updated Audio testing file

Presently/presently/users/audioemotiontest.py

@@ -18,18 +18,9 @@ import time
 from os import path
 from pydub import AudioSegment
 
-#convert video files to .wav format
-# src = input("In: ")
-# dst = input("Out: ")
-# src = input("F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\media\\video\\22")
-# dst = input("F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\media\\audio")
-
-# sound = AudioSegment.from_mp3(src)
-# sound.export(dst, format="wav")
-
 saved_model_path = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\models\\model8723.json"
 saved_weights_path = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\models\\model8723_weights.h5"
-audio_file = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\media\\audio\\fearful.wav"
+audio_file = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\media\\audio\\publicspeech.wav"
 
 with open(saved_model_path, 'r') as json_file:
     json_savedModel = json_file.read()
@@ -126,6 +117,7 @@ fig = plt.figure(figsize = (10, 2))
 plt.bar(emo_list, pred_np, color = 'darkturquoise')
 plt.ylabel("Probabilty (%)")
 plt.show()
+print(emo_list, pred_np)
     
 max_emo = np.argmax(predictions)
 print('max emotion:', emotions.get(max_emo,-1))
@@ -145,5 +137,6 @@ plt.bar(emo_list, total_predictions_np, color = 'indigo')
 plt.ylabel("Mean probabilty (%)")
 plt.title("Session Summary")
 plt.show()
+print(emo_list, total_predictions)
 
 print(f"Emotions analyzed for: {(toc - tic):0.4f} seconds")