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README.md

@@ -33,7 +33,7 @@ More details on model performance across various devices, can be found
 
 | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
 | ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 884.463 ms | 142 - 146 MB | FP32 | CPU |  [HuggingFace-WavLM-Base-Plus.tflite](https://huggingface.co/qualcomm/HuggingFace-WavLM-Base-Plus/blob/main/HuggingFace-WavLM-Base-Plus.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 938.575 ms | 124 - 137 MB | FP32 | CPU |  [HuggingFace-WavLM-Base-Plus.tflite](https://huggingface.co/qualcomm/HuggingFace-WavLM-Base-Plus/blob/main/HuggingFace-WavLM-Base-Plus.tflite) 
 
 
 ## Installation
@@ -91,16 +91,6 @@ device. This script does the following:
 python -m qai_hub_models.models.huggingface_wavlm_base_plus.export
 ```
 
-```
-Profile Job summary of HuggingFace-WavLM-Base-Plus
---------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 928.77 ms
-Estimated Peak Memory Range: 143.20-150.90 MB
-Compute Units: CPU (811) | Total (811)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/HuggingFace-WavLM-Base-Plus/export.py)