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By IBM Developer Staff | Published September 21, 2018
Artificial intelligenceDeep learningAudio Modeling
This model generates short samples based on an existing dataset of audio clips. It maps the sample space of the input data and generates audio clips that are “inbetween” or “combinations” of the dominant features of the sounds. The model architecture is a generative adversarial neural network, trained by the IBM CODAIT Team on lo-fi instrumental music tracks from the Free Music Archive and short spoken commands from the Speech Commands Dataset. The model can generate 1.5 second audio samples of the words up, down, left, right, stop, go, as well as lo-fi instrumental music. The model is based on the WaveGAN Model.
This model can be deployed using the following mechanisms:
docker run -it -p 5000:5000 codait/max-audio-sample-generator
kubectl apply -f https://raw.githubusercontent.com/IBM//master/max-audio-sample-generator.yaml
Once deployed, you can test the model from the command line. For example, the following command will generate a sample from the default model (lo-fi instrumental music):
curl -X GET 'http://localhost:5000/model/predict' -H 'accept: audio/wav' > result.wav
This will save the resulting audio file to result.wav, which you can then open in the audio player of your choice.
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