confusion matrix is output.

acoustic_model/check_novoapi.py

@@ -3,23 +3,27 @@ os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
 
 import sys
 import csv
-#import subprocess
-#from collections import Counter
-#import re
 
 import numpy as np
 import pandas as pd
-#import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt
-#from sklearn.metrics import confusion_matrix
+
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import accuracy_score
+import novoapi 
+
+from forced_alignment import pyhtk, convert_phone_set
 
 import acoustic_model_functions as am_func
 import convert_xsampa2ipa
 import defaultfiles as default
-
-from forced_alignment import pyhtk, convert_phone_set
-
-import novoapi 
 import novoapi_functions
+sys.path.append(default.accent_classification_dir)
+import output_confusion_matrix
+
+## procedure 
+forced_alignment_novo70 = True
+
 
 ## ===== load novo phoneset =====
 phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa = novoapi_functions.load_phonset()
@@ -145,9 +149,9 @@ for word in word_list:
 
 
 ## ===== forced alignment =====
-if forced_alignment:
+if forced_alignment_novo70:
 	Results = pd.DataFrame(index=[],
-		columns=['filename', 'ipa', 'word', 'result_ipa', 'result_novo70', 'llh'])
+		columns=['filename', 'word', 'ipa', 'result_ipa', 'result_novo70', 'llh'])
 	for word in word_list:
 	#for word in ['Oor']:
 		# pronunciation variants top 3
@@ -176,7 +180,7 @@ if forced_alignment:
 		samples = df_.query("ipa in @pronunciation_ipa")
 
 		results = pd.DataFrame(index=[],
-			columns=['filename', 'ipa', 'word', 'result_ipa', 'result_novo70', 'llh'])
+			columns=['filename', 'word', 'ipa', 'result_ipa', 'result_novo70', 'llh'])
 	
 		#j = 0
 		for i in range(0, len(samples)):
@@ -205,5 +209,23 @@ if forced_alignment:
 		Results.to_excel(os.path.join(default.stimmen_dir, 'Results.xlsx'), encoding="utf-8")
 else:
 	Results_xlsx = pd.ExcelFile(os.path.join(default.stimmen_dir, 'Results.xlsx'), encoding="utf-8")
-	R = pd.read_excel(Results_xlsx, 'Sheet1')
+	Results = pd.read_excel(Results_xlsx, 'Sheet1')
 
+
+## ===== analysis =====
+result_novoapi_dir = os.path.join(default.stimmen_dir, 'result', 'novoapi')
+for word in word_list:
+	if not word == 'Oog':
+#word = 'Reus'
+
+		Results_ = Results[Results['word'] == word]
+		y_true  = list(Results_['ipa'])
+		y_pred_ = [ipa.replace(' ', '') for ipa in list(Results_['result_ipa'])]
+		y_pred  = [ipa.replace('ː', ':') for ipa in y_pred_]
+		pronunciation_variants = list(set(y_true))
+		cm = confusion_matrix(y_true, y_pred, labels=pronunciation_variants)
+
+		plt.figure()
+		output_confusion_matrix.plot_confusion_matrix(cm, pronunciation_variants, normalize=False)
+		#plt.show()
+		plt.savefig(os.path.join(result_novoapi_dir, word + '.png'))

acoustic_model/defaultfiles.py

@@ -29,6 +29,7 @@ config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
 repo_dir = r'C:\Users\Aki\source\repos'
 ipa_xsampa_converter_dir    = os.path.join(repo_dir, 'ipa-xsama-converter')
 forced_alignment_module_dir = os.path.join(repo_dir, 'forced_alignment')
+accent_classification_dir   = os.path.join(repo_dir, 'accent_classification', 'accent_classification')
 
 WSL_dir   = r'C:\OneDrive\WSL'
 fame_dir        = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')