Merge pull request #2987 from NinaARM/feature/voice-assistant-github-move

Align learning path with recent move of voice assistant project to gi…

Author: pareenaverma

content/learning-paths/mobile-graphics-and-gaming/voice-assistant/2-overview.md

@@ -36,7 +36,7 @@ This process includes the following stages:
 The voice assistant pipeline imports and builds a separate module to provide this STT functionality. You can access this at:
 
 ```
-https://gitlab.arm.com/kleidi/kleidi-examples/speech-to-text
+https://github.com/Arm-Examples/STT-Runner
 ```
 
 You can build the pipeline for various platforms and independently benchmark the STT functionality:
@@ -50,7 +50,7 @@ You can build the pipeline for various platforms and independently benchmark the
 Currently, this module uses [whisper.cpp](https://github.com/ggml-org/whisper.cpp) and wraps the backend library with a thin C++ layer. The module also provides JNI bindings for developers targeting Android based applications.
 
 {{% notice %}}
-You can get more information on how to build and use this module in the [speech-to-text README](https://gitlab.arm.com/kleidi/kleidi-examples/speech-to-text/-/blob/main/README.md?ref_type=heads)
+You can get more information on how to build and use this module in the [speech-to-text README](http://github.com/Arm-Examples/STT-Runner/blob/main/README.md)
 {{% /notice %}}
 
 ## Large Language Model  
@@ -64,7 +64,7 @@ By default, the LLM runs asynchronously, streaming tokens as they are generated.
 The voice assistant pipeline imports and builds a separate module to provide this LLM functionality. You can access this at:
 
 ```
-https://gitlab.arm.com/kleidi/kleidi-examples/large-language-models
+https://github.com/Arm-Examples/LLM-Runner
 ```
 
 You can build this pipeline for various platforms and independently benchmark the LLM functionality:
@@ -86,7 +86,7 @@ Currently, this module provides a thin C++ layer as well as JNI bindings for dev
 
 
 {{% notice %}}
-You can get more information on how to build and use this module in the [large-language-models README](https://gitlab.arm.com/kleidi/kleidi-examples/large-language-models/-/blob/main/README.md?ref_type=heads)
+You can get more information on how to build and use this module in the [large-language-models README](https://github.com/Arm-Examples/LLM-Runner/blob/main/README.md)
 {{% /notice %}}
 
 ## Text-to-Speech 

content/learning-paths/mobile-graphics-and-gaming/voice-assistant/3-build.md

@@ -11,7 +11,7 @@ layout: learningpathall
 Start by cloning the repository with the complete example application code:
 
 ```bash
-git clone https://git.gitlab.arm.com/kleidi/kleidi-examples/real-time-voice-assistant.git voice-assistant.git
+git clone https://github.com/Arm-Examples/Real-Time-Voice-Assistant.git voice-assistant.git
 ```
 
 ## Build the Voice Assistant

content/learning-paths/mobile-graphics-and-gaming/voice-assistant/7-performance.md

@@ -15,7 +15,7 @@ layout: learningpathall
 You can also benchmark the LLM functionality on Android phone outside of RTVA application. For this, you can use the Large Language Models repository:
 
 ```
-https://gitlab.arm.com/kleidi/kleidi-examples/large-language-models
+https://github.com/Arm-Examples/LLM-Runner
 ```
 
 and build for your chosen LLM backend, ensure that `NDK_PATH` is set properly. SME kernels are enabled by default, so let's first build with SME disabled:
@@ -26,7 +26,7 @@ cmake --build ./build
 ```
 
 {{% notice %}}
-For troubleshooting any build issues, refer to [large-language-models README](https://gitlab.arm.com/kleidi/kleidi-examples/large-language-models/-/blob/main/README.md?ref_type=heads)
+For troubleshooting any build issues, refer to [large-language-models README](https://github.com/Arm-Examples/LLM-Runner/blob/main/README.md)
 {{% /notice %}}
 
 ### Phone setup