def test_dict(self): d = DictPron( DICT_FRA ) self.assertTrue( d.is_unk('azerty') ) self.assertFalse( d.is_unk('il_y_a') ) self.assertFalse( d.is_unk(u'être') ) self.assertEqual( d.get_pron(u'sil'), "s.i.l" ) self.assertEqual( d.get_pron(u'azerty'), "UNK" )
def gen_slm_dependencies(self, basename, N=3): """ Generate the dependencies (slm, dictionary) for julius. @param basename (str - IN) the base name of the slm file and of the dictionary file @param N (int) Language model N-gram length. """ dictname = basename + ".dict" slmname = basename + ".arpa" phoneslist = self._phones.split() tokenslist = self._tokens.split() dictpron = DictPron() for token,pron in zip(tokenslist,phoneslist): for variant in pron.split("|"): dictpron.add_pron( token, variant.replace("-"," ") ) if dictpron.is_unk(START_SENT_SYMBOL) is True: dictpron.add_pron( START_SENT_SYMBOL, "sil" ) if dictpron.is_unk(END_SENT_SYMBOL) is True: dictpron.add_pron( END_SENT_SYMBOL, "sil" ) dictpron.save_as_ascii( dictname, False ) # Write the SLM model = NgramsModel(N) model.append_sentences( [self._tokens] ) probas = model.probabilities( method="logml" ) arpaio = ArpaIO() arpaio.set( probas ) arpaio.save( slmname )