Nemotron Asr Finetune

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:

• Scope the problem (how much real audio, target eval set, latency/hardware budget, language/domain).
• Choose the cheapest sufficient path — word boosting, n-gram LM fusion, or fine-tuning — and escalate only when quality falls short.
• Delegate each stage to the right sub-skill (data generation, training, evaluation, deployment/optimization).
• 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, so the cheapest sufficient path is chosen and the right sub-skills are sequenced.

Orchestration Workflow

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

| # | Stage | What happens | Sub-skill |

|---|---|---|---|

| 1 | State the goal | Capture the target: domain/language, the errors, the metric. | Orchestration (this skill) |

| 2 | Clarify & scope | Ask the discovery questions: how much real audio? target eval set? latency/HW budget? deployment target? | Orchestration |

| 3 | Choose the path | Pick 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 |

| 4 | Get the data right | If 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 |

| 5 | Train | Apply the recipe (configs, hyperparameters, replay/curriculum, GPU/OOM preflight) and run. | Research / Training |

| 6 | Evaluate | Normalized WER on the domain set + A/B forgetting check on a general set; error-driven analysis to find the next lever. | Evaluation |

| 7 | Loop or ship | If short of target, loop to 4/5 with targeted data; else select/average checkpoints. Consult the user before more cycles. | Orchestration |

| 8 | Deploy | Export 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).

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) | Purpose | Sub-skill to invoke |

|---|---|---|

| Research / Training | NeMo configs, recipes, fine-tuning, checkpoint averaging | nemo-speech-asr-finetune |

| SDG / Data Designer | Synthetic transcripts/text, noise profiling, vendor-data impact, blends | data-designer (synthetic text; audio via TTS in nemotron-speech); *placeholder:* asr-data-profiling |

| Evaluation | Normalized WER, A/B forgetting, error analysis | Offline file WER → nemo-speech-asr-finetune; served-endpoint WER → nemotron-speech |

| Deployment / Optimization | NIM/Riva export, checkpoint swap, NIM-build optimization, serving | nemotron-speech |

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

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. Runtime, no training. → Deployment sub-skill.
• 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 that are different artifacts: *pilot (NeMo)* to prove lift offline (nemo-speech-asr-finetune), or *deploy (Riva)* to ship it (nemotron-speech). Don't ship the pilot LM — rebuild it in Riva word-level format. 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). → 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.
• 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

| Topic | Location |

|---|---|

| NIM Speech docs home | https://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-tune | https://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.