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we do already have a base VocalDataset but it's basically just used for prediction
there should be something like a base Dataset class similar to the hierarchy in torchvision that has an init that expect to get a path to a vocles dataset and then keeps that as an attribute
Two sub-classes would be AudioDataset and SpectrogramDataset, that each return as an __item__ the audio or spectrogram + any corresponding annotation from the row. We could just always return a dict with audio / spect and annot and let annot be None for unannotated data. This removes the need to have a separate dataset for prediction
Then e.g. a BFSongRepo dataset would sub-class the SpectrogramDataset?
But then we'd need to actually provide spectrograms 🤔
The text was updated successfully, but these errors were encountered:
I am realizing that the concept of Dataset as used in torch tends to conflate a static set of files with the dynamic series of pre-processing transformations applied to those files at load time; I feel like we should prefer the term "Dataset" to refer to a static set of files and a term like "Pipeline" to refer to a pre-defined set of transformations
related to #446
we do already have a base VocalDataset but it's basically just used for prediction
there should be something like a base
Dataset
class similar to the hierarchy intorchvision
that has an init that expect to get a path to avocles
dataset and then keeps that as an attributeTwo sub-classes would be
AudioDataset
andSpectrogramDataset
, that each return as an__item__
the audio or spectrogram + any corresponding annotation from the row. We could just always return a dict withaudio
/spect
andannot
and letannot
beNone
for unannotated data. This removes the need to have a separate dataset for predictionThen e.g. a BFSongRepo dataset would sub-class the
SpectrogramDataset
?But then we'd need to actually provide spectrograms 🤔
The text was updated successfully, but these errors were encountered: