Nemotron Speech

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 custom or fine-tuned TTS model deployment (.nemo → RMIR → NIM) to references/tts-custom.md.

• Route TTS synthesis pipeline configuration — SSML, zero-shot voice cloning, applying an existing pronunciation dictionary, audio encoding, sample rate, custom_configuration keys — to references/tts-pipelines.md. (For discovering and constructing the pronunciation itself, use tts-pronunciation.md.)

• Route TTS pronunciation discovery — finding, testing, and applying IPA pronunciations for specific words or phrases — to references/tts-pronunciation.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:

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.

"Deploy a custom fine-tuned TTS voice as a NIM" → load references/tts-custom.md for the 4-phase pipeline.

"Use zero-shot voice cloning with Magpie" → load references/tts-pipelines.md.

"Add SSML emphasis tags to my TTS request" → load references/tts-pipelines.md.

"'NVIDIA' sounds wrong in my Magpie TTS output — suggest a few IPA options to test" → load references/tts-pronunciation.md, generate IPA candidates, synthesize variants, then output all three delivery formats.

"How do I fix the pronunciation of 'NIM' in Riva TTS with a custom_dictionary in gRPC Python?" → load references/tts-pronunciation.md, propose IPA for 'NIM', show wire format and gRPC snippet.

"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