diff --git a/src/transformers/pipelines/__init__.py b/src/transformers/pipelines/__init__.py
index 34d6222d7165..3167c91fd720 100755
--- a/src/transformers/pipelines/__init__.py
+++ b/src/transformers/pipelines/__init__.py
@@ -989,9 +989,10 @@ def pipeline(
                     feature_extractor, _from_pipeline=task, **hub_kwargs, **model_kwargs
                 )
 
+                feature_extractor_type = feature_extractor.to_dict().get("feature_extractor_type", None)
                 if (
-                    feature_extractor._processor_class
-                    and feature_extractor._processor_class.endswith("WithLM")
+                    feature_extractor_type
+                    and feature_extractor_type.endswith("WithLM")
                     and isinstance(model_name, str)
                 ):
                     try:
diff --git a/src/transformers/pipelines/automatic_speech_recognition.py b/src/transformers/pipelines/automatic_speech_recognition.py
index 8e6f8b5cafcd..98fb8255d3bf 100644
--- a/src/transformers/pipelines/automatic_speech_recognition.py
+++ b/src/transformers/pipelines/automatic_speech_recognition.py
@@ -193,16 +193,14 @@ def __init__(
         device: Union[int, "torch.device"] | None = None,
         **kwargs,
     ):
+        feature_extractor_type = feature_extractor.to_dict().get("feature_extractor_type", None)
+
         # set the model type so we can check we have the right pre- and post-processing parameters
         if model.config.model_type == "whisper":
             self.type = "seq2seq_whisper"
         elif model.__class__.__name__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.values():
             self.type = "seq2seq"
-        elif (
-            feature_extractor._processor_class
-            and feature_extractor._processor_class.endswith("WithLM")
-            and decoder is not None
-        ):
+        elif feature_extractor_type and feature_extractor_type.endswith("WithLM") and decoder is not None:
             self.decoder = decoder
             self.type = "ctc_with_lm"
         else: