class Flite(object):
"""English G2P using the Flite speech synthesis system."""
def __init__(self, arpabet='arpabet', ligatures=False, cedict_file=None):
"""Construct a Flite "wrapper"
Args:
arpabet (str): file containing ARPAbet to IPA mapping
ligatures (bool): if True, use non-standard ligatures instead of
standard IPA
cedict_filename (str): path to CC-CEDict dictionary (included for
compatibility)
"""
arpabet = pkg_resources.resource_filename(
__name__, os.path.join('data', arpabet + '.csv'))
self.arpa_map = self._read_arpabet(arpabet)
self.chunk_re = re.compile(r"([A-Za-z'’]+|[^A-Za-z'’]+)", re.U)
self.letter_re = re.compile(r"[A-Za-z'’]+")
self.regexp = re.compile(r'[A-Za-z]')
self.puncnorm = PuncNorm()
self.ligatures = ligatures
self.ft = panphon.FeatureTable()
def _read_arpabet(self, arpabet):
arpa_map = {}
with open(arpabet, 'rb') as f:
reader = csv.reader(f, encoding='utf-8')
for arpa, ipa in reader:
arpa_map[arpa] = ipa
return arpa_map
def normalize(self, text):
text = unicode(text)
text = unicodedata.normalize('NFD', text)
text = ''.join(filter(lambda x: x in string.printable, text))
return text
def arpa_text_to_list(self, arpa_text):
return arpa_text.split(' ')[1:-1]
def arpa_to_ipa(self, arpa_text, ligatures=False):
arpa_text = arpa_text.strip()
arpa_list = self.arpa_text_to_list(arpa_text)
arpa_list = map(lambda d: re.sub('\d', '', d), arpa_list)
ipa_list = map(lambda d: self.arpa_map[d], arpa_list)
text = ''.join(ipa_list)
return text
def transliterate(self, text, normpunc=False, ligatures=False):
"""Convert English text to IPA transcription
Args:
text (unicode): English text
normpunc (bool): if True, normalize punctuation downward
ligatures (bool): if True, use non-standard ligatures instead of
standard IPA
"""
text = unicodedata.normalize('NFC', text)
acc = []
for chunk in self.chunk_re.findall(text):
if self.letter_re.match(chunk):
acc.append(self.english_g2p(chunk))
else:
acc.append(chunk)
text = ''.join(acc)
text = self.puncnorm.norm(text) if normpunc else text
text = ligaturize(text) if (ligatures or self.ligatures) else text
return text
def strict_trans(self, text, normpunc=False, ligatures=False):
return self.transliterate(text, normpunc, ligatures)
def word_to_tuples(self, word, normpunc=False):
"""Given a word, returns a list of tuples corresponding to IPA segments.
Args:
word (unicode): word to transliterate
normpunc (bool): If True, normalizes punctuation to ASCII inventory
Returns:
list: A list of (category, lettercase, orthographic_form,
phonetic_form, feature_vectors) tuples.
The "feature vectors" form a list consisting of (segment, vector) pairs.
For IPA segments, segment is a substring of phonetic_form such that the
concatenation of all segments in the list is equal to the phonetic_form.
The vectors are a sequence of integers drawn from the set {-1, 0, 1}
where -1 corresponds to '-', 0 corresponds to '0', and 1 corresponds to
'+'.
"""
def cat_and_cap(c):
cat, case = tuple(unicodedata.category(c))
case = 1 if case == 'u' else 0
return unicode(cat), case
def recode_ft(ft):
try:
return {'+': 1, '0': 0, '-': -1}[ft]
except KeyError:
return None
def vec2bin(vec):
return map(recode_ft, vec)
def to_vector(seg):
return seg, vec2bin(self.ft.segment_to_vector(seg))
def to_vectors(phon):
if phon == '':
return [(-1, [0] * self.num_panphon_fts)]
else:
return [to_vector(seg) for seg in self.ft.segs(phon)]
tuples = []
word = unicode(word)
# word = self.strip_diacritics.process(word)
word = unicodedata.normalize('NFKD', word)
word = unicodedata.normalize('NFC', word)
while word:
match = re.match('[A-Za-z]+', word)
if match:
span = match.group(0)
cat, case = cat_and_cap(span[0])
phonword = self.transliterate(span)
phonsegs = self.ft.segs(phonword)
maxlen = max(len(phonsegs), len(span))
orth = list(span) + [''] * (maxlen - len(span))
phonsegs += [''] * (maxlen - len(phonsegs))
for p, o in zip(phonsegs, orth):
tuples.append(('L', case, o, p, to_vectors(p)))
word = word[len(span):]
else:
span = word[0]
span = self.puncnorm.norm(span) if normpunc else span
cat, case = cat_and_cap(span)
cat = 'P' if normpunc and cat in self.puncnorm else cat
phon = ''
vecs = to_vectors(phon)
tuples.append((cat, case, span, phon, vecs))
word = word[1:]
return tuples
class Flite(object):
"""English G2P using the Flite speech synthesis system."""
def __init__(self, arpabet='arpabet', ligatures=False, cedict_file=None):
"""Construct a Flite "wrapper"
Args:
arpabet (str): file containing ARPAbet to IPA mapping
ligatures (bool): if True, use non-standard ligatures instead of
standard IPA
cedict_filename (str): path to CC-CEDict dictionary (included for
compatibility)
"""
arpabet = pkg_resources.resource_filename(__name__, os.path.join('data', arpabet + '.csv'))
self.arpa_map = self._read_arpabet(arpabet)
self.chunk_re = re.compile(r"([A-Za-z'’]+|[^A-Za-z'’]+)", re.U)
self.letter_re = re.compile(r"[A-Za-z'’]+")
self.regexp = re.compile(r'[A-Za-z]')
self.puncnorm = PuncNorm()
self.ligatures = ligatures
self.ft = panphon.FeatureTable()
def _read_arpabet(self, arpabet):
arpa_map = {}
with open(arpabet, 'rb') as f:
reader = csv.reader(f, encoding='utf-8')
for arpa, ipa in reader:
arpa_map[arpa] = ipa
return arpa_map
def normalize(self, text):
text = unicode(text)
text = unicodedata.normalize('NFD', text)
text = ''.join(filter(lambda x: x in string.printable, text))
return text
def arpa_text_to_list(self, arpa_text):
return arpa_text.split(' ')[1:-1]
def arpa_to_ipa(self, arpa_text, ligatures=False):
arpa_text = arpa_text.strip()
arpa_list = self.arpa_text_to_list(arpa_text)
arpa_list = map(lambda d: re.sub('\d', '', d), arpa_list)
ipa_list = map(lambda d: self.arpa_map[d], arpa_list)
text = ''.join(ipa_list)
return text
def transliterate(self, text, normpunc=False, ligatures=False):
"""Convert English text to IPA transcription
Args:
text (unicode): English text
normpunc (bool): if True, normalize punctuation downward
ligatures (bool): if True, use non-standard ligatures instead of
standard IPA
"""
text = unicodedata.normalize('NFC', text)
acc = []
for chunk in self.chunk_re.findall(text):
if self.letter_re.match(chunk):
acc.append(self.english_g2p(chunk))
else:
acc.append(chunk)
text = ''.join(acc)
text = self.puncnorm.norm(text) if normpunc else text
text = ligaturize(text) if ligatures else text
return text
def strict_trans(self, text, normpunc=False, ligatures=False):
return self.transliterate(text, normpunc, ligatures)
def word_to_tuples(self, word, normpunc=False):
"""Given a word, returns a list of tuples corresponding to IPA segments.
Args:
word (unicode): word to transliterate
normpunc (bool): If True, normalizes punctuation to ASCII inventory
Returns:
list: A list of (category, lettercase, orthographic_form,
phonetic_form, feature_vectors) tuples.
The "feature vectors" form a list consisting of (segment, vector) pairs.
For IPA segments, segment is a substring of phonetic_form such that the
concatenation of all segments in the list is equal to the phonetic_form.
The vectors are a sequence of integers drawn from the set {-1, 0, 1}
where -1 corresponds to '-', 0 corresponds to '0', and 1 corresponds to
'+'.
"""
def cat_and_cap(c):
cat, case = tuple(unicodedata.category(c))
case = 1 if case == 'u' else 0
return unicode(cat), case
def recode_ft(ft):
try:
return {'+': 1, '0': 0, '-': -1}[ft]
except KeyError:
return None
def vec2bin(vec):
return map(recode_ft, vec)
def to_vector(seg):
return seg, vec2bin(self.ft.segment_to_vector(seg))
def to_vectors(phon):
if phon == '':
return [(-1, [0] * self.num_panphon_fts)]
else:
return [to_vector(seg) for seg in self.ft.segs(phon)]
tuples = []
word = unicode(word)
# word = self.strip_diacritics.process(word)
word = unicodedata.normalize('NFKD', word)
word = unicodedata.normalize('NFC', word)
while word:
match = re.match('[A-Za-z]+', word)
if match:
span = match.group(0)
cat, case = cat_and_cap(span[0])
phonword = self.transliterate(span)
phonsegs = self.ft.segs(phonword)
maxlen = max(len(phonsegs), len(span))
orth = list(span) + [''] * (maxlen - len(span))
phonsegs += [''] * (maxlen - len(phonsegs))
for p, o in zip(phonsegs, orth):
tuples.append(('L', case, o, p, to_vectors(p)))
word = word[len(span):]
else:
span = word[0]
span = self.puncnorm.norm(span) if normpunc else span
cat, case = cat_and_cap(span)
cat = 'P' if normpunc and cat in self.puncnorm else cat
phon = ''
vecs = to_vectors(phon)
tuples.append((cat, case, span, phon, vecs))
word = word[1:]
return tuples
class SimpleEpitran(object):
def __init__(self, code, preproc=True, postproc=True, ligatures=False):
"""Constructs the backend object epitran uses for most languages
Args:
code (str): ISO 639-3 code and ISO 15924 code joined with a hyphen
preproc (bool): if True, apply preprocessor
postproc (bool): if True, apply postprocessors
ligatures (bool): if True, use phonetic ligatures for affricates
instead of standard IPA
"""
self.g2p = self._load_g2p_map(code)
self.regexp = self._construct_regex()
self.puncnorm = PuncNorm()
self.ft = panphon.FeatureTable()
self.num_panphon_fts = len(self.ft.names)
self.preprocessor = PrePostProcessor(code, 'pre')
self.postprocessor = PrePostProcessor(code, 'post')
self.strip_diacritics = StripDiacritics(code)
self.preproc = preproc
self.postproc = postproc
self.ligatures = ligatures
self.nils = defaultdict(int)
def __enter__(self):
return self
def __exit__(self, type_, val, tb):
for nil, count in self.nils.items():
sys.stderr.write('Unknown character "{}" occured {} times.\n'.format(nil, count))
def _one_to_many_gr_by_line_map(self, gr_by_line):
for g, ls in gr_by_line.items():
if len(ls) != 1:
return (g, ls)
return None
def _load_g2p_map(self, code):
"""Load the code table for the specified language.
Args:
code (str): ISO 639-3 code plus "-" plus ISO 15924 code for the
language/script to be loaded
"""
g2p = defaultdict(list)
gr_by_line = defaultdict(list)
try:
path = os.path.join('data', 'map', code + '.csv')
path = pkg_resources.resource_filename(__name__, path)
except IndexError:
raise DatafileError('Add an appropriately-named mapping to the data/maps directory.')
with open(path, 'rb') as f:
reader = csv.reader(f, encoding='utf-8')
next(reader)
for (i, fields) in enumerate(reader):
try:
graph, phon = fields
except ValueError:
raise DatafileError('Map file is not well formed at line {}.'.format(i + 2))
graph = unicodedata.normalize('NFC', graph)
phon = unicodedata.normalize('NFC', phon)
g2p[graph].append(phon)
gr_by_line[graph].append(i)
if self._one_to_many_gr_by_line_map(g2p):
graph, lines = self._one_to_many_gr_by_line_map(gr_by_line)
lines = [l + 2 for l in lines]
raise MappingError('One-to-many G2P mapping for "{}" on lines {}'.format(graph, ', '.join(map(str, lines))).encode('utf-8'))
return g2p
def _load_punc_norm_map(self):
"""Load the map table for normalizing 'down' punctuation."""
path = os.path.join('data', 'puncnorm.csv')
path = pkg_resources.resource_filename(__name__, path)
with open(path, 'rb') as f:
reader = csv.reader(f, encoding='utf-8',
delimiter=str(','),
quotechar=str('"'))
next(reader)
return {punc: norm for (punc, norm) in reader}
def _construct_regex(self):
"""Build a regular expression that will greadily match segments from
the mapping table.
"""
graphemes = sorted(self.g2p.keys(), key=len, reverse=True)
return re.compile(r'({})'.format(r'|'.join(graphemes)), re.I)
def general_trans(self, text, filter_func,
normpunc=False, ligatures=False):
"""Transliaterates a word into IPA, filtering with filter_func
Args:
text (str): word to transcribe; unicode strings
filter_func (function): function for filtering segments; takes
a <segment, is_ipa> tuple and returns a
boolean.
normpunc (bool): normalize punctuation
ligatures (bool): use precomposed ligatures instead of
standard IPA
Returns:
unicode: IPA string, filtered by filter_func.
"""
text = unicode(text)
text = self.strip_diacritics.process(text)
text = unicodedata.normalize('NFC', text.lower())
if self.preproc:
text = self.preprocessor.process(text)
tr_list = []
while text:
m = self.regexp.match(text)
if m:
source = m.group(0)
try:
target = self.g2p[source][0]
except KeyError:
logging.debug("source = '{}'".format(source))
logging.debug("self.g2p[source] = '{}'"
.format(self.g2p[source]))
target = source
tr_list.append((target, True))
text = text[len(source):]
else:
tr_list.append((text[0], False))
self.nils[text[0]] += 2
text = text[1:]
text = ''.join([s for (s, _) in filter(filter_func, tr_list)])
if self.postproc:
text = self.postprocessor.process(text)
if ligatures or self.ligatures:
text = ligaturize(text)
if normpunc:
text = self.puncnorm.norm(text)
return text
def transliterate(self, text, normpunc=False, ligatures=False):
"""Transliterates/transcribes a word into IPA
Passes unmapped characters through to output unchanged.
Args:
word (str): word to transcribe; unicode string
normpunc (bool): normalize punctuation
ligatures (bool): use precomposed ligatures instead of standard IPA
Returns:
unicode: IPA string with unrecognized characters included
"""
return self.general_trans(text, lambda x: True,
normpunc, ligatures)
def strict_trans(self, text, normpunc=False, ligatures=False):
"""Transliterates/transcribes a word into IPA
Ignores umapped characters.
Args:
word (str): word to transcribe; unicode string
normpunc (bool): normalize punctuation
ligatures (bool): use precomposed ligatures instead of standard IPA
Returns:
unicode: IPA string
"""
return self.general_trans(text, lambda x: x[1],
normpunc, ligatures)
def word_to_tuples(self, word, normpunc=False):
"""Given a word, returns a list of tuples corresponding to IPA segments.
Args:
word (unicode): word to transliterate
normpunc (bool): If True, normalizes punctuation to ASCII inventory
Returns:
list: A list of (category, lettercase, orthographic_form,
phonetic_form, feature_vectors) tuples.
The "feature vectors" form a list consisting of (segment, vector)
pairs. For IPA segments, segment is a substring of phonetic_form such
that the concatenation of all segments in the list is equal to
the phonetic_form. The vectors are a sequence of integers drawn from
the set {-1, 0, 1} where -1 corresponds to '-', 0 corresponds to '0',
and 1 corresponds to '+'.
"""
def cat_and_cap(c):
cat, case = tuple(unicodedata.category(c))
case = 1 if case == 'u' else 0
return unicode(cat), case
def recode_ft(ft):
try:
return {'+': 1, '0': 0, '-': -1}[ft]
except KeyError:
return None
def vec2bin(vec):
return list(map(recode_ft, vec))
def to_vector(seg):
return seg, vec2bin(self.ft.segment_to_vector(seg))
def to_vectors(phon):
if phon == '':
return [(-1, [0] * self.num_panphon_fts)]
else:
return [to_vector(seg) for seg in self.ft.ipa_segs(phon)]
tuples = []
word = unicode(word)
word = self.strip_diacritics.process(word)
word = unicodedata.normalize('NFC', word)
if self.preproc:
word = self.preprocessor.process(word)
while word:
match = self.regexp.match(word)
if match:
span = match.group(1)
cat, case = cat_and_cap(span[0])
phon = self.g2p[span.lower()][0]
vecs = to_vectors(phon)
tuples.append(('L', case, span, phon, vecs))
word = word[len(span):]
else:
span = word[0]
span = self.puncnorm.norm(span) if normpunc else span
cat, case = cat_and_cap(span)
cat = 'P' if normpunc and cat in self.puncnorm else cat
phon = ''
vecs = to_vectors(phon)
tuples.append((cat, case, span, phon, vecs))
word = word[1:]
return tuples
def ipa_segs(self, ipa):
"""Given an IPA string, decompose it into a list of segments
Args:
ipa (unicode): a Unicode IPA string
Returns:
list: a list of unicode strings corresponding to segments
(consonants and vowels) in the input string
"""
return self.ft.segs(ipa)
class SimpleEpitran(object):
def __init__(self, code, preproc=True, postproc=True, ligatures=False,
rev=False, rev_preproc=True, rev_postproc=True):
"""Constructs the backend object epitran uses for most languages
Args:
code (str): ISO 639-3 code and ISO 15924 code joined with a hyphen
preproc (bool): if True, apply preprocessor
postproc (bool): if True, apply postprocessors
ligatures (bool): if True, use phonetic ligatures for affricates
instead of standard IPA
rev (bool): if True, load reverse transliteration
rev_preproc (bool): if True, apply preprocessor when reverse transliterating
rev_postproc (bool): if True, apply postprocessor when reverse transliterating
"""
self.rev = rev
self.g2p = self._load_g2p_map(code, False)
self.regexp = self._construct_regex(self.g2p.keys())
self.puncnorm = PuncNorm()
self.ft = panphon.FeatureTable()
self.num_panphon_fts = len(self.ft.names)
self.preprocessor = PrePostProcessor(code, 'pre', False)
self.postprocessor = PrePostProcessor(code, 'post', False)
self.strip_diacritics = StripDiacritics(code)
self.preproc = preproc
self.postproc = postproc
self.ligatures = ligatures
self.rev_preproc = rev_preproc
self.rev_postproc = rev_postproc
if rev:
self.rev_g2p = self._load_g2p_map(code, True)
self.rev_regexp = self._construct_regex(self.rev_g2p.keys())
self.rev_preprocessor = PrePostProcessor(code, 'pre', True)
self.rev_postprocessor = PrePostProcessor(code, 'post', True)
self.nils = defaultdict(int)
def __enter__(self):
return self
def __exit__(self, type_, val, tb):
for nil, count in self.nils.items():
sys.stderr.write('Unknown character "{}" occured {} times.\n'.format(nil, count))
def _one_to_many_gr_by_line_map(self, gr_by_line):
for g, ls in gr_by_line.items():
if len(ls) != 1:
return (g, ls)
return None
def _load_g2p_map(self, code, rev):
"""Load the code table for the specified language.
Args:
code (str): ISO 639-3 code plus "-" plus ISO 15924 code for the
language/script to be loaded
rev (boolean): True for reversing the table (for reverse transliterating)
"""
g2p = defaultdict(list)
gr_by_line = defaultdict(list)
code += '_rev' if rev else ''
try:
path = os.path.join('data', 'map', code + '.csv')
path = pkg_resources.resource_filename(__name__, path)
except IndexError:
raise DatafileError('Add an appropriately-named mapping to the data/maps directory.')
with open(path, 'rb') as f:
reader = csv.reader(f, encoding='utf-8')
next(reader)
for (i, fields) in enumerate(reader):
try:
graph, phon = fields
except ValueError:
raise DatafileError('Map file is not well formed at line {}.'.format(i + 2))
graph = unicodedata.normalize('NFC', graph)
phon = unicodedata.normalize('NFC', phon)
g2p[graph].append(phon)
gr_by_line[graph].append(i)
if self._one_to_many_gr_by_line_map(g2p):
graph, lines = self._one_to_many_gr_by_line_map(gr_by_line)
lines = [l + 2 for l in lines]
raise MappingError('One-to-many G2P mapping for "{}" on lines {}'.format(graph, ', '.join(map(str, lines))).encode('utf-8'))
return g2p
def _load_punc_norm_map(self):
"""Load the map table for normalizing 'down' punctuation."""
path = os.path.join('data', 'puncnorm.csv')
path = pkg_resources.resource_filename(__name__, path)
with open(path, 'rb') as f:
reader = csv.reader(f, encoding='utf-8',
delimiter=str(','),
quotechar=str('"'))
next(reader)
return {punc: norm for (punc, norm) in reader}
def _construct_regex(self, g2p_keys):
"""Build a regular expression that will greadily match segments from
the mapping table.
"""
graphemes = sorted(g2p_keys, key=len, reverse=True)
return re.compile(r'({})'.format(r'|'.join(graphemes)), re.I)
def general_trans(self, text, filter_func,
normpunc=False, ligatures=False):
"""Transliaterates a word into IPA, filtering with filter_func
Args:
text (str): word to transcribe; unicode strings
filter_func (function): function for filtering segments; takes
a <segment, is_ipa> tuple and returns a
boolean.
normpunc (bool): normalize punctuation
ligatures (bool): use precomposed ligatures instead of
standard IPA
Returns:
unicode: IPA string, filtered by filter_func.
"""
text = unicode(text)
text = self.strip_diacritics.process(text)
text = unicodedata.normalize('NFC', text.lower())
if self.preproc:
text = self.preprocessor.process(text)
tr_list = []
while text:
m = self.regexp.match(text)
if m:
source = m.group(0)
try:
target = self.g2p[source][0]
except KeyError:
logging.debug("source = '{}'".format(source))
logging.debug("self.g2p[source] = '{}'"
.format(self.g2p[source]))
target = source
tr_list.append((target, True))
text = text[len(source):]
else:
tr_list.append((text[0], False))
self.nils[text[0]] += 2
text = text[1:]
text = ''.join([s for (s, _) in filter(filter_func, tr_list)])
if self.postproc:
text = self.postprocessor.process(text)
if ligatures or self.ligatures:
text = ligaturize(text)
if normpunc:
text = self.puncnorm.norm(text)
return text
def transliterate(self, text, normpunc=False, ligatures=False):
"""Transliterates/transcribes a word into IPA
Passes unmapped characters through to output unchanged.
Args:
word (str): word to transcribe; unicode string
normpunc (bool): normalize punctuation
ligatures (bool): use precomposed ligatures instead of standard IPA
Returns:
unicode: IPA string with unrecognized characters included
"""
return self.general_trans(text, lambda x: True,
normpunc, ligatures)
def general_reverse_trans(self, ipa):
"""Reconstructs word from IPA. Does the reverse of transliterate().
Ignores unmapped characters.
Args:
ipa (str): word transcription in ipa; unicode string
Returns:
unicode: reconstructed word
"""
text = unicode(ipa)
if self.rev_preproc:
text = self.rev_preprocessor.process(text)
tr_list = []
while text:
m = self.rev_regexp.match(text)
if m:
source = m.group(0)
try:
target = self.rev_g2p[source][0]
except KeyError:
logging.debug("source = '{}'".format(source))
logging.debug("self.rev_g2p[source] = '{}'"
.format(self.g2p[source]))
target = source
tr_list.append((target, True))
text = text[len(source):]
else:
tr_list.append((text[0], False))
self.nils[text[0]] += 2
text = text[1:]
text = ''.join([s for (s, _) in tr_list])
if self.rev_postproc:
text = self.rev_postprocessor.process(text)
return text
def reverse_transliterate(self, ipa):
"""Reconstructs word from IPA. Does the reverse of transliterate()
Args:
ipa (str): word transcription in ipa; unicode string
Returns:
unicode: reconstructed word
"""
if not self.rev:
raise ValueError('This Epitran object was initialized with no reverse transliteration loaded')
return self.general_reverse_trans(ipa)
def strict_trans(self, text, normpunc=False, ligatures=False):
"""Transliterates/transcribes a word into IPA
Ignores umapped characters.
Args:
word (str): word to transcribe; unicode string
normpunc (bool): normalize punctuation
ligatures (bool): use precomposed ligatures instead of standard IPA
Returns:
unicode: IPA string
"""
return self.general_trans(text, lambda x: x[1],
normpunc, ligatures)
def word_to_tuples(self, word, normpunc=False):
"""Given a word, returns a list of tuples corresponding to IPA segments.
Args:
word (unicode): word to transliterate
normpunc (bool): If True, normalizes punctuation to ASCII inventory
Returns:
list: A list of (category, lettercase, orthographic_form,
phonetic_form, feature_vectors) tuples.
The "feature vectors" form a list consisting of (segment, vector)
pairs. For IPA segments, segment is a substring of phonetic_form such
that the concatenation of all segments in the list is equal to
the phonetic_form. The vectors are a sequence of integers drawn from
the set {-1, 0, 1} where -1 corresponds to '-', 0 corresponds to '0',
and 1 corresponds to '+'.
"""
def cat_and_cap(c):
cat, case = tuple(unicodedata.category(c))
case = 1 if case == 'u' else 0
return unicode(cat), case
def recode_ft(ft):
try:
return {'+': 1, '0': 0, '-': -1}[ft]
except KeyError:
return None
def vec2bin(vec):
return list(map(recode_ft, vec))
def to_vector(seg):
return seg, vec2bin(self.ft.segment_to_vector(seg))
def to_vectors(phon):
if phon == '':
return [(-1, [0] * self.num_panphon_fts)]
else:
return [to_vector(seg) for seg in self.ft.ipa_segs(phon)]
tuples = []
word = unicode(word)
word = self.strip_diacritics.process(word)
word = unicodedata.normalize('NFC', word)
if self.preproc:
word = self.preprocessor.process(word)
while word:
match = self.regexp.match(word)
if match:
span = match.group(1)
cat, case = cat_and_cap(span[0])
phon = self.g2p[span.lower()][0]
vecs = to_vectors(phon)
tuples.append(('L', case, span, phon, vecs))
word = word[len(span):]
else:
span = word[0]
span = self.puncnorm.norm(span) if normpunc else span
cat, case = cat_and_cap(span)
cat = 'P' if normpunc and cat in self.puncnorm else cat
phon = ''
vecs = to_vectors(phon)
tuples.append((cat, case, span, phon, vecs))
word = word[1:]
return tuples
def ipa_segs(self, ipa):
"""Given an IPA string, decompose it into a list of segments
Args:
ipa (unicode): a Unicode IPA string
Returns:
list: a list of unicode strings corresponding to segments
(consonants and vowels) in the input string
"""
return self.ft.segs(ipa)