fame_asr phoneset is added including reduced version and htk compatible version.
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yemaozi88
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87abbbb95a
commit
8cda93de75
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@ -32,7 +32,9 @@
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<Compile Include="defaultfiles.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="fame_phoneset.py">
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<Compile Include="fame_asr.py" />
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<Compile Include="fame_ipa.py" />
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<Compile Include="fame_test.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="fa_test.py">
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@ -20,7 +20,7 @@ def split_word(word, multi_character_phones):
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Args:
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word (str): a word written in given phoneset.
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multi_character_phones (list): the list of multicharacter phones which is considered as one phone. this can be obtained with phoneset definition such as fame_phoneset.py.
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multi_character_phones (list): the list of multicharacter phones which is considered as one phone. this can be obtained with phoneset definition such as fame_ipa.py.
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Returns:
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(word_seperated) (list): the word splitted in given phoneset.
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127
acoustic_model/fame_asr.py
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127
acoustic_model/fame_asr.py
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@ -0,0 +1,127 @@
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""" definition of the phones to be used. """
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# phonese in {FAME}/lexicon/lex.asr
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phoneset = [
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# vowels
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'a',
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'a:',
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'e',
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'e:',
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'i',
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'i:',
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'i̯',
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'o',
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'o:',
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'ö',
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'ö:',
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'u',
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'u:',
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'ü',
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'ü:',
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#'ú', # only appears in word 'feeste'(út) and 'gaste'(út) which are 'f e: s t ə' and 'yn' in lex_asr. The pronunciation in Fries may be mistakes so I removed this phone.
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'ṷ',
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'y',
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'ɔ',
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'ɔ:',
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'ɔ̈',
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'ɔ̈:',
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'ə',
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'ɛ',
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'ɛ:',
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'ɪ',
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'ɪ:',
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# plosives
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'p',
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'b',
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't',
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'd',
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'k',
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'g',
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'ɡ', # = 'g'
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# nasals
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'm',
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'n',
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'ŋ',
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# fricatives
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'f',
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'v',
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's',
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's:',
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'z',
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'x',
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'h',
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# tap and flip
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'r',
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'r:',
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# approximant
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'j',
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'l'
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]
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## reduce the number of phones.
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# the phones which seldom occur are replaced with another more popular phones.
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# replacements are based on the advice from Martijn Wieling.
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reduction_key = {
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'y':'i:', 'e':'e:', 'ə:':'ɛ:', 'r:':'r', 'ɡ':'g'
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}
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# already removed beforehand in phoneset. Just to be sure.
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phones_to_be_removed = ['ú', 's:', 'ɔ̈:']
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phoneset_short = [reduction_key.get(i, i) for i in phoneset
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if not i in phones_to_be_removed]
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phoneset_short = list(set(phoneset_short))
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phoneset_short.sort()
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## translation_key to htk format (ascii).
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# phones which gives UnicodeEncodeError when phone.encode("ascii")
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# are replaced with other characters.
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translation_key_asr2htk = {
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'i̯': 'i_',
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'ṷ': 'u_',
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# on the analogy of German umlaut, 'e' is used.
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'ö': 'oe', 'ö:': 'oe:',
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'ü': 'ue', 'ü:': 'ue:',
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# on the analogy of Chinese...
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'ŋ': 'ng',
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# refer to Xsampa.
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'ɔ': 'O', 'ɔ:': 'O:', 'ɔ̈': 'Oe',
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'ɛ': 'E', 'ɛ:': 'E:',
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'ɪ': 'I', 'ɪ:': 'I:',
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# it is @ in Xsampa, but that is not handy on HTK.
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'ə': 'A'
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}
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phoneset_htk = [translation_key_asr2htk.get(i, i) for i in phoneset_short]
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## check
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#for i in phoneset_short:
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# try:
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# print("{0} --> {1}".format(i, i.encode("ascii")))
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# except UnicodeEncodeError:
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# print(">>> {}".format(i))
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## the list of multi character phones.
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# for example, the length of 'a:' is 3, but in the codes it is treated as one letter.
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# original.
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multi_character_phones = [i for i in phoneset if len(i) > 1]
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multi_character_phones.sort(key=len, reverse=True)
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# phonset reduced.
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multi_character_phones_short = [i for i in phoneset_short if len(i) > 1]
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multi_character_phones_short.sort(key=len, reverse=True)
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# htk compatible.
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multi_character_phones_htk = [i for i in phoneset_htk if len(i) > 1]
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multi_character_phones_htk.sort(key=len, reverse=True)
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@ -1,5 +1,4 @@
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import os
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os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
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import sys
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from collections import Counter
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@ -9,7 +8,7 @@ import numpy as np
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import pandas as pd
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import defaultfiles as default
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import fame_phoneset
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from phoneset import fame_ipa
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import convert_phone_set
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@ -110,14 +109,6 @@ import convert_phone_set
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# return ipa
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#def make_filelist(input_dir, output_txt):
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# """ Make a list of files in the input_dir. """
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# filenames = os.listdir(input_dir)
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# with open(output_txt, 'w') as fout:
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# for filename in filenames:
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# fout.write(input_dir + '\\' + filename + '\n')
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#def make_htk_dict(word, pronvar_, fileDic, output_type):
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# """
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@ -179,10 +170,11 @@ def make_hcopy_scp_from_filelist_in_fame(fame_dir, dataset, feature_dir, hcopy_s
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fout.write(wav_file + '\t' + mfc_file + '\n')
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return
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def load_lexicon(lexicon_file):
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""" load lexicon file as Data Frame.
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""" load lexicon file as data frame.
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Args:
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lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
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@ -196,25 +188,27 @@ def load_lexicon(lexicon_file):
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return lex
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def get_phoneset_from_lexicon(lexicon_file, phoneset='asr'):
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def get_phoneset_from_lexicon(lexicon_file, phoneset_name='asr'):
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""" Make a list of phones which appears in the lexicon.
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Args:
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lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
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phoneset (str): the phoneset with which lexicon_file is written. 'asr'(default) or 'ipa'.
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phoneset_name (str): the name of phoneset with which lexicon_file is written. 'asr'(default) or 'ipa'.
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Returns:
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(list_of_phones) (set): the set of phones included in the lexicon_file.
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"""
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assert phoneset in ['asr', 'ipa'], 'phoneset should be \'asr\' or \'ipa\''
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assert phoneset_name in ['asr', 'ipa'], 'phoneset_name should be \'asr\' or \'ipa\''
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lex = load_lexicon(lexicon_file)
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if phoneset == 'asr':
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if phoneset_name == 'asr':
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return set(' '.join(lex['pronunciation']).split(' '))
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elif phoneset == 'ipa':
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elif phoneset_name == 'ipa':
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join_pronunciations = ''.join(lex['pronunciation'])
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return set(convert_phone_set.split_word(join_pronunciations, fame_phoneset.multi_character_phones_ipa))
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return set(convert_phone_set.split_word(join_pronunciations, fame_ipa.multi_character_phones))
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return
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def extract_unknown_phones(ipa, known_phones):
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@ -228,7 +222,7 @@ def extract_unknown_phones(ipa, known_phones):
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(list_of_phones) (list): unknown phones not included in 'known_phones'.
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"""
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ipa_split = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
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ipa_split = convert_phone_set.split_word(ipa, fame_ipa.multi_character_phones)
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return [i for i in ipa_split if not i in known_phones]
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@ -247,14 +241,14 @@ def get_translation_key(lexicon_file_ipa, lexicon_file_asr):
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"""
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lex_ipa = load_lexicon(lexicon_file_ipa)
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lex_asr = load_lexicon(lexicon_file_asr)
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phone_unknown = fame_phoneset.phoneset_ipa[:]
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phone_unknown = fame_ipa.phoneset[:]
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translation_key = dict()
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for word in lex_ipa['word']:
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if np.sum(lex_ipa['word'] == word) == 1 and np.sum(lex_asr['word'] == word) == 1:
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ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
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asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
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ipa_list = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
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ipa_list = convert_phone_set.split_word(ipa, fame_ipa.multi_character_phones)
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asr_list = asr.split(' ')
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# if there are phones which is not in phone_unknown
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@ -268,13 +262,13 @@ def get_translation_key(lexicon_file_ipa, lexicon_file_asr):
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return translation_key, list(phone_unknown)
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def find_phone(lexicon_file, phone, phoneset='ipa'):
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def find_phone(lexicon_file, phone, phoneset_name='ipa'):
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""" extract rows where the phone is used in the lexicon_file.
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Args:
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lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
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phone (str): the phone to be searched.
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phoneset (str): the phoneset with which lexicon_file is written. 'asr' or 'ipa'(default).
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phoneset_name (str): the name of phoneset_name with which lexicon_file is written. 'asr' or 'ipa'(default).
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Returns:
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extracted (df): rows where the phone is used.
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@ -283,7 +277,7 @@ def find_phone(lexicon_file, phone, phoneset='ipa'):
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* develop when the phonset == 'asr'.
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"""
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assert phoneset in ['asr', 'ipa'], 'phoneset should be \'asr\' or \'ipa\''
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assert phoneset_name in ['asr', 'ipa'], 'phoneset_name should be \'asr\' or \'ipa\''
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lex = load_lexicon(lexicon_file)
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@ -292,8 +286,8 @@ def find_phone(lexicon_file, phone, phoneset='ipa'):
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extracted = pd.DataFrame(index=[], columns=['word', 'pronunciation'])
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for index, row in lex_.iterrows():
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if phoneset == 'ipa':
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pronunciation = convert_phone_set.split_word(row['pronunciation'], fame_phoneset.multi_character_phones_ipa)
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if phoneset_name == 'ipa':
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pronunciation = convert_phone_set.split_word(row['pronunciation'], fame_ipa.multi_character_phones)
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if phone in pronunciation:
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extracted_ = pd.Series([row['word'], pronunciation], index=extracted.columns)
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extracted = extracted.append(extracted_, ignore_index=True)
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@ -8,8 +8,8 @@ import tempfile
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#from collections import Counter
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import time
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#import numpy as np
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#import pandas as pd
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import numpy as np
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import pandas as pd
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import fame_functions
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import defaultfiles as default
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@ -54,6 +54,10 @@ conv_lexicon = 1
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#mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl']
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#FAME_dir = config['Settings']['FAME_dir']
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#lexicon_dir = os.path.join(default.fame_dir, 'lexicon')
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#lexicon_ipa = os.path.join(lexicon_dir, 'lex.ipa')
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#lexicon_asr = os.path.join(lexicon_dir, 'lex.asr')
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#lex_asr = FAME_dir + '\\lexicon\\lex.asr'
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#lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
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#lex_oov = FAME_dir + '\\lexicon\\lex.oov'
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@ -111,71 +115,6 @@ if extract_features:
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## ======================= convert lexicon from ipa to fame_htk =======================
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if conv_lexicon:
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print('==== convert lexicon from ipa 2 fame ====\n')
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#dir_out = r'c:\Users\Aki\source\repos\acoustic_model\_tmp'
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lexicon_dir = os.path.join(default.fame_dir, 'lexicon')
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lexicon_ipa = os.path.join(lexicon_dir, 'lex.ipa')
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lexicon_asr = os.path.join(lexicon_dir, 'lex.asr')
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# get the correspondence between lex_ipa and lex_asr.
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lex_asr = fame_functions.load_lexicon(lexicon_asr)
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lex_ipa = fame_functions.load_lexicon(lexicon_ipa)
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if 1:
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timer_start = time.time()
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translation_key, phone_unknown = fame_functions.get_translation_key(lexicon_ipa, lexicon_asr)
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print("elapsed time: {}".format(time.time() - timer_start))
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np.save('translation_key_ipa2asr.npy', translation_key)
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np.save('phone_unknown.npy', phone_unknown)
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else:
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translation_key = np.load('translation_key_ipa2asr.npy').item()
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phone_unknown = np.load('phone_unknown.npy')
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phone_unknown = list(phone_unknown)
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## manually check the correspondence for the phone in phone_unknown.
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#p = phone_unknown[0]
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#lex_ipa_ = find_phone(lexicon_ipa, p, phoneset='ipa')
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#for word in lex_ipa_['word']:
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# ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
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# if np.sum(lex_asr['word'] == word) > 0:
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# asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
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# ipa_list = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
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# asr_list = asr.split(' ')
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# if p in ipa_list and (len(ipa_list) == len(asr_list)):
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# print("{0}: {1} --> {2}".format(word, ipa_list, asr_list))
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# for ipa_, asr_ in zip(ipa_list, asr_list):
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# if ipa_ in phone_unknown:
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# translation_key[ipa_] = asr_
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# phone_unknown.remove(ipa_)
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## check if all the phones in lexicon_ipa are in fame_phoneset.py.
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#timer_start = time.time()
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#phoneset_lex = get_phoneset_from_lexicon(lexicon_ipa, phoneset='ipa')
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#print("elapsed time: {}".format(time.time() - timer_start))
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#phoneset_py = fame_phoneset.phoneset_ipa
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#set(phoneset_lex) - set(phoneset_py)
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##timer_start = time.time()
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##extracted = find_phone(lexicon_ipa, 'ⁿ')
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##print("elapsed time: {}".format(time.time() - timer_start))
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# lex.asr is Kaldi compatible version of lex.ipa.
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# to check...
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#lexicon_ipa = pd.read_table(lex_ipa, names=['word', 'pronunciation'])
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#with open(lex_ipa_, "w", encoding="utf-8") as fout:
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# for word, pronunciation in zip(lexicon_ipa['word'], lexicon_ipa['pronunciation']):
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# # ignore nasalization and '.'
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# pronunciation_ = pronunciation.replace(u'ⁿ', '')
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# pronunciation_ = pronunciation_.replace('.', '')
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# pronunciation_split = convert_phone_set.split_ipa_fame(pronunciation_)
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# fout.write("{0}\t{1}\n".format(word, ' '.join(pronunciation_split)))
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# convert each lexicon from ipa description to fame_htk phoneset.
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#am_func.ipa2famehtk_lexicon(lex_oov, lex_oov_htk)
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#am_func.ipa2famehtk_lexicon(lex_asr, lex_asr_htk)
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107
acoustic_model/fame_ipa.py
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107
acoustic_model/fame_ipa.py
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""" definition of the phones to be used. """
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phoneset = [
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# vowels
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'i̯',
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'i̯ⁿ',
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'y',
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'i',
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'i.',
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'iⁿ',
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'i:',
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'i:ⁿ',
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'ɪ',
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'ɪⁿ',
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'ɪ.',
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#'ɪ:', # not included in lex.ipa
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'ɪ:ⁿ',
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'e',
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'e:',
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'e:ⁿ',
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'ə',
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'əⁿ',
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'ə:',
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'ɛ',
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'ɛ.',
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'ɛⁿ',
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'ɛ:',
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'ɛ:ⁿ',
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'a',
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'aⁿ',
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'a.',
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'a:',
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'a:ⁿ',
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'ṷ',
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'ṷ.',
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'ṷⁿ',
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#'ú', # only appears in word 'feeste'(út) and 'gaste'(út) which are 'f e: s t ə' and 'yn' in lex_asr. The pronunciation in Fries may be mistakes so I removed this phone.
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'u',
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'uⁿ',
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'u.',
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||||
'u:',
|
||||
'u:ⁿ',
|
||||
'ü',
|
||||
'ü.',
|
||||
'üⁿ',
|
||||
'ü:',
|
||||
'ü:ⁿ',
|
||||
'o',
|
||||
'oⁿ',
|
||||
'o.',
|
||||
'o:',
|
||||
'o:ⁿ',
|
||||
'ö',
|
||||
'ö.',
|
||||
'öⁿ',
|
||||
'ö:',
|
||||
'ö:ⁿ',
|
||||
'ɔ',
|
||||
'ɔ.',
|
||||
'ɔⁿ',
|
||||
'ɔ:',
|
||||
'ɔ:ⁿ',
|
||||
#'ɔ̈', # not included in lex.ipa
|
||||
'ɔ̈.',
|
||||
'ɔ̈:',
|
||||
|
||||
# plosives
|
||||
'p',
|
||||
'b',
|
||||
't',
|
||||
'tⁿ',
|
||||
'd',
|
||||
'k',
|
||||
'g',
|
||||
'ɡ', # = 'g'
|
||||
|
||||
# nasals
|
||||
'm',
|
||||
'n',
|
||||
'ŋ',
|
||||
|
||||
# fricatives
|
||||
'f',
|
||||
'v',
|
||||
's',
|
||||
's:',
|
||||
'z',
|
||||
'zⁿ',
|
||||
'x',
|
||||
'h',
|
||||
|
||||
# tap and flip
|
||||
'r',
|
||||
'r.', # only appears in word 'mearpartijestelsel'(does not exist in lex_asr) and 'tenoarpartij'.
|
||||
'r:', # only appears in word 'mûsearflearmûs' and 'sjochdêr'.
|
||||
|
||||
# approximant
|
||||
'j',
|
||||
'j.',
|
||||
'l'
|
||||
]
|
||||
|
||||
|
||||
## the list of multi character phones.
|
||||
# for example, the length of 'i̯ⁿ' is 3, but in the codes it is treated as one letter.
|
||||
multi_character_phones = [i for i in phoneset if len(i) > 1]
|
||||
multi_character_phones.sort(key=len, reverse=True)
|
||||
93
acoustic_model/fame_test.py
Normal file
93
acoustic_model/fame_test.py
Normal file
@ -0,0 +1,93 @@
|
||||
import sys
|
||||
import os
|
||||
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
||||
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
import fame_functions
|
||||
import defaultfiles as default
|
||||
sys.path.append(default.toolbox_dir)
|
||||
from phoneset import fame_ipa, fame_asr
|
||||
|
||||
|
||||
lexicon_dir = os.path.join(default.fame_dir, 'lexicon')
|
||||
lexicon_ipa = os.path.join(lexicon_dir, 'lex.ipa')
|
||||
lexicon_asr = os.path.join(lexicon_dir, 'lex.asr')
|
||||
|
||||
|
||||
## check if all the phones in lexicon.ipa are in fame_ipa.py.
|
||||
#timer_start = time.time()
|
||||
#phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_ipa, phoneset='ipa')
|
||||
#phoneset_py = fame_ipa.phoneset
|
||||
#print("phones which is in lexicon.ipa but not in fame_ipa.py:\n{}".format(
|
||||
# set(phoneset_lex) - set(phoneset_py)))
|
||||
#print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
# check which word has the phone.
|
||||
#timer_start = time.time()
|
||||
#extracted = find_phone(lexicon_ipa, 'ⁿ')
|
||||
#print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
|
||||
## get the correspondence between lex_ipa and lex_asr.
|
||||
lex_asr = fame_functions.load_lexicon(lexicon_asr)
|
||||
lex_ipa = fame_functions.load_lexicon(lexicon_ipa)
|
||||
if 0:
|
||||
timer_start = time.time()
|
||||
translation_key_ipa2asr, phone_unknown = fame_functions.get_translation_key(lexicon_ipa, lexicon_asr)
|
||||
print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
np.save(os.path.join('phoneset', 'output_get_translation_key_translation_key.npy'), translation_key_ipa2asr)
|
||||
np.save(os.path.join('phoneset', 'output_get_translation_key_phone_unknown.npy'), phone_unknown)
|
||||
else:
|
||||
translation_key_ipa2asr = np.load(os.path.join('phoneset', 'output_get_translation_key_translation_key.npy')).item()
|
||||
phone_unknown = np.load(os.path.join('phoneset', 'output_get_translation_key_phone_unknown.npy'))
|
||||
phone_unknown = list(phone_unknown)
|
||||
|
||||
# manually check the correspondence for the phone in phone_unknown.
|
||||
#p = phone_unknown[0]
|
||||
#lex_ipa_ = find_phone(lexicon_ipa, p, phoneset='ipa')
|
||||
|
||||
#for word in lex_ipa_['word']:
|
||||
# ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
|
||||
# if np.sum(lex_asr['word'] == word) > 0:
|
||||
# asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
|
||||
|
||||
# ipa_list = convert_phone_set.split_word(ipa, fame_ipa.multi_character_phones)
|
||||
# asr_list = asr.split(' ')
|
||||
# if p in ipa_list and (len(ipa_list) == len(asr_list)):
|
||||
# print("{0}: {1} --> {2}".format(word, ipa_list, asr_list))
|
||||
# for ipa_, asr_ in zip(ipa_list, asr_list):
|
||||
# if ipa_ in phone_unknown:
|
||||
# translation_key_ipa2asr[ipa_] = asr_
|
||||
# phone_unknown.remove(ipa_)
|
||||
translation_key_ipa2asr['ə:'] = 'ə'
|
||||
translation_key_ipa2asr['r.'] = 'r'
|
||||
translation_key_ipa2asr['r:'] = 'r'
|
||||
np.save(os.path.join('phoneset', 'fame_ipa2asr.npy'), translation_key_ipa2asr)
|
||||
|
||||
|
||||
## check if all the phones in lexicon.asr are in translation_key_ipa2asr.
|
||||
timer_start = time.time()
|
||||
phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_asr, phoneset='asr')
|
||||
phoneset_lex.remove("")
|
||||
phoneset_asr = list(set(translation_key_ipa2asr.values()))
|
||||
print("phones which is in lexicon.asr but not in the translation_key_ipa2asr:\n{}".format(
|
||||
set(phoneset_lex) - set(phoneset_asr)))
|
||||
print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
## make the translation key between asr to htk.
|
||||
#multi_character_phones = [i for i in phoneset_asr if len(i) > 1]
|
||||
#multi_character_phones.sort(key=len, reverse=True)
|
||||
|
||||
#lexicon_ipa = pd.read_table(lex_ipa, names=['word', 'pronunciation'])
|
||||
#with open(lex_ipa_, "w", encoding="utf-8") as fout:
|
||||
# for word, pronunciation in zip(lexicon_ipa['word'], lexicon_ipa['pronunciation']):
|
||||
# # ignore nasalization and '.'
|
||||
# pronunciation_ = pronunciation.replace(u'ⁿ', '')
|
||||
# pronunciation_ = pronunciation_.replace('.', '')
|
||||
# pronunciation_split = convert_phone_set.split_ipa_fame(pronunciation_)
|
||||
# fout.write("{0}\t{1}\n".format(word, ' '.join(pronunciation_split)))
|
||||
@ -1,7 +1,6 @@
|
||||
""" definition of the phones to be used. """
|
||||
|
||||
## phones in IPA.
|
||||
phoneset_ipa = [
|
||||
phoneset = [
|
||||
# vowels
|
||||
'i̯',
|
||||
'i̯ⁿ',
|
||||
@ -103,5 +102,5 @@ phoneset_ipa = [
|
||||
|
||||
## the list of multi character phones.
|
||||
# for example, the length of 'i̯ⁿ' is 3, but in the codes it is treated as one letter.
|
||||
multi_character_phones_ipa = [i for i in phoneset_ipa if len(i) > 1]
|
||||
multi_character_phones_ipa.sort(key=len, reverse=True)
|
||||
multi_character_phones = [i for i in phoneset if len(i) > 1]
|
||||
multi_character_phones.sort(key=len, reverse=True)
|
||||
106
acoustic_model/phoneset/fame_ipa.py
Normal file
106
acoustic_model/phoneset/fame_ipa.py
Normal file
@ -0,0 +1,106 @@
|
||||
""" definition of the phones to be used. """
|
||||
|
||||
phoneset = [
|
||||
# vowels
|
||||
'i̯',
|
||||
'i̯ⁿ',
|
||||
'y',
|
||||
'i',
|
||||
'i.',
|
||||
'iⁿ',
|
||||
'i:',
|
||||
'i:ⁿ',
|
||||
'ɪ',
|
||||
'ɪⁿ',
|
||||
'ɪ.',
|
||||
#'ɪ:', # not included in lex.ipa
|
||||
'ɪ:ⁿ',
|
||||
'e',
|
||||
'e:',
|
||||
'e:ⁿ',
|
||||
'ə',
|
||||
'əⁿ',
|
||||
'ə:',
|
||||
'ɛ',
|
||||
'ɛ.',
|
||||
'ɛⁿ',
|
||||
'ɛ:',
|
||||
'ɛ:ⁿ',
|
||||
'a',
|
||||
'aⁿ',
|
||||
'a.',
|
||||
'a:',
|
||||
'a:ⁿ',
|
||||
'ṷ',
|
||||
'ṷ.',
|
||||
'ṷⁿ',
|
||||
#'ú', # only appears in word 'feeste'(út) and 'gaste'(út) which are 'f e: s t ə' and 'yn' in lex_asr.
|
||||
'u',
|
||||
'uⁿ',
|
||||
'u.',
|
||||
'u:',
|
||||
'u:ⁿ',
|
||||
'ü',
|
||||
'ü.',
|
||||
'üⁿ',
|
||||
'ü:',
|
||||
'ü:ⁿ',
|
||||
'o',
|
||||
'oⁿ',
|
||||
'o.',
|
||||
'o:',
|
||||
'o:ⁿ',
|
||||
'ö',
|
||||
'ö.',
|
||||
'öⁿ',
|
||||
'ö:',
|
||||
'ö:ⁿ',
|
||||
'ɔ',
|
||||
'ɔ.',
|
||||
'ɔⁿ',
|
||||
'ɔ:',
|
||||
'ɔ:ⁿ',
|
||||
#'ɔ̈', # not included in lex.ipa
|
||||
'ɔ̈.',
|
||||
'ɔ̈:',
|
||||
|
||||
# plosives
|
||||
'p',
|
||||
'b',
|
||||
't',
|
||||
'tⁿ',
|
||||
'd',
|
||||
'k',
|
||||
'g',
|
||||
'ɡ', # = 'g'
|
||||
|
||||
# nasals
|
||||
'm',
|
||||
'n',
|
||||
'ŋ',
|
||||
|
||||
# fricatives
|
||||
'f',
|
||||
'v',
|
||||
's',
|
||||
's:',
|
||||
'z',
|
||||
'zⁿ',
|
||||
'x',
|
||||
'h',
|
||||
|
||||
# tap and flip
|
||||
'r',
|
||||
'r.', # only appears in word 'mearpartijestelsel'(does not exist in lex_asr) and 'tenoarpartij'.
|
||||
'r:', # only appears in word 'mûsearflearmûs' and 'sjochdêr'.
|
||||
|
||||
# approximant
|
||||
'j',
|
||||
'j.',
|
||||
'l'
|
||||
]
|
||||
|
||||
## the list of multi character phones.
|
||||
# for example, the length of 'i̯ⁿ' is 3, but in the codes it is treated as one letter.
|
||||
multi_character_phones = [i for i in phoneset if len(i) > 1]
|
||||
multi_character_phones.sort(key=len, reverse=True)
|
||||
BIN
acoustic_model/phoneset/fame_ipa2asr.npy
Normal file
BIN
acoustic_model/phoneset/fame_ipa2asr.npy
Normal file
Binary file not shown.
Binary file not shown.
Binary file not shown.
Loading…
Reference in New Issue
Block a user