Firen Word Generator

Words: (Limit 250)
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Show glosses:
Show old orthography: (Sajem Tan only)
Show original IPA: (Sajem Tan only)
Show Ðab Tan: (Sajem Tan only)
Show Ðab Tan IPA: (Sajem Tan only)
Show ABC notation: (Jafren only)
Show alphabetical notation: (Jafren only)
Datafile:
Root Node:
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WordGloss
kůtganoun[I]-PEG
tesppaṙrolmatgaočonoun-noun[I]-COLL-PCL-ILA
růṙttaṙdanoun-noun[I]-BEN
doslaounovvomunaṙmbuidinoun-noun-noun-noun-noun-A;person-INS
naittifsenoun-D-CUZ
tippespillůṙneaṙmatasenoun-noun-noun[I]-COLL-PL-CUZ
retvukunoun-noun[I]-ERG
baijoṙmogoṙnoun-noun[I]-ELA
jaisenoun[I]-LOC
siboṙfočukosoanoun-noun-noun-A-PL[ABS]
kivkilitpaṙzzaṙsodmatdunoun-noun-noun-noun[I]-COLL-INS
fukufaṙponůṙskebaṙnoun-noun-def;person-FML.COLL-PL-LAT
spoṙmkůpsodoṙnoun-noun-A-EVT
kediagaṙnoun-A;person-PL-ELA
tipginoun[I]-DAT
zůṙzusbuvvotreigaṙnoun-noun-noun-noun-noun-A;person-ELA
tůmmegaṙnoun[I]-ELA
kaovuhallokelekiadůṙnoun-noun-noun-noun-noun[I]-PL-EVT
čaeganoun-noun[I]-PEG
linnibinoun[I]-VIA
nůllinollunůṙčůllitokunůṙenoun-noun-noun-noun-noun[I]-FML.COLL-LOC
čůṙatainoun[I]-PL-SPE
mevaṙfpeenoun-noun-def;person-LOC
kemmilůssaṙsebaṙnoun-noun-A-LAT
futtaojunoun[I]-NPT
kitvaftifdanoun-noun-D-BEN
zůṙnnůṙtifdinoun-D-INS
vupukkosoagoṙnoun-noun-A-PL-ELA
zaodinůṙdůṙnoun-A;person-FML.COLL-EVT
stubatonoun[I]-PL-INE
junnaṙnisseimatbinoun-noun-A;person-COLL-VIA
laogumlaittesenůṙabaṙnoun-noun-noun-A-FML.COLL-PL-LAT
lullaolůṙsaigaitainoun-noun[I]-PCL-SPE
raogesseskedůṙnoun-noun[I]-PL-EVT
čiůlugkůddaodoṙnoun-noun-noun[I]-EVT
tusksostsokunoun-noun-A-ERG
tidassaigaienoun-noun[I]-PCL-LOC
dunsomatnoun-A-COLL[ABS]
tisůtpetainoun-noun-def;person-SPE
bamčonoun[I]-ILA
sainanoun[I]-PL[ABS]
tiltifnůṙginoun-D-FML.COLL-DAT
gugsolunoun-A-ABL
dutaoveaṙnůggaṙiskedůṙnoun-noun-noun-A;person-PL-EVT
čaotaonoun[I]-SPE
lugunoun[I]-DAT
mipohiffaičenoun-noun-noun[I]-ILA
luttaofilliseenoun-noun-A-LOC
kuinůṙkinoun-A;person-FML.COLL-ERG
vallaṙpeginoun-def;person-DAT
joniginoun-A;person-DAT
kosmaosokkaofaotpodanoun-noun-noun-noun-def;person-BEN
tenniteaṙinůṙasenoun-noun-A;person-FML.COLL-PL-CUZ
roddaotusůčonoun-noun[I]-ILA
festostufdanoun-noun-D-BEN
laoffůtufnůṙbaṙnoun-D-FML.COLL-LAT
teppaṙkappitifnůṙagaṙnoun-noun-D-FML.COLL-PL-ELA
vůṙtokonailnůṙtainoun-noun-noun[I]-FML.COLL-SPE
kičesoṙfaoinůṙčenoun-noun-A;person-FML.COLL-ILA
tussuskodanoun[I]-PL-BEN
jodhilpeačenoun-noun-def;person-PL-ILA
sugunainenoun-noun[I]-LOC
guffogutosgzuzůṙkiagaṙnoun-noun-noun-noun-noun[I]-PL-ELA
snaṙbbaovallumatunoun-noun[I]-COLL-LOC
nimmainoun[I][ABS]
fufammoṙvutagunoun-noun-noun[I]-PL-DAT
vokkosodunoun-A-INS
maillaitůṙgaṙnoun-noun[I]-ELA
siůgulsogaotaonoun-noun-A-PCL-SPE
fiffaiienoun-A;person-LOC
lůṙruninoun-noun-A;person[ABS]
kotoponůṙenoun-def;person-FML.COLL-LOC
toloṙruvutaonoun-noun[I]-SPE
slilskůtskekinoun-noun[I]-PL-ERG
faifhaohuvaṙvspoataonoun-noun-noun-noun[I]-PL-SPE
dottugaodanoun[I]-PCL-BEN
naottaṙsolstuspiimatlenoun-noun-noun-A;person-COLL-ABL
saidtollukutufdanoun-noun-noun-D-BEN
ginaisenůṙagaṙnoun-A-FML.COLL-PL-ELA
slustusodanoun-A-BEN
jaotufunoun-D-LOC
dannehotruphoffonůṙadůṙnoun-noun-noun-noun[I]-FML.COLL-PL-EVT
faomatbonoun[I]-COLL-VIA
dozotufkunoun-D-ERG
gosknůṙčenoun[I]-FML.COLL-ILA
tunelmatsenoun-noun[I]-COLL-CUZ
salloṙzoṙnojunoun-noun[I]-NPT
fusounoun-A-LOC
foffososonoun-A-CUZ
tumnaostufčonoun-noun[I]-ILA
doddukůrolussosomattaonoun-noun-noun-A-COLL-SPE
slegaitifnůṙenoun-noun-D-FML.COLL-LOC
stevfugaokunoun-noun[I]-PCL-ERG
lansematbaṙnoun-A-COLL-LAT
rilleičenoun-A;person-ILA
neppůṙmůlkůtaikginoun-noun-noun[I]-DAT
tufsobonoun-A-VIA
lůsnaikkaiseadůṙnoun-noun-A-PL-EVT
taṙstaonoun[I]-SPE
jaolinůṙgaṙnoun-A;person-FML.COLL-ELA

Process returned 0

Utility Functions: Clear, Permalink

Noteworthy nodes in each datafile include:

LanguageDatafile nameRoot nodes (Click a root to generate from it)Remarks
Firensyllables.ymlSentence, Noun, Verb, NominalRoot, VerbalRoot, More information about Firen can be found on the Wiki.
Sajem Tansajemtan.ymlWord, Root, Suffix, UnlikelyWord, UnlikelyRoot, UnlikelySuffix, Sajem Tan is a collaborative conlang. It has a website here.
Englishenglish.ymlSentence, My (possibly poorly-considered) attempt to encode basic English grammar in WordGen. I apologise in advance to anyone who tries to make sense out of it.
Dab vi Suxi Kidapffb.ymlSentence, Word, Compound, Syllable, DVSK is a very simple isolating language that was created as a collaboration between me and 4 other people from the Sajem Tan tribe, however it was abandoned after working out the foundations.
Xanzxanz.ymlword, tricons, root, word1, Another collaborative language in the Sajem Tan universe. It is the source of triconsonantal roots in Sajem Tan.
Jafrenjafren.ymlSentence, Word, ChordL, Chord, Chord1, Chord2, Chord3, Chord4, Chord5, A musical language used in the same setting as Firen. It is currently much less well-developed.
Jokesjokes.ymlGender, Someone on Mastodon posted a silly CFG for making gender jokes, so I encoded it as a WordGen datafile. Nothing more to it.
Numbersnumbers.ymlnumber, phoneNumber, internationalPhoneNumber, This is one of the first files I ever wrote, and it shows. It makes use of outdated and deprecated features of WordGen and made the very questionable choice of using 'val' for a phonetic English reading of the number and 'ipa' for the digits.
TestsCFGs.ymlDyck, binPalindrome, Node, This file exists as a testing ground for things that are too simple to need their own files, and for new or experimental features. You will need to uncrease the recursion depth to use some of these roots, particularly Node or else get a million errors.

Note that CFGs.yml is not allowed on this web interface due to higher resource use than the other files and its reliance on WordGen/Cpp features.

Feel free to look at the sources for WordGen/Py and WordGen/Cpp. wordgen.py is the current version of the script, and syllables.yml is the current version of the Firen data file.

This is the web frontend for a Python program that will produce random words using a (rather nifty) weighted-randomized macro expansion approach. IPA transcriptions are generated from the same file, and are not directly attached to the orthography. This means that "digraph recognition" is not even a concept to worry about.

In a second phase, regular expressions and Mealy-type finite state machines are applied to transform the output.

The Firen datafile is generally quite well-developed, and produces generally good results. The IPA transcriptions are sometimes non-obvious because they include synchronic sound changes, and sometimes unnatural but generally still correct, such as with the overzealous syllabification.

The other datafiles are in various stages of development.

Not that it matters or anything, but unless you provide your own seeds, this web frontend has worse randomness because it is simply using Unix time as the seed. (It's required that the server generates the seed for the permalink to work, and time is the standard easy choice for these things.) When run from the command line without an explicit seed parameter, the randomness is much better (Python seeds its random generator from the system's main entropy source). Maybe I could make this Base64-encode some bytes from /dev/urandom or something for the seed instead, it wouldn't change too much.

Working-1.py is a less flexible earlier (Python 2 only) draft, which technically knows nothing about words, and only generates syllables. You may find it interesting or even useful. syllables1.yml is the data file for that version. The two versions are not compatible, but are mostly similar and a single file could in theory be agnostic between them.

Once this is "done", my next plan is to implement something with Markov chains, the more classical way to generate natural language.

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