Connectionist Temporal Classification loss in pytorch

There’s a lot of loss functions available in torch.nn. I am, currently, working on a speech recognizer. I am doing an end-to-end recognition. I need to have a connectionist temporal classification (CTC) layer as the outermost layer. Is there a neat way to do this?

In short, I want to have a bidrectional LSTM architecture which will have an objective to minimize CTC loss.

I don’t want to calculate the error gradients by hand obviously.

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Hey tom,

A great repo! Thanks for sharing.