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2017 GTC San Jose

S7544 - Efficient Inference for WaveNet Audio Synthesis Models

Session Speakers
Session Description

WaveNet is a generative neural network architecture for audio in the time domain. Due to the high sampling frequency of audio signals and the sequential dependencies between timesteps, inference in a WaveNet model is incredibly expensive, and can take many minutes to generate a single second of audio with an unoptimized implementation. We implement custom WaveNet inference kernels and demonstrate that an efficient implementation on a CPU or a GPU can provide faster than realtime audio generation, even though neither platform is perfectly suited to such a task due to the effective lack of parallelism and high compute requirements. To our knowledge, this is the first demonstration that neural audio generation can be done efficiently enough to deploy in a production text-to-speech system.


Additional Session Information
Advanced
Talk
Deep Learning and AI Signal and Audio Processing Tools and Libraries
Media & Entertainment Software
50 minutes
Session Schedule