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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Create and build a simple program

As a final step, you’ll build a simple program that runs inference on all three submodules directly on an Android device.

The program takes a text prompt as input and generates an audio file as output.

    

        
        
cd $WORKSPACE/ML-examples/kleidiai-examples/audiogen/app
mkdir build && cd build

Ensure the NDK path is set correctly and build with cmake:

    

        
        
cmake -DCMAKE_TOOLCHAIN_FILE=$NDK_PATH/build/cmake/android.toolchain.cmake \
      -DCMAKE_POLICY_VERSION_MINIMUM=3.5 \
      -DANDROID_ABI=arm64-v8a \
      -DTF_INCLUDE_PATH=$TF_SRC_PATH \
      -DTF_LIB_PATH=$TF_SRC_PATH/bazel-bin/tensorflow/lite \
      -DFLATBUFFER_INCLUDE_PATH=$TF_SRC_PATH/flatc-native-build/flatbuffers/include \
    ..

make -j

After the example application builds successfully, a binary file named audiogen is created.

A SentencePiece model is a type of subword tokenizer which is used by the audiogen application, you’ll need to download the spiece.model file from:

    

        
        
cd $WORKSPACE
wget https://huggingface.co/google-t5/t5-base/resolve/main/spiece.model

Verify this model was downloaded to your WORKSPACE.

    

        
        
ls $WORKSPACE/spiece.model

Connect your Android device to your development machine using a cable. adb (Android Debug Bridge) is available as part of the Android SDK.

You should see your device listed when you run the following command:

    

        
        
adb devices

    

    

        
        <DEVICE ID>     device

Note that you may have to approve the connection on your phone for this to work. Now, use adb to push all necessary files into the audiogen folder on Android device:

    

        
        
cd $WORKSPACE/ML-examples/kleidiai-examples/audiogen/app/build
adb shell mkdir -p /data/local/tmp/app
adb push audiogen /data/local/tmp/app
adb push $LITERT_MODELS_PATH/conditioners_float32.tflite /data/local/tmp/app
adb push $LITERT_MODELS_PATH/dit_model.tflite /data/local/tmp/app
adb push $LITERT_MODELS_PATH/autoencoder_model.tflite /data/local/tmp/app
adb push $WORKSPACE/spiece.model /data/local/tmp/app
adb push ${TF_SRC_PATH}/bazel-bin/tensorflow/lite/libtensorflowlite.so /data/local/tmp/app

Start a new shell to access the device’s system from your development machine:

    

        
        
adb shell

From there, you can then run the audiogen application, which requires just three input arguments:

  • Model Path: The directory containing your LiteRT models and spiece.model files
  • Prompt: A text description of the desired audio (e.g., warm arpeggios on house beats 120BPM with drums effect)
  • CPU Threads: The number of CPU threads to use (e.g., 4)

Play around with the advice from Download and test the model section.

    

        
        
cd /data/local/tmp/app
LD_LIBRARY_PATH=. ./audiogen . "warm arpeggios on house beats 120BPM with drums effect" 4
exit

You can now pull the generated output.wav back to your host machine and listen to the result.

    

        
        
adb pull /data/local/tmp/app/output.wav

You should now have gained hands-on experience running the Stable Audio Open Small model with LiteRT on Arm-based devices. This includes setting up the environment, optimizing the model for on-device inference, and understanding how efficient runtimes like LiteRT make low-latency generative AI possible at the edge. You’re now better equipped to explore and deploy AI-powered audio applications on mobile and embedded platforms.

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