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+__all__= ['load']
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+
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+import sys,threading,collections,time
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+import TLK
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+import lmodel,oepmodel,segmodel
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+
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+import logging as Log
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+
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+Hyp= collections.namedtuple('Hyp',['var','novar','err',
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+ 'score','nframes',
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+ 'eos'])
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+
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+class Recogniser:
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+
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+ def __init__(self,models,params,
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+# uppercase_tbl,uppercase_tbl_bi,
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+ sil_word,sil_sym,fea_freq,mng):
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+ args= {
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+ 'ifreq' : 16000,
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+ 'bsize' : 400,
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+ 'amodel' : models['amodel'],
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+ 'lm' : models['lm'],
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+ 'params' : params,
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+ 'output' : self,
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+ }
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+
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+ self._fea_freq= fea_freq
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+ if sil_word!=None:
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+ args['sil_word']= sil_word
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+ if sil_sym!=None:
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+ args['sil_sym']= sil_sym
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+ if models['dnn']!=None: args['dnn']= models['dnn']
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+ if models['dlm']!=None: args['dlm']= models['dlm']
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+ if models['mustd']!=None: args['mustd']= models['mustd']
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+ if models['oep-factory']!=None:
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+ oep= models['oep-factory'](models['oep-model'])
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+ args['oep']= oep
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+ self.reco= TLK.ORecogniser(**args)
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+ if models['seg-factory']!=None:
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+ self.seg= models['seg-factory'](models['seg-model'])
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+ else: self.seg= None
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+ self._mng= mng
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+ self._cv= threading.Condition()
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+
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+ def reset(self):
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+ self._err= False
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+ self._hyps= []
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+ self._prev= None
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+ if self.seg is not None:
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+ self.seg.reset()
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+ # Per a gestionar tokens insertats el que vaig a fer es tindre
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+ # una llista de llistes. En la primera llista estaran els
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+ # tokens del segment que s'està processant actualment i en
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+ # l'última els tokens de l'últim segment. Cada token serà una
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+ # tupla paraula i número de frames on es va insertar.
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+ self._itokens= [[]]
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+ self._inframes= 0.0 # Numero de frames del segment actual
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+ # insertats en el reconeixedor
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+
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+ def __register_itoken_nosec(self,token):
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+ token= token.strip()
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+ if token=='': return
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+ nframes= int(self._inframes) if self._inframes>0.0 else None
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+ self._itokens[-1].append((token,nframes))
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+
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+ def __register_itoken(self,token):
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+ try:
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+ self.__register_itoken_nosec(token)
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+ except:
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+ pass
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+
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+ def __register_itoken_eos(self):
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+ if self._inframes>0.0:
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+ self._itokens.append([])
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+ self._inframes= 0.0
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+
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+ def __merge_itokens_nosec(self,code,hyp):
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+ # Comprovacions inicials i eliminació
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+ itokens= self._itokens[0]
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+ if code==TLK.OUT_RES: self._itokens.pop(0)
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+ if itokens==[]: return hyp
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+ # Inserta
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+ hyp,pos_var= hyp[0],hyp[1]
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+ pref,novar,var= [],list(hyp[:pos_var]),list(hyp[pos_var:])
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+ # -> Inserta inici segment
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+ while len(itokens)>0 and itokens[0][1] is None:
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+ tok= itokens.pop(0)
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+ pref.append((tok[0],0,0))
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+ # --> Processa novar
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+ if len(itokens)>0 and len(novar)>0:
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+ i,new_novar= 0,[]
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+ while len(itokens)>0 and i<len(novar):
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+ w,b,e= novar[i]
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+ epos= b+e
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+ while len(itokens)>0 and itokens[0][1]<epos:
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+ tok= itokens.pop(0)
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+ new_novar.append((tok[0],b,0))
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+ new_novar.append((w,b,e))
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+ i+= 1
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+ while i<len(novar):
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+ new_novar.append(novar[i])
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+ i+= 1
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+ else: new_novar= novar
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+ # --> Processa var
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+ if len(itokens)>0 and len(var)>0:
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+ i,j,new_var= 0,0,[]
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+ while len(itokens)>0 and i<len(var):
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+ w,b,e= var[i]
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+ epos= b+e
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+ while j<len(itokens) and itokens[j][1]<epos:
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+ new_var.append((itokens[j][0],b,0))
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+ j+= 1
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+ new_var.append((w,b,e))
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+ i+= 1
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+ while i<len(var):
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+ new_var.append(var[i])
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+ i+= 1
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+ else: new_var= var
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+ # --> Afegeix pendents si code==TLK.OUT_RES
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+ if code==TLK.OUT_RES and len(itokens)>0:
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+ assert var==[]
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+ pos= new_novar[-1][1]+new_novar[-1][2] if len(new_novar)>0 else -1
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+ for tok in itokens:
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+ tmp_pos= pos if pos!=-1 else tok[1]
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+ new_novar.append((tok[0],tmp_pos,0))
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+ return pref+new_novar+new_var,len(pref)+len(new_novar)
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+ # end __merge_itokens_nosec
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+
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+ def __merge_itokens(self,code,hyp):
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+ try:
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+ return self.__merge_itokens_nosec(code,hyp)
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+ except:
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+ return hyp
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+
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+ # None denotes end of segment
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+ def feed(self,data=None):
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+ if data==None:
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+ self.reco.feed()
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+ elif not self._err:
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+ if type(data)==str:
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+ self.__register_itoken(data)
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+ elif len(data)==0:
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+ self.reco.split()
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+ self.__register_itoken_eos()
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+ else:
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+ self._inframes+= (len(data)/32000)*self._fea_freq
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+ self.reco.feed(data)
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+
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+ def __write_hyp(self,novar,var,score,nframes,err,eos):
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+ h= Hyp(novar=novar,var=var,err=err,
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+ score=score,nframes=nframes,
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+ eos=eos)
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+ with self._cv:
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+ self._hyps.append(h)
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+ self._cv.notify_all()
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+
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+ def process_out(self,code,hyp,stats):
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+ #def totxt(rec,last_word):
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+ # aux= []
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+ # for x in rec:
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+ # w= x[0].lower()
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+ # aux.append(toupper(w,last_word))
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+ # last_word= w
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+ # ret= ' '.join(aux)
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+ # ret= ret.replace('[hesitation]','').replace('<unk>','')
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+ # return ret
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+ def totxt(rec):
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+ ret= ' '.join([x[0] for x in rec])
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+ ret= ret.replace('[hesitation]','').replace('<unk>','')
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+ return ret
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+ hyp,pos_var= hyp[0],hyp[1]
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+ novar= hyp[:pos_var]
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+ #new_last_word= novar[-1][0] if len(novar)>0 else self.last_word
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+ #novar= totxt(novar,self.last_word)
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+ #var= totxt(hyp[pos_var:],new_last_word)
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+ #self.last_word= new_last_word
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+ novar= totxt(novar)
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+ var= totxt(hyp[pos_var:])
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+ if novar=='' and var==self._prev and code==TLK.OUT_HYP: return
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+ self._prev= var
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+ score,nframes= stats
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+ self.__write_hyp(novar,var,score,nframes,False,
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+ code==TLK.OUT_RES)
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+
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+ # Output method
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+ def write(self,code,hyp,stats):
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+ hyp= self.__merge_itokens(code,hyp)
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+ if code==TLK.OUT_ERR:
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+ txt= self._prev if self._prev!=None else ''
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+ self.__write_hyp(txt,'',0,0,False,False)
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+ self._err= True
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+ return
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+ if code==TLK.OUT_END:
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+ self.__write_hyp(None,None,0,0,self._err,False)
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+ return
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+ if self.seg is not None:
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+ hyp= self.seg(hyp)
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+ if code==TLK.OUT_RES:
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+ hyp0,pos_var0= hyp
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+ hyp1= self.seg.eos()
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+ if hyp1 is not None:
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+ hyp1,pos_var1= hyp1
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+ hyp= hyp0[:pos_var0]+hyp1[:pos_var1]
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+ hyp= hyp,len(hyp)
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+ self.seg.reset()
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+ self.process_out(code,hyp,stats)
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+
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+ @property
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+ def output(self):
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+ end= False
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+ while not end:
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+ with self._cv:
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+ self._cv.wait_for(lambda: len(self._hyps)>0)
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+ ret= self._hyps.pop(0)
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+ end= ret.novar is None
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+ yield ret
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+
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+# end Recogniser
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+
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+class RecogniserManager:
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+
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+ def __init__(self,name,tag,date,lang,
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+ models,params,nreco,
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+ sil_word,sil_sym,fea_freq):
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+ self.v= []
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+ self.vv= [] # Reference copy
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+ #self.cmllr= params.cmllr_enabled
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+ for n in range(0,nreco):
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+ reco= Recogniser(models,params,
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+ sil_word,sil_sym,
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+ fea_freq,self)
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+ self.v.append(reco)
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+ self.vv.append(reco)
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+ self.name= name
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+ self.tag= tag
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+ self.date= date
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+ self.lang= lang
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+ self._lock= threading.Lock()
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+ self._models= models
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+ self._enabled= True
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+
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+ def __len__(self):
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+ return len(self.vv)
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+
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+ def get_reco(self):
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+ with self._lock:
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+ if self.v==[]: return None
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+ if not self._enabled: return None
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+ ret= self.v.pop()
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+ ret.reset()
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+ #ret.reset_cmllr()
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+ Log.info(('Recogniser taken from %s '+
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+ '(available: %d of %d)')%(self.name,
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+ len(self.v),
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+ len(self.vv)))
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+ return ret
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+
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+ def append(self,reco):
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+ with self._lock:
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+ self.v.append(reco)
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+ Log.info(('Recogniser from %s realeased'+
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+ '(available: %d of %d)')%(self.name,
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+ len(self.v),
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+ len(self.vv)))
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+
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+ def set_enabled(self,value):
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+ with self._lock:
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+ self._enabled= value
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+
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+ @property
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+ def num_recos_available(self):
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+ with self._lock:
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+ return len(self.v)
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+
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+ @property
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+ def enabled(self):
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+ with self._lock:
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+ return self._enabled
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+
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+# end RecogniserManager
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+
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+def prepare_params(cfg):
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+ params= TLK.OParameters()
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+ aux= cfg.get('hp')
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+ if aux!=None: params.hp= int(aux)
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+ aux= cfg.get('hp_min')
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+ if aux!=None: params.hp_min= int(aux)
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+ aux= cfg.get('wep')
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+ if aux!=None: params.wep= float(aux)
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+ aux= cfg.get('beam')
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+ if aux!=None: params.beam= float(aux)
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+ aux= cfg.get('dynormthr')
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+ if aux!=None: params.dynormthr= int(aux)
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+ aux= cfg.get('meannorm')
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+ if aux!=None: params.meannorm= bool(aux)
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+ aux= cfg.get('cmllr')
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+ if aux!=None: params.cmllr_enabled= bool(aux)
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+ aux= cfg.get('cmllr_nframes_step0')
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+ if aux!=None: params.cmllr_nframes_step0= int(aux)
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+ aux= cfg.get('cmllr_nframes')
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+ if aux!=None: params.cmllr_nframes= int(aux)
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+ aux= cfg.get('cmllr_niters')
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+ if aux!=None: params.cmllr_niters= int(aux)
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+ aux= cfg.get('numceps')
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+ if aux!=None: params.numceps= int(aux)
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+ aux= cfg.get('numchans')
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+ if aux!=None: params.numchans= int(aux)
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+ aux= cfg.get('accwindow')
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+ if aux!=None: params.accwindow= int(aux)
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+ aux= cfg.get('deltawindow')
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+ if aux!=None: params.deltawindow= int(aux)
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+ aux= cfg.get('hp_lm')
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+ if aux!=None: params.hp_lm= int(aux)
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+ aux= cfg.get('order')
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+ if aux!=None: params.order= int(aux)
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+ aux= cfg.get('ftype')
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+ if aux!=None: params.ftype= str(aux)
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+ aux= cfg.get('mustdnorm')
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+ if aux!=None: params.mustdnorm= bool(aux)
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+ aux= cfg.get('gsf')
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+ gsf= float(aux) if aux!=None else 1.0
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+ params.gsf= gsf
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+ aux= cfg.get('wip')
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+ wip= float(aux) if aux!=None else 0.0
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+ params.wip= wip
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+ params.amla= cfg.get('amla-fs')!=None
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+ params.sil= cfg.get('sil_sym','SP')
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+
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+ # IMPORTANT !!!!! Deshabilita l'heurístic de segmentació
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+ params.sil_length= 20000
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+ params.sil_thr= 0.0
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+
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+ return params
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+# end prepare_params
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+
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+def load_models(cfg,name,params,sil_word):
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+ Log.info("Loading '%s' models..."%name)
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|
|
+ mustd= cfg.get('mustd')
|
|
|
|
+ dnn= cfg.get('dnn')
|
|
|
|
+ if params.cmllr_enabled:
|
|
|
|
+ sys.exit('CMLLR NOT SUPPORTED !!!!')
|
|
|
|
+ target= cfg.get('target')
|
|
|
|
+ dnn_cmllr= cfg.get('dnn_cmllr')
|
|
|
|
+ else:
|
|
|
|
+ target= dnn_cmllr= None
|
|
|
|
+ lexicon= cfg.get('lexicon')
|
|
|
|
+
|
|
|
|
+ amodel= TLK.AModel(cfg['amodel'])
|
|
|
|
+ if lexicon!=None:
|
|
|
|
+ lex= TLK.Lexicon(lexicon, syms=amodel.syms)
|
|
|
|
+ lm= TLK.LM()
|
|
|
|
+ lm.load(cfg['lm'],lexicon=lex)
|
|
|
|
+ else:
|
|
|
|
+ try:
|
|
|
|
+ lm= TLK.SearchGraph(cfg['lm'], syms=amodel.syms)
|
|
|
|
+ except:
|
|
|
|
+ lm= TLK.StaticLookaheadTables(cfg['lm'], syms=amodel.syms)
|
|
|
|
+ dlm= cfg.get('dlm')
|
|
|
|
+ if dlm!=None:
|
|
|
|
+ dlm= lmodel.load_model(dlm)(lm.words)
|
|
|
|
+ oep= cfg.get('oep')
|
|
|
|
+ if oep!=None:
|
|
|
|
+ step= cfg.get('oep-step')
|
|
|
|
+ if step==None:
|
|
|
|
+ sys.exit('oep-step not defined')
|
|
|
|
+ step= int(step)
|
|
|
|
+ lookahead= cfg.get('oep-lookahead')
|
|
|
|
+ if lookahead==None:
|
|
|
|
+ sys.exit('oep-lookahead not defined')
|
|
|
|
+ lookahead= int(lookahead)
|
|
|
|
+ priors_fn= cfg.get('oep-priors')
|
|
|
|
+ amla_fs= cfg.get('amla-fs')
|
|
|
|
+ tmp= oepmodel.load_model(oep,step,lookahead,
|
|
|
|
+ priors_fn,amla_fs)
|
|
|
|
+ oep_factory,oep_model= tmp
|
|
|
|
+ else: oep_factory= oep_model= None
|
|
|
|
+ seg= cfg.get('seg')
|
|
|
|
+ if seg!=None:
|
|
|
|
+ tmp= segmodel.load_model(seg,sil_word=sil_word)
|
|
|
|
+ seg_factory,seg_model= tmp
|
|
|
|
+ else: seg_factory= seg_model= None
|
|
|
|
+ aux= cfg.get('gsf')
|
|
|
|
+ gsf= float(aux) if aux!=None else 1.0
|
|
|
|
+ aux= cfg.get('wip')
|
|
|
|
+ wip= float(aux) if aux!=None else 0.0
|
|
|
|
+ models= {}
|
|
|
|
+ models["amodel"]=amodel
|
|
|
|
+ models["lm"]=lm
|
|
|
|
+ models["dnn"]=dnn
|
|
|
|
+ models["gsf"]=gsf
|
|
|
|
+ models["wip"]=wip
|
|
|
|
+ models['dlm']= dlm
|
|
|
|
+ models['oep-factory']= oep_factory
|
|
|
|
+ models['oep-model']= oep_model
|
|
|
|
+ models['mustd']= mustd
|
|
|
|
+ models['seg-factory']= seg_factory
|
|
|
|
+ models['seg-model']= seg_model
|
|
|
|
+ return models
|
|
|
|
+
|
|
|
|
+# end load_models
|
|
|
|
+
|
|
|
|
+def load_system(conf):
|
|
|
|
+ sil_word= conf.get('sil_word')
|
|
|
|
+ sil_sym= conf.get('sil_sym')
|
|
|
|
+ fea_freq= conf.get('fea_freq',100)
|
|
|
|
+ params= prepare_params(conf)
|
|
|
|
+ name= conf['id']
|
|
|
|
+ tag= conf.get('tag',name)
|
|
|
|
+ date= tuple(conf.get('date',[1,1,1971]))
|
|
|
|
+ models= load_models(conf,tag,params,sil_word)
|
|
|
|
+ Log.info("Creating '%s' recognisers..."%name)
|
|
|
|
+ return RecogniserManager(name,tag,date,conf['lang'],
|
|
|
|
+ models,params,conf['nreco'],
|
|
|
|
+ sil_word,sil_sym,fea_freq)
|
|
|
|
+# uppercase_tbl,uppercase_tbl_bi,
|
|
|
|
+ # Uppercase-table (Deshabilitat de moment)
|
|
|
|
+ #aux= cfg.get('uppercase-table')
|
|
|
|
+ #uppercase_tbl= load_uppercase_table(aux) if aux!=None else None
|
|
|
|
+ #aux= cfg.get('uppercase-table-bi')
|
|
|
|
+ #uppercase_tbl_bi= load_uppercase_table_bi(aux) if aux!=None else None
|
|
|
|
+
|
|
|
|
+# end create_recognisers
|
|
|
|
+
|