Real-Time Music Generation Using Text-to-Audio Token Pipelines
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Solution Overview
Problem
Users face challenges in easily finding music that fits their mood or style in real-time and generating original audio content is time-consuming and inefficient, often requiring complex processes and significant time to produce even a small portion of audio.
Innovation Solution
A system utilizing machine learning models to convert user input into text encoding, generate tokens representing acoustic information, and transform them into audio vectors and waveforms, enabling real-time generation of audio content through a two-stage process involving an autoregressive transformer decoder and flow matching model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional audio generation processes are used, then audio content can be generated, but the process is slow and inefficient, requiring significant time (minutes to hours) to generate even a small portion of audio
Solution Approach 1:
The audio generation process is divided into two distinct stages: a fast autoregressive transformer decoder that generates initial audio tokens efficiently, and a flow matching model that refines these tokens into final audio waveforms. This segmentation allows each stage to optimize for its specific function, with the first stage prioritizing speed and the second stage prioritizing quality, thereby resolving the contradiction between generation speed and time consumption.
Solution Approach 2:
The autoregressive transformer decoder performs preliminary action by generating initial audio tokens quickly based on text encoding before the flow matching model refines them. This preliminary generation step creates a fast baseline that reduces the overall time required, as the heavy computational refinement is deferred to a separate stage that can work on pre-computed tokens rather than generating audio from scratch.
2Reliability
If complex computer programs and processes are used to generate original audio, then audibly pleasing music with unique musical tastes can be created, but the processes are slow and inefficient
Solution Approach 1:
The system segments the audio generation into two specialized models: an autoregressive transformer decoder that ensures musical coherence and structure through its language-modeling capabilities, and a flow matching model that ensures audibly pleasing quality through its diffusion-based refinement. Each model focuses on specific aspects of audio quality, allowing the system to maintain high reliability while improving overall efficiency through parallel processing and specialized optimization.
Solution Approach 2:
The flow matching model acts as an intermediary between the initial text encoding and the final audio waveform. It receives audio tokens from the transformer decoder and refines them into high-quality waveforms, serving as a mediator that bridges the gap between fast generation and high quality, thereby resolving the contradiction between reliability and productivity.
3Adaptability or versatility
If users want to find music that fits their mood or style in real-time, then they can access existing catalogs, but user choice is limited and it is challenging to easily find music that fits their particular mood or style in the moment
Solution Approach 1:
The system enables self-service music generation where users directly input their mood or style preferences and the system automatically generates customized audio content without requiring manual selection from catalogs. The text-to-audio transformation process handles the complexity of music selection and creation automatically, making the system both highly adaptable to user preferences and easy to operate.
Solution Approach 2:
The patent replaces the mechanical system of manual music selection from fixed catalogs with an AI-based text-to-audio generation system. Users interact through simple text input describing their mood or style, and the machine learning models automatically transform this text into customized audio waveforms, substituting the mechanical browsing process with an intelligent generation process that is both highly adaptable and easy to use.
Data Source
AI summary
Systems and methods to generate audio content are provided. The systems and methods include converting, at a communication device, user input to a text encoding. The systems and methods also include generating, by a first machine learning model associated with the communication device, at least one token representing acoustic information based on the text encoding. A first token of the at least one token may represent at least one audio feature. The systems and methods further include generating at least one audio vector based on the at least one token and the text encoding. The systems and methods further include transforming the at least one audio vector to an audio waveform including at least one segment of audio content associated with the at least one audio feature.