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GTC ON-DEMAND

Artificial Intelligence and Deep Learning
Presentation
Media
Efficient Inference for WaveNet Audio Synthesis Models
Abstract:
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.
 
Topics:
Artificial Intelligence and Deep Learning, Tools & Libraries, Signal and Audio Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7544
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