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Official agent skill

Nemotron Asr Finetune

by NVIDIA in NVIDIA/skills

Orchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Nemotron Asr Finetune

skills CLI
$ npx skills add NVIDIA/skills --skill nemotron-asr-finetune -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA/skills nemotron-asr-finetune --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nemotron-asr-finetune .claude/skills/nemotron-asr-finetune && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nemotron-asr-finetune
GitHub stars
3.6k
Token cost
~3k tokens
SKILL.md length
1,313 words
Files
13 (incl. scripts, references, assets)
Skills in repo
390
Repo updated
First seen
Licence
Apache-2.0

At a glance

Orchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation.

  • Works in 4 steps: Scope the problem (how much real audio,… → Choose the cheapest sufficient path —… → Delegate each stage to the right… → …
  • Tasks that involve Speech recognition and synthesis
  • SKILL.md covers What This Skill Is, When to Use, Orchestration Workflow and Sub-Skills This Skill Calls, plus 4 more sections
  • Runs Shell and Python scripts from its folder

What it does

Nemotron Asr Finetune is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Orchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation. Given a goal like "improve/fine-tune ASR for my domain or language", it scopes the task, picks the cheapest sufficient path (word boosting → n-gram LM → fine-tuning), delegates each stage to the right sub-skill (data generation, training, evaluation, deployment), and answers cost/time/data questions along the way.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including scripts, reference files and assets (for example `BENCHMARK.md`, `assets/experiment-ledger-template.md` and `evals/EVAL.md`).

It sits in AI & LLM Engineering, covering Speech recognition and synthesis and Fine-tuning. It works with NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Speech recognition and synthesis
  • Tasks that involve Fine-tuning

Example prompts

  • “improve/fine-tune ASR for my domain or language”
  • “/nemotron-asr-finetune”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Scope the problem (how much real audio, target eval set, latency/hardware budget, language/domain).
  2. Choose the cheapest sufficient path — word boosting, n-gram LM fusion, or fine-tuning — and escalate only when quality falls short.
  3. Delegate each stage to the right sub-skill (data generation, training, evaluation, deployment/optimization).
  4. Answer cost/time/data questions along the way (how many hours to hit X% WER, synthetic vs real, L40S vs H100, expected cost).

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Shell and Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.nvidia.com
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nemotron Asr Finetune loads about 3k tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 1,313 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~15k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 1,313 words, ~2,955 tokens.

Download SKILL.mdSave it as .claude/skills/nemotron-asr-finetune/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.
name
nemotron-asr-finetune
description
Orchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation. Given a goal like "improve/fine-tune ASR for my domain or language", it scopes the task, picks the cheapest sufficient path (word boosting → n-gram LM → fine-tuning), delegates each stage to the right sub-skill (data generation, training, evaluation, deployment), and answers cost/time/data questions along the way.
triggers
fine-tune ASR for my domain, adapt ASR to my language, improve ASR accuracy, customize ASR, ASR domain adaptation, new language ASR, reduce WER, my ASR gets…
version
1.3.0
license
Apache-2.0
metadata.author
Nemotron Speech Team
metadata.team
riva
metadata.tags
nvidia, nemotron-speech, riva, nemo, asr, speech-to-text, orchestration, customization, domain-adaptation, fine-tuning, word-boosting, language-model…
metadata.domain
ml

Nemotron Speech ASR Customization — Orchestration Skill

Note: "Nemotron Speech" is the public-facing name for what NVIDIA documents today as Riva / Riva NIM; the acoustic models are trained and fine-tuned with NVIDIA NeMo. Commands, config paths, imports, and doc URLs still use "Riva" / "NeMo" — the rename is brand-only. Do not rename them.

What This Skill Is

This is a high-level orchestration skill, not a step-by-step training manual. Its job, given a goal such as "I want to fine-tune ASR for my domain/language", is to:

  1. Scope the problem (how much real audio, target eval set, latency/hardware budget, language/domain).
  2. Choose the cheapest sufficient path — word boosting, n-gram LM fusion, or fine-tuning — and escalate only when quality falls short.
  3. Delegate each stage to the right sub-skill (data generation, training, evaluation, deployment/optimization).
  4. Answer cost/time/data questions along the way (how many hours to hit X% WER, synthetic vs real, L40S vs H100, expected cost).

It owns the plan and the routing; the sub-skills own the execution. When a needed sub-skill does not exist yet, this skill names it as a placeholder and gives interim guidance.

When to Use

Use for any request to make a Nemotron Speech / Riva ASR model work better on a specific domain or language — improving accuracy, reducing WER, adding a language, or planning a fine-tune. Start here even when the user names a specific technique: treat it as a candidate until the orchestration step confirms it is the cheapest sufficient path, then sequence the right sub-skills.

Orchestration Workflow

Run the loop below; each stage names the sub-skill it invokes. Full detail in references/workflow.md.

#StageWhat happensSub-skill
1State the goalCapture the target: domain/language, the errors, the metric.Orchestration (this skill)
2Clarify & scopeAsk the discovery questions: how much real audio? target eval set? latency/HW budget? deployment target?Orchestration
3Choose the pathPick the cheapest sufficient rung (boosting → n-gram LM → fine-tune). Escalate only if quality is short; experiment while proposing the full plan.Orchestration → Research/Training
4Get the data rightIf data is scarce/noisy: synthetic (TTS), TTS-friendly formatting, noise profiling/harvest, blend, score vendor samples; align customer data to training format; flag missing real data.SDG / Data
5TrainApply the recipe (configs, hyperparameters, replay/curriculum, GPU/OOM preflight) and run.Research / Training
6EvaluateNormalized WER on the domain set + A/B forgetting check on a general set; error-driven analysis to find the next lever.Evaluation
7Loop or shipIf short of target, loop to 4/5 with targeted data; else select/average checkpoints. Consult the user before more cycles.Orchestration
8DeployExport to NIM/HF, hot-swap the checkpoint, serve.Deployment / Optimization

Stages 4–8 are the fine-tune path (data → NeMo train → NeMo eval → Riva deploy). Cheaper rungs (boosting, custom vocab, n-gram LM) take a shorter branch owned by a single sub-skill — don't force them through the full loop. See the branch-by-rung table in references/workflow.md (§3b).

Before training (Stage 5), run the pre-flight dataset quality check (§4a): verify/convert audio to 16 kHz, and, on user request, audit transcript quality by running a reference pretrained model and checking WER against the provided ground truth. See references/workflow.md §4a.

Also before training (Stage 5), run the pre-flight environment/dependency check (§4b): if no NeMo is provided, pull the latest main (local execution) or the latest published container tag (container execution). If a NeMo checkout/install is already provided, check its version, but still recommend switching to latest main (staleness risk) and ask the user — if they insist on the provided one, proceed with it and only revisit once a concrete version issue (e.g. unsupported functionality) is actually hit. See references/workflow.md §4b.

Throughout, answer the "along the way" questions (data volume, synthetic vs real, hours to reach a WER target, cost, GPU choice) — see references/planning-answers.md.

Sub-Skills This Skill Calls

Detailed registry, invocation, and handoff contracts in references/sub-skills.md.

Role (per the architecture)PurposeSub-skill to invoke
Research / TrainingNeMo configs, recipes, fine-tuning, checkpoint averaging; also owns the NeMo-side word-boosting and n-gram LM pilotsnemo-speech-asr-finetune
SDG / Data DesignerSynthetic transcripts/text, noise profiling, vendor-data impact, blendsdata-designer (synthetic text; audio via TTS in nemotron-speech); placeholder: asr-data-profiling
EvaluationNormalized WER, A/B forgetting, error analysisOffline file WER → nemo-speech-asr-finetune; served-endpoint WER → nemotron-speech
Deployment / OptimizationNIM/Riva export, checkpoint swap, NIM-build optimization, servingnemotron-speech

If a sub-skill is unavailable, say so, give the interim guidance from the reference, and continue the plan.

Show full SKILL.md (594 more words)Show less

Choosing The Path (cheapest first)

The scoping in Stage 3 selects the lowest-cost rung that can meet the target. Summary; full docs-grounded ladder in references/path-selection.md.

  • Word boosting — a bounded set of known words/names/jargon. Two realizations that are different artifacts: pilot (NeMo) to prove lift offline via GPU-PB context biasing (nemo-speech-asr-finetune), or deploy (Riva) to ship it via runtime boosted_lm_score (nemotron-speech) — different score systems, don't reuse one for the other. Runtime, no training either way. See references/path-selection.md.
  • Custom vocabulary / pronunciation — OOV or consistently mispronounced terms. Deploy-time. → Deployment sub-skill.
  • N-gram (KenLM) LM — domain phrasing/word-sequences when you have text but little audio. Two realizations, built the same way but deployed differently by architecture: pilot (NeMo, NGPU-LM) to prove lift offline (nemo-speech-asr-finetune), or deploy (Riva) to ship it (nemotron-speech). For CTC, rebuild the pilot corpus into a Riva word-level LM — don't ship the pilot artifact as-is. For RNN-T/TDT, the opposite: hand the pilot's .nemo artifact to Riva unchanged, no rebuild. See references/path-selection.md.
  • Fine-tune — real acoustic gaps (accents, noise, channel) with enough transcribed audio (NIM guide: 100+ h; ~10 h floor only if mixed to avoid catastrophic forgetting). Below ~10 h, do not recommend fine-tuning — recommend word boosting instead (severe catastrophic-forgetting/overfitting risk). → Research/Training.
  • Train from scratch / cross-language transfer — a new language with no suitable checkpoint (last resort). → Research/Training.

Ordering and per-model support follow the NVIDIA Speech NIM ASR customization guide: https://docs.nvidia.com/nim/speech/latest/asr/customization/customization.html.

Key Principles

  • Scope before you pick. Don't recommend fine-tuning before the discovery questions and a measured baseline.
  • Cheapest sufficient path. Escalate rungs only when the current one provably can't hit the target; you may experiment on a cheap rung while presenting the full fine-tuning plan.
  • Measure with a contract. Report normalized WER on the domain set plus an A/B forgetting check on a general set — never in-training logs alone.
  • Verify before you train. Run the pre-flight dataset quality check before Stage 5: verify/convert audio to 16 kHz, and, when the user asks, audit ground-truth transcripts by comparing them to a reference model's WER. Don't train on an unresampled or unaudited-on-request dataset. See references/workflow.md §4a.
  • Know your environment before you train. If no NeMo is provided, pull the latest main (or latest published container tag). If one is provided, check its version but still recommend latest main and ask the user first — a provided checkout always carries staleness risk. If they insist on keeping it, don't pull preemptively; only revisit once a concrete version issue (e.g. unsupported functionality) is actually hit. Record what was resolved in the ledger. See references/workflow.md §4b.
  • Delegate, don't reimplement. Route execution to the sub-skills; keep this skill focused on the plan, sequencing, and cost/time/data answers.
  • Real target-domain audio is the usual bottleneck. Prefer real data; use synthetic to fill measured gaps, kept separately weighted so it can be ablated.
  • Consult the user before extra tuning cycles, and when a needed sub-skill is a placeholder.

Source of Truth

TopicLocation
NIM Speech docs homehttps://docs.nvidia.com/nim/speech/latest/index.html
ASR customization guide (methods, per-model support)https://docs.nvidia.com/nim/speech/latest/asr/customization/customization.html
ASR support matrix (models & features)https://docs.nvidia.com/nim/speech/latest/reference/support-matrix/asr.html
NeMo fine-tuning (flags/config)docs/source/asr/fine_tuning.rst, and the nemo-speech-asr-finetune sub-skill
Riva ASR tutorials (boosting, LM, fine-tune)https://github.com/nvidia-riva/tutorials
Tokenizer extension to new language + acoustic fine-tunehttps://github.com/nvidia-riva/tutorials/blob/main/asr-extend-tokenizer-to-newlang-ft-acoustic-model.ipynb

Limitations

  • Orchestration only — execution happens in the sub-skills. Where a sub-skill is a placeholder, guidance is interim until it exists.
  • GPU required for the training rungs; deployment/serving is owned by the nemotron-speech sub-skill.
  • Model names, config paths, flags, and per-model feature support drift across NeMo/Riva releases — verify against the support matrix and the current checkout.
  • Public branding is "Nemotron Speech"; commands, imports, config paths, and doc URLs still use "Riva" / "NeMo" — do not rename.

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 12 other files (scripts, references, assets) in skills/nemotron-asr-finetune of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • assets/experiment-ledger-template.md
  • evals/EVAL.md
  • evals/evals.json
  • references/path-selection.md
  • references/planning-answers.md
  • references/sub-skills.md
  • references/workflow.md
  • scripts/link-nemo-subskill.sh
  • scripts/main.py
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 14a98ae

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Works with

Categories

Questions about Nemotron Asr Finetune

What does Nemotron Asr Finetune do?

Orchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation. Nemotron Asr Finetune is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Orchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation.

When should I use Nemotron Asr Finetune?

Nemotron Asr Finetune fits situations like: tasks that involve Speech recognition and synthesis; tasks that involve Fine-tuning.

How do I install Nemotron Asr Finetune in Claude Code?

Run `npx skills add NVIDIA/skills --skill nemotron-asr-finetune -a claude-code`. Or copy the skill folder (skills/nemotron-asr-finetune in NVIDIA/skills) into .claude/skills/nemotron-asr-finetune in your project. Claude Code loads it when a task matches its description.

How do I install Nemotron Asr Finetune in Codex?

Run `npx skills add NVIDIA/skills --skill nemotron-asr-finetune -a codex`. Or copy the skill folder (skills/nemotron-asr-finetune in NVIDIA/skills) into .agents/skills/nemotron-asr-finetune in your project. Codex loads it when a task matches its description.

Can I use Nemotron Asr Finetune in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill nemotron-asr-finetune -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nemotron-asr-finetune, .gemini/skills/nemotron-asr-finetune, .github/skills/nemotron-asr-finetune and .opencode/skills/nemotron-asr-finetune in your project.

What does Nemotron Asr Finetune need to run?

Going by SKILL.md and its folder, Nemotron Asr Finetune needs a shell and Python for the scripts in its folder. Our summary lists: Python 3; A Bash shell.

Does Nemotron Asr Finetune access the network?

SKILL.md names 2 domains. As links in the text: docs.nvidia.com and github.com. This is read from the text; nothing was executed.

Is Nemotron Asr Finetune safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Nemotron Asr Finetune use?

Nemotron Asr Finetune is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nemotron Asr Finetune use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 13k tokens, read only when the agent opens those files.

What are the alternatives to Nemotron Asr Finetune?

Skills that share tags, products or a category with Nemotron Asr Finetune: Nemotron Add Step (NVIDIA-NeMo/Nemotron, 2.1k stars), Setup Workshop Nemoclaw (brevdev/workshop-build-an-agent, 146 stars), Nemotron Super3 (NVIDIA-NeMo/Nemotron, 2.1k stars) and Nemotron 3 Ultra Text2sql Lora (NVIDIA-NeMo/Nemotron, 2.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nemotron Asr Finetune?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.