fix loading of hyb ctc rnnt bpe models when using from pretrained (NVIDIA-NeMo#15042)

* fix loading of hyb ctc rnnt bpe models when using from pretrained

Signed-off-by: nithinraok <[email protected]>

* Apply isort and black reformatting

Signed-off-by: nithinraok <[email protected]>

---------

Signed-off-by: nithinraok <[email protected]>
Signed-off-by: nithinraok <[email protected]>
Co-authored-by: nithinraok <[email protected]>
Signed-off-by: genquan9 <[email protected]>

Author: nithinraok

nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models_prompt.py

@@ -36,7 +36,7 @@
 from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTBPEDecoding
 from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis
 from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config
-from nemo.core.classes.common import typecheck
+from nemo.core.classes.common import PretrainedModelInfo, typecheck
 from nemo.core.classes.mixins import AccessMixin
 from nemo.core.neural_types import (
     AcousticEncodedRepresentation,
@@ -1000,3 +1000,13 @@ def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]):
         # preserve config
         self._update_dataset_config(dataset_name='test', config=test_data_config)
         self._test_dl = self._setup_dataloader_from_config(config=test_data_config)
+
+    @classmethod
+    def list_available_models(cls) -> List[PretrainedModelInfo]:
+        """
+        This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
+
+        Returns:
+            List of available pre-trained models.
+        """
+        return None