def guessStress(input_string):
b = BlickLoader()
vows = {x[:-1]: { y[-1] for y in b.vowels if y[:-1] == x[:-1]} for x in b.vowels}
phones = input_string.split(" ")
stress_pattern_space = [ vows[x] for x in phones if x in vows]
revised_string = []
for p in phones:
if p in vows:
revised_string.append(p+"%s")
else:
revised_string.append(p)
revised_string = ' '.join(revised_string)
possible_patterns = list(itertools.product(*stress_pattern_space))
possible_strings = [revised_string % x for x in possible_patterns]
best = 1000
pattern = ''
for p in possible_strings:
if p in word_list:
return p
score = b.assessWord(p)
if score < best:
best = score
pattern = p
return pattern
def getPhonScores(puns):
b = BlickLoader()
scoredPuns = {}
for pun in puns:
words = pun.split('#')
del words[len(words) - 1]
goodWords = []
for w in words:
if "D I C T" not in w:
goodWords.append(w)
scoredPuns[pun] = sum(b.assessWord(w.strip()) for w in goodWords)
return scoredPuns
def train_wakeword_model(audio_train_loader,
vocab_list,
label_model,
beam_size=3,
num_hypotheses=5,
query_by_string=False):
wakeword_model = {}
if query_by_string:
# load ww model produced by MFA from config
keywords = config["wakeword_model"]
# load blick
b = BlickLoader()
for i, _, y_hat in enumerate(keywords.items()):
w = b.assessWord(y_hat)
# for each keyword, append the tuple(hypotheses + weights) to the list
# only one hypothesis if using MFA
wakeword_model[i] = (y_hat, w)
else:
# train ww model from scratch
for i in audio_train_loader:
posteriors_i = label_model(i)
# decode using CTC, vocab_list is A (labels)
decoder = CTCBeamDecoder(self.vocab_list,
beam_width=self.beam_size,
blank_id=self.vocab_list.index('_'))
beam, beam_scores, _, _ = decoder.decode(posteriors_i)
for j in range(num_hypotheses):
y_hat = beam[j] # hypothesis
log_prob_post = beam_scores[j]
w = log_prob_post**-1
# for each keyword, append the tuple(hypotheses + weights) to the list
wakeword_model[i].append((y_hat, w))
return wakeword_model
def getPhonotacticProb(input_string,use_blick=True,no_stress=False):
if use_blick:
if no_stress:
b = BlickLoader(grammarType='NoStress')
else:
b = BlickLoader()
return b.assessWord(str(input_string))
if no_stress:
input_string = re.sub('[0-9]','',input_string)
SPprob = 0.0
BPprob = 0.0
phones = input_string.split(" ")
for i in range(len(phones)):
patt = [any_segment] * i
patt.append(phones[i])
pattern = '^'+' '.join(patt) +'.*$'
totPattern = '^'+' '.join([any_segment] * (i+1)) +'.*$'
if no_stress:
count = len(PhonoString.objects.filter(NoStress__regex = pattern))
totCount = len(PhonoString.objects.filter(NoStress__regex = totPattern))
else:
count = len(PhonoString.objects.filter(Transcription__regex = pattern))
totCount = len(PhonoString.objects.filter(Transcription__regex = totPattern))
SPprob += float(count) / float(totCount)
if i != len(phones)-1:
patt = [any_segment] * i
patt.extend([phones[i],phones[i+1]])
pattern = '^'+' '.join(patt) +'.*$'
totPattern = '^'+' '.join([any_segment] * (i+2)) +'.*$'
if no_stress:
count = len(PhonoString.objects.filter(NoStress__regex = pattern))
totCount = len(PhonoString.objects.filter(NoStress__regex = totPattern))
else:
count = len(PhonoString.objects.filter(Transcription__regex = pattern))
totCount = len(PhonoString.objects.filter(Transcription__regex = totPattern))
BPprob += float(count) / float(totCount)
SPprob = SPprob / float(len(phones))
BPprob = BPprob / float(len(phones)-1)
return (SPprob,BPprob)
#!/usr/bin/env python
import argparse
from blick import BlickLoader
parser = argparse.ArgumentParser(description='Add phonotactic probability to a file of phone strings.')
parser.add_argument('filename', type=open,
help='name of file of phone strings')
parser.add_argument('-d','--debug', action='store_true',
default=False,
help='whether or not debug mode should be activated')
parser.add_argument('-c','--constraints', action='store_true',
default=False,
help='whether or not debug mode should be activated')
parser.add_argument('-g','--grammar', type=str, choices = set(['HayesWhite','NoStress','default']), default='default',
help='type of grammar to be used')
args = parser.parse_args()
argdict = vars(args)
#print argdict
b = BlickLoader(debug=argdict['debug'],grammarType=argdict['grammar'])
b.assessFile(argdict['filename'].name,includeConstraints=argdict['constraints'])
if syl.nucleus in SHORT_VOWELS:
return False
if is_first and syl.onset and syl.onset[0] == "ZH":
return False
# if is_last and stress_lvl == 1 and len(syl.coda) == 0:
# return False
if syl.onset and syl.coda and syl.onset[0] == "S" and not syl.onset[
-1] == "T" and syl.nucleus in SHORT_VOWELS:
if syl.coda[0] == syl.onset[-1]:
return False
if stress_lvl != 1 and syl.nucleus not in {
"AH", "ER", "IH", "IY", "OW", "UW"
}:
return False
return True
blick_rater = BlickLoader()
words = []
for i in range(100):
word = getWord()
score, rules = blick_rater.assessWord(word.replace(" ", " "),
includeConstraints=True)
score = exp(-score)
words.append([word, score, rules])
for word, score, rules in sorted(words, key=itemgetter(1)):
if score > 0.00001:
print(word, score, rules)
print()
from blick import BlickLoader
parser = argparse.ArgumentParser(
description='Add phonotactic probability to a file of phone strings.')
parser.add_argument('filename',
type=open,
help='name of file of phone strings')
parser.add_argument('-d',
'--debug',
action='store_true',
default=False,
help='whether or not debug mode should be activated')
parser.add_argument('-c',
'--constraints',
action='store_true',
default=False,
help='whether or not debug mode should be activated')
parser.add_argument('-g',
'--grammar',
type=str,
choices=set(['HayesWhite', 'NoStress', 'default']),
default='default',
help='type of grammar to be used')
args = parser.parse_args()
argdict = vars(args)
#print argdict
b = BlickLoader(debug=argdict['debug'], grammarType=argdict['grammar'])
b.assessFile(argdict['filename'].name,
includeConstraints=argdict['constraints'])
import markov, time, random from blick import BlickLoader from google import search from random import randrange b = BlickLoader(grammarType="default") phonetics = [] phonetics.append(["th", " TH", " DH"]) phonetics.append(["sh", " SH"]) phonetics.append(["ee", " IY1"]) phonetics.append(["ai", " EY1"]) phonetics.append(["oo", " UW1", " UH1"]) phonetics.append(["ou", " AW1", " AW2", " UW2"]) phonetics.append(["oi", " OY2"]) phonetics.append(["oy", " OY1"]) phonetics.append(["oa", " OW1"]) phonetics.append(["ng", " NG"]) phonetics.append(["e", " IY2", " EH1", " EH2", " EY2", " ER1", " ER2", " ER0"]) phonetics.append(["i", " IH1", " IH2", " IH0", " AY1", " AY2"]) phonetics.append(["a", " AE1", " AE2", " AO2", " AH0"]) phonetics.append(["o", " AO1", " AA1", " AA2", " OW2", " OW0"]) phonetics.append(["u", " UW0", " UH2", " AH1", " AH2"]) phonetics.append(["p", " P"]) phonetics.append(["b", " B"]) phonetics.append(["f", " F"]) phonetics.append(["v", " V"]) phonetics.append(["m", " M"]) phonetics.append(["w", " W"]) phonetics.append(["t", " T"]) phonetics.append(["d", " D"]) phonetics.append(["s", " S"])