|
--- |
|
dataset_info: |
|
features: |
|
- name: image |
|
dtype: image |
|
- name: audio_file |
|
dtype: string |
|
- name: slice |
|
dtype: int16 |
|
splits: |
|
- name: train |
|
num_bytes: 1903861364.293 |
|
num_examples: 10663 |
|
download_size: 1903696036 |
|
dataset_size: 1903861364.293 |
|
pretty_name: Mel spectrograms of music |
|
size_categories: |
|
- 10K<n<100K |
|
source_datasets: [] |
|
tags: |
|
- audio |
|
- spectrograms |
|
task_categories: |
|
- image-to-image |
|
task_ids: [] |
|
--- |
|
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 |
|
``` |
