label alignment using HVite is added.

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>htk_vs_kaldi.py</StartupFile>
     <SearchPath>
     </SearchPath>
     <WorkingDirectory>.</WorkingDirectory>

acoustic_model/convert_phoneset.py

@@ -38,3 +38,9 @@ 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]

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

@@ -321,9 +321,11 @@ 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):
+def fix_lexicon(lexicon_file):
-	""" add '\' before all single quote at the beginning of words.
+	""" fix lexicon 
-	convert special characters to ascii compatible characters.
+	- 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.
@@ -331,6 +333,12 @@ 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.
@@ -346,10 +354,11 @@ 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)
 
 
@@ -360,5 +369,6 @@ def ipa2htk(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)
 	htk_splitted = convert_phoneset.convert_phoneset(asr_splitted, fame_asr.translation_key_asr2htk)
 	return ''.join(htk_splitted)

acoustic_model/fame_hmm.py

@@ -27,7 +27,8 @@ extract_features  = 0
 flat_start		  = 0
 train_model_without_sp = 0
 add_sp = 0
-train_model_with_sp    = 1
+train_model_with_sp    = 0
+train_model_with_sp_align_mlf = 1
 
 
 
@@ -75,6 +76,7 @@ if not os.path.exists(label_dir):
 ## training
 hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
 mlf_file_train   = os.path.join(label_dir, 'train_phone.mlf')
+mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
 
 ## train without sp
 niter_max = 10
@@ -102,7 +104,8 @@ if make_lexicon:
 	# (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)
+	print('>>> fixing the lexicon...')
+	fame_functions.fix_lexicon(lexicon_htk)
 	print("elapsed time: {}".format(time.time() - timer_start))
 
 
@@ -269,11 +272,11 @@ if train_model_without_sp:
 		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)
+			mlf_file=mlf_file_train, 
+			macros=os.path.join(modeln_dir_pre, 'macros'))
 		print("elapsed time: {}".format(time.time() - timer_start))
 
 
@@ -321,7 +324,6 @@ if add_sp:
 ## ======================= 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()
 		hmm_n = 'iter' + str(niter)
@@ -333,9 +335,31 @@ if train_model_with_sp:
 		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)
+			mlf_file=mlf_file_train, 
+			macros=os.path.join(modeln_dir_pre, 'macros'))
+		print("elapsed time: {}".format(time.time() - timer_start))
+		
+
+## ======================= train model with short pause =======================
+if train_model_with_sp_align_mlf:
+	print('==== train model with sp with align.mlf ====')
+	for niter in range(50, 60):
+		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, hmmdefs_name), 
+			modeln_dir,
+			hcompv_scp_train, phonelist_txt,
+			mlf_file=mlf_file_train_aligned, 
+			macros=os.path.join(modeln_dir_pre, 'macros'))
 		print("elapsed time: {}".format(time.time() - timer_start))

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 = 1
+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
 
+## 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')
-#pyhtk.create_word_lattice_file(
+phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
-#	os.path.join(config_dir, 'stimmen.net'), 
+stimmen_dic = os.path.join(default.htk_dir, 'lexicon', 'stimmen_recognition.dic')
-#	lattice_file)
+hcopy_scp   = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hcopy.scp')
-hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.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 ======================
@@ -85,36 +110,159 @@ 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]
-	print('{0} has {1} variants'.format(word, len(np.unique(df_['htk'])))
+			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'))
 
-#fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
+	# 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'))
 
-#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)
 
-			#pyhtk.create_label_file(
+## ======================= test data for recognition ======================
-			#	row['word'], 
+# only target pronunciation variants.
-			#	os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
+df_rec = pd.DataFrame(index=[], columns=list(df.keys()))
+for word in word_list:
+	variants = [htk.replace(' ', '') 
+				   for htk in stimmen_functions.load_pronunciations(word.upper(), stimmen_dic)]
+	df_ = df[df['word'] == word]
+	for index, row in df_.iterrows():
+		if row['htk'] in variants:
+			df_rec = df_rec.append(row, ignore_index=True)
 
-## ======================= make 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 dic file.
+## ======================= make files required for HTK ======================
-        #am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
+if make_HTK_files:
+	# make a word lattice file.
+	pyhtk.create_word_lattice_file(
+		os.path.join(config_dir, 'stimmen.net'), 
+		lattice_file)
+
+	# extract features.
+	with open(hcopy_scp, 'wb') as f:
+		filelist = [os.path.join(stimmen_test_dir, filename) + '\t' 
+	+ os.path.join(feature_dir, os.path.basename(filename).replace('.wav', '.mfc')) 
+				for filename in df['filename']]
+		f.write(bytes('\n'.join(filelist), 'ascii'))
+	pyhtk.wav2mfc(config_hcopy, hcopy_scp)
+
+	# make label files.
+	for index, row in df.iterrows():
+		filename = row['filename'].replace('.wav', '.lab')
+		label_file = os.path.join(feature_dir, filename)
+		with open(label_file, 'wb') as f:
+			label_string = 'START\n' + row['word'].upper() + '\nEND\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(1, 50):
+	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']))
+
 
 
 ## ======================= forced alignment using HTK =======================
@@ -168,54 +316,7 @@ if do_forced_alignment_htk:
 		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 =======================

acoustic_model/phoneset/fame_asr.py

@@ -68,14 +68,21 @@ 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.
+	"""
 	return [reduction_key.get(i, i) for i in phones
 				  if not i in phones_to_be_removed]
+
 phoneset_short = list(set(phone_reduction(phoneset)))
 phoneset_short.sort()
 
@@ -97,6 +104,7 @@ translation_key_asr2htk = {
 				
 	# refer to Xsampa. 
 	'ɔ': 'O', 'ɔ:': 'O:', 'ɔ̈': 'Oe', 
+	#'ɔ̈:': 'O:', # does not appear in FAME, but used in stimmen.
 	'ɛ': 'E', 'ɛ:': 'E:',
 	'ɪ': 'I', 'ɪ:': 'I:', 
 

acoustic_model/stimmen_functions.py

@@ -81,3 +81,25 @@ 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 +63,18 @@ 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)