diff --git a/libs/community/langchain_community/document_loaders/parsers/audio.py b/libs/community/langchain_community/document_loaders/parsers/audio.py
index ab54c67ed3..3b96f9860c 100644
--- a/libs/community/langchain_community/document_loaders/parsers/audio.py
+++ b/libs/community/langchain_community/document_loaders/parsers/audio.py
@@ -64,7 +64,7 @@ class OpenAIWhisperParser(BaseBlobParser):
                 file_obj.name = f"part_{split_number}.mp3"
 
             # Transcribe
-            print(f"Transcribing part {split_number+1}!")
+            print(f"Transcribing part {split_number + 1}!")
             attempts = 0
             while attempts < 3:
                 try:
@@ -116,6 +116,8 @@ class OpenAIWhisperParserLocal(BaseBlobParser):
         self,
         device: str = "0",
         lang_model: Optional[str] = None,
+        batch_size: int = 8,
+        chunk_length: int = 30,
         forced_decoder_ids: Optional[Tuple[Dict]] = None,
     ):
         """Initialize the parser.
@@ -126,6 +128,10 @@ class OpenAIWhisperParserLocal(BaseBlobParser):
               Defaults to None.
             forced_decoder_ids: id states for decoder in a multilanguage model.
               Defaults to None.
+            batch_size: batch size used for decoding
+              Defaults to 8.
+            chunk_length: chunk length used during inference.
+              Defaults to 30s.
         """
         try:
             from transformers import pipeline
@@ -141,47 +147,37 @@ class OpenAIWhisperParserLocal(BaseBlobParser):
                 "torch package not found, please install it with " "`pip install torch`"
             )
 
-        # set device, cpu by default check if there is a GPU available
+        # Determine the device to use
         if device == "cpu":
             self.device = "cpu"
-            if lang_model is not None:
-                self.lang_model = lang_model
-                print("WARNING! Model override. Using model: ", self.lang_model)
-            else:
-                # unless overridden, use the small base model on cpu
-                self.lang_model = "openai/whisper-base"
         else:
-            if torch.cuda.is_available():
-                self.device = "cuda:0"
-                # check GPU memory and select automatically the model
-                mem = torch.cuda.get_device_properties(self.device).total_memory / (
-                    1024**2
-                )
-                if mem < 5000:
-                    rec_model = "openai/whisper-base"
-                elif mem < 7000:
-                    rec_model = "openai/whisper-small"
-                elif mem < 12000:
-                    rec_model = "openai/whisper-medium"
-                else:
-                    rec_model = "openai/whisper-large"
-
-                # check if model is overridden
-                if lang_model is not None:
-                    self.lang_model = lang_model
-                    print("WARNING! Model override. Might not fit in your GPU")
-                else:
-                    self.lang_model = rec_model
+            self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+        if self.device == "cpu":
+            default_model = "openai/whisper-base"
+            self.lang_model = lang_model if lang_model else default_model
+        else:
+            # Set the language model based on the device and available memory
+            mem = torch.cuda.get_device_properties(self.device).total_memory / (1024**2)
+            if mem < 5000:
+                rec_model = "openai/whisper-base"
+            elif mem < 7000:
+                rec_model = "openai/whisper-small"
+            elif mem < 12000:
+                rec_model = "openai/whisper-medium"
             else:
-                "cpu"
+                rec_model = "openai/whisper-large"
+            self.lang_model = lang_model if lang_model else rec_model
 
         print("Using the following model: ", self.lang_model)
 
+        self.batch_size = batch_size
+
         # load model for inference
         self.pipe = pipeline(
             "automatic-speech-recognition",
             model=self.lang_model,
-            chunk_length_s=30,
+            chunk_length_s=chunk_length,
             device=self.device,
         )
         if forced_decoder_ids is not None:
@@ -224,7 +220,7 @@ class OpenAIWhisperParserLocal(BaseBlobParser):
 
         y, sr = librosa.load(file_obj, sr=16000)
 
-        prediction = self.pipe(y.copy(), batch_size=8)["text"]
+        prediction = self.pipe(y.copy(), batch_size=self.batch_size)["text"]
 
         yield Document(
             page_content=prediction,