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danjacobellis commited on May 10, 2023

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@@ -14,7 +14,7 @@ dataset_info:
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 # 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.
@@ -22,8 +22,6 @@ Other audio diffusion models operate in the space of the magnitude spectrogram o
 Operating in the MDCT space does not require a vocoder, nor does it oversample or undersample any range of frequencies.
-More information is provided in the [report](MP3_diffusion.pdf).
 Please see [this notebook showing how to load the dataset and convert from the MDCT images back to audio](load_dataset.ipynb)
 Additionally, [this notebook includes an example of the audio generated by fine tuning on this dataset and shows how to use the inference pipeline](music_inference.ipynb)

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 # MDCT-1k
+Over 1000 audio clips from the [Google music captions dataset](https://huggingface.co/datasets/google/MusicCaps) represented as 512x512 time-frequency images. More information is provided in the [report](MP3_diffusion.pdf).
 The time-frequency images are created from the MDCT coefficients of the 0-12kHz frequency band for 20 second audio clips.
 Operating in the MDCT space does not require a vocoder, nor does it oversample or undersample any range of frequencies.
 Please see [this notebook showing how to load the dataset and convert from the MDCT images back to audio](load_dataset.ipynb)
 Additionally, [this notebook includes an example of the audio generated by fine tuning on this dataset and shows how to use the inference pipeline](music_inference.ipynb)