Cell In[21], line 3
1 # Initialize NeMo MSDD diarization model
2 msdd_model = NeuralDiarizer(cfg=create_config(temp_path)).to("cpu")
----> 3 msdd_model.diarize()
5 del msdd_model
6 torch.cuda.empty_cache()
File [c:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\_contextlib.py:115](file:///C:/Users/T_Care/AppData/Local/Programs/Python/Python38/lib/site-packages/torch/utils/_contextlib.py:115), in context_decorator..decorate_context(*args, **kwargs)
112 @functools.wraps(func)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
File [c:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\msdd_models.py:1180](file:///C:/Users/T_Care/AppData/Local/Programs/Python/Python38/lib/site-packages/nemo/collections/asr/models/msdd_models.py:1180), in NeuralDiarizer.diarize(self)
1173 @torch.no_grad()
1174 def diarize(self) -> Optional[List[Optional[List[Tuple[DiarizationErrorRate, Dict]]]]]:
1175 """
1176 Launch diarization pipeline which starts from VAD (or a oracle VAD stamp generation), initialization clustering and multiscale diarization decoder (MSDD).
1177 Note that the result of MSDD can include multiple speakers at the same time. Therefore, RTTM output of MSDD needs to be based on `make_rttm_with_overlap()`
1178 function that can generate overlapping timestamps. `self.run_overlap_aware_eval()` function performs DER evaluation.
1179 """
-> 1180 self.clustering_embedding.prepare_cluster_embs_infer()
1181 self.msdd_model.pairwise_infer = True
...
58 def dump(obj, file, protocol=None):
59 '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60 ForkingPickler(file, protocol).dump(obj)
PicklingError: Can't pickle : attribute lookup SpeechLabelEntity on nemo.collections.common.parts.preprocessing.collections failed```