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DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling

arXiv.org Artificial Intelligence

Rap generation, which aims to produce lyrics and corresponding singing beats, needs to model both rhymes and rhythms. Previous works for rap generation focused on rhyming lyrics but ignored rhythmic beats, which are important for rap performance. In this paper, we develop DeepRapper, a Transformer-based rap generation system that can model both rhymes and rhythms. Since there is no available rap dataset with rhythmic beats, we develop a data mining pipeline to collect a large-scale rap dataset, which includes a large number of rap songs with aligned lyrics and rhythmic beats. Second, we design a Transformer-based autoregressive language model which carefully models rhymes and rhythms. Specifically, we generate lyrics in the reverse order with rhyme representation and constraint for rhyme enhancement and insert a beat symbol into lyrics for rhythm/beat modeling. To our knowledge, DeepRapper is the first system to generate rap with both rhymes and rhythms. Both objective and subjective evaluations demonstrate that DeepRapper generates creative and high-quality raps with rhymes and rhythms. Code will be released on GitHub.


Having treble with songwriting? AI lyric generation is here to help

#artificialintelligence

LyricJam, a real-time system that uses artificial intelligence (AI) to generate lyric lines for live instrumental music, was created by members of the University of Waterloo's Natural Language Processing Lab. The lab, led by Olga Vechtomova, a Waterloo Engineering Professor cross-appointed in Computer Science, has been researching creative applications of AI for several years. The lab's initial work led to the creation of a system that learns musical expressions of artists and generates lyrics in their style. Recently, Vechtomova, along with Waterloo graduate students Gaurav Sahu and Dhruv Kumar, developed technology that relies on various aspects of music such as chord progressions, tempo and instrumentation to synthesise lyrics, reflecting the mood and emotions expressed by live music. As a musician or a band plays instrumental music, the system continuously receives the raw audio clips, which the neural network processes to generate new lyric lines.


A Syllable-Structured, Contextually-Based Conditionally Generation of Chinese Lyrics

arXiv.org Artificial Intelligence

This paper presents a novel, syllable-structured Chinese lyrics generation model given a piece of original melody. Most previously reported lyrics generation models fail to include the relationship between lyrics and melody. In this work, we propose to interpret lyrics-melody alignments as syllable structural information and use a multi-channel sequence-to-sequence model with considering both phrasal structures and semantics. Two different RNN encoders are applied, one of which is for encoding syllable structures while the other for semantic encoding with contextual sentences or input keywords. Moreover, a large Chinese lyrics corpus for model training is leveraged. With automatic and human evaluations, results demonstrate the effectiveness of our proposed lyrics generation model. To the best of our knowledge, there is few previous reports on lyrics generation considering both music and linguistic perspectives.


AI generates melodies from lyrics

#artificialintelligence

Generating sequences of musical notes from lyrics might sound like the stuff of science fiction, but thanks to AI, it might someday become as commonplace as internet radio. In a paper published on the preprint server Arxiv.org "Melody generation from lyrics has been a challenging research issue in the field of artificial intelligence and music, which enables to learn and discover latent relationship between interesting lyrics and accompanying melody," wrote the paper's coauthors. "With the development of available lyrics and melody dataset and [AI], musical knowledge mining between lyrics and melody has gradually become possible." As the researchers explain, notes have two musical attributes: pitch and duration.


Weird AI Yankovic: Generating Parody Lyrics

arXiv.org Artificial Intelligence

Lyrics parody swaps one set of words that accompany a melody with a new set of words, preserving the number of syllables per line and the rhyme scheme. Lyrics parody generation is a challenge for controllable text generation. We show how a specialized sampling procedure, combined with backward text generation with XLNet can produce parody lyrics that reliably meet the syllable and rhyme scheme constraints. We introduce the Weird AI Yankovic system and provide a case study evaluation. We conclude with societal implications of neural lyric parody generation.