Code is cleaned up.

acoustic_model.sln

@@ -11,6 +11,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
 		..\forced_alignment\forced_alignment\convert_phone_set.py = ..\forced_alignment\forced_alignment\convert_phone_set.py
 		..\toolbox\evaluation.py = ..\toolbox\evaluation.py
 		..\forced_alignment\forced_alignment\forced_alignment.pyproj = ..\forced_alignment\forced_alignment\forced_alignment.pyproj
+		..\forced_alignment\forced_alignment\htk_dict.py = ..\forced_alignment\forced_alignment\htk_dict.py
 		..\forced_alignment\forced_alignment\lexicon.py = ..\forced_alignment\forced_alignment\lexicon.py
 		..\forced_alignment\forced_alignment\mlf.py = ..\forced_alignment\forced_alignment\mlf.py
 		..\forced_alignment\forced_alignment\pronunciations.py = ..\forced_alignment\forced_alignment\pronunciations.py

acoustic_model/acoustic_model_functions.py

@@ -38,7 +38,7 @@ def make_filelist(input_dir, output_txt):
 			fout.write(input_dir + '\\' + filename + '\n')
 
 
-def make_dic(word, pronvar_, fileDic, output_type):
+def make_htk_dict(word, pronvar_, fileDic, output_type):
     """
     make dict files which can be used for HTK.
     param word: target word.
@@ -98,8 +98,8 @@ def find_phone(lexicon_file, phone):
 	for line in lines:
 		line = line.split('\t')
 		if len(line) > 1:
-			pron = line[1]
+			pronunciation = line[1]
-			if phone in pron:
+			if phone in pronunciation:
 				extracted.append(line)
 	return extracted
 
@@ -149,3 +149,54 @@ def read_fileFA(fileFA):
             phones.append(line_split[2])
 
     return ' '.join(phones)
+
+
+def fame_pronunciation_variant(ipa):
+    ipa = ipa.replace('æ', 'ɛ')
+    ipa = ipa.replace('ɐ', 'a')
+    ipa = ipa.replace('ɑ', 'a')
+    ipa = ipa.replace('ɾ', 'r')
+    ipa = ipa.replace('ɹ', 'r') # ???
+    ipa = ipa.replace('ʁ', 'r')
+    ipa = ipa.replace('ʀ', 'r') # ???
+    ipa = ipa.replace('ʊ', 'u')
+    ipa = ipa.replace('χ', 'x')
+
+    pronvar_list = [ipa]
+    while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
+        pronvar_list_ = []
+        for p in pronvar_list:
+            if 'ø:' in p:
+                pronvar_list_.append(p.replace('ø:', 'ö'))
+                pronvar_list_.append(p.replace('ø:', 'ö:'))
+            if 'œ' in p:
+                pronvar_list_.append(p.replace('œ', 'ɔ̈'))
+                pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
+            if 'ɒ' in p:
+                pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
+                pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
+        pronvar_list = np.unique(pronvar_list_)
+    return pronvar_list
+
+
+def make_fame2ipa_variants(fame):
+    fame = 'rɛös'
+    ipa = [fame]
+    ipa.append(fame.replace('ɛ', 'æ'))
+    ipa.append(fame.replace('a', 'ɐ'))
+    ipa.append(fame.replace('a', 'ɑ'))
+    ipa.append(fame.replace('r', 'ɾ'))
+    ipa.append(fame.replace('r', 'ɹ'))
+    ipa.append(fame.replace('r', 'ʁ'))
+    ipa.append(fame.replace('r', 'ʀ'))
+    ipa.append(fame.replace('u', 'ʊ'))
+    ipa.append(fame.replace('x', 'χ'))
+        
+    ipa.append(fame.replace('ö', 'ø:'))
+    ipa.append(fame.replace('ö:', 'ø:'))
+    ipa.append(fame.replace('ɔ̈', 'œ'))
+    ipa.append(fame.replace('ɔ̈:', 'œ'))
+    ipa.append(fame.replace('ɔ̈', 'ɒ'))
+    ipa.append(fame.replace('ɔ̈:', 'ɒ'))
+
+    return ipa

acoustic_model/defaultfiles.py

@@ -3,6 +3,7 @@ import os
 #default_hvite_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', 'htk', 'config.HVite')
 
 cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
+kaldi_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
 #config_hcopy = os.path.join(cygwin_dir, 'config', 'config.HCopy')
 #config_train = os.path.join(cygwin_dir, 'config', 'config.train')
 config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')

acoustic_model/performance_check.py

@@ -16,25 +16,15 @@ import acoustic_model_functions as am_func
 import convert_xsampa2ipa
 import defaultfiles as default
 
+from forced_alignment import pyhtk
+
 
 ## ======================= user define =======================
-#curr_dir   = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
-#config_ini = 'config.ini'
-#repo_dir   = r'C:\Users\Aki\source\repos'
-#forced_alignment_module     = repo_dir + '\\forced_alignment'
-#forced_alignment_module_old = repo_dir + '\\aki_tools'
-#ipa_xsampa_converter_dir    = repo_dir + '\\ipa-xsama-converter'
-#accent_classification_dir   = repo_dir + '\\accent_classification\accent_classification'
 excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
-
-
-#experiments_dir = r'C:\OneDrive\Research\rug\experiments'
 data_dir   = os.path.join(default.experiments_dir, 'stimmen', 'data')
-#csvfile         = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
+
-#wav_dir	= os.path.join(default.experiments_dir, 'stimmen', 'wav_44k') # 44.1k
 wav_dir	= r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen' # 16k
 
-#wav_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen' # 16k
 acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
 htk_dict_dir       = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
 fa_dir             = os.path.join(default.experiments_dir, 'stimmen', 'FA_44k')
@@ -44,35 +34,32 @@ kaldi_data_dir = os.path.join(default.kaldi_dir, 'data', 'alignme')
 kaldi_dict_dir = os.path.join(default.kaldi_dir, 'data', 'local', 'dict')
 lexicon_txt    = os.path.join(kaldi_dict_dir, 'lexicon.txt')
 
-#cygwin_dir      = r'C:\cygwin64\home\Aki\acoustic_model'
 #lex_asr	 = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
 #lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
 
-from forced_alignment import pyhtk
 
 # procedure
-make_dic_files = 0
+make_htk_dict_files = 0
 do_forced_alignment_htk = 0
+eval_forced_alignment_htk = 0
 make_kaldi_data_files = 0
 make_kaldi_lexicon_txt = 0
 load_forced_alignment_kaldi = 1
-eval_forced_alignment = 0
+eval_forced_alignment_kaldi = 1
+
 
 
 
 ## ======================= add paths =======================
-
 sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
 from forced_alignment import convert_phone_set
 from forced_alignment import pyhtk
 
 sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
-#import pyHTK
 from evaluation import plot_confusion_matrix
 
 
 ## ======================= convert phones ======================
-
 mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
 
 xls = pd.ExcelFile(excel_file)
@@ -128,7 +115,7 @@ word_list = np.unique(df['word'])
 
 
 ## ======================= make dict files used for HTK. ======================
-if make_dic_files:
+if make_htk_dict_files:
     output_type = 3
     
     for word in word_list:
@@ -138,14 +125,12 @@ if make_dic_files:
         pronvar_ = df['famehtk'][df['word'].str.match(word)]
 
         # make dic file.
-        am_func.make_dic(word, pronvar_, htk_dict_file, output_type)
+        am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
         
 
 ## ======================= forced alignment using HTK =======================
 if do_forced_alignment_htk:
     
-    #hmm_num = 2
-    #for hmm_num in [1]:
     #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)
@@ -178,26 +163,19 @@ if do_forced_alignment_htk:
                                 default.phonelist, acoustic_model)
                     os.remove(label_file)
 
-
                     prediction = am_func.read_fileFA(fa_file)
-                    #predictions.append(prediction)
 
                     print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
                 else:
                     prediction = ''
-                    #predictions.append('')
                     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 = ''
-                #predictions.append('')
                 print('!!!!! invalid entry.')
 
-
-        #predictions = np.array(predictions)
-        #np.save(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'), predictions)
         predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
 
 
@@ -208,9 +186,6 @@ if make_kaldi_data_files:
     text_file = os.path.join(kaldi_data_dir, 'text')
     utt2spk   = os.path.join(kaldi_data_dir, 'utt2spk')
 
-    #predictions = []
-    #file_num_max = len(filenames)
-
     # remove previous files.
     if os.path.exists(wav_scp):
         os.remove(wav_scp)
@@ -224,22 +199,11 @@ if make_kaldi_data_files:
     f_utt2spk   = open(utt2spk, 'a', encoding="utf-8", newline='\n')
 
     # make wav.scp, text, and utt2spk files.
-    predictions = pd.DataFrame({'filename': [''], 
+    for i in df.index:
-                'word': [''], 
+        filename = df['filename'][i]
-                'xsampa': [''],
+        print('=== {0}: {1} ==='.format(i, filename))
-                'ipa': [''],
-                'famehtk': [''],
-                'prediction': ['']})
-    #for i in range(0, file_num_max):
-    #for i in range(400, 410):
-    for i, filename in enumerate(df['filename']):
 
-        #print('=== {0}/{1} ==='.format(i+1, file_num_max))
+        #if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
-        #filename = filenames[i]
-
-        print('=== {0}/{1} ==='.format(i, len(df)))
-        wav_file = wav_dir + '\\' + 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)
@@ -254,7 +218,6 @@ if make_kaldi_data_files:
             f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix))
 
             # text file
-                #word = words[i].lower()
             word = df['word'][i].lower()
             f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
 
@@ -268,8 +231,7 @@ if make_kaldi_data_files:
 
 ## ======================= make lexicon txt which is used by Kaldi =======================
 if make_kaldi_lexicon_txt:
-    #lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
+    option_num = 6
-    option_num = 5
 
     # remove previous file.
     if os.path.exists(lexicon_txt):
@@ -278,87 +240,35 @@ if make_kaldi_lexicon_txt:
     if os.path.exists(lexiconp_txt):
         os.remove(lexiconp_txt)
                 
-    #mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
-
-    #with open(csvfile, encoding="utf-8") as fin:
-    #    lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
-    #    next(lines, None) # skip the headers
-
-    #    filenames	   = []
-    #    words		   = []
-    #    pronunciations = []
-    #    p = []
-    #    for line in lines:
-    #        if line[1] is not '' and len(line) > 5:
-    #            filenames.append(line[0])
-    #            words.append(line[1])
-    #            pron_xsampa = line[3]
-    #            pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
-    #            pron_ipa = pron_ipa.replace('ː', ':')
-            
-    #            # adjust to phones used in the acoustic model.
-    #            pronunciations.append(pron_ipa)
-
-    ## check if all phones are in the phonelist of the acoustic model.
-    ##'y', 'b', 'ɾ', 'u', 'ɔ:', 'ø', 't', 'œ', 'n', 'ɒ', 'ɐ', 'f', 'o', 'k', 'x', 'ɡ', 'v', 's', 'ɛ:', 'ɪ:', 'ɑ', 'ɛ', 'a', 'd', 'z', 'ɪ', 'ɔ', 'l', 'i:', 'm', 'p', 'a:', 'i', 'e', 'j', 'o:', 'ʁ', 'h', ':', 'e:', 'ə', 'æ', 'χ', 'w', 'r', 'ə:', 'sp', 'ʊ', 'u:', 'ŋ'
-
-    #filenames	   = np.array(filenames)
-    #words		   = np.array(words)
-    #wordlist       = np.unique(words)
-    #pronunciations = np.array(pronunciations)
-                
     # 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.
-        #pronvar_ = pronunciations[words == word]
         pronunciation_variants = df['ipa'][df['word'].str.match(word)]
-        #pronunciation_variants = np.unique(pronunciation_variants)
-        # remove ''
-        #pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
 
         c = Counter(pronunciation_variants)
         total_num = sum(c.values())
 
-        for key, value in c.most_common(option_num):
+        #with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f:
-            #print('{0}\t{1}\t{2}\t{3}'.format(word, key, value, total_num))
+        #    for key in c.keys():
-            key = key.replace('æ', 'ɛ')
+        #        f.write("{0},{1}\n".format(key,c[key]))
-            key = key.replace('ɐ', 'a')
-            key = key.replace('ɑ', 'a')
-            key = key.replace('ɾ', 'r')
-            key = key.replace('ɹ', 'r') # ???
-            key = key.replace('ʁ', 'r')
-            key = key.replace('ʀ', 'r') # ???
-            key = key.replace('ʊ', 'u')
-            key = key.replace('χ', 'x')
-            #print('-->{0}\t{1}\t{2}\t{3}\n'.format(word, key, value, total_num))
 
+        for key, value in c.most_common(option_num):
             # make possible pronounciation variant list.
-            pronvar_list = [key]
+            pronvar_list = am_func.fame_pronunciation_variant(key)
-            while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
-                pronvar_list_ = []
-                for p in pronvar_list:
-                    if 'ø:' in p:
-                        pronvar_list_.append(p.replace('ø:', 'ö'))
-                        pronvar_list_.append(p.replace('ø:', 'ö:'))
-                    if 'œ' in p:
-                        pronvar_list_.append(p.replace('œ', 'ɔ̈'))
-                        pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
-                    if 'ɒ' in p:
-                        pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
-                        pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
-                pronvar_list = np.unique(pronvar_list_)
 
             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])
 
-    # output
     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]))
@@ -368,9 +278,8 @@ if make_kaldi_lexicon_txt:
 
 ## ======================= load kaldi forced alignment result =======================
 if load_forced_alignment_kaldi:
-    kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
+    phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
-    phones_txt = os.path.join(kaldi_work_dir, 'data', 'lang', 'phones.txt')
+    merged_alignment_txt = os.path.join(default.kaldi_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt')
-    merged_alignment_txt = os.path.join(kaldi_work_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt')
     
     #filenames	   = np.load(data_dir + '\\filenames.npy')
     #words		   = np.load(data_dir + '\\words.npy')
@@ -380,98 +289,72 @@ if load_forced_alignment_kaldi:
 
     # load the mapping between phones and ids.
     with open(phones_txt, 'r', encoding="utf-8") as f:
-        mappings = f.read().split('\n')
+        mapping_phone2id = f.read().split('\n')
 
     phones = []
-    phone_ids = []
+    phone_ids = [] # ID of phones
-    for m in mappings:
+    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')
 
-    fa_filenames = []
+    predictions = pd.DataFrame({'filename': [''], 
-    fa_pronunciations = []
+                'word': [''], 
-    filename_ = ''
+                'xsampa': [''],
-    pron = []
+                'ipa': [''],
+                'famehtk': [''],
+                'prediction': ['']})
+    #fa_filenames = []
+    #fa_pronunciations = []
+    utterance_id_ = ''
+    pronunciation = []
     for line in lines:
         line = line.split(' ')
         if len(line) == 5:
-            filename = line[0]
+            utterance_id = line[0]
-            if filename == filename_:
+            if utterance_id == utterance_id_:
                 phone_id = int(line[4])
                 #if not phone_id == 1:
-                phone    = phones[phone_ids.index(phone_id)]
+                phone_ = phones[phone_ids.index(phone_id)]
-                pron_ = re.sub(r'_[A-Z]', '', phone)
+                phone  = re.sub(r'_[A-Z]', '', phone_)
-                if not pron_ == 'SIL':
+                if not phone == 'SIL':
-                    pron.append(pron_)
+                    pronunciation.append(phone)
             else:
-                fa_filenames.append(re.sub(r'speaker_[0-9]{4}-', '', filename))
+                filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_)
-                fa_pronunciations.append(' '.join(pron))
+                prediction = ''.join(pronunciation)
-                pron = []
+                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 = []
 
-            filename_ = filename
+            utterance_id_ = utterance_id
-
+    predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
-    # correct or not.
-    #for filename, fa_pronunciation in zip(fa_filenames, fa_pronunciations):
-        
- 
-    #    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')
-
-    #                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)
-    #                #predictions.append(prediction)
-
-    #                print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
-    #            else:
-    #                prediction = ''
-    #                #predictions.append('')
-    #                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 = ''
-    #            #predictions.append('')
-    #            print('!!!!! invalid entry.')
-
-
-    #    #predictions = np.array(predictions)
-    #    #np.save(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'), predictions)
-    #    predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
 
 
 ## ======================= evaluate the result of forced alignment =======================
-if eval_forced_alignment:
+if eval_forced_alignment_htk:
     htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
 
     compare_hmm_num = 1
@@ -524,3 +407,81 @@ if eval_forced_alignment:
     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'))
+
+    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]
+
+    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(' ', ''))
+
+        # 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]
+
+            # 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
+
+                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'])
+
+        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'])
+
+        #plt.figure()
+        #plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
+        #plt.savefig(result_dir + '\\cm_' + word + '.png')