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  # Musika Model: halvany\_oszi\_rozsa
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  ## Model provided by: DarkDude31
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- Finetuned (from misc) halvany\_oszi\_rozsa model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
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  Introduced in [this paper](https://arxiv.org/abs/2208.08706).
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  ## How to use
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- You can generate music from this finetuned (from misc) musika_halvany_oszi_rozsa model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r).
 
 
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  ### Model description
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  This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio.
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  The generator has a context window of about 12 seconds of audio.
 
 
 
 
 
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  # Musika Model: halvany\_oszi\_rozsa
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  ## Model provided by: DarkDude31
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+ Fine-tuned (from misc) halvany\_oszi\_rozsa model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
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  Introduced in [this paper](https://arxiv.org/abs/2208.08706).
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  ## How to use
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+ You can generate music from this fine-tuned (from misc) halvany_oszi_rozsa model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r).
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+ Only the `gen_ema.h5` file is needed to generate music. Place it in your `checkpoints` folder.
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  ### Model description
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  This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio.
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  The generator has a context window of about 12 seconds of audio.
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+ ### Training description
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+ I had trained this on colab for 5 epochs. (about 5 \* 9000 iterations)