Upon the start-up the demo application reads command line parameters and loads a network to Inference engine. It uses only audio files 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:
The command yields the following usage message:
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:
To run the demo, you can use public or pre-trained models. You can download the pre-trained models with the OpenVINO Model Downloader. The list of models supported by the demo is in the
models.lst file in the demo's directory.
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.
The demo uses console to display the predictions. It shows classification of each clip with timing of it and total prediction of whole audio.