nemotron-speech

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Routes NVIDIA Nemotron Speech (Riva) NIM tasks — deploys, runs, and tests ASR, TTS, and NMT NIMs on build.nvidia.com or self-hosted.

12installs

Sourcenvidia/skills

Added on2026-06-03

NPX Install

npx skill4agent add nvidia/skills nemotron-speech

SKILL.md Content

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Nemotron Speech Skills

Note: "Nemotron Speech" is the public-facing name for what NVIDIA documents today as Riva / Riva NIM. All commands, container images, gRPC APIs, Python imports, and documentation URLs still use "Riva" — the rename is brand-only. Do not rename commands, images, or doc URLs.

Agent: When walking the user through a multi-step workflow, announce each step before presenting it: Step N/M — Step Title (e.g., "Step 1/4 — Deploy the Container").

Purpose

Single entry point for all NVIDIA Nemotron Speech (Riva) NIM workflows: ASR (speech-to-text), TTS (text-to-speech), and NMT (translation). Covers cloud-hosted inference via build.nvidia.com, self-hosted Docker deployment, client-protocol choice for ASR (gRPC, HTTP, WebSocket), custom NeMo model deployment via

riva-build

, ASR pipeline tuning (VAD, diarization, language models), and the prerequisite Docker / NGC / driver setup.

When to Use This Skill

Use this skill for any Nemotron Speech / Riva NIM task — deployment, testing, custom model build, system requirements check, or model selection across ASR / TTS / NMT modalities.

Workflow

Identify the user's task type, then load the corresponding reference file from

references/

. The reference files contain the detailed per-workflow content; this SKILL.md is a routing surface. Load only the reference relevant to the task at hand.

Prerequisites

For self-hosted deployment: NVIDIA AI Enterprise (NVAIE) entitlement, then complete the environment setup — NVIDIA drivers, Docker, Container Toolkit, NGC API key, Riva Python client. See
```
references/setup.md
```
.
For cloud-hosted inference:
```
pip install -U nvidia-riva-client
```
and a valid
```
NVIDIA_API_KEY
```
from https://build.nvidia.com.
Treat
```
NVIDIA_API_KEY
```
and
```
NGC_API_KEY
```
as secrets: never print, paste, commit, or log real key values. Prefer
```
--password-stdin
```
for Docker login and store persistent keys in a credential manager or a
```
chmod 600
```
env file rather than world-readable shell startup files.
For self-hosted Docker model caching: host directories mounted at
```
/opt/nim/.cache
```
must be writable by the container user (the NIM container runs as
```
nvs:1000
```
internally), not just the host user. Run
```
sudo chown 1000:1000 $LOCAL_NIM_CACHE
```
after creating the directory so the container can write to it. Avoid world-writable modes — they let any local user replace cached model artifacts. Also avoid
```
-u "$(id -u):$(id -g)"
```
on the docker run —
```
/opt/nim/workspace
```
inside the container isn't writable to arbitrary UIDs. If you see
```
I/O error Permission denied (os error 13)
```
during model download, the host directory ownership is the issue.

Instructions

Match the user's task to one reference file and load only that file; the references are detailed, so progressive disclosure keeps context tight.
Route setup requests for drivers, Docker, Container Toolkit, and NGC to
```
references/setup.md
```
.
Route GPU compatibility, deployment readiness, and container health checks to
```
references/deployment-readiness-checks.md
```
.
Route model choice across ASR, TTS, and NMT to
```
references/model-selection.md
```
.
Route ASR deployment or inference for Parakeet, Canary, Whisper, and Nemotron ASR Streaming to
```
references/asr.md
```
.
Route custom-trained NeMo ASR deployment (
```
.nemo
```
→ RMIR → NIM) to
```
references/asr-custom.md
```
.
Route ASR pipeline configuration for VAD, diarization, language models, and chunk size to
```
references/pipelines.md
```
.
Route TTS deployment or inference for Magpie to
```
references/tts.md
```
.
Route NMT deployment or inference for Riva Translate, language pairs, and DNT tags to
```
references/nmt.md
```
.

Source of truth

For per-release detail — current model catalog, container IDs, function IDs, voice lists, VRAM minimums, per-model feature support — fetch or open the canonical NVIDIA doc rather than relying on text in this SKILL.md or the references. Each reference file includes its own routing table to the relevant doc pages.

Top-level landing pages:

Topic	URL
ASR support matrix	https://docs.nvidia.com/nim/speech/latest/reference/support-matrix/asr.html
TTS support matrix	https://docs.nvidia.com/nim/speech/latest/reference/support-matrix/tts.html
NMT support matrix	https://docs.nvidia.com/nim/speech/latest/reference/support-matrix/nmt.html
Prerequisites (driver / GPU / OS)	https://docs.nvidia.com/nim/speech/latest/get-started/prerequisites.html
ASR pipeline configuration	https://docs.nvidia.com/nim/speech/latest/asr/customization/pipeline-configuration.html
ASR runtime customization	https://docs.nvidia.com/nim/speech/latest/asr/customization/customization.html
Cloud function IDs (per model)	`https://build.nvidia.com/<org>/<model>/api`
NGC catalog	https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/models

Examples

"Deploy a Parakeet ASR NIM" → load

references/asr.md

, follow Option B (self-hosted), Steps 1–4.

"Synthesize speech with Magpie" → load

references/tts.md

, follow Option A (cloud) or Option B (self-hosted).

"Translate English to German" → load

references/nmt.md

, follow the 4-step flow.

"Convert my fine-tuned
.nemo
to a NIM" → load

references/asr-custom.md

for the 4-phase pipeline and

references/pipelines.md

for build-time config.

"Can my GPU run this?" → load

references/deployment-readiness-checks.md

and run the 6-step system check.

"Which Riva model should I use?" → load

references/model-selection.md

, apply the decision framework, then fetch the support matrix for the specific current model name.

Naming & Terminology

Skill brand: Nemotron Speech (public-facing name).
Internal naming preserved: commands (
```
riva-build
```
,
```
riva-deploy
```
,
```
riva_streaming_asr_client
```
), Python client (
```
riva.client
```
), gRPC namespace (
```
nvidia.riva.asr.*
```
), container registry (
```
nvcr.io/nim/nvidia/*
```
), and all NVIDIA documentation URLs still use "Riva". Do not rename these in code, commands, or docs.

Troubleshooting

For task-specific runtime or modality issues, use the relevant reference file (

references/<task>.md

). Cross-cutting readiness checks:

Container does not become ready →
```
references/deployment-readiness-checks.md
```
(system check + health check table)

Health check fails →

references/deployment-readiness-checks.md

docker pull
from
nvcr.io
returns 403 →
```
references/setup.md
```
(Step 5 — Docker login)
Wrong base image / model architecture mismatch →
```
references/asr-custom.md
```
(Phase 2 base image)
VRAM / GPU compatibility →
```
references/deployment-readiness-checks.md
```
, then verify on the support matrix

Limitations

x86_64 architecture only — WSL2 on Windows requires Podman and supports a subset of NIMs (see
```
references/setup.md
```
)
Self-hosted deployment requires an NVIDIA AI Enterprise license
Cloud-hosted inference requires an active
```
NVIDIA_API_KEY
```
and internet access
Public skill branding is "Nemotron Speech"; commands, container images, Python imports (
```
riva.client
```
), gRPC services (
```
nvidia.riva.*
```
), and NVIDIA documentation URLs still use "Riva" — follow official docs and catalogs for naming, do not rename these in commands or code

Next Steps

Verify hardware compatibility:

references/deployment-readiness-checks.md

Set up the environment:
```
references/setup.md
```
Pick a model:
```
references/model-selection.md
```

Deploy:

references/asr.md

references/tts.md

, or

references/nmt.md