def test_word_mean_duration(summarized_config):
with CorpusContext(summarized_config) as g:
print("mean duration (word):")
res = g.get_measure('duration', 'mean', 'word')
print(res)
assert (len(res) == 44)
for i, r in enumerate(res):
if r[0] == 'words':
break
assert res[i][1] == approx(0.5340040000000001, 1e-3)
def test_ilg_mismatched(graph_db, ilg_test_dir):
mismatched_path = os.path.join(ilg_test_dir, 'mismatched.txt')
basic_path = os.path.join(ilg_test_dir, 'basic.txt')
parser = inspect_ilg(basic_path)
with CorpusContext('mismatch', **graph_db) as c:
c.reset()
with pytest.raises(ILGWordMismatchError):
c.load(parser, mismatched_path)
def test_strings(timed_config):
with CorpusContext(timed_config) as g:
q = g.query_graph(g.word).filter(g.word.label == 'are')
q = q.columns(g.word.label.column_name('label'),
g.word.phone.label.column_name('phones'))
print(q.cypher())
results = q.all()
assert (all(x['label'] == 'are' for x in results))
assert (all(x['phones'] == ['aa', 'r'] for x in results))
def run_query(self):
config = self.kwargs['config']
acoustics = self.kwargs['acoustics']
with CorpusContext(config) as c:
acoustic_analysis(c,
stop_check=self.kwargs['stop_check'],
call_back=self.kwargs['call_back'],
acoustics=acoustics)
self.actionCompleted.emit('analysing acousics')
return True
def test_baseline_syllable(acoustic_config):
syllabics = [
'ae', 'aa', 'uw', 'ay', 'eh', 'ih', 'aw', 'ey', 'iy', 'uh', 'ah', 'ao',
'er', 'ow'
]
with CorpusContext(acoustic_config) as g:
g.encode_syllabic_segments(syllabics)
g.encode_syllables()
res = g.get_measure('duration', 'baseline', 'syllable')
print(res)
def test_phone_mean_duration_speaker(summarized_config):
with CorpusContext(summarized_config) as g:
print("phone mean:")
res = g.get_measure('duration', 'mean', 'phone', False, 'unknown')
print(res)
assert (len(res) == 33)
for i, r in enumerate(res):
if r[0] == 'uw':
break
assert res[i][1] == approx(0.08043999999999973, 1e-3)
def test_baseline_word(summarized_config):
with CorpusContext(summarized_config) as g:
g.reset_pauses()
g.reset_syllables()
g.reset_utterances()
res = g.get_measure('duration', 'baseline', 'word')
print(res)
assert res['this'] == approx(0.20937191666666685, 1e-3)
assert (len(res) == 44)
def test_word_mean_duration_with_speaker_buckeye(graph_db, buckeye_test_dir):
with CorpusContext('directory_buckeye', **graph_db) as g:
g.encode_utterances()
res = g.get_measure('duration', 'mean', 'word', True)
print(res)
for i, r in enumerate(res):
if r[1] == 'that\'s':
break
assert (len(res) == 9)
assert res[i][2] == approx(0.17431200000000002, 1e-3)
def acoustic_config(graph_db, textgrid_test_dir):
config = CorpusConfig('acoustic', **graph_db)
acoustic_path = os.path.join(textgrid_test_dir, 'acoustic_corpus.TextGrid')
with CorpusContext(config) as c:
c.reset()
parser = inspect_textgrid(acoustic_path)
c.load(parser, acoustic_path)
#c.analyze_acoustics()
return config
def test_relativize_intensity(acoustic_utt_config):
with CorpusContext(acoustic_utt_config) as g:
mean_f0 = 97.72
sd_f0 = 1.88997
expected_intensity = {
Decimal('4.23'): {
'Intensity': 98,
'Intensity_relativized': (98 - mean_f0) / sd_f0
},
Decimal('4.24'): {
'Intensity': 100,
'Intensity_relativized': (100 - mean_f0) / sd_f0
},
Decimal('4.25'): {
'Intensity': 99,
'Intensity_relativized': (99 - mean_f0) / sd_f0
},
Decimal('4.26'): {
'Intensity': 95.8,
'Intensity_relativized': (95.8 - mean_f0) / sd_f0
},
Decimal('4.27'): {
'Intensity': 95.8,
'Intensity_relativized': (95.8 - mean_f0) / sd_f0
}
}
g.relativize_acoustic_measure('intensity', by_speaker=True)
q = g.query_graph(g.phone)
q = q.filter(g.phone.label == 'ow')
q = q.order_by(g.phone.begin.column_name('begin'))
ac = g.phone.intensity
q = q.columns(g.phone.label, ac.track)
results = q.all()
assert (len(results[0].track) == len(expected_intensity.items()))
print(sorted(expected_intensity.items()))
print(results[0].track)
for point in results[0].track:
print(point)
assert (round(point['Intensity_relativized'], 5) == round(
expected_intensity[point.time]['Intensity_relativized'], 5))
g.reset_relativized_acoustic_measure('intensity')
assert g.hierarchy.acoustic_properties['intensity'] == {('Intensity',
float)}
q = g.query_graph(g.phone)
q = q.filter(g.phone.label == 'ow')
q = q.order_by(g.phone.begin.column_name('begin'))
ac = g.phone.intensity
q = q.columns(g.phone.label, ac.track)
results = q.all()
assert len(results[0].track) == 5
for r in results:
for p in r.track:
assert not p.has_value('Intensity_relativized')
def test_position_query(timed_config):
with CorpusContext(timed_config) as g:
q = g.query_graph(g.phone).filter(g.phone.label == 'k')
q = q.columns(g.phone.word.phone.position.column_name('position'))
q = q.order_by(g.phone.word.begin)
print(q.cypher())
results = q.all()
expected = [1, 1]
assert (len(results) == len(expected))
for i in range(len(expected)):
assert (results[i]['position'] == expected[i])
def test_reset_utterances(acoustic_utt_config):
with CorpusContext(acoustic_utt_config) as g:
g.reset_utterances()
g.encode_utterances(0.15)
q = g.query_graph(g.phone).filter(g.phone.label == 'ow')
q = q.columns(g.phone.begin, g.phone.end, g.phone.pitch.track)
print(q.cypher())
results = q.all()
assert (len(results) > 0)
for r in results:
assert len(r.track)
def test_query_count_group_by(acoustic_config):
with CorpusContext(acoustic_config) as g:
q = g.query_graph(g.phone).filter(g.phone.label.in_(['aa','ae']))
results = q.group_by(g.phone.label.column_name('label')).aggregate(Count())
assert(len(results) == 2)
print(results)
assert(results[0]['label'] == 'aa')
assert(results[0]['count_all'] == 3)
assert(results[1]['label'] == 'ae')
assert(results[1]['count_all'] == 7)
def updateConfig(self, config):
self.config = config
self.discourseList.clear()
if self.config is None or self.config.corpus_name == '':
return
try:
with CorpusContext(self.config) as c:
for d in sorted(c.discourses):
self.discourseList.addItem(d)
except GraphQueryError:
self.discourseList.clear()
def test_phone_std_dev(summarized_config):
with CorpusContext(summarized_config) as g:
print("phone std dev:")
res = g.get_measure('duration', 'stdev', 'phone')
print(res)
for i, r in enumerate(res):
if r[0] == 'uw':
break
assert (len(res) == 33)
assert (abs(res[i][1] - 0.026573072836990105) < .0000000000001)
def test_phone_mean_duration_with_speaker(summarized_config):
with CorpusContext(summarized_config) as g:
print("phone mean by speaker:")
# res =g.phone_mean_duration_with_speaker()
res = g.get_measure('duration', 'mean', 'phone', True)
print(res)
assert (len(res) == 33)
for i, r in enumerate(res):
if r[1] == 'uw':
break
assert res[i][2] == approx(0.08043999999999973, 1e-3)
def test_word_std_dev(summarized_config):
with CorpusContext(summarized_config) as g:
print("word std dev:")
res = g.get_measure('duration', 'stdev', 'word')
print(res)
assert (len(res) == 44)
for i, r in enumerate(res):
if r[0] == 'words':
break
assert (abs(res[i][1] - 0.26996736762060747) < .0000000000001)
def test_phone_mean_duration_speaker(summarized_config):
with CorpusContext(summarized_config) as g:
print("phone mean:")
g.encode_utterances()
res = g.get_measure('duration', 'mean', 'phone', False, 'unknown')
print(res)
assert (len(res) == 33)
for i, r in enumerate(res):
if r[0] == 'uw':
break
assert (abs(res[i][1] - 0.08043999999999973) < .0000000000001)
def test_order_by(timed_config):
with CorpusContext(timed_config) as g:
q = g.query_graph(g.word).filter(g.word.label == 'are').order_by(
g.word.begin.column_name('begin')) #.times('begin','end')
prev = 0
print(q.cypher())
print(q.all())
for x in q.all():
assert (x['begin'] > prev)
prev = x['begin']
assert ('timed' in get_corpora_list(timed_config))
def export(request, corpus):
response = HttpResponse(content_type='text/csv')
a_type = request.data['annotation_type']
corpus = Corpus.objects.get(pk=corpus)
ordering = request.data.get('ordering', '')
filters = request.data['filters']
columns = request.data['columns']
response[
'Content-Disposition'] = 'attachment; filename="{}_query_export.csv"'.format(
a_type)
print(filters)
print(columns)
with CorpusContext(corpus.config) as c:
a = getattr(c, a_type)
q = c.query_graph(a)
for k, v in filters.items():
if v[0] == '':
continue
if v[0] == 'null':
v = None
else:
try:
v = float(v[0])
except ValueError:
v = v[0]
k = k.split('__')
att = a
for f in k:
att = getattr(att, f)
q = q.filter(att == v)
if ordering:
desc = False
if ordering.startswith('-'):
desc = True
ordering = ordering[1:]
ordering = ordering.split('.')
att = a
for o in ordering:
att = getattr(att, o)
q = q.order_by(att, desc)
else:
q = q.order_by(getattr(a, 'label'))
columns_for_export = []
for c in columns:
att = a
for f in c.split('__'):
att = getattr(att, f)
columns_for_export.append(att)
q = q.columns(*columns_for_export)
writer = csv.writer(response)
q.to_csv(writer)
return response
def test_query_time(timed_config):
with CorpusContext(timed_config) as g:
q = g.query_graph(g.word).filter(g.word.label == 'are')
q = q.filter(g.word.begin > 2)
print(q.cypher())
assert (len(list(q.all())) == 1)
q = g.query_graph(g.word).filter(g.word.label == 'are')
q = q.filter(g.word.begin < 2)
print(q.cypher())
assert (len(list(q.all())) == 1)
def test_export_spelling(graph_db, export_test_dir):
export_path = os.path.join(export_test_dir, 'export_spelling.txt')
with CorpusContext('spelling_no_ignore', **graph_db) as c:
export_discourse_spelling(c,
'text_spelling',
export_path,
words_per_line=10)
with open(export_path, 'r') as f:
assert (f.read() == 'ab cab\'d ad ab ab.')
def test_query_duration_aggregate_average_group_by(acoustic_config):
with CorpusContext(acoustic_config) as g:
q = g.query_graph(g.phone).filter(g.phone.label.in_(['aa','ae']))
results = q.group_by(g.phone.label.column_name('label')).aggregate(Average(g.phone.duration))
assert(len(results) == 2)
assert(results[0]['label'] == 'aa')
assert(abs(results[0]['average_duration'] - 0.08) < 0.001)
assert(results[1]['label'] == 'ae')
assert(abs(results[1]['average_duration'] - 0.193) < 0.001)
def test_discourse_query(timed_config):
with CorpusContext(timed_config) as g:
q = g.query_graph(g.word).columns(
g.word.discourse.name.column_name('discourse'))
print(q.cypher())
assert (all(x['discourse'] == 'test_timed' for x in q.all()))
q = g.query_graph(g.word).filter(g.word.discourse.name == 'test')
q = q.columns(g.word.discourse.name.column_name('discourse'))
print(q.cypher())
assert (all(x['discourse'] == 'test_timed' for x in q.all()))
def reset(corpus_name):
"""Remove the database files produced from import."""
with ensure_local_database_running(corpus_name,
port=8080,
ip=server_ip,
token=load_token()) as params:
config = CorpusConfig(corpus_name, **params)
with CorpusContext(config) as c:
print('Resetting the corpus.')
c.reset()
def test_analyze_pitch_gendered_praat(acoustic_utt_config, praat_path):
with CorpusContext(acoustic_utt_config) as g:
g.reset_acoustics()
g.reset_acoustics()
g.config.praat_path = praat_path
g.config.pitch_algorithm = 'speaker_adjusted'
g.analyze_pitch(source='praat')
assert (g.has_pitch('acoustic_corpus'))
g.reset_pitch()
assert not g.has_pitch(g.discourses[0])
def test_load(textgrid_test_dir, graph_db):
path = os.path.join(textgrid_test_dir, 'phone_word.TextGrid')
with CorpusContext('test_textgrid', **graph_db) as c:
c.reset()
parser = inspect_textgrid(path)
parser.annotation_tiers[1].linguistic_type = 'word'
parser.annotation_tiers[2].ignored = True
parser.hierarchy['word'] = None
parser.hierarchy['phone'] = 'word'
print([(x.linguistic_type, x.name) for x in parser.annotation_tiers])
c.load(parser, path)
def test_subset_enrichment(acoustic_config):
syllabics = [
'ae', 'aa', 'uw', 'ay', 'eh', 'ih', 'aw', 'ey', 'iy', 'uh', 'ah', 'ao',
'er', 'ow'
]
phone_class = ['ae', 'aa', 'd', 'r']
with CorpusContext(acoustic_config) as c:
c.reset_class('syllabic')
c.reset_class('test')
c.encode_class(syllabics, "syllabic")
c.encode_class(phone_class, "test")
assert (len(c.hierarchy.subset_types['phone']) == 2)
def test_load_pronunciation_ignore(textgrid_test_dir, graph_db):
path = os.path.join(textgrid_test_dir, 'pronunc_variants_corpus.TextGrid')
with CorpusContext('test_pronunc', **graph_db) as c:
c.reset()
parser = inspect_textgrid(path)
parser.annotation_tiers[1].ignored = True
parser.annotation_tiers[2].ignored = True
c.load(parser, path)
with pytest.raises(GraphQueryError):
q = c.query_graph(c.actualPron)
results = q.all()
def test_load_transcription_morpheme(graph_db, text_transcription_test_dir):
transcription_morphemes_path = os.path.join(
text_transcription_test_dir,
'text_transcription_morpheme_boundaries.txt')
parser = inspect_transcription(transcription_morphemes_path)
parser.annotation_types[0].morph_delimiters = set('-=')
with CorpusContext('transcription_morpheme', **graph_db) as c:
c.reset()
c.load(parser, transcription_morphemes_path)
#assert(c.lexicon['cab'].frequency == 2)
assert (str(c.lexicon['cab'].transcription) == 'c.a.b')
