Sound Classification Python* Demo

Demo application for sound classification algorithm.

How It Works

Upon the start-up the demo application reads command line parameters and loads a network to Inference engine. It uses only audiofiles in wav format. Audio should be converted to model's sample rate using -sr/--samplerate option, if sample rate of audio differs from sample rate of model (e.g. AclNet expected 16kHz audio). After reading the audio, it is sliced into clips to fit model input (clips are allowed to overlap with -ol/--overlap option) and each clip is processed separately with its own prediction.


Run the application with the -h option to see the usage message:

python3 -h

The command yields the following usage message:

usage: [-h] -i INPUT -m MODEL [-l CPU_EXTENSION]
[-d DEVICE] [--labels LABELS]
-h, --help Show this help message and exit.
-i INPUT, --input INPUT
Required. Input to process
-m MODEL, --model MODEL
Required. Path to an .xml file with a trained model.
Optional. Required for CPU custom layers. Absolute
path to a shared library with the kernels
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, FPGA, HDDL or MYRIAD is acceptable. The sample
will look for a suitable plugin for device specified.
Default value is CPU
--labels LABELS Optional. Labels mapping file
-sr SAMPLERATE, --sample_rate SAMPLERATE
Optional. Set sample rate for audio input
-ol OVERLAP, --overlap OVERLAP
Optional. Set the overlapping between audio clip in
samples or percent

Running the application with the empty list of options yields the usage message given above and an error message. You can use the following command to do inference on GPU with a pre-trained sound classification model and conversion of input audio to samplerate of 16000:

python3 -i <path_to_wav>/input_audio.wav -m <path_to_model>/aclnet.xml -d GPU --samplerate 16000

To run the demo, you can use public or pre-trained models. You can download the pre-trained models with the OpenVINO Model Downloader or from

NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

Demo Output

The demo uses console to display the predictions. It shows classification of each clip with timing of it and total prediction of whole audio.

See Also