formatting

Author: MahmoudAshraf97

diarization/msdd/msdd.py

@@ -48,9 +48,7 @@ def diarize(self, audio: torch.Tensor):
                 num_workers=0,
                 verbose=True,
             )
-            self.model.clustering_embedding.clus_diar_model._diarizer_params.out_dir = (
-                temp_path
-            )
+            self.model.clustering_embedding.clus_diar_model._diarizer_params.out_dir = temp_path
             self.model.clustering_embedding.clus_diar_model._diarizer_params.manifest_filepath = (
                 manifest_path
             )
@@ -74,18 +72,14 @@ def diarize(self, audio: torch.Tensor):
 
 
 def create_config():
-    config = OmegaConf.load(
-        os.path.join(os.path.dirname(__file__), "diar_infer_telephonic.yaml")
-    )
+    config = OmegaConf.load(os.path.join(os.path.dirname(__file__), "diar_infer_telephonic.yaml"))
     pretrained_vad = "vad_multilingual_marblenet"
     pretrained_speaker_model = "titanet_large"
 
     config.diarizer.out_dir = None
     config.diarizer.manifest_filepath = None
     config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
-    config.diarizer.oracle_vad = (
-        False  # compute VAD provided with model_path to vad config
-    )
+    config.diarizer.oracle_vad = False  # compute VAD provided with model_path to vad config
     config.diarizer.clustering.parameters.oracle_num_speakers = False
 
     # Here, we use our in-house pretrained NeMo VAD model

diarization/sortformer/sortformer.py

@@ -35,9 +35,7 @@ def diarize(self, audio: torch.Tensor):
             )
             processed_signal = processed_signal[:, :, : processed_signal_length.max()]
 
-            preds = self.model.forward_streaming(
-                processed_signal, processed_signal_length
-            )
+            preds = self.model.forward_streaming(processed_signal, processed_signal_length)
             preds = preds.cpu()
 
         # TODO: make this tunable
@@ -65,9 +63,7 @@ def diarize(self, audio: torch.Tensor):
 
         self.model._diarize_audio_rttm_map = audio_rttm_map_dict
         uniq_ids = list(self.model._diarize_audio_rttm_map.keys())
-        processed_outputs = self.model._diarize_output_processing(
-            preds, uniq_ids, diarize_cfg
-        )
+        processed_outputs = self.model._diarize_output_processing(preds, uniq_ids, diarize_cfg)
         self.model._diarize_audio_rttm_map = {}
 
         labels = []