Update README.md

README.md

@@ -102,6 +102,7 @@ def process_func(
     # then we put it on the device
     y = processor(x, sampling_rate=sampling_rate)
     y = y['input_values'][0]
+    y = y.reshape(1, -1)
     y = torch.from_numpy(y).to(device)
 
     # run through model
@@ -114,12 +115,12 @@ def process_func(
     return y
 
 
-process_func(signal, sampling_rate)
+print(process_func(signal, sampling_rate))
 #  Arousal    dominance valence
-# [[0.5460759 0.6062269 0.4043165]]
+# [[0.5460754  0.6062266  0.40431657]]
 
-process_func(signal, sampling_rate, embeddings=True)
+print(process_func(signal, sampling_rate, embeddings=True))
 # Pooled hidden states of last transformer layer
-# [[-0.00752167  0.0065819  -0.00746339 ...  0.00663631  0.00848747
-#   0.00599209]]
+# [[-0.00752167  0.0065819  -0.00746342 ...  0.00663632  0.00848748
+#    0.00599211]]
 ```