def filter_babel_gum(target, source, env):
with meta_open(source[0].rstr()) as pron_ifd, meta_open(source[1].rstr()) as prob_ifd, meta_open(source[2].rstr()) as lim_ifd:
pron = Pronunciations(pron_ifd)
logging.info("Old pronunciations: %s", pron)
prob = ProbabilityList(prob_ifd)
logging.info("Old probabilities: %s", prob)
filt = Vocabulary(lim_ifd)
logging.info("Correct words: %s", filt)
pron.filter_by(filt)
logging.info("New pronunciations: %s", pron)
prob.filter_by(filt)
logging.info("New probabilities: %s", prob)
with meta_open(target[0].rstr(), "w") as pron_ofd, meta_open(target[1].rstr(), "w") as prob_ofd:
pron_ofd.write(pron.format())
prob_ofd.write(prob.format())
return None
def augment_language_model(target, source, env):
"""
Input: old language model, old pronunciations, new pronunciations
Output: new language model, new vocab, new pronunciations
"""
from arpabo import Arpabo, Pronunciations
if len(source) == 4:
old_prons = Pronunciations(meta_open(source[0].rstr()))
old_lm = Arpabo(meta_open(source[1].rstr()))
new_prons = Pronunciations(meta_open(source[2].rstr()))
mass = source[3].read()
elif len(source) == 5:
old_prons = Pronunciations(meta_open(source[0].rstr()))
old_lm = Arpabo(meta_open(source[1].rstr()))
new_prons = Pronunciations(meta_open(source[2].rstr()))
new_probs = arpabo.ProbabilityList(meta_open(source[3].rstr()))
mass = source[4].read()
logging.info("Old LM: %s", old_lm)
logging.info("Old Pronunciations: %s", old_prons)
logging.info("Words to add: %s", new_prons)
old_prons.add_entries(new_prons)
if len(source) == 4:
old_lm.add_unigrams(new_prons.get_words(), mass)
else:
old_lm.add_unigrams_with_probs(new_probs, mass)
logging.info("New Pronunciations: %s", old_prons)
logging.info("New LM: %s", old_lm)
logging.info("New words have weight %s", old_lm.get_probability_of_words(new_prons.get_words()))
logging.info("Old words have weight %s", old_lm.get_probability_of_not_words(new_prons.get_words()))
with meta_open(target[0].rstr(), "w") as new_vocab, meta_open(target[1].rstr(), "w") as new_prons, meta_open(target[2].rstr(), "w") as new_lm:
new_lm.write(old_lm.format())
new_vocab.write(old_prons.format_vocabulary())
new_prons.write(old_prons.format())
return None
