Force diarizer to use CUDA if cuda is available and if device=None.

nemo/collections/asr/models/clustering_diarizer.py

@@ -74,10 +74,10 @@ def get_available_model_names(class_name):
 
 class ClusteringDiarizer(torch.nn.Module, Model, DiarizationMixin):
     """
-    Inference model Class for offline speaker diarization. 
-    This class handles required functionality for diarization : Speech Activity Detection, Segmentation, 
-    Extract Embeddings, Clustering, Resegmentation and Scoring. 
-    All the parameters are passed through config file 
+    Inference model Class for offline speaker diarization.
+    This class handles required functionality for diarization : Speech Activity Detection, Segmentation,
+    Extract Embeddings, Clustering, Resegmentation and Scoring.
+    All the parameters are passed through config file
     """
 
     def __init__(self, cfg: Union[DictConfig, Any], speaker_model=None):
@@ -137,7 +137,10 @@ def _init_speaker_model(self, speaker_model=None):
         Initialize speaker embedding model with model name or path passed through config
         """
         if speaker_model is not None:
-            self._speaker_model = speaker_model
+            if self._cfg.device is None and torch.cuda.is_available():
+                self._speaker_model = speaker_model.to(torch.device('cuda'))
+            else:
+                self._speaker_model = speaker_model
         else:
             model_path = self._cfg.diarizer.speaker_embeddings.model_path
             if model_path is not None and model_path.endswith('.nemo'):
@@ -158,7 +161,6 @@ def _init_speaker_model(self, speaker_model=None):
                 self._speaker_model = EncDecSpeakerLabelModel.from_pretrained(
                     model_name=model_path, map_location=self._cfg.device
                 )
-
         self.multiscale_args_dict = parse_scale_configs(
             self._diarizer_params.speaker_embeddings.parameters.window_length_in_sec,
             self._diarizer_params.speaker_embeddings.parameters.shift_length_in_sec,
@@ -171,7 +173,9 @@ def _setup_vad_test_data(self, manifest_vad_input):
             'sample_rate': self._cfg.sample_rate,
             'batch_size': self._cfg.get('batch_size'),
             'vad_stream': True,
-            'labels': ['infer',],
+            'labels': [
+                'infer',
+            ],
             'window_length_in_sec': self._vad_window_length_in_sec,
             'shift_length_in_sec': self._vad_shift_length_in_sec,
             'trim_silence': False,
@@ -192,8 +196,8 @@ def _setup_spkr_test_data(self, manifest_file):
 
     def _run_vad(self, manifest_file):
         """
-        Run voice activity detection. 
-        Get log probability of voice activity detection and smoothes using the post processing parameters. 
+        Run voice activity detection.
+        Get log probability of voice activity detection and smoothes using the post processing parameters.
         Using generated frame level predictions generated manifest file for later speaker embedding extraction.
         input:
         manifest_file (str) : Manifest file containing path to audio file and label as infer
@@ -338,7 +342,7 @@ def _perform_speech_activity_detection(self):
     def _extract_embeddings(self, manifest_file: str, scale_idx: int, num_scales: int):
         """
         This method extracts speaker embeddings from segments passed through manifest_file
-        Optionally you may save the intermediate speaker embeddings for debugging or any use. 
+        Optionally you may save the intermediate speaker embeddings for debugging or any use.
         """
         logging.info("Extracting embeddings for Diarization")
         self._setup_spkr_test_data(manifest_file)