This example uses TensorFlow Lite with Python on a Raspberry Pi to perform real-time audio classification using audio streamed from the microphone.

At the end of this page, there are extra steps to accelerate the example using the Coral USB Accelerator to increase inference speed.

Before you begin, you need to set up your Raspberry Pi with Raspberry Pi OS (preferably updated to Buster).

Raspberry Pi doesn't have a microphone integrated on its board, so you need to plug in a USB microphone to record audio.

In this project, all you need from the TensorFlow Lite API is the Interpreter class. So instead of installing the large tensorflow package, we're using the much smaller tflite_runtime package.

To install this on your Raspberry Pi, follow the instructions in the Python quickstart.

You can install the TFLite runtime using this script.

sh setup.sh

First, clone this Git repo onto your Raspberry Pi like this:

git clone https://github.com/tensorflow/examples.git --depth 1

Then use our script to install a couple Python packages, and download the TFLite model:

cd lite/examples/sound_classification/raspberry_pi

# Run this script to install the required dependencies and download the TFLite models.
sh setup.sh

python3 classify.py

• You can optionally specify the model parameter to set the TensorFlow Lite model to be used:
  • The default value is yamnet.tflite
• You can optionally specify the maxResults parameter to limit the list of classification results:
  • Supported value: A positive integer.
  • Default value: 5.
• Example usage:

python3 classify.py \
  --model yamnet.tflite \
  --maxResults 5

If you want to speed up the inference time, you can attach an ML accelerator such as the Coral USB Accelerator—a USB accessory that adds the Edge TPU ML accelerator to any Linux-based system.

If you have a Coral USB Accelerator, you can run the sample with it enabled:

1. First, be sure you have completed the USB Accelerator setup instructions.

2. Run the audio classification script using the Edge TPU TFLite model and enable the Edge TPU option.

python3 classify.py \
  --model yamnet_edgetpu.tflite \
  --enableEdgeTPU

For more information about creating and running TensorFlow Lite models with Coral devices, read TensorFlow models on the Edge TPU.

For more information about executing inferences with TensorFlow Lite, read TensorFlow Lite inference.