Update README.md

README.md

@@ -22,28 +22,27 @@ diarization = pipeline(SAMPLE_FILE)
 
 # print output
 print(diarization)
-# [ 00:00:06.730 -->  00:00:06.747] A speaker90
-# [ 00:00:06.747 -->  00:00:07.169] B speaker91
-# [ 00:00:07.169 -->  00:00:07.185] C speaker90
-# [ 00:00:07.590 -->  00:00:07.624] D speaker90
-# [ 00:00:07.624 -->  00:00:08.029] E speaker91
-# [ 00:00:08.029 -->  00:00:09.970] F speaker90
-# [ 00:00:09.970 -->  00:00:10.982] G speaker91
-# [ 00:00:10.459 -->  00:00:14.729] H speaker90
-# [ 00:00:14.307 -->  00:00:17.884] I speaker91
-# [ 00:00:18.019 -->  00:00:21.512] J 2
-# [ 00:00:18.188 -->  00:00:18.407] K speaker91
-# [ 00:00:21.765 -->  00:00:28.499] L speaker91
-# [ 00:00:27.824 -->  00:00:29.967] M 2
+# [ 00:00:06.730 -->  00:00:07.185] A 1
+# [ 00:00:07.590 -->  00:00:08.316] B 1
+# [ 00:00:08.316 -->  00:00:09.852] C speaker90
+# [ 00:00:09.852 -->  00:00:09.902] D 1
+# [ 00:00:09.902 -->  00:00:10.982] E speaker91
+# [ 00:00:10.459 -->  00:00:10.527] F 1
+# [ 00:00:10.527 -->  00:00:14.729] G speaker90
+# [ 00:00:14.307 -->  00:00:17.884] H speaker91
+# [ 00:00:18.019 -->  00:00:21.495] I 3
+# [ 00:00:18.239 -->  00:00:18.374] J speaker91
+# [ 00:00:21.765 -->  00:00:28.515] K speaker91
+# [ 00:00:27.824 -->  00:00:29.984] L speaker90
 
 # compute diarization error rate
 from pyannote.metrics.diarization import DiarizationErrorRate
 metric = DiarizationErrorRate()
 metric(SAMPLE_FILE['annotation'], diarization, detailed=True)
-# {'confusion': 6.2540312500000015,
-#  'missed detection': 0.5480625000000003,
-#  'correct': 17.547906249999997,
-#  'false alarm': 0.4811874999999999,
+# {'missed detection': 0.6146562499999995,
+#  'correct': 19.108875,
+#  'false alarm': 0.48028125,
+#  'confusion': 4.626468749999998,
 #  'total': 24.349999999999998,
-#  'diarization error rate': 0.2991080595482547}
+#  'diarization error rate': 0.23496534907597527}
 ```