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87abbbb95a
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7844a56281
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@ -10,6 +10,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
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..\forced_alignment\forced_alignment\__init__.py = ..\forced_alignment\forced_alignment\__init__.py
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..\forced_alignment\forced_alignment\convert_phone_set.py = ..\forced_alignment\forced_alignment\convert_phone_set.py
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..\toolbox\evaluation.py = ..\toolbox\evaluation.py
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..\toolbox\toolbox\file_handling.py = ..\toolbox\toolbox\file_handling.py
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..\forced_alignment\forced_alignment\htk_dict.py = ..\forced_alignment\forced_alignment\htk_dict.py
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..\forced_alignment\forced_alignment\lexicon.py = ..\forced_alignment\forced_alignment\lexicon.py
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..\forced_alignment\forced_alignment\mlf.py = ..\forced_alignment\forced_alignment\mlf.py
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@ -22,7 +23,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
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..\forced_alignment\forced_alignment\test_environment.py = ..\forced_alignment\forced_alignment\test_environment.py
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EndProjectSection
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EndProject
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Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "toolbox", "..\toolbox\toolbox.pyproj", "{F0D46C9C-51C6-4989-8A2F-35F2A0C048BE}"
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Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "pyhtk", "..\pyhtk\pyhtk\pyhtk.pyproj", "{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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@ -32,8 +33,8 @@ Global
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GlobalSection(ProjectConfigurationPlatforms) = postSolution
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{4D8C8573-32F0-4A62-9E62-3CE5CC680390}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{4D8C8573-32F0-4A62-9E62-3CE5CC680390}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{F0D46C9C-51C6-4989-8A2F-35F2A0C048BE}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{F0D46C9C-51C6-4989-8A2F-35F2A0C048BE}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}.Release|Any CPU.ActiveCfg = Release|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
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@ -23,18 +23,12 @@
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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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<SubType>Code</SubType>
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</Compile>
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<Compile Include="convert_xsampa2ipa.py">
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<SubType>Code</SubType>
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</Compile>
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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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<SubType>Code</SubType>
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</Compile>
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<Compile Include="fa_test.py">
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<SubType>Code</SubType>
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</Compile>
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@ -1,29 +0,0 @@
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"""Module to convert phonemes."""
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def multi_character_tokenize(line, multi_character_tokens):
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"""Tries to match one of the tokens in multi_character_tokens at each position of line, starting at position 0,
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if so tokenizes and eats that token. Otherwise tokenizes a single character"""
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while line != '':
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for token in multi_character_tokens:
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if line.startswith(token) and len(token) > 0:
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yield token
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line = line[len(token):]
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break
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else:
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yield line[:1]
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line = line[1:]
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def split_word(word, multi_character_phones):
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"""
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split a line by given phoneset.
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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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Returns:
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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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@ -4,8 +4,7 @@ import os
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#cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
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#htk_dir = r'C:\Aki\htk_fame'
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htk_dir = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk'
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htk_dir = r'C:\Aki\htk_fame'
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config_hcopy = os.path.join(htk_dir, 'config', 'config.HCopy')
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#config_train = os.path.join(cygwin_dir, 'config', 'config.train')
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@ -29,21 +28,22 @@ config_hcopy = os.path.join(htk_dir, 'config', 'config.HCopy')
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#filePhoneList = config['pyHTK']['filePhoneList']
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#AcousticModel = config['pyHTK']['AcousticModel']
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repo_dir = r'C:\Users\Aki\source\repos'
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repo_dir = r'C:\Users\A.Kunikoshi\source\repos'
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ipa_xsampa_converter_dir = os.path.join(repo_dir, 'ipa-xsama-converter')
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forced_alignment_module_dir = os.path.join(repo_dir, 'forced_alignment')
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accent_classification_dir = os.path.join(repo_dir, 'accent_classification', 'accent_classification')
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toolbox_dir = os.path.join(repo_dir, 'toolbox')
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pyhtk_dir = os.path.join(repo_dir, 'pyhtk', 'pyhtk')
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toolbox_dir = os.path.join(repo_dir, 'toolbox', 'toolbox')
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#htk_config_dir = r'c:\Users\A.Kunikoshi\source\repos\forced_alignment\forced_alignment\data\htk\preset_models\aki_dutch_2017'
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#config_hvite = os.path.join(htk_config_dir, 'config.HVite')
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htk_config_dir = r'c:\Users\A.Kunikoshi\source\repos\forced_alignment\forced_alignment\data\htk\preset_models\aki_dutch_2017'
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config_hvite = os.path.join(htk_config_dir, 'config.HVite')
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#acoustic_model = os.path.join(htk_config_dir, 'hmmdefs.compo')
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#acoustic_model = r'c:\cygwin64\home\A.Kunikoshi\acoustic_model\model\barbara\hmm128-2\hmmdefs.compo'
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#phonelist_txt = os.path.join(htk_config_dir, 'phonelist.txt')
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acoustic_model = r'c:\cygwin64\home\A.Kunikoshi\acoustic_model\model\barbara\hmm128-2\hmmdefs.compo'
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phonelist_txt = os.path.join(htk_config_dir, 'phonelist.txt')
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WSL_dir = r'C:\OneDrive\WSL'
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#fame_dir = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
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fame_dir = r'd:\_corpus\fame'
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fame_dir = r'f:\_corpus\fame'
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fame_s5_dir = os.path.join(fame_dir, 's5')
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fame_corpus_dir = os.path.join(fame_dir, 'corpus')
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@ -1,5 +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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os.chdir(r'C:\Users\A.Kunikoshi\source\repos\acoustic_model\acoustic_model')
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import sys
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from collections import Counter
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@ -9,8 +9,24 @@ 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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import convert_phone_set
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#sys.path.append(default.forced_alignment_module_dir)
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#from forced_alignment import convert_phone_set
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#def find_phone(lexicon_file, phone):
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# """ Search where the phone is used in the lexicon. """
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# with open(lexicon_file, "rt", encoding="utf-8") as fin:
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# lines = fin.read()
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# lines = lines.split('\n')
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# extracted = []
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# for line in lines:
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# line = line.split('\t')
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# if len(line) > 1:
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# pronunciation = line[1]
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# if phone in pronunciation:
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# extracted.append(line)
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# return extracted
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#def ipa2famehtk_lexicon(lexicon_file_in, lexicon_file_out):
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@ -110,6 +126,25 @@ import convert_phone_set
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# return ipa
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def make_hcopy_scp_from_filelist_in_fame(fame_dir, dataset, feature_dir, hcopy_scp):
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""" Make a script file for HCopy using the filelist in FAME! corpus. """
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filelist_txt = os.path.join(fame_dir, 'fame', 'filelists', dataset + 'list.txt')
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with open(filelist_txt) as fin:
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filelist = fin.read()
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filelist = filelist.split('\n')
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with open(hcopy_scp, 'w') as fout:
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for filename_ in filelist:
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filename = filename_.replace('.TextGrid', '')
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if len(filename) > 3: # remove '.', '..' and ''
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wav_file = os.path.join(fame_dir, 'fame', 'wav', dataset, filename + '.wav')
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mfc_file = os.path.join(feature_dir, filename + '.mfc')
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fout.write(wav_file + '\t' + mfc_file + '\n')
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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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@ -154,147 +189,64 @@ import convert_phone_set
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# f.write('{0}\t{1}\n'.format(WORD, key))
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def make_hcopy_scp_from_filelist_in_fame(fame_dir, dataset, feature_dir, hcopy_scp):
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""" Make a script file for HCopy using the filelist in FAME! corpus.
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Args:
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fame_dir (path): the directory of FAME corpus.
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dataset (str): 'devel', 'test' or 'train'.
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feature_dir (path): the directory where feature will be stored.
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hcopy_scp (path): a script file for HCopy to be made.
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"""
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filelist_txt = os.path.join(fame_dir, 'fame', 'filelists', dataset + 'list.txt')
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with open(filelist_txt) as fin:
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filelist = fin.read()
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filelist = filelist.split('\n')
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with open(hcopy_scp, 'w') as fout:
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for filename_ in filelist:
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filename = filename_.replace('.TextGrid', '')
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if len(filename) > 3: # remove '.', '..' and ''
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wav_file = os.path.join(fame_dir, 'fame', 'wav', dataset, filename + '.wav')
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mfc_file = os.path.join(feature_dir, filename + '.mfc')
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fout.write(wav_file + '\t' + mfc_file + '\n')
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def load_lexicon(lexicon_file):
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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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Returns:
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lex (df): lexicon as Data Frame, which has columns 'word' and 'pronunciation'.
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"""
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lex = pd.read_csv(lexicon_file, delimiter='\t', header=None, encoding="utf-8")
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lex.rename(columns={0: 'word', 1: 'pronunciation'}, inplace=True)
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return lex
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def get_phoneset_from_lexicon(lexicon_file, phoneset='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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def get_phonelist(lexicon_asr):
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""" Make a list of phones which appears in the lexicon. """
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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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#with open(lexicon_file, "rt", encoding="utf-8") as fin:
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# lines = fin.read()
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# lines = lines.split('\n')
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# phonelist = set([])
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# for line in lines:
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# line = line.split('\t')
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# if len(line) > 1:
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# pronunciation = set(line[1].split())
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# phonelist = phonelist | pronunciation
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lex = load_lexicon(lexicon_asr)
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return set(' '.join(lex['pronunciation']).split(' '))
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"""
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assert phoneset in ['asr', 'ipa'], 'phoneset should be \'asr\' or \'ipa\''
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import time
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lex = load_lexicon(lexicon_file)
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if phoneset == 'asr':
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return set(' '.join(lex['pronunciation']).split(' '))
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elif phoneset == '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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timer_start = time.time()
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#def get_translation_key():
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dir_tmp = r'c:\Users\A.Kunikoshi\source\repos\acoustic_model\_tmp'
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lexicon_ipa = r'f:\_corpus\FAME\lexicon\lex.ipa'
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lexicon_asr = r'f:\_corpus\FAME\lexicon\lex.asr'
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def extract_unknown_phones(ipa, known_phones):
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"""extract unknown phones in the pronunciation written in IPA.
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Args:
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ipa (str): a pronunciation written in IPA.
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known_phones (list): list of phones already know.
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Returns:
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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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return [i for i in ipa_split if not i in known_phones]
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def get_translation_key(lexicon_file_ipa, lexicon_file_asr):
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""" get correspondence between lexicon_file_ipa and lexicon_file_asr.
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Args:
|
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lexicon_file_ipa (path): lexicon in the format of 'word' /t 'pronunciation (IPA)'.
|
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lexicon_file_asr (path): lexicon in the format of 'word' /t 'pronunciation (asr)'.
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the each character of 'pronunciation' should be delimited by ' '.
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Returns:
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translation_key (dict): translation key from ipa to asr.
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(phone_unknown) (list): the list of IPA phones, which does not appear in 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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lex_ipa = load_lexicon(lexicon_ipa)
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lex_asr = load_lexicon(lexicon_asr)
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if 0:
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phone_to_be_searched = get_phonelist(lexicon_asr)
|
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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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for word in lex_asr['word']:
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if np.sum(lex_asr['word'] == word) == 1 and np.sum(lex_ipa['word'] == word) == 1:
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asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
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ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
|
||||
|
||||
ipa_list = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
|
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asr_list = asr.split(' ')
|
||||
# if there are phones which is not in phone_to_be_searched
|
||||
if len([True for i in asr_list if i in phone_to_be_searched]) > 0:
|
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if(len(ipa) == len(asr_list)):
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||||
print("{0}: {1} --> {2}".format(word, ipa, asr))
|
||||
for ipa_, asr_ in zip(ipa, asr_list):
|
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if asr_ in phone_to_be_searched:
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#if not translation_key[ipa_] == asr_:
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||||
translation_key[ipa_] = asr_
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||||
phone_to_be_searched.remove(asr_)
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||||
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||||
# if there are phones which is not in phone_unknown
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#if len([True for i in asr_list if i in phone_unknown]) > 0:
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||||
if(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_
|
||||
phone_unknown.remove(ipa_)
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||||
return translation_key, list(phone_unknown)
|
||||
print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
|
||||
def find_phone(lexicon_file, phone, phoneset='ipa'):
|
||||
""" extract rows where the phone is used in the lexicon_file.
|
||||
|
||||
Args:
|
||||
lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
|
||||
phone (str): the phone to be searched.
|
||||
phoneset (str): the phoneset with which lexicon_file is written. 'asr' or 'ipa'(default).
|
||||
|
||||
Returns:
|
||||
extracted (df): rows where the phone is used.
|
||||
|
||||
ToDo:
|
||||
* develop when the phonset == 'asr'.
|
||||
|
||||
"""
|
||||
assert phoneset in ['asr', 'ipa'], 'phoneset should be \'asr\' or \'ipa\''
|
||||
|
||||
lex = load_lexicon(lexicon_file)
|
||||
|
||||
# to reduce the calculation time, only target rows which include 'phone' at least once.
|
||||
lex_ = lex[lex['pronunciation'].str.count(phone)>0]
|
||||
|
||||
extracted = pd.DataFrame(index=[], columns=['word', 'pronunciation'])
|
||||
for index, row in lex_.iterrows():
|
||||
if phoneset == 'ipa':
|
||||
pronunciation = convert_phone_set.split_word(row['pronunciation'], fame_phoneset.multi_character_phones_ipa)
|
||||
if phone in pronunciation:
|
||||
extracted_ = pd.Series([row['word'], pronunciation], index=extracted.columns)
|
||||
extracted = extracted.append(extracted_, ignore_index=True)
|
||||
return extracted
|
||||
np.save(os.path.join(dir_tmp, 'translation_key.npy'), translation_key)
|
||||
np.save(os.path.join(dir_tmp, 'phone_to_be_searched.npy'), phone_to_be_searched)
|
||||
else:
|
||||
translation_key = np.load(os.path.join(dir_tmp, 'translation_key.npy')).item()
|
||||
phone_to_be_searched = np.load(os.path.join(dir_tmp, 'phone_to_be_searched.npy')).item()
|
||||
|
|
|
|||
|
|
@ -1,21 +1,21 @@
|
|||
import sys
|
||||
import os
|
||||
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
||||
os.chdir(r'C:\Users\A.Kunikoshi\source\repos\acoustic_model\acoustic_model')
|
||||
|
||||
import tempfile
|
||||
#import configparser
|
||||
#import subprocess
|
||||
#from collections import Counter
|
||||
import time
|
||||
|
||||
#import numpy as np
|
||||
#import pandas as pd
|
||||
|
||||
import fame_functions
|
||||
import defaultfiles as default
|
||||
sys.path.append(default.pyhtk_dir)
|
||||
import pyhtk
|
||||
sys.path.append(default.toolbox_dir)
|
||||
import file_handling as fh
|
||||
from htk import pyhtk
|
||||
import file_handling
|
||||
|
||||
|
||||
## ======================= user define =======================
|
||||
|
|
@ -28,8 +28,8 @@ from htk import pyhtk
|
|||
dataset_list = ['devel', 'test', 'train']
|
||||
|
||||
# procedure
|
||||
extract_features = 0
|
||||
conv_lexicon = 1
|
||||
extract_features = 1
|
||||
#conv_lexicon = 0
|
||||
#check_lexicon = 0
|
||||
#make_mlf = 0
|
||||
#combine_files = 0
|
||||
|
|
@ -85,12 +85,14 @@ if not os.path.exists(tmp_dir):
|
|||
## ======================= extract features =======================
|
||||
if extract_features:
|
||||
for dataset in dataset_list:
|
||||
#for dataset in ['test']:
|
||||
print('==== {} ===='.format(dataset))
|
||||
|
||||
# a script file for HCopy
|
||||
print(">>> making a script file for HCopy... \n")
|
||||
hcopy_scp = tempfile.NamedTemporaryFile(mode='w', delete=False)
|
||||
hcopy_scp.close()
|
||||
#hcopy_scp = os.path.join(default.htk_dir, 'tmp', 'HCopy.scp')
|
||||
|
||||
# get a list of features (hcopy.scp) from the filelist in FAME! corpus
|
||||
feature_dir_ = os.path.join(feature_dir, dataset)
|
||||
|
|
@ -100,70 +102,31 @@ if extract_features:
|
|||
# extract features
|
||||
print(">>> extracting features... \n")
|
||||
fame_functions.make_hcopy_scp_from_filelist_in_fame(default.fame_dir, dataset, feature_dir_, hcopy_scp.name)
|
||||
|
||||
#subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name
|
||||
#subprocess.call(subprocessStr, shell=True)
|
||||
pyhtk.wav2mfc(default.config_hcopy, hcopy_scp.name)
|
||||
|
||||
# a script file for HCompV
|
||||
print(">>> making a script file for HCompV... \n")
|
||||
|
||||
## ======================= make a list of features =======================
|
||||
#if make_feature_list:
|
||||
# print("==== make a list of features ====\n")
|
||||
|
||||
# for dataset in dataset_list:
|
||||
# print(dataset)
|
||||
|
||||
#feature_dir = output_dir + '\\mfc\\' + dataset
|
||||
hcompv_scp = os.path.join(tmp_dir, dataset + '.scp')
|
||||
fh.make_filelist(feature_dir_, hcompv_scp, '.mfc')
|
||||
|
||||
#am_func.make_filelist(feature_dir, hcompv_scp)
|
||||
file_handling.make_filelist(feature_dir_, hcompv_scp, '.mfc')
|
||||
|
||||
|
||||
## ======================= convert lexicon from ipa to fame_htk =======================
|
||||
if conv_lexicon:
|
||||
print('==== convert lexicon from ipa 2 fame ====\n')
|
||||
|
||||
#dir_out = r'c:\Users\Aki\source\repos\acoustic_model\_tmp'
|
||||
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')
|
||||
|
||||
# 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 1:
|
||||
timer_start = time.time()
|
||||
translation_key, phone_unknown = fame_functions.get_translation_key(lexicon_ipa, lexicon_asr)
|
||||
print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
np.save('translation_key_ipa2asr.npy', translation_key)
|
||||
np.save('phone_unknown.npy', phone_unknown)
|
||||
else:
|
||||
translation_key = np.load('translation_key_ipa2asr.npy').item()
|
||||
phone_unknown = np.load('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_phoneset.multi_character_phones_ipa)
|
||||
# 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[ipa_] = asr_
|
||||
# phone_unknown.remove(ipa_)
|
||||
|
||||
|
||||
## check if all the phones in lexicon_ipa are in fame_phoneset.py.
|
||||
#timer_start = time.time()
|
||||
#phoneset_lex = get_phoneset_from_lexicon(lexicon_ipa, phoneset='ipa')
|
||||
#print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
#phoneset_py = fame_phoneset.phoneset_ipa
|
||||
#set(phoneset_lex) - set(phoneset_py)
|
||||
|
||||
##timer_start = time.time()
|
||||
##extracted = find_phone(lexicon_ipa, 'ⁿ')
|
||||
##print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
|
||||
# lex.asr is Kaldi compatible version of lex.ipa.
|
||||
# to check...
|
||||
|
|
@ -177,13 +140,13 @@ if conv_lexicon:
|
|||
# fout.write("{0}\t{1}\n".format(word, ' '.join(pronunciation_split)))
|
||||
|
||||
# convert each lexicon from ipa description to fame_htk phoneset.
|
||||
#am_func.ipa2famehtk_lexicon(lex_oov, lex_oov_htk)
|
||||
#am_func.ipa2famehtk_lexicon(lex_asr, lex_asr_htk)
|
||||
am_func.ipa2famehtk_lexicon(lex_oov, lex_oov_htk)
|
||||
am_func.ipa2famehtk_lexicon(lex_asr, lex_asr_htk)
|
||||
|
||||
# combine lexicon
|
||||
# pronunciations which is not found in lex.asr are generated using G2P and listed in lex.oov.
|
||||
# therefore there is no overlap between lex_asr and lex_oov.
|
||||
#am_func.combine_lexicon(lex_asr_htk, lex_oov_htk, lex_htk)
|
||||
am_func.combine_lexicon(lex_asr_htk, lex_oov_htk, lex_htk)
|
||||
|
||||
|
||||
## ======================= check if all the phones are successfully converted =======================
|
||||
|
|
|
|||
|
|
@ -1,107 +0,0 @@
|
|||
""" definition of the phones to be used. """
|
||||
|
||||
## phones in IPA.
|
||||
phoneset_ipa = [
|
||||
# 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_ipa = [i for i in phoneset_ipa if len(i) > 1]
|
||||
multi_character_phones_ipa.sort(key=len, reverse=True)
|
||||
Loading…
Reference in New Issue