---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 378108023.375
    num_examples: 1581
  download_size: 373552088
  dataset_size: 378108023.375
---
# MDCT-1k

Over 1000 audio clips from the [Google music captions dataset](https://huggingface.co/datasets/google/MusicCaps) represented as 512x512 time-frequency images.

The time-frequency images are created from the MDCT coefficients of the 0-12kHz frequency band for 20 second audio clips.

Please see [this notebook showing how to load the dataset and convert from the MDCT images back to audio](load_dataset.ipynb)

Most other audio diffusion models operate in the space of the magnitude spectrogram or mel magnitude spectrogram. Since the phase is discarded, this requires the use of a vocoder for audio generation. When operating in the space of the mel-spectrogram, high frequencies are represented with insufficient time resolution, leading to a noticable loss of quality.

Operating in the MDCT space does not require a vocoder, nor does it oversample or undersample any range of frequencies.