Hi,
I’ve got a dataset consisting of x number of 10-second recordings. I’ve got a data loader which can load these in one by one. However, I want my model to predict a value for each timestep and was wanting to feed it something like audio_file1[timestep_n -2: timestep_n +2] as training examples, which will then make a prediction for timestep_n.  I’m not sure the best way to go about this with PyTorch.
Currently, I’m thinking that I might have to include another nested loop after the data loader loads in the audio file where I break that into blocks and run through us it using a moving average style indexing, once the model has been fed all those blocks, the data loader then loads the next audio file.
Not sure if it’s relevant, but at present, I’m just looking to predict one feature per timestep per frequency band. However, in the future, I’m looking to extend this to predict multiple features per time step, per frequency band.