def hdc_slu(fn_input, constructor, fn_output):
"""
Use for transcription a HDC SLU model.
:param fn_model:
:param fn_input:
:param constructor:
:param fn_reference:
:return:
"""
print "="*120
print "HDC SLU: ", fn_input, fn_output
print "-"*120
from alex.components.slu.base import CategoryLabelDatabase
from alex.applications.PublicTransportInfoCS.preprocessing import PTICSSLUPreprocessing
from alex.applications.PublicTransportInfoCS.hdc_slu import PTICSHDCSLU
from alex.corpustools.wavaskey import load_wavaskey, save_wavaskey
from alex.corpustools.semscore import score
cldb = CategoryLabelDatabase('../data/database.py')
preprocessing = PTICSSLUPreprocessing(cldb)
hdc_slu = PTICSHDCSLU(preprocessing, cfg = {'SLU': {PTICSHDCSLU: {'utt2da': as_project_path("applications/PublicTransportInfoCS/data/utt2da_dict.txt")}}})
test_utterances = load_wavaskey(fn_input, constructor, limit=1000000)
parsed_das = {}
for utt_key, utt in sorted(test_utterances.iteritems()):
if isinstance(utt, Utterance):
obs = {'utt': utt}
elif isinstance(utt, UtteranceNBList):
obs = {'utt_nbl': utt}
else:
raise BaseException('Unsupported observation type')
print '-' * 120
print "Observation:"
print utt_key, " ==> "
print unicode(utt)
da_confnet = hdc_slu.parse(obs, verbose=False)
print "Conf net:"
print unicode(da_confnet)
da_confnet.prune()
dah = da_confnet.get_best_da_hyp()
print "1 best: "
print unicode(dah)
parsed_das[utt_key] = dah.da
if 'CL_' in str(dah.da):
print '*' * 120
print utt
print dah.da
hdc_slu.parse(obs, verbose=True)
save_wavaskey(fn_output, parsed_das, trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
def main():
import autopath
files = []
for i in range(1, len(sys.argv)):
k = load_wavaskey(sys.argv[i], unicode)
print sys.argv[i], len(k)
files.append(k)
keys = set()
keys.update(set(files[0].keys()))
ukeys = set()
for f in files:
keys = keys.intersection(set(f.keys()))
ukeys = ukeys.union(set(f.keys()))
print len(keys), len(ukeys), len(ukeys - keys)
for f in files:
rk = set(f.keys()) - keys
for k in rk:
if k in f:
del f[k]
for i in range(1, len(sys.argv)):
save_wavaskey(sys.argv[i]+'.pruned',files[i-1])
def main():
files = []
for i in range(1, len(sys.argv)):
k = load_wavaskey(sys.argv[i], unicode)
print sys.argv[i], len(k)
files.append(k)
keys = set()
keys.update(set(files[0].keys()))
ukeys = set()
for f in files:
keys = keys.intersection(set(f.keys()))
ukeys = ukeys.union(set(f.keys()))
print len(keys), len(ukeys), len(ukeys - keys)
for f in files:
rk = set(f.keys()) - keys
for k in rk:
if k in f:
del f[k]
for i in range(1, len(sys.argv)):
save_wavaskey(sys.argv[i] + '.pruned', files[i - 1])
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 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 compute_rt_factor(outdir, trn_dict, dec_dict, wavlen_dict, declen_dict, fwlen_dict):
reference = os.path.join(outdir, 'ref_trn.txt')
hypothesis = os.path.join(outdir, 'dec_trn.txt')
save_wavaskey(reference, trn_dict)
save_wavaskey(hypothesis, dec_dict)
save_wavaskey(os.path.join(outdir, 'wavlen.txt'), wavlen_dict)
save_wavaskey(os.path.join(outdir, 'dec_duration.txt'), declen_dict)
rtf, latency, fw_rtf, fw_latency, d_tot, w_tot = [], [], [], [], 0, 0
for k in declen_dict.keys():
w, d, f = wavlen_dict[k], declen_dict[k], fwlen_dict[k]
d_tot, w_tot = d_tot + d, w_tot + w
rtf.append(float(d) / w)
latency.append(float(d) - w)
fw_rtf.append(float(f) / w)
fw_latency.append(max(float(f) - w, 0))
rtf_global = float(d_tot) / w_tot
print
print """ # waws: %d""" % len(rtf)
print """ Global RTF mean: %(rtfglob)f""" % {'rtfglob': rtf_global}
try:
rtf.sort()
rm = rtf[int(len(rtf)*0.5)]
rc95 = rtf[int(len(rtf)*0.95)]
print """ RTF median: %(median)f RTF < %(c95)f [in 95%%]""" % {'median': rm, 'c95': rc95}
except:
pass
try:
fw_rtf.sort()
fm = fw_rtf[int(len(fw_rtf)*0.5)]
fc95 = fw_rtf[int(len(fw_rtf)*0.95)]
print """ Forward RTF median: %(median)f FWRTF < %(c95)f [in 95%%]""" % {'median': fm, 'c95': fc95}
except:
pass
try:
latency.sort()
lm = latency[int(len(latency)*0.5)]
lc95 = latency[int(len(latency)*0.95)]
print """ Latency median: %(median)f Latency < %(c95)f [in 95%%]""" % {'median': lm, 'c95': lc95}
except:
pass
try:
fw_latency.sort()
flm = fw_latency[int(len(fw_latency)*0.5)]
flc95 = fw_latency[int(len(fw_latency)*0.95)]
print """ Forward latency median: %(median)f FWLatency < %(c95)f [in 95%%]
""" % {'median': flm, 'c95': flc95}
except:
pass
try:
print " 95%RTF={rtf:0.2f} 95%FWRTF={fwrtf:0.2f} " \
"95%LAT={lat:0.2f} 95%FWLAT={fwlat:0.2f}".format(rtf=rc95, fwrtf=fc95, lat=lc95, fwlat=flc95)
print
except:
pass
def main():
cldb = CategoryLabelDatabase('../data/database.py')
f_dupl = 0
f_subs = 0
examples = defaultdict(list)
for f in cldb.form2value2cl:
if len(cldb.form2value2cl[f]) >= 2:
f_dupl += 1
if len(f) < 2:
continue
for w in f:
w = (w,)
if w in cldb.form2value2cl:
for v in cldb.form2value2cl[w]:
for c in cldb.form2value2cl[w][v]:
cc = c
break
print '{w},{cc} -> {f}'.format(w=w, cc=cc, f=' '.join(f))
break
else:
continue
f_subs += 1
for v in cldb.form2value2cl[f]:
for c in cldb.form2value2cl[f][v]:
examples[(c,cc)].extend(inform(f,v,c))
examples[(c,cc)].extend(confirm(f,v,c))
print "There were {f} surface forms.".format(f=len(cldb.form2value2cl))
print "There were {f_dupl} surface form duplicits.".format(f_dupl=f_dupl)
print "There were {f_subs} surface forms with substring surface forms.".format(f_subs=f_subs)
max_examples = 100
ex = []
for c in sorted(examples.keys()):
print c
z = examples[c]
if max_examples < len(z):
z = random.sample(z, max_examples)
for s, t in z:
print ' - ', s, '<=>', t
ex.append((s, t))
examples = ex
examples.sort()
sem = {}
trn = {}
for i, e in enumerate(examples):
key = 'bootstrap_gen_{i:06d}.wav'.format(i=i)
sem[key] = e[0]
trn[key] = e[1]
save_wavaskey('bootstrap_gen.sem', sem)
save_wavaskey('bootstrap_gen.trn', trn)
def compute_rt_factor(outdir, trn_dict, dec_dict, wavlen_dict, declen_dict,
fwlen_dict):
"""
Prints RTF statistics for decoding and (decoding + ASR extraction)
Args:
outdir(str): path to directory for the generated log files are saved.
trn_dict(dict): (Wave name, transcription) dictionary
dec_dict(dict): (Wave name, decoded transcription) dictionary
wavlen_dict(dict): (Wave name, Wave length) dictionary
declen_dict(dict): (Wave name, (decoding time + extraction time)) dictionary
fwlen_dict(dict): (Wave name, decoding time) dictionary
"""
reference = os.path.join(outdir, 'ref_trn.txt')
hypothesis = os.path.join(outdir, 'dec_trn.txt')
save_wavaskey(reference, trn_dict)
save_wavaskey(hypothesis, dec_dict)
save_wavaskey(os.path.join(outdir, 'wavlen.txt'), wavlen_dict)
save_wavaskey(os.path.join(outdir, 'dec_duration.txt'), declen_dict)
rtf, latency, fw_rtf, fw_latency, d_tot, w_tot = [], [], [], [], 0, 0
for k in declen_dict.keys():
w, d, f = wavlen_dict[k], declen_dict[k], fwlen_dict[k]
d_tot, w_tot = d_tot + d, w_tot + w
rtf.append(float(d) / w)
latency.append(float(d) - w)
fw_rtf.append(float(f) / w)
fw_latency.append(max(float(f) - w, 0))
rtf_global = float(d_tot) / w_tot
print
print """ # waws: %d""" % len(rtf)
print """ Global RTF mean: %(rtfglob)f""" % {
'rtfglob': rtf_global
}
try:
rtf.sort()
rm = rtf[int(len(rtf) * 0.5)]
rc95 = rtf[int(len(rtf) * 0.95)]
print """ RTF median: %(median)f RTF < %(c95)f [in 95%%]""" % {
'median': rm,
'c95': rc95
}
except:
pass
try:
fw_rtf.sort()
fm = fw_rtf[int(len(fw_rtf) * 0.5)]
fc95 = fw_rtf[int(len(fw_rtf) * 0.95)]
print """ Forward RTF median: %(median)f FWRTF < %(c95)f [in 95%%]""" % {
'median': fm,
'c95': fc95
}
except:
pass
try:
latency.sort()
lm = latency[int(len(latency) * 0.5)]
lc95 = latency[int(len(latency) * 0.95)]
print """ Latency median: %(median)f Latency < %(c95)f [in 95%%]""" % {
'median': lm,
'c95': lc95
}
except:
pass
try:
fw_latency.sort()
flm = fw_latency[int(len(fw_latency) * 0.5)]
flc95 = fw_latency[int(len(fw_latency) * 0.95)]
print """ Forward latency median: %(median)f FWLatency < %(c95)f [in 95%%]
""" % {
'median': flm,
'c95': flc95
}
except:
pass
try:
print " 95%RTF={rtf:0.2f} 95%FWRTF={fwrtf:0.2f} " \
"95%LAT={lat:0.2f} 95%FWLAT={fwlat:0.2f}".format(rtf=rc95, fwrtf=fc95, lat=lc95, fwlat=flc95)
print
except:
pass
def hdc_slu_test(fn_input, constructor, fn_reference):
"""
Tests the HDC SLU.
:param fn_model:
:param fn_input:
:param constructor:
:param fn_reference:
:return:
"""
print "=" * 120
print "Testing HDC SLU: ", fn_input, fn_reference
print "-" * 120
from alex.components.slu.base import CategoryLabelDatabase
from alex.applications.PublicTransportInfoCS.preprocessing import PTICSSLUPreprocessing
from alex.applications.PublicTransportInfoCS.hdc_slu import PTICSHDCSLU
from alex.corpustools.wavaskey import load_wavaskey, save_wavaskey
from alex.corpustools.semscore import score
cldb = CategoryLabelDatabase('../data/database.py')
preprocessing = PTICSSLUPreprocessing(cldb)
hdc_slu = PTICSHDCSLU(
preprocessing,
cfg={
'SLU': {
PTICSHDCSLU: {
'utt2da':
as_project_path(
"applications/PublicTransportInfoCS/data/utt2da_dict.txt"
)
}
}
})
test_utterances = load_wavaskey(fn_input, constructor, limit=100000)
parsed_das = {}
for utt_key, utt in sorted(test_utterances.iteritems()):
if isinstance(utt, Utterance):
obs = {'utt': utt}
elif isinstance(utt, UtteranceNBList):
obs = {'utt_nbl': utt}
else:
raise BaseException('Unsupported observation type')
print '-' * 120
print "Observation:"
print utt_key, " ==> "
print unicode(utt)
da_confnet = hdc_slu.parse(obs, verbose=False)
print "Conf net:"
print unicode(da_confnet)
da_confnet.prune()
dah = da_confnet.get_best_da_hyp()
print "1 best: "
print unicode(dah)
parsed_das[utt_key] = dah.da
if 'CL_' in str(dah.da):
print '*' * 120
print utt
print dah.da
hdc_slu.parse(obs, verbose=True)
fn_sem = os.path.basename(fn_input) + '.hdc.sem.out'
save_wavaskey(fn_sem,
parsed_das,
trans=lambda da: '&'.join(sorted(unicode(da).split('&'))))
f = codecs.open(os.path.basename(fn_sem) + '.score',
'w+',
encoding='UTF-8')
score(fn_reference, fn_sem, True, True, f)
f.close()
sf_train = sorted(sf[:int(train_data_size*len(sf))], key=lambda k: k[1][0])
sf_dev = sorted(sf[int(train_data_size*len(sf)):], key=lambda k: k[1][0])
t_train = [a for a, b in sf_train]
pt_train = [b for a, b in sf_train]
t_dev = [a for a, b in sf_dev]
pt_dev = [b for a, b in sf_dev]
with codecs.open(indomain_data_text_trn,"w", "UTF-8") as w:
w.write('\n'.join(t_train))
with codecs.open(indomain_data_text_dev,"w", "UTF-8") as w:
w.write('\n'.join(t_dev))
save_wavaskey(fn_pt_trn, dict(pt_train))
save_wavaskey(fn_pt_dev, dict(pt_dev))
# train data
cmd = r"cat %s %s | iconv -f UTF-8 -t UTF-8//IGNORE | sed 's/\. /\n/g' | sed 's/[[:digit:]]/ /g; s/[^[:alnum:]_]/ /g; s/[ˇ]/ /g; s/ \+/ /g' | sed 's/[[:lower:]]*/\U&/g' | sed s/[\%s→€…│]//g > %s" % \
(bootstrap_text,
indomain_data_text_trn,
"'",
indomain_data_text_trn_norm)
print cmd
os.system(cmd)
# dev data
cmd = r"cat %s | iconv -f UTF-8 -t UTF-8//IGNORE | sed 's/\. /\n/g' | sed 's/[[:digit:]]/ /g; s/[^[:alnum:]_]/ /g; s/[ˇ]/ /g; s/ \+/ /g' | sed 's/[[:lower:]]*/\U&/g' | sed s/[\%s→€…│]//g > %s" % \
(indomain_data_text_dev,
def save_das(file_name, das, encoding = 'UTF-8'):
save_wavaskey(file_name, das, encoding)
def hdc_slu_test(fn_input, constructor, fn_reference):
"""
Tests a SLU DAILogRegClassifier model.
:param fn_model:
:param fn_input:
:param constructor:
:param fn_reference:
:return:
"""
print "="*120
print "Testing HDC SLU: ", fn_input, fn_reference
print "-"*120
from alex.components.slu.base import CategoryLabelDatabase
from alex.applications.PublicTransportInfoCS.preprocessing import PTICSSLUPreprocessing
from alex.applications.PublicTransportInfoCS.hdc_slu import PTICSHDCSLU
from alex.corpustools.wavaskey import load_wavaskey, save_wavaskey
from alex.corpustools.semscore import score
cldb = CategoryLabelDatabase('../data/database.py')
preprocessing = PTICSSLUPreprocessing(cldb)
hdc_slu = PTICSHDCSLU(preprocessing)
test_utterances = load_wavaskey(fn_input, constructor, limit=100000)
parsed_das = {}
for utt_key, utt in sorted(test_utterances.iteritems()):
if isinstance(utt, Utterance):
obs = {'utt': utt}
elif isinstance(utt, UtteranceNBList):
obs = {'utt_nbl': utt}
else:
raise BaseException('Unsupported observation type')
print '-' * 120
print "Observation:"
print utt_key, " ==> "
print unicode(utt)
da_confnet = hdc_slu.parse(obs, verbose=False)
print "Conf net:"
print unicode(da_confnet)
da_confnet.prune()
dah = da_confnet.get_best_da_hyp()
print "1 best: "
print unicode(dah)
parsed_das[utt_key] = dah.da
if 'CL_' in str(dah.da):
print '*' * 120
print utt
print dah.da
hdc_slu.parse(obs, verbose=True)
fn_sem = os.path.basename(fn_input)+'.hdc.sem.out'
save_wavaskey(fn_sem, parsed_das, trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
f = codecs.open(os.path.basename(fn_sem)+'.score', 'w+', encoding='UTF-8')
score(fn_reference, fn_sem, True, True, f)
f.close()
def compute_rt_factor(outdir, trn_dict, dec_dict, wavlen_dict, declen_dict, fwlen_dict):
"""
Prints RTF statistics for decoding and (decoding + ASR extraction)
Args:
outdir(str): path to directory for the generated log files are saved.
trn_dict(dict): (Wave name, transcription) dictionary
dec_dict(dict): (Wave name, decoded transcription) dictionary
wavlen_dict(dict): (Wave name, Wave length) dictionary
declen_dict(dict): (Wave name, (decoding time + extraction time)) dictionary
fwlen_dict(dict): (Wave name, decoding time) dictionary
"""
reference = os.path.join(outdir, 'ref_trn.txt')
hypothesis = os.path.join(outdir, 'dec_trn.txt')
save_wavaskey(reference, trn_dict)
save_wavaskey(hypothesis, dec_dict)
save_wavaskey(os.path.join(outdir, 'wavlen.txt'), wavlen_dict)
save_wavaskey(os.path.join(outdir, 'dec_duration.txt'), declen_dict)
rtf, latency, fw_rtf, fw_latency, d_tot, w_tot = [], [], [], [], 0, 0
for k in declen_dict.keys():
w, d, f = wavlen_dict[k], declen_dict[k], fwlen_dict[k]
d_tot, w_tot = d_tot + d, w_tot + w
rtf.append(float(d) / w)
latency.append(float(d) - w)
fw_rtf.append(float(f) / w)
fw_latency.append(max(float(f) - w, 0))
rtf_global = float(d_tot) / w_tot
print
print """ # waws: %d""" % len(rtf)
print """ Global RTF mean: %(rtfglob)f""" % {'rtfglob': rtf_global}
try:
rtf.sort()
rm = rtf[int(len(rtf)*0.5)]
rc95 = rtf[int(len(rtf)*0.95)]
print """ RTF median: %(median)f RTF < %(c95)f [in 95%%]""" % {'median': rm, 'c95': rc95}
except:
pass
try:
fw_rtf.sort()
fm = fw_rtf[int(len(fw_rtf)*0.5)]
fc95 = fw_rtf[int(len(fw_rtf)*0.95)]
print """ Forward RTF median: %(median)f FWRTF < %(c95)f [in 95%%]""" % {'median': fm, 'c95': fc95}
except:
pass
try:
latency.sort()
lm = latency[int(len(latency)*0.5)]
lc95 = latency[int(len(latency)*0.95)]
print """ Latency median: %(median)f Latency < %(c95)f [in 95%%]""" % {'median': lm, 'c95': lc95}
except:
pass
try:
fw_latency.sort()
flm = fw_latency[int(len(fw_latency)*0.5)]
flc95 = fw_latency[int(len(fw_latency)*0.95)]
print """ Forward latency median: %(median)f FWLatency < %(c95)f [in 95%%]
""" % {'median': flm, 'c95': flc95}
except:
pass
try:
print " 95%RTF={rtf:0.2f} 95%FWRTF={fwrtf:0.2f} " \
"95%LAT={lat:0.2f} 95%FWLAT={fwlat:0.2f}".format(rtf=rc95, fwrtf=fc95, lat=lc95, fwlat=flc95)
print
except:
pass
def main():
global asr_log
global num_workers
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="""This program prepares data for training Alex PTIcs SLU.
""")
parser.add_argument('--num_workers',
action="store",
default=num_workers,
type=int,
help='number of workers used for ASR: default %d' %
num_workers)
parser.add_argument('--asr_log',
action="store",
default=asr_log,
type=int,
help='use ASR results from logs: default %d' % asr_log)
args = parser.parse_args()
asr_log = args.asr_log
num_workers = args.num_workers
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_nbl = 'all.nbl'
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_nbl = 'train.nbl'
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_nbl = 'dev.nbl'
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_nbl = 'test.nbl'
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)
files = files[:100000]
asr = []
nbl = []
sem = []
trn = []
trn_hdc_sem = []
p_process_call_logs = multiprocessing.Pool(num_workers)
processed_cls = p_process_call_logs.imap_unordered(process_call_log, files)
count = 0
for pcl in processed_cls:
count += 1
#process_call_log(fn) # uniq utterances
#print pcl
print "=" * 80
print "Processed files ", count, "/", len(files)
print "=" * 80
asr.extend(pcl[0])
nbl.extend(pcl[1])
sem.extend(pcl[2])
trn.extend(pcl[3])
trn_hdc_sem.extend(pcl[4])
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('&'))))
save_wavaskey(fn_all_asr, dict(asr))
save_wavaskey(fn_all_nbl, dict(nbl))
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(nbl)
# 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))
# 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))
def trained_slu_test(fn_model, fn_input, constructor, fn_reference):
"""
Tests a SLU DAILogRegClassifier model.
:param fn_model:
:param fn_input:
:param constructor:
:param fn_reference:
:return:
"""
print "="*120
print "Testing: ", fn_model, fn_input, fn_reference
print "-"*120
from alex.applications.PublicTransportInfoCS.preprocessing import PTICSSLUPreprocessing
from alex.components.slu.base import CategoryLabelDatabase
from alex.components.slu.dailrclassifier import DAILogRegClassifier
from alex.corpustools.wavaskey import load_wavaskey, save_wavaskey
from alex.corpustools.semscore import score
cldb = CategoryLabelDatabase('../data/database.py')
preprocessing = PTICSSLUPreprocessing(cldb)
slu = DAILogRegClassifier(cldb, preprocessing)
slu.load_model(fn_model)
test_utterances = load_wavaskey(fn_input, constructor, limit=100000)
parsed_das = {}
for utt_key, utt in sorted(test_utterances.iteritems()):
if isinstance(utt, Utterance):
obs = {'utt': utt}
elif isinstance(utt, UtteranceNBList):
obs = {'utt_nbl': utt}
else:
raise BaseException('Unsupported observation type')
print '-' * 120
print "Observation:"
print utt_key, " ==> "
print unicode(utt)
da_confnet = slu.parse(obs, verbose=False)
print "Conf net:"
print unicode(da_confnet)
da_confnet.prune()
dah = da_confnet.get_best_da_hyp()
print "1 best: "
print unicode(dah)
parsed_das[utt_key] = dah.da
if 'CL_' in str(dah.da):
print '*' * 120
print utt
print dah.da
slu.parse(obs, verbose=True)
if 'trn' in fn_model:
fn_sem = os.path.basename(fn_input)+'.model.trn.sem.out'
elif 'asr' in fn_model:
fn_sem = os.path.basename(fn_input)+'.model.asr.sem.out'
elif 'nbl' in fn_model:
fn_sem = os.path.basename(fn_input)+'.model.nbl.sem.out'
else:
fn_sem = os.path.basename(fn_input)+'.XXX.sem.out'
save_wavaskey(fn_sem, parsed_das, trans = lambda da: '&'.join(sorted(unicode(da).split('&'))))
f = codecs.open(os.path.basename(fn_sem)+'.score', 'w+', encoding='UTF-8')
score(fn_reference, fn_sem, True, True, f)
f.close()
def save_das(file_name, das, encoding='UTF-8'):
save_wavaskey(file_name, das, encoding)
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 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('&'))))
sf_train = sorted(sf[:int(train_data_size*len(sf))], key=lambda k: k[1][0])
sf_dev = sorted(sf[int(train_data_size*len(sf)):], key=lambda k: k[1][0])
t_train = [a for a, b in sf_train]
pt_train = [b for a, b in sf_train]
t_dev = [a for a, b in sf_dev]
pt_dev = [b for a, b in sf_dev]
with codecs.open(indomain_data_text_trn,"w", "UTF-8") as w:
w.write('\n'.join(t_train))
with codecs.open(indomain_data_text_dev,"w", "UTF-8") as w:
w.write('\n'.join(t_dev))
save_wavaskey(fn_pt_trn, dict(pt_train))
save_wavaskey(fn_pt_dev, dict(pt_dev))
# train data
cmd = r"cat %s %s | iconv -f UTF-8 -t UTF-8//IGNORE | sed 's/\. /\n/g' | sed 's/[[:digit:]]/ /g; s/[^[:alnum:]_]/ /g; s/[ˇ]/ /g; s/ \+/ /g' | sed 's/[[:lower:]]*/\U&/g' | sed s/[\%s→€…│]//g > %s" % \
(bootstrap_text,
indomain_data_text_trn,
"'",
indomain_data_text_trn_norm)
print cmd
exit_on_system_fail(cmd)
# dev data
cmd = r"cat %s | iconv -f UTF-8 -t UTF-8//IGNORE | sed 's/\. /\n/g' | sed 's/[[:digit:]]/ /g; s/[^[:alnum:]_]/ /g; s/[ˇ]/ /g; s/ \+/ /g' | sed 's/[[:lower:]]*/\U&/g' | sed s/[\%s→€…│]//g > %s" % \
(indomain_data_text_dev,
def main():
global asr_log
global num_workers
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="""This program prepares data for training Alex PTIcs SLU.
""")
parser.add_argument('--num_workers', action="store", default=num_workers, type=int,
help='number of workers used for ASR: default %d' % num_workers)
parser.add_argument('--asr_log', action="store", default=asr_log, type=int,
help='use ASR results from logs: default %d' % asr_log)
args = parser.parse_args()
asr_log = args.asr_log
num_workers = args.num_workers
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_nbl = 'all.nbl'
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_nbl = 'train.nbl'
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_nbl = 'dev.nbl'
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_nbl = 'test.nbl'
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)
files = files[:100000]
asr = []
nbl = []
sem = []
trn = []
trn_hdc_sem = []
p_process_call_logs = multiprocessing.Pool(num_workers)
processed_cls = p_process_call_logs.imap_unordered(process_call_log, files)
count = 0
for pcl in processed_cls:
count += 1
#process_call_log(fn) # uniq utterances
#print pcl
print "="*80
print "Processed files ", count, "/", len(files)
print "="*80
asr.extend(pcl[0])
nbl.extend(pcl[1])
sem.extend(pcl[2])
trn.extend(pcl[3])
trn_hdc_sem.extend(pcl[4])
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('&'))))
save_wavaskey(fn_all_asr, dict(asr))
save_wavaskey(fn_all_nbl, dict(nbl))
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(nbl)
# 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))
# 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))
