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See the below video for a demo of Riva capabilities. Support multiple network formats: ONNX, TensorRT plans, PyTorch TorchScript models.ĭeployement on multiple platforms: from datacenter to edge servers, via Helm to K8s cluster, on NVIDIA Volta/Turing GPUs or Jetson Xavier platforms. Optimize neural network performance and latency using NVIDIA TensorRTĭeploy AI applications with TensorRT Inference Server: NeMo is a toolkit and platform that enables researchers to define and build new state-of-the-art speech and natural language processing models. Transfer learning: re-train your model on domain-specific data, with NVIDIA NeMo. With the Riva platform, you can:īuild speech and visual AI applications using pretrained NVIDIA Neural Modules ( NeMo) available at NVIDIA GPU Cloud ( NGC). It offers a complete workflow to build, train and deploy AI systems that can use visual cues such as gestures and gaze along with speech in context. NVIDIA Riva is a platform for building and deploying AI applications that fuse vision, speech and other sensors. Finetune your own domain specific Speech or NLP model and deploy into Riva. Create Riva clients and connect to Riva Speech API server.Introduction the Riva Speech and Natural Languages services.Defining the Client Service and Ingress Route.Downloading and Modifying the Traefik Helm Chart.Downloading and Modifying the Riva API Helm Chart.Virtual Assistant (with Google Dialogflow).

Launching the Servers and Client Container.Downloading Required Models and Containers from NGC.Binary Offline/Batch (non-streaming) Example.Option 2: Using riva-deploy and the Riva Speech Container (Advanced).Option 1: Using Quick Start Scripts to Deploy Your Models (Recommended path).Speech Synthesis Markup Language (SSML).Riva Build: Tacotron2 and Waveglow Pipeline Configuration.Riva Build: FastPitch and HiFi-GAN Pipeline Configuration.Model Architectures - Mel Spectrogram Generators.Token Classification (Named Entity Recognition).Bidirectional Encoder Representations from Transformers (BERT).Generating Multiple Transcript Hypotheses.Citrinet and Conformer-CTC Acoustic Models.Running the Riva Client and Converting Text to Audio Files.Running the Riva Client and Transcribing Audio Files.Local Deployment using Quick Start Scripts.