YamNet Example

This document explains how to use the QAI AppBuilder Python API to perform inference with the YamNet audio classification model using the Qualcomm® Hexagon™ Processor (NPU).

Supported devices

Device	SoC
Fogwise® AIRbox Q900	QCS9075

Install QAI AppBuilder

tip

1. Install QAI AppBuilder by following the QAI AppBuilder installation guide.

2. Configure ADSP environment variables as described in Create ADSP environment variables.

Run the sample

Install dependencies

Install sample dependencies in the activated virtual environment:

Device

pip3 install requests tqdm qai-hub py3-wget Pillow torch torchvision opencv-python-headless soxr soundfile resampy

Run the script

• Enter the upstream samples directory

  Device

  cd qai-appbuilder/samples

• Prepare input data (use the sample input if provided, or pass script arguments)

input audio

• Run inference

  Device

  python3 audio/Audio_Classification/yamnet/yamnet.py --chipset 9075

The default input is the bundled input.wav. On success the terminal prints top-5 labels, for example:

Top 5 predictions:
Whistling | Alarm | Whistle | Field recording | Rattle (instrument)

tip

The first run downloads the model via Qualcomm® AI Hub and needs audio libraries such as soxr. You can also use the launcher from samples:

python3 run_inference.py --list
python3 run_inference.py --model yamnet --args "--chipset 9075"