Lyrics Model Training with Attribute-Guided Prompt Construction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing lyrics generation models struggle to satisfy multiple dimensions of lyrics requirements and lack adaptability in downstream song generation tasks due to insufficient training data and reliance on lyrics data alone.
Innovation Solution
Construct a set of candidate lyrics content based on reference lyrics, determine target lyrics content with predetermined requirements, generate description information indicating attributes, and train a lyrics generation model using prompts and target lyrics content to enhance training data quality and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If lyrics generation models are trained using only lyrics data, then the training process is simple, but the model lacks adaptability in downstream song generation tasks
Solution Approach 1:
The patent segments the training data construction process into multiple independent modules: candidate generation module, filtering module, attribute generation module, and prompt construction module. Each module handles a specific aspect of data processing, making the complex training data construction manageable and adaptable to different downstream tasks.
Solution Approach 2:
The patent adds multiple dimensions to the training data by incorporating attribute information (theme, song style, voice information, structure) beyond just lyrics content. This multi-dimensional enrichment enables the model to adapt to diverse song generation requirements while maintaining a systematic training framework.
2Manufacturing precision
If lyrics generation models use basic training data, then the training process is fast, but the generated lyrics quality is insufficient
Solution Approach 1:
The patent performs preliminary actions by pre-generating candidate lyrics content and pre-computing attribute information before actual model training. This preparation work ensures high-quality training data is ready in advance, improving generation quality without significantly increasing training time.
Solution Approach 2:
The patent changes the parameters of training data by incorporating multiple attribute dimensions (theme, song style, voice information, structure) and using prompt engineering techniques. These parameter enhancements improve lyrics generation quality while the efficient pipeline maintains reasonable training time.
3Manufacturing precision
If the model is trained with multi-dimensional attributes, then the lyrics generation quality improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the complex data processing into distinct modules: candidate generation, filtering based on evaluation information, attribute generation, and prompt construction. Each module handles specific processing tasks independently, reducing overall system complexity while maintaining multi-dimensional attribute processing.
Solution Approach 2:
The patent introduces intermediary components such as the filtering module that processes candidate lyrics based on evaluation information, and the attribute generation module that creates structured attribute data. These intermediaries simplify the processing of multi-dimensional attributes by handling them in a systematic, step-by-step manner.
Data Source
AI summary
A method, an apparatus, a device, and a storage medium for training a model are provided. The method includes: constructing a set of candidate lyrics content based on reference lyrics content, each candidate lyrics content including at least one paragraph in the reference lyrics content; determining target lyrics content satisfying a predetermined requirement from the set of candidate lyrics content based on evaluation information of the set of candidate lyrics content; generating description information corresponding to the target lyrics content, the description information indicating a plurality of attributes of the target lyrics content; constructing a set of prompts corresponding to the target lyrics content based on the description information; and training a lyrics generation model based on the set of prompts and the target lyrics content.