144 lines
3.5 KiB
Python
144 lines
3.5 KiB
Python
import pandas as pd
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import pronouncing
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import re
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import numpy as np
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import random
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# mapping = {
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# s.split()[0]: s.split()[1]
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# for s in """
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# AA AA
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# AE AE
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# AH UX
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# AO AO
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# AW AW
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# AY AY
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# B b
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# CH C
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# D d
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# DH D
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# EH EH
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# ER UXr
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# EY EY
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# F f
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# G g
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# HH h
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# IH IH
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# IY IY
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# JH J
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# K k
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# L l
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# M m
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# N n
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# NG N
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# OW OW
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# OY OY
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# P p
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# R r
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# S s
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# SH S
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# T t
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# TH T
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# UH UH
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# UW UW
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# V v
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# W w
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# Y y
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# Z z
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# ZH Z
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# """.strip().split('\n')
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# }
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# sim_mat = pd.read_csv('./similarity.csv', header=0, index_col=0)
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#
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#
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# def convert_ph(ph):
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# stress_level = re.search("(\w+)([0-9])", ph)
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# if stress_level:
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# return stress_level.group(2) + mapping[stress_level.group(1)]
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# else:
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# return mapping[ph]
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#
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#
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# def sim_mat_to_apple_table(smt):
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# colnames = [convert_ph(ph) for ph in smt.index.tolist()]
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# smt = pd.DataFrame(np.nan_to_num(smt.values))
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# fsmt = (smt.T + smt)
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# np.fill_diagonal(fsmt.values, 100.0)
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# asmt = pd.DataFrame.copy(fsmt)
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# asmt.columns = colnames
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# asmt.index = colnames
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# apple_sim_table = asmt.stack().reset_index()
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# apple_sim_table.columns = ['q', 'r', 's']
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# return apple_sim_table
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#
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#
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# apple_sim_table = sim_mat_to_apple_table(sim_mat)
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#
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#
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# def top_match(ph):
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# selected = apple_sim_table[(apple_sim_table.q == ph)
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# & (apple_sim_table.s < 100) &
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# (apple_sim_table.s >= 70)]
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# tm = ph
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# if len(selected) > 0:
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# tm = pd.DataFrame.sort_values(selected, 's', ascending=False).iloc[0].r
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# return tm
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apple_phonemes = [
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'%', '@', 'AE', 'EY', 'AO', 'AX', 'IY', 'EH', 'IH', 'AY', 'IX', 'AA', 'UW',
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'UH', 'UX', 'OW', 'AW', 'OY', 'b', 'C', 'd', 'D', 'f', 'g', 'h', 'J', 'k',
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'l', 'm', 'n', 'N', 'p', 'r', 's', 'S', 't', 'T', 'v', 'w', 'y', 'z', 'Z'
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]
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class ApplePhoneme(object):
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"""docstring for ApplePhoneme."""
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def __init__(self, phone, stress, vowel=False):
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super(ApplePhoneme, self).__init__()
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self.phone = phone
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self.stress = stress
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self.vowel = vowel
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def __str__(self):
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return (str(self.stress) if (self.vowel and self.stress>0) else '') + self.phone
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def __repr__(self):
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return "'{}'".format(str(self))
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def adjust_stress(self):
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self.stress = random.choice([i for i in range(3) if i != self.stress])
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def parse_apple_phonemes(ph_str):
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for i in range(len(ph_str)):
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pref, rest = ph_str[:i + 1], ph_str[i + 1:]
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if pref in apple_phonemes:
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vowel = pref[0] in 'AEIOU'
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return [ApplePhoneme(pref, 0, vowel)] + parse_apple_phonemes(rest)
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elif pref[0].isdigit() and pref[1:] in apple_phonemes:
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return [ApplePhoneme(pref[1:], int(pref[0]) , True)] + parse_apple_phonemes(rest)
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elif not pref.isalnum():
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return [ApplePhoneme(pref, -1, False)] + parse_apple_phonemes(rest)
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return []
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def segmentable_phoneme(ph_str):
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return [p for p in parse_apple_phonemes(ph_str) if p.stress >=0]
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def similar_phoneme_word(ph_str):
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phons = parse_apple_phonemes(ph_str)
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vowels = [i for i in phons if i.vowel]
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random.choice(vowels).adjust_stress()
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return ''.join([str(i) for i in phons])
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def similar_phoneme_phrase(ph_str):
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return ' '.join([similar_phoneme_word(w) for w in ph_str.split()])
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def similar_word(word_str):
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similar = pronouncing.rhymes(word_str)
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return random.choice(similar) if len(similar) > 0 else word_str
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def similar_phrase(ph_str):
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return ' '.join([similar_word(w) for w in ph_str.split()])
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