At GTC 21, NVIDIA announced several major breakthroughs in conversational AI for building and deploying automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS) applications. The conference also hosted over 60 engaging sessions and workshops featuring the latest tools, technologies and research in conversational AI and NLP.
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Conversational AI Demystified
Speaker: Meriem Bendris, Senior Solution Architect, NVIDIA
Conversational AI technologies are becoming ubiquitous, with countless products taking advantage of automatic speech recognition, natural language understanding, and speech synthesis coming to market. Thanks to new tools and technologies, developing conversational AI applications is easier than ever, enabling a much broader range of applications, such as virtual assistants, real-time transcription, and many more. We will give an overview of the conversational AI landscape and discuss how any organization can get started developing conversational AI applications today.
Building and Deploying a Custom Conversational AI App with NVIDIA Transfer Learning Toolkit and Jarvis
Speakers: Tripti Singhal, Solutions Architect, NVIDIA; Nikhil Srihari, Technical Marketing Engineer – Deep Learning, NVIDIA; Arun Venkatesan, Product Manager, NVIDIA
Tailoring the deep learning models in a conversational AI pipeline to your enterprise needs is time-consuming. Developing a domain-specific application typically requires several cycles of re-training, fine-tuning, and deploying the model until it satisfies the requirements. NVIDIA Jarvis helps you easily build production-ready conversational AI applications and provides tools for fine-tuning on your domain. In this session, we will walk you through the process of customizing automatic speech recognition and natural language processing pipelines to build a truly customized production-ready Conversational AI application.
Megatron GPT-3 Large Model Inference with Triton and ONNX Runtime
Speaker: Denis Timonin, AI Solutions Architect, NVIDIA
Huge NLP models like Megatron-LM GPT-3, Megatron-LM Bert require tens/hundreds of gigabytes of memory to store their weights or run inference. Frequently, one GPU is not enough for such a task. One way to run inference and maximize throughput of these models is to divide them into smaller sub-parts in the pipeline-parallelism (in-depth) style and run these subparts on multiple GPUs. This method will allow us to use bigger batch size and run inference through an ensemble of subparts in a conveyor manner. TRITON inference server is an open-source inference serving software that lets teams deploy trained AI models from any framework. And this is a perfect tool that allows us to run this ensemble. In this talk, we will take Megatron LM with billions of parameters, convert it in ONNX format, and will learn how to divide it into subparts with the new tool – ONNX-GraphSurgeon. Then, we will use TRITON ensemble API and ONNX runtime background and run this model inference on an NVIDIA DGX.
Announcing Megatron for Training Trillion Parameter Models and NVIDIA Jarvis Availability
NVIDIA announced Megatron for training giant transformer-based language models and major capabilities in NVIDIA Jarvis for building state-of-the-art interactive conversational AI applications.
World-Class ASR | Real-Time Machine Translation | Controllable Text-to-Speech
Watch this demo to see Jarvis’ automatic speech recognition (ASR) accuracy when fine-tuned on medical jargon, real-time neural machine translation from English to Spanish and Japanese, and powerful controllability of neural text-to-speech.
New pre-trained models, notebooks, and sample applications for conversational AI are all available to try from the NGC catalog. You can also find tutorials for building and deploying conversational AI applications at the NVIDIA Developer Blog.
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