removed noise

app.py

@@ -23,15 +23,15 @@ def preprocess_mp3(sample, index):
     return spectrogram
 
 def greet(name):
-    wave = load_audio_to_tensor(name)
-    power = sum(wave * 2) / len(wave)   # audio signal power
-    SNR = 3.5                           # signal-to-noise ratio
-    SNR_linear = 10 ** (SNR / 10)       # convert SNR to linear scale
-    noise_power = power / SNR_linear    # noise power
-
-    # add noise to audio to simulate environment
-    noise = np.random.normal(0, noise_power ** 0.5, wave.shape)  # generate noise
-    wave = (wave + noise) * 32768.0     # add noise to the audio signal
+    wave, volume = load_audio_to_tensor(name)
+    # power = sum(wave * 2) / len(wave)   # audio signal power
+    # SNR = 3.5                           # signal-to-noise ratio
+    # SNR_linear = 10 ** (SNR / 10)       # convert SNR to linear scale
+    # noise_power = power / SNR_linear    # noise power
+
+    # # add noise to audio to simulate environment
+    # noise = np.random.normal(0, noise_power ** 0.5, wave.shape)  # generate noise
+    # wave = (wave + noise) * 32768.0     # add noise to the audio signal
     tensor_wave = tf.convert_to_tensor(wave, dtype=tf.float32)  # convert to tensor
     min_wave = min(wave)  
     if len(wave) > 16000: