def input_usr_utt_nblist(self):
"""Reads an N-best list of utterances from the input. """
self.init_readline()
nblist = UtteranceNBList()
i = 1
while i < 100:
l = raw_input("User %d: " % i)
l = l.decode('utf8')
if self.f_output_script:
self.f_output_script.write(l + '\n')
if l.startswith("."):
print
break
try:
prob, da = self.parse_input_utt(l)
except TextHubException as e:
print e
continue
nblist.add(prob, da)
i += 1
nblist.merge()
nblist.scale()
nblist.add_other()
self.write_readline()
return nblist
def input_usr_utt_nblist(self):
"""Reads an N-best list of utterances from the input. """
self.init_readline()
nblist = UtteranceNBList()
i = 1
while i < 100:
l = raw_input("User %d: " % i)
try:
l = l.decode('utf8')
except: # if we use ipdb, it already gives us UTF-8-encoded input :-(
pass
if l.startswith("."):
print
break
try:
prob, da = self.parse_input_utt(l)
except TextHubException as e:
print e
continue
nblist.add(prob, da)
i += 1
nblist.merge()
nblist.scale()
nblist.add_other()
self.write_readline()
return nblist
def test_parse_with_mutliple_date_rel(self):
asr_hyp = UtteranceNBList()
asr_hyp.add(0.1, Utterance("CHTEL BYCH ZITRA ZITRA JET"))
cn = self.slu.parse(asr_hyp)
self.assert_(DialogueActItem(dai="inform(date_rel=tomorrow)") in cn)
def get_response(self, utt_text):
try:
self.cfg['Logging']['system_logger'].info('User: '******'utt_nbl': utt_nblist})
return self.process_dm()
except Exception:
self.cfg['Logging']['system_logger'].exception(
'Uncaught exception in WTHUB process.')
return None
def test_parse_X(self):
from alex.components.slu.dainnclassifier import DAINNClassifier
np.random.seed(0)
cldb = CategoryLabelDatabase()
class db:
database = {
"task": {
"find_connection": ["najít spojení", "najít spoj", "zjistit spojení",
"zjistit spoj", "hledám spojení", 'spojení', 'spoj',
],
"find_platform": ["najít nástupiště", "zjistit nástupiště", ],
'weather': ['pocasi', 'jak bude', ],
},
"number": {
"1": ["jednu"]
},
"time": {
"now": ["nyní", "teď", "teďka", "hned", "nejbližší", "v tuto chvíli", "co nejdřív"],
},
}
cldb.load(db_mod=db)
preprocessing = SLUPreprocessing(cldb)
clf = DAINNClassifier(cldb, preprocessing, features_size=4)
# Train a simple classifier.
das = {
'1': DialogueAct('inform(task=weather)'),
'2': DialogueAct('inform(time=now)'),
'3': DialogueAct('inform(task=weather)'),
'4': DialogueAct('inform(task=connection)'),
}
utterances = {
'1': Utterance('pocasi pocasi pocasi pocasi pocasi'),
'2': Utterance('hned ted nyni hned ted nyni'),
'3': Utterance('jak bude jak bude jak bude jak bude'),
'4': Utterance('kdy a odkat mi to jede'),
}
clf.extract_classifiers(das, utterances, verbose=False)
clf.prune_classifiers(min_classifier_count=0)
clf.gen_classifiers_data(min_pos_feature_count=0,
min_neg_feature_count=0,
verbose2=False)
clf.train(inverse_regularisation=1e1, verbose=False)
# Parse some sentences.
utterance_list = UtteranceNBList()
utterance_list.add(0.7, Utterance('pocasi'))
utterance_list.add(0.7, Utterance('jak bude pocasi'))
utterance_list.add(0.2, Utterance('hned'))
utterance_list.add(0.2, Utterance('hned'))
da_confnet = clf.parse_X(utterance_list, verbose=False)
self.assertTrue(da_confnet.get_prob(DialogueActItem(dai='inform(task=weather)')) != 0.0)
self.assertTrue(da_confnet.get_prob(DialogueActItem(dai='inform(time=now)')) != 0.0)
def hyp_out(self):
""" This defines asynchronous interface for speech recognition.
Returns:
ASR hypothesis about the input speech audio.
"""
start = time.time()
# Get hypothesis
self.decoder.prune_final()
utt_lik, lat = self.decoder.get_lattice(
) # returns acceptor (py)fst.LogVectorFst
self.decoder.reset(keep_buffer_data=False)
if self.calibration_table:
lat = lattice_calibration(lat, self.calibration_table)
self.last_lattice = lat
# Convert lattice to nblist
nbest = lattice_to_nbest(lat, self.n_best)
nblist = UtteranceNBList()
for w, word_ids in nbest:
words = u' '.join([self.wst[i] for i in word_ids])
if self.cfg['ASR']['Kaldi']['debug']:
self.syslog.debug(words)
p = exp(-w)
nblist.add(p, Utterance(words))
# Log
if len(nbest) == 0:
nblist.add(1.0, Utterance('Empty hypothesis: Kaldi __FAIL__'))
nblist.merge()
if self.cfg['ASR']['Kaldi']['debug']:
self.syslog.info('utterance "likelihood" is %f' % utt_lik)
self.syslog.debug('hyp_out: get_lattice+nbest in %s secs' %
str(time.time() - start))
return nblist
def process_call_log(fn):
name = multiprocessing.current_process().name
asr = []
nbl = []
sem = []
trn = []
trn_hdc_sem = []
fcount = 0
tcount = 0
f_dir = os.path.dirname(fn)
print "Process name:", name
print "File #", fcount
fcount += 1
print "Processing:", fn
doc = xml.dom.minidom.parse(fn)
turns = doc.getElementsByTagName("turn")
for i, turn in enumerate(turns):
if turn.getAttribute('speaker') != 'user':
continue
recs = turn.getElementsByTagName("rec")
trans = turn.getElementsByTagName("asr_transcription")
asrs = turn.getElementsByTagName("asr")
if len(recs) != 1:
print "Skipping a turn {turn} in file: {fn} - recs: {recs}".format(
turn=i, fn=fn, recs=len(recs))
continue
if len(asrs) == 0 and (i + 1) < len(turns):
next_asrs = turns[i + 1].getElementsByTagName("asr")
if len(next_asrs) != 2:
print "Skipping a turn {turn} in file: {fn} - asrs: {asrs} - next_asrs: {next_asrs}".format(
turn=i, fn=fn, asrs=len(asrs), next_asrs=len(next_asrs))
continue
print "Recovered from missing ASR output by using a delayed ASR output from the following turn of turn {turn}. File: {fn} - next_asrs: {asrs}".format(
turn=i, fn=fn, asrs=len(next_asrs))
hyps = next_asrs[0].getElementsByTagName("hypothesis")
elif len(asrs) == 1:
hyps = asrs[0].getElementsByTagName("hypothesis")
elif len(asrs) == 2:
print "Recovered from EXTRA ASR outputs by using a the last ASR output from the turn. File: {fn} - asrs: {asrs}".format(
fn=fn, asrs=len(asrs))
hyps = asrs[-1].getElementsByTagName("hypothesis")
else:
print "Skipping a turn {turn} in file {fn} - asrs: {asrs}".format(
turn=i, fn=fn, asrs=len(asrs))
continue
if len(trans) == 0:
print "Skipping a turn in {fn} - trans: {trans}".format(
fn=fn, trans=len(trans))
continue
wav_key = recs[0].getAttribute('fname')
wav_path = os.path.join(f_dir, wav_key)
# FIXME: Check whether the last transcription is really the best! FJ
t = various.get_text_from_xml_node(trans[-1])
t = normalise_text(t)
if '--asr-log' not in sys.argv:
asr_rec_nbl = asr_rec.rec_wav_file(wav_path)
a = unicode(asr_rec_nbl.get_best())
else:
a = various.get_text_from_xml_node(hyps[0])
a = normalise_semi_words(a)
if exclude_slu(t) or 'DOM Element:' in a:
print "Skipping transcription:", unicode(t)
print "Skipping ASR output: ", unicode(a)
continue
# The silence does not have a label in the language model.
t = t.replace('_SIL_', '')
trn.append((wav_key, t))
print
print "Transcritpiton #", tcount
tcount += 1
print "Parsing transcription:", unicode(t)
print " ASR:", unicode(a)
# HDC SLU on transcription
s = slu.parse_1_best({'utt': Utterance(t)}).get_best_da()
trn_hdc_sem.append((wav_key, s))
# 1 best ASR
asr.append((wav_key, a))
# N best ASR
n = UtteranceNBList()
if '--asr-log' not in sys.argv:
n = asr_rec_nbl
print 'ASR RECOGNITION NBLIST\n', unicode(n)
else:
for h in hyps:
txt = various.get_text_from_xml_node(h)
txt = normalise_semi_words(txt)
n.add(abs(float(h.getAttribute('p'))), Utterance(txt))
n.merge()
n.normalise()
nbl.append((wav_key, n.serialise()))
# there is no manual semantics in the transcriptions yet
sem.append((wav_key, None))
return asr, nbl, sem, trn, trn_hdc_sem, fcount, tcount
def recognize(self, wav):
""" Produces hypotheses for the input audio data.
Remember that GoogleASR works only with complete wave files.
Returns an n-best list of hypotheses.
"""
# making a file temp for manipulation
handle, flac_file_name = mkstemp('TmpSpeechFile.flac')
try:
# convert wav to flac
audio.save_flac(self.cfg, flac_file_name, wav)
json_hypotheses = self.get_asr_hypotheses(flac_file_name)
except (urllib2.HTTPError, urllib2.URLError) as e:
self.syslog.exception('GoogleASR HTTP/URL error: %s' % unicode(e))
json_hypotheses = [
[{'confidence': 1.0, 'utterance': '__google__ __asr__ __exception__'}, ], ]
finally:
os.close(handle)
remove(flac_file_name)
try:
hyp = json.loads(json_hypotheses)
# print "###", hyp
nblist = UtteranceNBList()
if hyp['status'] == 0:
n = len(hyp['hypotheses'])
for i, h in enumerate(hyp['hypotheses']):
if i == 0:
nblist.add(h['confidence'], Utterance(h['utterance']))
conf1 = hyp['hypotheses'][0]['confidence']
else:
# guess the confX score
nblist.add((1.0-conf1)*(n-i)/(n-1.0)/(n-0.0)*2.0, Utterance(h['utterance']))
elif hyp['status'] == 5:
nblist.add(1.0, Utterance('_other_'))
except:
nblist = UtteranceNBList()
nblist.merge()
nblist.add_other()
return nblist
def test_conversion_of_confnet_into_nblist(self):
A1, A2, A3 = 0.90, 0.05, 0.05
B1, B2, B3 = 0.50, 0.35, 0.15
C1, C2, C3 = 0.60, 0.30, 0.10
correct_nblist = UtteranceNBList()
correct_nblist.add(A1*B1*C1, Utterance("A1 B1 C1"))
correct_nblist.add(A1*B2*C1, Utterance("A1 B2 C1"))
correct_nblist.add(A1*B1*C2, Utterance("A1 B1 C2"))
correct_nblist.add(A1*B2*C2, Utterance("A1 B2 C2"))
correct_nblist.add(A1*B3*C1, Utterance("A1 B3 C1"))
correct_nblist.add(A1*B1*C3, Utterance("A1 B1 C3"))
correct_nblist.add(A1*B3*C2, Utterance("A1 B3 C2"))
correct_nblist.add(A1*B2*C3, Utterance("A1 B2 C3"))
correct_nblist.merge()
correct_nblist.add_other()
confnet = UtteranceConfusionNetwork()
confnet.add([[A1, 'A1'], [A2, 'A2'], [A3, 'A3'],])
confnet.add([[B1, 'B1'], [B2, 'B2'], [B3, 'B3'],])
confnet.add([[C1, 'C1'], [C2, 'C2'], [C3, 'C3'],])
confnet.merge().sort()
gen_nblist = confnet.get_utterance_nblist(10)
s = []
s.append("")
s.append("Confusion network:")
s.append(unicode(confnet))
s.append("")
s.append("Generated nblist:")
s.append(unicode(gen_nblist))
s.append("")
s.append("Correct nblist:")
s.append(unicode(correct_nblist))
s.append("")
self.assertEqual(unicode(gen_nblist), unicode(correct_nblist))
def main():
cldb = CategoryLabelDatabase('../data/database.py')
preprocessing = PTIENSLUPreprocessing(cldb)
slu = PTIENHDCSLU(preprocessing, cfg={'SLU': {PTIENHDCSLU: {'utt2da': as_project_path("applications/PublicTransportInfoEN/data/utt2da_dict.txt")}}})
cfg = Config.load_configs(['../kaldi.cfg',], use_default=True)
asr_rec = asr_factory(cfg)
fn_uniq_trn = 'uniq.trn'
fn_uniq_trn_hdc_sem = 'uniq.trn.hdc.sem'
fn_uniq_trn_sem = 'uniq.trn.sem'
fn_all_sem = 'all.sem'
fn_all_trn = 'all.trn'
fn_all_trn_hdc_sem = 'all.trn.hdc.sem'
fn_all_asr = 'all.asr'
fn_all_asr_hdc_sem = 'all.asr.hdc.sem'
fn_all_nbl = 'all.nbl'
fn_all_nbl_hdc_sem = 'all.nbl.hdc.sem'
fn_train_sem = 'train.sem'
fn_train_trn = 'train.trn'
fn_train_trn_hdc_sem = 'train.trn.hdc.sem'
fn_train_asr = 'train.asr'
fn_train_asr_hdc_sem = 'train.asr.hdc.sem'
fn_train_nbl = 'train.nbl'
fn_train_nbl_hdc_sem = 'train.nbl.hdc.sem'
fn_dev_sem = 'dev.sem'
fn_dev_trn = 'dev.trn'
fn_dev_trn_hdc_sem = 'dev.trn.hdc.sem'
fn_dev_asr = 'dev.asr'
fn_dev_asr_hdc_sem = 'dev.asr.hdc.sem'
fn_dev_nbl = 'dev.nbl'
fn_dev_nbl_hdc_sem = 'dev.nbl.hdc.sem'
fn_test_sem = 'test.sem'
fn_test_trn = 'test.trn'
fn_test_trn_hdc_sem = 'test.trn.hdc.sem'
fn_test_asr = 'test.asr'
fn_test_asr_hdc_sem = 'test.asr.hdc.sem'
fn_test_nbl = 'test.nbl'
fn_test_nbl_hdc_sem = 'test.nbl.hdc.sem'
indomain_data_dir = "indomain_data"
print "Generating the SLU train and test data"
print "-"*120
###############################################################################################
files = []
files.append(glob.glob(os.path.join(indomain_data_dir, 'asr_transcribed.xml')))
files.append(glob.glob(os.path.join(indomain_data_dir, '*', 'asr_transcribed.xml')))
files.append(glob.glob(os.path.join(indomain_data_dir, '*', '*', 'asr_transcribed.xml')))
files.append(glob.glob(os.path.join(indomain_data_dir, '*', '*', '*', 'asr_transcribed.xml')))
files.append(glob.glob(os.path.join(indomain_data_dir, '*', '*', '*', '*', 'asr_transcribed.xml')))
files.append(glob.glob(os.path.join(indomain_data_dir, '*', '*', '*', '*', '*', 'asr_transcribed.xml')))
files = various.flatten(files)
sem = []
trn = []
trn_hdc_sem = []
asr = []
asr_hdc_sem = []
nbl = []
nbl_hdc_sem = []
for fn in files[:100000]:
f_dir = os.path.dirname(fn)
print "Processing:", fn
doc = xml.dom.minidom.parse(fn)
turns = doc.getElementsByTagName("turn")
for i, turn in enumerate(turns):
if turn.getAttribute('speaker') != 'user':
continue
recs = turn.getElementsByTagName("rec")
trans = turn.getElementsByTagName("asr_transcription")
asrs = turn.getElementsByTagName("asr")
if len(recs) != 1:
print "Skipping a turn {turn} in file: {fn} - recs: {recs}".format(turn=i,fn=fn, recs=len(recs))
continue
if len(asrs) == 0 and (i + 1) < len(turns):
next_asrs = turns[i+1].getElementsByTagName("asr")
if len(next_asrs) != 2:
print "Skipping a turn {turn} in file: {fn} - asrs: {asrs} - next_asrs: {next_asrs}".format(turn=i, fn=fn, asrs=len(asrs), next_asrs=len(next_asrs))
continue
print "Recovered from missing ASR output by using a delayed ASR output from the following turn of turn {turn}. File: {fn} - next_asrs: {asrs}".format(turn=i, fn=fn, asrs=len(next_asrs))
hyps = next_asrs[0].getElementsByTagName("hypothesis")
elif len(asrs) == 1:
hyps = asrs[0].getElementsByTagName("hypothesis")
elif len(asrs) == 2:
print "Recovered from EXTRA ASR outputs by using a the last ASR output from the turn. File: {fn} - asrs: {asrs}".format(fn=fn, asrs=len(asrs))
hyps = asrs[-1].getElementsByTagName("hypothesis")
else:
print "Skipping a turn {turn} in file {fn} - asrs: {asrs}".format(turn=i,fn=fn, asrs=len(asrs))
continue
if len(trans) == 0:
print "Skipping a turn in {fn} - trans: {trans}".format(fn=fn, trans=len(trans))
continue
wav_key = recs[0].getAttribute('fname')
wav_path = os.path.join(f_dir, wav_key)
# FIXME: Check whether the last transcription is really the best! FJ
t = various.get_text_from_xml_node(trans[-1])
t = normalise_text(t)
if '--asr-log' not in sys.argv:
asr_rec_nbl = asr_rec.rec_wav_file(wav_path)
a = unicode(asr_rec_nbl.get_best())
else:
a = various.get_text_from_xml_node(hyps[0])
a = normalise_semi_words(a)
if exclude_slu(t) or 'DOM Element:' in a:
print "Skipping transcription:", unicode(t)
print "Skipping ASR output: ", unicode(a)
continue
# The silence does not have a label in the language model.
t = t.replace('_SIL_','')
trn.append((wav_key, t))
print "Parsing transcription:", unicode(t)
print " ASR:", unicode(a)
# HDC SLU on transcription
s = slu.parse_1_best({'utt':Utterance(t)}).get_best_da()
trn_hdc_sem.append((wav_key, s))
if '--uniq' not in sys.argv:
# HDC SLU on 1 best ASR
if '--asr-log' not in sys.argv:
a = unicode(asr_rec_nbl.get_best())
else:
a = various.get_text_from_xml_node(hyps[0])
a = normalise_semi_words(a)
asr.append((wav_key, a))
s = slu.parse_1_best({'utt':Utterance(a)}).get_best_da()
asr_hdc_sem.append((wav_key, s))
# HDC SLU on N best ASR
n = UtteranceNBList()
if '--asr-log' not in sys.argv:
n = asr_rec_nbl
print 'ASR RECOGNITION NBLIST\n',unicode(n)
else:
for h in hyps:
txt = various.get_text_from_xml_node(h)
txt = normalise_semi_words(txt)
n.add(abs(float(h.getAttribute('p'))),Utterance(txt))
n.merge()
n.normalise()
nbl.append((wav_key, n.serialise()))
if '--fast' not in sys.argv:
s = slu.parse_nblist({'utt_nbl':n}).get_best_da()
nbl_hdc_sem.append((wav_key, s))
# there is no manual semantics in the transcriptions yet
sem.append((wav_key, None))
uniq_trn = {}
uniq_trn_hdc_sem = {}
uniq_trn_sem = {}
trn_set = set()
sem = dict(trn_hdc_sem)
for k, v in trn:
if not v in trn_set:
trn_set.add(v)
uniq_trn[k] = v
uniq_trn_hdc_sem[k] = sem[k]
uniq_trn_sem[k] = v + " <=> " + unicode(sem[k])
save_wavaskey(fn_uniq_trn, uniq_trn)
save_wavaskey(fn_uniq_trn_hdc_sem, uniq_trn_hdc_sem, trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
save_wavaskey(fn_uniq_trn_sem, uniq_trn_sem)
# all
save_wavaskey(fn_all_trn, dict(trn))
save_wavaskey(fn_all_trn_hdc_sem, dict(trn_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
if '--uniq' not in sys.argv:
save_wavaskey(fn_all_asr, dict(asr))
save_wavaskey(fn_all_asr_hdc_sem, dict(asr_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
save_wavaskey(fn_all_nbl, dict(nbl))
save_wavaskey(fn_all_nbl_hdc_sem, dict(nbl_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
seed_value = 10
random.seed(seed_value)
random.shuffle(trn)
random.seed(seed_value)
random.shuffle(trn_hdc_sem)
random.seed(seed_value)
random.shuffle(asr)
random.seed(seed_value)
random.shuffle(asr_hdc_sem)
random.seed(seed_value)
random.shuffle(nbl)
random.seed(seed_value)
random.shuffle(nbl_hdc_sem)
# trn
train_trn = trn[:int(0.8*len(trn))]
dev_trn = trn[int(0.8*len(trn)):int(0.9*len(trn))]
test_trn = trn[int(0.9*len(trn)):]
save_wavaskey(fn_train_trn, dict(train_trn))
save_wavaskey(fn_dev_trn, dict(dev_trn))
save_wavaskey(fn_test_trn, dict(test_trn))
# trn_hdc_sem
train_trn_hdc_sem = trn_hdc_sem[:int(0.8*len(trn_hdc_sem))]
dev_trn_hdc_sem = trn_hdc_sem[int(0.8*len(trn_hdc_sem)):int(0.9*len(trn_hdc_sem))]
test_trn_hdc_sem = trn_hdc_sem[int(0.9*len(trn_hdc_sem)):]
save_wavaskey(fn_train_trn_hdc_sem, dict(train_trn_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
save_wavaskey(fn_dev_trn_hdc_sem, dict(dev_trn_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
save_wavaskey(fn_test_trn_hdc_sem, dict(test_trn_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
# asr
train_asr = asr[:int(0.8*len(asr))]
dev_asr = asr[int(0.8*len(asr)):int(0.9*len(asr))]
test_asr = asr[int(0.9*len(asr)):]
save_wavaskey(fn_train_asr, dict(train_asr))
save_wavaskey(fn_dev_asr, dict(dev_asr))
save_wavaskey(fn_test_asr, dict(test_asr))
# asr_hdc_sem
train_asr_hdc_sem = asr_hdc_sem[:int(0.8*len(asr_hdc_sem))]
dev_asr_hdc_sem = asr_hdc_sem[int(0.8*len(asr_hdc_sem)):int(0.9*len(asr_hdc_sem))]
test_asr_hdc_sem = asr_hdc_sem[int(0.9*len(asr_hdc_sem)):]
save_wavaskey(fn_train_asr_hdc_sem, dict(train_asr_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
save_wavaskey(fn_dev_asr_hdc_sem, dict(dev_asr_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
save_wavaskey(fn_test_asr_hdc_sem, dict(test_asr_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
# n-best lists
train_nbl = nbl[:int(0.8*len(nbl))]
dev_nbl = nbl[int(0.8*len(nbl)):int(0.9*len(nbl))]
test_nbl = nbl[int(0.9*len(nbl)):]
save_wavaskey(fn_train_nbl, dict(train_nbl))
save_wavaskey(fn_dev_nbl, dict(dev_nbl))
save_wavaskey(fn_test_nbl, dict(test_nbl))
# nbl_hdc_sem
train_nbl_hdc_sem = nbl_hdc_sem[:int(0.8*len(nbl_hdc_sem))]
dev_nbl_hdc_sem = nbl_hdc_sem[int(0.8*len(nbl_hdc_sem)):int(0.9*len(nbl_hdc_sem))]
test_nbl_hdc_sem = nbl_hdc_sem[int(0.9*len(nbl_hdc_sem)):]
save_wavaskey(fn_train_nbl_hdc_sem, dict(train_nbl_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
save_wavaskey(fn_dev_nbl_hdc_sem, dict(dev_nbl_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
save_wavaskey(fn_test_nbl_hdc_sem, dict(test_nbl_hdc_sem), trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
def test_parse_meta(self):
utterances_to_understand = [
(
u"ahoj",
"hello()",
),
(
u"sbohem čau",
"bye()",
),
(
u"jiné",
"reqalts()",
),
(
u"začneme znovu",
"restart()",
),
(
u"zopakuj",
"repeat()",
),
(
u"promiň",
"apology()",
),
(
u"co se zeptat",
"help()",
),
(
u"haló",
"canthearyou()",
),
(
u"nerozuměl jsem",
"notunderstood()",
),
(
u"ano jo",
"affirm()",
),
(
u"ne ano nechci",
"negate()",
),
(
u"děkuji",
"thankyou()",
),
(
u"dobře",
"ack()",
),
(
u"chci jet",
"inform(task=find_connection)",
),
(
u"jak bude",
"inform(task=weather)",
),
(
u"nástupiště",
"inform(task=find_platform)",
),
(
u"z jaké jede",
"request(from_stop)",
),
(
u"kam to jede",
"request(to_stop)",
),
(
u"kdy to jede",
"request(departure_time)",
),
(
u"za jak dlouho",
"request(departure_time_rel)",
),
(
u"kdy tam budem",
"request(arrival_time)",
),
(
u"za jak dlouho tam přijedu",
"request(arrival_time_rel)",
),
(
u"jak dlouho bude trvat cesta",
"request(duration)",
),
(
u"kolik je hodin",
"request(current_time)",
),
(
u"jak dlouho trvá přestup",
"request(time_transfers)",
),
(
u"kolik přestupů",
"request(num_transfers)",
),
(
u"nechci přestup bez jet přímo",
"inform(num_transfers=0)",
),
(
u"jeden přestup",
"inform(num_transfers=1)",
),
(
u"dva přestupy",
"inform(num_transfers=2)",
),
(
u"tři přestupy",
"inform(num_transfers=3)",
),
(
u"čtyři přestupy",
"inform(num_transfers=4)",
),
(
u"libovolně přestupů",
"inform(num_transfers=dontcare)",
),
(
u"jet přímo",
"inform(num_transfers=0)",
),
(
u"alternativa libovolný",
"inform(alternative=dontcare)",
),
(
u"alternativa první",
"inform(alternative=1)",
),
(
u"alternativa druhá",
"inform(alternative=2)",
),
(
u"alternativa třetí",
"inform(alternative=3)",
),
(
u"alternativa čtvrtá",
"inform(alternative=4)",
),
(
u"alternativa páté",
"inform(alternative=5)",
),
(
u"předchozí spoj",
"inform(alternative=prev)",
),
(
u"nechci předchozí spoj",
"deny(alternative=prev)",
),
(
u"poslední spoj",
"inform(alternative=last)",
),
(
u"nechci poslední spoj",
"deny(alternative=last)",
),
(
u"další spoj",
"inform(alternative=next)",
),
(
u"další",
"inform(alternative=next)",
),
(
u"předchozí",
"inform(alternative=prev)",
),
(
u"jako ve dne",
"inform(ampm=pm)",
),
]
for utt, res in utterances_to_understand:
asr_hyp = UtteranceNBList()
asr_hyp.add(0.79, Utterance(utt))
cn = self.slu.parse(asr_hyp)
self.assertIn(DialogueActItem(dai=res), cn)
def get_results(self, timeout=0.6):
""""
Waits for the complete recognition results from the Julius ASR server.
Timeout specifies how long it will wait for the end of message.
"""
msg = ""
# Get results from the server.
time_slept = 0.0
while time_slept < timeout:
msg_part = self.read_server_message(self.msg_timeout)
if not msg_part:
# Wait and check whether there is a message.
time.sleep(self.cfg['Hub']['main_loop_sleep_time'])
time_slept += self.cfg['Hub']['main_loop_sleep_time']
if self.debug >= 2:
print "gr.time_slept:", time_slept
continue
msg += msg_part + '\n'
if self.debug:
print msg
if '<CONFNET>' in msg:
break
else:
raise JuliusASRTimeoutException(
"Timeout when waiting for the Julius server results.")
# Process the results.
""" Typical result returned by the Julius ASR.
<STARTPROC/>
<INPUT STATUS="LISTEN" TIME="1343896296"/>
<INPUT STATUS="STARTREC" TIME="1343896311"/>
<STARTRECOG/>
<INPUT STATUS="ENDREC" TIME="1343896312"/>
<ENDRECOG/>
<INPUTPARAM FRAMES="164" MSEC="1640"/>
<RECOGOUT>
<SHYPO RANK="1" SCORE="-7250.111328">
<WHYPO WORD="" CLASSID="<s>" PHONE="sil" CM="0.887"/>
<WHYPO WORD="I'M" CLASSID="I'M" PHONE="ah m" CM="0.705"/>
<WHYPO WORD="LOOKING" CLASSID="LOOKING" PHONE="l uh k ih ng" CM="0.992"/>
<WHYPO WORD="FOR" CLASSID="FOR" PHONE="f er" CM="0.757"/>
<WHYPO WORD="A" CLASSID="A" PHONE="ah" CM="0.672"/>
<WHYPO WORD="PUB" CLASSID="PUB" PHONE="p ah b" CM="0.409"/>
<WHYPO WORD="" CLASSID="</s>" PHONE="sil" CM="1.000"/>
</SHYPO>
</RECOGOUT>
<GRAPHOUT NODENUM="43" ARCNUM="70">
<NODE GID="0" WORD="" CLASSID="<s>" PHONE="sil" BEGIN="0" END="2"/>
<NODE GID="1" WORD="" CLASSID="<s>" PHONE="sil" BEGIN="0" END="3"/>
<NODE GID="2" WORD="" CLASSID="<s>" PHONE="sil" BEGIN="0" END="4"/>
<NODE GID="3" WORD="I" CLASSID="I" PHONE="ay" BEGIN="3" END="5"/>
<NODE GID="4" WORD="NO" CLASSID="NO" PHONE="n ow" BEGIN="3" END="7"/>
<NODE GID="5" WORD="I" CLASSID="I" PHONE="ay" BEGIN="4" END="6"/>
<NODE GID="6" WORD="UH" CLASSID="UH" PHONE="ah" BEGIN="4" END="6"/>
<NODE GID="7" WORD="I'M" CLASSID="I'M" PHONE="ay m" BEGIN="4" END="27"/>
...
<NODE GID="38" WORD="PUB" CLASSID="PUB" PHONE="p ah b" BEGIN="79" END="104"/>
<NODE GID="39" WORD="AH" CLASSID="AH" PHONE="aa" BEGIN="81" END="110"/>
<NODE GID="40" WORD="LOT" CLASSID="LOT" PHONE="l aa t" BEGIN="81" END="110"/>
<NODE GID="41" WORD="" CLASSID="</s>" PHONE="sil" BEGIN="105" END="163"/>
<NODE GID="42" WORD="" CLASSID="</s>" PHONE="sil" BEGIN="111" END="163"/>
<ARC FROM="0" TO="4"/>
<ARC FROM="0" TO="3"/>
<ARC FROM="1" TO="7"/>
<ARC FROM="1" TO="5"/>
<ARC FROM="1" TO="6"/>
...
<ARC FROM="38" TO="41"/>
<ARC FROM="39" TO="42"/>
<ARC FROM="40" TO="42"/>
</GRAPHOUT>
<CONFNET>
<WORD>
<ALTERNATIVE PROB="1.000"></ALTERNATIVE>
</WORD>
<WORD>
<ALTERNATIVE PROB="0.950">I</ALTERNATIVE>
<ALTERNATIVE PROB="0.020">HI</ALTERNATIVE>
<ALTERNATIVE PROB="0.013">NO</ALTERNATIVE>
<ALTERNATIVE PROB="0.010"></ALTERNATIVE>
<ALTERNATIVE PROB="0.006">UH</ALTERNATIVE>
</WORD>
<WORD>
<ALTERNATIVE PROB="0.945">AM</ALTERNATIVE>
<ALTERNATIVE PROB="0.055">I'M</ALTERNATIVE>
</WORD>
<WORD>
<ALTERNATIVE PROB="1.000">LOOKING</ALTERNATIVE>
</WORD>
<WORD>
<ALTERNATIVE PROB="1.000">FOR</ALTERNATIVE>
</WORD>
<WORD>
<ALTERNATIVE PROB="1.000">A</ALTERNATIVE>
</WORD>
<WORD>
<ALTERNATIVE PROB="0.963">PUB</ALTERNATIVE>
<ALTERNATIVE PROB="0.016">AH</ALTERNATIVE>
<ALTERNATIVE PROB="0.012">BAR</ALTERNATIVE>
<ALTERNATIVE PROB="0.008">LOT</ALTERNATIVE>
</WORD>
<WORD>
<ALTERNATIVE PROB="1.000"></ALTERNATIVE>
</WORD>
</CONFNET>
<INPUT STATUS="LISTEN" TIME="1343896312"/>
"""
msg = "<RESULTS>" + msg + "</RESULTS>"
msg = msg.replace("<s>", "<s>").replace("</s>", "</s>")
nblist = UtteranceNBList()
doc = xml.dom.minidom.parseString(msg)
recogout = doc.getElementsByTagName("RECOGOUT")
for el in recogout:
shypo = el.getElementsByTagName("SHYPO")
for el in shypo:
whypo = el.getElementsByTagName("WHYPO")
utterance = ""
cm = 1.0
for el in whypo:
word = el.getAttribute("WORD")
utterance += " " + word
if word:
cm *= float(el.getAttribute("CM"))
nblist.add(cm, Utterance(utterance))
nblist.merge()
nblist.add_other()
cn = UtteranceConfusionNetwork()
confnet = doc.getElementsByTagName("CONFNET")
for el in confnet:
word = el.getElementsByTagName("WORD")
for el in word:
alternative = el.getElementsByTagName("ALTERNATIVE")
word_list = []
for el in alternative:
prob = float(el.getAttribute("PROB"))
text = get_text_from_xml_node(el)
word_list.append([prob, text])
# Filter out empty hypotheses.
if len(word_list) == 0:
continue
if len(word_list) == 1 and len(word_list[0][1]) == 0:
continue
# Add the word into the confusion network.
cn.add(word_list)
cn.merge()
cn.normalise()
cn.prune()
cn.normalise()
cn.sort()
return nblist, cn