Over 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.
2
9 commits
1 linked in READMEs
updated Jun 19, 2023
Over 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
Over 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.
2
9 commits
1 linked in READMEs
updated Jun 19, 2023
Over 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