Generate audio with Stable Audio Open Small on LiteRT
Introduction
Set up your development environment
Download and test the model
Convert Stable Audio Open Small model to LiteRT
Build LiteRT
Create a simple program for Android target
Create a simple program for macOS target
Next Steps

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LiteRT

LiteRT (short for Lite Runtime), formerly known as TensorFlow Lite, is Google’s high-performance runtime for on-device AI. Designed for low-latency, resource-efficient execution, LiteRT is optimized for mobile and embedded environments — making it a natural fit for Arm CPUs running models like Stable Audio Open Small. You’ll build the runtime using the Bazel build tool.

Build LiteRT libraries

Clone the repository and get the latest modules

    

        
        
cd $WORKSPACE
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
cd tensorflow_src

Check out the specified commit of TensorFlow, and set the TF_SRC_PATH:

    

        
        
git checkout 84dd28bbc29d75e6a6d917eb2998e4e8ea90ec56
export TF_SRC_PATH=$(pwd)

A script is available to configure the bazel build environment. Run it to create a custom TFLite build for Android:

Reminder

Ensure the NDK_PATH variable is set to your previously installed Android NDK:

    

        
        

export NDK_PATH=$WORKSPACE/android-ndk-r25b/
export ANDROID_NDK_HOME=$NDK_PATH
export PATH=$NDK_PATH/toolchains/llvm/prebuilt/linux-x86_64/bin/:$PATH
  

    

    

        
        

export NDK_PATH=$WORKSPACE/android-ndk-r25b/
export ANDROID_NDK_HOME=$NDK_PATH
export PATH=$NDK_PATH/toolchains/llvm/prebuilt/darwin-x86_64/bin/:$PATH

The configuration script is interactive. Run it using the command below, and use the table to set the parameters for this Learning Path use-case.

    

        
        
python3 ./configure.py
QuestionInput
Please specify the location of python. [Default is $WORKSPACE/bin/python3]:Enter (default)
Please input the desired Python library path to use[$WORKSPACE/lib/python3.10/site-packages]Enter
Do you wish to build TensorFlow with ROCm support? [y/N]N (No)
Do you wish to build TensorFlow with CUDA support?N
Please specify optimization flags to use during compilation when bazel option “–config=opt” is specified [Default is -Wno-sign-compare]:Enter
Do you want to use Clang to build TensorFlow? [Y/n]N
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]y (Yes)
Please specify the home path of the Android NDK to use. [Default is /home/user/Android/Sdk/ndk-bundle]Enter
Please specify the (min) Android NDK API level to use. [Default is 21]27
Please specify the home path of the Android SDK to use. [Default is /home/user/Android/Sdk]Enter
Please specify the Android SDK API level to use. [Default is 35]Enter
Please specify an Android build tools version to use. [Default is 35.0.0]Enter
Do you wish to build TensorFlow with iOS support? [y/N]:n

Once the Bazel configuration is complete, you can build LiteRT for your target platform as follows:

    

        
        

bazel build -c opt --config android_arm64 //tensorflow/lite:libtensorflowlite.so \
    --define tflite_with_xnnpack=true \
    --define=xnn_enable_arm_i8mm=true \
    --define tflite_with_xnnpack_qs8=true \
    --define tflite_with_xnnpack_qu8=true
  

    

    

        
        

bazel build -c opt --config macos //tensorflow/lite:libtensorflowlite.so \
    --define tflite_with_xnnpack=true \
    --define xnn_enable_arm_i8mm=true \
    --define tflite_with_xnnpack_qs8=true \
    --define tflite_with_xnnpack_qu8=true

The final step is to build flatbuffers used by the application:

    

        
        
cd $WORKSPACE/tensorflow_src
mkdir flatc-native-build && cd flatc-native-build
cmake ../tensorflow/lite/tools/cmake/native_tools/flatbuffers
cmake --build .

Now that LiteRT and FlatBuffers are built, you’re ready to compile and deploy the Stable Audio Open Small inference application on your Android or macOS device.

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