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README.md

@@ -137,16 +137,16 @@ import qai_hub as hub
 from qai_hub_models.models.whisper_tiny_en import WhisperEncoder,WhisperDecoder
 
 # Load the model
-        encoder_model = WhisperEncoder.from_pretrained()
-        
-        decoder_model = WhisperDecoder.from_pretrained()
-        
+encoder_model = WhisperEncoder.from_pretrained()
+
+decoder_model = WhisperDecoder.from_pretrained()
+
 
 # Device
 device = hub.Device("Samsung Galaxy S23")
 
 
-        # Trace model
+# Trace model
 encoder_input_shape = encoder_model.get_input_spec()
 encoder_sample_inputs = encoder_model.sample_inputs()
 
@@ -163,7 +163,7 @@ encoder_compile_job = hub.submit_compile_job(
 encoder_target_model = encoder_compile_job.get_target_model()
 
 
-        # Trace model
+# Trace model
 decoder_input_shape = decoder_model.get_input_spec()
 decoder_sample_inputs = decoder_model.sample_inputs()
 
@@ -191,10 +191,10 @@ provisioned in the cloud.  Once the job is submitted, you can navigate to a
 provided job URL to view a variety of on-device performance metrics.
 ```python
 profile_job = hub.submit_profile_job(
-    model=target_model,
-    device=device,
-)
-
+        model=target_model,
+        device=device,
+        )
+        
 ```
 
 Step 3: **Verify on-device accuracy**
@@ -203,13 +203,12 @@ To verify the accuracy of the model on-device, you can run on-device inference
 on sample input data on the same cloud hosted device.
 ```python
 input_data = torch_model.sample_inputs()
-inference_job = hub.submit_inference_job(
-    model=target_model,
-    device=device,
-    inputs=input_data,
-)
-
-on_device_output = inference_job.download_output_data()
+    inference_job = hub.submit_inference_job(
+        model=target_model,
+        device=device,
+        inputs=input_data,
+    )
+    on_device_output = inference_job.download_output_data()
 
 ```
 With the output of the model, you can compute like PSNR, relative errors or