The Stimmen excel file is loaded as Data Frame. Default values are given by defaultfiles.py.

acoustic_model/performance_check.py

@@ -10,7 +10,7 @@ import re
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-#from sklearn.metrics import confusion_matrix
+from sklearn.metrics import confusion_matrix
 
 import acoustic_model_functions as am_func
 import convert_xsampa2ipa
@@ -31,22 +31,31 @@ excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian V
 #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')
+#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')
+fa_dir  = os.path.join(default.experiments_dir, 'stimmen', 'FA_44k')
+result_dir = os.path.join(default.experiments_dir, 'stimmen', 'result')
+
+kaldi_data_dir = os.path.join(default.kaldi_dir, 'data', 'alignme') 
+kaldi_dict_dir = os.path.join(default.kaldi_dir, 'data', 'local', 'dict')
+lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
 
 #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
-do_forced_alignment_htk = 1
+do_forced_alignment_htk = 0
 make_kaldi_data_files = 0
 make_kaldi_lexicon_txt = 0
-load_forced_alignment_kaldi = 0
+load_forced_alignment_kaldi = 1
 eval_forced_alignment = 0
 
 
@@ -115,11 +124,11 @@ df = pd.DataFrame({'filename': df['Filename'],
 # cleansing. 
 df = df[~df['famehtk'].isin(['/', ''])]
 
+word_list = np.unique(df['word'])
+
 
 ## ======================= make dict files used for HTK. ======================
 if make_dic_files:
-    word_list = np.unique(df['word'])
-
     output_type = 3
     
     for word in word_list:
@@ -134,61 +143,73 @@ if make_dic_files:
 
 ## ======================= forced alignment using HTK =======================
 if do_forced_alignment_htk:
-
+    
     #hmm_num = 2
-    for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
-
+    #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)
         acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
 
-        predictions = []
+        predictions = pd.DataFrame({'filename': [''], 
+                    'word': [''], 
+                    'xsampa': [''],
+                    'ipa': [''],
+                    'famehtk': [''],
+                    'prediction': ['']})
         for i, filename in enumerate(df['filename']):
             print('=== {0}/{1} ==='.format(i, len(df)))
-            wav_file = os.path.join(wav_dir, filename)
-    
-            if os.path.exists(wav_file) and i in df['filename'].keys():
-                word = df['word'][i]
-                WORD = word.upper()
-
-                # 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')
-                fa_file       = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
-                pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, default.phonelist, acoustic_model)
-
-                prediction = am_func.read_fileFA(fa_file)
-                predictions.append(prediction)
-
-                os.remove(label_file)
-                print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
+            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:
-                predictions.append('')
-                print('!!!!! file not found.')
+                prediction = ''
+                #predictions.append('')
+                print('!!!!! invalid entry.')
 
-        predictions = np.array(predictions)
-        #match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
-        np.save(os.path.join(data_dir, 'predictions_hmm' + hmm_num_str + '.npy'), predictions)
+
+        #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'))
 
 
 ## ======================= make files which is used for forced alignment by Kaldi =======================
 if make_kaldi_data_files:
-    wav_dir	 = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
-    kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
-    kaldi_data_dir = os.path.join(kaldi_work_dir, 'data', 'alignme') 
-    kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
-    htk_dict_dir   = os.path.join(experiments_dir, 'stimmen', 'dic_top3')
 
     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')
 
-    lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
-
-    predictions = []
-    file_num_max = len(filenames)
+    #predictions = []
+    #file_num_max = len(filenames)
 
     # remove previous files.
     if os.path.exists(wav_scp):
@@ -203,30 +224,42 @@ if make_kaldi_data_files:
     f_utt2spk   = open(utt2spk, 'a', encoding="utf-8", newline='\n')
 
     # make wav.scp, text, and utt2spk files.
-    for i in range(0, file_num_max):
+    predictions = pd.DataFrame({'filename': [''], 
+                'word': [''], 
+                'xsampa': [''],
+                'ipa': [''],
+                'famehtk': [''],
+                'prediction': ['']})
+    #for i in range(0, file_num_max):
     #for i in range(400, 410):
-        print('=== {0}/{1} ==='.format(i+1, file_num_max))
-        filename = filenames[i]
-        wav_file = wav_dir + '\\' + filename
+    for i, filename in enumerate(df['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
+        #print('=== {0}/{1} ==='.format(i+1, file_num_max))
+        #filename = filenames[i]
 
-            # wav.scp file
-            wav_file_unix = wav_file.replace('\\', '/')
-            wav_file_unix = wav_file_unix.replace('c:/', '/mnt/c/')
+        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)
+                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))
+                f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix))
 
-            # text file
-            word = words[i].lower()
-            f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
+                # text file
+                #word = words[i].lower()
+                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))
+                # utt2spk
+                f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
 
     f_wav_scp.close()
     f_text_file.close()
@@ -235,54 +268,58 @@ if make_kaldi_data_files:
 
 ## ======================= make lexicon txt which is used by Kaldi =======================
 if make_kaldi_lexicon_txt:
-    kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
-    kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
-    lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
+    #lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
     option_num = 5
 
     # remove previous file.
     if os.path.exists(lexicon_txt):
         os.remove(lexicon_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', ':')
+    lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.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)
+    #            # 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'
+    ## 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)
+    #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')
+    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]
+        #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_==''))
+        #pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
 
-        c = Counter(pronvar_)
+        c = Counter(pronunciation_variants)
         total_num = sum(c.values())
 
         for key, value in c.most_common(option_num):
@@ -291,7 +328,9 @@ if make_kaldi_lexicon_txt:
             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))
@@ -320,23 +359,24 @@ if make_kaldi_lexicon_txt:
     # output
     pronvar_list_all = np.array(pronvar_list_all)
     pronvar_list_all = np.unique(pronvar_list_all, axis=0)
-    #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.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:
     kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
-    phones_txt = kaldi_work_dir + '\\data\\lang\\phones.txt'
-    merged_alignment_txt = kaldi_work_dir + '\\exp\\tri1_alignme\\merged_alignment.txt'
+    phones_txt = os.path.join(kaldi_work_dir, 'data', 'lang', 'phones.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')
-    pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy')
-    pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy')
-    word_list = np.unique(words)    
+    #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:
@@ -379,59 +419,108 @@ if load_forced_alignment_kaldi:
     # 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:
-    match_num = []
-    for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
-    #hmm_num = 256
+    htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
+
+    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")
+
+    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)
-        match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
-    
-        # use dic_short?
-        if 1:
-            pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
-            for word in word_list:
-                fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
-                pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
+        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'))
+
+
+        # 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.
-            match_short = []
-            for line in match:
-                word = line[0]
-                WORD = word.upper()
-                pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
-        
-                if line[1] in pronvar:
-                    match_short.append(line)
+            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_short = np.array(match_short)
-            match = np.copy(match_short)
+            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'])
 
-            # number of match
-            total_match = sum(match[:, 1] == match[:, 2])
-            print("{}: {}/{}".format(hmm_num_str, total_match, match.shape[0]))
-            match_num.append([hmm_num, total_match, match.shape[0]])
+                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')
 
-    # number of mixtures vs accuracy
-    match_num = np.array(match_num)
-    plt.xscale("log")
-    plt.plot(match_num[:, 0], match_num[:, 1]/match_num[0, 2], 'o-')
-    plt.xlabel('number of mixtures', fontsize=14, fontweight='bold')
-    plt.ylabel('accuracy', fontsize=14, fontweight='bold')
-    plt.show()
+    if compare_hmm_num:
+        f_result.close()
 
-    # confusion matrix
-    #dir_out   = r'C:\OneDrive\Research\rug\experiments\stimmen\result'
-    #word_list = np.unique(match[:, 0])
-    
-    #for word in word_list:
-    #    match_ = match[match[:, 0] == word, :]
-    #    cm = confusion_matrix(match_[:, 1], match_[:, 2])
-    #    pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
-
-    #    plt.figure()
-    #    plot_confusion_matrix(cm, classes=pronvar, normalize=True)
-    #    plt.savefig(dir_out + '\\cm_' + word + '.png')