diff --git a/speech_recognition/__init__.py b/speech_recognition/__init__.py
index 253ab0fe..5ddd6055 100644
--- a/speech_recognition/__init__.py
+++ b/speech_recognition/__init__.py
@@ -1411,6 +1411,17 @@ def recognize_tensorflow(self, audio_data, tensor_graph='tensorflow-data/conv_ac
                 human_string = self.tflabels[node_id]
                 return human_string
 
+    def pre_load_whisper_model(self, model="base", load_options=None):
+        """
+        Pre-load whisper model
+        model can be any of tiny, base, small, medium, large, tiny.en, base.en, small.en, medium.en. See https://github.com/openai/whisper for more details.
+        """
+        import whisper
+
+        if load_options or not hasattr(self, "whisper_model") or self.whisper_model.get(model) is None:
+            self.whisper_model = getattr(self, "whisper_model", {})
+            self.whisper_model[model] = whisper.load_model(model, **load_options or {})
+
     def recognize_whisper(self, audio_data, model="base", show_dict=False, load_options=None, language=None, translate=False, **transcribe_options):
         """
         Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using Whisper.
@@ -1430,11 +1441,8 @@ def recognize_whisper(self, audio_data, model="base", show_dict=False, load_opti
         import numpy as np
         import soundfile as sf
         import torch
-        import whisper
 
-        if load_options or not hasattr(self, "whisper_model") or self.whisper_model.get(model) is None:
-            self.whisper_model = getattr(self, "whisper_model", {})
-            self.whisper_model[model] = whisper.load_model(model, **load_options or {})
+        self.pre_load_whisper_model(model=model, load_options=load_options)
 
         # 16 kHz https://github.com/openai/whisper/blob/28769fcfe50755a817ab922a7bc83483159600a9/whisper/audio.py#L98-L99
         wav_bytes = audio_data.get_wav_data(convert_rate=16000)