nemotron-speech
Routes NVIDIA Nemotron Speech (Riva) NIM tasks — deploys, runs, and tests ASR, TTS, and NMT NIMs on build.nvidia.com or self-hosted.
Security Assessment
About nemotron-speech
The nemotron-speech skill is a single routing entry point for all NVIDIA Nemotron Speech (Riva) NIM workflows, covering automatic speech recognition (ASR), text-to-speech (TTS), and neural machine translation (NMT). It solves the problem of navigating NVIDIA's sprawling Riva tooling by acting as a thin routing surface that identifies a user's task type and loads only the relevant detailed reference file, keeping context tight through progressive disclosure. The skill notes that "Nemotron Speech" is a brand-only rename of what NVIDIA still documents as Riva, so all commands, images, gRPC APIs, and doc URLs continue to use the Riva name.
Its capabilities span cloud-hosted inference via build.nvidia.com, self-hosted Docker deployment, client-protocol selection for ASR (gRPC, HTTP, WebSocket), custom NeMo model deployment through the four-phase riva-build / riva-deploy pipeline, ASR pipeline tuning (VAD, diarization, language models), and prerequisite driver/Docker/NGC setup. Reference files cover model selection across Parakeet, Canary, Whisper, Nemotron ASR Streaming, and Magpie TTS, plus deployment-readiness and GPU compatibility checks. The skill is explicitly security-conscious: it instructs treating NVIDIA_API_KEY and NGC_API_KEY as secrets, using --password-stdin for Docker login, storing keys in a credential manager or chmod 600 file, and avoiding world-writable cache directories.
Target users are ML engineers and DevOps practitioners deploying NVIDIA speech models, whether via cloud NIM endpoints or self-hosted GPU infrastructure. Typical use cases include standing up an ASR/TTS/NMT NIM, building a custom fine-tuned ASR pipeline from a .nemo or .riva checkpoint, checking GPU/driver prerequisites, and choosing the right model family for a task.
FAQ
What speech tasks does this skill support?
ASR (speech-to-text), TTS (text-to-speech), and NMT (translation), across model families including Parakeet, Canary, Whisper, Nemotron ASR Streaming, and Magpie TTS, either cloud-hosted on build.nvidia.com or self-hosted via Docker.
What are the prerequisites for self-hosted deployment?
An NVIDIA AI Enterprise entitlement plus completed environment setup: NVIDIA drivers, Docker, the NVIDIA Container Toolkit, an exported NGC_API_KEY, and the Riva Python client. Cloud inference only needs the nvidia-riva-client pip package and a valid NVIDIA_API_KEY.
How are API keys handled?
The skill treats NVIDIA_API_KEY and NGC_API_KEY as secrets and instructs users never to print, commit, or log them, preferring --password-stdin for Docker login and storing keys in a credential manager or a chmod 600 env file.
Can I deploy a custom fine-tuned model?
Yes. It documents a four-phase pipeline: obtain a deployable .riva or fine-tuned .nemo file, build an RMIR with riva-build, deploy the model repository with riva-deploy, and launch the NIM. Per-model syntax should be verified against NVIDIA's live docs and riva-build --help.
Is the reference content authoritative for exact build flags?
No. The skill deliberately routes version-specific details (container images, nemo2riva config blocks, riva-build defaults) to NVIDIA's canonical docs, NGC catalog, and --help output rather than inferring them from the skill text.
All Files
15 filesInstall nemotron-speech
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
nvidia/skills