**OpenFst version 1.7.7 is now available for download.**

**OpenFst** is a library for constructing, combining, optimizing, and searching *weighted finite-state transducers* (FSTs). Weighted finite-state transducers are automata where each transition has an input label, an output label, and a weight. The more familiar finite-state acceptor is represented as a transducer with each transition's input and output label equal. Finite-state acceptors are used to represent sets of strings (specifically, *regular* or *rational sets*); finite-state transducers are used to represent binary relations between pairs of strings (specifically, *rational transductions*). The weights can be used to represent the cost of taking a particular transition. FSTs have key applications in speech recognition and synthesis, machine translation, optical character recognition, pattern matching, string processing, machine learning, information extraction and retrieval among others. Often a weighted transducer is used to represent a probabilistic model (e.g., an *n-gram model*, *pronunciation model*). FSTs can be optimized by *determinization* and *minimization*, models can be applied to hypothesis sets (also represented as automata) or cascaded by finite-state *composition*, and the best results can be selected by *shortest-path* algorithms.

This library was developed by contributors from Google Research and NYU's Courant Institute. It is intended to be comprehensive, reliable, flexible, efficient, and to scale well. It is an open source project distributed under the Apache license.

OpenFst is now available on conda-forge. Linux (x86) and Mac OS X users who already have conda can install using the following command: `conda install -c conda-forge openfst`

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Topic revision: r115 - 2020-03-16 - KyleGorman

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