Fix linter

Signed-off-by: Piotr Żelasko <[email protected]>

Author: pzelasko

nemo/collections/asr/models/msdd_models.py

@@ -1258,7 +1258,7 @@ def get_range_average(
             seq_len = end - stt
             if stt < clus_label_tensor.shape[0]:
                 target_clus_label_tensor = clus_label_tensor[stt:end]
-                emb_seq, seg_length = (
+                emb_seq, _ = (
                     signals[stt:end, :, :],
                     min(
                         self.diar_window_length,
@@ -1291,7 +1291,7 @@ def get_range_average(
         return emb_vectors_split, emb_seq, seq_len
 
     def get_range_clus_avg_emb(
-        self, test_batch: List[torch.Tensor], _test_data_collection: List[Any], device: torch.device('cpu')
+        self, test_batch: List[torch.Tensor], _test_data_collection: List[Any], device: torch.device
     ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         This function is only used when `get_range_average` function is called. This module calculates
@@ -1470,7 +1470,7 @@ def run_overlap_aware_eval(
                 verbose=self._cfg.verbose,
             )
             outputs.append(output)
-        logging.info(f"  \n")
+        logging.info("  \n")
         return outputs
 
     @classmethod

nemo/collections/audio/losses/__init__.py

@@ -13,3 +13,5 @@
 # limitations under the License.
 
 from nemo.collections.audio.losses.audio import MAELoss, MSELoss, SDRLoss
+
+__all__ = ["MAELoss", "MSELoss", "SDRLoss"]

nemo/collections/audio/models/__init__.py

@@ -20,3 +20,12 @@
     SchroedingerBridgeAudioToAudioModel,
     ScoreBasedGenerativeAudioToAudioModel,
 )
+
+__all__ = [
+    "AudioToAudioModel",
+    "EncMaskDecAudioToAudioModel",
+    "FlowMatchingAudioToAudioModel",
+    "PredictiveAudioToAudioModel",
+    "SchroedingerBridgeAudioToAudioModel",
+    "ScoreBasedGenerativeAudioToAudioModel",
+]

nemo/core/classes/modelPT.py

@@ -1529,27 +1529,27 @@ def extract_state_dict_from(
         Example:
             To convert the .nemo tarfile into a single Model level PyTorch checkpoint::
 
-                state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.nemo', './asr_ckpts')
+                state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.nemo', './ckpts')
 
             To restore a model from a Model level checkpoint::
 
                 model = nemo.collections.asr.models.EncDecCTCModel(cfg)  # or any other method of restoration
-                model.load_state_dict(torch.load("./asr_ckpts/model_weights.ckpt"))
+                model.load_state_dict(torch.load("./ckpts/model_weights.ckpt"))
 
             To convert the .nemo tarfile into multiple Module level PyTorch checkpoints::
 
                 state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from(
-                    'asr.nemo', './asr_ckpts', split_by_module=True
+                    'asr.nemo', './ckpts', split_by_module=True
                 )
 
             To restore a module from a Module level checkpoint::
 
                 model = nemo.collections.asr.models.EncDecCTCModel(cfg)  # or any other method of restoration
 
                 # load the individual components
-                model.preprocessor.load_state_dict(torch.load("./asr_ckpts/preprocessor.ckpt"))
-                model.encoder.load_state_dict(torch.load("./asr_ckpts/encoder.ckpt"))
-                model.decoder.load_state_dict(torch.load("./asr_ckpts/decoder.ckpt"))
+                model.preprocessor.load_state_dict(torch.load("./ckpts/preprocessor.ckpt"))
+                model.encoder.load_state_dict(torch.load("./ckpts/encoder.ckpt"))
+                model.decoder.load_state_dict(torch.load("./ckpts/decoder.ckpt"))
 
         Returns:
             The state dict that was loaded from the original .nemo checkpoint

nemo/core/connectors/save_restore_connector.py

@@ -299,27 +299,27 @@ def extract_state_dict_from(self, restore_path: str, save_dir: str, split_by_mod
         Example:
             To convert the .nemo tarfile into a single Model level PyTorch checkpoint::
 
-                state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.nemo', './asr_ckpts')
+                state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.nemo', './ckpts')
 
             To restore a model from a Model level checkpoint::
 
                 model = nemo.collections.asr.models.EncDecCTCModel(cfg)  # or any other method of restoration
-                model.load_state_dict(torch.load("./asr_ckpts/model_weights.ckpt"))
+                model.load_state_dict(torch.load("./ckpts/model_weights.ckpt"))
 
             To convert the .nemo tarfile into multiple Module level PyTorch checkpoints::
 
                 state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from(
-                    'asr.nemo', './asr_ckpts', split_by_module=True
+                    'asr.nemo', './ckpts', split_by_module=True
                 )
 
             To restore a module from a Module level checkpoint::
 
                 model = nemo.collections.asr.models.EncDecCTCModel(cfg)  # or any other method of restoration
 
                 # load the individual components
-                model.preprocessor.load_state_dict(torch.load("./asr_ckpts/preprocessor.ckpt"))
-                model.encoder.load_state_dict(torch.load("./asr_ckpts/encoder.ckpt"))
-                model.decoder.load_state_dict(torch.load("./asr_ckpts/decoder.ckpt"))
+                model.preprocessor.load_state_dict(torch.load("./ckpts/preprocessor.ckpt"))
+                model.encoder.load_state_dict(torch.load("./ckpts/encoder.ckpt"))
+                model.decoder.load_state_dict(torch.load("./ckpts/decoder.ckpt"))
 
         Returns:
             The state dict that was loaded from the original .nemo checkpoint