https://github.com/saattrupdan/autopoet

Build poems from text sources
https://github.com/saattrupdan/autopoet

haiku neural-network poems syllables trump twitter

Last synced: about 1 month ago
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Build poems from text sources

• Host: GitHub
• URL: https://github.com/saattrupdan/autopoet
• Owner: saattrupdan
• Created: 2019-09-29T16:37:05.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-10-18T01:49:35.000Z (about 1 year ago)
• Last Synced: 2024-11-10T14:42:58.852Z (about 2 months ago)
• Topics: haiku, neural-network, poems, syllables, trump, twitter
• Language: Python
• Homepage:
• Size: 43.1 MB
• Stars: 6
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# AutoPoet 

Build poems from text sources. 

## Todos

- [x] Fetch Trump tweet data

- [x] Generate vocabulary from Trump tweets

- [x] Build baseline model to count syllables in English words

- [x] Optimise syllable counter

- [x] Use model to build Haikus from Trump tweets

- [ ] Build progressive web app that generates poems

- [ ] Enable working with live tweets

- [ ] Enable working with other text sources

## Syllable model

A large part of this project was to develop a model that counts syllables in English words. 

The syllable counter is trained on a (slightly modified version of) the [Gutenberg](https://en.wikipedia.org/wiki/Project_Gutenberg) syllable corpus, consisting of ~170,000 English words split into syllables. The `process_gutsyls` notebook converts these into a format which is more convenient for our purposes. The raw dataset can be freely downloaded [here](http://onlinebooks.library.upenn.edu/webbin/gutbook/lookup?num=3204), and the preprocessed versions used for this project can be found [here](https://filedn.com/lRBwPhPxgV74tO0rDoe8SpH/autopoet_data/).

The model is a recurrent neural network that works at the character level, with the following rough architecture:

1. Embed the characters into 64-dimensional vectors

2. Process the characters through three bidirectional GRU layers, each having 2x128 = 256 hidden units

3. Process the GRU outputs through a time-distributed dense layers with 256 hidden units followed by a ReLU activation

4. Finally project the outputs from the dense layer down to a single dimension across time, outputting a sequence of real numbers between 0 and 1, of the same length as we started with

5. To get the syllable count, we sum up the probabilities and round to nearest integer

To get a more detailed view of the model's architecture see `syllablecounter.py`, and check out `core.py` for an idea of how the model is trained.

This model currently achieves a 96.89% validation accuracy.

The reason why we sum up (aggregates of) the *probabilities* in point (5), rather than firstly rounding the probabilities, is to deal with the situation where the model is unsure whether two consecutive characters begin a new syllable. These will have probabilities ~50% and so will constitute a single syllable rather than two.