# Build and run on macOS

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/)
- [Set up your development environment](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/1-prerequisites/)
- [Download the Stable Audio Open Small model](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/2-download-model/)
- [Convert the model to ExecuTorch format](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/3-convert-model/)
- [Build and run on macOS](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/4-build-run-macos/)
- [Build and run on Android](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/5-build-run-android/)
- [Next Steps](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_next-steps/)

## Overview
In this section, you will build and run the audio generation application on macOS. The application uses ExecuTorch with XNNPack and Arm KleidiAI for optimized inference on Arm CPUs.

> **Note:** These steps assume you are using macOS running on Apple Silicon (Arm64).

## Set up the environment
Start a fresh virtual environment to avoid dependency conflicts from earlier steps:

```bash
cd $WORKSPACE/ML-examples/kleidiai-examples/audiogen-et/
python3.10 -m venv new-venv
source new-venv/bin/activate
```

If you haven’t already installed ExecuTorch, install it now:

```bash
pip install executorch==1.0.0
```

## Set model path
Set the `EXECUTORCH_MODELS_PATH` environment variable to the directory containing your exported ExecuTorch models:

```bash
export EXECUTORCH_MODELS_PATH=$WORKSPACE/ML-examples/kleidiai-examples/audiogen-et
```

This should be the directory where the `.pte` model files were created during the conversion step.

## Build the application
Navigate to the `app` directory and create a build directory:

```bash
cd app
mkdir build && cd build
```

Run CMake to configure the build:

```bash
cmake ..
```

Build the application:

```bash
make -j8
```

The build process creates an `audiogen` executable in the `build` directory.

## Download the tokenizer model
The audio generation application uses a SentencePiece-based tokenizer. Download the `spiece.model` file from Hugging Face:

```bash
curl -L https://huggingface.co/google-t5/t5-base/resolve/main/spiece.model -o $EXECUTORCH_MODELS_PATH/spiece.model
```

Verify the file was downloaded:

```bash
ls -lh $EXECUTORCH_MODELS_PATH/spiece.model
```

## Run the application
Run the `audiogen` application with three required arguments:
- Model Path (`-m`): Directory containing your ExecuTorch models and `spiece.model` files
- Prompt (`-p`): Text description of the desired audio
- CPU Threads (`-t`): Number of CPU threads to use

Run the application with an example prompt:

```bash
./audiogen -m $EXECUTORCH_MODELS_PATH -p "warm arpeggios on house beats 120BPM with drums effect" -t 4
```

The application processes the prompt through the three model submodules and generates audio.

The output is similar to:

```
__output__ I 00:00:00.002858 executorch:main.cpp:280] Resetting threadpool with num threads = 4
__output__ I 00:00:00.003228 executorch:threadpool.cpp:48] Resetting threadpool to 4 threads.
__output__ I 00:00:00.003543 executorch:main.cpp:288] Using 4 threads
__output__ I 00:00:00.003620 executorch:main.cpp:294] Model (/Users/parver01/my-workspace/ML-examples/kleidiai-examples/audiogen-et/conditioners_model.pte) loaded
__output__ I 00:00:00.003627 executorch:main.cpp:298] Model (/Users/parver01/my-workspace/ML-examples/kleidiai-examples/audiogen-et/dit_model.pte) loaded
__output__ I 00:00:00.003629 executorch:main.cpp:302] Model (/Users/parver01/my-workspace/ML-examples/kleidiai-examples/audiogen-et/autoencoder_model.pte) loaded
__output__ I 00:00:04.723761 executorch:main.cpp:478] Output saved to warm_arpeggios_on_house_beats_120bpm_with_drums_effect_99.wav
__output__ I 00:00:04.723770 executorch:main.cpp:487] T5: 72 ms
__output__ I 00:00:04.723771 executorch:main.cpp:488] DiT: 1474 ms
__output__ I 00:00:04.723772 executorch:main.cpp:489] DiT Avg per step: 184.250000 ms
__output__ I 00:00:04.723784 executorch:main.cpp:490] AutoEncoder: 3127 ms
__output__ I 00:00:04.723785 executorch:main.cpp:491] Total execution time: 4673 ms
```

## Verify the output
If successful, the generated audio is saved as a `.wav` file in the current directory. The filename is based on the prompt text:

```bash
ls -lh warm_arpeggios_on_house_beats_120bpm_with_drums_effect_99.wav
```

You can play the audio file using any audio player on your macOS system:

```bash
open warm_arpeggios_on_house_beats_120bpm_with_drums_effect_99.wav
```

## What you’ve accomplished and what’s next
You’ve built the audio generation application for macOS, downloaded the tokenizer model, and generated audio from a text prompt using ExecuTorch with Arm KleidiAI optimizations. The application delivers efficient inference on Arm CPUs, and lets you experiment with different prompts to generate audio.

If you want to deploy to Android instead, proceed to the next section.