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README.md

@@ -31,9 +31,12 @@ More details on model performance across various devices, can be found
   - Model size: 363 MB
 
 
+
+
 | 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 | 950.768 ms | 134 - 136 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 | 920.916 ms | 141 - 148 MB | FP32 | CPU |  [HuggingFace-WavLM-Base-Plus.tflite](https://huggingface.co/qualcomm/HuggingFace-WavLM-Base-Plus/blob/main/HuggingFace-WavLM-Base-Plus.tflite) 
+
 
 
 ## Installation
@@ -91,9 +94,21 @@ 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: 932.00 ms
+Estimated Peak Memory Range: 142.46-146.71 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)
+This [export script](https://aihub.qualcomm.com/models/huggingface_wavlm_base_plus/qai_hub_models/models/HuggingFace-WavLM-Base-Plus/export.py)
 leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
 on-device. Lets go through each step below in detail:
 
@@ -171,6 +186,7 @@ AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
 
+
 ## Deploying compiled model to Android
 
 
@@ -192,7 +208,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of HuggingFace-WavLM-Base-Plus can be found
   [here](https://github.com/microsoft/unilm/blob/master/LICENSE).
-- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
+- The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
 
 ## References
 * [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900)