acoustic_model.sln

@@ -10,6 +10,7 @@ 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
@@ -22,7 +23,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
 		..\forced_alignment\forced_alignment\test_environment.py = ..\forced_alignment\forced_alignment\test_environment.py
 	EndProjectSection
 EndProject
-Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "toolbox", "..\toolbox\toolbox.pyproj", "{F0D46C9C-51C6-4989-8A2F-35F2A0C048BE}"
+Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "pyhtk", "..\pyhtk\pyhtk\pyhtk.pyproj", "{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}"
 EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
@@ -32,8 +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
-		{F0D46C9C-51C6-4989-8A2F-35F2A0C048BE}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{F0D46C9C-51C6-4989-8A2F-35F2A0C048BE}.Release|Any CPU.ActiveCfg = Release|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

@@ -23,18 +23,12 @@
   </PropertyGroup>
   <ItemGroup>
     <Compile Include="check_novoapi.py" />
-    <Compile Include="convert_phone_set.py">
-      <SubType>Code</SubType>
-    </Compile>
     <Compile Include="convert_xsampa2ipa.py">
       <SubType>Code</SubType>
     </Compile>
     <Compile Include="defaultfiles.py">
       <SubType>Code</SubType>
     </Compile>
-    <Compile Include="fame_phoneset.py">
-      <SubType>Code</SubType>
-    </Compile>
     <Compile Include="fa_test.py">
       <SubType>Code</SubType>
     </Compile>

acoustic_model/convert_phone_set.py

@@ -1,29 +0,0 @@
-"""Module to convert phonemes."""
-
-def multi_character_tokenize(line, multi_character_tokens):
-	"""Tries to match one of the tokens in multi_character_tokens at each position of line, starting at position 0,
-	if so tokenizes and eats that token. Otherwise tokenizes a single character"""
-	while line != '':
-		for token in multi_character_tokens:
-			if line.startswith(token) and len(token) > 0:
-				yield token
-				line = line[len(token):]
-				break
-		else:
-			yield line[:1]
-			line = line[1:]
-
-
-def split_word(word, multi_character_phones):
-	"""
-	split a line by given phoneset.
-	
-	Args:
-		word (str): a word written in given phoneset.
-		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. 
-
-	Returns:
-		(word_seperated) (list): the word splitted in given phoneset. 
-
-	"""
-	return [phone for phone in multi_character_tokenize(word.strip(), multi_character_phones)]

acoustic_model/defaultfiles.py

@@ -4,8 +4,7 @@ import os
 
 #cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
 
-#htk_dir = r'C:\Aki\htk_fame'
+htk_dir = r'C:\Aki\htk_fame'
-htk_dir = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk'
 
 config_hcopy = os.path.join(htk_dir, 'config', 'config.HCopy')
 #config_train = os.path.join(cygwin_dir, 'config', 'config.train')
@@ -29,21 +28,22 @@ config_hcopy = os.path.join(htk_dir, 'config', 'config.HCopy')
 #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')
-toolbox_dir					= os.path.join(repo_dir, 'toolbox')
+pyhtk_dir                   = os.path.join(repo_dir, 'pyhtk', 'pyhtk')
+toolbox_dir					= os.path.join(repo_dir, 'toolbox', 'toolbox')
 
-#htk_config_dir = r'c:\Users\A.Kunikoshi\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')
+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\A.Kunikoshi\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')
+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 = r'd:\_corpus\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

@@ -1,5 +1,5 @@
 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 sys
 from collections import Counter
@@ -9,8 +9,24 @@ import numpy as np
 import pandas as pd
 
 import defaultfiles as default
-import fame_phoneset
+
-import convert_phone_set
+#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):
@@ -110,6 +126,25 @@ import convert_phone_set
 
 #    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)
@@ -154,147 +189,64 @@ import convert_phone_set
 #                            f.write('{0}\t{1}\n'.format(WORD, key))
 
 
-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. 
-	
-	Args:
-		fame_dir (path): the directory of FAME corpus.
-		dataset (str): 'devel', 'test' or 'train'.
-		feature_dir (path): the directory where feature will be stored.
-		hcopy_scp (path): a script file for HCopy to be made.
-
-	"""
-	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 load_lexicon(lexicon_file):
-	""" load lexicon file as Data Frame.
-
-	Args:
-		lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
-	
-	Returns:
-		lex (df): lexicon as Data Frame, which has columns 'word' and 'pronunciation'.
-
-	"""
 	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_phoneset_from_lexicon(lexicon_file, phoneset='asr'):
+def get_phonelist(lexicon_asr):
-	""" Make a list of phones which appears in the lexicon. 
+	""" Make a list of phones which appears in the lexicon. """
 
-	Args:
+	#with open(lexicon_file, "rt", encoding="utf-8") as fin:
-		lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
+	#	lines = fin.read()
-		phoneset (str): the phoneset with which lexicon_file is written. 'asr'(default) or 'ipa'.
+	#	lines = lines.split('\n')
-
+	#	phonelist = set([])
-	Returns:
+	#	for line in lines:
-		(list_of_phones) (set): the set of phones included in the lexicon_file.
+	#		line = line.split('\t')
-
+	#		if len(line) > 1:
-	"""
+	#			pronunciation = set(line[1].split())
-	assert phoneset in ['asr', 'ipa'], 'phoneset should be \'asr\' or \'ipa\''
+	#			phonelist = phonelist | pronunciation
-
+	lex = load_lexicon(lexicon_asr)
-	lex = load_lexicon(lexicon_file)
-	if phoneset == 'asr':
 	return set(' '.join(lex['pronunciation']).split(' '))
-	elif phoneset == 'ipa':
-		join_pronunciations = ''.join(lex['pronunciation'])
-		return set(convert_phone_set.split_word(join_pronunciations, fame_phoneset.multi_character_phones_ipa))
 
+import time
 
-def extract_unknown_phones(ipa, known_phones):
+timer_start = time.time()
-	"""extract unknown phones in the pronunciation written in IPA.
 
-	Args:
+#def get_translation_key():
-		ipa (str): a pronunciation written in IPA. 
+dir_tmp = r'c:\Users\A.Kunikoshi\source\repos\acoustic_model\_tmp'
-		known_phones (list): list of phones already know.
+lexicon_ipa = r'f:\_corpus\FAME\lexicon\lex.ipa'
+lexicon_asr = r'f:\_corpus\FAME\lexicon\lex.asr'
 
-	Returns:
+lex_ipa = load_lexicon(lexicon_ipa)
-		(list_of_phones) (list): unknown phones not included in 'known_phones'.
+lex_asr = load_lexicon(lexicon_asr)
-
+if 0:
-	"""
+	phone_to_be_searched = get_phonelist(lexicon_asr)
-	ipa_split = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
-	return [i for i in ipa_split if not i in known_phones]
-
-
-def get_translation_key(lexicon_file_ipa, lexicon_file_asr):
-	""" get correspondence between lexicon_file_ipa and lexicon_file_asr.
-
-	Args:
-		lexicon_file_ipa (path): lexicon in the format of 'word' /t 'pronunciation (IPA)'.
-		lexicon_file_asr (path): lexicon in the format of 'word' /t 'pronunciation (asr)'.
-			the each character of 'pronunciation' should be delimited by ' '.
-
-	Returns:
-		translation_key (dict): translation key from ipa to asr. 
-		(phone_unknown) (list): the list of IPA phones, which does not appear in lexicon_file_asr. 
-
-	"""
-	lex_ipa = load_lexicon(lexicon_file_ipa)
-	lex_asr = load_lexicon(lexicon_file_asr)
-	phone_unknown = fame_phoneset.phoneset_ipa[:]
 	translation_key = dict()
-	for word in lex_ipa['word']:
+	for word in lex_asr['word']:
-		if np.sum(lex_ipa['word'] == word) == 1 and np.sum(lex_asr['word'] == word) == 1:
+		if np.sum(lex_asr['word'] == word) == 1 and np.sum(lex_ipa['word'] == word) == 1:
-			ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
 			asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
+			ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
 	
-			ipa_list = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
 			asr_list = asr.split(' ')
-
+			# if there are phones which is not in phone_to_be_searched
-			# if there are phones which is not in phone_unknown
+			if len([True for i in asr_list if i in phone_to_be_searched]) > 0:
-			#if len([True for i in asr_list if i in phone_unknown]) > 0:
+				if(len(ipa) == len(asr_list)):
-			if(len(ipa_list) == len(asr_list)):
+					print("{0}: {1} --> {2}".format(word, ipa, asr))
-				print("{0}: {1} --> {2}".format(word, ipa_list, asr_list))
+					for ipa_, asr_ in zip(ipa, asr_list):
-				for ipa_, asr_ in zip(ipa_list, asr_list):
+						if asr_ in phone_to_be_searched:
-					if ipa_ in phone_unknown:
+							#if not translation_key[ipa_] == asr_:
 							translation_key[ipa_] = asr_
-						phone_unknown.remove(ipa_)
+							phone_to_be_searched.remove(asr_)
-	return translation_key, list(phone_unknown)
 
+	print("elapsed time: {}".format(time.time() - timer_start))
 
-def find_phone(lexicon_file, phone, phoneset='ipa'):
+	np.save(os.path.join(dir_tmp, 'translation_key.npy'), translation_key)
-	""" extract rows where the phone is used in the lexicon_file. 
+	np.save(os.path.join(dir_tmp, 'phone_to_be_searched.npy'), phone_to_be_searched)
-
+else:
-	Args:
+	translation_key		 = np.load(os.path.join(dir_tmp, 'translation_key.npy')).item()
-		lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
+	phone_to_be_searched = np.load(os.path.join(dir_tmp, 'phone_to_be_searched.npy')).item()
-		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

acoustic_model/fame_hmm.py

@@ -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
+import file_handling
-from htk import pyhtk
 
 
 ## ======================= user define =======================
@@ -28,8 +28,8 @@ from htk import pyhtk
 dataset_list = ['devel', 'test', 'train']
 
 # procedure
-extract_features  = 0
+extract_features  = 1
-conv_lexicon	  = 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)
@@ -100,71 +102,32 @@ 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... 
 	#lexicon_ipa = pd.read_table(lex_ipa, names=['word', 'pronunciation'])
@@ -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_oov, lex_oov_htk)
-	#am_func.ipa2famehtk_lexicon(lex_asr, lex_asr_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 =======================

acoustic_model/fame_phoneset.py

@@ -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)