Generate audio with Stable Audio Open Small on LiteRT
Introduction
Set up your development environment
Download and test the model
Convert Open Stable Audio Small model to LiteRT
Build LiteRT
Create a simple program
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.

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

We will use a specific commit of tensorflow for build so you can checkout and set the TF_SRC_PATH:

    

        
        
git checkout 84dd28bbc29d75e6a6d917eb2998e4e8ea90ec56
export TF_SRC_PATH=$(pwd)

We can use bazel to build LiteRT libraries, first we use configure script to create a custom configuration for this:

You can now create a custom TFLite build for android:

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

    

        
        

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

    

    

        
        

export TF_CXX_FLAGS="-DTF_MAJOR_VERSION=0 -DTF_MINOR_VERSION=0 -DTF_PATCH_VERSION=0 -DTF_VERSION_SUFFIX=''"
export ANDROID_NDK=~/Library/Android/sdk/ndk/27.0.12077973/
export PATH=$PATH:$ANDROID_NDK/toolchains/llvm/prebuilt/darwin-x86_64/bin
export PATH=$PATH:~/Library/Android/sdk/cmdline-tools/latest/bin

Set the TensorFlow version

    

        
        
export TF_CXX_FLAGS="-DTF_MAJOR_VERSION=0 -DTF_MINOR_VERSION=0 -DTF_PATCH_VERSION=0 -DTF_VERSION_SUFFIX=''"

Now you can configure TensorFlow. Here you can set the custom build parameters needed as follows:

    

        
        python3 ./configure.py
__output__Please specify the location of python. [Default is $WORKSPACE/bin/python3]:
__output__Please input the desired Python library path to use. Default is [$WORKSPACE/lib/python3.10/site-packages]
__output__Do you wish to build TensorFlow with ROCm support? [y/N]: n
__output__Do you wish to build TensorFlow with CUDA support? [y/N]: n
__output__Do you want to use Clang to build TensorFlow? [Y/n]: n
__output__Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: y
__output__Please specify the home path of the Android NDK to use. [Default is /home/user/Android/Sdk/ndk-bundle]: /home/user/Workspace/tools/ndk/android-ndk-r25b
__output__Please specify the (min) Android NDK API level to use. [Available levels: [16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33]] [Default is 21]: 30
__output__Please specify the home path of the Android SDK to use. [Default is /home/user/Android/Sdk]:
__output__Please specify the Android SDK API level to use. [Available levels: ['31', '33', '34', '35']] [Default is 35]:
__output__Please specify an Android build tools version to use. [Available versions: ['30.0.3', '34.0.0', '35.0.0']] [Default is 35.0.0]:

Once the bazel configuration is complete, you can build TFLite 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

This will produce a libtensorflowlite.so shared library for android with XNNPack enabled, which we will use to build the example next.

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