def main():
cldb = CategoryLabelDatabase('../data/database.py')
preprocessing = PTICSSLUPreprocessing(cldb)
slu = PTICSHDCSLU(preprocessing, cfg={'SLU': {PTICSHDCSLU: {'utt2da': as_project_path("applications/PublicTransportInfoCS/data/utt2da_dict.txt")}}})
output_alignment = False
output_utterance = True
output_abstraction = False
output_da = True
if len(sys.argv) < 2:
fn_uniq_trn = 'uniq.trn'
else:
fn_uniq_trn = sys.argv[1]
fn_uniq_trn_sem = fn_uniq_trn + '.sem.tmp'
print "Processing input from file", fn_uniq_trn
uniq_trn = codecs.open(fn_uniq_trn, "r", encoding='utf8')
uniq_trn_sem = {}
for line in uniq_trn:
wav_key, utterance = line.split(" => ", 2)
annotation = []
if output_alignment:
norm_utterance = slu.preprocessing.normalise_utterance(Utterance(utterance))
abutterance, _, _ = slu.abstract_utterance(norm_utterance)
abutterance = slu.handle_false_abstractions(abutterance)
da = slu.parse_1_best({'utt': Utterance(utterance)}).get_best_da()
max_alignment_idx = lambda _dai: max(_dai.alignment) if _dai.alignment else len(abutterance)
for i, dai in enumerate(sorted(da, key=max_alignment_idx)):
if not dai.alignment:
print "Empty alignment:", unicode(abutterance), ";", dai
if not dai.alignment or dai.alignment == {-1}:
dai_alignment_idx = len(abutterance)
else:
dai_alignment_idx = max(dai.alignment) + i + 1
abutterance.insert(dai_alignment_idx, "[{} - {}]".format(unicode(dai), list(dai.alignment if dai.alignment else [])))
annotation += [unicode(abutterance)]
else:
if output_utterance:
annotation += [utterance.rstrip()]
if output_abstraction:
norm_utterance = slu.preprocessing.normalise_utterance(Utterance(utterance))
abutterance, _ = slu.abstract_utterance(norm_utterance)
annotation += [abutterance]
if output_da:
da = slu.parse_1_best({'utt': Utterance(utterance)}).get_best_da()
annotation += [unicode(da)]
uniq_trn_sem[wav_key] = " <=> ".join(annotation)
print "Saving output to file", fn_uniq_trn_sem
save_wavaskey(fn_uniq_trn_sem, uniq_trn_sem)
def process_file(file_path):
cldb = CategoryLabelDatabase(as_project_path('applications/PublicTransportInfoCS/data/database.py'))
preprocessing = PTICSSLUPreprocessing(cldb)
hdc_slu = PTICSHDCSLU(preprocessing, cfg = {'SLU': {PTICSHDCSLU: {'utt2da': as_project_path('applications/PublicTransportInfoCS/data/utt2da_dict.txt')}}})
stdout = codecs.getwriter('UTF-8')(sys.stdout)
with open(file_path, 'r') as fh:
for line in codecs.getreader('UTF-8')(fh):
line = line.strip("\r\n")
# skip empty lines (dialogue boundaries)
if not line:
continue
person, da, utt = line.split("\t")
# skip system utterances, use just user utterances
if 'SYSTEM' in person:
continue
# reparse utterance using transcription
utt = re.sub(r',', r' ', utt)
utt = Utterance(utt)
sem = hdc_slu.parse({'utt': utt})
# get abstracted utterance text
abutt = hdc_slu.abstract_utterance(utt)
abutt_str = get_abutt_str(utt, abutt)
# get abstracted DA
best_da = sem.get_best_da()
best_da_str = unicode(best_da)
abstract_da(best_da)
print >> stdout, unicode(utt) + "\t" + abutt_str + "\t" + best_da_str + "\t" + unicode(best_da)
def main():
cldb = CategoryLabelDatabase('../data/database.py')
preprocessing = PTICSSLUPreprocessing(cldb)
slu = PTICSHDCSLU(
preprocessing,
cfg={
'SLU': {
PTICSHDCSLU: {
'utt2da':
as_project_path(
"applications/PublicTransportInfoCS/data/utt2da_dict.txt"
)
}
}
})
output_utterance = True
output_abstraction = False
output_da = True
fn_uniq_trn_sem = 'uniq.trn.sem.tmp'
if len(sys.argv) < 2:
fn_uniq_trn = 'uniq.trn'
else:
fn_uniq_trn = sys.argv[1]
print "Processing input from file", fn_uniq_trn
uniq_trn = codecs.open(fn_uniq_trn, "r", encoding='utf8')
uniq_trn_sem = {}
for line in uniq_trn:
wav_key, utterance = line.split(" => ", 2)
annotation = []
if output_utterance:
annotation += [utterance.rstrip()]
if output_abstraction:
norm_utterance = slu.preprocessing.normalise_utterance(utterance)
abutterance, _ = slu.abstract_utterance(norm_utterance)
annotation += [abutterance]
if output_da:
da = slu.parse_1_best({'utt': Utterance(utterance)}).get_best_da()
annotation += [unicode(da)]
uniq_trn_sem[wav_key] = " <=> ".join(annotation)
print "Saving output to file", fn_uniq_trn_sem
save_wavaskey(fn_uniq_trn_sem, uniq_trn_sem)
def main():
import autopath
cldb = CategoryLabelDatabase('../data/database.py')
preprocessing = PTICSSLUPreprocessing(cldb)
slu = PTICSHDCSLU(preprocessing, cfg = {'SLU': {PTICSHDCSLU: {'utt2da': as_project_path("applications/PublicTransportInfoCS/data/utt2da_dict.txt")}}})
output_utterance = True
output_abstraction = False
output_da = True
fn_uniq_trn_sem = 'uniq.trn.sem.tmp'
if len(sys.argv) < 2:
fn_uniq_trn = 'uniq.trn'
else:
fn_uniq_trn = sys.argv[1]
print "Processing input from file", fn_uniq_trn
uniq_trn = codecs.open(fn_uniq_trn, "r", encoding='utf8')
uniq_trn_sem = {}
for line in uniq_trn:
wav_key, utterance = line.split(" => ", 2)
annotation = []
if output_utterance:
annotation += [utterance.rstrip()]
if output_abstraction:
norm_utterance = slu.preprocessing.normalise_utterance(utterance)
abutterance, _ = slu.abstract_utterance(norm_utterance)
annotation += [abutterance]
if output_da:
da = slu.parse_1_best({'utt': Utterance(utterance)}).get_best_da()
annotation += [unicode(da)]
uniq_trn_sem[wav_key] = " <=> ".join(annotation)
print "Saving output to file", fn_uniq_trn_sem
save_wavaskey(fn_uniq_trn_sem, uniq_trn_sem)
def main():
cldb = CategoryLabelDatabase('../data/database.py')
preprocessing = PTICSSLUPreprocessing(cldb)
slu = PTICSHDCSLU(
preprocessing,
cfg={
'SLU': {
PTICSHDCSLU: {
'utt2da':
as_project_path(
"applications/PublicTransportInfoCS/data/utt2da_dict.txt"
)
}
}
})
output_alignment = False
output_utterance = True
output_abstraction = False
output_da = True
if len(sys.argv) < 2:
fn_uniq_trn = 'uniq.trn'
else:
fn_uniq_trn = sys.argv[1]
fn_uniq_trn_sem = fn_uniq_trn + '.sem.tmp'
print "Processing input from file", fn_uniq_trn
uniq_trn = codecs.open(fn_uniq_trn, "r", encoding='utf8')
uniq_trn_sem = {}
for line in uniq_trn:
wav_key, utterance = line.split(" => ", 2)
annotation = []
if output_alignment:
norm_utterance = slu.preprocessing.normalise_utterance(
Utterance(utterance))
abutterance, _, _ = slu.abstract_utterance(norm_utterance)
abutterance = slu.handle_false_abstractions(abutterance)
da = slu.parse_1_best({'utt': Utterance(utterance)}).get_best_da()
max_alignment_idx = lambda _dai: max(
_dai.alignment) if _dai.alignment else len(abutterance)
for i, dai in enumerate(sorted(da, key=max_alignment_idx)):
if not dai.alignment:
print "Empty alignment:", unicode(abutterance), ";", dai
if not dai.alignment or dai.alignment == {-1}:
dai_alignment_idx = len(abutterance)
else:
dai_alignment_idx = max(dai.alignment) + i + 1
abutterance.insert(
dai_alignment_idx, "[{} - {}]".format(
unicode(dai),
list(dai.alignment if dai.alignment else [])))
annotation += [unicode(abutterance)]
else:
if output_utterance:
annotation += [utterance.rstrip()]
if output_abstraction:
norm_utterance = slu.preprocessing.normalise_utterance(
Utterance(utterance))
abutterance, _ = slu.abstract_utterance(norm_utterance)
annotation += [abutterance]
if output_da:
da = slu.parse_1_best({
'utt': Utterance(utterance)
}).get_best_da()
annotation += [unicode(da)]
uniq_trn_sem[wav_key] = " <=> ".join(annotation)
print "Saving output to file", fn_uniq_trn_sem
save_wavaskey(fn_uniq_trn_sem, uniq_trn_sem)
utterance = u"CHTĚL BYCH JET ZE ZASTÁVKY ANDĚL DO ZASTÁVKY MALOSTRANSKÉ NÁMĚSTÍ"
else:
utterance = sys.argv[1].decode("utf-8")
sys.argv = sys.argv[:1]
cldb = CategoryLabelDatabase("../data/database.py")
preprocessing = PTICSSLUPreprocessing(cldb)
slu = PTICSHDCSLU(
preprocessing,
cfg={
"SLU": {PTICSHDCSLU: {"utt2da": as_project_path("applications/PublicTransportInfoCS/data/utt2da_dict.txt")}}
},
)
norm_utterance = slu.preprocessing.normalise_utterance(Utterance(utterance))
abutterance, _, _ = slu.abstract_utterance(norm_utterance)
da = slu.parse_1_best({"utt": Utterance(utterance)}, verbose=True).get_best_da()
print "Abstracted utterance:", unicode(abutterance)
print "Dialogue act:", unicode(da)
max_alignment_idx = lambda _dai: max(_dai.alignment) if _dai.alignment else len(abutterance)
for i, dai in enumerate(sorted(da, key=max_alignment_idx)):
if not dai.alignment:
print "Empty alignment:", unicode(abutterance), ";", dai
if not dai.alignment or dai.alignment == -1:
dai_alignment_idx = len(abutterance)
else:
dai_alignment_idx = max(dai.alignment) + i + 1
abutterance.insert(dai_alignment_idx, "[{} - {}]".format(dai, dai.alignment))
slu = PTICSHDCSLU(
preprocessing,
cfg={
'SLU': {
PTICSHDCSLU: {
'utt2da':
as_project_path(
"applications/PublicTransportInfoCS/data/utt2da_dict.txt"
)
}
}
})
norm_utterance = slu.preprocessing.normalise_utterance(
Utterance(utterance))
abutterance, _, _ = slu.abstract_utterance(norm_utterance)
da = slu.parse_1_best({
'utt': Utterance(utterance)
}, verbose=True).get_best_da()
print "Abstracted utterance:", unicode(abutterance)
print "Dialogue act:", unicode(da)
max_alignment_idx = lambda _dai: max(
_dai.alignment) if _dai.alignment else len(abutterance)
for i, dai in enumerate(sorted(da, key=max_alignment_idx)):
if not dai.alignment:
print "Empty alignment:", unicode(abutterance), ";", dai
if not dai.alignment or dai.alignment == -1:
dai_alignment_idx = len(abutterance)
else:
