Update README.md

README.md

@@ -164,7 +164,7 @@ language_id = EncoderClassifier.from_hparams(source="speechbrain/lang-id-voxling
 signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
 prediction =  language_id.classify_batch(signal)
 print(prediction)
-  (tensor([[-2.8646e+01, -3.0346e+01, -2.0748e+01, -2.9562e+01, -2.2187e+01,
+#  (tensor([[-2.8646e+01, -3.0346e+01, -2.0748e+01, -2.9562e+01, -2.2187e+01,
          -3.2668e+01, -3.6677e+01, -3.3573e+01, -3.2545e+01, -2.4365e+01,
          -2.4688e+01, -3.1171e+01, -2.7743e+01, -2.9918e+01, -2.4770e+01,
          -3.2250e+01, -2.4727e+01, -2.6087e+01, -2.1870e+01, -3.2821e+01,
@@ -190,10 +190,10 @@ print(prediction)
 # the given utterance belongs to the given language (i.e., the larger the better)
 # The linear-scale likelihood can be retrieved using the following:
 print(prediction[1].exp())
-  tensor([0.9850])
+#  tensor([0.9850])
 # The identified language ISO code is given in prediction[3]
 print(prediction[3])
-  ['th: Thai']
+#  ['th: Thai']
   
 # Alternatively, use the utterance embedding extractor:
 emb =  language_id.encode_batch(signal)