def test_lexicon_enrichment(timed_config, csv_test_dir):
path = os.path.join(csv_test_dir, 'timed_enrichment.txt')
with CorpusContext(timed_config) as c:
enrich_lexicon_from_csv(c, path)
q = c.query_graph(c.word).filter(c.word.neighborhood_density < 10)
q = q.columns(c.word.label.column_name('label'))
res = q.all()
assert (all(x['label'] == 'guess' for x in res))
q = c.query_graph(c.word).filter(c.word.label == 'i')
res = q.all()
assert (res[0]['frequency'] == 150)
assert (res[0]['part_of_speech'] == 'PRP')
assert (res[0]['neighborhood_density'] == 17)
q = c.query_graph(c.word).filter(c.word.label == 'cute')
res = q.all()
assert (res[0]['frequency'] is None)
assert (res[0]['part_of_speech'] == 'JJ')
assert (res[0]['neighborhood_density'] == 14)
levels = c.lexicon.get_property_levels('part_of_speech')
assert (set(levels) == set(['NN', 'VB', 'JJ', 'IN', 'PRP']))
def test_lexicon_enrichment(timed_config, csv_test_dir):
path = os.path.join(csv_test_dir, 'timed_enrichment.txt')
with CorpusContext(timed_config) as c:
enrich_lexicon_from_csv(c, path)
q = c.query_graph(c.word).filter(c.word.neighborhood_density < 10)
q = q.columns(c.word.label.column_name('label'))
res = q.all()
assert(all(x['label'] == 'guess' for x in res))
q = c.query_graph(c.word).filter(c.word.label == 'i')
res = q.all()
assert(res[0]['frequency'] == 150)
assert(res[0]['part_of_speech'] == 'PRP')
assert(res[0]['neighborhood_density'] == 17)
q = c.query_graph(c.word).filter(c.word.label == 'cute')
res = q.all()
assert(res[0]['frequency'] is None)
assert(res[0]['part_of_speech'] == 'JJ')
assert(res[0]['neighborhood_density'] == 14)
levels = c.lexicon.get_property_levels('part_of_speech')
assert(set(levels) == set(['NN','VB','JJ','IN','PRP']))
def lexicon_enrichment(config, unisyn_spade_directory, dialect_code):
enrichment_dir = os.path.join(unisyn_spade_directory, 'enrichment_files')
if not os.path.exists(enrichment_dir):
print('Could not find enrichment_files directory from {}, skipping lexical enrichment.'.format(
unisyn_spade_directory))
return
with CorpusContext(config) as g:
for lf in os.listdir(enrichment_dir):
path = os.path.join(enrichment_dir, lf)
if lf == 'rule_applications.csv':
if g.hierarchy.has_type_property('word', 'UnisynPrimStressedVowel1'.lower()):
print('Dialect independent enrichment already loaded, skipping.')
continue
elif lf.startswith(dialect_code):
if g.hierarchy.has_type_property('word', 'UnisynPrimStressedVowel2_{}'.format(
dialect_code).lower()):
print('Dialect specific enrichment already loaded, skipping.')
continue
else:
continue
begin = time.time()
enrich_lexicon_from_csv(g, path)
time_taken = time.time() - begin
print('Lexicon enrichment took: {}'.format(time.time() - begin))
save_performance_benchmark(config, 'lexicon_enrichment', time_taken)
def run_query(self):
config = self.kwargs['config']
case_sensitive = self.kwargs['case_sensitive']
path = self.kwargs['path']
stop_check = self.kwargs['stop_check']
call_back = self.kwargs['call_back']
call_back('Enriching lexicon...')
call_back(0, 0)
with CorpusContext(config) as c:
enrich_lexicon_from_csv(c, path)
if stop_check():
call_back('Resetting lexicon...')
call_back(0, 0)
c.reset_lexicon()
return False
return True
def run_query(self):
config = self.kwargs['config']
case_sensitive = self.kwargs['case_sensitive']
path = self.kwargs['path']
stop_check = self.kwargs['stop_check']
call_back = self.kwargs['call_back']
call_back('Enriching lexicon...')
call_back(0, 0)
with CorpusContext(config) as c:
enrich_lexicon_from_csv(c, path)
if stop_check():
call_back('Resetting lexicon...')
call_back(0, 0)
c.reset_lexicon()
return False
return True
def run_query(self):
print("in the lexical worker")
config = self.kwargs['config']
case_sensitive = self.kwargs['case_sensitive']
path = self.kwargs['path']
stop_check = self.kwargs['stop_check']
call_back = self.kwargs['call_back']
call_back('Enriching lexicon...')
call_back(0, 0)
with CorpusContext(config) as c:
enrich_lexicon_from_csv(c, path)
self.actionCompleted.emit('enriching lexicon')
if stop_check():
call_back('Resetting lexicon...')
call_back(0, 0)
c.reset_lexicon()
return False
return True
def run_query(self):
print("in the lexical worker")
config = self.kwargs['config']
case_sensitive = self.kwargs['case_sensitive']
path = self.kwargs['path']
stop_check = self.kwargs['stop_check']
call_back = self.kwargs['call_back']
call_back('Enriching lexicon...')
call_back(0, 0)
with CorpusContext(config) as c:
enrich_lexicon_from_csv(c, path)
self.actionCompleted.emit('enriching lexicon')
if stop_check():
call_back('Resetting lexicon...')
call_back(0, 0)
c.reset_lexicon()
return False
return True
def duration_export(config,
corpus_name,
corpus_directory,
dialect_code,
speakers,
vowels,
stressed_vowels=None,
baseline=False,
ignored_speakers=None):
csv_path = os.path.join(base_dir, corpus_name,
'{}_duration.csv'.format(corpus_name))
with CorpusContext(config) as c:
if corpus_name == 'spade-Buckeye':
print("Processing {}".format(corpus_name))
if not c.hierarchy.has_type_property('word',
"ContainsVowelObstruent"):
print('Classifying Buckeye vowel-obstruent pairs')
enrich_lexicon_from_csv(
c,
os.path.join(
corpus_directory,
"corpus-data/enrichment/buckeye_obstruents.csv"))
print("Beginning duration export")
beg = time.time()
consonants = [
'p', 'P', 't', 'T', 'k', 'K', 'b', 'B', 'd', 'D', 'g', 'G', 'F',
'f', 'V', 'v', 'N', 'n', 'm', 'M', 'NG', 'TH', 'DH', 'l', 'L',
'ZH', 'x', 'X', 'r', 'R', 's', 'S', 'sh', 'SH', 'z', 'Z', 'zh',
'ZH', 'J', 'C', 'tS', 'dZ', 'tq'
]
if stressed_vowels:
q = c.query_graph(c.phone).filter(
c.phone.label.in_(stressed_vowels))
q = q.filter(c.phone.following.end == c.phone.syllable.end)
q = q.filter(
c.phone.following.end == c.phone.syllable.word.utterance.end)
q = q.filter(c.phone.following.label.in_(consonants))
q = q.filter(c.phone.syllable.word.num_syllables == 1)
else:
q = c.query_graph(c.phone).filter(c.phone.label.in_(vowels))
q = q.filter(c.phone.following.end == c.phone.syllable.end)
q = q.filter(
c.phone.following.end == c.phone.syllable.word.utterance.end)
q = q.filter(c.phone.following.label.in_(consonants))
q = q.filter(c.phone.word.stresspattern == "1")
q = q.filter(c.phone.syllable.stress == "1")
print(c.hierarchy)
if c.hierarchy.has_type_property('word', 'containsvowelobstruent'):
q = q.filter(c.phone.word.containsvowelobstruent == True)
if speakers:
q = q.filter(c.phone.speaker.name.in_(speakers))
if ignored_speakers:
q = q.filter(c.phone.speaker.name.not_in_(ignored_speakers))
print("Applied filters")
q = q.columns(
c.phone.label.column_name('phone_label'),
c.phone.begin.column_name('phone_begin'),
c.phone.end.column_name('phone_end'),
c.phone.duration.column_name('phone_duration'),
c.phone.previous.label.column_name('previous_phone'),
c.phone.following.label.column_name('following_phone'),
c.phone.following.duration.column_name('following_duration'),
c.phone.word.unisynprimstressedvowel1.column_name('word_unisyn'),
c.phone.word.label.column_name('word_label'),
c.phone.word.begin.column_name('word_begin'),
c.phone.word.end.column_name('word_end'),
c.phone.word.duration.column_name('word_duration'),
c.phone.syllable.label.column_name('syllable_label'),
c.phone.syllable.duration.column_name('syllable_duration'),
c.phone.word.stresspattern.column_name('word_stresspattern'),
c.phone.syllable.stress.column_name('syllable_stress'),
c.phone.utterance.speech_rate.column_name('speech_rate'),
c.phone.utterance.id.column_name('utterance_label'),
c.phone.speaker.name.column_name('speaker_name'),
c.phone.syllable.end.column_name('syllable_end'),
c.phone.utterance.end.column_name('utterance_end'))
for sp, _ in c.hierarchy.speaker_properties:
if sp == 'name':
continue
q = q.columns(getattr(c.phone.speaker, sp).column_name(sp))
if c.hierarchy.has_token_property('word', 'surface_transcription'):
print('getting underlying and surface transcriptions')
q = q.columns(
c.phone.word.transcription.column_name(
'word_underlying_transcription'),
c.phone.word.surface_transcription.column_name(
'word_surface_transcription'))
if c.hierarchy.has_type_property('word', 'containsvowelobstruent'):
q = q.columns(
c.phone.word.containsvowelobstruent.column_name(
'word_containsvowelobstruent'))
# get baseline duration:
# for most corpora this should be done over words
# as buckeye has many-to-one correspondence between transcriptions and words
# buckeye should have duration calculated over its underlying transcription
if baseline:
if not c.hierarchy.has_type_property('word', 'baseline'):
print('getting baseline from word')
c.encode_baseline('word', 'duration')
q = q.columns(
c.phone.word.baseline_duration.column_name(
'word_baseline_duration'))
print("Writing CSV")
q.to_csv(csv_path)
end = time.time()
time_taken = time.time() - beg
print('Query took: {}'.format(end - beg))
print("Results for query written to " + csv_path)
common.save_performance_benchmark(config, 'duration_export',
time_taken)
