v0.48.0

See https://github.com/qualcomm/ai-hub-models/releases/v0.48.0 for changelog.

README.md

@@ -15,7 +15,7 @@ pipeline_tag: automatic-speech-recognition
 HuggingFaceWavLMBasePlus is a real time speech processing backbone based on Microsoft's WavLM model.
 
 This is based on the implementation of HuggingFace-WavLM-Base-Plus found [here](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-base-plus/tree/main).
-This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/huggingface_wavlm_base_plus) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
+This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/huggingface_wavlm_base_plus) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
 
 Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
 
@@ -28,21 +28,21 @@ Below are pre-exported model assets ready for deployment.
 
 | Runtime | Precision | Chipset | SDK Versions | Download |
 |---|---|---|---|---|
-| TFLITE | float | Universal | TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/huggingface_wavlm_base_plus/releases/v0.47.0/huggingface_wavlm_base_plus-tflite-float.zip)
+| TFLITE | float | Universal | TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/huggingface_wavlm_base_plus/releases/v0.48.0/huggingface_wavlm_base_plus-tflite-float.zip)
 
 For more device-specific assets and performance metrics, visit **[HuggingFace-WavLM-Base-Plus on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/huggingface_wavlm_base_plus)**.
 
 
 ### Option 2: Export with Custom Configurations
 
-Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/huggingface_wavlm_base_plus) Python library to compile and export the model with your own:
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/huggingface_wavlm_base_plus) Python library to compile and export the model with your own:
 - Custom weights (e.g., fine-tuned checkpoints)
 - Custom input shapes
 - Target device and runtime configurations
 
 This option is ideal if you need to customize the model beyond the default configuration provided here.
 
-See our repository for [HuggingFace-WavLM-Base-Plus on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/huggingface_wavlm_base_plus) for usage instructions.
+See our repository for [HuggingFace-WavLM-Base-Plus on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/huggingface_wavlm_base_plus) for usage instructions.
 
 ## Model Details