update app.py

app.py

@@ -154,9 +154,9 @@ audio_output_prediction, sample_rate, min_duration, frame_length, hop_length_fra
     print("Loaded model from disk")
 
     # Extracting noise and voice from folder and convert to numpy
-    # audio = audio_files_to_numpy(audio_dir_prediction, audio_input_prediction, sample_rate,
-                                #  frame_length, hop_length_frame, min_duration)
-    audio = audioData
+    audio = audio_files_to_numpy(audio_dir_prediction, audio_input_prediction, sample_rate,
+                                 frame_length, hop_length_frame, min_duration)
+    # audio = audioData
     #Dimensions of squared spectrogram
     dim_square_spec = int(n_fft / 2) + 1
     print(dim_square_spec)
@@ -190,17 +190,13 @@ audio_output_prediction, sample_rate, min_duration, frame_length, hop_length_fra
     sf.write(dir_save_prediction + audio_output_prediction , denoise_long[0, :], sample_rate)
 
 def denoise_audio(audioName):
-#   print("typppppeeeeee:",type(audioName))
-  testNo = audioName
-#   audio_dir_prediction = os.path.abspath("/")+ str(testNo) +".wav"
-  sample_rate, datalist = audioName[0], audioName[1]
-  print("typppppeeeeee:")
-  print(type(datalist))
-  data = np.ndarray(datalist)
+  sr, data = audioName
+  sf.write("temp.wav",data, sr)
+  testNo = "temp"
+  audio_dir_prediction = os.path.abspath("/")+ str(testNo) +".wav"
+  sample_rate, data = audioName[0], audioName[1]
   len_data = len(data)  # holds length of the numpy array
-  print("typppppeeeeee:")
-  print(type(data))
-  print(data)
+
   
   t = len_data / sample_rate # returns duration but in floats
   print("t:",t)
@@ -233,7 +229,7 @@ examples = [
 
 
 iface = gr.Interface(fn = denoise_audio,
-                     inputs=[gr.Audio(source="microphone", type="numpy")],
+                     inputs = 'audio',
                      outputs = 'audio', 
                      verbose = True,
                      title = 'audio to denoised Audio Application',