HTK related functions are moved to pyhtk project. fame acoustic models are made using fame_hmm.py. feature extraction is completed. A function is being made to get translation key from ipa to asr.
This commit is contained in:
a.kunikoshi
parent
04a862b2fd
commit
7844a56281
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_tmp/phone_to_be_searched.npy
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_tmp/phone_to_be_searched.npy
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_tmp/translation_key.npy
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_tmp/translation_key.npy
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@ -10,19 +10,21 @@ 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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..\forced_alignment\forced_alignment\pronunciations.py = ..\forced_alignment\forced_alignment\pronunciations.py
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..\toolbox\pyHTK.py = ..\toolbox\pyHTK.py
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..\forced_alignment\forced_alignment\pyhtk.py = ..\forced_alignment\forced_alignment\pyhtk.py
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reus-test\reus-test.py = reus-test\reus-test.py
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..\forced_alignment\forced_alignment\scripts.py = ..\forced_alignment\forced_alignment\scripts.py
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..\..\..\..\..\Python36-32\Lib\site-packages\novoapi\backend\session.py = ..\..\..\..\..\Python36-32\Lib\site-packages\novoapi\backend\session.py
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..\forced_alignment\forced_alignment\tempfilename.py = ..\forced_alignment\forced_alignment\tempfilename.py
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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}") = "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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Debug|Any CPU = Debug|Any CPU
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@ -31,6 +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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{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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@ -4,7 +4,8 @@
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<SchemaVersion>2.0</SchemaVersion>
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<ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
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<ProjectHome>.</ProjectHome>
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<StartupFile>forced_aligner_comparison.py</StartupFile>
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<StartupFile>
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</StartupFile>
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<SearchPath>
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</SearchPath>
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<WorkingDirectory>.</WorkingDirectory>
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@ -21,10 +22,6 @@
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<EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
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</PropertyGroup>
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<ItemGroup>
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<Compile Include="acoustic_model.py" />
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<Compile Include="acoustic_model_functions.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="check_novoapi.py" />
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<Compile Include="convert_xsampa2ipa.py">
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<SubType>Code</SubType>
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@ -35,9 +32,8 @@
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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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<Compile Include="forced_aligner_comparison.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="fame_functions.py" />
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<Compile Include="forced_aligner_comparison.py" />
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<Compile Include="novoapi_forced_alignment.py">
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<SubType>Code</SubType>
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</Compile>
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@ -47,6 +43,7 @@
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<Compile Include="novoapi_functions.py">
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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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</ItemGroup>
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<ItemGroup>
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<Content Include="config.ini" />
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@ -1,202 +0,0 @@
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import os
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import sys
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from collections import Counter
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import numpy as np
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import pandas as pd
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import defaultfiles as default
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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 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 = 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 = FAME_dir + '\\fame\\wav\\' + dataset + '\\' + filename + '.wav'
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mfc_file = 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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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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make dict files which can be used for HTK.
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param word: target word.
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param pronvar_: pronunciation variant. nx2 (WORD /t pronunciation) ndarray.
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param fileDic: output dic file.
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param output_type: 0:full, 1:statistics, 2:frequency <2% entries are removed. 3:top 3.
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"""
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#assert(output_type < 4 and output_type >= 0, 'output_type should be an integer between 0 and 3.')
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WORD = word.upper()
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if output_type == 0: # full
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pronvar = np.unique(pronvar_)
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with open(fileDic, 'w') as f:
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for pvar in pronvar:
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f.write('{0}\t{1}\n'.format(WORD, pvar))
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else:
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c = Counter(pronvar_)
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total_num = sum(c.values())
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with open(fileDic, 'w') as f:
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if output_type == 3:
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for key, value in c.most_common(3):
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f.write('{0}\t{1}\n'.format(WORD, key))
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else:
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for key, value in c.items():
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percentage = value/total_num*100
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if output_type == 1: # all
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f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, percentage, WORD, key))
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elif output_type == 2: # less than 2 percent
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if percentage < 2:
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f.write('{0}\t{1}\n'.format(WORD, key))
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def get_phonelist(lexicon_file):
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""" Make a list of phones which appears 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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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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return phonelist
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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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""" 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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def read_fileFA(fileFA):
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"""
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read the result file of HTK forced alignment.
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this function only works when input is one word.
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"""
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with open(fileFA, 'r') as f:
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lines = f.read()
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lines = lines.split('\n')
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phones = []
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for line in lines:
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line_split = line.split()
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if len(line_split) > 1:
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phones.append(line_split[2])
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return ' '.join(phones)
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def fame_pronunciation_variant(ipa):
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ipa = ipa.replace('æ', 'ɛ')
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ipa = ipa.replace('ɐ', 'a')
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ipa = ipa.replace('ɑ', 'a')
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ipa = ipa.replace('ɾ', 'r')
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ipa = ipa.replace('ɹ', 'r') # ???
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ipa = ipa.replace('ʁ', 'r')
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ipa = ipa.replace('ʀ', 'r') # ???
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ipa = ipa.replace('ʊ', 'u')
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ipa = ipa.replace('χ', 'x')
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pronvar_list = [ipa]
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while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
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pronvar_list_ = []
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for p in pronvar_list:
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if 'ø:' in p:
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pronvar_list_.append(p.replace('ø:', 'ö'))
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pronvar_list_.append(p.replace('ø:', 'ö:'))
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if 'œ' in p:
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pronvar_list_.append(p.replace('œ', 'ɔ̈'))
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pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
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if 'ɒ' in p:
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pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
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pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
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pronvar_list = np.unique(pronvar_list_)
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return pronvar_list
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def make_fame2ipa_variants(fame):
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fame = 'rɛös'
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ipa = [fame]
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ipa.append(fame.replace('ɛ', 'æ'))
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ipa.append(fame.replace('a', 'ɐ'))
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ipa.append(fame.replace('a', 'ɑ'))
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ipa.append(fame.replace('r', 'ɾ'))
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ipa.append(fame.replace('r', 'ɹ'))
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ipa.append(fame.replace('r', 'ʁ'))
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ipa.append(fame.replace('r', 'ʀ'))
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ipa.append(fame.replace('u', 'ʊ'))
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ipa.append(fame.replace('x', 'χ'))
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ipa.append(fame.replace('ö', 'ø:'))
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ipa.append(fame.replace('ö:', 'ø:'))
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ipa.append(fame.replace('ɔ̈', 'œ'))
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ipa.append(fame.replace('ɔ̈:', 'œ'))
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ipa.append(fame.replace('ɔ̈', 'ɒ'))
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ipa.append(fame.replace('ɔ̈:', 'ɒ'))
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return ipa
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@ -2,11 +2,13 @@ import os
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#default_hvite_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', 'htk', 'config.HVite')
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cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
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#cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
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#config_hcopy = os.path.join(cygwin_dir, 'config', 'config.HCopy')
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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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config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
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#config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
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#mkhmmdefs_pl = os.path.join(cygwin_dir, 'src', 'acoustic_model', 'mkhmmdefs.pl')
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#dbLexicon = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\lexicon.accdb
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@ -26,19 +28,23 @@ config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
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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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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\Aki\source\repos\forced_alignment\forced_alignment\data\htk\preset_models\aki_dutch_2017'
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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\Aki\acoustic_model\model\barbara\hmm128-2\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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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 = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', '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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252
acoustic_model/fame_functions.py
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252
acoustic_model/fame_functions.py
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@ -0,0 +1,252 @@
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import os
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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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import pickle
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import numpy as np
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import pandas as pd
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import defaultfiles as default
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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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# """ 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):
|
||||
# """ Combine two lexicon files and sort by words. """
|
||||
|
||||
# with open(lexicon_file1, "rt", encoding="utf-8") as fin:
|
||||
# lines1 = fin.read()
|
||||
# lines1 = lines1.split('\n')
|
||||
# with open(lexicon_file2, "rt", encoding="utf-8") as fin:
|
||||
# lines2 = fin.read()
|
||||
# lines2 = lines2.split('\n')
|
||||
|
||||
# lex1 = pd.read_table(lexicon_file1, names=['word', 'pronunciation'])
|
||||
# lex2 = pd.read_table(lexicon_file2, names=['word', 'pronunciation'])
|
||||
# lex = pd.concat([lex1, lex2])
|
||||
# lex = lex.sort_values(by='word', ascending=True)
|
||||
# lex.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
|
||||
|
||||
|
||||
#def read_fileFA(fileFA):
|
||||
# """
|
||||
# read the result file of HTK forced alignment.
|
||||
# this function only works when input is one word.
|
||||
# """
|
||||
# with open(fileFA, 'r') as f:
|
||||
# lines = f.read()
|
||||
# lines = lines.split('\n')
|
||||
|
||||
# phones = []
|
||||
# for line in lines:
|
||||
# line_split = line.split()
|
||||
# if len(line_split) > 1:
|
||||
# phones.append(line_split[2])
|
||||
|
||||
# return ' '.join(phones)
|
||||
|
||||
|
||||
#def fame_pronunciation_variant(ipa):
|
||||
# ipa = ipa.replace('æ', 'ɛ')
|
||||
# ipa = ipa.replace('ɐ', 'a')
|
||||
# ipa = ipa.replace('ɑ', 'a')
|
||||
# ipa = ipa.replace('ɾ', 'r')
|
||||
# ipa = ipa.replace('ɹ', 'r') # ???
|
||||
# ipa = ipa.replace('ʁ', 'r')
|
||||
# ipa = ipa.replace('ʀ', 'r') # ???
|
||||
# ipa = ipa.replace('ʊ', 'u')
|
||||
# ipa = ipa.replace('χ', 'x')
|
||||
|
||||
# pronvar_list = [ipa]
|
||||
# while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
|
||||
# pronvar_list_ = []
|
||||
# for p in pronvar_list:
|
||||
# if 'ø:' in p:
|
||||
# pronvar_list_.append(p.replace('ø:', 'ö'))
|
||||
# pronvar_list_.append(p.replace('ø:', 'ö:'))
|
||||
# if 'œ' in p:
|
||||
# pronvar_list_.append(p.replace('œ', 'ɔ̈'))
|
||||
# pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
|
||||
# if 'ɒ' in p:
|
||||
# pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
|
||||
# pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
|
||||
# pronvar_list = np.unique(pronvar_list_)
|
||||
# return pronvar_list
|
||||
|
||||
|
||||
#def make_fame2ipa_variants(fame):
|
||||
# fame = 'rɛös'
|
||||
# ipa = [fame]
|
||||
# ipa.append(fame.replace('ɛ', 'æ'))
|
||||
# ipa.append(fame.replace('a', 'ɐ'))
|
||||
# ipa.append(fame.replace('a', 'ɑ'))
|
||||
# ipa.append(fame.replace('r', 'ɾ'))
|
||||
# ipa.append(fame.replace('r', 'ɹ'))
|
||||
# ipa.append(fame.replace('r', 'ʁ'))
|
||||
# ipa.append(fame.replace('r', 'ʀ'))
|
||||
# ipa.append(fame.replace('u', 'ʊ'))
|
||||
# ipa.append(fame.replace('x', 'χ'))
|
||||
|
||||
# ipa.append(fame.replace('ö', 'ø:'))
|
||||
# ipa.append(fame.replace('ö:', 'ø:'))
|
||||
# ipa.append(fame.replace('ɔ̈', 'œ'))
|
||||
# ipa.append(fame.replace('ɔ̈:', 'œ'))
|
||||
# ipa.append(fame.replace('ɔ̈', 'ɒ'))
|
||||
# ipa.append(fame.replace('ɔ̈:', 'ɒ'))
|
||||
|
||||
# return ipa
|
||||
|
||||
def make_hcopy_scp_from_filelist_in_fame(fame_dir, dataset, feature_dir, hcopy_scp):
|
||||
""" Make a script file for HCopy using the filelist in FAME! corpus. """
|
||||
|
||||
filelist_txt = os.path.join(fame_dir, 'fame', 'filelists', dataset + 'list.txt')
|
||||
with open(filelist_txt) as fin:
|
||||
filelist = fin.read()
|
||||
filelist = filelist.split('\n')
|
||||
|
||||
with open(hcopy_scp, 'w') as fout:
|
||||
for filename_ in filelist:
|
||||
filename = filename_.replace('.TextGrid', '')
|
||||
|
||||
if len(filename) > 3: # remove '.', '..' and ''
|
||||
wav_file = os.path.join(fame_dir, 'fame', 'wav', dataset, filename + '.wav')
|
||||
mfc_file = os.path.join(feature_dir, filename + '.mfc')
|
||||
|
||||
fout.write(wav_file + '\t' + mfc_file + '\n')
|
||||
|
||||
|
||||
#def make_filelist(input_dir, output_txt):
|
||||
# """ Make a list of files in the input_dir. """
|
||||
# filenames = os.listdir(input_dir)
|
||||
|
||||
# with open(output_txt, 'w') as fout:
|
||||
# for filename in filenames:
|
||||
# fout.write(input_dir + '\\' + filename + '\n')
|
||||
|
||||
|
||||
#def make_htk_dict(word, pronvar_, fileDic, output_type):
|
||||
# """
|
||||
# make dict files which can be used for HTK.
|
||||
# param word: target word.
|
||||
# param pronvar_: pronunciation variant. nx2 (WORD /t pronunciation) ndarray.
|
||||
# param fileDic: output dic file.
|
||||
# param output_type: 0:full, 1:statistics, 2:frequency <2% entries are removed. 3:top 3.
|
||||
# """
|
||||
# #assert(output_type < 4 and output_type >= 0, 'output_type should be an integer between 0 and 3.')
|
||||
# WORD = word.upper()
|
||||
|
||||
# if output_type == 0: # full
|
||||
# pronvar = np.unique(pronvar_)
|
||||
|
||||
# with open(fileDic, 'w') as f:
|
||||
# for pvar in pronvar:
|
||||
# f.write('{0}\t{1}\n'.format(WORD, pvar))
|
||||
# else:
|
||||
# c = Counter(pronvar_)
|
||||
# total_num = sum(c.values())
|
||||
# with open(fileDic, 'w') as f:
|
||||
# if output_type == 3:
|
||||
# for key, value in c.most_common(3):
|
||||
# f.write('{0}\t{1}\n'.format(WORD, key))
|
||||
# else:
|
||||
# for key, value in c.items():
|
||||
# percentage = value/total_num*100
|
||||
|
||||
# if output_type == 1: # all
|
||||
# f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, percentage, WORD, key))
|
||||
# elif output_type == 2: # less than 2 percent
|
||||
# if percentage < 2:
|
||||
# f.write('{0}\t{1}\n'.format(WORD, key))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def load_lexicon(lexicon_file):
|
||||
lex = pd.read_csv(lexicon_file, delimiter='\t', header=None, encoding="utf-8")
|
||||
lex.rename(columns={0: 'word', 1: 'pronunciation'}, inplace=True)
|
||||
return lex
|
||||
|
||||
|
||||
def get_phonelist(lexicon_asr):
|
||||
""" Make a list of phones which appears in the lexicon. """
|
||||
|
||||
#with open(lexicon_file, "rt", encoding="utf-8") as fin:
|
||||
# lines = fin.read()
|
||||
# lines = lines.split('\n')
|
||||
# phonelist = set([])
|
||||
# for line in lines:
|
||||
# line = line.split('\t')
|
||||
# if len(line) > 1:
|
||||
# pronunciation = set(line[1].split())
|
||||
# phonelist = phonelist | pronunciation
|
||||
lex = load_lexicon(lexicon_asr)
|
||||
return set(' '.join(lex['pronunciation']).split(' '))
|
||||
|
||||
import time
|
||||
|
||||
timer_start = time.time()
|
||||
|
||||
#def get_translation_key():
|
||||
dir_tmp = r'c:\Users\A.Kunikoshi\source\repos\acoustic_model\_tmp'
|
||||
lexicon_ipa = r'f:\_corpus\FAME\lexicon\lex.ipa'
|
||||
lexicon_asr = r'f:\_corpus\FAME\lexicon\lex.asr'
|
||||
|
||||
lex_ipa = load_lexicon(lexicon_ipa)
|
||||
lex_asr = load_lexicon(lexicon_asr)
|
||||
if 0:
|
||||
phone_to_be_searched = get_phonelist(lexicon_asr)
|
||||
translation_key = dict()
|
||||
for word in lex_asr['word']:
|
||||
if np.sum(lex_asr['word'] == word) == 1 and np.sum(lex_ipa['word'] == word) == 1:
|
||||
asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
|
||||
ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
|
||||
|
||||
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:
|
||||
if(len(ipa) == len(asr_list)):
|
||||
print("{0}: {1} --> {2}".format(word, ipa, asr))
|
||||
for ipa_, asr_ in zip(ipa, asr_list):
|
||||
if asr_ in phone_to_be_searched:
|
||||
#if not translation_key[ipa_] == asr_:
|
||||
translation_key[ipa_] = asr_
|
||||
phone_to_be_searched.remove(asr_)
|
||||
|
||||
print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
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()
|
||||
@ -1,105 +1,127 @@
|
||||
import os
|
||||
import sys
|
||||
import tempfile
|
||||
import configparser
|
||||
import subprocess
|
||||
from collections import Counter
|
||||
import os
|
||||
os.chdir(r'C:\Users\A.Kunikoshi\source\repos\acoustic_model\acoustic_model')
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import tempfile
|
||||
#import configparser
|
||||
#import subprocess
|
||||
#from collections import Counter
|
||||
|
||||
#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
|
||||
|
||||
|
||||
## ======================= user define =======================
|
||||
repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
|
||||
curr_dir = repo_dir + '\\acoustic_model'
|
||||
config_ini = curr_dir + '\\config.ini'
|
||||
output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
|
||||
forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
|
||||
#repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
|
||||
#curr_dir = repo_dir + '\\acoustic_model'
|
||||
#config_ini = curr_dir + '\\config.ini'
|
||||
#output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
|
||||
#forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
|
||||
|
||||
dataset_list = ['devel', 'test', 'train']
|
||||
|
||||
# procedure
|
||||
extract_features = 0
|
||||
make_feature_list = 0
|
||||
conv_lexicon = 0
|
||||
check_lexicon = 0
|
||||
make_mlf = 0
|
||||
combine_files = 0
|
||||
flat_start = 0
|
||||
train_model = 1
|
||||
extract_features = 1
|
||||
#conv_lexicon = 0
|
||||
#check_lexicon = 0
|
||||
#make_mlf = 0
|
||||
#combine_files = 0
|
||||
#flat_start = 0
|
||||
#train_model = 1
|
||||
|
||||
|
||||
sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
|
||||
sys.path.append(forced_alignment_module)
|
||||
from forced_alignment import convert_phone_set
|
||||
#sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
|
||||
#sys.path.append(forced_alignment_module)
|
||||
#from forced_alignment import convert_phone_set
|
||||
|
||||
import acoustic_model_functions as am_func
|
||||
|
||||
|
||||
## ======================= load variables =======================
|
||||
|
||||
config = configparser.ConfigParser()
|
||||
config.sections()
|
||||
config.read(config_ini)
|
||||
#config = configparser.ConfigParser()
|
||||
#config.sections()
|
||||
#config.read(config_ini)
|
||||
|
||||
config_hcopy = config['Settings']['config_hcopy']
|
||||
config_train = config['Settings']['config_train']
|
||||
mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl']
|
||||
FAME_dir = config['Settings']['FAME_dir']
|
||||
#config_hcopy = config['Settings']['config_hcopy']
|
||||
#config_train = config['Settings']['config_train']
|
||||
#mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl']
|
||||
#FAME_dir = config['Settings']['FAME_dir']
|
||||
|
||||
lex_asr = FAME_dir + '\\lexicon\\lex.asr'
|
||||
lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
|
||||
lex_oov = FAME_dir + '\\lexicon\\lex.oov'
|
||||
lex_oov_htk = FAME_dir + '\\lexicon\\lex.oov_htk'
|
||||
#lex_ipa = FAME_dir + '\\lexicon\\lex.ipa'
|
||||
#lex_ipa_ = FAME_dir + '\\lexicon\\lex.ipa_'
|
||||
#lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk'
|
||||
lex_htk = FAME_dir + '\\lexicon\\lex_original.htk'
|
||||
lex_htk_ = FAME_dir + '\\lexicon\\lex.htk'
|
||||
#lex_asr = FAME_dir + '\\lexicon\\lex.asr'
|
||||
#lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
|
||||
#lex_oov = FAME_dir + '\\lexicon\\lex.oov'
|
||||
#lex_oov_htk = FAME_dir + '\\lexicon\\lex.oov_htk'
|
||||
##lex_ipa = FAME_dir + '\\lexicon\\lex.ipa'
|
||||
##lex_ipa_ = FAME_dir + '\\lexicon\\lex.ipa_'
|
||||
##lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk'
|
||||
#lex_htk = FAME_dir + '\\lexicon\\lex_original.htk'
|
||||
#lex_htk_ = FAME_dir + '\\lexicon\\lex.htk'
|
||||
|
||||
hcompv_scp = output_dir + '\\scp\\combined.scp'
|
||||
combined_mlf = output_dir + '\\label\\combined.mlf'
|
||||
#hcompv_scp = output_dir + '\\scp\\combined.scp'
|
||||
#combined_mlf = output_dir + '\\label\\combined.mlf'
|
||||
|
||||
model_dir = output_dir + '\\model'
|
||||
model0_dir = model_dir + '\\hmm0'
|
||||
proto_init = model_dir + '\\proto38'
|
||||
proto_name = 'proto'
|
||||
phonelist = output_dir + '\\config\\phonelist_friesian.txt'
|
||||
hmmdefs_name = 'hmmdefs'
|
||||
#model_dir = output_dir + '\\model'
|
||||
#model0_dir = model_dir + '\\hmm0'
|
||||
#proto_init = model_dir + '\\proto38'
|
||||
#proto_name = 'proto'
|
||||
#phonelist = output_dir + '\\config\\phonelist_friesian.txt'
|
||||
#hmmdefs_name = 'hmmdefs'
|
||||
|
||||
feature_dir = os.path.join(default.htk_dir, 'mfc')
|
||||
if not os.path.exists(feature_dir):
|
||||
os.makedirs(feature_dir)
|
||||
tmp_dir = os.path.join(default.htk_dir, 'tmp')
|
||||
if not os.path.exists(tmp_dir):
|
||||
os.makedirs(tmp_dir)
|
||||
|
||||
|
||||
## ======================= extract features =======================
|
||||
if extract_features:
|
||||
print("==== extract features ====\n")
|
||||
|
||||
for dataset in dataset_list:
|
||||
print(dataset)
|
||||
#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 = output_dir + '\\mfc\\' + dataset
|
||||
am_func.make_hcopy_scp_from_filelist_in_fame(FAME_dir, dataset, feature_dir, hcopy_scp.name)
|
||||
feature_dir_ = os.path.join(feature_dir, dataset)
|
||||
if not os.path.exists(feature_dir_):
|
||||
os.makedirs(feature_dir_)
|
||||
|
||||
# extract features
|
||||
subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name
|
||||
subprocess.call(subprocessStr, shell=True)
|
||||
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")
|
||||
#if make_feature_list:
|
||||
# print("==== make a list of features ====\n")
|
||||
|
||||
for dataset in dataset_list:
|
||||
print(dataset)
|
||||
# for dataset in dataset_list:
|
||||
# print(dataset)
|
||||
|
||||
feature_dir = output_dir + '\\mfc\\' + dataset
|
||||
hcompv_scp = output_dir + '\\scp\\' + dataset + '.scp'
|
||||
#feature_dir = output_dir + '\\mfc\\' + dataset
|
||||
hcompv_scp = os.path.join(tmp_dir, dataset + '.scp')
|
||||
|
||||
am_func.make_filelist(feature_dir, hcompv_scp)
|
||||
#am_func.make_filelist(feature_dir, hcompv_scp)
|
||||
file_handling.make_filelist(feature_dir_, hcompv_scp, '.mfc')
|
||||
|
||||
|
||||
## ======================= convert lexicon from ipa to fame_htk =======================
|
||||
Loading…
Reference in New Issue
Block a user