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
go, as well as lo-fi instrumental music. The model is based on the WaveGAN Model.
|Domain||Application||Industry||Framework||Training Data||Input Data Format|
|Audio||Audio Modeling||General||TensorFlow||Speech Commands & FMA tracks||WAV Audio Files|
- Chris Donahue, Julian McAuley, Miller Puckette, “Synthesizing Audio with Generative Adversarial Networks”, arXiv, 2018.
- WaveGAN Github repository
- Speech Commands Dataset release blog
- Free Music Archive
|Model GitHub Repository||Apache 2.0||LICENSE|
|Model Weights||Apache 2.0||LICENSE|
|Model Code (3rd party)||MIT||LICENSE|
Options available for deploying this model
This model can be deployed using the following mechanisms:
Deploy from Dockerhub:
docker run -it -p 5000:5000 codait/max-audio-sample-generator
Deploy on Red Hat OpenShift:
Follow the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial and specify
codait/max-audio-sample-generatoras the image name.
Deploy on Kubernetes:
kubectl apply -f https://raw.githubusercontent.com/IBM//master/max-audio-sample-generator.yaml
Locally: follow the instructions in the model README on GitHub
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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