Update README.md

README.md

@@ -73,33 +73,33 @@ The model used in this project is the **Whisper-V3-Turbo**. Whisper is a multili
 
 To use the fine-tuned model, follow the steps below:
 
-    ```python
-    import torch
-    from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-    
-    device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-    
-    model_id = "suzii/vi-whisper-large-v3-turbo-v1"
-    
-    model = AutoModelForSpeechSeq2Seq.from_pretrained(
-        model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-    )
-    model.to(device)
-    
-    processor = AutoProcessor.from_pretrained(model_id)
-    
-    pipe = pipeline(
-        "automatic-speech-recognition",
-        model=model,
-        tokenizer=processor.tokenizer,
-        feature_extractor=processor.feature_extractor,
-        torch_dtype=torch_dtype,
-        device=device,
-    )
-    result = pipe("your-audio.mp3", return_timestamps=True)
-
-    ```
+  ```python
+  import torch
+  from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+  
+  device = "cuda:0" if torch.cuda.is_available() else "cpu"
+  torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+  
+  model_id = "suzii/vi-whisper-large-v3-turbo-v1"
+  
+  model = AutoModelForSpeechSeq2Seq.from_pretrained(
+      model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+  )
+  model.to(device)
+  
+  processor = AutoProcessor.from_pretrained(model_id)
+  
+  pipe = pipeline(
+      "automatic-speech-recognition",
+      model=model,
+      tokenizer=processor.tokenizer,
+      feature_extractor=processor.feature_extractor,
+      torch_dtype=torch_dtype,
+      device=device,
+  )
+  result = pipe("your-audio.mp3", return_timestamps=True)
+
+  ```
 
 ## Acknowledgements