Streaming Music Generation With Chunked Local Attention
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Solution Overview
Problem
Existing music generation technologies face challenges in improving sound quality, generation speed, and generating long audio, with traditional models being inefficient in processing long audio data in real time and requiring extensive long-term audio information and training data.
Innovation Solution
A method involving obtaining tokens based on input information, generating encoded representations using a target model with local attention mechanisms, and decoding these representations to produce music content in a streaming manner, reducing dependence on long audio training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional models process long audio data in real time, then generation speed may be improved, but sound quality and processing efficiency deteriorate due to inefficiency and extensive data requirements
Solution Approach 1:
The patent divides the audio generation process into discrete chunks that are processed sequentially. Each chunk is encoded independently using attention parameters from previous chunks, enabling real-time processing while maintaining quality. This segmentation allows the system to handle long audio data without requiring extensive training data for the entire duration.
Solution Approach 2:
The system pre-calculates and stores attention parameters from earlier chunks before processing subsequent chunks. This preliminary action enables faster real-time processing of later chunks without re-computing historical context, thereby improving generation speed while maintaining processing efficiency through cached attention mechanisms.
2Manufacturing precision
If extensive long-term audio information and training data are used, then sound quality is improved, but training resources and data acquisition requirements increase
Solution Approach 1:
Instead of requiring the model to learn from extensive long-term audio information globally, the patent applies local attention mechanisms that focus on relevant local patterns within each chunk. This local quality approach maintains sound quality by attending to pertinent features while reducing dependence on vast amounts of training data.
Solution Approach 2:
The system copies and reuses attention parameters from previously processed chunks when generating subsequent chunks. This copying mechanism allows the model to maintain consistent sound quality across long audio generations without re-learning patterns from extensive training data, thereby reducing data acquisition requirements.
3Stability of the object's composition
If traditional models generate complete music content before playback, then audio continuity is maintained, but generation time and resource consumption increase
Solution Approach 1:
The patent segments the music generation into_CHUNKS that can be decoded and played progressively as they are generated. Each chunk maintains audio continuity through attention-based context from previous chunks, enabling streaming playback without requiring complete generation beforehand, thus reducing generation time and resource consumption.
Solution Approach 2:
The system maintains continuous generation and decoding of audio chunks in a streaming manner. By using attention parameters to preserve contextual continuity across chunks, the system enables uninterrupted audio playback while generating content incrementally, eliminating the need to wait for complete generation and reducing overall time loss.
Data Source
AI summary
Embodiments of the disclosure relate to a method, apparatus, device and storage medium for generating music content. The method provided herein includes: obtaining a set of tokens generated based on input information; providing the set of tokens to a target model to generate a plurality of encoded representations corresponding to a plurality of chunks, wherein a target encoded representation corresponding to a first chunk is generated based on a first set of attention parameters associated with a second chunk, the second chunk is earlier in time than the first chunk; and generating target music content by decoding the plurality of encoded representations.