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PyTorch edit-distance functions

Project description

PyTorch edit-distance functions

Useful functions for E2E Speech Recognition training with PyTorch and CUDA.

Here is a simple use case with Reinforcement Learning and RNN-T loss:

blank = torch.tensor([0],
space = torch.tensor([1],

xs = model.greedy_decode(xs, sampled=True)

torch_edit_distance.remove_blank(xs, xn, blank)

rewards = 1 - torch_edit_distance.compute_wer(xs, ys, xn, yn, blank, space)

nll = rnnt_loss(zs, ys, xn, yn)

loss = nll * rewards


Levenshtein edit-distance with detailed statistics for ins/del/sub operations.


Merge repeated tokens, useful for CTC-based model.


Remove unnecessary blank tokens, useful for CTC, RNN-T, RNA models.


Remove leading, trailing and repeated middle separators.


  • C++11 compiler (tested with GCC 9.4.0).
  • Python: 3.5, 3.6, 3.7, 3.8, 3.9 (tested with version 3.8).
  • PyTorch >= 1.5.0 (tested with version 1.13.1+cu116).
  • CUDA Toolkit (tested with version 11.2).


There is no compiled version of the package. The following setup instructions compile the package from the source code locally.

From Pypi

pip install torch_edit_distance

From GitHub

git clone
cd pytorch-edit-distance
python install


python -m torch_edit_distance.test

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torch_edit_distance-0.4.0.tar.gz (8.5 kB view hashes)

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