Sound Classification Python* Demo¶
Demo application for sound classification algorithm.
How It Works¶
On startup the demo application reads command line parameters and loads a model to OpenVINO™ Runtime plugin. It uses only audio files in
wav format. Audio should be converted to model’s sample rate using
-sr/--sample_rate 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.
Preparing to Run¶
For demo input image or video files, refer to the section Media Files Available for Demos in the Open Model Zoo Demos Overview. The list of models supported by the demo is in
<omz_dir>/demos/sound_classification_demo/python/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO IR format (*.xml + *.bin).
An example of using the Model Downloader:
omz_downloader --list models.lst
An example of using the Model Converter:
omz_converter --list models.lst
Run the application with the
-h option to see the usage message:
usage: sound_classification_demo.py [-h] -i INPUT -m MODEL [-d DEVICE]
[--labels LABELS] [-sr SAMPLE_RATE]
-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.
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, HDDL or MYRIAD is acceptable. The demo
will look for a suitable plugin for device specified.
Default value is CPU
--labels LABELS Optional. Labels mapping file
-sr SAMPLE_RATE, --sample_rate SAMPLE_RATE
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 sample rate of 16000:
python3 sound_classification_demo.py -i <path_to_wav>/input_audio.wav -m <path_to_model>/aclnet.xml -d GPU --sample_rate 16000
The demo uses console to display the predictions. It shows classification of each clip and total prediction of whole audio. The demo reports
Latency : total processing time required to process input data (from reading the data to displaying the results).