Modern Voice AI
Large Language Models and generative text applications have become essential tools for many enterprise teams, helping automate communication, search internal knowledge, and support daily workflows. However, the next meaningful step for enterprise AI may come through voice, where natural sounding agents, accurate transcription, and real-time translation play a central role. Companies such as ElevenLabs, OpenAI, and Deepgram now provide speech synthesis, transcription, and real time translation tools that show how capable modern voice models have become.
While these applications are effective, they all rely on cloud based services. For many organizations, that creates limitations when the information being processed is sensitive or confidential. If a team needs live translation for an internal meeting, or wants to test a voice assistant that handles private data, sending audio to a third party cloud service may not align with security requirements. In these situations, running a speech model on-prem becomes the preferred option, and building an internal voice AI pipeline becomes a practical next step.
As organizations explore voice enabled applications, many require speech recognition, text to speech, and translation that run locally. NVIDIA® Riva allows teams to build their own AI voice tools in a controlled environment, supporting tasks such as call transcription, internal productivity, customer support, accessibility, and spoken language interfaces.
What is NVIDIA Riva?
NVIDIA Riva is a GPU-accelerated SDK for building Speech AI applications that are customized for your use case and deliver real-time performance. It supports automatic speech recognition, text to speech, and neural machine translation across more than 26 languages. Riva is included in the NVIDIA AI Enterprise Software platform which provides tested performance and enterprise support for production deployments or as a part of the NVIDIA API Catalog.
Riva can run in data centers, at the edge, or on-prem, giving organizations control over latency, data privacy, and system optimization. Its modular design makes it suitable for teams that need reliable, low latency speech pipelines for enterprise use cases.
Inside the Riva Speech Pipeline
Riva’s automatic speech recognition pipeline converts audio into clean, structured text through a sequence of GPU accelerated steps. Each component can be customized or tuned for specific enterprise workflows.
- Custom vocabulary
Teams can add product names, acronyms, and industry terminology so the system correctly recognizes domain specific language. - Feature extraction
Raw audio is transformed into numerical features such as spectrograms. This representation captures pitch, frequency, and timing patterns that the models rely on for accurate interpretation. - Acoustic model
The acoustic model analyzes the extracted features and predicts the most likely phonemes and sound units within the audio stream. - Decoder or N gram language model
These models convert acoustic predictions into complete words and phrases. They use statistical language patterns to choose the most likely sequence based on context. - BERT punctuation model
A transformer based punctuation model adds sentence boundaries, capitalization, and formatting to produce readable text suitable for transcription or downstream AI processing.
The result is a structured text output that can be routed into applications for transcription, translation, summarization, or integration with larger language models.
How AMAX Builds with NVIDIA Riva
AMAX has been developing on-prem voice AI applications using NVIDIA Riva to show how organizations can run real time speech systems securely within their own environment. The work focuses on practical tools that support day to day workflows across technical teams, sales, and operations.
- Real time translation
Using Riva’s speech recognition and neural machine translation capabilities, AMAX is building on-prem translation pipelines that support live multilingual communication without sending audio to external services.
- Real time conversation transcription
AMAX has developed a prototype application that captures live dialogue between two speakers and generates continuous, structured transcripts. This creates a reliable foundation for meeting support, internal documentation, and productivity tools. - RAG assisted conversations
One of the ongoing projects combines Riva with a retrieval augmented generation model. As the conversation unfolds, the system identifies key topics and surfaces relevant internal documents or reference material on screen. This can assist sales engineers, solution architects, or support teams who need quick access to technical content during customer discussions.
See how an AMAX intern built a RAG assisted voice application using NVIDIA Riva
The Path Forward for On-Prem Voice AI
Running speech and translation models locally is becoming an important capability for teams that need real time transcription, multilingual communication, and tools that combine voice with retrieval augmented generation. These early applications show how on-prem systems can support secure workflows without relying on external providers.
In Part 2, we will examine how NVIDIA Riva performs in real development environments. This includes the streaming path, the client application work, and the progression from early prototypes to a functioning voice pipeline.
These early applications show how voice AI can run securely in customer environments. AMAX also supports large scale voice model development, including work with customers training advanced models on NVIDIA DGX SuperPOD™ deployments.
AMAX supports both the software and the infrastructure required to deploy these workloads. Riva is part of the NVIDIA AI Enterprise software platform, which is included with NVIDIA DGX™ systems. As an NVIDIA Elite Partner, AMAX helps customers design, build, and deploy the GPU systems needed for advanced AI training and inference. This gives organizations a clear path from early experimentation to validated production environments.