Update README.md

README.md

@@ -6,12 +6,44 @@ tags:
 ---
 
 ```python
+
+# load pretrained pipeline
 from pyannote.audio import Pipeline
 pipeline = Pipeline.from_pretrained('hbredin/utter-project-diarization')
 
+# send it to MPS device (on Apple Silicon)
 import torch
 mps = torch.device('mps')
 pipeline.to(mps)
 
-diarization = pipeline('audio.wav')
+# apply it on sample file
+from pyannote.audio.sample import SAMPLE_FILE
+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
+
+# 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,
+#  'total': 24.349999999999998,
+#  'diarization error rate': 0.2991080595482547}
 ```