What does this skill do, and when should you use it?
This skill orchestrates domain and language adaptation for NVIDIA Nemotron Speech (Riva) / NeMo ASR. It scopes audio data, evaluation, target metrics, latency, and hardware before choosing among word boosting, n-gram language modeling, and fine-tuning. It owns planning, routing, and answers about cost, time, and data, while dedicated sub-skills handle training, evaluation, and deployment. It fits teams trying to reduce WER, correct jargon errors, handle noise, or add a language, but it is not a step-by-step training manual.
Collects the target domain or language, error patterns, evaluation metric, real-audio volume, deployment target, and latency or hardware budget; selects word boosting, custom vocabulary/pronunciation, n-gram LM, fine-tuning, or cross-language transfer; routes data generation, training, evaluation, and deployment to named sub-skills; plans blends of real and synthetic data; requires normalized domain-set WER plus a general-set forgetting check; loops for targeted improvements or proceeds to Riva/NIM deployment; and answers questions about data volume, GPUs, time, and cost.
- A call-center team sees industry jargon misrecognized in noisy calls and needs to decide whether boosting, a language model, or fine-tuning is appropriate.
- A speech product team wants to adapt Riva ASR to a new language and needs to assess checkpoints, data requirements, and cross-language transfer.
- An enterprise has domain text but little labeled audio and wants to pilot an n-gram LM for domain phrasing before investing in acoustic fine-tuning.
- An ASR engineering team has transcribed target-domain audio and needs a plan covering NeMo fine-tuning, WER evaluation, forgetting checks, and deployment.
How do you install this skill?
- Do not treat this orchestration skill as a training or deployment executor; confirm required sub-skills, versions, and external-service availability first.
- Before processing customer audio, transcripts, or synthetic data, establish privacy, access-control, retention, and data-transfer requirements.
- Before deployment or expensive training, obtain confirmation of budget, hardware, model versions, rollback, and the current Riva/NeMo support matrix.
- For mainland-China users, separately verify reachability of NVIDIA documentation, images, models, and service endpoints.
NVIDIA NeMoNVIDIA Riva/NIMGPU for training rungsRelevant NVIDIA ASR sub-skills
Install the individual skill from NVIDIA’s collection with the skills CLI:
npx skills add nvidia/skills --skill nemotron-asr-finetune --yes
The README does not document a client-specific destination; the CLI prompts for the installation target.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/NVIDIA/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/nemotron-asr-finetune" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Fine-tune ASR for my noisy call-center domain and reduce WER.
After installation, give the agent a concrete ASR customization request, such as: "Fine-tune ASR for my noisy call-center domain and reduce WER." The agent should first ask about real-audio volume, the target evaluation set, latency/hardware budget, and deployment target, then select the cheapest sufficient path. Available sub-skills perform the detailed training, evaluation, and deployment work; missing sub-skills are identified as placeholders.
What are this skill's strengths and limitations?
- Uses a cost-ordered ladder from word boosting and language modeling to fine-tuning.
- Covers data planning, training, evaluation, forgetting checks, and deployment routing.
- Requires domain WER and a general-set A/B check instead of relying only on training logs.
- Addresses scarce real data, synthetic data, noise, GPU choice, time, and cost questions.
- Orchestration only; it does not provide the complete training or deployment execution itself.
- Some required sub-skills may be placeholders and then provide only interim guidance.
- Training paths require a GPU, and NeMo/Riva commands, configurations, and model support can change by release.
- The supplied sources provide no skill-specific test results, concrete pricing, or guaranteed WER improvement.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Nemotron Speech ASR Customization Orchestrator this page ✓ NVIDIA · Official | 56 · Use with care | ★ 3.8k | today | Apache-2.0 |
| Clinical ASR Fine-Tuning ✓ NVIDIA · Official | 50 · Use with care | ★ 3.8k | today | Apache-2.0 |
| Clinical ASR Evaluation and KER Leaderboard ✓ NVIDIA · Official | 55 · Use with care | ★ 3.8k | today | Apache-2.0 |
| Nemotron Customization Pipelines ✓ NVIDIA · Official | 57 · Use with care | ★ 3.8k | today | Apache-2.0 |
| Azure AI Foundry Fine-Tuning ✓ Microsoft · Official | 48 · Use with care | ★ 2.7k | 3mo ago | MIT |
The skill explicitly chooses among word boosting, custom vocabulary/pronunciation, n-gram LM, fine-tuning, and finally training from scratch or cross-language transfer. These are staged ASR customization paths rather than competing products.
How did FollowSkills review this skill?
The skill requires scoping first, baseline measurement, least-cost path selection, and user consultation before additional tuning cycles; it also distinguishes offline evaluation from served evaluation, reducing operational confusion. Points are deducted because handling of sensitive speech/customer data, least-privilege permissions, dependency security, rollback, and explicit pre-deployment confirmation are not specified.
The workflow, path ladder, sub-skill ownership, and placeholder fallback guidance are broadly self-consistent, and missing sub-skills are to be disclosed rather than silently assumed. Points are deducted because this review is static and does not reproduce key paths; detailed version compatibility, command failures, and abnormal-input diagnostics are largely delegated to external sub-skills.
Triggers, audience scenarios, input questions, path branches, and non-fit cases are relatively clear, including exclusions for pure deployment, OpenAI Whisper, and text-LLM fine-tuning. Points are deducted because Chinese-language support and mainland-China network reachability are not addressed, and the core workflow depends on multiple external NVIDIA/Riva/NeMo components.
The documentation is layered and includes workflow, path selection, sub-skill registry, planning answers, limitations, version 1.0.0, Apache-2.0 licensing, and repository update guidance. Points are deducted for missing recommended Instructions and Examples sections, nonconforming author metadata, and incomplete skill-level changelog, maintainer, and troubleshooting details.
The skill provides useful high-level ASR adaptation planning, cost-aware routing, delegation, and evaluation criteria; the committed benchmark report indicates positive evaluation results, though it is only source-file evidence here. Points are deducted because the skill does not itself execute training, evaluation, or deployment, final utility depends on other skills, and no independent direct-output verification was performed.
A revision-pinned source, cited primary documentation, evaluation guidance, evals.json, and an NVSkills-Eval report provide some auditability. Points are deducted because independently reproducible execution logs, raw evaluation artifacts, and corroboration from multiple evidence types are absent, so a higher static evidence score is not justified.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →