diff --git a/versions/2022/supervised/python/kws-infer.py b/versions/2022/supervised/python/kws-infer.py
index 8eb7a55..a98d9b0 100644
--- a/versions/2022/supervised/python/kws-infer.py
+++ b/versions/2022/supervised/python/kws-infer.py
@@ -8,12 +8,19 @@
 To use microphone input, run:
     python3 kws-infer.py --gui
 
+    On RPi 4:
+    python3 kws-infer.py --rpi --gui
+
 Dependencies:
     pip3 install pysimplegui
     pip3 install sounddevice 
+    pip3 install librosa
+
+    sudo apt-get install libasound2-dev libportaudio2 
 
 Inference time:
     0.03 sec Quad Core Intel i7 2.3GHz
+    0.09 sec on RPi 4
 '''
 
 
@@ -38,6 +45,7 @@ def get_args():
     parser.add_argument("--wav-file", type=str, default=None)
     parser.add_argument("--model-path", type=str, default="resnet18-kws-best-acc.pt")
     parser.add_argument("--gui", default=False, action="store_true")
+    parser.add_argument("--rpi", default=False, action="store_true")
     args = parser.parse_args()
     return args
 
@@ -82,18 +90,6 @@ def get_args():
 
     if not args.gui:
         waveform, sample_rate = torchaudio.load(wav_file)
-    
-    #wav = np.expand_dims(waveform.numpy().squeeze(), axis=1)
-
-    #print("Shape:", wav.shape)
-    #sd.default.samplerate = sample_rate
-    #sd.default.channels = 1
-    #sd.play(wav, blocking=True)
-
-    #print("Shape:", waveform.shape)
-    #sd.fs = sample_rate
-    #sd.channel_count = waveform.shape[1]
-    #sd.play(waveform.numpy(), sample_rate)
 
     transform = torchaudio.transforms.MelSpectrogram(sample_rate=sample_rate,
                                                      n_fft=args.n_fft,
@@ -127,8 +123,17 @@ def get_args():
         if waveform.max() > 1.0:
             continue
         start_time = time.time()
-        waveform = torch.from_numpy(waveform).unsqueeze(0)
-        mel = ToTensor()(librosa.power_to_db(transform(waveform).squeeze().numpy(), ref=np.max))
+        if args.rpi:
+            waveform = torch.FloatTensor(waveform.tolist())
+            mel = np.array(transform(waveform).squeeze().tolist())
+            mel = librosa.power_to_db(mel, ref=np.max).tolist()
+            
+            mel = torch.FloatTensor(mel)
+            mel = mel.unsqueeze(0)
+
+        else:
+            waveform = torch.from_numpy(waveform).unsqueeze(0)
+            mel = ToTensor()(librosa.power_to_db(transform(waveform).squeeze().numpy(), ref=np.max))
         mel = mel.unsqueeze(0)
         pred = scripted_module(mel)
         max_prob =  pred.max()