Projects / Treba

Treba

Treba is a commandline tool for training, decoding, and calculating with weighted (probabilistic) finite state automata (WFSA/PFSA). Training algorithms include Baum-Welch (EM), Viterbi training, and Baum-Welch augmented with deterministic annealing. Treba is optimized for speed and numerical stability, and training algorithms can be run multi-threaded on hardware with multiple cores/CPUs. Forward, backward, and Viterbi decoding are supported. Automata for training/decoding are read from a text file, or can be generated randomly or with uniform transition probabilities with different topologies (ergodic or fully connected, Bakis or left-to-right, or deterministic). Observations used for training or decoding are read from text files compatible with AT&T finite state tools and OpenFST.

Tags
Licenses
Operating Systems
Implementation
Screenshot

Project Spotlight

OpenStack4j

A Fluent OpenStack client API for Java.

Screenshot

Project Spotlight

TurnKey TWiki Appliance

A TWiki appliance that is easy to use and lightweight.