lexicon is made.
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yemaozi88
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@ -23,7 +23,7 @@
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</PropertyGroup>
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<ItemGroup>
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<Compile Include="check_novoapi.py" />
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<Compile Include="convert_phone_set.py">
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<Compile Include="convert_phoneset.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="convert_xsampa2ipa.py">
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@ -32,8 +32,6 @@
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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_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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@ -52,9 +50,20 @@
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="fame_hmm.py" />
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<Compile Include="phoneset\fame_asr.py" />
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<Compile Include="phoneset\fame_ipa.py" />
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</ItemGroup>
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<ItemGroup>
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<Content Include="config.ini" />
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<Content Include="phoneset\fame_ipa2asr.npy" />
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<Content Include="phoneset\output_get_translation_key_phone_unknown.npy" />
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<Content Include="phoneset\output_get_translation_key_translation_key.npy" />
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<Content Include="phoneset\__pycache__\fame_asr.cpython-36.pyc" />
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<Content Include="phoneset\__pycache__\fame_ipa.cpython-36.pyc" />
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</ItemGroup>
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<ItemGroup>
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<Folder Include="phoneset\" />
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<Folder Include="phoneset\__pycache__\" />
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</ItemGroup>
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<Import Project="$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)\Python Tools\Microsoft.PythonTools.targets" />
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<!-- Uncomment the CoreCompile target to enable the Build command in
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@ -26,4 +26,15 @@ def split_word(word, multi_character_phones):
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(word_seperated) (list): the word splitted in given phoneset.
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"""
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return [phone for phone in multi_character_tokenize(word.strip(), multi_character_phones)]
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return [phone
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for phone in multi_character_tokenize(word.strip(), multi_character_phones)
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]
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def convert_phoneset(word_list, translation_key):
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"""
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Args:
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word_list (str): a list of phones written in given phoneset.
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translation_key (dict):
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"""
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return [translation_key.get(phone, phone) for phone in word_list]
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@ -1,127 +0,0 @@
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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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@ -1,4 +1,5 @@
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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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@ -8,38 +9,8 @@ import numpy as np
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import pandas as pd
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import defaultfiles as default
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from phoneset import fame_ipa
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import convert_phone_set
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#def ipa2famehtk_lexicon(lexicon_file_in, lexicon_file_out):
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# """ Convert a lexicon file from IPA to HTK format for FAME! corpus. """
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# lexicon_in = pd.read_table(lexicon_file_in, names=['word', 'pronunciation'])
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# with open(lexicon_file_out, "w", encoding="utf-8") as fout:
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# for word, pronunciation in zip(lexicon_in['word'], lexicon_in['pronunciation']):
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# pronunciation_no_space = pronunciation.replace(' ', '')
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# pronunciation_famehtk = convert_phone_set.ipa2famehtk(pronunciation_no_space)
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# if 'ceh' not in pronunciation_famehtk and 'sh' not in pronunciation_famehtk:
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# fout.write("{0}\t{1}\n".format(word.upper(), pronunciation_famehtk))
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#def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
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# """ Combine two lexicon files and sort by words. """
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# with open(lexicon_file1, "rt", encoding="utf-8") as fin:
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# lines1 = fin.read()
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# lines1 = lines1.split('\n')
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# with open(lexicon_file2, "rt", encoding="utf-8") as fin:
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# lines2 = fin.read()
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# lines2 = lines2.split('\n')
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# lex1 = pd.read_table(lexicon_file1, names=['word', 'pronunciation'])
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# lex2 = pd.read_table(lexicon_file2, names=['word', 'pronunciation'])
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# lex = pd.concat([lex1, lex2])
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# lex = lex.sort_values(by='word', ascending=True)
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# lex.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
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import convert_phoneset
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from phoneset import fame_ipa, fame_asr
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#def read_fileFA(fileFA):
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# """
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@ -292,3 +263,73 @@ def find_phone(lexicon_file, phone, phoneset_name='ipa'):
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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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return extracted
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def asr2htk_space_delimited(pronunciation):
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"""convert phoneset from asr to htk.
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Args:
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pronunciation (str): space delimited asr phones.
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Returns:
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(pronunciation) (str): space delimited asr phones in htk format (ascii).
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"""
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pronunciation_short = [fame_asr.reduction_key.get(i, i) for i in pronunciation.split(' ')
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if not i in fame_asr.phones_to_be_removed]
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return ' '.join(convert_phoneset.convert_phoneset(
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pronunciation_short, fame_asr.translation_key_asr2htk))
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def lexicon_asr2htk(lexicon_file_asr, lexicon_file_htk):
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""" Convert a lexicon file from asr to htk format (ascii).
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Args:
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lexicon_file_asr (path): a lexicon file written in asr format e.g. fame/lex.asr.
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lexicon_file_htk (path): a lexicon file written in htk format (ascii).
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"""
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lex_asr = load_lexicon(lexicon_file_asr)
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def asr2htk_space_delimited_(row):
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return asr2htk_space_delimited(row['pronunciation'])
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lex_htk = pd.DataFrame({
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'word': lex_asr['word'],
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'pronunciation': lex_asr.apply(asr2htk_space_delimited_, axis=1)
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})
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lex_htk = lex_htk.ix[:, ['word', 'pronunciation']]
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lex_htk.to_csv(lexicon_file_htk, header=None, index=None, sep='\t')
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return
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def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
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""" Combine two lexicon files and sort by words.
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Args:
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lexicon_file1, lexicon_file2 (path): input lexicon files.
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Returns:
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lexicon_file_out (path): lexicon_file which lexcion_file1 and 2 are combined and sorted.
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"""
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lex1 = load_lexicon(lexicon_file1)
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lex2 = load_lexicon(lexicon_file2)
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lex = pd.concat([lex1, lex2])
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lex = lex.sort_values(by='word', ascending=True)
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lex.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
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def fix_single_quote(lexicon_file):
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""" add '\' before all single quote at the beginning of words.
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Args:
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lexicon_file (path): lexicon file, which will be overwitten.
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"""
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lex = load_lexicon(lexicon_file)
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for i in lex[lex['word'].str.startswith('\'')].index.values:
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lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
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# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
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#lex.to_csv(lexicon_file, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
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lex.to_csv(lexicon_file, index=False, header=False, encoding="utf-8", sep='\t')
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return
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@ -5,7 +5,6 @@ os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
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import tempfile
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#import configparser
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#import subprocess
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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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@ -29,44 +28,21 @@ dataset_list = ['devel', 'test', 'train']
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# procedure
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extract_features = 0
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conv_lexicon = 1
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#check_lexicon = 0
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#make_mlf = 0
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#combine_files = 0
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#flat_start = 0
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#train_model = 1
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#sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
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#sys.path.append(forced_alignment_module)
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#from forced_alignment import convert_phone_set
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make_lexicon = 0
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make_mlf = 0
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combine_files = 0
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flat_start = 0
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train_model = 0
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## ======================= load variables =======================
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#config = configparser.ConfigParser()
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#config.sections()
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#config.read(config_ini)
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#config_hcopy = config['Settings']['config_hcopy']
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#config_train = config['Settings']['config_train']
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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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#lex_oov_htk = FAME_dir + '\\lexicon\\lex.oov_htk'
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##lex_ipa = FAME_dir + '\\lexicon\\lex.ipa'
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##lex_ipa_ = FAME_dir + '\\lexicon\\lex.ipa_'
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##lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk'
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#lex_htk = FAME_dir + '\\lexicon\\lex_original.htk'
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#lex_htk_ = FAME_dir + '\\lexicon\\lex.htk'
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lexicon_dir = os.path.join(default.fame_dir, 'lexicon')
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lexicon_asr = os.path.join(lexicon_dir, 'lex.asr')
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lexicon_oov = os.path.join(lexicon_dir, 'lex.oov')
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lexicon_htk_asr = os.path.join(default.htk_dir, 'lexicon', 'lex.htk_asr')
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lexicon_htk_oov = os.path.join(default.htk_dir, 'lexicon', 'lex.htk_oov')
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lexicon_htk = os.path.join(default.htk_dir, 'lexicon', 'lex.htk')
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#hcompv_scp = output_dir + '\\scp\\combined.scp'
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#combined_mlf = output_dir + '\\label\\combined.mlf'
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@ -88,8 +64,10 @@ if not os.path.exists(tmp_dir):
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## ======================= extract features =======================
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if extract_features:
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print('==== extract features ====\n')
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for dataset in dataset_list:
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print('==== {} ===='.format(dataset))
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print('==== dataset: {} ===='.format(dataset))
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# a script file for HCopy
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print(">>> making a script file for HCopy... \n")
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@ -112,41 +90,20 @@ if extract_features:
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fh.make_filelist(feature_dir_, hcompv_scp, '.mfc')
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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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# 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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## ======================= make lexicon for HTK =======================
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if make_lexicon:
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print('==== make lexicon for HTK ====\n')
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# convert each lexicon from fame_asr phoneset to fame_htk phoneset.
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print('>>> converting each lexicon from fame_asr phoneset to fame_htk phoneset... \n')
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fame_functions.lexicon_asr2htk(lexicon_asr, lexicon_htk_asr)
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fame_functions.lexicon_asr2htk(lexicon_oov, lexicon_htk_oov)
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# combine lexicon
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print('>>> combining lexicon files into one lexicon... \n')
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# pronunciations which is not found in lex.asr are generated using G2P and listed in lex.oov.
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# therefore there is no overlap between lex_asr and lex_oov.
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#am_func.combine_lexicon(lex_asr_htk, lex_oov_htk, lex_htk)
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## ======================= check if all the phones are successfully converted =======================
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if check_lexicon:
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print("==== check if all the phones are successfully converted. ====\n")
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# the phones used in the lexicon.
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phonelist_asr = am_func.get_phonelist(lex_asr)
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phonelist_oov = am_func.get_phonelist(lex_oov)
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phonelist_htk = am_func.get_phonelist(lex_htk)
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phonelist = phonelist_asr.union(phonelist_oov)
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# the lines which include a specific phone.
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lines = am_func.find_phone(lex_asr, 'g')
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# statistics over the lexicon
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lexicon_htk = pd.read_table(lex_htk, names=['word', 'pronunciation'])
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pronunciation = lexicon_htk['pronunciation']
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phones_all = []
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for word in pronunciation:
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phones_all = phones_all + word.split()
|
||||
c = Counter(phones_all)
|
||||
|
||||
fame_functions.combine_lexicon(lexicon_htk_asr, lexicon_htk_oov, lexicon_htk)
|
||||
|
||||
## =======================
|
||||
## manually make changes to the pronunciation dictionary and save it as lex.htk
|
||||
@ -154,6 +111,7 @@ if check_lexicon:
|
||||
# (1) Replace all tabs with single space;
|
||||
# (2) Put a '\' before any dictionary entry beginning with single quote
|
||||
#http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
|
||||
fame_functions.fix_single_quote(lexicon_htk)
|
||||
|
||||
|
||||
## ======================= make label file =======================
|
||||
|
||||
@ -1,107 +0,0 @@
|
||||
""" 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. The pronunciation in Fries may be mistakes so I removed this phone.
|
||||
'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)
|
||||
@ -1,7 +1,7 @@
|
||||
import sys
|
||||
import os
|
||||
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
||||
|
||||
from collections import Counter
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
@ -11,12 +11,12 @@ import fame_functions
|
||||
import defaultfiles as default
|
||||
sys.path.append(default.toolbox_dir)
|
||||
from phoneset import fame_ipa, fame_asr
|
||||
|
||||
import convert_phoneset
|
||||
|
||||
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')
|
||||
|
||||
lexicon_htk = os.path.join(default.htk_dir, 'lexicon', 'lex.htk')
|
||||
|
||||
## check if all the phones in lexicon.ipa are in fame_ipa.py.
|
||||
#timer_start = time.time()
|
||||
@ -64,6 +64,7 @@ else:
|
||||
# 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'
|
||||
@ -71,23 +72,32 @@ 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))
|
||||
|
||||
|
||||
## check if all the phones in lexicon.htk are in fame_asr.py.
|
||||
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)))
|
||||
phoneset_htk = fame_asr.phoneset_htk
|
||||
phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_htk)
|
||||
phoneset_lex.remove('')
|
||||
print("phones which is in lexicon.htk but not in the fame_asr.py are:\n{}".format(
|
||||
set(phoneset_htk) - set(phoneset_lex)))
|
||||
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)
|
||||
# statistics over the lexicon
|
||||
lex_htk = fame_functions.load_lexicon(lexicon_htk)
|
||||
phones_all = (' '.join(lex_htk['pronunciation'])).split(' ')
|
||||
c = Counter(phones_all)
|
||||
|
||||
#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)))
|
||||
lexicon_out = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\lexicon\lex.htk2'
|
||||
for i in lex_htk[lex_htk['word'].str.startswith('\'')].index.values:
|
||||
lex_htk.iat[i, 0] = lex_htk.iat[i, 0].replace('\'', '\\\'')
|
||||
# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
|
||||
#lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
|
||||
lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
|
||||
|
||||
@ -1,74 +1,40 @@
|
||||
""" definition of the phones to be used. """
|
||||
|
||||
# phonese in {FAME}/lexicon/lex.asr
|
||||
phoneset = [
|
||||
# vowels
|
||||
'i̯',
|
||||
'i̯ⁿ',
|
||||
'y',
|
||||
'i',
|
||||
'i.',
|
||||
'iⁿ',
|
||||
'i:',
|
||||
'i:ⁿ',
|
||||
'ɪ',
|
||||
'ɪⁿ',
|
||||
'ɪ.',
|
||||
#'ɪ:', # not included in lex.ipa
|
||||
'ɪ:ⁿ',
|
||||
'a',
|
||||
'a:',
|
||||
'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',
|
||||
'i',
|
||||
'i:',
|
||||
'i̯',
|
||||
'o',
|
||||
'oⁿ',
|
||||
'o.',
|
||||
'o:',
|
||||
'o:ⁿ',
|
||||
'ö',
|
||||
'ö.',
|
||||
'öⁿ',
|
||||
'ö:',
|
||||
'ö:ⁿ',
|
||||
'u',
|
||||
'u:',
|
||||
'ü',
|
||||
'ü:',
|
||||
#'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.
|
||||
'ṷ',
|
||||
'y',
|
||||
'ɔ',
|
||||
'ɔ.',
|
||||
'ɔⁿ',
|
||||
'ɔ:',
|
||||
'ɔ:ⁿ',
|
||||
#'ɔ̈', # not included in lex.ipa
|
||||
'ɔ̈.',
|
||||
'ɔ̈',
|
||||
'ɔ̈:',
|
||||
'ə',
|
||||
'ɛ',
|
||||
'ɛ:',
|
||||
'ɪ',
|
||||
'ɪ:',
|
||||
|
||||
# plosives
|
||||
'p',
|
||||
'b',
|
||||
't',
|
||||
'tⁿ',
|
||||
'd',
|
||||
'k',
|
||||
'g',
|
||||
@ -85,22 +51,77 @@ phoneset = [
|
||||
'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'.
|
||||
'r:',
|
||||
|
||||
# approximant
|
||||
'j',
|
||||
'j.',
|
||||
'l'
|
||||
]
|
||||
|
||||
|
||||
## reduce the number of phones.
|
||||
# the phones which seldom occur are replaced with another more popular phones.
|
||||
# replacements are based on the advice from Martijn Wieling.
|
||||
reduction_key = {
|
||||
'y':'i:', 'e':'e:', 'ə:':'ɛ:', 'r:':'r', 'ɡ':'g'
|
||||
}
|
||||
# already removed beforehand in phoneset. Just to be sure.
|
||||
phones_to_be_removed = ['ú', 's:', 'ɔ̈:']
|
||||
|
||||
phoneset_short = [reduction_key.get(i, i) for i in phoneset
|
||||
if not i in phones_to_be_removed]
|
||||
phoneset_short = list(set(phoneset_short))
|
||||
phoneset_short.sort()
|
||||
|
||||
|
||||
## translation_key to htk format (ascii).
|
||||
# phones which gives UnicodeEncodeError when phone.encode("ascii")
|
||||
# are replaced with other characters.
|
||||
translation_key_asr2htk = {
|
||||
'i̯': 'i_',
|
||||
'ṷ': 'u_',
|
||||
|
||||
# on the analogy of German umlaut, 'e' is used.
|
||||
'ö': 'oe', 'ö:': 'oe:',
|
||||
'ü': 'ue', 'ü:': 'ue:',
|
||||
|
||||
# on the analogy of Chinese...
|
||||
'ŋ': 'ng',
|
||||
|
||||
# refer to Xsampa.
|
||||
'ɔ': 'O', 'ɔ:': 'O:', 'ɔ̈': 'Oe',
|
||||
'ɛ': 'E', 'ɛ:': 'E:',
|
||||
'ɪ': 'I', 'ɪ:': 'I:',
|
||||
|
||||
# it is @ in Xsampa, but that is not handy on HTK.
|
||||
'ə': 'A'
|
||||
}
|
||||
phoneset_htk = [translation_key_asr2htk.get(i, i) for i in phoneset_short]
|
||||
|
||||
## check
|
||||
#for i in phoneset_short:
|
||||
# try:
|
||||
# print("{0} --> {1}".format(i, i.encode("ascii")))
|
||||
# except UnicodeEncodeError:
|
||||
# print(">>> {}".format(i))
|
||||
|
||||
|
||||
## the list of multi character phones.
|
||||
# for example, the length of 'i̯ⁿ' is 3, but in the codes it is treated as one letter.
|
||||
# for example, the length of 'a:' is 3, but in the codes it is treated as one letter.
|
||||
|
||||
# original.
|
||||
multi_character_phones = [i for i in phoneset if len(i) > 1]
|
||||
multi_character_phones.sort(key=len, reverse=True)
|
||||
|
||||
# phonset reduced.
|
||||
multi_character_phones_short = [i for i in phoneset_short if len(i) > 1]
|
||||
multi_character_phones_short.sort(key=len, reverse=True)
|
||||
|
||||
# htk compatible.
|
||||
multi_character_phones_htk = [i for i in phoneset_htk if len(i) > 1]
|
||||
multi_character_phones_htk.sort(key=len, reverse=True)
|
||||
|
||||
@ -34,7 +34,7 @@ phoneset = [
|
||||
'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.
|
||||
#'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.
|
||||
'u',
|
||||
'uⁿ',
|
||||
'u.',
|
||||
@ -100,6 +100,7 @@ phoneset = [
|
||||
'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]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user