A web app for generating sonnets from a corpus of plays
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A French sonnet generator

Based on this contribution to NaNoGenMo 2018

The code for this project generates sonnets in French with alexandrines using the Théâtre Classique's online collection of French plays from the sixteenth to the nineteenth centuries. A functional prototype is available here.

Here's the procedure:

  1. Scrape the links for all the XML files of the plays from this page and then download them to build a corpus.
  2. Make a vector space for all words in the corpus using Gensim's Word2Vec module. The words are lemmatized using SpaCy to simplify the vector space.
  3. Build a tf-idf matrix for all the verses in all the plays in the corpus.
  4. Choose a pair of words to form the basis of an analogy (femme and homme, for instance). The pair will enable a modification of a verse by replacing words according to the analogy (roi is to homme as reine is to femme).
  5. Select a verse from the corpus.
  6. Modify the verse with word substitutions based on the vector space.
  7. Construct a tf-idf vector for the modified verse based on the matrix for the whole corpus.
  8. Find a verse in the corpus that is most similar to the modified verse using cosine similarity. The poem's rhyming scheme should follow the pattern abba abba ccd eed where the rhymes alternate between feminine (the last word of the verse ending in a silent e) and masculine (the last word ending in some other letter). The epitran module is useful for transliterating text into IPA, although it is imperfect (as its authors acknowledge) because the relationship between word spellings and phonetics in French is complicated.
  9. Return to step 6 with the selected verse and continue until the generated text contains 14 verses.

Here is an example of a generated sonnet:

1. Que l'amour est déchu de son autorité (CREBILLON_CATILINA.xml:351)
2. Quoi ! Vous feriez cet illustre Molière, (IMBERT_POINSINETMOLIERE.xml:1)
3. Venir sur son tombeau jurer a votre père (CREBILLON_ELECTRE.xml:1102)
4. Le feront adorer de la postérité. (MONTFLEURY_IMPROMPTUCONDE.xml:60)

5. Vous prenez sa défense avec vivacité ! (MARCHADIER_PLAISIR.xml:394)
6. On n'est pas innocent lorsqu'on peut leur déplaire : (CREBILLON_IDOMENEE.xml:60)
7. Je ne puis oublier que leur chef est mon frère. (CORNEILLEP_MORTPOMPEE.xml:1434)
8. Servant aveuglement une Divinité. (TRISTAN_MORTDECHRISPE.xml:1366)

9. Le Sabre de Galas, celui de Jean de Verth. (MARESCHALA_VERITABLECAPITAINEMATAMORE.xml:1172)
10. Je lui donne au souper d'un poignard pour dessert. (TRISTAN_MORTSENEQUE.xml:432)
11. Lesquels sans contourner le rond du voisinage (AURE_DIPNE.xml:243)

12. Voudrait charger sa main de cet horrible emploi ? (CREBILLON_IDOMENEE.xml:1218)
13. Et que sur ce penchant il se fasse une loi... (CORNEILLET_PYRRHUS.xml:35)
14. Comme vous gai, brillant, aimable ; mais volage ; (MARCHADIER_PLAISIR.xml:145)

At the end of each verse is a reference to its source text and line number, which can be referenced in the Théâtre Classique.


You will need to install the following Python modules (the current repo works for Python 3.7):

  • epitran
  • xml.etree.ElementTree
  • mysql.connector
  • spacy (with fr_core_news_sm)
  • gensim
  • flask

Here is how you set up the web application:

  1. Create a directory and create subdirectories lib and Fievre. Install this repo as a subdirectory as well.
  2. Change directories to the repo.
  3. Run scrapeTheatreClassique.py. This will download the files for plays from the Théâtre Classique website and place them in the directory ../Fievre.
  4. Create a MySQL database by importing songenapp.sql. You will need to create two users in MySQL, one to build the database (with INSERT and SELECT privileges) and one for the web server to access the database (with SELECT privileges). You can create these accounts by looking at the parameters set in buildCorpus.py and makeSonnet.py.
  5. Run build.Corpus.py, which inserts all alexandrines from verse plays in ../Fievre into the corpus database along with their IPA transliterations. There are formatting errors in the source data downloaded in ../Fievre. These errors are easy to fix when this script throws an exception: just edit the source data and restart.
  6. Run buildVectorSpace.py. This will build the word embedding model and save it as ../lib/Fievre_model. It will also build the dictionary file ../lib/pos_dict.pkl.
  7. The web application makeSonnet.py can now be deployed. I recommend Gunicorn for deployment:
gunicorn -b localhost:5000 -w 4 -t 120 --preload makeSonnet:app

When the server starts up it will first load the word embedding model and vectorize all the verses in the database. This takes a while (about a minute on my 3-year-old MacBook Pro).