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.

acoustic_model.sln

@@ -10,19 +10,21 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
 		..\forced_alignment\forced_alignment\__init__.py = ..\forced_alignment\forced_alignment\__init__.py
 		..\forced_alignment\forced_alignment\convert_phone_set.py = ..\forced_alignment\forced_alignment\convert_phone_set.py
 		..\toolbox\evaluation.py = ..\toolbox\evaluation.py
+		..\toolbox\toolbox\file_handling.py = ..\toolbox\toolbox\file_handling.py
 		..\forced_alignment\forced_alignment\htk_dict.py = ..\forced_alignment\forced_alignment\htk_dict.py
 		..\forced_alignment\forced_alignment\lexicon.py = ..\forced_alignment\forced_alignment\lexicon.py
 		..\forced_alignment\forced_alignment\mlf.py = ..\forced_alignment\forced_alignment\mlf.py
 		..\forced_alignment\forced_alignment\pronunciations.py = ..\forced_alignment\forced_alignment\pronunciations.py
 		..\toolbox\pyHTK.py = ..\toolbox\pyHTK.py
 		..\forced_alignment\forced_alignment\pyhtk.py = ..\forced_alignment\forced_alignment\pyhtk.py
-		reus-test\reus-test.py = reus-test\reus-test.py
 		..\forced_alignment\forced_alignment\scripts.py = ..\forced_alignment\forced_alignment\scripts.py
 		..\..\..\..\..\Python36-32\Lib\site-packages\novoapi\backend\session.py = ..\..\..\..\..\Python36-32\Lib\site-packages\novoapi\backend\session.py
 		..\forced_alignment\forced_alignment\tempfilename.py = ..\forced_alignment\forced_alignment\tempfilename.py
 		..\forced_alignment\forced_alignment\test_environment.py = ..\forced_alignment\forced_alignment\test_environment.py
 	EndProjectSection
 EndProject
+Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "pyhtk", "..\pyhtk\pyhtk\pyhtk.pyproj", "{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -31,6 +33,8 @@ Global
 	GlobalSection(ProjectConfigurationPlatforms) = postSolution
 		{4D8C8573-32F0-4A62-9E62-3CE5CC680390}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
 		{4D8C8573-32F0-4A62-9E62-3CE5CC680390}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}.Release|Any CPU.ActiveCfg = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

acoustic_model/acoustic_model.pyproj

@@ -4,7 +4,8 @@
     <SchemaVersion>2.0</SchemaVersion>
     <ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
     <ProjectHome>.</ProjectHome>
-    <StartupFile>forced_aligner_comparison.py</StartupFile>
+    <StartupFile>
+    </StartupFile>
     <SearchPath>
     </SearchPath>
     <WorkingDirectory>.</WorkingDirectory>
@@ -21,10 +22,6 @@
     <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
   </PropertyGroup>
   <ItemGroup>
-    <Compile Include="acoustic_model.py" />
-    <Compile Include="acoustic_model_functions.py">
-      <SubType>Code</SubType>
-    </Compile>
     <Compile Include="check_novoapi.py" />
     <Compile Include="convert_xsampa2ipa.py">
       <SubType>Code</SubType>
@@ -35,9 +32,8 @@
     <Compile Include="fa_test.py">
       <SubType>Code</SubType>
     </Compile>
-    <Compile Include="forced_aligner_comparison.py">
+    <Compile Include="fame_functions.py" />
-      <SubType>Code</SubType>
+    <Compile Include="forced_aligner_comparison.py" />
-    </Compile>
     <Compile Include="novoapi_forced_alignment.py">
       <SubType>Code</SubType>
     </Compile>
@@ -47,6 +43,7 @@
     <Compile Include="novoapi_functions.py">
       <SubType>Code</SubType>
     </Compile>
+    <Compile Include="fame_hmm.py" />
   </ItemGroup>
   <ItemGroup>
     <Content Include="config.ini" />

acoustic_model/acoustic_model_function.py

@@ -1,202 +0,0 @@
-import os
-import sys
-from collections import Counter
-
-import numpy as np
-import pandas as pd
-
-import defaultfiles as default
-
-sys.path.append(default.forced_alignment_module_dir)
-from forced_alignment import convert_phone_set
-
-
-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 = 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 = FAME_dir + '\\fame\\wav\\' + dataset + '\\' + filename + '.wav'
-				mfc_file = 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 get_phonelist(lexicon_file):
-	""" 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
-	return phonelist
-
-
-def find_phone(lexicon_file, phone):
-	""" Search where the phone is used in the lexicon. """
-	with open(lexicon_file, "rt", encoding="utf-8") as fin:
-		lines = fin.read()
-		lines = lines.split('\n')
-	
-	extracted = []
-	for line in lines:
-		line = line.split('\t')
-		if len(line) > 1:
-			pronunciation = line[1]
-			if phone in pronunciation:
-				extracted.append(line)
-	return extracted
-
-
-def ipa2famehtk_lexicon(lexicon_file_in, lexicon_file_out):
-	""" Convert a lexicon file from IPA to HTK format for FAME! corpus. """
-
-	lexicon_in = pd.read_table(lexicon_file_in, names=['word', 'pronunciation'])
-	with open(lexicon_file_out, "w", encoding="utf-8") as fout:
-		for word, pronunciation in zip(lexicon_in['word'], lexicon_in['pronunciation']):
-			pronunciation_no_space = pronunciation.replace(' ', '')
-			pronunciation_famehtk  = convert_phone_set.ipa2famehtk(pronunciation_no_space)
-			if 'ceh' not in pronunciation_famehtk and 'sh' not in pronunciation_famehtk:
-				fout.write("{0}\t{1}\n".format(word.upper(), pronunciation_famehtk))
-
-
-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

acoustic_model/defaultfiles.py

@@ -2,11 +2,13 @@ import os
 
 #default_hvite_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', 'htk', 'config.HVite')
 
-cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
+#cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
 
-#config_hcopy = os.path.join(cygwin_dir, 'config', 'config.HCopy')
+htk_dir = r'C:\Aki\htk_fame'
+
+config_hcopy = os.path.join(htk_dir, 'config', 'config.HCopy')
 #config_train = os.path.join(cygwin_dir, 'config', 'config.train')
-config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
+#config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
 #mkhmmdefs_pl = os.path.join(cygwin_dir, 'src', 'acoustic_model', 'mkhmmdefs.pl')
 
 #dbLexicon	  = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\lexicon.accdb
@@ -26,19 +28,23 @@ config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
 #filePhoneList = config['pyHTK']['filePhoneList']
 #AcousticModel = config['pyHTK']['AcousticModel']
 
-repo_dir = r'C:\Users\Aki\source\repos'
+repo_dir = r'C:\Users\A.Kunikoshi\source\repos'
 ipa_xsampa_converter_dir    = os.path.join(repo_dir, 'ipa-xsama-converter')
 forced_alignment_module_dir = os.path.join(repo_dir, 'forced_alignment')
 accent_classification_dir   = os.path.join(repo_dir, 'accent_classification', 'accent_classification')
+pyhtk_dir                   = os.path.join(repo_dir, 'pyhtk', 'pyhtk')
+toolbox_dir					= os.path.join(repo_dir, 'toolbox', 'toolbox')
 
-htk_config_dir = r'c:\Users\Aki\source\repos\forced_alignment\forced_alignment\data\htk\preset_models\aki_dutch_2017'
+htk_config_dir = r'c:\Users\A.Kunikoshi\source\repos\forced_alignment\forced_alignment\data\htk\preset_models\aki_dutch_2017'
 config_hvite = os.path.join(htk_config_dir, 'config.HVite')
 #acoustic_model = os.path.join(htk_config_dir, 'hmmdefs.compo')
-acoustic_model = r'c:\cygwin64\home\Aki\acoustic_model\model\barbara\hmm128-2\hmmdefs.compo'
+acoustic_model = r'c:\cygwin64\home\A.Kunikoshi\acoustic_model\model\barbara\hmm128-2\hmmdefs.compo'
 phonelist_txt = os.path.join(htk_config_dir, 'phonelist.txt')
 
 WSL_dir   = r'C:\OneDrive\WSL'
-fame_dir        = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
+#fame_dir        = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
+fame_dir = r'f:\_corpus\fame'
+
 fame_s5_dir     = os.path.join(fame_dir, 's5')
 fame_corpus_dir = os.path.join(fame_dir, 'corpus')
 

acoustic_model/fame_functions.py

@@ -0,0 +1,252 @@
+import os
+os.chdir(r'C:\Users\A.Kunikoshi\source\repos\acoustic_model\acoustic_model')
+
+import sys
+from collections import Counter
+import pickle
+
+import numpy as np
+import pandas as pd
+
+import defaultfiles as default
+
+#sys.path.append(default.forced_alignment_module_dir)
+#from forced_alignment import convert_phone_set
+
+#def find_phone(lexicon_file, phone):
+#	""" Search where the phone is used in the lexicon. """
+#	with open(lexicon_file, "rt", encoding="utf-8") as fin:
+#		lines = fin.read()
+#		lines = lines.split('\n')
+	
+#	extracted = []
+#	for line in lines:
+#		line = line.split('\t')
+#		if len(line) > 1:
+#			pronunciation = line[1]
+#			if phone in pronunciation:
+#				extracted.append(line)
+#	return extracted
+
+
+#def ipa2famehtk_lexicon(lexicon_file_in, lexicon_file_out):
+#	""" Convert a lexicon file from IPA to HTK format for FAME! corpus. """
+
+#	lexicon_in = pd.read_table(lexicon_file_in, names=['word', 'pronunciation'])
+#	with open(lexicon_file_out, "w", encoding="utf-8") as fout:
+#		for word, pronunciation in zip(lexicon_in['word'], lexicon_in['pronunciation']):
+#			pronunciation_no_space = pronunciation.replace(' ', '')
+#			pronunciation_famehtk  = convert_phone_set.ipa2famehtk(pronunciation_no_space)
+#			if 'ceh' not in pronunciation_famehtk and 'sh' not in pronunciation_famehtk:
+#				fout.write("{0}\t{1}\n".format(word.upper(), pronunciation_famehtk))
+
+
+#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()

acoustic_model/fame_hmm.py

@@ -1,105 +1,127 @@
-import os
 import sys
-import tempfile
+import os
-import configparser
+os.chdir(r'C:\Users\A.Kunikoshi\source\repos\acoustic_model\acoustic_model')
-import subprocess
-from collections import Counter
 
-import numpy as np
+import tempfile
-import pandas as pd
+#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'
+#repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
-curr_dir = repo_dir + '\\acoustic_model'
+#curr_dir = repo_dir + '\\acoustic_model'
-config_ini = curr_dir + '\\config.ini'
+#config_ini = curr_dir + '\\config.ini'
-output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
+#output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
-forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
+#forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
 
 dataset_list = ['devel', 'test', 'train']
 
 # procedure
-extract_features  = 0
+extract_features  = 1
-make_feature_list = 0
+#conv_lexicon	  = 0
-conv_lexicon	  = 0
+#check_lexicon	  = 0
-check_lexicon	  = 0
+#make_mlf		  = 0
-make_mlf		  = 0
+#combine_files	  = 0
-combine_files	  = 0
+#flat_start		  = 0
-flat_start		  = 0
+#train_model		  = 1
-train_model		  = 1
 
 
-sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
+#sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
-sys.path.append(forced_alignment_module)
+#sys.path.append(forced_alignment_module)
-from forced_alignment import convert_phone_set
+#from forced_alignment import convert_phone_set
 
-import acoustic_model_functions as am_func
 
 
 ## ======================= load variables =======================
 
-config = configparser.ConfigParser()
+#config = configparser.ConfigParser()
-config.sections()
+#config.sections()
-config.read(config_ini)
+#config.read(config_ini)
 
-config_hcopy = config['Settings']['config_hcopy']
+#config_hcopy = config['Settings']['config_hcopy']
-config_train = config['Settings']['config_train']
+#config_train = config['Settings']['config_train']
-mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl']
+#mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl']
-FAME_dir	 = config['Settings']['FAME_dir']
+#FAME_dir	 = config['Settings']['FAME_dir']
 
-lex_asr		= FAME_dir + '\\lexicon\\lex.asr'
+#lex_asr		= FAME_dir + '\\lexicon\\lex.asr'
-lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
+#lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
-lex_oov		= FAME_dir + '\\lexicon\\lex.oov'
+#lex_oov		= FAME_dir + '\\lexicon\\lex.oov'
-lex_oov_htk = FAME_dir + '\\lexicon\\lex.oov_htk'
+#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_	= FAME_dir + '\\lexicon\\lex.ipa_'
+##lex_ipa_	= FAME_dir + '\\lexicon\\lex.ipa_'
-#lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk'
+##lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk'
-lex_htk		= FAME_dir + '\\lexicon\\lex_original.htk'
+#lex_htk		= FAME_dir + '\\lexicon\\lex_original.htk'
-lex_htk_	= FAME_dir + '\\lexicon\\lex.htk'
+#lex_htk_	= FAME_dir + '\\lexicon\\lex.htk'
 
-hcompv_scp = output_dir + '\\scp\\combined.scp'
+#hcompv_scp = output_dir + '\\scp\\combined.scp'
-combined_mlf = output_dir + '\\label\\combined.mlf'
+#combined_mlf = output_dir + '\\label\\combined.mlf'
 
-model_dir  = output_dir + '\\model'
+#model_dir  = output_dir + '\\model'
-model0_dir = model_dir + '\\hmm0'
+#model0_dir = model_dir + '\\hmm0'
-proto_init = model_dir + '\\proto38'
+#proto_init = model_dir + '\\proto38'
-proto_name = 'proto'
+#proto_name = 'proto'
-phonelist  = output_dir + '\\config\\phonelist_friesian.txt'
+#phonelist  = output_dir + '\\config\\phonelist_friesian.txt'
-hmmdefs_name = 'hmmdefs'
+#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
+		feature_dir_ = os.path.join(feature_dir, dataset)
-		am_func.make_hcopy_scp_from_filelist_in_fame(FAME_dir, dataset, feature_dir, hcopy_scp.name)
+		if not os.path.exists(feature_dir_):
+			os.makedirs(feature_dir_)
 
 		# extract features
-		subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name
+		print(">>> extracting features... \n")
-		subprocess.call(subprocessStr, shell=True)
+		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:
+#if make_feature_list:
-	print("==== make a list of features ====\n")
+#	print("==== make a list of features ====\n")
 
-	for dataset in dataset_list:
+#	for dataset in dataset_list:
-		print(dataset)
+#		print(dataset)
 
-		feature_dir = output_dir + '\\mfc\\' + dataset
+		#feature_dir = output_dir + '\\mfc\\' + dataset
-		hcompv_scp  = output_dir + '\\scp\\' + dataset + '.scp'
+		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 =======================