Summary of Raply: a Profanity-mitigated Rap Generator, by Omar Manil Bendali et al.
Raply: A profanity-mitigated rap generator
by Omar Manil Bendali, Samir Ferroum, Ekaterina Kozachenko, Youssef Parviz, Hanna Shcharbakova, Anna Tokareva, Shemair Williams
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Raply, a fine-tuned GPT-2 model, generates meaningful rhyming text in the style of rap while producing less offensive content. The model’s capabilities are achieved through fine-tuning on the Mitislurs dataset, a profanity-mitigated corpus. Raply is evaluated based on rhyme density metric and list of profanities for English language. This paper presents the first attempt at profanity mitigation for rap lyrics generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rap music is hard to make because it needs complex rhyming patterns with meaningful words. We created a special computer program called Raply that can do this. It’s like a super smart rapper! We trained Raply on a new set of words and ideas, making sure the rap songs are nice and respectful. We tested Raply by looking at how well it did on two important things: making rhymes sound good and not using bad words. This is the first time anyone has tried to make a computer program that can create nice rap music. |
Keywords
» Artificial intelligence » Fine tuning » Gpt
