def __init__(self, grammar, length=1):
"""Convert the grammar to Chomsky Normal Form and do preprocessing.
`grammar` can be:
(1) an instance of nltk.grammar.ContextFreeGrammar,
(2) a string representing the path to a .cfg file, or
(3) a string that can be parsed into a grammar by parse_cfg
`length` is the maximum string length that should be preprocessed.
"""
if length < 1:
raise ValueError('length must be greater than 0.')
# self.grammar must be instance of nltk.grammar.Grammar
if isinstance(grammar, ContextFreeGrammar):
self.grammar = grammar
elif isinstance(grammar, str) and grammar.endswith('.cfg'):
self.grammar = nltk.data.load('file:' + grammar)
elif isinstance(grammar, str):
self.grammar = parse_cfg(grammar)
else:
raise ValueError('Arg grammar must be nltk.grammar.Grammar or str.')
if not self.grammar.is_chomsky_normal_form():
#raise ValueError('Input grammar must be in CNF '
# '(conversion method isn\'t implemented)')
self.grammar = convert_to_cnf(self.grammar)
assert self.grammar.is_chomsky_normal_form()
self.productions = self.grammar.productions()
# TODO: Is it ok to assume all nonterminals occur on a LHS?
# Technically yes, but check whether nltk's is_cnf ensures it.
self.nonterminals = set([p.lhs() for p in self.productions])
self.terminals = set([token for prod in self.productions
for token in prod.rhs()
if not isinstance(token, Nonterminal)])
# Initialize self._counts then populate it in _preprocess().
# self.length is the string length that has been preprocessed.
self._counts = {}
self.length = 0
self._preprocess(length)
VOWEL -> MONOPHTHONG | DIPHTHONG
MONOPHTHONG -> 'ɔ' | 'ɑ' | 'i' | 'u' | 'ɛ' | 'ɪ' | 'ʊ' | 'ʌ' | 'ə' | 'æ'
DIPHTHONG -> 'eɪ' | 'aɪ' | 'oʊ' | 'aʊ' | 'ɔɪ'
GLIDE -> 'j' | 'w'
SYLLABIC_CONSONANT -> 'm̩' | 'n̩' | 'ɹ̩' | 'ɫ̩'
NASAL -> 'm' | 'n' | 'ŋ'
LIQUID -> 'ɹ' | 'l'
FRICATIVE -> 'f' | 'v' | 'θ' | 'ð' | 's' | 'z' | 'ʃ' | 'ʒ'
STOP -> 'p' | 'b' | 't' | 'd' | 'k' | 'g'
AFFRICATE -> 'tʃ' | 'dʒ'
""")
grammar = cnf.convert_to_cnf(non_cnf_grammar)
assert grammar.is_chomsky_normal_form()
generator = cfl.CFLGenerator(grammar)
def main(args):
if args:
num_phones = int(args[0])
else:
num_phones = 50
print ''.join(generator.generate(num_phones))
if __name__ == '__main__':
main(sys.argv[1:])
