diff --git a/whisperplus/pipelines/whisper.py b/whisperplus/pipelines/whisper.py
index 050915c..0a5612d 100644
--- a/whisperplus/pipelines/whisper.py
+++ b/whisperplus/pipelines/whisper.py
@@ -18,17 +18,6 @@ def __init__(self, model_id: str = "openai/whisper-large-v3"):
         else:
             logging.info("Model already loaded.")
 
-        self.set_device()
-
-    def set_device(self):
-        """Sets the device to be used for inference based on availability."""
-        if torch.backends.mps.is_available():
-            self.device = "mps"
-        else:
-            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        logging.info(f"Using device: {self.device}")
-
     def load_model(self, model_id: str = "openai/whisper-large-v3"):
         """
         Loads the pre-trained speech recognition model and moves it to the specified device.
@@ -38,35 +27,39 @@ def load_model(self, model_id: str = "openai/whisper-large-v3"):
         """
         logging.info("Loading model...")
         model = AutoModelForSpeechSeq2Seq.from_pretrained(
-            model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, use_safetensors=True)
-        model.to(self.device)
+            model_id,
+            low_cpu_mem_usage=True,
+            use_safetensors=True,
+            attn_implementation="flash_attention_2",
+            load_in_4bit=True,
+            device_map="auto")
         logging.info("Model loaded successfully.")
 
+        processor = AutoProcessor.from_pretrained(model_id)
+
+        self.processor = processor
         self.model = model
 
-    def __call__(self, audio_path: str, model_id: str = "openai/whisper-large-v3", language: str = "turkish"):
+    def __call__(self, audio_path: str, language: str = "turkish"):
         """
         Converts audio to text using the pre-trained speech recognition model.
 
         Args:
             audio_path (str): Path to the audio file to be transcribed.
-            model_id (str): Identifier of the pre-trained model to be used for transcription.
 
         Returns:
             str: Transcribed text from the audio.
         """
-        processor = AutoProcessor.from_pretrained(model_id)
         pipe = pipeline(
             "automatic-speech-recognition",
             model=self.model,
-            torch_dtype=torch.float16,
             chunk_length_s=30,
             max_new_tokens=128,
             batch_size=24,
+            device_map="auto",
             return_timestamps=True,
-            device=self.device,
-            tokenizer=processor.tokenizer,
-            feature_extractor=processor.feature_extractor,
+            tokenizer=self.processor.tokenizer,
+            feature_extractor=self.processor.feature_extractor,
             model_kwargs={"use_flash_attention_2": True},
             generate_kwargs={"language": language},
         )