def main(options, args):
# 1. load reference lexicon
print('loading reference lexicon ...')
lexicon = loadBlissLexicon(options.lexicon)
knownWords = set([ orth for orth, phon in lexicon ])
# 2. load model for fragmentizing unknown words
if options.subliminal_lexicon:
print('loading subliminal lexicon ...')
subliminalLexicon = loadBlissLexicon(options.subliminal_lexicon)
else:
subliminalLexicon = None
if options.subliminal_g2p:
print('loading subliminal g2p model ...')
subliminalG2p = pickle.load(open(options.subliminal_g2p))
else:
subliminalG2p = None
if options.g2pModel:
print('loading g2p model ...')
model = pickle.load(open(options.g2pModel))
oldSize, newSize = model.strip()
print('stripped number of multigrams from %d to %d' % (oldSize, newSize))
fragmentizer = Fragmentizer(model)
if subliminalLexicon:
fragmentizer.addSupervised(subliminalLexicon)
if subliminalG2p:
fragmentizer.addSupervised(subliminalG2p)
graphones = model.sequitur.symbols()
graphones.remove(model.sequitur.symbol(model.sequitur.term))
else:
model = fragmentizer = graphones = None
# 3. add fragments to lexicon
if options.write_lexicon:
print('creating extended lexicon ...')
xmlLexicon = ElementTree(file = options.lexicon)
if options.model_type == 'phonemes':
changeSyntaticToPhonetic(xmlLexicon)
else:
addGraphonesToLexicon(xmlLexicon, graphones)
xmlLexicon.write(gOpenOut(options.write_lexicon), defaultEncoding)
# 4. determine set of LM tokens
vocabulary = mGramCounts.ClosedVocablary()
vocabulary.add(['<s>', '</s>'])
if options.model_type == 'flat-hybrid':
vocabulary.add(filter(isLmToken, knownWords), soft=True)
if graphones:
vocabulary.add(starmap(lmToken, graphones))
vocabulary.sort()
if options.write_tokens:
f = gOpenOut(options.write_tokens, defaultEncoding)
if options.model_type == 'phonemes':
phonemes = set(p for orth, phon in lexicon for p in phon)
phonemes.add('#1')
if 'si' in phonemes: phonemes.remove('si')
for p in sorted(phonemes):
print(p, file=f)
else:
for w in vocabulary:
if w is not None:
print(w, file=f)
# 5./6. set-up LM event generator
if options.write_counts or options.write_events:
order = options.order - 1
if options.model_type == 'flat-hybrid':
events = HybridEventGenerator(knownWords, fragmentizer, order)
if options.range_type == 'fragments':
events.setFragmentRange()
elif options.range_type == 'words':
events.setTrueWordRange()
else:
assert ValueError(options.range_type)
elif options.model_type == 'fragments':
events = OovEventGenerator(knownWords, fragmentizer, order)
elif options.model_type == 'phonemes':
events = PhonemeEventGenerator(lexicon, order)
# 5. create modified LM training corpus counts
if options.write_events:
print('creating sequence model events ...')
f = gOpenOut(options.write_events, defaultEncoding)
for event, count in events(gOpenIn(options.text, defaultEncoding)):
print(repr(event), '\t', count, file=f)
# 6. count LM events
if options.write_counts:
print('creating sequence model counts ...')
counts = mGramCounts.SimpleMultifileStorage()
counts.addIter(events(gOpenIn(options.text, defaultEncoding)))
mGramCounts.TextStorage.write(gOpenOut(options.write_counts, defaultEncoding), counts)
# 7. dump list of OOV words and their corresponding fragmentation
if options.write_fragments:
print('dumping fragments ...')
f = gOpenOut(options.write_fragments, defaultEncoding)
events = OovFragmentGenerator(knownWords, fragmentizer)
fragments = events(gOpenIn(options.text, defaultEncoding))
for event in list(fragments.keys()):
print(event, '\t', ' '.join(fragments[event]), file=f)
# 8. dump modified LM training text
if options.write_lm_text:
print('dumping modified LM training text ...')
f = gOpenOut(options.write_lm_text, defaultEncoding)
events = OovFragmentGenerator(knownWords, fragmentizer)
for line in gOpenIn(options.text, defaultEncoding):
words = line.split()
modWords = events.modifyLmText(words)
print(" ".join(modWords), file=f)
def main(options, args):
# 1. load reference lexicon
print 'loading reference lexicon ...'
lexicon = loadBlissLexicon(options.lexicon)
knownWords = set([ orth for orth, phon in lexicon ])
# 2. load model for fragmentizing unknown words
if options.subliminal_lexicon:
print 'loading subliminal lexicon ...'
subliminalLexicon = loadBlissLexicon(options.subliminal_lexicon)
else:
subliminalLexicon = None
if options.subliminal_g2p:
print 'loading subliminal g2p model ...'
subliminalG2p = pickle.load(open(options.subliminal_g2p))
else:
subliminalG2p = None
if options.g2pModel:
print 'loading g2p model ...'
model = pickle.load(open(options.g2pModel))
oldSize, newSize = model.strip()
print 'stripped number of multigrams from %d to %d' % (oldSize, newSize)
fragmentizer = Fragmentizer(model)
if subliminalLexicon:
fragmentizer.addSupervised(subliminalLexicon)
if subliminalG2p:
fragmentizer.addSupervised(subliminalG2p)
graphones = model.sequitur.symbols()
graphones.remove(model.sequitur.symbol(model.sequitur.term))
else:
model = fragmentizer = graphones = None
# 3. add fragments to lexicon
if options.write_lexicon:
print 'creating extended lexicon ...'
xmlLexicon = ElementTree(file = options.lexicon)
if options.model_type == 'phonemes':
changeSyntaticToPhonetic(xmlLexicon)
else:
addGraphonesToLexicon(xmlLexicon, graphones)
xmlLexicon.write(gOpenOut(options.write_lexicon), defaultEncoding)
# 4. determine set of LM tokens
vocabulary = mGramCounts.ClosedVocablary()
vocabulary.add(['<s>', '</s>'])
if options.model_type == 'flat-hybrid':
vocabulary.add(ifilter(isLmToken, knownWords), soft=True)
if graphones:
vocabulary.add(starmap(lmToken, graphones))
vocabulary.sort()
if options.write_tokens:
f = gOpenOut(options.write_tokens, defaultEncoding)
if options.model_type == 'phonemes':
phonemes = set(p for orth, phon in lexicon for p in phon)
phonemes.add('#1')
if 'si' in phonemes: phonemes.remove('si')
for p in sorted(phonemes):
print >> f, p
else:
for w in vocabulary:
if w is not None:
print >> f, w
# 5./6. set-up LM event generator
if options.write_counts or options.write_events:
order = options.order - 1
if options.model_type == 'flat-hybrid':
events = HybridEventGenerator(knownWords, fragmentizer, order)
if options.range_type == 'fragments':
events.setFragmentRange()
elif options.range_type == 'words':
events.setTrueWordRange()
else:
assert ValueError(options.range_type)
elif options.model_type == 'fragments':
events = OovEventGenerator(knownWords, fragmentizer, order)
elif options.model_type == 'phonemes':
events = PhonemeEventGenerator(lexicon, order)
# 5. create modified LM training corpus counts
if options.write_events:
print 'creating sequence model events ...'
f = gOpenOut(options.write_events, defaultEncoding)
for event, count in events(gOpenIn(options.text, defaultEncoding)):
print >> f, repr(event), '\t', count
# 6. count LM events
if options.write_counts:
print 'creating sequence model counts ...'
counts = mGramCounts.SimpleMultifileStorage()
counts.addIter(events(gOpenIn(options.text, defaultEncoding)))
mGramCounts.TextStorage.write(gOpenOut(options.write_counts, defaultEncoding), counts)
# 7. dump list of OOV words and their corresponding fragmentation
if options.write_fragments:
print 'dumping fragments ...'
f = gOpenOut(options.write_fragments, defaultEncoding)
events = OovFragmentGenerator(knownWords, fragmentizer)
fragments = events(gOpenIn(options.text, defaultEncoding))
for event in fragments.keys():
print >> f, event, '\t', ' '.join(fragments[event])
# 8. dump modified LM training text
if options.write_lm_text:
print 'dumping modified LM training text ...'
f = gOpenOut(options.write_lm_text, defaultEncoding)
events = OovFragmentGenerator(knownWords, fragmentizer)
for line in gOpenIn(options.text, defaultEncoding):
words = line.split()
modWords = events.modifyLmText(words)
print >> f, " ".join(modWords)