SEQUITUR-G2P(1)

NAME
sequitur-g2p - grapheme-to-phoneme conversion tool

SYNOPSIS
sequitur-g2p [OPTION]... FILE...

DESCRIPTION
Grapheme-to-Phoneme Conversion

Samples can be either in plain format (one word per line followed by phonetic transcription) or Bliss XML Lexicon format.

OPTIONS

--version
    show program's version number and exit

-h, --help
    show this help message and exit

-p FILE, --profile=FILE
    Profile execution time and store result in FILE

-R, --resource-usage
    Report resource usage execution time

-Y, --psyco
    Use Psyco to speed up execution

--tempdir=PATH
    store temporary files in PATH

-t FILE, --train=FILE
    read training sample from FILE

-d FILE / N%, --devel=FILE / N%
    read held-out training sample from FILE or use N% of the training data

-x FILE, --test=FILE
    read test sample from FILE

--checkpoint
    save state of training in regular time intervals. The name of the checkpoint file is derived from --writemodel.

--resume-from-checkpoint=FILE
    load checkpoint FILE and continue training

-T, --transpose
    Transpose model, i.e. do phoneme-to-grapheme conversion

-m FILE, --model=FILE
    read model from FILE

-n FILE, --write-model=FILE
    write model to FILE

--continuous-test
    report error rates on development and test set in each iteration

-S, --self-test
    apply model to development set and report error rates

-s l1,l2,r1,r2, --size-constraints=l1,l2,r1,r2
    multigrams must have l1 ... l2 left-symbols and r1 ... r2 right-symbols

-E, --no-emergence
    do not allow new joint-multigrams to be added to the model

--viterbi
    estimate model using maximum approximation rather than true EM

-r, --ramp-up
    ramp up the model

-W, --wipe-out
    wipe out probabilities, retain only model structure

-C, --initialize-with-counts
    initialize probabilities estimation by counting how many times every graphone occurs in the training set, disregarding possible overlaps

-i MINITERATIONS, --min-iterations=MINITERATIONS
    minimum number of EM iterations during training

-I MAXITERATIONS, --max-iterations=MAXITERATIONS
    maximum number of EM iterations during training

--eager-discount-adjustment
    re-adjust discounts in each iteration

--fixed-discount=D
    set discount to D and keep it fixed

-e ENC, --encoding=ENC
    use character set encoding ENC

-P, --phoneme-to-phoneme
    train/apply a phoneme-to-phoneme converter

--test-segmental
    evaluate only at segmental level, i.e. do not count syllable boundaries and stress marks

-B FILE, --result=FILE
    store test result in table FILE (for use with bootlog or R)

-a FILE, --apply=FILE
    apply grapheme-to-phoneme conversion to words read from FILE

-V Q, --variants-mass=Q
    generate pronunciation variants until \sum_i p(var_i) >= Q (only effective with --apply)

--variants-number=N
    generate up to N pronunciation variants (only effective with --apply)

-f FILE, --fake=FILE
    use a translation memory (read from sample FILE) instead of a genuine model (use in combination with -x to evaluate two files against each other)

--stack-limit=N
    limit size of search stack to N elements