Controllable Lyrics Generation With Melody Attribute Matching
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Solution Overview
Problem
Existing machine learning models for generating song lyrics lack control over music-related attributes, leading to mismatches between lyrics and song melodies.
Innovation Solution
A system is developed that includes a training data generator to reconstruct prompts with music-related attributes, a first sub-model to generate lyrics based on these attributes, and a scoring and ranking mechanism to select the best-fitting lyrics for a given melody, ensuring accurate control over the number of lines and syllable range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing machine learning models are used for lyric generation, then the generation process is simple, but the control over music-related attributes (number of lines and syllable range) is poor
Solution Approach 1:
The system is divided into multiple specialized sub-models: a training data generator for reconstructing prompts with music-related attributes, a first sub-model for generating lyrics based on these attributes, a second sub-model for receiving user inputs and format attributes, and a third sub-model for scoring and ranking. This segmentation allows each component to focus on a specific aspect of lyric generation, achieving precise control over music-related attributes while maintaining manageable system complexity through modular design.
2Reliability
If existing lyric generation models are used, then the operation is easy, but the mismatch between lyrics and song melody occurs due to lack of control over music attributes
Solution Approach 1:
The system implements a feedback mechanism through the third sub-model that scores and ranks generated lyrics based on their adherence to music-related attributes such as number of lines and syllable range. This feedback loop allows the system to evaluate and select the best-fitting lyrics for a given melody, significantly improving the match accuracy between lyrics and melody while providing users with controllable parameters to guide the generation process.
3Measurement precision
If existing models generate lyrics without music-related attribute control, then the generation process is fast, but the accuracy in controlling number of lines and syllable range is low
Solution Approach 1:
The training data generator performs preliminary action by reconstructing prompts that explicitly include music-related attributes (number of lines, syllable range) before the lyric generation process begins. This preliminary structuring of generation instructions ensures that the first sub-model has clear guidance on the desired output format, achieving high measurement precision in controlling number of lines and syllable range while maintaining efficient generation through pre-planned constraints.
Data Source
AI summary
The present disclosure describes techniques for implementing controllable lyrics generation. A first sub-model of a system is trained on training data pairs. Each pair of training data is generated based at least in part on extracting music-related attributes from lyrics. Each pair of training data comprises the lyrics and a reconstructed prompt indicative of the music-related attributes. A user input is received by a second sub-model of the system. The user input comprises text specifying a theme of lyrics to be generated by the system. A prompt is generated based on the text and format attributes by the second sub-model. A plurality of sets of lyrics are generated by the first sub-model based on the generated prompt. The plurality of sets of lyrics are ranked by a third sub-model of the system. The plurality of sets of lyrics are ranked based on predetermined music-related rules.