Modern text-to-speech (TTS) systems have come a long way in using neural networks to mimic the nuances of human voice. To generate humanlike audio, one second of speech can require a TTS system to output as many as 24,000 samples — sometimes even more. The size and complexity of state-of-the-art models require massive computation, which often needs to run on GPUs or other specialized hardware. At Facebook, our long-term goal is to deliver high-quality, efficient voices to the billions of people in our community. In order to achieve this, we’ve built and deployed a neural TTS system with state-of-the-art audio quality. With strong engineering and extensive model optimization, we have attained a 160x speedup over our baseline while retaining state-of-the-art audio quality, which enables the whole service to be hosted in real time using regular CPUs — without any specialized hardware. The system is highly flexible and will play an important role in creating and scaling new voice applications that sound more human and expressive and are more enjoyable to use. It’s currently powering Portal, our video-calling device, and it’s available as a service for other applications, like reading assistance and virtual reality. Today, we’re sharing details on our approach and how we solved core efficiency challenges to deploy this at scale.