rustfst-python

Introduction

Rust implementation of Weighted Finite States Transducers.

Rustfst 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.

Naming

• rustfst is the Rust re-implementation of Openfst.
• rustfst-python is a python biding on top of rustfst.

References

Implementation heavily inspired from Mehryar Mohri's, Cyril Allauzen's and Michael Riley's work :

• Weighted automata algorithms
• The design principles of a weighted finite-state transducer library
• OpenFst: A general and efficient weighted finite-state transducer library
• Weighted finite-state transducers in speech recognition