dataset for experiments in check_novoapi is updated.

acoustic_model/acoustic_model.pyproj

@@ -4,8 +4,7 @@
     <SchemaVersion>2.0</SchemaVersion>
     <ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
     <ProjectHome>.</ProjectHome>
-    <StartupFile>
-    </StartupFile>
+    <StartupFile>check_novoapi.py</StartupFile>
     <SearchPath>
     </SearchPath>
     <WorkingDirectory>.</WorkingDirectory>
@@ -23,7 +22,7 @@
   </PropertyGroup>
   <ItemGroup>
     <Compile Include="check_novoapi.py" />
-    <Compile Include="convert_phone_set.py">
+    <Compile Include="convert_phoneset.py">
       <SubType>Code</SubType>
     </Compile>
     <Compile Include="convert_xsampa2ipa.py">
@@ -32,7 +31,7 @@
     <Compile Include="defaultfiles.py">
       <SubType>Code</SubType>
     </Compile>
-    <Compile Include="fame_phoneset.py">
+    <Compile Include="fame_test.py">
       <SubType>Code</SubType>
     </Compile>
     <Compile Include="fa_test.py">
@@ -50,9 +49,25 @@
       <SubType>Code</SubType>
     </Compile>
     <Compile Include="fame_hmm.py" />
+    <Compile Include="phoneset\fame_asr.py" />
+    <Compile Include="phoneset\fame_ipa.py" />
+    <Compile Include="phoneset\fame_phonetics.py">
+      <SubType>Code</SubType>
+    </Compile>
+    <Compile Include="stimmen_functions.py" />
+    <Compile Include="stimmen_test.py" />
   </ItemGroup>
   <ItemGroup>
     <Content Include="config.ini" />
+    <Content Include="phoneset\fame_ipa2asr.npy" />
+    <Content Include="phoneset\output_get_translation_key_phone_unknown.npy" />
+    <Content Include="phoneset\output_get_translation_key_translation_key.npy" />
+    <Content Include="phoneset\__pycache__\fame_asr.cpython-36.pyc" />
+    <Content Include="phoneset\__pycache__\fame_ipa.cpython-36.pyc" />
+  </ItemGroup>
+  <ItemGroup>
+    <Folder Include="phoneset\" />
+    <Folder Include="phoneset\__pycache__\" />
   </ItemGroup>
   <Import Project="$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)\Python Tools\Microsoft.PythonTools.targets" />
   <!-- Uncomment the CoreCompile target to enable the Build command in

acoustic_model/check_novoapi.py

@@ -20,57 +20,56 @@ from forced_alignment import convert_phone_set
 #import acoustic_model_functions as am_func
 import convert_xsampa2ipa
 import novoapi_functions
+import stimmen_functions
 sys.path.append(default.accent_classification_dir)
 import output_confusion_matrix
 
 ## procedure 
 forced_alignment_novo70 = True
-balance_sample_numbers  = False
 
 
 ## ===== load novo phoneset =====
-phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa = novoapi_functions.load_phonset()
+phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa = novoapi_functions.load_novo70_phoneset()
 
 
 ## ===== extract pronunciations written in novo70 only (not_in_novo70) =====
 
-# As per Nederlandse phoneset_aki.xlsx recieved from David
-# [ɔː] oh / ohr
-# [ɪː] ih / ihr
-# [iː] iy
-# [œː] uh
-# [ɛː] eh
-# [w] wv in IPA written as ʋ. 
-david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'w']
+
 
 ## read pronunciation variants.
-stimmen_transcription_ = pd.ExcelFile(default.stimmen_transcription_xlsx)
-df = pd.read_excel(stimmen_transcription_, 'frequency')
-transcription_ipa = list(df['IPA'])
+#stimmen_transcription_ = pd.ExcelFile(default.stimmen_transcription_xlsx)
+#df = pd.read_excel(stimmen_transcription_, 'frequency')
+#transcription_ipa = list(df['IPA'])
 
-# transcription mistake?
-transcription_ipa = [ipa.replace(';', 'ː') for ipa in transcription_ipa if not ipa=='pypɪl' and not pd.isnull(ipa)]
-transcription_ipa = [ipa.replace('ˑ', '') for ipa in transcription_ipa] # only one case.
 
-not_in_novo70 = []
-all_in_novo70 = []
-for ipa in transcription_ipa:
-	ipa = ipa.replace(':', 'ː')
-	ipa = convert_phone_set.split_ipa(ipa)
+stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
+df = stimmen_functions.load_transcriptions_novo70(stimmen_test_dir)
 
-	# list of phones not in novo70 phoneset.
-	not_in_novo70_ = [phone for phone in ipa 
-				   if not phone in phoneset_ipa and not phone in david_suggestion]
-	not_in_novo70_ = [phone.replace('sp', '') for phone in not_in_novo70_]
-	not_in_novo70_ = [phone.replace(':', '') for phone in not_in_novo70_]
-	not_in_novo70_ = [phone.replace('ː', '') for phone in not_in_novo70_]
+	
 
-	if len(not_in_novo70_) == 0:
-		all_in_novo70.append(''.join(ipa))
+## transcription mistake?
+#transcription_ipa = [ipa.replace(';', 'ː') for ipa in transcription_ipa if not ipa=='pypɪl' and not pd.isnull(ipa)]
+#transcription_ipa = [ipa.replace('ˑ', '') for ipa in transcription_ipa] # only one case.
 
-	#translation_key.get(phone, phone)
-	not_in_novo70.extend(not_in_novo70_)
-not_in_novo70_list = list(set(not_in_novo70))
+#not_in_novo70 = []
+#all_in_novo70 = []
+#for ipa in transcription_ipa:
+#	ipa = ipa.replace(':', 'ː')
+#	ipa = convert_phone_set.split_ipa(ipa)
+
+#	# list of phones not in novo70 phoneset.
+#	not_in_novo70_ = [phone for phone in ipa 
+#				   if not phone in phoneset_ipa and not phone in david_suggestion]
+#	not_in_novo70_ = [phone.replace('sp', '') for phone in not_in_novo70_]
+#	not_in_novo70_ = [phone.replace(':', '') for phone in not_in_novo70_]
+#	not_in_novo70_ = [phone.replace('ː', '') for phone in not_in_novo70_]
+
+#	if len(not_in_novo70_) == 0:
+#		all_in_novo70.append(''.join(ipa))
+
+#	#translation_key.get(phone, phone)
+#	not_in_novo70.extend(not_in_novo70_)
+#not_in_novo70_list = list(set(not_in_novo70))
 
 
 ## check which phones used in stimmen but not in novo70
@@ -85,70 +84,43 @@ not_in_novo70_list = list(set(not_in_novo70))
 # [ʊ] 'ʊ'(1) --> can be ʏ (uh)??
 # [χ] --> can be x??
 
-def search_phone_ipa(x, phone_list):
-	x_in_item = []
-	for ipa in phone_list:
-		ipa_original = ipa
-		ipa = ipa.replace(':', 'ː')
-		ipa = convert_phone_set.split_ipa(ipa)
-		if x in ipa and not x+':' in ipa:
-			x_in_item.append(ipa_original)
-	return x_in_item
+#def search_phone_ipa(x, phone_list):
+#	x_in_item = []
+#	for ipa in phone_list:
+#		ipa_original = ipa
+#		ipa = ipa.replace(':', 'ː')
+#		ipa = convert_phone_set.split_ipa(ipa)
+#		if x in ipa and not x+':' in ipa:
+#			x_in_item.append(ipa_original)
+#	return x_in_item
 #search_phone_ipa('ø', transcription_ipa)
 
 
 ## ===== load all transcriptions (df) =====
-df = pd.read_excel(stimmen_transcription_, 'original')
-
-# mapping from ipa to xsampa
-mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
-#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
-#    ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
-#    if not ipa_converted == ipa:
-#        print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
-
-ipas     = []
-famehtks = []
-for xsampa in df['Self Xsampa']:
-    if not isinstance(xsampa, float): # 'NaN'
-        # typo?
-        xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t')
-        xsampa = xsampa.replace(';', ':')
-
-        ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
-        ipa = ipa.replace('ː', ':')
-        ipa = ipa.replace(' ', '')
-        ipas.append(ipa)
-    else:
-        ipas.append('')
-        
-# extract interesting cols.
-df = pd.DataFrame({'filename': df['Filename'], 
-                    'word': df['Word'], 
-                    'xsampa': df['Self Xsampa'],
-                    'ipa': pd.Series(ipas)})
-
+#df = stimmen_functions.load_transcriptions()
 word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
 word_list = sorted(word_list)
 
 
 ## check frequency of each pronunciation variants
-cols = ['word', 'ipa', 'frequency']
-df_samples = pd.DataFrame(index=[], columns=cols)
-for ipa in all_in_novo70:
-	ipa = ipa.replace('ː', ':')
-	samples = df[df['ipa'] == ipa]
-	word = list(set(samples['word']))[0]
-	samples_Series = pd.Series([word, ipa, len(samples)], index=df_samples.columns)
-	df_samples = df_samples.append(samples_Series, ignore_index=True)
+#cols = ['word', 'ipa', 'frequency']
+#df_samples = pd.DataFrame(index=[], columns=cols)
+#for ipa in all_in_novo70:
+#	ipa = ipa.replace('ː', ':')
+#	samples = df[df['ipa'] == ipa]
+#	word = list(set(samples['word']))[0]
+#	samples_Series = pd.Series([word, ipa, len(samples)], index=df_samples.columns)
+#	df_samples = df_samples.append(samples_Series, ignore_index=True)
 
 # each word
-df_per_word = pd.DataFrame(index=[], columns=df_samples.keys())
+#df_per_word = pd.DataFrame(index=[], columns=df_samples.keys())
 
-for word in word_list:
-	df_samples_ = df_samples[df_samples['word']==word]
-	df_samples_ = df_samples_[df_samples_['frequency']>2]
-	df_per_word = df_per_word.append(df_samples_, ignore_index=True)
+#for word in word_list:
+word = word_list[2]
+df_ = df[df['word']==word]
+np.unique(list(df_['ipa']))
+	#df_samples_ = df_samples_[df_samples_['frequency']>2]
+	#df_per_word = df_per_word.append(df_samples_, ignore_index=True)
 #df_per_word.to_excel(os.path.join(default.stimmen_dir, 'pronunciation_variants_novo70.xlsx'), encoding="utf-8")
 
 
@@ -183,21 +155,6 @@ if forced_alignment_novo70:
 
 		# samples in which all pronunciations are written in novo70.
 		samples = df_.query("ipa in @pronunciation_ipa")
-		
-
-		## ===== balance sample numbers =====
-		if balance_sample_numbers:
-			c = Counter(samples['ipa'])
-			sample_num_list = [c[key] for key in c.keys()]
-			sample_num		= np.min(sample_num_list)
-
-			samples_balanced = pd.DataFrame(index=[], columns=list(samples.keys()))
-			for key in c.keys():
-				samples_ = samples[samples['ipa'] == key]
-				samples_balanced = samples_balanced.append(samples_.sample(sample_num), ignore_index = True)
-
-			samples = samples_balanced
-
 
 		results = pd.DataFrame(index=[],
 			columns=['filename', 'word', 'xsampa', 'ipa', 'result_ipa', 'result_novo70', 'llh'])

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/convert_phoneset.py

@@ -0,0 +1,58 @@
+"""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, phoneset):
+	"""
+	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_ipa.py. 
+		phoneset (list): the list of phones.
+
+	Returns:
+		(word_seperated) (list): the word splitted in given phoneset. 
+
+	"""
+	multi_character_phones = extract_multi_character_phones(phoneset)
+	return [phone 
+		 for phone in multi_character_tokenize(word.strip(), multi_character_phones)
+		 ]
+
+
+def convert_phoneset(word_list, translation_key):
+	"""
+	Args:
+		word_list (str): a list of phones written in given phoneset.
+		translation_key (dict): 
+	"""
+	return [translation_key.get(phone, phone) for phone in word_list]
+
+
+def phone_reduction(phones, reduction_key):
+	multi_character_tokenize(wo.strip(), multi_character_phones)
+	return [reduction_key.get(i, i) for i in phones
+				  if not i in phones_to_be_removed]
+
+
+def extract_multi_character_phones(phoneset):
+	""" 
+	Args:
+		phoneset (list): 
+	"""
+	multi_character_phones = [i for i in phoneset if len(i) > 1]
+	multi_character_phones.sort(key=len, reverse=True)
+	return multi_character_phones

acoustic_model/defaultfiles.py

@@ -1,65 +1,42 @@
 import os
+# add path of the parent directory
+#os.path.dirname(os.path.realpath(__file__))
 
-#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'
-
-#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')
-#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
-#scriptBarbara = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\pronvars_barbara.perl
-#exeG2P		  = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\string2phon.exe
-
-#[pyHTK]
-#configHVite   = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\config.HVite
-#filePhoneList = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\phonelist_barbara.txt
-#AcousticModel = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\hmmdefs_16-2_barbara.compo
-
-#dbLexicon	  = config['cLexicon']['dbLexicon']
-#scriptBarbara = config['cLexicon']['scriptBarbara']
-#exeG2P		  = config['cLexicon']['exeG2P']
-
-#configHVite	  = config['pyHTK']['configHVite']
-#filePhoneList = config['pyHTK']['filePhoneList']
-#AcousticModel = config['pyHTK']['AcousticModel']
-
+# repos
 repo_dir = r'C:\Users\Aki\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')
 
-#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\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 = r'd:\_corpus\fame'
+novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
+#novo_api_dir = r'c:\Python36-32\Lib\site-packages\novoapi'
 
-fame_s5_dir     = os.path.join(fame_dir, 's5')
-fame_corpus_dir = os.path.join(fame_dir, 'corpus')
-
-experiments_dir = r'c:\OneDrive\Research\rug\experiments'
+# working directories
+rug_dir = r'c:\OneDrive\Research\rug'
+experiments_dir = os.path.join(rug_dir, 'experiments')
+htk_dir		= os.path.join(experiments_dir, 'acoustic_model', 'fame', 'htk')
+kaldi_dir   = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', '_stimmen')
 stimmen_dir = os.path.join(experiments_dir, 'stimmen')
-stimmen_data_dir = os.path.join(stimmen_dir, 'data')
+
+# data
+fame_dir = os.path.join(rug_dir, '_data', 'FAME')
+#fame_dir = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
 # 44.1 kHz
 #stimmen_wav_dir  = os.path.join(stimmen_dir, 'wav')
 # 16 kHz
 stimmen_wav_dir  = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
-stimmen_result_novoapi_dir = os.path.join(stimmen_dir, 'result', 'novoapi')
-
-stimmen_transcription_xlsx = os.path.join(stimmen_data_dir, 'Frisian Variants Picture Task Stimmen.xlsx')
+stimmen_transcription_xlsx = os.path.join(stimmen_dir, 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
 phonelist_friesian_txt     = os.path.join(experiments_dir, 'friesian', 'acoustic_model', 'config', 'phonelist_friesian.txt')
+novo70_phoneset = os.path.join(novo_api_dir, 'asr', 'phoneset', 'nl', 'novo70.phoneset')
+
+
+
+#phonelist_txt = os.path.join(htk_dir, 'config', 'phonelist.txt')
+#fame_s5_dir     = os.path.join(fame_dir, 's5')
+#fame_corpus_dir = os.path.join(fame_dir, 'corpus')
+#stimmen_result_novoapi_dir = os.path.join(stimmen_dir, 'result', 'novoapi')
+# novoapi_functions
+
 
-novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
-#novo_api_dir = r'c:\Python36-32\Lib\site-packages\novoapi'
-novo70_phoneset = os.path.join(novo_api_dir, 'asr', 'phoneset', 'nl', 'novo70.phoneset')

acoustic_model/fame_functions.py

@@ -9,37 +9,11 @@ import numpy as np
 import pandas as pd
 
 import defaultfiles as default
-import fame_phoneset
-import convert_phone_set
+import convert_phoneset
+from phoneset import fame_ipa, fame_asr
 
-
-#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')
+sys.path.append(default.toolbox_dir)
+from htk import pyhtk
 
 
 #def read_fileFA(fileFA):
@@ -110,14 +84,6 @@ import convert_phone_set
 
 #    return ipa
 
-#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):
 #    """
@@ -179,10 +145,11 @@ def make_hcopy_scp_from_filelist_in_fame(fame_dir, dataset, feature_dir, hcopy_s
 
 				fout.write(wav_file + '\t' + mfc_file + '\n')
 
+	return
 
 
 def load_lexicon(lexicon_file):
-	""" load lexicon file as Data Frame.
+	""" load lexicon file as data frame.
 
 	Args:
 		lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
@@ -196,25 +163,27 @@ def load_lexicon(lexicon_file):
 	return lex
 
 
-def get_phoneset_from_lexicon(lexicon_file, phoneset='asr'):
+def get_phoneset_from_lexicon(lexicon_file, phoneset_name='asr'):
 	""" Make a list of phones which appears in the lexicon. 
 	
 	Args:
 		lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
-		phoneset (str): the phoneset with which lexicon_file is written. 'asr'(default) or 'ipa'.
+		phoneset_name (str): the name of phoneset with which lexicon_file is written. 'asr'(default) or 'ipa'.
 
 	Returns:
 		(list_of_phones) (set): the set of phones included in the lexicon_file.
 
 	"""
-	assert phoneset in ['asr', 'ipa'], 'phoneset should be \'asr\' or \'ipa\''
+	assert phoneset_name in ['asr', 'ipa'], 'phoneset_name should be \'asr\' or \'ipa\''
 
 	lex = load_lexicon(lexicon_file)
-	if phoneset == 'asr':
+	if phoneset_name == 'asr':
 		return set(' '.join(lex['pronunciation']).split(' '))
-	elif phoneset == 'ipa':
+	elif phoneset_name == 'ipa':
 		join_pronunciations = ''.join(lex['pronunciation'])
-		return set(convert_phone_set.split_word(join_pronunciations, fame_phoneset.multi_character_phones_ipa))
+		return set(convert_phone_set.split_word(join_pronunciations, fame_ipa.multi_character_phones))
+
+	return
 
 
 def extract_unknown_phones(ipa, known_phones):
@@ -228,7 +197,7 @@ def extract_unknown_phones(ipa, known_phones):
 		(list_of_phones) (list): unknown phones not included in 'known_phones'.
 
 	"""
-	ipa_split = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
+	ipa_split = convert_phone_set.split_word(ipa, fame_ipa.multi_character_phones)
 	return [i for i in ipa_split if not i in known_phones]
 
 
@@ -247,14 +216,14 @@ def get_translation_key(lexicon_file_ipa, lexicon_file_asr):
 	"""
 	lex_ipa = load_lexicon(lexicon_file_ipa)
 	lex_asr = load_lexicon(lexicon_file_asr)
-	phone_unknown = fame_phoneset.phoneset_ipa[:]
+	phone_unknown = fame_ipa.phoneset[:]
 	translation_key = dict()
 	for word in lex_ipa['word']:
 		if np.sum(lex_ipa['word'] == word) == 1 and np.sum(lex_asr['word'] == word) == 1:
 			ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
 			asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
 	
-			ipa_list = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
+			ipa_list = convert_phone_set.split_word(ipa, fame_ipa.multi_character_phones)
 			asr_list = asr.split(' ')
 
 			# if there are phones which is not in phone_unknown
@@ -268,13 +237,13 @@ def get_translation_key(lexicon_file_ipa, lexicon_file_asr):
 	return translation_key, list(phone_unknown)
 
 
-def find_phone(lexicon_file, phone, phoneset='ipa'):
+def find_phone(lexicon_file, phone, phoneset_name='ipa'):
 	""" extract rows where the phone is used in the lexicon_file. 
 
 	Args:
 		lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
 		phone (str): the phone to be searched.
-		phoneset (str): the phoneset with which lexicon_file is written. 'asr' or 'ipa'(default).
+		phoneset_name (str): the name of phoneset_name with which lexicon_file is written. 'asr' or 'ipa'(default).
 
 	Returns:
 		extracted (df): rows where the phone is used.
@@ -283,7 +252,7 @@ def find_phone(lexicon_file, phone, phoneset='ipa'):
 		* develop when the phonset == 'asr'.
 
 	"""
-	assert phoneset in ['asr', 'ipa'], 'phoneset should be \'asr\' or \'ipa\''
+	assert phoneset_name in ['asr', 'ipa'], 'phoneset_name should be \'asr\' or \'ipa\''
 	
 	lex = load_lexicon(lexicon_file)
 	
@@ -292,9 +261,146 @@ def find_phone(lexicon_file, phone, phoneset='ipa'):
 
 	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 phoneset_name == 'ipa':
+			pronunciation = convert_phone_set.split_word(row['pronunciation'], fame_ipa.multi_character_phones)
 		if phone in pronunciation:
 			extracted_ = pd.Series([row['word'], pronunciation], index=extracted.columns)
 			extracted  = extracted.append(extracted_, ignore_index=True)
-	return extracted
+	return extracted
+
+
+def asr2htk_space_delimited(pronunciation):
+	"""convert phoneset from asr to htk.
+	
+	Args:
+		pronunciation (str): space delimited asr phones. 
+
+	Returns:
+		(pronunciation) (str): space delimited asr phones in htk format (ascii).
+
+	"""
+	pronunciation_short = [fame_asr.reduction_key.get(i, i) for i in pronunciation.split(' ') 
+			   if not i in fame_asr.phones_to_be_removed]
+	return ' '.join(convert_phoneset.convert_phoneset(
+		pronunciation_short, fame_asr.translation_key_asr2htk))
+
+
+def lexicon_asr2htk(lexicon_file_asr, lexicon_file_htk):
+	""" Convert a lexicon file from asr to htk format (ascii). 
+
+	Args:
+		lexicon_file_asr (path): a lexicon file written in asr format e.g. fame/lex.asr.
+		lexicon_file_htk (path): a lexicon file written in htk format (ascii).
+
+	"""
+	lex_asr = load_lexicon(lexicon_file_asr)
+	def word2htk_(row):
+		return word2htk(row['word'])
+	def asr2htk_space_delimited_(row):
+		return asr2htk_space_delimited(row['pronunciation'])
+
+	lex_htk = pd.DataFrame({
+		'word': lex_asr.apply(word2htk_, axis=1).str.upper(),
+        'pronunciation': lex_asr.apply(asr2htk_space_delimited_, axis=1)
+		})
+	lex_htk = lex_htk.ix[:, ['word', 'pronunciation']]
+	lex_htk.to_csv(lexicon_file_htk, header=None, index=None, sep='\t', encoding='utf-8')
+	return
+
+
+def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
+	""" Combine two lexicon files and sort by words. 
+
+	Args:
+		lexicon_file1, lexicon_file2 (path): input lexicon files.
+		
+	Returns:
+		lexicon_file_out (path): lexicon_file which lexcion_file1 and 2 are combined and sorted.
+
+	"""
+	lex1 = load_lexicon(lexicon_file1)
+	lex2 = load_lexicon(lexicon_file2)
+	lex  = pd.concat([lex1, lex2])
+	lex  = lex.sort_values(by='word', ascending=True)
+	lex.to_csv(lexicon_out, index=False, header=False, sep='\t', encoding='utf-8')
+
+
+def fix_lexicon(lexicon_file):
+	""" fix lexicon 
+	- add '\' before all single quote at the beginning of words.
+	- convert special characters to ascii compatible characters.
+	- add silence.
+
+	Args:
+		lexicon_file (path): lexicon file, which will be overwitten.
+
+	"""
+	lex = load_lexicon(lexicon_file)
+	lex = lex.dropna() # remove N/A.
+
+	# add 'sil'
+	row = pd.Series(['SILENCE', 'sil'], index=lex.columns)
+	lex = lex.append(row, ignore_index=True)
+	lex = lex.sort_values(by='word', ascending=True)
+
+	for i in lex[lex['word'].str.startswith('\'')].index.values:
+		lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
+
+	# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
+	#lex.to_csv(lexicon_file, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
+	lex.to_csv(lexicon_file, index=False, header=False, sep='\t', encoding='utf-8')
+	return
+
+
+def word2htk(word):
+	return ''.join([fame_asr.translation_key_word2htk.get(i, i) for i in word])
+
+
+def ipa2asr(ipa):
+	curr_dir = os.path.dirname(os.path.abspath(__file__))
+	translation_key_ipa2asr = np.load(os.path.join(curr_dir, 'phoneset', 'fame_ipa2asr.npy')).item(0)
+	#ipa_ = fame_asr.phone_reduction(ipa)
+	ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
+	ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
+	asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
+	asr_splitted = fame_asr.phone_reduction(asr_splitted)
+	return ''.join(asr_splitted)
+
+
+def ipa2htk(ipa):
+	curr_dir = os.path.dirname(os.path.abspath(__file__))
+	translation_key_ipa2asr = np.load(os.path.join(curr_dir, 'phoneset', 'fame_ipa2asr.npy')).item(0)
+	#translation_key_ipa2asr = np.load(r'c:\Users\Aki\source\repos\acoustic_model\acoustic_model\phoneset\fame_ipa2asr.npy').item(0)
+
+	ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
+	ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
+	asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
+	asr_splitted = fame_asr.phone_reduction(asr_splitted)
+	htk_splitted = convert_phoneset.convert_phoneset(asr_splitted, fame_asr.translation_key_asr2htk)
+	return ''.join(htk_splitted)
+
+
+def performance_on_stimmen(config_dir, stimmen_dir, hmmdefs):
+	lattice_file = os.path.join(stimmen_dir, 'word_lattice.ltc')
+	hvite_scp	 = os.path.join(stimmen_dir, 'hvite.scp')
+	#fh.make_filelist(os.path.join(stimmen_dir, 'mfc'), hvite_scp, file_type='mfc')
+	hresult_scp  = os.path.join(stimmen_dir, 'hresult.scp')
+	#fh.make_filelist(os.path.join(stimmen_dir, 'mfc'), hresult_scp, file_type='rec')
+	lexicon_file = os.path.join(stimmen_dir, 'lexicon_recognition.dic')
+	
+	# get feature_size from hmmdefs.
+	with open(hmmdefs) as f:
+		line = f.readline()
+		line = f.readline().strip()
+	feature_size = int(line.split(' ')[2])
+
+	chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_file, feature_size)
+
+	result = chtk.recognition(
+		lattice_file,
+		hmmdefs,
+		hvite_scp
+		)
+	per_sentence, per_word = chtk.calc_recognition_performance(hresult_scp)
+
+	return per_sentence['accuracy']

acoustic_model/fame_hmm.py

@@ -3,376 +3,564 @@ import os
 os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
 
 import tempfile
-#import configparser
-#import subprocess
-#from collections import Counter
+import shutil
+import glob
 import time
 
-#import numpy as np
-#import pandas as pd
+import numpy as np
+import pandas as pd
 
 import fame_functions
+from phoneset import fame_ipa, fame_asr, fame_phonetics
 import defaultfiles as default
 sys.path.append(default.toolbox_dir)
 import file_handling as fh
 from htk import pyhtk
+#from scripts import run_command
 
 
 ## ======================= 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'
-
-dataset_list = ['devel', 'test', 'train']
-
 # procedure
+combine_all = 1
+
+make_lexicon	  = 0
+make_label		  = 0 # it takes roughly 4800 sec on Surface pro 2.
+make_mlf		  = 0
 extract_features  = 0
-conv_lexicon	  = 1
-#check_lexicon	  = 0
-#make_mlf		  = 0
-#combine_files	  = 0
-#flat_start		  = 0
-#train_model		  = 1
+flat_start		  = 1
+train_monophone_without_sp = 1
+add_sp = 1
+train_monophone_with_re_aligned_mlf = 1
+increase_mixture = 1
+train_triphone = 0
+train_triphone_tied = 0
 
 
-#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
+# pre-defined values.
+dataset_list = ['devel', 'test', 'train']
+feature_size = 30
+improvement_threshold = 0.3
 
+lexicon_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
+lexicon_oov = os.path.join(default.fame_dir, 'lexicon', 'lex.oov')
 
+config_dir = os.path.join(default.htk_dir, 'config')
+phonelist_full_txt = os.path.join(config_dir, 'phonelist_full.txt')
+tree_hed    = os.path.join(config_dir, 'tree.hed')
+quests_hed  = os.path.join(config_dir, 'quests.hed')
 
-## ======================= load variables =======================
+model_dir     = os.path.join(default.htk_dir, 'model')
+model_mono0_dir    = os.path.join(model_dir, 'mono0')
+model_mono1_dir    = os.path.join(model_dir, 'mono1')
+model_mono1sp_dir  = os.path.join(model_dir, 'mono1sp')
+model_mono1sp2_dir = os.path.join(model_dir, 'mono1sp2')
+model_tri1_dir	   = os.path.join(model_dir, 'tri1')
+model_tri1tied_dir = os.path.join(model_dir, 'tri1tied')
 
-#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']
-
-#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'
-
-#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'
+# directories / files to be made.
+lexicon_dir = os.path.join(default.htk_dir, 'lexicon') 
+lexicon_htk_asr = os.path.join(lexicon_dir, 'lex.htk_asr')
+lexicon_htk_oov = os.path.join(lexicon_dir, 'lex.htk_oov')
+lexicon_htk     = os.path.join(lexicon_dir, 'lex.htk')
+lexicon_htk_with_sp  = os.path.join(lexicon_dir, 'lex_with_sp.htk')
+lexicon_htk_triphone = os.path.join(lexicon_dir, 'lex_triphone.htk')
 
 feature_dir = os.path.join(default.htk_dir, 'mfc')
-if not os.path.exists(feature_dir):
-	os.makedirs(feature_dir)
+fh.make_new_directory(feature_dir, existing_dir='leave')
 tmp_dir = os.path.join(default.htk_dir, 'tmp')
-if not os.path.exists(tmp_dir):
-	os.makedirs(tmp_dir)
+fh.make_new_directory(tmp_dir, existing_dir='leave')
+label_dir = os.path.join(default.htk_dir, 'label')
+fh.make_new_directory(label_dir, existing_dir='leave')
+
+
+## training
+if combine_all:
+	hcompv_scp_train		 = os.path.join(tmp_dir, 'all.scp')
+	mlf_file_train			 = os.path.join(label_dir, 'all_phone.mlf')
+	mlf_file_train_word		 = os.path.join(label_dir, 'all_word.mlf')
+	mlf_file_train_with_sp   = os.path.join(label_dir, 'all_phone_with_sp.mlf')
+	mlf_file_train_aligned   = os.path.join(label_dir, 'all_phone_aligned.mlf')
+	triphone_mlf			 = os.path.join(label_dir, 'all_triphone.mlf')
+else:
+	hcompv_scp_train		 = os.path.join(tmp_dir, 'train.scp')
+	mlf_file_train			 = os.path.join(label_dir, 'train_phone.mlf')
+	mlf_file_train_word		 = os.path.join(label_dir, 'train_word.mlf')
+	mlf_file_train_with_sp   = os.path.join(label_dir, 'train_phone_with_sp.mlf')
+	mlf_file_train_aligned   = os.path.join(label_dir, 'train_phone_aligned.mlf')
+	triphone_mlf			 = os.path.join(label_dir, 'train_triphone.mlf')
+hcompv_scp_train_updated = hcompv_scp_train.replace('.scp', '_updated.scp')
+
+## testing
+htk_stimmen_dir = os.path.join(default.htk_dir, 'stimmen')
+
+
+## ======================= make lexicon for HTK =======================
+if make_lexicon:
+	timer_start = time.time()
+	print('==== making lexicon for HTK ====')
+
+	# convert each lexicon from fame_asr phoneset to fame_htk phoneset.
+	print('>>> converting each lexicon from fame_asr phoneset to fame_htk phoneset...')
+	fame_functions.lexicon_asr2htk(lexicon_asr, lexicon_htk_asr)
+	fame_functions.lexicon_asr2htk(lexicon_oov, lexicon_htk_oov)
+
+	# combine lexicon
+	print('>>> combining lexicon files into one 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.   
+	fame_functions.combine_lexicon(lexicon_htk_asr, lexicon_htk_oov, lexicon_htk)
+
+	## fixing the lexicon for HTK. 
+	# (1) Replace all tabs with single space;
+	# (2) Put a '\' before any dictionary entry beginning with single quote 
+	# http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
+	print('>>> fixing the lexicon...')
+	fame_functions.fix_lexicon(lexicon_htk)
+
+	## adding sp to the lexicon for HTK. 
+	print('>>> adding sp to the lexicon...')
+	with open(lexicon_htk) as f:
+		lines = f.read().split('\n')
+	with open(lexicon_htk_with_sp, 'wb') as f:
+		f.write(bytes(' sp\n'.join(lines), 'ascii'))
+
+	print("elapsed time: {}".format(time.time() - timer_start))
+	
+
+## intialize the instance for HTK.
+chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_htk_with_sp, feature_size)
+
+
+## ======================= make label files =======================
+if make_label:
+	for dataset in dataset_list:
+		timer_start = time.time()
+		print("==== making label files on dataset {}".format(dataset))
+
+		script_list = os.path.join(default.fame_dir, 'data', dataset, 'text')
+		wav_dir_	= os.path.join(default.fame_dir, 'fame', 'wav', dataset)
+		label_dir_		= os.path.join(label_dir, dataset)
+		dictionary_file = os.path.join(label_dir_, 'temp.dic')
+		fh.make_new_directory(label_dir_, existing_dir='leave')
+
+		# list of scripts 
+		with open(script_list, "rt", encoding="utf-8") as fin:
+			scripts = fin.read().split('\n')
+
+		for line in scripts:
+			# sample line:
+			# sp0457m_test_1968_plakkenfryslanterhorne_2168 en dan begjinne je natuerlik
+			filename_ = line.split(' ')[0]
+			filename  = '_'.join(filename_.split('_')[1:])
+			sentence  = ' '.join(line.split(' ')[1:])
+			sentence_htk = fame_functions.word2htk(sentence)
+
+			wav_file = os.path.join(wav_dir_, filename + '.wav')
+			if os.path.exists(wav_file) and chtk.can_be_ascii(sentence_htk) == 0:
+				if chtk.get_number_of_missing_words(
+					sentence_htk, dictionary_file) == 0:
+					# when the file name is too long, HDMan command does not work.
+					# therefore first temporary dictionary_file is made, then renamed. 
+					shutil.move(dictionary_file, os.path.join(label_dir_, filename + '.dic'))
+
+					label_file = os.path.join(label_dir_, filename + '.lab')
+					chtk.make_label_file(sentence_htk, label_file)
+				else:
+					os.remove(dictionary_file)
+
+		print("elapsed time: {}".format(time.time() - timer_start))
+
+
+## ======================= make master label files =======================
+if make_mlf:
+	timer_start = time.time()
+	print("==== making master label files ====")
+	
+	# train_2002_gongfansaken_10347.lab is empty. should be removed.
+	empty_lab_file = os.path.join(label_dir, 'train', 'train_2002_gongfansaken_10347.lab')
+	empty_dic_file = empty_lab_file.replace('.lab', '.dic')
+
+	if os.path.exists(empty_lab_file):
+		os.remove(empty_lab_file)
+	if os.path.exists(empty_dic_file):
+		os.remove(empty_dic_file)
+
+	for dataset in dataset_list:
+		feature_dir_ = os.path.join(feature_dir, dataset)
+		label_dir_   = os.path.join(label_dir, dataset)
+		mlf_word  = os.path.join(label_dir, dataset + '_word.mlf')
+		mlf_phone = os.path.join(label_dir, dataset + '_phone.mlf')
+		mlf_phone_with_sp = os.path.join(label_dir, dataset + '_phone_with_sp.mlf')
+
+		print(">>> generating a word level mlf file for {}...".format(dataset))
+		chtk.label2mlf(label_dir_, mlf_word)
+
+		print(">>> generating a phone level mlf file for {}...".format(dataset))
+		chtk.mlf_word2phone(mlf_phone, mlf_word, with_sp=False)
+		chtk.mlf_word2phone(mlf_phone_with_sp, mlf_word, with_sp=True)
+		
+	print("elapsed time: {}".format(time.time() - timer_start))
 
 
 ## ======================= extract features =======================
 if extract_features:
 	for dataset in dataset_list:
-		print('==== {} ===='.format(dataset))
+		timer_start = time.time()
+		print('==== extract features on dataset {} ===='.format(dataset))
+
+		wav_dir_	 = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
+		label_dir_   = os.path.join(label_dir, dataset)
+		feature_dir_ = os.path.join(feature_dir, dataset)
+		fh.make_new_directory(feature_dir_, existing_dir='delete')
 
 		# a script file for HCopy 
-		print(">>> making a script file for HCopy... \n")
+		print(">>> making a script file for HCopy...")
 		hcopy_scp = tempfile.NamedTemporaryFile(mode='w', delete=False)
 		hcopy_scp.close()
 
-		# get a list of features (hcopy.scp) from the filelist in FAME! corpus
-		feature_dir_ = os.path.join(feature_dir, dataset)
-		if not os.path.exists(feature_dir_):
-			os.makedirs(feature_dir_)
+		# get a list of features (hcopy.scp) 
+		# from the filelist in FAME! corpus.
+		#fame_functions.make_hcopy_scp_from_filelist_in_fame(default.fame_dir, dataset, feature_dir_, hcopy_scp.name)
+		# from the list of label files.
+		lab_list = glob.glob(os.path.join(label_dir_, '*.lab'))
+		feature_list = [
+			os.path.join(wav_dir_, os.path.basename(lab_file).replace('.lab', '.wav')) + '\t'
+			+ os.path.join(feature_dir_, os.path.basename(lab_file).replace('.lab', '.mfc'))
+				  for lab_file in lab_list]
 
-		# extract features
-		print(">>> extracting features... \n")
-		fame_functions.make_hcopy_scp_from_filelist_in_fame(default.fame_dir, dataset, feature_dir_, hcopy_scp.name)
-		pyhtk.wav2mfc(default.config_hcopy, hcopy_scp.name)
-
-		# a script file for HCompV
-		print(">>> making a script file for HCompV... \n")
-		hcompv_scp  = os.path.join(tmp_dir, dataset + '.scp')
-		fh.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'])
-	#with open(lex_ipa_, "w", encoding="utf-8") as fout:
-	#	for word, pronunciation in zip(lexicon_ipa['word'], lexicon_ipa['pronunciation']):
-	#		# ignore nasalization and '.'
-	#		pronunciation_ = pronunciation.replace(u'ⁿ', '')
-	#		pronunciation_ = pronunciation_.replace('.', '')
-	#		pronunciation_split = convert_phone_set.split_ipa_fame(pronunciation_)
-	#		fout.write("{0}\t{1}\n".format(word, ' '.join(pronunciation_split)))
-
-	# convert each lexicon from ipa description to fame_htk phoneset.
-	#am_func.ipa2famehtk_lexicon(lex_oov, lex_oov_htk)
-	#am_func.ipa2famehtk_lexicon(lex_asr, lex_asr_htk)
-
-	# 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)
-
-
-## ======================= check if all the phones are successfully converted =======================
-if check_lexicon:
-	print("==== check if all the phones are successfully converted. ====\n")
-
-	# the phones used in the lexicon.
-	phonelist_asr = am_func.get_phonelist(lex_asr)
-	phonelist_oov = am_func.get_phonelist(lex_oov)
-	phonelist_htk = am_func.get_phonelist(lex_htk)
-
-	phonelist = phonelist_asr.union(phonelist_oov)
-
-	# the lines which include a specific phone.
-	lines = am_func.find_phone(lex_asr, 'g')
-
-	# statistics over the lexicon
-	lexicon_htk = pd.read_table(lex_htk, names=['word', 'pronunciation'])
-	pronunciation = lexicon_htk['pronunciation']
-	phones_all = []
-	for word in pronunciation:
-		phones_all = phones_all + word.split()
-	c = Counter(phones_all)
-
-
-## ======================= 
-## manually make changes to the pronunciation dictionary and save it as lex.htk 
-## =======================
-# (1) Replace all tabs with single space;
-# (2) Put a '\' before any dictionary entry beginning with single quote 
-#http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
-
-
-## ======================= make label file =======================
-if make_mlf:
-	print("==== make mlf ====\n")
-
-	print("generating word level transcription...\n")
-	for dataset in dataset_list:
-		hcompv_scp  = output_dir + '\\scp\\' + dataset + '.scp'
-		hcompv_scp2 = output_dir + '\\scp\\' + dataset + '_all_words_in_lexicon.scp'
-		script_list = FAME_dir + '\\data\\' + dataset + '\\text'
-		mlf_word	= output_dir + '\\label\\' + dataset + '_word.mlf'
-		mlf_phone   = output_dir + '\\label\\' + dataset + '_phone.mlf'
-
-		# lexicon
-		lexicon_htk = pd.read_table(lex_htk, names=['word', 'pronunciation'])
-
-		# list of features
-		with open(hcompv_scp) as fin:
-			features = fin.read()
-			features = features.split('\n')
-
-		# list of scripts 
-		with open(script_list, "rt", encoding="utf-8") as fin:
-			scripts = fin.read()
-			scripts = pd.Series(scripts.split('\n'))
-
-		i = 0
-		missing_words = []
-		fscp = open(hcompv_scp2, 'wt')
-		fmlf = open(mlf_word, "wt", encoding="utf-8")
-		fmlf.write("#!MLF!#\n")
-		feature_nr = 1
-		for feature in features:
-			sys.stdout.write("\r%d/%d" % (feature_nr, len(features)))
-			sys.stdout.flush()
-			feature_nr += 1
-			file_basename = os.path.basename(feature).replace('.mfc', '')
-
-			# get words from scripts.
-			try:
-				script = scripts[scripts.str.contains(file_basename)]
-			except IndexError:
-				script = []
-
-			if len(script) != 0:
-				script_id  = script.index[0]
-				script_txt = script.get(script_id)
-				script_words = script_txt.split(' ')
-				del script_words[0]
-
-				# check if all words can be found in the lexicon.
-				SCRIPT_WORDS = []
-				script_prons = []
-				is_in_lexicon = 1
-				for word in script_words:
-					WORD = word.upper()
-					SCRIPT_WORDS.append(WORD)
-					extracted = lexicon_htk[lexicon_htk['word']==WORD]
-					if len(extracted) == 0:
-						missing_words.append(word)
-					script_prons.append(extracted)
-					is_in_lexicon *= len(extracted)
-
-				# if all pronunciations are found in the lexicon, update scp and mlf files.
-				if is_in_lexicon:
-					# add the feature filename into the .scp file.
-					fscp.write("{}\n".format(feature))
-					i += 1
-
-					# add the words to the mlf file.
-					fmlf.write('\"*/{}.lab\"\n'.format(file_basename))
-					#fmlf.write('{}'.format('\n'.join(SCRIPT_WORDS)))
-					for word_ in SCRIPT_WORDS:
-						if word_[0] == '\'':
-							word_ = '\\' + word_
-						fmlf.write('{}\n'.format(word_))
-					fmlf.write('.\n')
-		print("\n{0} has {1} samples.\n".format(dataset, i))
-		np.save(output_dir + '\\missing_words' + '_' + dataset + '.npy', missing_words)
-
-		fscp.close()
-		fmlf.close()
-
-
-		## generate phone level transcription 
-		print("generating phone level transcription...\n")
-		mkphones = output_dir + '\\label\\mkphones0.txt'
-		subprocessStr = r"HLEd -l * -d " + lex_htk_ + ' -i ' + mlf_phone + ' ' + mkphones + ' ' + mlf_word
-		subprocess.call(subprocessStr, shell=True)
-	
-
-## ======================= combined scps and mlfs =======================
-if combine_files:
-	print("==== combine scps and mlfs ====\n")
-
-	fscp = open(hcompv_scp, 'wt')
-	fmlf = open(combined_mlf, 'wt')
-
-	for dataset in dataset_list:
-		fmlf.write("#!MLF!#\n")
-		for dataset in dataset_list:
-			each_mlf = output_dir + '\\label\\' + dataset + '_phone.mlf'
-			each_scp = output_dir + '\\scp\\' + dataset + '_all_words_in_lexicon.scp'
+		#if os.path.exists(empty_mfc_file):
+		#	os.remove(empty_mfc_file)
+		with open(hcopy_scp.name, 'wb') as f:
+			f.write(bytes('\n'.join(feature_list), 'ascii'))
 		
-		with open(each_mlf, 'r') as fin:
-			lines = fin.read()
-			lines = lines.split('\n')
-		fmlf.write('\n'.join(lines[1:]))
+		# extract features.
+		print(">>> extracting features on {}...".format(dataset))
+		chtk.wav2mfc(hcopy_scp.name)
+		os.remove(hcopy_scp.name)
 
-		with open(each_scp, 'r') as fin:
-			lines = fin.read()
-		fscp.write(lines)
+		# make hcompv.scp.
+		print(">>> making a script file for {}...".format(dataset))
+		listdir    = glob.glob(os.path.join(label_dir_, '*.dic'))
+		mfc_list   = [filename.replace(label_dir_, feature_dir_).replace('.dic', '.mfc') for filename in listdir]
+		hcompv_scp = os.path.join(tmp_dir, dataset + '.scp')
+		with open(hcompv_scp, 'wb') as f:
+			f.write(bytes('\n'.join(mfc_list) + '\n', 'ascii'))
 
-	fscp.close()
-	fmlf.close()
+		print(">>> extracting features on stimmen...")
+		chtk.wav2mfc(os.path.join(htk_stimmen_dir, 'hcopy.scp'))
+
+		print("elapsed time: {}".format(time.time() - timer_start))
 
 
 ## ======================= flat start monophones =======================
-if flat_start:	
-	subprocessStr = 'HCompV -T 1 -C ' + config_train + ' -m -v 0.01 -S ' + hcompv_scp + ' -M ' + model0_dir + ' ' + proto_init
-	subprocess.call(subprocessStr, shell=True)
+if combine_all:
+	# script files.
+	fh.concatenate(
+		os.path.join(tmp_dir, 'devel.scp'),
+		os.path.join(tmp_dir, 'test.scp'),
+		hcompv_scp_train
+		)
+	fh.concatenate(
+		hcompv_scp_train,
+		os.path.join(tmp_dir, 'train.scp'),
+		hcompv_scp_train
+		)
+
+	# phone level mlfs.
+	fh.concatenate(
+		os.path.join(label_dir, 'devel_phone.mlf'),
+		os.path.join(label_dir, 'test_phone.mlf'),
+		mlf_file_train
+		)
+	fh.concatenate(
+		mlf_file_train,
+		os.path.join(label_dir, 'train_phone.mlf'),
+		mlf_file_train
+		)
+
+	# phone level mlfs with sp.
+	fh.concatenate(
+		os.path.join(label_dir, 'devel_phone_with_sp.mlf'),
+		os.path.join(label_dir, 'test_phone_with_sp.mlf'),
+		mlf_file_train_with_sp
+		)
+	fh.concatenate(
+		mlf_file_train_with_sp,
+		os.path.join(label_dir, 'train_phone_with_sp.mlf'),
+		mlf_file_train_with_sp
+		)
+
+
+	# word level mlfs.
+	fh.concatenate(
+		os.path.join(label_dir, 'devel_word.mlf'),
+		os.path.join(label_dir, 'test_word.mlf'),
+		mlf_file_train_word
+		)
+	fh.concatenate(
+		mlf_file_train_word,
+		os.path.join(label_dir, 'train_word.mlf'),
+		mlf_file_train_word
+		)
+
+
+## ======================= flat start monophones =======================
+if flat_start:
+	timer_start = time.time()
+	print('==== flat start ====')
+	fh.make_new_directory(model_mono0_dir, existing_dir='leave')
+
+	chtk.flat_start(hcompv_scp_train, model_mono0_dir)
+
+	# make macros.
+	vFloors = os.path.join(model_mono0_dir, 'vFloors')
+	if os.path.exists(vFloors):
+		chtk.make_macros(vFloors)
 
 	# allocate mean & variance to all phones in the phone list
-	subprocessStr = 'perl ' + mkhmmdefs_pl + ' ' + model0_dir + '\\proto38' + ' ' + phonelist + ' > ' + model0_dir + '\\' + hmmdefs_name 
-	subprocess.call(subprocessStr, shell=True)
-
-
-## ======================= estimate monophones =======================
-if train_model:
-	iter_num_max = 3
-	for mix_num in [128, 256, 512, 1024]:
-		for iter_num in range(1, iter_num_max+1):
-			print("===== mix{}, iter{} =====".format(mix_num, iter_num))
-			iter_num_pre = iter_num - 1
-			modelN_dir = model_dir + '\\hmm' + str(mix_num) + '-' + str(iter_num)
-			if not os.path.exists(modelN_dir):
-				os.makedirs(modelN_dir)
-
-			if iter_num == 1 and mix_num == 1:
-				modelN_dir_pre = model0_dir
-			else:
-				modelN_dir_pre = model_dir + '\\hmm' + str(mix_num) + '-' + str(iter_num_pre)
-		
-			## re-estimation
-			subprocessStr = 'HERest -T 1 -C ' + config_train + ' -v 0.01 -I ' + combined_mlf + ' -H ' + modelN_dir_pre + '\\' + hmmdefs_name + ' -M ' + modelN_dir + ' ' + phonelist + ' -S ' + hcompv_scp
-			subprocess.call(subprocessStr, shell=True)
-
-		mix_num_next = mix_num * 2
-		modelN_dir_next = model_dir + '\\hmm' + str(mix_num_next) + '-0'
-		if not os.path.exists(modelN_dir_next):
-			os.makedirs(modelN_dir_next)
+	print('>>> allocating mean & variance to all phones in the phone list...')
+	chtk.make_hmmdefs(model_mono0_dir)
 	
-		header_file = modelN_dir + '\\mix' + str(mix_num_next) + '.hed'
-		with open(header_file, 'w') as fout:
-			fout.write("MU %d {*.state[2-4].mix}" % (mix_num_next))
+	print("elapsed time: {}".format(time.time() - timer_start))
 
-		subprocessStr =	'HHEd -T 1 -H ' + modelN_dir + '\\' + hmmdefs_name + ' -M ' + modelN_dir_next + ' ' + header_file + ' ' + phonelist
+
+## ======================= train model without short pause =======================
+if train_monophone_without_sp:
+	print('==== train monophone without sp ====')
+	
+	timer_start = time.time()
+	niter = chtk.re_estimation_until_saturated(
+		model_mono1_dir,
+		model_mono0_dir, improvement_threshold, hcompv_scp_train, 
+		os.path.join(htk_stimmen_dir, 'mfc'), 
+		'mfc', 
+		os.path.join(htk_stimmen_dir, 'word_lattice.ltc'), 
+		mlf_file=mlf_file_train, 
+		lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic')
+		)
+
+	print("elapsed time: {}".format(time.time() - timer_start))
+
+
+## ======================= adding sp to the model =======================
+if add_sp:
+	print('==== adding sp to the model ====')
+	# reference:
+	# http://www.f.waseda.jp/yusukekondo/htk.html#flat_start_estimation
+	timer_start = time.time()
+
+	# make model with sp.
+	print('>>> adding sp state to the last model in the previous step...')
+	fh.make_new_directory(model_mono1sp_dir, existing_dir='leave')
+	niter = chtk.get_niter_max(model_mono1_dir)
+	modeln_dir_pre = os.path.join(model_mono1_dir, 'iter'+str(niter))
+	modeln_dir	   = os.path.join(model_mono1sp_dir, 'iter0')
+
+	chtk.add_sp(modeln_dir_pre, modeln_dir)
+	
+	print('>>> re-estimation...')
+	niter = chtk.re_estimation_until_saturated(
+		model_mono1sp_dir, modeln_dir, improvement_threshold, hcompv_scp_train, 
+		os.path.join(htk_stimmen_dir, 'mfc'), 
+		'mfc', 
+		os.path.join(htk_stimmen_dir, 'word_lattice.ltc'), 
+		mlf_file=mlf_file_train_with_sp, 
+		lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'), 
+		model_type='monophone_with_sp'
+		)
+	print("elapsed time: {}".format(time.time() - timer_start))
+	
+
+## ======================= train model with re-aligned mlf =======================
+if train_monophone_with_re_aligned_mlf:
+	print('==== traina monophone with re-aligned mlf ====')
+	timer_start = time.time()
+
+	print('>>> re-aligning the training data... ')
+	niter = chtk.get_niter_max(model_mono1sp_dir)
+	modeln_dir = os.path.join(model_mono1sp_dir, 'iter'+str(niter))
+	chtk.make_aligned_label(
+		os.path.join(modeln_dir, 'macros'),
+		os.path.join(modeln_dir, 'hmmdefs'), 
+		mlf_file_train_aligned, 		
+		mlf_file_train_word, 
+		hcompv_scp_train)
+	chtk.fix_mlf(mlf_file_train_aligned)
+
+	print('>>> updating the script file... ')
+	chtk.update_script_file(
+		mlf_file_train_aligned, 
+		mlf_file_train_with_sp, 
+		hcompv_scp_train, 
+		hcompv_scp_train_updated)
+
+	print('>>> re-estimation... ')
+	timer_start = time.time()
+	fh.make_new_directory(model_mono1sp2_dir, existing_dir='leave')
+	niter = chtk.get_niter_max(model_mono1sp_dir)
+	niter = chtk.re_estimation_until_saturated(
+		model_mono1sp2_dir, 
+		os.path.join(model_mono1sp_dir, 'iter'+str(niter)), 
+		improvement_threshold, 
+		hcompv_scp_train_updated, 
+		os.path.join(htk_stimmen_dir, 'mfc'), 
+		'mfc', 
+		os.path.join(htk_stimmen_dir, 'word_lattice.ltc'), 
+		mlf_file=mlf_file_train_aligned, 
+		lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'), 
+		model_type='monophone_with_sp'
+		)
+	print("elapsed time: {}".format(time.time() - timer_start))
+
+
+## ======================= increase mixture =======================
+if increase_mixture:
+	print('==== increase mixture ====')
+	timer_start = time.time()
+	for nmix in [2, 4, 8, 16]:
+		if nmix == 2:
+			modeln_dir_ = model_mono1sp2_dir
+		else:
+			modeln_dir_ = os.path.join(model_dir, 'mono'+str(nmix_))
+		modeln_dir	= os.path.join(model_dir, 'mono'+str(nmix))
+
+		print('mixture: {}'.format(nmix))
+		fh.make_new_directory(modeln_dir, existing_dir='delete')	
+		niter = chtk.get_niter_max(modeln_dir_)
+		chtk.increase_mixture(
+			os.path.join(modeln_dir_, 'iter'+str(niter), 'hmmdefs'), 
+			nmix, 
+			os.path.join(modeln_dir, 'iter0'), 
+			model_type='monophone_with_sp')
+		shutil.copy2(os.path.join(modeln_dir_, 'iter'+str(niter), 'macros'), 
+				  os.path.join(modeln_dir, 'iter0', 'macros'))
+
+		#improvement_threshold = -10
+		niter = chtk.re_estimation_until_saturated(
+			modeln_dir, 
+			os.path.join(modeln_dir_, 'iter0'), 
+			improvement_threshold, 
+			hcompv_scp_train_updated, 
+			os.path.join(htk_stimmen_dir, 'mfc'), 
+			'mfc', 
+			os.path.join(htk_stimmen_dir, 'word_lattice.ltc'), 
+			mlf_file=mlf_file_train_aligned, 
+			lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'), 
+			model_type='monophone_with_sp'
+			)
+		nmix_ = nmix
+
+	print("elapsed time: {}".format(time.time() - timer_start))
+
+
+## ======================= train triphone =======================
+print('>>> making triphone list... ')
+chtk.make_triphonelist( 
+	mlf_file_train_aligned,
+	triphone_mlf)
+
+if train_triphone:
+	print('==== train triphone model ====')
+	timer_start = time.time()
+
+	print('>>> init triphone model... ')
+	niter = chtk.get_niter_max(model_mono1sp2_dir)
+	fh.make_new_directory(os.path.join(model_tri1_dir, 'iter0'), existing_dir='leave')
+	chtk.init_triphone(
+		os.path.join(model_mono1sp2_dir, 'iter'+str(niter)),
+		os.path.join(model_tri1_dir, 'iter0')
+		)
+
+	print('>>> re-estimation... ')
+	## I wanted to train until satulated:
+	#niter = chtk.re_estimation_until_saturated(
+	#	model_tri1_dir, 
+	#	os.path.join(model_tri1_dir, 'iter0'), 
+	#	improvement_threshold, 
+	#	hcompv_scp_train_updated, 
+	#	os.path.join(htk_stimmen_dir, 'mfc'), 
+	#	'mfc', 
+	#	os.path.join(htk_stimmen_dir, 'word_lattice.ltc'), 
+	#	mlf_file=triphone_mlf, 
+	#	lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'), 
+	#	model_type='triphone'
+	#	)
+	#
+	# but because the data size is limited, some triphone cannot be trained and received the error:
+	#   ERROR [+8231]  GetHCIModel: Cannot find hmm [i:-]r[+???]
+	# therefore only two times re-estimation is performed.
+	output_dir = model_tri1_dir
+	for niter in range(1, 4):
+		hmm_n = 'iter' + str(niter)
+		hmm_n_pre = 'iter' + str(niter-1)
+		_modeln_dir	    = os.path.join(output_dir, hmm_n)
+		_modeln_dir_pre = os.path.join(output_dir, hmm_n_pre) 
 		
-		subprocess.call(subprocessStr, shell=True)
+		fh.make_new_directory(_modeln_dir, 'leave')
+		chtk.re_estimation(
+			os.path.join(_modeln_dir_pre, 'hmmdefs'), 
+			_modeln_dir,
+			hcompv_scp_train_updated,
+			mlf_file=triphone_mlf,
+			macros=os.path.join(_modeln_dir_pre, 'macros'),
+			model_type='triphone')
 
+	print("elapsed time: {}".format(time.time() - timer_start))
+
+
+## ======================= train tied-state triphones =======================
+if train_triphone_tied:
+	print('==== train tied-state triphones ====')
+	timer_start = time.time()
+
+	print('>>> making lexicon for triphone... ')
+	chtk.make_lexicon_triphone(phonelist_full_txt, lexicon_htk_triphone)
+	chtk.combine_phonelists(phonelist_full_txt)
+
+	print('>>> making a tree header... ')
+	fame_phonetics.make_quests_hed(quests_hed)
+	stats = os.path.join(r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\model\tri1\iter3', 'stats')
+	chtk.make_tree_header(tree_hed, quests_hed, stats, config_dir)
+
+	print('>>> init triphone model... ')
+	niter = chtk.get_niter_max(model_tri1_dir)
+	fh.make_new_directory(os.path.join(model_tri1tied_dir, 'iter0'), existing_dir='leave')
+	chtk.init_triphone(
+		os.path.join(model_tri1_dir, 'iter'+str(niter)),
+		os.path.join(model_tri1tied_dir, 'iter0'),
+		tied=True)
+
+	# I wanted to train until satulated:
+	#niter = chtk.re_estimation_until_saturated(
+	#	model_tri1tied_dir, 
+	#	os.path.join(model_tri1tied_dir, 'iter0'), 
+	#	improvement_threshold, 
+	#	hcompv_scp_train_updated, 
+	#	os.path.join(htk_stimmen_dir, 'mfc'), 
+	#	'mfc', 
+	#	os.path.join(htk_stimmen_dir, 'word_lattice.ltc'), 
+	#	mlf_file=triphone_mlf, 
+	#	lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'), 
+	#	model_type='triphone'
+	#	)
+	#
+	# but because the data size is limited, some triphone cannot be trained and received the error:
+	#   ERROR [+8231]  GetHCIModel: Cannot find hmm [i:-]r[+???]
+	# therefore only 3 times re-estimation is performed.
+	output_dir = model_tri1tied_dir
+	for niter in range(1, 4):
+		hmm_n = 'iter' + str(niter)
+		hmm_n_pre = 'iter' + str(niter-1)
+		_modeln_dir	    = os.path.join(output_dir, hmm_n)
+		_modeln_dir_pre = os.path.join(output_dir, hmm_n_pre) 
+		
+		fh.make_new_directory(_modeln_dir, 'leave')
+		chtk.re_estimation(
+			os.path.join(_modeln_dir_pre, 'hmmdefs'), 
+			_modeln_dir,
+			hcompv_scp_train_updated,
+			mlf_file=triphone_mlf,
+			macros=os.path.join(_modeln_dir_pre, 'macros'),
+			model_type='triphone')
+
+	print("elapsed time: {}".format(time.time() - timer_start))

acoustic_model/fame_test.py

@@ -0,0 +1,138 @@
+import sys
+import os
+os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
+from collections import Counter
+import time
+import re
+
+import numpy as np
+import pandas as pd
+
+import fame_functions
+import defaultfiles as default
+sys.path.append(default.toolbox_dir)
+from phoneset import fame_ipa, fame_asr
+import convert_phoneset
+
+lexicon_dir = os.path.join(default.fame_dir, 'lexicon') 
+lexicon_ipa = os.path.join(lexicon_dir, 'lex.ipa')
+lexicon_asr = os.path.join(lexicon_dir, 'lex.asr')
+lexicon_htk = os.path.join(default.htk_dir, 'lexicon', 'lex.htk')
+
+## check if all the phones in lexicon.ipa are in fame_ipa.py.
+#timer_start = time.time()
+#phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_ipa, phoneset='ipa')	
+#phoneset_py = fame_ipa.phoneset
+#print("phones which is in lexicon.ipa but not in fame_ipa.py:\n{}".format(
+#	set(phoneset_lex) - set(phoneset_py)))
+#print("elapsed time: {}".format(time.time() - timer_start))
+
+# check which word has the phone. 
+#timer_start = time.time()
+#extracted = find_phone(lexicon_ipa, 'ⁿ')
+#print("elapsed time: {}".format(time.time() - timer_start))
+
+
+## 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 0:
+	timer_start = time.time()
+	translation_key_ipa2asr, phone_unknown = fame_functions.get_translation_key(lexicon_ipa, lexicon_asr)
+	print("elapsed time: {}".format(time.time() - timer_start))
+
+	np.save(os.path.join('phoneset', 'output_get_translation_key_translation_key.npy'), translation_key_ipa2asr)
+	np.save(os.path.join('phoneset', 'output_get_translation_key_phone_unknown.npy'), phone_unknown)
+else:
+	translation_key_ipa2asr = np.load(os.path.join('phoneset', 'output_get_translation_key_translation_key.npy')).item()
+	phone_unknown   = np.load(os.path.join('phoneset', 'output_get_translation_key_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_ipa.multi_character_phones)
+#		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_ipa2asr[ipa_] = asr_
+#					phone_unknown.remove(ipa_)
+
+translation_key_ipa2asr['ə:'] = 'ə'
+translation_key_ipa2asr['r.'] = 'r'
+translation_key_ipa2asr['r:'] = 'r'
+# added for stimmen.
+translation_key_ipa2asr['ɪ:'] = 'ɪ:'
+translation_key_ipa2asr['y:'] = 'y'
+
+np.save(os.path.join('phoneset', 'fame_ipa2asr.npy'), translation_key_ipa2asr)
+
+
+## check if all the phones in lexicon.asr are in translation_key_ipa2asr.
+#timer_start = time.time()
+#phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_asr, phoneset='asr')
+#phoneset_lex.remove("")
+#phoneset_asr = list(set(translation_key_ipa2asr.values()))
+#print("phones which is in lexicon.asr but not in the translation_key_ipa2asr:\n{}".format(
+#	set(phoneset_lex) - set(phoneset_asr)))
+#print("elapsed time: {}".format(time.time() - timer_start))
+
+
+## check if all the phones in lexicon.htk are in fame_asr.py.
+#timer_start = time.time()
+#phoneset_htk = fame_asr.phoneset_htk
+#phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_htk)
+#phoneset_lex.remove('')
+#print("phones which is in lexicon.htk but not in the fame_asr.py are:\n{}".format(
+#	set(phoneset_htk) - set(phoneset_lex)))
+#print("elapsed time: {}".format(time.time() - timer_start))
+
+## statistics over the lexicon
+#lex_htk = fame_functions.load_lexicon(lexicon_htk)
+#phones_all = (' '.join(lex_htk['pronunciation'])).split(' ')
+#c = Counter(phones_all)
+
+#lexicon_out = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\lexicon\lex.htk2'
+#for i in lex_htk[lex_htk['word'].str.startswith('\'')].index.values:
+#	lex_htk.iat[i, 0] = lex_htk.iat[i, 0].replace('\'', '\\\'')
+## to_csv does not work with space seperator. therefore all tabs should manually be replaced.
+##lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
+#lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
+
+
+## check which letters are not coded in ascii. 
+#print('asr phones which cannot be coded in ascii:\n')
+#for i in fame_asr.phoneset_short:
+#	try:
+#		i_encoded = i.encode("ascii")
+#		#print("{0} --> {1}".format(i, i.encode("ascii")))
+#	except UnicodeEncodeError:
+#		print(">>> {}".format(i))
+
+#print("letters in the scripts which is not coded in ascii:\n")
+#for dataset in ['train', 'devel', 'test']:
+#	timer_start = time.time()
+
+#	script_list = os.path.join(default.fame_dir, 'data', dataset, 'text')
+#	with open(script_list, "rt", encoding="utf-8") as fin:
+#		scripts = fin.read().split('\n')
+
+#	for line in scripts:
+#		sentence  = ' '.join(line.split(' ')[1:])
+#		sentence_htk = fame_functions.word2htk(sentence)
+
+#		#if len(re.findall(r'[âêôûč\'àéèúćäëïöü]', sentence))==0:
+#		try:
+#			sentence_htk = bytes(sentence_htk, 'ascii')
+#		except UnicodeEncodeError:
+#			print(sentence)
+#			print(sentence_htk)
+

acoustic_model/htk_vs_kaldi.py

@@ -1,487 +1,587 @@
 import os
 os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
-
 import sys
-import csv
-import subprocess
-from collections import Counter
-import re
+
+#import csv
+#import subprocess
+#from collections import Counter
+#import re
+import shutil
+import glob
 
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-from sklearn.metrics import confusion_matrix
+from collections import Counter
+#import matplotlib.pyplot as plt
+#from sklearn.metrics import confusion_matrix
 
-import acoustic_model_functions as am_func
-import convert_xsampa2ipa
+#import acoustic_model_functions as am_func
+#import convert_xsampa2ipa
 import defaultfiles as default
 
-from forced_alignment import pyhtk
+#from forced_alignment import pyhtk
+#sys.path.append(default.forced_alignment_module_dir)
+#from forced_alignment import convert_phone_set
+#import acoustic_model_functions as am_func
+import convert_xsampa2ipa
+import stimmen_functions
+import fame_functions
+import convert_phoneset
+from phoneset import fame_ipa, fame_asr
+sys.path.append(default.toolbox_dir)
+import file_handling as fh
+from htk import pyhtk
 
 
 ## ======================= user define =======================
-excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
-data_dir   = os.path.join(default.experiments_dir, 'stimmen', 'data')
+#excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
+#data_dir   = os.path.join(default.experiments_dir, 'stimmen', 'data')
 
-wav_dir	= r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen' # 16k
+#wav_dir	= r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen' # 16k
 
-acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
-htk_dict_dir       = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
-fa_dir             = os.path.join(default.experiments_dir, 'stimmen', 'FA_44k')
-result_dir         = os.path.join(default.experiments_dir, 'stimmen', 'result')
+#acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
+#htk_dict_dir       = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
+#fa_dir             = os.path.join(default.experiments_dir, 'stimmen', 'FA_44k')
+#result_dir         = os.path.join(default.experiments_dir, 'stimmen', 'result')
 
-kaldi_data_dir = os.path.join(default.kaldi_dir, 'data', 'alignme') 
-kaldi_dict_dir = os.path.join(default.kaldi_dir, 'data', 'local', 'dict')
-lexicon_txt    = os.path.join(kaldi_dict_dir, 'lexicon.txt')
+#kaldi_data_dir = os.path.join(default.kaldi_dir, 'data', 'alignme') 
+#kaldi_dict_dir = os.path.join(default.kaldi_dir, 'data', 'local', 'dict')
+#lexicon_txt    = os.path.join(kaldi_dict_dir, 'lexicon.txt')
 
 #lex_asr	 = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
 #lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
 
-
 # procedure
-make_htk_dict_files = 0
-do_forced_alignment_htk = 0
-eval_forced_alignment_htk = 0
-make_kaldi_data_files = 0
-make_kaldi_lexicon_txt = 0
-load_forced_alignment_kaldi = 1
-eval_forced_alignment_kaldi = 1
+make_dic_file = 0
+make_HTK_files = 0
+extract_features = 0
+#make_htk_dict_files = 0
+#do_forced_alignment_htk = 0
+#eval_forced_alignment_htk = 0
+make_kaldi_files = 0
+#make_kaldi_lexicon_txt = 0
+#load_forced_alignment_kaldi = 1
+#eval_forced_alignment_kaldi = 1
+
+#sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
+#from forced_alignment import convert_phone_set
+#from forced_alignment import pyhtk
+
+#sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
+#from evaluation import plot_confusion_matrix
+
+## HTK related files.
+config_dir	 = os.path.join(default.htk_dir, 'config')
+model_dir	 = os.path.join(default.htk_dir, 'model')
+feature_dir  = os.path.join(default.htk_dir, 'mfc', 'stimmen')
+
+config_hcopy = os.path.join(config_dir, 'config.HCopy')
+
+# files to be made.
+lattice_file  = os.path.join(config_dir, 'stimmen.ltc')
+phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
+stimmen_dic = os.path.join(default.htk_dir, 'lexicon', 'stimmen_recognition.dic')
+hcopy_scp   = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hcopy.scp')
+hvite_scp   = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hvite.scp')
+hresult_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_result.scp')
+
+
+## Kaldi related files.
+kaldi_data_dir = os.path.join(default.kaldi_dir, 'data')
+
+# files to be made.
+wav_scp    = os.path.join(kaldi_data_dir, 'test', 'wav.scp')
+text_file  = os.path.join(kaldi_data_dir, 'test', 'text')
+utt2spk    = os.path.join(kaldi_data_dir, 'test', 'utt2spk')
+corpus_txt = os.path.join(kaldi_data_dir, 'local', 'corpus.txt')
+lexicon_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'lexicon.txt')
+nonsilence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'nonsilence_phones.txt')
+silence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'silence_phones.txt')
+optional_silence_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'optional_silence.txt')
+
+
+## ======================= load test data ======================
+stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
+
+df = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)
+df = stimmen_functions.add_row_asr(df)
+df = stimmen_functions.add_row_htk(df)
+
+word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
+word_list = sorted(word_list)
+
+
+## ======================= make dic file to check pronunciation variants ======================
+# dic file should be manually modified depends on the task - recognition / forced-alignemnt.
+if make_dic_file:
+	# for HTK.
+	with open(stimmen_dic, mode='wb') as f:
+		for word in word_list:
+			df_ = df[df['word']==word]
+			pronunciations = list(np.unique(df_['htk']))
+			pronunciations_ = [word.upper() + ' sil ' + ' '.join(convert_phoneset.split_word(
+				htk, fame_asr.multi_character_phones_htk)) + ' sil'
+				for htk in pronunciations]
+			f.write(bytes('\n'.join(pronunciations_) + '\n', 'ascii'))
+			f.write(bytes('SILENCE sil\n', 'ascii'))
+
+	# for Kaldi.
+	fh.make_new_directory(os.path.join(kaldi_data_dir, 'local', 'dict'))
+	with open(lexicon_txt, mode='wb') as f:
+		f.write(bytes('!SIL sil\n', 'utf-8'))		
+		f.write(bytes('<UNK> spn\n', 'utf-8'))
+		for word in word_list:
+			df_ = df[df['word']==word]
+			pronunciations = list(np.unique(df_['asr']))
+			pronunciations_ = [word.lower() + ' ' + ' '.join(convert_phoneset.split_word(
+				asr, fame_asr.multi_character_phones))
+				for asr in pronunciations]
+			f.write(bytes('\n'.join(pronunciations_) + '\n', 'utf-8'))
+
+
+## ======================= test data for recognition ======================
+# only target pronunciation variants.
+df_rec = pd.DataFrame(index=[], columns=list(df.keys()))
+for word in word_list:
+	variants = [htk.replace(' ', '') 
+				   for htk in stimmen_functions.load_pronunciations(word.upper(), stimmen_dic)]
+	df_ = df[df['word'] == word]
+	for index, row in df_.iterrows():
+		if row['htk'] in variants:
+			df_rec = df_rec.append(row, ignore_index=True)
+
+
+## ======================= make files required for HTK ======================
+if make_HTK_files:
+	# make a word lattice file.
+	pyhtk.create_word_lattice_file(
+		os.path.join(config_dir, 'stimmen.net'), 
+		lattice_file)
+
+	# extract features.
+	with open(hcopy_scp, 'wb') as f:
+		filelist = [os.path.join(stimmen_test_dir, filename) + '\t' 
+	+ os.path.join(feature_dir, os.path.basename(filename).replace('.wav', '.mfc')) 
+				for filename in df['filename']]
+		f.write(bytes('\n'.join(filelist), 'ascii'))
+	pyhtk.wav2mfc(config_hcopy, hcopy_scp)
+
+	# make label files.
+	for index, row in df.iterrows():
+		filename = row['filename'].replace('.wav', '.lab')
+		label_file = os.path.join(feature_dir, filename)
+		with open(label_file, 'wb') as f:
+			label_string = 'SILENCE\n' + row['word'].upper() + '\nSILENCE\n'
+			f.write(bytes(label_string, 'ascii'))
+
+
+## ======================= make files required for Kaldi =======================
+if make_kaldi_files:
+	fh.make_new_directory(os.path.join(kaldi_data_dir, 'test'))
+	fh.make_new_directory(os.path.join(kaldi_data_dir, 'test', 'local'))
+	fh.make_new_directory(os.path.join(kaldi_data_dir, 'conf'))
+	
+	# remove previous files.
+	if os.path.exists(wav_scp):
+		os.remove(wav_scp)
+	if os.path.exists(text_file):
+		os.remove(text_file)
+	if os.path.exists(utt2spk):
+		os.remove(utt2spk)
+
+	f_wav_scp   = open(wav_scp, 'a', encoding="utf-8", newline='\n')
+	f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
+	f_utt2spk   = open(utt2spk, 'a', encoding="utf-8", newline='\n')
+
+	# make wav.scp, text, and utt2spk files.
+	for i, row in df_rec.iterrows():
+		filename = row['filename']
+		print('=== {0}: {1} ==='.format(i, filename))
+
+		wav_file = os.path.join(stimmen_test_dir, filename)
+		#if os.path.exists(wav_file):
+		speaker_id = 'speaker_' + str(i).zfill(4)
+		utterance_id = filename.replace('.wav', '')
+		utterance_id = utterance_id.replace(' ', '_')
+		utterance_id = speaker_id + '-' + utterance_id
+
+		# output
+		f_wav_scp.write('{0} {1}\n'.format(
+			utterance_id, 
+			wav_file.replace('c:/', '/mnt/c/').replace('\\', '/'))) # convert path to unix format.
+		f_text_file.write('{0}\t{1}\n'.format(utterance_id, df_rec['word'][i].lower()))
+		f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
+
+	f_wav_scp.close()
+	f_text_file.close()
+	f_utt2spk.close()
+
+	with open(corpus_txt, 'wb') as f:
+		f.write(bytes('\n'.join([word.lower() for word in word_list]) + '\n', 'utf-8'))
+
+	with open(nonsilence_phones_txt, 'wb') as f:
+		f.write(bytes('\n'.join(fame_asr.phoneset_short) + '\n', 'utf-8'))
+
+	with open(silence_phones_txt, 'wb') as f:
+		f.write(bytes('sil\nspn\n', 'utf-8'))
+
+	with open(optional_silence_txt, 'wb') as f:
+		f.write(bytes('sil\n', 'utf-8'))
+
+	with open(os.path.join(kaldi_data_dir, 'conf', 'decode.config'), 'wb') as f:
+		f.write(bytes('first_beam=10.0\n', 'utf-8'))
+		f.write(bytes('beam=13.0\n', 'utf-8'))
+		f.write(bytes('lattice_beam=6.0\n', 'utf-8'))
+
+	with open(os.path.join(kaldi_data_dir, 'conf', 'mfcc.conf'), 'wb') as f:
+		f.write(bytes('--use-energy=false', 'utf-8'))
+
+
+## ======================= recognition ======================
+
+listdir = glob.glob(os.path.join(feature_dir, '*.mfc'))
+with open(hvite_scp, 'wb') as f:
+	f.write(bytes('\n'.join(listdir), 'ascii'))
+
+with open(hresult_scp, 'wb') as f:
+	f.write(bytes('\n'.join(listdir).replace('.mfc', '.rec'), 'ascii'))
+
+
+# calculate result
+performance = np.zeros((1, 2))
+for niter in range(50, 60):
+	output = pyhtk.recognition(
+		os.path.join(config_dir, 'config.rec'),
+		lattice_file,
+		os.path.join(default.htk_dir, 'model', 'hmm1', 'iter' + str(niter), 'hmmdefs'),
+		stimmen_dic, phonelist_txt, hvite_scp)
+
+	output = pyhtk.calc_recognition_performance(
+		stimmen_dic, hresult_scp)
+	per_sentence, per_word = pyhtk.load_recognition_output_all(output)
+	performance_ = np.array([niter, per_sentence['accuracy']]).reshape(1, 2)
+	performance = np.r_[performance, performance_]
+	print('{0}: {1}[%]'.format(niter, per_sentence['accuracy']))
 
 
 
+	#output = run_command_with_output([
+	#	'HVite', '-T', '1', 
+	#	'-C', config_rec, 
+	#	'-w', lattice_file, 
+	#	'-H', hmm, 
+	#	dictionary_file, phonelist_txt, 
+	#	'-S', HVite_scp
+	#])
 
-## ======================= add paths =======================
-sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
-from forced_alignment import convert_phone_set
-from forced_alignment import pyhtk
-
-sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
-from evaluation import plot_confusion_matrix
-
-
-## ======================= convert phones ======================
-mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
-
-xls = pd.ExcelFile(excel_file)
-
-## check conversion
-#df = pd.read_excel(xls, 'frequency')
-#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
-#    #ipa_converted = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, xsampa_)
-#    ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
-#    if not ipa_converted == ipa:
-#        print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
-
-
-## check phones included in FAME!
-# the phones used in the lexicon.
-#phonelist = am_func.get_phonelist(lex_asr)
-
-# the lines which include a specific phone.
-#lines = am_func.find_phone(lex_asr, 'x')
-
-
-# Filename, Word, Self Xsampa
-df = pd.read_excel(xls, 'original')
-    
-ipas     = []
-famehtks = []
-for xsampa in df['Self Xsampa']:
-    if not isinstance(xsampa, float): # 'NaN'
-        # typo?
-        xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t')
-        xsampa = xsampa.replace(';', ':')
-
-        ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
-        ipa = ipa.replace('ː', ':')
-        ipa = ipa.replace(' ', '')
-        ipas.append(ipa)
-        famehtk = convert_phone_set.ipa2famehtk(ipa)
-        famehtks.append(famehtk)
-    else:
-        ipas.append('')
-        famehtks.append('')
-        
-# extract interesting cols.
-df = pd.DataFrame({'filename': df['Filename'], 
-                    'word': df['Word'], 
-                    'xsampa': df['Self Xsampa'],
-                    'ipa': pd.Series(ipas),
-                    'famehtk': pd.Series(famehtks)})
-# cleansing. 
-df = df[~df['famehtk'].isin(['/', ''])]
-
-word_list = np.unique(df['word'])
-
-
-## ======================= make dict files used for HTK. ======================
-if make_htk_dict_files:
-    output_type = 3
-    
-    for word in word_list:
-        htk_dict_file = htk_dict_dir + '\\' + word + '.dic'
-
-        # pronunciation variant of the target word.
-        pronvar_ = df['famehtk'][df['word'].str.match(word)]
-
-        # make dic file.
-        am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
-        
 
 ## ======================= forced alignment using HTK =======================
 if do_forced_alignment_htk:
-    
-    #for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
-    for hmm_num in [256, 512, 1024]:
-        hmm_num_str = str(hmm_num)
-        acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
+	
+	#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
+	for hmm_num in [256, 512, 1024]:
+		hmm_num_str = str(hmm_num)
+		acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
 
-        predictions = pd.DataFrame({'filename': [''], 
-                    'word': [''], 
-                    'xsampa': [''],
-                    'ipa': [''],
-                    'famehtk': [''],
-                    'prediction': ['']})
-        for i, filename in enumerate(df['filename']):
-            print('=== {0}/{1} ==='.format(i, len(df)))
-            if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
-                wav_file = os.path.join(wav_dir, filename)
-                if os.path.exists(wav_file):
-                    word = df['word'][i]
-                    WORD = word.upper()
-                    fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
-                    
-                    #if not os.path.exists(fa_file):
-                    # make label file.
-                    label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
-                    with open(label_file, 'w') as f:
-                        lines = f.write(WORD)
+		predictions = pd.DataFrame({'filename': [''], 
+					'word': [''], 
+					'xsampa': [''],
+					'ipa': [''],
+					'famehtk': [''],
+					'prediction': ['']})
+		for i, filename in enumerate(df['filename']):
+			print('=== {0}/{1} ==='.format(i, len(df)))
+			if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
+				wav_file = os.path.join(wav_dir, filename)
+				if os.path.exists(wav_file):
+					word = df['word'][i]
+					WORD = word.upper()
+					fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
+					
+					#if not os.path.exists(fa_file):
+					# make label file.
+					label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
+					with open(label_file, 'w') as f:
+						lines = f.write(WORD)
 
-                    htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
+					htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
 
-                    pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, 
-                                default.phonelist, acoustic_model)
-                    os.remove(label_file)
+					pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, 
+								default.phonelist, acoustic_model)
+					os.remove(label_file)
 
-                    prediction = am_func.read_fileFA(fa_file)
+					prediction = am_func.read_fileFA(fa_file)
 
-                    print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
-                else:
-                    prediction = ''
-                    print('!!!!! file not found.')
+					print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
+				else:
+					prediction = ''
+					print('!!!!! file not found.')
 
-                line = pd.Series([df['filename'][i], df['word'][i], df['xsampa'][i], df['ipa'][i], df['famehtk'][i], prediction], index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], name=i)
-                predictions = predictions.append(line)
-            else:
-                prediction = ''
-                print('!!!!! invalid entry.')
+				line = pd.Series([df['filename'][i], df['word'][i], df['xsampa'][i], df['ipa'][i], df['famehtk'][i], prediction], index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], name=i)
+				predictions = predictions.append(line)
+			else:
+				prediction = ''
+				print('!!!!! invalid entry.')
 
-        predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
+		predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
 
 
-## ======================= make files which is used for forced alignment by Kaldi =======================
-if make_kaldi_data_files:
 
-    wav_scp   = os.path.join(kaldi_data_dir, 'wav.scp')
-    text_file = os.path.join(kaldi_data_dir, 'text')
-    utt2spk   = os.path.join(kaldi_data_dir, 'utt2spk')
-
-    # remove previous files.
-    if os.path.exists(wav_scp):
-        os.remove(wav_scp)
-    if os.path.exists(text_file):
-        os.remove(text_file)
-    if os.path.exists(utt2spk):
-        os.remove(utt2spk)
-
-    f_wav_scp   = open(wav_scp, 'a', encoding="utf-8", newline='\n')
-    f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
-    f_utt2spk   = open(utt2spk, 'a', encoding="utf-8", newline='\n')
-
-    # make wav.scp, text, and utt2spk files.
-    for i in df.index:
-        filename = df['filename'][i]
-        print('=== {0}: {1} ==='.format(i, filename))
-
-        #if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
-        wav_file = os.path.join(wav_dir, filename)
-        if os.path.exists(wav_file):
-            speaker_id = 'speaker_' + str(i).zfill(4)
-            utterance_id = filename.replace('.wav', '')
-            utterance_id = utterance_id.replace(' ', '_')
-            utterance_id = speaker_id + '-' + utterance_id
-
-            # wav.scp file
-            wav_file_unix = wav_file.replace('\\', '/')
-            wav_file_unix = wav_file_unix.replace('c:/', '/mnt/c/')
-
-            f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix))
-
-            # text file
-            word = df['word'][i].lower()
-            f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
-
-            # utt2spk
-            f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
-
-    f_wav_scp.close()
-    f_text_file.close()
-    f_utt2spk.close()
 
 
 ## ======================= make lexicon txt which is used by Kaldi =======================
 if make_kaldi_lexicon_txt:
-    option_num = 6
+	option_num = 6
 
-    # remove previous file.
-    if os.path.exists(lexicon_txt):
-        os.remove(lexicon_txt)
-    lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
-    if os.path.exists(lexiconp_txt):
-        os.remove(lexiconp_txt)
-                
-    # output lexicon.txt
-    f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
-    pronvar_list_all = []
-    for word in word_list:
+	# remove previous file.
+	if os.path.exists(lexicon_txt):
+		os.remove(lexicon_txt)
+	lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
+	if os.path.exists(lexiconp_txt):
+		os.remove(lexiconp_txt)
+				
+	# output lexicon.txt
+	f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
+	pronvar_list_all = []
+	for word in word_list:
 
-        # pronunciation variant of the target word.
-        pronunciation_variants = df['ipa'][df['word'].str.match(word)]
+		# pronunciation variant of the target word.
+		pronunciation_variants = df['ipa'][df['word'].str.match(word)]
 
-        c = Counter(pronunciation_variants)
-        total_num = sum(c.values())
+		c = Counter(pronunciation_variants)
+		total_num = sum(c.values())
 
-        #with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f:
-        #    for key in c.keys():
-        #        f.write("{0},{1}\n".format(key,c[key]))
+		#with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f:
+		#    for key in c.keys():
+		#        f.write("{0},{1}\n".format(key,c[key]))
 
-        for key, value in c.most_common(option_num):
-            # make possible pronunciation variant list.
-            pronvar_list = am_func.fame_pronunciation_variant(key)
+		for key, value in c.most_common(option_num):
+			# make possible pronunciation variant list.
+			pronvar_list = am_func.fame_pronunciation_variant(key)
 
-            for pronvar_ in pronvar_list:
-                split_ipa   = convert_phone_set.split_fame_ipa(pronvar_)
-                pronvar_out = ' '.join(split_ipa) 
-                pronvar_list_all.append([word, pronvar_out])
+			for pronvar_ in pronvar_list:
+				split_ipa   = convert_phone_set.split_fame_ipa(pronvar_)
+				pronvar_out = ' '.join(split_ipa) 
+				pronvar_list_all.append([word, pronvar_out])
 
-    pronvar_list_all = np.array(pronvar_list_all)
-    pronvar_list_all = np.unique(pronvar_list_all, axis=0)
+	pronvar_list_all = np.array(pronvar_list_all)
+	pronvar_list_all = np.unique(pronvar_list_all, axis=0)
 
-    
-    # output
-    f_lexicon_txt.write('<UNK>\tSPN\n')
-    for line in pronvar_list_all:
-        f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1]))
+	
+	# output
+	f_lexicon_txt.write('<UNK>\tSPN\n')
+	for line in pronvar_list_all:
+		f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1]))
 
-    f_lexicon_txt.close()
+	f_lexicon_txt.close()
 
 
 ## ======================= load kaldi forced alignment result =======================
 if load_forced_alignment_kaldi:
-    phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
-    merged_alignment_txt = os.path.join(default.kaldi_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt')
-    
-    #filenames	   = np.load(data_dir + '\\filenames.npy')
-    #words		   = np.load(data_dir + '\\words.npy')
-    #pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy')
-    #pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy')
-    #word_list = np.unique(words)    
+	phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
+	merged_alignment_txt = os.path.join(default.kaldi_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt')
+	
+	#filenames	   = np.load(data_dir + '\\filenames.npy')
+	#words		   = np.load(data_dir + '\\words.npy')
+	#pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy')
+	#pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy')
+	#word_list = np.unique(words)    
 
-    # load the mapping between phones and ids.
-    with open(phones_txt, 'r', encoding="utf-8") as f:
-        mapping_phone2id = f.read().split('\n')
+	# load the mapping between phones and ids.
+	with open(phones_txt, 'r', encoding="utf-8") as f:
+		mapping_phone2id = f.read().split('\n')
 
-    phones = []
-    phone_ids = [] # ID of phones
-    for m in mapping_phone2id:
-        m = m.split(' ')
-        if len(m) > 1:
-            phones.append(m[0])
-            phone_ids.append(int(m[1]))
+	phones = []
+	phone_ids = [] # ID of phones
+	for m in mapping_phone2id:
+		m = m.split(' ')
+		if len(m) > 1:
+			phones.append(m[0])
+			phone_ids.append(int(m[1]))
 
 
-    # load the result of FA.
-    with open(merged_alignment_txt, 'r') as f:
-        lines = f.read()
-        lines = lines.split('\n')
+	# load the result of FA.
+	with open(merged_alignment_txt, 'r') as f:
+		lines = f.read()
+		lines = lines.split('\n')
 
-    predictions = pd.DataFrame({'filename': [''], 
-                'word': [''], 
-                'xsampa': [''],
-                'ipa': [''],
-                'famehtk': [''],
-                'prediction': ['']})
-    #fa_filenames = []
-    #fa_pronunciations = []
-    utterance_id_ = ''
-    pronunciation = []
-    for line in lines:
-        line = line.split(' ')
-        if len(line) == 5:
-            utterance_id = line[0]
-            if utterance_id == utterance_id_:
-                phone_id = int(line[4])
-                #if not phone_id == 1:
-                phone_ = phones[phone_ids.index(phone_id)]
-                phone  = re.sub(r'_[A-Z]', '', phone_)
-                if not phone == 'SIL':
-                    pronunciation.append(phone)
-            else:
-                filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_)
-                prediction = ''.join(pronunciation)
-                df_ = df[df['filename'].str.match(filename)]
-                df_idx = df_.index[0]
-                prediction_ = pd.Series([#filename,
-                                    #df_['word'][df_idx], 
-                                    #df_['xsampa'][df_idx], 
-                                    #df_['ipa'][df_idx], 
-                                    #df_['famehtk'][df_idx],
-                                    df_.iloc[0,1],
-                                    df_.iloc[0,3],
-                                    df_.iloc[0,4],
-                                    df_.iloc[0,2],
-                                    df_.iloc[0,0],
-                                    prediction], 
-                                    index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], 
-                                    name=df_idx)
-                predictions = predictions.append(prediction_)
-                #fa_filenames.append()
-                #fa_pronunciations.append(' '.join(pronunciation))
-                pronunciation = []
+	predictions = pd.DataFrame({'filename': [''], 
+				'word': [''], 
+				'xsampa': [''],
+				'ipa': [''],
+				'famehtk': [''],
+				'prediction': ['']})
+	#fa_filenames = []
+	#fa_pronunciations = []
+	utterance_id_ = ''
+	pronunciation = []
+	for line in lines:
+		line = line.split(' ')
+		if len(line) == 5:
+			utterance_id = line[0]
+			if utterance_id == utterance_id_:
+				phone_id = int(line[4])
+				#if not phone_id == 1:
+				phone_ = phones[phone_ids.index(phone_id)]
+				phone  = re.sub(r'_[A-Z]', '', phone_)
+				if not phone == 'SIL':
+					pronunciation.append(phone)
+			else:
+				filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_)
+				prediction = ''.join(pronunciation)
+				df_ = df[df['filename'].str.match(filename)]
+				df_idx = df_.index[0]
+				prediction_ = pd.Series([#filename,
+									#df_['word'][df_idx], 
+									#df_['xsampa'][df_idx], 
+									#df_['ipa'][df_idx], 
+									#df_['famehtk'][df_idx],
+									df_.iloc[0,1],
+									df_.iloc[0,3],
+									df_.iloc[0,4],
+									df_.iloc[0,2],
+									df_.iloc[0,0],
+									prediction], 
+									index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], 
+									name=df_idx)
+				predictions = predictions.append(prediction_)
+				#fa_filenames.append()
+				#fa_pronunciations.append(' '.join(pronunciation))
+				pronunciation = []
 
-            utterance_id_ = utterance_id
-    predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
+			utterance_id_ = utterance_id
+	predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
 
 
 ## ======================= evaluate the result of forced alignment =======================
 if eval_forced_alignment_htk:
-    htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
+	htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
 
-    compare_hmm_num = 1
+	compare_hmm_num = 1
 
-    if compare_hmm_num:
-        f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
-        f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n")
+	if compare_hmm_num:
+		f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
+		f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n")
 
-    for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
-    #for hmm_num in [256]:
-        hmm_num_str = str(hmm_num)
-        if compare_hmm_num:
-            f_result.write("{},".format(hmm_num_str))
+	for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
+	#for hmm_num in [256]:
+		hmm_num_str = str(hmm_num)
+		if compare_hmm_num:
+			f_result.write("{},".format(hmm_num_str))
 
-        #match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
-        #prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
-        #prediction = pd.Series(prediction, index=df.index, name='prediction')
-        #result = pd.concat([df, prediction], axis=1)
-        result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
+		#match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
+		#prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
+		#prediction = pd.Series(prediction, index=df.index, name='prediction')
+		#result = pd.concat([df, prediction], axis=1)
+		result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
 
 
-        # load pronunciation variants
-        for word in word_list:
-            htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
-            with open(htk_dict_file, 'r') as f:
-                lines = f.read().split('\n')[:-1]
-                pronunciation_variants = [line.split('\t')[1] for line in lines]
+		# load pronunciation variants
+		for word in word_list:
+			htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
+			with open(htk_dict_file, 'r') as f:
+				lines = f.read().split('\n')[:-1]
+				pronunciation_variants = [line.split('\t')[1] for line in lines]
 
-            # see only words which appears in top 3.
-            result_ = result[result['word'].str.match(word)]
-            result_ = result_[result_['famehtk'].isin(pronunciation_variants)]
+			# see only words which appears in top 3.
+			result_ = result[result['word'].str.match(word)]
+			result_ = result_[result_['famehtk'].isin(pronunciation_variants)]
 
-            match_num = sum(result_['famehtk'] == result_['prediction'])
-            total_num = len(result_)
+			match_num = sum(result_['famehtk'] == result_['prediction'])
+			total_num = len(result_)
 
-            print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100))
-            if compare_hmm_num:
-                f_result.write("{0},{1},".format(match_num, total_num))
-            else:
-                # output confusion matrix
-                cm = confusion_matrix(result_['famehtk'], result_['prediction'])
+			print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100))
+			if compare_hmm_num:
+				f_result.write("{0},{1},".format(match_num, total_num))
+			else:
+				# output confusion matrix
+				cm = confusion_matrix(result_['famehtk'], result_['prediction'])
 
-                plt.figure()
-                plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
-                plt.savefig(result_dir + '\\cm_' + word + '.png')
+				plt.figure()
+				plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
+				plt.savefig(result_dir + '\\cm_' + word + '.png')
 
-        if compare_hmm_num:
-            f_result.write('\n')
+		if compare_hmm_num:
+			f_result.write('\n')
 
-    if compare_hmm_num:
-        f_result.close()
+	if compare_hmm_num:
+		f_result.close()
 
 
 ## ======================= evaluate the result of forced alignment of kaldi =======================
 if eval_forced_alignment_kaldi:
-    result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
+	result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
 
-    f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
-    f_result.write("word,total,valid,match,[%]\n")
+	f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
+	f_result.write("word,total,valid,match,[%]\n")
 
-    # load pronunciation variants
-    with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f:
-        lines = f.read().split('\n')[:-1]
-        pronunciation_variants_all = [line.split('\t') for line in lines]
+	# load pronunciation variants
+	with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f:
+		lines = f.read().split('\n')[:-1]
+		pronunciation_variants_all = [line.split('\t') for line in lines]
 
-    word_list = np.delete(word_list, [0], 0) # remove 'Oog'
-    for word in word_list:
+	word_list = np.delete(word_list, [0], 0) # remove 'Oog'
+	for word in word_list:
 
-        # load pronunciation variant of the word.
-        pronunciation_variants = []
-        for line in pronunciation_variants_all:
-            if line[0] == word.lower():
-                pronunciation_variants.append(line[1].replace(' ', ''))
+		# load pronunciation variant of the word.
+		pronunciation_variants = []
+		for line in pronunciation_variants_all:
+			if line[0] == word.lower():
+				pronunciation_variants.append(line[1].replace(' ', ''))
 
-        # see only words which appears in top 3.
-        result_ = result[result['word'].str.match(word)]        
-        result_tolerant = pd.DataFrame({
-            'filename': [''], 
-                'word': [''], 
-                'xsampa': [''],
-                'ipa': [''],
-                'prediction': [''],
-                'match': ['']})
+		# see only words which appears in top 3.
+		result_ = result[result['word'].str.match(word)]        
+		result_tolerant = pd.DataFrame({
+			'filename': [''], 
+				'word': [''], 
+				'xsampa': [''],
+				'ipa': [''],
+				'prediction': [''],
+				'match': ['']})
 
-        for i in range(0, len(result_)):
-            line = result_.iloc[i]
+		for i in range(0, len(result_)):
+			line = result_.iloc[i]
 
-            # make a list of all possible pronunciation variants of ipa description.
-            # i.e. possible answers from forced alignment.
-            ipa = line['ipa']
-            pronvar_list = [ipa]
-            pronvar_list_ = am_func.fame_pronunciation_variant(ipa)
-            if not pronvar_list_ is None: 
-                pronvar_list += list(pronvar_list_)
+			# make a list of all possible pronunciation variants of ipa description.
+			# i.e. possible answers from forced alignment.
+			ipa = line['ipa']
+			pronvar_list = [ipa]
+			pronvar_list_ = am_func.fame_pronunciation_variant(ipa)
+			if not pronvar_list_ is None: 
+				pronvar_list += list(pronvar_list_)
 
-            # only focus on pronunciations which can be estimated from ipa. 
-            if len(set(pronvar_list) & set(pronunciation_variants)) > 0:
-                if line['prediction'] in pronvar_list:
-                    ismatch = True
-                else:
-                    ismatch = False
+			# only focus on pronunciations which can be estimated from ipa. 
+			if len(set(pronvar_list) & set(pronunciation_variants)) > 0:
+				if line['prediction'] in pronvar_list:
+					ismatch = True
+				else:
+					ismatch = False
 
-                line_df = pd.DataFrame(result_.iloc[i]).T
-                df_idx = line_df.index[0]
-                result_tolerant_ = pd.Series([line_df.loc[df_idx, 'filename'],
-                                              line_df.loc[df_idx, 'word'],
-                                              line_df.loc[df_idx, 'xsampa'],
-                                              line_df.loc[df_idx, 'ipa'],
-                                              line_df.loc[df_idx, 'prediction'],
-                                              ismatch], 
-                                        index=['filename', 'word', 'xsampa', 'ipa', 'prediction', 'match'], 
-                                        name=df_idx)
-                result_tolerant = result_tolerant.append(result_tolerant_)
-        # remove the first entry (dummy)
-        result_tolerant = result_tolerant.drop(0, axis=0)
+				line_df = pd.DataFrame(result_.iloc[i]).T
+				df_idx = line_df.index[0]
+				result_tolerant_ = pd.Series([line_df.loc[df_idx, 'filename'],
+											  line_df.loc[df_idx, 'word'],
+											  line_df.loc[df_idx, 'xsampa'],
+											  line_df.loc[df_idx, 'ipa'],
+											  line_df.loc[df_idx, 'prediction'],
+											  ismatch], 
+										index=['filename', 'word', 'xsampa', 'ipa', 'prediction', 'match'], 
+										name=df_idx)
+				result_tolerant = result_tolerant.append(result_tolerant_)
+		# remove the first entry (dummy)
+		result_tolerant = result_tolerant.drop(0, axis=0)
 
-        total_num = len(result_)
-        valid_num = len(result_tolerant)
-        match_num = np.sum(result_tolerant['match'])
+		total_num = len(result_)
+		valid_num = len(result_tolerant)
+		match_num = np.sum(result_tolerant['match'])
 
-        print("word '{0}': {1}/{2} ({3:.2f} %) originally {4}".format(word, match_num, valid_num, match_num/valid_num*100, total_num))
-        f_result.write("{0},{1},{2},{3},{4}\n".format(word, total_num, valid_num, match_num, match_num/valid_num*100))
+		print("word '{0}': {1}/{2} ({3:.2f} %) originally {4}".format(word, match_num, valid_num, match_num/valid_num*100, total_num))
+		f_result.write("{0},{1},{2},{3},{4}\n".format(word, total_num, valid_num, match_num, match_num/valid_num*100))
 
-    f_result.close()
-        ## output confusion matrix
-        #cm = confusion_matrix(result_['ipa'], result_['prediction'])
+	f_result.close()
+		## output confusion matrix
+		#cm = confusion_matrix(result_['ipa'], result_['prediction'])
 
-        #plt.figure()
-        #plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
-        #plt.savefig(result_dir + '\\cm_' + word + '.png')
+		#plt.figure()
+		#plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
+		#plt.savefig(result_dir + '\\cm_' + word + '.png')

acoustic_model/novoapi_forced_alignment.py

@@ -52,7 +52,7 @@ p = argparse.ArgumentParser()
 #p.add_argument("--user", default=None)
 #p.add_argument("--password", default=None)
 p.add_argument("--user", default='martijn.wieling')
-p.add_argument("--password", default='fa0Thaic')
+p.add_argument("--password", default='xxxxxx')
 args = p.parse_args()
 
 #wav_file = 'c:\\OneDrive\\WSL\\test\\onetwothree.wav'

acoustic_model/novoapi_functions.py

@@ -1,20 +1,19 @@
 ## this script should be used only by Aki Kunikoshi.
 
+import os
+
 import numpy as np
+import pandas as pd
 import argparse
 import json
 
 from novoapi.backend import session
 
-import os
-#os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
 import defaultfiles as default
+import convert_phoneset
 
 
-def load_phonset():
-	translation_key_ipa2novo70 = dict()
-	translation_key_novo702ipa = dict()
-
+def load_novo70_phoneset():
 	#phonelist_novo70_      = pd.ExcelFile(default.phonelist_novo70_xlsx)
 	#df = pd.read_excel(phonelist_novo70_, 'list')
 	## *_simple includes columns which has only one phone in.
@@ -23,21 +22,23 @@ def load_phonset():
 	#        print('{0}:{1}'.format(ipa, novo70))
 	#        translation_key[ipa] = novo70
 	#phonelist_novo70 = np.unique(list(df['novo70_simple']))
+	novo70_phoneset = pd.read_csv(default.novo70_phoneset, delimiter='\t', header=None, encoding="utf-8")
+	novo70_phoneset.rename(columns={0: 'novo70', 1: 'ipa', 2: 'description'}, inplace=True)
 
-	phoneset_ipa = []
-	phoneset_novo70 = []
-	with open(default.novo70_phoneset, "rt", encoding="utf-8") as fin:
-		lines = fin.read()
-		lines = lines.split('\n')
-		for line in lines:
-			words = line.split('\t')
-			if len(words) > 1:
-				novo70 = words[0]
-				ipa	   = words[1]
-				phoneset_ipa.append(ipa)
-				phoneset_novo70.append(novo70)
-				translation_key_ipa2novo70[ipa] = novo70
-				translation_key_novo702ipa[novo70] = ipa
+	#phoneset_ipa = []
+	#phoneset_novo70 = []
+	#with open(default.novo70_phoneset, "rt", encoding="utf-8") as fin:
+	#	lines = fin.read()
+	#	lines = lines.split('\n')
+	#	for line in lines:
+	#		words = line.split('\t')
+	#		if len(words) > 1:
+	#			novo70 = words[0]
+	#			ipa	   = words[1]
+	#			phoneset_ipa.append(ipa)
+	#			phoneset_novo70.append(novo70)
+	#			translation_key_ipa2novo70[ipa] = novo70
+	#			translation_key_novo702ipa[novo70] = ipa
 
 	# As per Nederlandse phoneset_aki.xlsx recieved from David
 	# [ɔː] oh / ohr # from ipa->novo70, only oh is used.
@@ -47,15 +48,26 @@ def load_phonset():
 	# [ɛː] eh
 	# [w] wv in IPA written as ʋ. 
 	extra_ipa	 = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'ʋ']
-	extra_novo70 = ['oh', 'ih', 'iy', 'uh', 'eh', 'wv'] 
-	for ipa, novo70 in zip(extra_ipa, extra_novo70):
-		phoneset_ipa.append(ipa)
-		phoneset_novo70.append(novo70)
+	extra_novo70 = ['oh', 'ih', 'iy', 'uh', 'eh', 'wv']
+
+	phoneset_ipa = list(novo70_phoneset['ipa'])
+	phoneset_ipa.extend(extra_ipa)
+	phoneset_ipa = [i.replace('ː', ':') for i in phoneset_ipa]
+
+	phoneset_novo70 = list(novo70_phoneset['novo70'])
+	phoneset_novo70.extend(extra_novo70)
+	phoneset_novo70 = [i.replace('ː', ':') for i in phoneset_novo70]
+
+	translation_key_ipa2novo70 = dict()
+	translation_key_novo702ipa = dict()
+	for ipa, novo70 in zip(phoneset_ipa, phoneset_novo70):
+		#phoneset_ipa.append(ipa)
+		#phoneset_novo70.append(novo70)
 		translation_key_ipa2novo70[ipa] = novo70
 		translation_key_novo702ipa[novo70] = ipa
 
-	translation_key_novo702ipa['ohr'] = 'ɔː'
-	translation_key_novo702ipa['ihr'] = 'ɪː'
+	translation_key_novo702ipa['ohr'] = 'ɔ:'
+	translation_key_novo702ipa['ihr'] = 'ɪ:'
 
 	phoneset_ipa    = np.unique(phoneset_ipa)
 	phoneset_novo70 = np.unique(phoneset_novo70)
@@ -63,25 +75,6 @@ def load_phonset():
 	return phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa
 
 
-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.
-
-	Copied from forced_alignment.convert_phone_set.py
-	"""
-	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_ipa(line):
 	"""
 	Split a line by IPA phones.
@@ -89,13 +82,16 @@ def split_ipa(line):
 	:param string line: one line written in IPA.
 	:return string lineSeperated: the line splitted in IPA phone. 
 	"""
+	phoneset_ipa, _, _, _ = load_novo70_phoneset()
+	#multi_character_phones = [i for i in phoneset_ipa if len(i) > 1]
+	#multi_character_phones.sort(key=len, reverse=True)
+	#multi_character_phones = [
+	#	# IPAs in CGN.
+	#	u'ʌu', u'ɛi', u'œy', u'aː', u'eː', u'iː', u'oː', u'øː', u'ɛː', u'œː', u'ɔː', u'ɛ̃ː', u'ɑ̃ː', u'ɔ̃ː', u'œ̃', u'ɪː'
+	#	]
+	#return [phone for phone in multi_character_tokenize(line.strip(), multi_character_phones)]
 
-	multi_character_phones = [
-		# IPAs in CGN.
-		u'ʌu', u'ɛi', u'œy', u'aː', u'eː', u'iː', u'oː', u'øː', u'ɛː', u'œː', u'ɔː', u'ɛ̃ː', u'ɑ̃ː', u'ɔ̃ː', u'œ̃', u'ɪː'
-		]
-
-	return [phone for phone in multi_character_tokenize(line.strip(), multi_character_phones)]
+	return convert_phoneset.split_word(line, phoneset_ipa)
 
 
 def split_novo70(line):
@@ -104,30 +100,33 @@ def split_novo70(line):
 	:param string line: one line written in novo70.
 	:return string lineSeperated: the line splitted by novo70 phones. 
 	"""
-	_, phoneset_novo70, _, _ = load_phonset()
-	multi_character_phones = [p for p in phoneset_novo70 if len(p) > 1]
-	multi_character_phones = sorted(multi_character_phones, key=len, reverse=True)
+	_, phoneset_novo70, _, _ = load_novo70_phoneset()
+	#multi_character_phones = [p for p in phoneset_novo70 if len(p) > 1]
+	#multi_character_phones = sorted(multi_character_phones, key=len, reverse=True)
+	multi_character_phones = convert_phoneset.extract_multi_character_phones(phoneset_novo70)
 
 	return ['sp' if phone == ' ' else phone
 			for phone in multi_character_tokenize(line.strip(), multi_character_phones)]
 
 
-def novo702ipa(tokens):
-	pronunciation = []
-	_, _, _, translation_key = load_phonset()
-	for phone in split_novo70(tokens):
-		pronunciation.append(translation_key.get(phone, phone))
-	return ' '.join(pronunciation)
+def novo702ipa(line):
+	#pronunciation = []
+	_, _, _, translation_key = load_novo70_phoneset()
+	#for phone in split_novo70(tokens):
+	#	pronunciation.append(translation_key.get(phone, phone))
+	#return ' '.join(pronunciation)
+	return ' '.join(convert_phoneset.convert_phoneset(split_novo70(line), translation_key))
 
 
 # numbering of novo70 should be checked.
-def ipa2novo70(tokens):
-	pronunciation = []
-	_, _, translation_key, _ = load_phonset()
-	for phone in split_ipa(tokens):
-		pronunciation.append(translation_key.get(phone, phone))
-	return ' '.join(pronunciation)
-	
+def ipa2novo70(line):
+	#pronunciation = []
+	_, _, translation_key, _ = load_novo70_phoneset()
+	#for phone in split_ipa(tokens):
+	#	pronunciation.append(translation_key.get(phone, phone))
+	#return ' '.join(pronunciation)
+	return ' '.join(convert_phoneset.convert_phoneset(split_ipa(line), translation_key))
+
 
 def make_grammar(word, pronunciation_ipa):
 	"""
@@ -173,7 +172,10 @@ def forced_alignment(wav_file, word, pronunciation_ipa):
 	# username / password cannot be passed as artuments...
 	p = argparse.ArgumentParser()
 	p.add_argument("--user", default='martijn.wieling')
-	p.add_argument("--password", default='fa0Thaic')
+	p.add_argument("--password", default='xxxxxx')
+
+
+
 	args = p.parse_args()
 	
 	rec = session.Recognizer(grammar_version="1.0", lang="nl", snodeid=101, user=args.user, password=args.password, keepopen=True) # , modeldir=modeldir)
@@ -194,6 +196,25 @@ def result2pronunciation(result, word):
 	return pronunciation_ipa, pronunciation_novo70, llh
 
 
+def phones_not_in_novo70(ipa):
+	""" extract phones which is not in novo70 phoneset. """
+
+	phoneset_ipa, _, _, _ = load_novo70_phoneset()
+
+	# As per Nederlandse phoneset_aki.xlsx recieved from David
+	# [ɔː] oh / ohr
+	# [ɪː] ih / ihr
+	# [iː] iy
+	# [œː] uh
+	# [ɛː] eh
+	# [w] wv in IPA written as ʋ. 
+	david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'w']
+
+	return [phone for phone in split_ipa(ipa) 
+				   if not phone in phoneset_ipa and not phone in david_suggestion]
+
+
 if __name__ == 'main':
 	pronunciation_ipa = ['rø:s', 'mɑn', 'mɑntsjə']
-	grammar = make_grammar('reus', pronunciation_ipa)
+	#grammar = make_grammar('reus', pronunciation_ipa)
+	phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa = load_novo70_phoneset()

acoustic_model/phoneset/fame_asr.py

@@ -0,0 +1,154 @@
+""" definition of the phones to be used. """
+
+# phonese in {FAME}/lexicon/lex.asr
+phoneset = [
+	# vowels
+	'a',
+	'a:',
+	'e',
+	'e:',
+	'i',
+	'i:',
+	'i̯',
+	'o',
+	'o:',
+	'ö',
+	'ö:',
+	'u',
+	'u:',
+	'ü',
+	'ü:',
+	#'ú', # only appears in word 'feeste'(út) and 'gaste'(út) which are 'f e: s t ə' and 'yn' in lex_asr. The pronunciation in Fries may be mistakes so I removed this phone. 
+	'ṷ',
+	'y',
+	'ɔ',
+	'ɔ:',
+	'ɔ̈', 
+	'ɔ̈:',
+	'ə',
+	'ɛ',
+	'ɛ:',
+	'ɪ',
+	'ɪ:',
+
+	# plosives
+	'p', 
+	'b', 
+	't',
+	'd', 
+	'k',
+	'g',
+	'ɡ', # = 'g'
+
+	# nasals
+	'm',
+	'n',
+	'ŋ',
+	
+	# fricatives
+	'f',
+	'v',
+	's',
+	's:',
+	'z',
+	'x',
+	'h',
+	
+	# tap and flip
+	'r',
+	'r:',
+
+	# approximant
+	'j',
+	'l'
+	]
+
+
+## reduce the number of phones.
+# the phones which seldom occur are replaced with another more popular phones.
+# replacements are based on the advice from Martijn Wieling.
+reduction_key = {
+	'y':'i:', 'e':'e:', 'ə:':'ɛ:', 'r:':'r', 'ɡ':'g',
+	# aki added because this is used in stimmen_project.
+	'ɔ̈:':'ɔ:'
+	}
+# already removed beforehand in phoneset. Just to be sure.
+phones_to_be_removed = ['ú', 's:']
+
+def phone_reduction(phones):
+	""" 
+	Args:
+		phones (list): list of phones.
+	"""
+	if sum([phone in phones for phone in phones_to_be_removed]) != 0:
+		print('input includes phone(s) which is not defined in fame_asr.')
+		print('those phone(s) are removed.')		
+	return [reduction_key.get(i, i) for i in phones
+		 if i not in phones_to_be_removed]
+
+phoneset_short = list(set(phone_reduction(phoneset)))
+phoneset_short.sort()
+
+
+
+## translation_key to htk format (ascii).
+# phones which gives UnicodeEncodeError when phone.encode("ascii")
+# are replaced with other characters.
+translation_key_asr2htk = {
+	'i̯': 'i_',
+	'ṷ': 'u_',
+
+	# on the analogy of German umlaut, 'e' is used.
+	'ö': 'oe', 'ö:': 'oe:', ''
+	'ü': 'ue', 'ü:': 'ue:',
+
+	# on the analogy of Chinese...
+	'ŋ': 'ng',
+				
+	# refer to Xsampa. 
+	'ɔ': 'O', 'ɔ:': 'O:', 'ɔ̈': 'Oe', 
+	#'ɔ̈:': 'O:', # does not appear in FAME, but used in stimmen.
+	'ɛ': 'E', 'ɛ:': 'E:',
+	'ɪ': 'I', 'ɪ:': 'I:', 
+
+	# it is @ in Xsampa, but that is not handy on HTK.
+	'ə': 'A'
+	}
+phoneset_htk = [translation_key_asr2htk.get(i, i) for i in phoneset_short]
+
+#not_in_ascii = [
+#	'\'', 
+#	'â', 'ê', 'ô', 'û', 'č', 
+#	'à', 'í', 'é', 'è', 'ú', 'ć', 
+#	'ä', 'ë', 'ï', 'ö', 'ü'
+#]
+translation_key_word2htk = {
+	#'\'': '\\\'',
+	'í':'i1', 'é':'e1', 'ú':'u1', 'ć':'c1',
+	'à':'a2', 'è':'e2', 	
+	'â':'a3', 'ê':'e3', 'ô':'o3', 'û':'u3', 
+	'č':'c4',
+	'ä': 'ao', 'ë': 'ee', 'ï': 'ie', 'ö': 'oe', 'ü': 'ue',
+}
+#[translation_key_word2htk.get(i, i) for i in not_in_ascii]
+#Stop: p, b, t, d, k, g  
+#Nasal: m, n, ng(ŋ)
+#Fricative: s, z, f, v, h, x
+#Liquid: l, r
+#Vowel: a, a:, e:, i, i:, i_(i̯), o, o:, u, u:, u_(ṷ), oe(ö), oe:(ö:), ue(ü), ue:(ü:), O(ɔ), O:(ɔ:), Oe(ɔ̈), A(ə), E(ɛ), E:(ɛ:), I(ɪ), I:(ɪ:)
+
+
+## the list of multi character phones. 
+# for example, the length of 'a:' is 3, but in the codes it is treated as one letter.
+
+# original.
+multi_character_phones = [i for i in phoneset if len(i) > 1]
+multi_character_phones.sort(key=len, reverse=True)
+
+# phonset reduced.
+multi_character_phones_short = [i for i in phoneset_short if len(i) > 1]
+multi_character_phones_short.sort(key=len, reverse=True)
+
+# htk compatible.
+multi_character_phones_htk = [i for i in phoneset_htk if len(i) > 1]
+multi_character_phones_htk.sort(key=len, reverse=True)

acoustic_model/phoneset/fame_ipa.py

@@ -1,11 +1,11 @@
 """ definition of the phones to be used. """
 
-## phones in IPA.
-phoneset_ipa = [
+phoneset = [
 	# vowels
 	'i̯',
 	'i̯ⁿ',
 	'y',
+	'y:', # not included in lex.ipa, but in stimmen.
 	'i',
 	'i.',
 	'iⁿ',
@@ -14,7 +14,7 @@ phoneset_ipa = [
 	'ɪ',
 	'ɪⁿ',
 	'ɪ.',
-	#'ɪ:', # not included in lex.ipa
+	'ɪ:', # not included in lex.ipa, but in stimmen.
 	'ɪ:ⁿ',
 	'e',
 	'e:',
@@ -35,7 +35,7 @@ phoneset_ipa = [
 	'ṷ',
 	'ṷ.',
 	'ṷⁿ',
-	#'ú', # only appears in word 'feeste'(út) and 'gaste'(út) which are 'f e: s t ə' and 'yn' in lex_asr. 
+	#'ú', # only appears in word 'feeste'(út) and 'gaste'(út) which are 'f e: s t ə' and 'yn' in lex_asr. The pronunciation in Fries may be mistakes so I removed this phone. 
 	'u',
 	'uⁿ',
 	'u.',
@@ -61,7 +61,7 @@ phoneset_ipa = [
 	'ɔⁿ',
 	'ɔ:',
 	'ɔ:ⁿ',
-	#'ɔ̈', # not included in lex.ipa 
+	'ɔ̈', # not included in lex.ipa 
 	'ɔ̈.',
 	'ɔ̈:',
 
@@ -101,7 +101,38 @@ phoneset_ipa = [
 	'l'
 	]
 
+## reduce the number of phones.
+# the phones which are used in stimmen transcription but not in FAME corpus.
+# replacements are based on the advice from Jelske Dijkstra on 2018/06/21.
+stimmen_replacement = {
+	'æ': 'ɛ', 
+	'ø': 'ö', # or 'ö:'
+	'ø:': 'ö:', # Aki added.
+	'œ': 'ɔ̈', # or 'ɔ̈:'
+	'œ:': 'ɔ̈:', # Aki added.
+	'ɐ': 'a', # or 'a:'
+	'ɐ:': 'a:', # Aki added.
+	'ɑ': 'a', # or 'a:'
+	'ɑ:': 'a:', # Aki added
+	'ɒ': 'ɔ', # or 'ɔ:'
+	'ɒ:': 'ɔ:', # Aki added.
+	'ɾ': 'r',
+	'ʁ': 'r',
+	'ʊ': 'u',
+	'χ': 'x',
+
+	# aki guessed.
+	'ʀ': 'r',
+	'ɹ': 'r',
+	'w': 'ö'
+	}
+phoneset.extend(list(stimmen_replacement.keys()))
+
+def phone_reduction(phones):
+	return [stimmen_replacement.get(i, i) for i in phones]
+
+	
 ## 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)
+multi_character_phones = [i for i in phoneset if len(i) > 1]
+multi_character_phones.sort(key=len, reverse=True)

acoustic_model/phoneset/fame_phonetics.py

@@ -0,0 +1,197 @@
+import sys
+import os
+os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
+
+import fame_functions
+from phoneset import fame_ipa, fame_asr
+import convert_phoneset
+
+
+## general
+stop	  = 'p, b, t, d, k, g'  
+nasal	  = 'm, n, ŋ'
+fricative = 's, z, f, v, h, x, j'
+liquid	  = 'l, r'
+vowel	  = 'a, a:, e:, i, i:, i̯, o, o:, u, u:, ṷ, ö, ö:, ü, ü:, ɔ, ɔ:, ɔ̈, ə, ɛ, ɛ:, ɪ, ɪ:'
+
+## consonant
+c_front   = 'p, b, m, f, v'
+c_central = 't, d, n, s, z, l, r' 
+c_back	  = 'k, g, ŋ, h, x, j'
+
+fortis = 'p, t, k, f, s'
+lenis  = 'b, d, g, v, z, j'
+neither_fortis_nor_lenis = 'm, n, ŋ, h, l, r, x'
+
+coronal		= 't, d, n, s, z, l, r, j'
+non_coronal = 'p, b, m, k, g, ŋ, f, v, h, x'
+
+anterior	 = 'p, b, m, t, d, n, f, v, s, z, l'
+non_anterior = 'k, g, ŋ, h, x, j, r'
+
+continuent	   = 'm, n, ŋ, f, v, s, z, h, l, r'
+non_continuent = 'p, b, t, d, k, g, x, j'
+
+strident	 = 's, z, j'
+non_strident = 'f, v, h'
+unstrident	 = 'p, b, t, d, m, n, ŋ, k, g, r, x'
+
+glide	 = 'h, l, r'
+syllabic = 'm, l, ŋ'
+
+unvoiced = 'p, t, k, s, f, x, h'
+voiced	 = 'b, d, g, z, v, m, n, ŋ, l, r, j'
+
+#affricate: ???
+non_affricate = 's, z, f, v'
+
+voiced_stop   = 'b, d, g'
+unvoiced_stop = 'p, t, k'
+front_stop	  = 'p, b'
+central_stop  = 't, d'
+back_stop	  = 'k, g'
+
+voiced_fricative   = 'z, v'
+unvoiced_fricative = 's, f'
+front_fricative	   = 'f, v'
+central_fricative  = 's, z'
+back_fricative	   = 'j'
+
+
+## vowel
+v_front   = 'i, i:, i̯,  ɪ, ɪ:, e:, ə, ɛ, ɛ:, a, a:'
+v_central = 'ə, ɛ, ɛ:, a, a:'
+v_back	  = 'u, u:, ü, ü:, ṷ, ɔ, ɔ:, ɔ̈,  ö, ö:, o, o:'
+
+long  = 'a:, e:, i:, o:, u:, ö:, ü:, ɔ:, ɛ:, ɪ:' 
+short = 'a, i, i̯, o, u, ṷ, ö, ü, ɔ, ɔ̈, ə, ɛ, ɪ'
+
+#Dipthong: ???
+#Front-Start: ???
+#Fronting: ???
+
+high   = 'i, i:, i̯,  ɪ, ɪ: u, u:, ṷ, ə, e:, o, o:, ö, ö:, ü, ü:'
+medium = 'e:, ə, ɛ, ɛ:, ɔ, ɔ:, ɔ̈, o, o:, ö, ö:'
+low	   = 'a, a:, ɛ, ɛ:, ɔ, ɔ:, ɔ̈'
+
+rounded   = 'a, a:, o, o:, u, u:, ṷ, ö, ö:, ü, ü:, ɔ, ɔ:, ɔ̈' 
+unrounded = 'i, i:, i̯, e:, ə, ɛ, ɛ:, ɪ, ɪ:' 
+
+i_vowel = 'i, i:, i̯, ɪ, ɪ:'
+e_vowel = 'e:,ə, ɛ, ɛ:'
+a_vowel = 'a, a:'
+o_vowel = 'o, o:, ö, ö:, ɔ, ɔ:, ɔ̈'
+u_vowel = 'u, u:, ṷ, ü, ü:'
+
+## htk phoneset
+phoneset = fame_asr.phoneset_htk
+
+## convert ipa group to htk format for quests.hed.
+def _ipa2quest(R_or_L, ipa_text):
+	assert R_or_L in ['R', 'L'], print('the first argument should be either R or L.')
+	ipa_list = ipa_text.replace(' ', '').split(',')
+	if R_or_L == 'R':
+		quests_list = ['*+' + fame_functions.ipa2htk(ipa) for ipa in ipa_list]
+	else:
+		quests_list = [fame_functions.ipa2htk(ipa) + '-*' for ipa in ipa_list]
+	return ','.join(quests_list)
+
+
+def make_quests_hed(quest_hed):
+	def _add_quests_item(R_or_L, item_name_, ipa_text):
+		assert R_or_L in ['R', 'L'], print('the first argument should be either R or L.')
+		item_name = R_or_L + '_' + item_name_
+		with open(quest_hed, 'ab') as f:
+			f.write(bytes('QS  "' + item_name + '"\t{ ' + _ipa2quest(R_or_L, ipa_text) + ' }\n', 'ascii'))
+
+	if os.path.exists(quest_hed):
+		os.remove(quest_hed)
+
+	for R_or_L in ['R', 'L']:
+		_add_quests_item(R_or_L, 'NonBoundary', '*')
+		_add_quests_item(R_or_L, 'Silence', 'sil')
+
+		_add_quests_item(R_or_L, 'Stop', stop)
+		_add_quests_item(R_or_L, 'Nasal', nasal)
+		_add_quests_item(R_or_L, 'Fricative', fricative)
+		_add_quests_item(R_or_L, 'Liquid', liquid)
+		_add_quests_item(R_or_L, 'Vowel', vowel)
+
+		_add_quests_item(R_or_L, 'C-Front', c_front)
+		_add_quests_item(R_or_L, 'C-Central', c_central)
+		_add_quests_item(R_or_L, 'C-Back', c_back)
+
+		_add_quests_item(R_or_L, 'V-Front', v_front)
+		_add_quests_item(R_or_L, 'V-Central', v_central)
+		_add_quests_item(R_or_L, 'V-Back', v_back)
+
+		_add_quests_item(R_or_L, 'Front', c_front + v_front)
+		_add_quests_item(R_or_L, 'Central', c_central + v_central)
+		_add_quests_item(R_or_L, 'Back', c_front + v_back)
+
+		_add_quests_item(R_or_L, 'Fortis', fortis)
+		_add_quests_item(R_or_L, 'Lenis', lenis)
+		_add_quests_item(R_or_L, 'UnFortLenis', neither_fortis_nor_lenis)
+
+		_add_quests_item(R_or_L, 'Coronal', coronal)
+		_add_quests_item(R_or_L, 'NonCoronal', non_coronal)
+
+		_add_quests_item(R_or_L, 'Anterior', anterior)
+		_add_quests_item(R_or_L, 'NonAnterior', non_anterior)
+
+		_add_quests_item(R_or_L, 'Continuent', continuent)
+		_add_quests_item(R_or_L, 'NonContinuent', non_continuent)
+
+		_add_quests_item(R_or_L, 'Strident', strident)
+		_add_quests_item(R_or_L, 'NonStrident', non_strident)
+		_add_quests_item(R_or_L, 'UnStrident', unstrident)
+
+		_add_quests_item(R_or_L, 'Glide', glide)
+		_add_quests_item(R_or_L, 'Syllabic', syllabic)
+
+		_add_quests_item(R_or_L, 'Unvoiced-Cons', unvoiced)
+		_add_quests_item(R_or_L, 'Voiced-Cons', voiced)
+		_add_quests_item(R_or_L, 'Unvoiced-All', unvoiced + ', sil')
+
+		_add_quests_item(R_or_L, 'Long', long)
+		_add_quests_item(R_or_L, 'Short', short)
+
+		#_add_quests_item(R_or_L, 'Dipthong', xxx)
+		#_add_quests_item(R_or_L, 'Front-Start', xxx)
+		#_add_quests_item(R_or_L, 'Fronting', xxx)
+
+		_add_quests_item(R_or_L, 'High', high)
+		_add_quests_item(R_or_L, 'Medium', medium)
+		_add_quests_item(R_or_L, 'Low', low)
+
+		_add_quests_item(R_or_L, 'Rounded', rounded)
+		_add_quests_item(R_or_L, 'UnRounded', unrounded)
+
+		#_add_quests_item(R_or_L, 'Affricative', rounded)
+		_add_quests_item(R_or_L, 'NonAffricative', non_affricate)
+
+		_add_quests_item(R_or_L, 'IVowel', i_vowel)
+		_add_quests_item(R_or_L, 'EVowel', e_vowel)
+		_add_quests_item(R_or_L, 'AVowel', a_vowel)
+		_add_quests_item(R_or_L, 'OVowel', o_vowel)
+		_add_quests_item(R_or_L, 'UVowel', u_vowel)
+
+		_add_quests_item(R_or_L, 'Voiced-Stop', voiced_stop)
+		_add_quests_item(R_or_L, 'UnVoiced-Stop', unvoiced_stop)
+
+		_add_quests_item(R_or_L, 'Front-Stop', front_stop)
+		_add_quests_item(R_or_L, 'Central-Stop', central_stop)
+		_add_quests_item(R_or_L, 'Back-Stop', back_stop)
+
+		_add_quests_item(R_or_L, 'Voiced-Fric', voiced_fricative)
+		_add_quests_item(R_or_L, 'UnVoiced-Fric', unvoiced_fricative)
+
+		_add_quests_item(R_or_L, 'Front-Fric', front_fricative)
+		_add_quests_item(R_or_L, 'Central-Fric', central_fricative)
+		_add_quests_item(R_or_L, 'Back-Fric', back_fricative)
+
+		for p in phoneset:
+			_add_quests_item(R_or_L, p, p)
+
+	return
+	

acoustic_model/stimmen_functions.py

@@ -0,0 +1,119 @@
+import os
+os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
+import glob
+
+import pandas as pd
+
+import convert_xsampa2ipa
+import defaultfiles as default
+import fame_functions
+import novoapi_functions
+
+
+def _load_transcriptions():
+	stimmen_transcription = pd.ExcelFile(default.stimmen_transcription_xlsx)
+	df = pd.read_excel(stimmen_transcription, 'original')
+
+	# mapping from ipa to xsampa
+	mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
+	#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
+	#    ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
+	#    if not ipa_converted == ipa:
+	#        print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
+
+	ipas     = []
+	for xsampa in df['Self Xsampa']:
+		if not isinstance(xsampa, float): # 'NaN'
+			# typo?
+			xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t').replace(';', ':')
+
+			ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
+			ipa = ipa.replace('ː', ':').replace(' ', '')
+			ipas.append(ipa)
+		else:
+			ipas.append('')
+
+	df_ = pd.DataFrame({'filename': df['Filename'], 
+						'word': df['Word'], 
+						'xsampa': df['Self Xsampa'],
+						'ipa': pd.Series(ipas)})
+	
+	# not valid inputs, but seperator.
+	df_ = df_[~df_['ipa'].str.contains('/')] 
+	return df_.dropna()
+
+
+def load_transcriptions():
+	""" in default.stimmen_transcription_xlsx 
+	rows of which wav files can be easily found"""
+	df = _load_transcriptions()
+	df_ = pd.DataFrame(index=[], columns=list(df.keys()))
+	for index, row in df.iterrows():
+		filename = row['filename']
+		if isinstance(filename, str):
+			wav_file = os.path.join(default.stimmen_wav_dir, filename)
+			if os.path.exists(wav_file):
+				df_ = df_.append(row, ignore_index=True)
+	return df_
+
+
+def load_transcriptions_clean(clean_wav_dir):
+	df = _load_transcriptions()
+	wav_file_list = glob.glob(os.path.join(clean_wav_dir, '*.wav'))
+	df_clean = pd.DataFrame(index=[], columns=list(df.keys()))
+	for wav_file in wav_file_list:
+		filename = os.path.basename(wav_file)
+		df_ = df[df['filename'].str.match(filename)]
+		df_clean = pd.concat([df_clean, df_])
+	return df_clean
+
+
+def load_transcriptions_novo70(clean_wav_dir):
+	""" extract rows of which ipa is written in novo70 phonset. """
+	df = load_transcriptions_clean(clean_wav_dir)
+
+	df_novo70 = pd.DataFrame(index=[], columns=list(df.keys()))
+	for index, row in df.iterrows():
+		not_in_novo70 = novoapi_functions.phones_not_in_novo70(row['ipa'])
+		if len(not_in_novo70) == 0:
+			df_novo70 = df_novo70.append(row, ignore_index=True)
+
+	return df_novo70
+
+
+def add_row_htk(df):
+	""" df['htk'] is made from df['ipa'] and added. """
+	htk = []
+	for index, row in df.iterrows():
+		htk.append(fame_functions.ipa2htk(row['ipa']))
+	return df.assign(htk=htk)
+
+
+def add_row_asr(df):
+	""" df['asr'] is made from df['ipa'] and added. """
+	asr = []
+	for index, row in df.iterrows():
+		asr.append(fame_functions.ipa2asr(row['ipa']))
+	return df.assign(asr=asr)
+
+
+def load_pronunciations(WORD, htk_dic):
+	""" load pronunciation variants from HTK dic file. 
+
+	Args:
+		WORD (str): word in capital letters.
+		htk_dic (path): HTK dict file. 
+
+	Returns:
+		(pronunciations) (list): pronunciation variants of WORD.
+		
+	Notes:
+		Because this function loads all contents from htk_dic file, 
+		it is not recommended to use for large lexicon. 
+
+	"""
+	with open(htk_dic) as f:
+		lines = f.read().replace(' sil', '')
+		lines = lines.split('\n')
+	return [' '.join(line.split(' ')[1:]) 
+		 for line in lines if line.split(' ')[0]==WORD]

acoustic_model/stimmen_test.py

@@ -0,0 +1,93 @@
+import os
+os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
+import sys
+import shutil
+from collections import Counter
+
+import numpy as np
+import pandas as pd
+
+import defaultfiles as default
+import convert_xsampa2ipa
+import stimmen_functions
+import fame_functions
+import convert_phoneset
+from phoneset import fame_ipa, fame_asr
+sys.path.append(default.toolbox_dir)
+import file_handling as fh
+from htk import pyhtk
+
+
+## ======================= user define =======================
+
+
+
+## ======================= make test data ======================
+stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
+
+## copy wav files which is in the stimmen data.
+df = stimmen_functions.load_transcriptions()
+#for index, row in df.iterrows():
+#	filename = row['filename']
+#	wav_file = os.path.join(default.stimmen_wav_dir, filename)
+#	shutil.copy(wav_file, os.path.join(stimmen_test_dir, filename))
+
+# after manually removed files which has too much noise and multiple words...
+# update the info.
+df_clean = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)
+
+# count how many files are removed due to the quality.
+word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
+word_list = sorted(word_list)
+for word in word_list:
+	df_ = df[df['word']==word]
+	df_clean_ = df_clean[df_clean['word']==word]
+	print('word {0} has {1} clean files among {2} files ({3:.2f} [%]).'.format(
+		word, len(df_clean_), len(df_), len(df_clean_)/len(df_)*100))
+
+
+## check phones included in stimmen but not in FAME!
+splitted_ipas = [' '.join(
+		convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)) 
+		for ipa in df['ipa']]
+stimmen_phones = set(' '.join(splitted_ipas))
+stimmen_phones = list(stimmen_phones)
+fame_phones = fame_ipa.phoneset
+stimmen_phones.sort()
+fame_phones.sort()
+print('phones which are used in stimmen transcription but not in FAME corpus are:\n{}'.format(
+	set(stimmen_phones) - set(fame_phones)
+	))
+for ipa in df['ipa']:
+	ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
+	if ':' in ipa_splitted:
+		print(ipa_splitted)
+
+
+## check pronunciation variants
+df_clean = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)
+df_clean = stimmen_functions.add_row_asr(df_clean)
+df_clean = stimmen_functions.add_row_htk(df_clean)
+
+for word in word_list:
+#word = word_list[1]
+	df_ = df_clean[df_clean['word']==word]
+	c = Counter(df_['htk'])
+	pronunciations = dict()
+	for key, value in zip(c.keys(), c.values()):
+		if value > 3:
+			pronunciations[key] = value
+	print(pronunciations)
+
+
+monophone_mlf = os.path.join(default.htk_dir, 'label', 'train_phone_aligned.mlf')
+triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
+def filenames_in_mlf(file_mlf):
+	with open(file_mlf) as f:
+		lines_ = f.read().split('\n')
+	lines = [line for line in lines_ if len(line.split(' ')) == 1 and line != '.']
+	filenames = [line.replace('"', '').replace('*/', '') for line in lines[1:-1]]
+	return filenames
+filenames_mono = filenames_in_mlf(monophone_mlf)
+filenames_tri  = filenames_in_mlf(triphone_mlf)
+