Wordgen is a random word generator that can learn from natural language text. It uses the sound combinations it finds in sample texts to generate new pronounceable words.
Use learn_distribution.py at the command line to learn a wordgen object, and then use generate_words.py on that wordgen.
Use merge_wordgens.py to merge different wordgens and create a totally unique sounding wordgen.
This could be a useful tool for the generation of names or entire vocabularies for fictional settings.
Here are some words that were made by merging two wordgens, one trained on an English novel and another trained on a Hungarian novel:
anean, wojtsan, kaeat, meret, lahazam, azet, alambar, howrga, andenkart,
porogwa, alrenaɟan, angarn, annon, fakt, nawmea, cwar, cudnakethna, annapeant,
enantajangalesh, cart, anam, wttsthannea, alkanne, anea, lawarjedetta, nahataken, annee
Here are some words that were made by merging the English wordgen with one trained on some Polish text:
rentnem, uyona, envervrarumi, tam, 'jethov, dutshi, 'amrer, thundayond, wotner, letsh,
nyebu, tamten, rojothtuti, tjimurone, thame, dodjer, 'odobje, idzi, rejundaytshe, gyowrdz,
wudnyaw, do'reterano, retsht, envervye, 'ovdutel, nyemeeni, 'ovdu, nyengu, murun, anto
How are these pronounced? There is actually a "correct" pronunciation, if you want it, because the output is produced in IPA. It's up to you how to convert the IPA into a natural looking spelling system. The words above are the result of one such conversion.
You need a Python 3 environment with some packages:
pip3 install marisa_trie regex unicodecsv numpy
For work with English text, you also need flite 2.0.5 or later. Download that, build it, then navigate to the testsuite subdirectory and build the target lex_lookup. Then make sure you are working in an environment that can find the compiled lex_lookup executable. Example steps for all this:
$ tar xjf flite-2.0.5-current.tar.bz2
$ cd flite-2.0.5-current
$ ./configure && make
$ sudo make install
$ cd testsuite
$ make lex_lookup
$ sudo cp lex_lookup /usr/local/bin
For detailed help:
python learn_distribution.py -h
Example: suppose you've got some Spanish text in a file spanish_text.txt. Then
python learn_distribution.py spanish_text.txt spa-Latn --window_size 4
would go through each word in the text and look at which 4-letter groups of sounds occur. In order to focus on sounds, wordgen works with the IPA for each word. It determines the IPA using epitran, and this is why the language code spa-Latn needs to be provided. Once the entire file is processed, a family of probability distributions is saved in the file spanish_text.pkl. These distributions can be used to generate fake words that sound like they could have come from Spanish.
For a list of languages and epitran language codes, see the table in the epitran readme.
Watch out for the file size of the saved distributions. The window_size option has an exponential effect.
For detailed help:
python generate_words.py -h
Continuing the example from above, suppose we ended up with the saved object spanish_text.pkl. To generate some words:
python generate_words.py spanish_text.pkl
By default generate_words.py will print words in IPA.
To give the words some flavor (e.g. as though they come from some fictional culture) they can be spelled out using some mapping out of IPA into some chosen target symbols. This mapping is specified in orthography_table.csv.
To print this sort of spelling in addition to the IPA, use the flag -o when running generate_words.py.
Each row in orthography_table.csv contains a phoneme (an IPA token) and a space-separated list of possible ways to spell out that phoneme.
When a wordgen object is created (e.g. when learn_distribution.py is used),
it chooses for itself an orthography by choosing one of the possible spellings for each phoneme in orthography_table.csv.
Make changes in orthography_table.csv to set up your own possible "flavors" of spelling.
For each row in the table, each possible spelling gets chosen with equal probability. You can repeat some possible spellings to give them different weights.
When an IPA token does not have an entry in orthography_table.csv, the IPA is defaulted to as the spelling. Feel free to add entries in this case.
Clarification: The choices of possible spellings do not vary with each run of generate_words.py.
One set of choices is made when you run learn_distribution.py and that set of choices is frozen into the resuling pkl file.
The idea is that a particular and consistent way of spelling things can give some sense of culture.
You can merge learned word generators to create some more unique sound distributions. For detailed help:
python merge_wordgens.py -h
Suppose that we used learn_distribution.py on some spanish text and some hindi text to generate the saved Wordgen objects spanish_text.pkl and hindi_text.pkl.
Then we can do the following to generate a new distribution that randomly combines elements of spanish and hindi sound combinations:
python merge_wordgens.py spanish_text.pkl hindi_text.pkl
Wordgens that were learned based on different texts or different languages could involve completely different sets of IPA tokens. Omitting some tokens isn't so good, because too many sound combinations become impossible and the resulting wordgen starts to become repetitive with the few sound combinations that were compatible with all the source wordgens. Unioning all token sets together wouldn't be good either, because it creates a a wordgen with an unreasonable amount of possible sounds.
The solution we use here is to union the token sets together and then cut down the size of the token set by identifying some tokens as equivalent. In a natural language, different sounds that are treated as equivalent by the language are called allophones. We need a reasonable way to identify some IPA tokens across different languages as being allophones in a merged language.
Here I have used the PHOIBLE database of phonological features and allophone pairs.
It allows me to convert each IPA token into a phonological feature vector.
I used PyTorch to train a linear embedding from phonological feature space into a euclidean space such that points that are close together tend to have
come from allophones. Allophone pairs from 1270 different natural languages were used to do this.
By identifying sounds that are close together per the embedding, the large unioned set of IPA tokens can be cut down to a reasonable size.
The code used to produce the embedding can be found in exploration/exploration4.ipynb.
Finally, each grouping of n sounds (where n is the window_size arg from learn_distribution.py) derives its frequency from one of the merged
wordgens chosen at random.
Clarification: The choices of allophone classes, the choices of representative IPA tokens for each allophone class, and the choices of wordgens used for each
n-sound-grouping do not vary with each run of generate_words.py. One set of choices is made when you run merge_wordgens.py and that set of choices is frozen into the resuling pkl file. Repeated runs of merge_wordgens.py can yield different and interesting results each time.
This is a successor to wordgen. Where the approach here is statistical, the approach in the first wordgen was focused on manually coding phonotactic constraints for specific languages.