improve train.py

backend/backend/cms/model/train.py

@@ -97,37 +97,47 @@ def train():
 
     train_audio_path = r'./backend/data/train/train/audio/'
 
-    # all_wave = []
-    # all_label = []
-    # for label in labels:
-    #     print(label)
-    #     waves = [f for f in os.listdir(
-    #         train_audio_path + '/' + label) if f.endswith('.wav')]
-    #     for wav in waves:
-    #         samples, sample_rate = librosa.load(
-    #             train_audio_path + '/' + label + '/' + wav, sr=16000)
-    #         samples = librosa.resample(samples, sample_rate, 8000)
-    #         if(len(samples) == 8000):
-    #             all_wave.append(samples)
-    #             all_label.append(label)
-
-    # print('3')
+    all_wave = []
+    all_label = []
 
     f1 = open('all_label.txt', 'rb')
     all_label = pickle.load(f1)
-    print('loaded labels')
 
     f2 = open('all_waves_file.txt', 'rb')
     all_wave = pickle.load(f2)
-    print('loaded waves')
+
+    if(all_wave and all_label):
+        print('loaded labels and waves')
+    else:
+        print('Creating labels and waves files')
+        for label in labels:
+            print(label)
+            waves = [f for f in os.listdir(
+                train_audio_path + '/' + label) if f.endswith('.wav')]
+            for wav in waves:
+                samples, sample_rate = librosa.load(
+                    train_audio_path + '/' + label + '/' + wav, sr=16000)
+                samples = librosa.resample(samples, sample_rate, 8000)
+                if(len(samples) == 8000):
+                    all_wave.append(samples)
+                    all_label.append(label)
+
+        # print('3')
+
+        all_labels_file = open('all_label.txt', 'wb+')
+        pickle.dump(file=all_labels_file, obj=all_label)
+        all_labels_file.close()
+
+        all_waves_file = open('all_waves_file.txt', 'wb+')
+        pickle.dump(file=all_waves_file, obj=all_wave)
+        all_waves_file.close()
+
+        print('Done: creating labels and waves files')
 
     le = LabelEncoder()
     y = le.fit_transform(all_label)
     classes = list(le.classes_)
 
-    print(classes)
-    print(all_wave)
-
     print('4')
 
     y = np_utils.to_categorical(y, num_classes=len(labels))