datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
audio-diffusion-1024Over 20,000 256x256 mel spectrograms of 5 second samples of music from my Spotify liked playlist. 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 using De-noising Diffusion Probabilistic Models.
x_res = 1024
y_res = 1024
sample_rate = 44100
n_fft = 2048
hop_length = 512
audio-diffusion-512Over 20,000 512x512 mel spectrograms of 5 second samples of music from my Spotify liked playlist. 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 using De-noising Diffusion Probabilistic Models.
x_res = 512
y_res = 512
sample_rate = 22050
n_fft = 2048
hop_length = 512
audio-diffusion-instrumental-hiphop-256256x256 mel spectrograms of 5 second samples of instrumental Hip Hop. 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 using De-noising Diffusion Probabilistic Models.
x_res = 256
y_res = 256
sample_rate = 22050
n_fft = 2048
hop_length = 512
audio-diffusion-breaks-25630,000 256x256 mel spectrograms of 5 second 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 using De-noising Diffusion Probabilistic Models.
x_res = 256
y_res = 256
sample_rate = 22050
n_fft = 2048
hop_length = 512
audio-diffusion-256Over 20,000 256x256 mel spectrograms of 5 second samples of music from my Spotify liked playlist. 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 using De-noising Diffusion Probabilistic Models.
x_res = 256
y_res = 256
sample_rate = 22050
n_fft = 2048
hop_length = 512
audio-diffusion-512
Dataset Card for "audio-diffusion-512"
More Information needed
audio-diffusion-breaks-25630,000 256x256 mel spectrograms of 5 second 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 using De-noising Diffusion Probabilistic Models.
x_res = 256
y_res = 256
sample_rate = 22050
n_fft = 2048
hop_length = 512
smd-audio-diffusion-256
Dataset Card for "smd-audio-diffusion-256"
More Information needed
audio-diffusion-256-isolated-drums
Dataset Card for "audio-diffusion-256-isolated-drums"
More Information needed
audio-diffusion-128-DPaudio-diffusion-256
Dataset Card for "audio-diffusion"
More Information needed
smd-bach-audio-diffusion-128
Dataset Card for "smd-bach-audio-diffusion-128"
More Information needed
