You'll see our IPU-M2000 system significantly outperforms the Nvidia A100 DGX across the board, with orders of magnitude performance improvements for some models. Graphcore customers are already making big leaps forward with our second generation IPU systems – whether they prioritise faster time to result, model accuracy, better efficiency, lower TCO (Total Cost of Ownership) or the chance to make new breakthroughs in AI with the IPU. We've chosen a range of the most popular models our customers frequently turn to as proxies for their proprietary production AI workloads in natural language processing, computer vision and more, both in training and inference. We are also delighted to share results in this blog using our new PyTorch framework support. We are continuing to develop and expand this capability – you can find out more in our blog here.

A lightweight end-to-end acoustic system is crucial in the deployment of text-to-speech tasks. Finding one that produces good audios with small time latency and fewer errors remains a problem. In this paper, we propose a new non-autoregressive, fully parallel acoustic system that utilizes a new attention structure and a recently proposed convolutional structure. Compared with the most popular end-to-end text-to-speech systems, our acoustic system can produce equal or better quality audios with fewer errors and reach at least 10 times speed up of inference.

We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. We scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on one single-GPU server.