Neural Audio Generation Using Dilated Causal Convolutions
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Solution Overview
Problem
Existing neural network-based audio generation systems face challenges in achieving high-quality audio generation with reduced computational resources and training time, particularly when generating speech from text, due to the inefficiencies of recurrent neural networks.
Innovation Solution
Employing convolutional neural network layers, specifically causal and dilated causal convolutional layers, with residual and skip connections, to process audio data in an autoregressive manner, allowing for efficient computation and improved receptive field without excessive resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional neural network architectures are used for audio generation, then the model can capture complex audio patterns, but the computational resources and training time required become excessively large
Solution Approach 1:
The audio generation task is segmented into multiple parallel autoregressive streams, where each stream generates a subset of audio samples independently. This segmentation allows the model to process audio data in smaller, manageable chunks, reducing the computational burden on each individual processing unit while maintaining overall generation quality through the parallel composition of multiple streams.
2Manufacturing precision
If traditional neural network architectures are used for audio generation, then the model can capture complex audio patterns, but the training time becomes excessively long
Solution Approach 1:
The training process is segmented into multiple parallel tasks corresponding to different autoregressive streams. Each stream can be trained independently or with reduced coordination overhead, allowing for more efficient utilization of training resources and shorter overall training time while maintaining the ability to capture complex audio patterns through the collective capability of multiple streams.
3Measurement precision
If the audio sample granularity is increased to achieve higher quality, then the detail and fidelity improve, but the computational complexity increases significantly
Solution Approach 1:
The high-resolution audio generation task is divided into multiple parallel autoregressive streams, each handling a portion of the audio samples. This segmentation enables the system to maintain fine audio sample granularity for high fidelity while distributing the computational complexity across multiple independent processing paths, preventing any single unit from becoming a computational bottleneck.
4Stability of the object's composition
If autoregressive modeling is used for audio generation, then the system can generate coherent audio sequences, but the processing speed decreases due to sequential nature
Solution Approach 1:
The audio generation process is segmented into multiple independent autoregressive streams that operate in parallel. Each stream maintains the sequential autoregressive property for generating coherent audio samples within its domain, while the overall system achieves faster processing through parallel execution of multiple streams, effectively combining sequential coherence with parallel speedup.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output sequence of audio data that comprises a respective audio sample at each of a plurality of time steps. One of the methods includes, for each of the time steps: providing a current sequence of audio data as input to a convolutional subnetwork, wherein the current sequence comprises the respective audio sample at each time step that precedes the time step in the output sequence, and wherein the convolutional subnetwork is configured to process the current sequence of audio data to generate an alternative representation for the time step; and providing the alternative representation for the time step as input to an output layer, wherein the output layer is configured to: process the alternative representation to generate an output that defines a score distribution over a plurality of possible audio samples for the time step.