Denoising Diffusion Probabilistic Model trained on [teticio/audio-diffusion-breaks-256](https://huggingface.co/datasets/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](https://whosampled.com) and [YouTube](https://youtube.com). 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.
{"tags": ["audio", "spectrograms"], "datasets": ["teticio/audio-diffusion-breaks-256"]}
diffusers tensorboard audio spectrograms dataset:teticio/audio-diffusion-breaks-256 has_space diffusers:AudioDiffusionPipeline region:us
TAGS
#diffusers #tensorboard #audio #spectrograms #dataset-teticio/audio-diffusion-breaks-256 #has_space #diffusers-AudioDiffusionPipeline #region-us 

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 URL along with scripts to train and run inference.
TAGS
#diffusers #tensorboard #audio #spectrograms #dataset-teticio/audio-diffusion-breaks-256 #has_space #diffusers-AudioDiffusionPipeline #region-us 

passage: TAGS
#diffusers #tensorboard #audio #spectrograms #dataset-teticio/audio-diffusion-breaks-256 #has_space #diffusers-AudioDiffusionPipeline #region-us 
