Poetic sound similarity vectors using phonetic features
This is the source code used to implement the algorithms, experiments and applications in my paper, "Poetic sound similarity vectors using phonetic features." The source code is written in Python.
Almost everything should be covered in a standard Anaconda install, i.e.:
conda config --add channels conda-forge conda install spacy python -m spacy download en_core_web_md
conda config --add channels conda-forge conda install python-annoy
(Note: You don't need any of this stuff if you just want to play around with the pre-computed vectors! Though I definitely recommend using a fast nearest-neighbor search library like Annoy.)
CMU Dict and pre-computed vectors
cmudict-0.7b-simvecs contains pre-computed vectors for all of the
cmudict-0.7b-with-vitz-nonce. This is probably the file you want if
you just want to play around with the vectors! The vectors are formatted just
like the CMU Pronouncing Dictionary (i.e., word in all caps, two spaces, then
space-separated values per dimension).
generate.py to create your own set of vectors from a CMU Pronouncing
Dictionary source file. Pass the dictionary as input and redirect the output to
your desired file, e.g.:
PYTHONIOENCODING=latin1 python generate.py <cmudict-0.7b-with-vitz-nonce >cmudict-0.7b-simvecs
Note that you must specify
latin1 encoding when running this script (unless
your dictionary uses some other character set).
similarity.py script is a quick script for checking your vectors. Pass
the file with your similarity vectors as a command line argument, and the
program will respond to every line of standard input with the most similar
items from the embedding:
$ python similarity.py cmudict-0.7b-simvecs loading... done. ELEPHANT ['ELEPHANT', "ELEPHANT'S", 'ELEPHANTS', "ELEPHANTS'", 'ENTOFFEN', 'UFFELMAN', 'UNRUFFLED', 'MUFFLE', "ENTOFFEN'S", 'KALAFUT'] AARDVARK ['AARDVARK', 'AARDVARKS', 'AARGH', 'ARGH', 'ARC', 'ARK', 'ARB', 'ARTCARVED', 'ALSGAARD', 'ARCHARD'] BADGER ['BADGER', 'BADER', 'BATHER(1)', 'BADGERED', 'BISER', "BADGER'S", 'BADGERS', 'BADDERS', 'MADAR', 'MADDER'] DOLPHIN ['DOLPHIN', 'DOLPHINS', "DOLPHINS'", 'DALFEN', 'GALVEN', 'DONELSON', 'GALVAN', 'JARVIS', 'GALVESTON', 'DARTH']
^D when you're done.
featurephone.py contains a few helper functions that help build the
bigram analysis of the dictionary.
experiment.ipynbcontains the code to run the experiments comparing the phonetic similarity reported by the embeddings in my paper to the similarity scores obtained from human subjects in Vitz and Winkler (1973).
some-applications.ipynbcontains a number of playful and poetic experimental applications of the phonetic similarity embeddings (including sound analogies, averages, symbolism tinting, etc.)
Still forthcoming: An example of how to use the embeddings for longer stretches of text, as in the "random walk" example in the paper.
LICENSE, which applies to everything in this repository except the copy
of the CMU Pronouncing Dictionary, which is used under the terms of their
license (included in the header of the file), and the data from Vitz and
Winkler, for which I claim fair use.