audio.wav'),st,dur,"/tmp/model.wav")
print "feature_path ", feature_path
inst = create_instance('audio', False, token=token) # impostare id modello
set_instance_feature(inst['id'], feature_path, token)
#if name.find("GiacomoMameli")>-1:
# print "trovato giacomino"
# id_persona=create_person("Giacomo","Mameli", token)["id"]
# #createTagKeyword(id_item, 'Giacomo', 'Mameli', token)
# print "id persona ",id_persona
#else:
if True:
mai=p.findall(name)
print "mai ",mai
#if len(mai)==2:
if True:
#f_name=name.split(mai[1])[0]
#s_name=mai[1]+name.split(mai[1])[1]
f_name="Unknown"
s_name=name + '_' + str(id_item)
print "f_name, s_name ", f_name, s_name
# corregge il problema delle identita' duplicate
persona = None
#feature_path = split4diarization(fp,st,dur,"/tmp/model_wav")
if f_name == "Unknown":
print "name Unknown"
persona=create_person(f_name,s_name, token)
#feature_path = split4diarization(fp,0,None,"/tmp/model_wav")
#model_path=_build_model("/tmp/model.wav", None, [f_name, s_name],dur, token) # crea il modello associato alla persona sconosciuta
#model_path= create_new_model (classpath, ubm,feature_path, 0, dur,None) #f_name+"_"+ s_name)
#print "model_path ",model_path
#model = create_model(persona['id'], 'audio', f_name + ' ' + s_name, last_update=None, token=token)
##set_model_file(model['id'], model_path, token=token)
#else:
# print " persona nota ", f_name, s_name
# persona=create_person(f_name, s_name, token)
# print "persona ", persona
# # list out of bound
# model = get_models_by_entity(persona['id'], token=token)[0]
# print "model ", model
# create a tag for person name
#createTagKeyword(id_item, persona['first_name'], persona['last_name'], token)
print "calcolo id persona"
id_persona=persona["id"]
print "id_persona ",id_persona
#else:
# persona=create_person("Il","Manutentore", token)
# id_persona=persona["id"]
print "create_tag id_item,id_persona ", id_item, " ",id_persona
tag=create_tag(id_item,id_persona, "speaker", token)
print "tag ",tag
dtag=create_dtag(tag["id"], st*10, dur*10, token=token)
print "dtag ",dtag
uniform_tag = create_tag(id_item, id_persona, "face+speaker", token)
print 'uniform tag', uniform_tag
uniform_tag_ids_arr.append(uniform_tag['id'])
# update the instance with the model id
print 'instance, model', inst, model
#edit_instance(inst['id'], model_id=model['id'], token=token)
print 'ascallo'
except Exception, e:
print e
