Denoising Diffusion Probabilistic Model trained on teticio/audio-diffusion-breaks-256 to generate mel spectrograms of 256x256 corresponding to 5 seconds of audio. The audio consists of 30,000 samples that have been used in music, sourced from WhoSampled and YouTube. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference.
4
19 commits
1 repos using this model
1 linked in READMEs
updated Jan 25, 2024
Denoising Diffusion Probabilistic Model trained on teticio/audio-diffusion-breaks-256 to generate mel spectrograms of 256x256 corresponding to 5 seconds of audio. The audio consists of 30,000 samples that have been used in music, sourced from WhoSampled and YouTube. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference.
Denoising Diffusion Probabilistic Model trained on teticio/audio-diffusion-breaks-256 to generate mel spectrograms of 256x256 corresponding to 5 seconds of audio. The audio consists of 30,000 samples that have been used in music, sourced from WhoSampled and YouTube. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference.
4
19 commits
1 repos using this model
1 linked in READMEs
updated Jan 25, 2024
Denoising Diffusion Probabilistic Model trained on teticio/audio-diffusion-breaks-256 to generate mel spectrograms of 256x256 corresponding to 5 seconds of audio. The audio consists of 30,000 samples that have been used in music, sourced from WhoSampled and YouTube. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference.
