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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: text |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 378108023.375 |
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num_examples: 1581 |
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download_size: 373552088 |
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dataset_size: 378108023.375 |
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--- |
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# MDCT-1k |
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Over 1000 audio clips from the [Google music captions dataset](https://huggingface.co/datasets/google/MusicCaps) represented as 512x512 time-frequency images. |
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The time-frequency images are created from the MDCT coefficients of the 0-12kHz frequency band for 20 second audio clips. |
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Please see [this notebook showing how to load the dataset and convert from the MDCT images back to audio](load_dataset.ipynb) |
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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. |
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Operating in the MDCT space does not require a vocoder, nor does it oversample or undersample any range of frequencies. |
