Install any skill in seconds. Free to start, no credit card required.
Get Started Free →nvidia/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.
Passed 16 of 22 test cases with the skill, and 13 of 22 without it.
and 5 more in the full report
6 fixed10 passed either way3 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
Since the product was rebranded to Nemotron Speech, should I update my CI scripts to use `nemotron-deploy` and `nemotron.client` instead of the old Riva names?
| Checked | Without skill | With skill |
|---|---|---|
| Instructs the user to keep using the Riva names for commands and code | Passed | Passed |
| Explains that the rename is brand-only and does not affect internal naming | Missed | Passed |
The instructions your model reads when the skill loads.
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").
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.
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.
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.
references/setup.md.pip install -U nvidia-riva-client and a valid NVIDIA_API_KEY from https://build.nvidia.com.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./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.references/setup.md.references/deployment-readiness-checks.md.references/model-selection.md.references/asr.md..nemo → RMIR → NIM) to references/asr-custom.md.references/pipelines.md.references/tts.md..nemo → RMIR → NIM) to references/tts-custom.md.custom_configuration keys — to references/tts-pipelines.md. (For discovering and constructing the pronunciation itself, use tts-pronunciation.md.)references/tts-pronunciation.md.references/nmt.md.test cases passed, vs 13 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.