Noise Estimation Neural Network for Low-Latency Audio Synthesis
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Solution Overview
Problem
Existing machine learning models, particularly auto-regressive models, require a large number of iterations to generate network outputs, resulting in high latency and resource consumption due to their sequential nature, whereas non-autoregressive models often produce lower quality outputs.
Innovation Solution
A method involving a noise estimation neural network that iteratively refines an initial network output by processing model inputs and noise levels, allowing for non-autoregressive generation of high-fidelity outputs in a constant number of steps, specifically for audio synthesis where it can produce high-quality audio samples in six or fewer iterations with reduced latency and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If auto-regressive models are used to generate network outputs, then output quality is improved, but latency and resource consumption increase due to sequential processing
Solution Approach 1:
The patent segments the output generation process into multiple independent parallel streams, where each stream generates a portion of the output simultaneously. This is achieved by dividing the output dimension into segments that can be processed independently, allowing parallel computation while maintaining overall output quality comparable to sequential auto-regressive models.
Solution Approach 2:
The patent transitions from sequential processing in one dimension (time steps) to parallel processing by introducing another dimension (output segments). The model processes multiple output segments simultaneously across different computational paths, converting the time-sequential operation into a spatial-parallel operation that reduces latency.
2Manufacturing precision
If auto-regressive models are used to generate network outputs, then output quality is improved, but resource consumption increases due to sequential processing
Solution Approach 1:
By segmenting the output generation into parallel independent streams, the computational workload is distributed across multiple processors or computational units simultaneously. This parallelization reduces the total computational resources required compared to sequential processing, as operations that would otherwise be performed one after another are executed concurrently.
Solution Approach 2:
The patent exploits the output dimension to create parallel computational paths, transforming a resource-intensive sequential process into a more efficient parallel process. By organizing computation along the output dimension rather than the time dimension, the model achieves better resource utilization through parallel hardware acceleration.
3Loss of time
If non-autoregressive models are used to generate network outputs, then latency and resource consumption are reduced, but output quality deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms where the generated output segments are used to condition subsequent generation steps. The model uses the already-generated portions of the output as input conditions for generating remaining segments, ensuring coherence and quality while maintaining parallel processing efficiency. This feedback loop allows the model to adapt and refine outputs without requiring fully sequential processing.
Solution Approach 2:
The patent modifies key parameters of the generation process, including the noise schedule, temperature, and conditioning strength, to optimize both speed and quality. By carefully tuning these parameters during training and inference, the model achieves high output quality despite the parallel non-autoregressive structure, overcoming the typical quality degradation associated with faster generation methods.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating outputs conditioned on network inputs using neural networks. In one aspect, a method comprises obtaining the network input; initializing a current network output; and generating the final network output by updating the current network output at each of a plurality of iterations, wherein each iteration corresponds to a respective noise level, and wherein the updating comprises, at each iteration: processing a model input for the iteration comprising (i) the current network output and (ii) the network input using a noise estimation neural network that is configured to process the model input to generate a noise output, wherein the noise output comprises a respective noise estimate for each value in the current network output; and updating the current network output using the noise estimate and the noise level for the iteration.