digital-health-clinical-asr-setup
Stage 1 of Clinical ASR Flywheel. Use when bootstrapping a cycle: NVCF+MW disclosure, NVIDIA_API_KEY check, deps install, TTS+ASR smoke test.
git clone --depth 1 https://github.com/NVIDIA/skills /tmp/digital-health-clinical-asr-setup && cp -r /tmp/digital-health-clinical-asr-setup/skills/digital-health-clinical-asr-setup ~/.claude/skills/digital-health-clinical-asr-setupSKILL.md
<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0 --> # Clinical ASR Flywheel — Stage 1 (Setup) > **Agent: this file is the complete Stage 1 procedure.** Do not invoke `find`, `ls`, `rg`, or `grep` looking for an installer or hidden config — there isn't one. The four sections below (outbound-data disclosure, three numbered checks, sibling hand-off) are all required reading; don't skip any. Function IDs, env-var conventions, and the smoke-test gate are inlined further down — answer from what's actually written here rather than from prior Riva/NVCF familiarity. Stage 1 has one job: prove the user can reach NVIDIA's hosted speech stack with the `NVIDIA_API_KEY` they currently hold. Once a single clinical sentence round-trips through Magpie TTS → Parakeet/Nemotron ASR successfully, the user is cleared to advance to `/digital-health-clinical-asr-build`. The four-stage flywheel exists to drive down **KER (keyword error rate)** on clinical entities — drugs, procedures, anatomy, conditions, labs, roles. WER averages obscure the failures that hurt clinically; KER is what Stage 3 will measure you against. There is **no installer script** anywhere in this skill — not `install.sh`, not `setup.py`, nothing hidden. Stage 1 *is* the three steps below: verify the key, install Python deps, run the smoke test. Anything past Stage 1 is composed from sibling skills (`/data-designer`, `/riva-tts`, the inlined Stage 3 ASR recipe, `/riva-asr-custom`). If a user asks "what script installs everything?", answer from this paragraph; don't go searching. ## Outbound data flows — surface before any text or audio is sent Two external endpoints receive data during this flywheel. The user has to acknowledge both before Stage 2 begins, against whatever data-governance policy their organization enforces. **Render the table below word-for-word in your response — a paraphrase doesn't satisfy the disclosure; the literal phrasing is what counts.** | Service | What gets sent | When | Hosted by | |---|---|---|---| | **NVIDIA NVCF** (`grpc.nvcf.nvidia.com`) | The clinical sentences you synthesize (text), and the WAV files you transcribe (audio) | Every Stage 2 TTS call and every Stage 3 ASR call | NVIDIA, governed by build.nvidia.com terms | | **Merriam-Webster** (`dictionaryapi.com` JSON API **or** the public `merriam-webster.com` HTML site) | Individual clinical terms (drug names, anatomy, procedures), one HTTP request per term | Stage 2 IPA tagging — see "Two MW paths" below for which endpoint applies | Merriam-Webster, governed by their API or site terms | The data is **synthetic by construction** — the flywheel manufactures sentences and audio from a user-curated term list, never from real patient encounters. That said: **do not feed real patient transcripts, recorded clinical audio, or any PHI through any stage.** If the term list itself contains sensitive material (codename drugs, unreleased product names), the user should consult their organization's external-API policy before proceeding. Either endpoint can be turned off: - **Skip Merriam-Webster entirely:** leave `DICTIONARY_API_KEY` unset and don't run a scraper. Stage 2 falls back to Magpie G2P, which still works but with weaker coverage on long-tail clinical terms. - **Skip NVCF:** this is a hard stop. Magpie TTS + Parakeet/Nemotron ASR *are* the workload; without them this skill family is the wrong tool — a self-hosted ASR/TTS pipeline is what you want instead. Recommend a copy of this notice lands in the user's workspace `README.md`; bring it forward on first invocation if it isn't already there. ## Purpose Get a fresh environment ready for Stage 2. Three things to confirm: key is present, deps import cleanly, hosted stack actually answers. Close by naming which skill to run next. The four `digital-health-clinical-asr-*` skills are **self-contained** — every TTS, ASR, IPA-tagging, and scoring recipe lives inside them; no other agent skill needs installing to run the flywheel end-to-end. This skill takes no opinion on workspace layout. The user decides where their cycle artifacts live; `data/eval_sets/cycle<N>/` is not imposed. ## When to use this skill Activate on user phrases like: - "Set up the Clinical ASR Flywheel" - "Initialize the clinical-asr eval" - "I want to evaluate ASR on clinical terminology — where do I start?" - "Bootstrap my environment for the flywheel" - "What do I need installed before I run the flywheel?" Do **not** activate when: - The user already has a manifest and wants to score it → `/digital-health-clinical-asr-eval` - The user already has the env set up and wants to curate terms → `/digital-health-clinical-asr-build` - The user is asking about Stage 4 fine-tune NGC/Docker setup specifically → that's covered inside `/digital-health-clinical-asr-finetune` ## Prerequisites | Requirement | Required? | Why | How | |---|---|---|---| | `NVIDIA_API_KEY` (`nvapi-…`) | **Required** | Hosted Magpie TTS + Parakeet/Nemotron ASR via NVCF | Issue at <https://build.nvidia.com>; `export NVIDIA_API_KEY=...` in shell | | Python ≥ 3.10 | **Required** | NeMo client, scoring, manifest tools | `python3 --version` | | `nvidia-riva-client`, `pandas`, `soundfile`, `requests` | **Required** | TTS + ASR clients, manifest I/O, MW lookup | `pip install nvidia-riva-client pandas soundfile requests` | | `DICTIONARY_API_KEY` | Optional | Merriam-Webster Medical Dictionary lookup via the JSON API (Path A in the build skill — recommended) | Free key at <https://dictionaryapi.com>. Path B (HTML scrape of `merriam-webster.com`, no key, brittle) is also documented in the build skill if you can't get a key. Without either path, Stage 2 falls through to Magpie G2P with weaker long-tail coverage. | | `jiwer` | Optional | Reference WER/CER against the inlined Levenshtein implementation | `pip install jiwer` — the eval skill includes a pure-Python fallback | | (Stage 4 o
>-
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
|
|
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP/live streams, use amc-run-rtsp-calibration instead.
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.