Description
We want to add Viterbi decoding to PyTorch. Viterbi decoding is a well-known algorithm that finds the path of maximum likelihood over a time-varying distribution. It is used in automatic speech recognition, bioinformatics, digital communications, and other tasks that produce models that infer or generate sequences of probability distributions. No implementation of Viterbi decoding exists in PyTorch, and no convenient alternative implementation exists for ML practitioners that is fast enough to scale to large datasets. We have created batched CPU and GPU implementations of Viterbi decoding significantly faster than available implementations. We have found our implementations useful for our own research tasks, and believe the community may find them useful as well.
Here is the link to the RFCS pull request pytorch/rfcs#62
co-author:
@maxrmorrison
cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki