dataset for experiments in check_novoapi is updated.

acoustic_model/acoustic_model.pyproj

@@ -4,7 +4,7 @@
     <SchemaVersion>2.0</SchemaVersion>
     <ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
     <ProjectHome>.</ProjectHome>
-    <StartupFile>fame_hmm.py</StartupFile>
+    <StartupFile>check_novoapi.py</StartupFile>
     <SearchPath>
     </SearchPath>
     <WorkingDirectory>.</WorkingDirectory>
@@ -51,6 +51,9 @@
     <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>

acoustic_model/check_novoapi.py

@@ -29,48 +29,47 @@ forced_alignment_novo70 = True
 
 
 ## ===== 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
@@ -85,41 +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 = stimmen_functions.load_transcriptions()
+#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")
 
 

acoustic_model/convert_phoneset.py

@@ -14,18 +14,20 @@ def multi_character_tokenize(line, multi_character_tokens):
 			line = line[1:]
 
 
-def split_word(word, multi_character_phones):
+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. 
+		#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)
 		 ]
@@ -38,3 +40,19 @@ def convert_phoneset(word_list, translation_key):
 		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

@@ -17,6 +17,7 @@ novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
 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')
 
 # data

acoustic_model/fame_functions.py

@@ -12,6 +12,10 @@ import defaultfiles as default
 import convert_phoneset
 from phoneset import fame_ipa, fame_asr
 
+sys.path.append(default.toolbox_dir)
+from htk import pyhtk
+
+
 #def read_fileFA(fileFA):
 #    """
 #    read the result file of HTK forced alignment.
@@ -321,9 +325,11 @@ def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
 	lex.to_csv(lexicon_out, index=False, header=False, sep='\t', encoding='utf-8')
 
 
-def fix_single_quote(lexicon_file):
-	""" add '\' before all single quote at the beginning of words.
-	convert special characters to ascii compatible characters.
+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.
@@ -331,8 +337,15 @@ def fix_single_quote(lexicon_file):
 	"""
 	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')
@@ -346,19 +359,48 @@ def word2htk(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)
+	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

@@ -11,73 +11,87 @@ import numpy as np
 import pandas as pd
 
 import fame_functions
-from phoneset import fame_ipa, fame_asr
+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 =======================
 # procedure
+combine_all = 1
+
 make_lexicon	  = 0
 make_label		  = 0 # it takes roughly 4800 sec on Surface pro 2.
-make_htk_files    = 0
+make_mlf		  = 0
 extract_features  = 0
-flat_start		  = 0
-train_model_without_sp = 0
-add_sp = 0
-train_model_with_sp    = 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
 
 
 # pre-defined values.
-
 dataset_list = ['devel', 'test', 'train']
-hmmdefs_name = 'hmmdefs'
-proto_name   = 'proto39'
+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')
-config_hcopy = os.path.join(config_dir, 'config.HCopy')
-config_train = os.path.join(config_dir, 'config.train')
-global_ded   = os.path.join(config_dir, 'global.ded')
-mkphones_led = os.path.join(config_dir, 'mkphones.led')
-sil_hed		 = os.path.join(config_dir, 'sil.hed')
-prototype    = os.path.join(config_dir, proto_name)
-
-model_dir    = os.path.join(default.htk_dir, 'model')
+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')
 
+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')
 
 # 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')
-
-phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
-model0_dir	  = os.path.join(model_dir, 'hmm0')
-model1_dir	  = os.path.join(model_dir, 'hmm1')
+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')
-if not os.path.exists(label_dir):
-	os.makedirs(label_dir)
+fh.make_new_directory(label_dir, existing_dir='leave')
+
 
 ## training
-hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
-mlf_file_train   = os.path.join(label_dir, 'train_phone.mlf')
+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')
 
-## train without sp
-niter_max = 10
+## testing
+htk_stimmen_dir = os.path.join(default.htk_dir, 'stimmen')
 
 
 ## ======================= make lexicon for HTK =======================
@@ -96,19 +110,29 @@ if make_lexicon:
 	# therefore there is no overlap between lex_asr and lex_oov.   
 	fame_functions.combine_lexicon(lexicon_htk_asr, lexicon_htk_oov, lexicon_htk)
 
-	## ======================= 
-	## manually make changes to the pronunciation dictionary and save it as lex.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
-	fame_functions.fix_single_quote(lexicon_htk)
+	# 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:
-	# train_2002_gongfansaken_10347.lab is empty. should be removed.
 	for dataset in dataset_list:
 		timer_start = time.time()
 		print("==== making label files on dataset {}".format(dataset))
@@ -117,7 +141,7 @@ if make_label:
 		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_)
+		fh.make_new_directory(label_dir_, existing_dir='leave')
 
 		# list of scripts 
 		with open(script_list, "rt", encoding="utf-8") as fin:
@@ -132,56 +156,50 @@ if make_label:
 			sentence_htk = fame_functions.word2htk(sentence)
 
 			wav_file = os.path.join(wav_dir_, filename + '.wav')
-			if os.path.exists(wav_file) and pyhtk.can_be_ascii(sentence_htk) == 0:
-				if pyhtk.create_dictionary_without_log(
-					sentence_htk, global_ded, dictionary_file, lexicon_htk) == 0:
+			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')
-					pyhtk.create_label_file(sentence_htk, label_file)
+					chtk.make_label_file(sentence_htk, label_file)
 				else:
 					os.remove(dictionary_file)
+
 		print("elapsed time: {}".format(time.time() - timer_start))
 
 
-## ======================= make other required files =======================
-if make_htk_files:
+## ======================= make master label files =======================
+if make_mlf:
 	timer_start = time.time()
-	print("==== making files required for HTK ====")
+	print("==== making master label files ====")
 	
-	print(">>> making a phonelist...")
-	pyhtk.create_phonelist_file(fame_asr.phoneset_htk, phonelist_txt)
+	# 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:
-		wav_dir_	 = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
 		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(">>> making a script file for {}...".format(dataset))
-		#listdir    = glob.glob(os.path.join(wav_dir_, '*.dic'))
-		#mfc_list   = [filename.replace(wav_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'))
+		print(">>> generating a word level mlf file for {}...".format(dataset))
+		chtk.label2mlf(label_dir_, mlf_word)
 
-		print(">>> making a mlf file for {}...".format(dataset))
-		lab_list = glob.glob(os.path.join(label_dir_, '*.lab'))
-		with open(mlf_word, 'wb') as fmlf:
-			fmlf.write(bytes('#!MLF!#\n', 'ascii'))
-			for label_file in lab_list:
-				filename = os.path.basename(label_file)
-				fmlf.write(bytes('\"*/{}\"\n'.format(filename), 'ascii'))
-				with open(label_file) as flab:
-					lines = flab.read()
-				fmlf.write(bytes(lines + '.\n', 'ascii'))
-
-		print(">>> generating phone level transcription for {}...".format(dataset))
-		pyhtk.mlf_word2phone(lexicon_htk, mlf_phone, mlf_word, mkphones_led)
-		print("elapsed time: {}".format(time.time() - timer_start))
+		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 =======================
@@ -190,10 +208,10 @@ if extract_features:
 		timer_start = time.time()
 		print('==== extract features on dataset {} ===='.format(dataset))
 
-		wav_dir_ = os.path.join(default.fame_dir, 'fame', 'wav', 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_)
+		fh.make_new_directory(feature_dir_, existing_dir='delete')
 
 		# a script file for HCopy 
 		print(">>> making a script file for HCopy...")
@@ -209,12 +227,15 @@ if extract_features:
 			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]
+
+		#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'))
 		
 		# extract features.
 		print(">>> extracting features on {}...".format(dataset))
-		pyhtk.wav2mfc(config_hcopy, hcopy_scp.name)
+		chtk.wav2mfc(hcopy_scp.name)
 		os.remove(hcopy_scp.name)
 
 		# make hcompv.scp.
@@ -225,117 +246,321 @@ if extract_features:
 		with open(hcompv_scp, 'wb') as f:
 			f.write(bytes('\n'.join(mfc_list) + '\n', 'ascii'))
 
+		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 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 ====')
-	pyhtk.flat_start(config_train, hcompv_scp_train, model0_dir, prototype)
+	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
 	print('>>> allocating mean & variance to all phones in the phone list...')
-	pyhtk.create_hmmdefs(
-		os.path.join(model0_dir, proto_name),
-	    os.path.join(model0_dir, 'hmmdefs'), 
-		phonelist_txt)
-
-	# make macros
-	print('>>> making macros...')
-	with open(os.path.join(model0_dir, 'vFloors')) as f:
-		lines = f.read()
-	with open(os.path.join(model0_dir, 'macros'), 'wb') as f:
-		f.write(bytes('~o <MFCC_0_D_A> <VecSize> 39\n' + lines, 'ascii'))
+	chtk.make_hmmdefs(model_mono0_dir)
 	
 	print("elapsed time: {}".format(time.time() - timer_start))
 
 
 ## ======================= train model without short pause =======================
-if train_model_without_sp:
-	fh.make_new_directory(model1_dir)
+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('==== train model without sp ====')
-	if not os.path.exists(os.path.join(model1_dir, 'iter0')):
-		shutil.copytree(model0_dir, os.path.join(model1_dir, 'iter0'))
-	for niter in range(1, niter_max):
-		timer_start = time.time()
-		hmm_n = 'iter' + str(niter)
-		hmm_n_pre = 'iter' + str(niter-1)
-		modeln_dir	   = os.path.join(model1_dir, hmm_n)
-		modeln_dir_pre = os.path.join(model1_dir, hmm_n_pre) 
-		
-		# re-estimation
-		fh.make_new_directory(modeln_dir)
-		pyhtk.re_estimation(
-			config_train,
-			os.path.join(modeln_dir_pre, 'macros'),
-			os.path.join(modeln_dir_pre, hmmdefs_name), 
-			modeln_dir,
-			hcompv_scp_train, phonelist_txt,
-			mlf_file=mlf_file_train)
-		print("elapsed time: {}".format(time.time() - timer_start))
+	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('>>> modifying the last model in the previous step...')
-	modeln_dir_pre = os.path.join(model1_dir, 'iter'+str(niter_max-1))
-	modeln_dir = modeln_dir_pre.replace('iter' + str(niter_max-1), 'iter' + str(niter_max))
-	fh.make_new_directory(modeln_dir)
-	shutil.copy(
-		os.path.join(modeln_dir_pre, 'macros'), 
-		os.path.join(modeln_dir, 'macros'))
-	shutil.copy(
-		os.path.join(modeln_dir_pre, hmmdefs_name), 
-		os.path.join(modeln_dir, hmmdefs_name))
+	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')
 
-	## ======================= 
-	## manually make changes to modeln_dir/hmmdefs 
-	## ======================= 
-	# add states 'sil'.
-	# http://www.f.waseda.jp/yusukekondo/htk.html#flat_start_estimation
-	#shutil.copy(
-	#	os.path.join(model_dir, 'hmmdefs.txt'), 
-	#	os.path.join(modeln_dir, hmmdefs_name))
-
-	#hmmdefs_file_pre = os.path.join(modeln_dir_pre, hmmdefs_name)
-	hmmdefs_file = os.path.join(modeln_dir, hmmdefs_name)
-	macros_file  = os.path.join(modeln_dir, 'macros')
-	#with open(hmmdefs_file_pre) as f:
-	#	lines = f.read()
-	#lines_ = lines.split('~h ')
-	#sil_model = [line for line in lines_ if line.split('\n')[0].replace('"', '') == 'sil'][0]
+	chtk.add_sp(modeln_dir_pre, modeln_dir)
 	
-	# update hmmdefs and macros.
-	print('>>> updating hmmdefs and macros...')
-	modeln_dir_pre = modeln_dir
-	modeln_dir = modeln_dir.replace('iter' + str(niter_max), 'iter' + str(niter_max+1))
-	fh.make_new_directory(modeln_dir)
-	pyhtk.include_sil_in_hmmdefs(macros_file, hmmdefs_file, modeln_dir, sil_hed, phonelist_txt)
+	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))
 
 
-## ======================= train model with short pause =======================
-if train_model_with_sp:
-	print('==== train model with sp ====')
-	#for niter in range(niter_max+1, niter_max*2+1):
-	for niter in range(20, 50):
-		timer_start = time.time()
+## ======================= 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(model1_dir, hmm_n)
-		modeln_dir_pre = os.path.join(model1_dir, hmm_n_pre) 
+		_modeln_dir	    = os.path.join(output_dir, hmm_n)
+		_modeln_dir_pre = os.path.join(output_dir, hmm_n_pre) 
 		
-		# re-estimation
-		fh.make_new_directory(modeln_dir)
-		pyhtk.re_estimation(
-			config_train,
-			os.path.join(modeln_dir_pre, 'macros'),
-			os.path.join(modeln_dir_pre, hmmdefs_name), 
-			modeln_dir,
-			hcompv_scp_train, phonelist_txt,
-			mlf_file=mlf_file_train)
-		print("elapsed time: {}".format(time.time() - timer_start))
+		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

@@ -109,30 +109,30 @@ np.save(os.path.join('phoneset', 'fame_ipa2asr.npy'), translation_key_ipa2asr)
 
 
 ## 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('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()
+#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')
+#	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)
+#	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)
+#		#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

@@ -11,6 +11,7 @@ import glob
 
 import numpy as np
 import pandas as pd
+from collections import Counter
 #import matplotlib.pyplot as plt
 #from sklearn.metrics import confusion_matrix
 
@@ -50,11 +51,14 @@ from htk import pyhtk
 #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
+# procedure
+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_data_files = 0
+make_kaldi_files = 0
 #make_kaldi_lexicon_txt = 0
 #load_forced_alignment_kaldi = 1
 #eval_forced_alignment_kaldi = 1
@@ -66,13 +70,34 @@ from htk import pyhtk
 #sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
 #from evaluation import plot_confusion_matrix
 
-config_dir = os.path.join(default.htk_dir, 'config')
-model_dir = os.path.join(default.htk_dir, 'model')
-lattice_file = os.path.join(config_dir, 'stimmen.ltc')
-#pyhtk.create_word_lattice_file(
-#	os.path.join(config_dir, 'stimmen.net'), 
-#	lattice_file)
-hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.scp')
+## 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 ======================
@@ -85,392 +110,478 @@ 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)
 
-# pronunciation variants
+
+## ======================= 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:
-	df_ = df[df['word']==word]
-	print('{0} has {1} variants'.format(word, len(np.unique(df_['htk'])))
+	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)
 
-#fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
 
-#output = pyhtk.recognition(
-#	os.path.join(default.htk_dir, 'config', 'config.rec', 
-#	lattice_file, 
-#	os.path.join(model_dir, 'hmm1', 'iter13'),
-#	dictionary_file, 
-#	os.path.join(config_dir, 'phonelist.txt'), 
-#	hvite_scp)
+## ======================= 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)
 
-			#pyhtk.create_label_file(
-			#	row['word'], 
-			#	os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
+	# 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 a HTK dic file ======================
-#if make_htk_dic_file:
-#    output_type = 3
-dictionary_txt = os.path.join(default.htk_dir, 'lexicon', 'stimmen.dic') 
-#for word in word_list:
-word = word_list[2]
-# pronunciation variant of the target word.
-pronunciations = df_test['asr'][df_test['word'].str.match(word)]
+	# 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
+	#])
 
-        # 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_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):
 	"""
@@ -174,6 +173,9 @@ def forced_alignment(wav_file, word, pronunciation_ipa):
 	p = argparse.ArgumentParser()
 	p.add_argument("--user", default='martijn.wieling')
 	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

@@ -68,14 +68,24 @@ phoneset = [
 # 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'
+	'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:', 'ɔ̈:']
+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 not i in phones_to_be_removed]
+		 if i not in phones_to_be_removed]
+
 phoneset_short = list(set(phone_reduction(phoneset)))
 phoneset_short.sort()
 
@@ -89,14 +99,15 @@ translation_key_asr2htk = {
 	'ṷ': 'u_',
 
 	# on the analogy of German umlaut, 'e' is used.
-	'ö': 'oe', 'ö:': 'oe:',
+	'ö': 'oe', 'ö:': 'oe:', ''
 	'ü': 'ue', 'ü:': 'ue:',
 
 	# on the analogy of Chinese...
 	'ŋ': 'ng',
 				
 	# refer to Xsampa. 
-	'ɔ': 'O', 'ɔ:': 'O:', 'ɔ̈': 'Oe',
+	'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:', 
 
@@ -120,7 +131,11 @@ translation_key_word2htk = {
 	'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. 

acoustic_model/phoneset/fame_ipa.py

@@ -61,7 +61,7 @@ phoneset = [
 	'ɔⁿ',
 	'ɔ:',
 	'ɔ:ⁿ',
-	#'ɔ̈', # not included in lex.ipa 
+	'ɔ̈', # not included in lex.ipa 
 	'ɔ̈.',
 	'ɔ̈:',
 

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

@@ -7,6 +7,7 @@ import pandas as pd
 import convert_xsampa2ipa
 import defaultfiles as default
 import fame_functions
+import novoapi_functions
 
 
 def _load_transcriptions():
@@ -67,6 +68,19 @@ def load_transcriptions_clean(clean_wav_dir):
 	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 = []
@@ -81,3 +95,25 @@ def add_row_asr(df):
 	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

@@ -2,8 +2,9 @@ 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 numpy as np
 import pandas as pd
 
 import defaultfiles as default
@@ -62,3 +63,31 @@ for ipa in df['ipa']:
 	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)
+