Digital Health Clinical Asr Setup

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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.

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

Instructions

Scope. This skill performs read-only environment checks: confirming a key is exported (length-only), the Python version, that libraries import, and that the hosted NVCF stack responds to a single smoke-test round-trip. It does not install system packages, modify shell rc files, write to disk outside an explicit .venv/, or attempt to authenticate with the real key value. Validate; never mutate without explicit user direction.

1a. Verify NVIDIA_API_KEY (length-only — never echo the value)

# Export NVIDIA_API_KEY in your shell — never echo or commit the value
export NVIDIA_API_KEY=nvapi-...     # from https://build.nvidia.com

# Length-only check; the key value never appears in any log
test -n "$NVIDIA_API_KEY" && echo "NVIDIA_API_KEY len=${#NVIDIA_API_KEY}"

A length of 70+ is normal. If the output is empty or shows len=0, the user must paste a key from https://build.nvidia.com. Do not print the key, even truncated. To persist across shell sessions, add the export line to your shell rc (~/.bashrc, ~/.zshrc) — or use a per-directory tool like direnv.

1b. Install Python dependencies

python3 -m venv .venv
source .venv/bin/activate
pip install nvidia-riva-client pandas soundfile requests
# optional
pip install jiwer

For Stage 4 (fine-tune) only: nemo-toolkit and Docker + NVIDIA Container Toolkit are also required. Defer those to /digital-health-clinical-asr-finetune — there is no point installing them up front if the user may never reach Stage 4.

1c. Smoke-test the hosted NVCF stack

NVIDIA_API_KEY handling — load-bearing, do not deviate:

• The agent harness reads $NVIDIA_API_KEY from the shell and passes it as an explicit function argument to smoke_test(api_key=…).

• Auditors can grep the recipe for every wire crossing — every api_key use is visible in auth_for(...).

• Do not echo, print, or log the key value (including truncated). Length-only checks are fine (see §1a).

• Do not let the recipe read os.environ["NVIDIA_API_KEY"] itself — the explicit-argument pattern is the auditability guarantee.

• Do not commit the key to any file, including .env examples or notebook outputs.

Verify the NVIDIA_API_KEY actually works against Magpie TTS and Parakeet/Nemotron ASR before advancing. The four skills inline every recipe needed; this round-trip just confirms the API key + network path are real.

The agent harness loads the NVIDIA_API_KEY shell variable and passes it as an explicit function argument to the helpers below. The recipe code itself does not read environment variables — auditors can see exactly which API keys cross the wire.

import wave, tempfile
import riva.client

NVCF_HOST = "grpc.nvcf.nvidia.com:443"
MAGPIE_FUNCTION_ID    = "877104f7-e885-42b9-8de8-f6e4c6303969"   # Magpie TTS
PARAKEET_FUNCTION_ID  = "d3fe9151-442b-4204-a70d-5fcc597fd610"   # Parakeet TDT 0.6B v2 (offline ASR)

def auth_for(function_id: str, api_key: str) -> riva.client.Auth:
    return riva.client.Auth(
        use_ssl=True, uri=NVCF_HOST,
        metadata_args=[
            ["function-id", function_id],
            ["authorization", f"Bearer {api_key}"],
        ],
    )

def smoke_test(api_key: str) -> str:
    """Caller passes api_key (the harness reads $NVIDIA_API_KEY at the shell;
    this code never touches the environment). Returns the ASR transcript."""

    # 1. TTS: "The patient was prescribed cefazolin."
    tts = riva.client.SpeechSynthesisService(auth_for(MAGPIE_FUNCTION_ID, api_key))
    pcm = b"".join(c.audio for c in tts.synthesize_online(
        text="The patient was prescribed cefazolin.",
        voice_name="Magpie-Multilingual.EN-US.Mia",
        language_code="en-US", sample_rate_hz=16000,
    ))
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
        with wave.open(f, "wb") as w:
            w.setnchannels(1); w.setsampwidth(2); w.setframerate(16000); w.writeframes(pcm)
        wav_path = f.name

    # 2. ASR: transcribe the WAV we just synthesized.
    asr = riva.client.ASRService(auth_for(PARAKEET_FUNCTION_ID, api_key))
    with open(wav_path, "rb") as f:
        audio_bytes = f.read()
    config = riva.client.RecognitionConfig(
        encoding=riva.client.AudioEncoding.LINEAR_PCM,
        sample_rate_hertz=16000, language_code="en-US",
        max_alternatives=1, enable_automatic_punctuation=True,
    )
    response = asr.offline_recognize(audio_bytes, config)
    transcript = response.results[0].alternatives[0].transcript if response.results else ""
    print(f"TTS:  The patient was prescribed cefazolin.")
    print(f"ASR:  {transcript}")
    return transcript

# Invoke from the agent (api_key sourced by the harness, not by this code):
# smoke_test(api_key="<NVIDIA_API_KEY value>")

Run the smoke test — don't defer it. This is the gate that proves Stages 2–4 can reach the hosted stack with the user's current key. "I can run it later" is not an acceptable completion of Stage 1; either invoke smoke_test(api_key=…) now or, if the user has explicitly opted out, log the deferral in your closing summary so they know what they're missing.

If the transcript matches the input within ~1 token, the hosted stack is reachable and the user can advance to Stage 2. If either call fails:

• 401 Unauthorized / PERMISSION_DENIED → NVIDIA_API_KEY is wrong, expired, or not exported in this shell. Re-export and re-test.

• 404 / INVALID_ARGUMENT: function not found → the function ID is stale. Look up the current ID at https://build.nvidia.com and update the constant above.

• RESOURCE_EXHAUSTED → NVCF rate limit. Retry after 30 seconds; this is normal under load.

• Network/TLS errors → corporate proxy or DNS issue. Test curl https://build.nvidia.com first.

1d. (Optional) Verify Merriam-Webster lookup

Two paths produce a merriam-webster-tagged manifest row in Stage 2. Pick one (or neither — Magpie G2P fall-through is a valid posture):

• Path A — JSON API + key. Recommended for standalone use of this skill. Check the key is set:

  Copy

  test -n "$DICTIONARY_API_KEY" && echo "DICTIONARY_API_KEY len=${#DICTIONARY_API_KEY}" \
    || echo "DICTIONARY_API_KEY not set — Path A is off"

  Free key issues instantly at https://dictionaryapi.com.

• Path B — HTML scraping. No API key needed; reachability is the only prerequisite. Brittle to MW site HTML changes; recipe inlined in the build skill's references/pronunciation-pipeline.md.

  Copy

  curl -fsS -o /dev/null -w "merriam-webster.com reachable, HTTP %{http_code}\n" \
    https://www.merriam-webster.com/medical/cefazolin

  If you don't want to maintain a scraper, use Path A instead.

Remember the data-disclosure note at the top: under either path, each clinical term in your seed list goes out as an HTTP request to a Merriam-Webster endpoint.

Examples

Fresh shell, never run before. User says something like "I want to start the flywheel." → Quote the disclosure table first, then walk through 1a → 1b → 1c in order. On a green smoke test, point them at /digital-health-clinical-asr-build and explicitly name KER as the metric Stage 3 will judge them by.

Returning user, env already up. User says "I already have the env, just confirm I'm good to go." → Skip the venv + pip install (1b). Run only the length check (1a) and the smoke test (1c). On green, advance.

Artifacts produced

• NVIDIA_API_KEY exported in the user's shell

• An activated virtualenv with nvidia-riva-client, pandas, soundfile, requests

• A confirmed TTS→ASR round-trip on a clinical sentence (proof the hosted stack works)

No manifest, audio, or model artifact is produced at this stage — those come at Stages 2–4.

Troubleshooting

• Length check shows nothing or len=0 → NVIDIA_API_KEY isn't exported in this shell. Run export NVIDIA_API_KEY=nvapi-... and re-check.

• Variable is set in one shell but not another → exports don't persist across sessions. Add the export line to your shell rc (~/.bashrc, ~/.zshrc), or use a per-directory loader like direnv.

• 401 Unauthorized on the smoke test → key value is wrong or expired. Re-issue at https://build.nvidia.com.

• grpc.RpcError: function not found → the inlined function IDs need updating against the current NVCF catalog. Check https://build.nvidia.com and edit the constants in 1c. The eval skill (/digital-health-clinical-asr-eval) provides a catalog of current function IDs in its Step 3a "Other catalog options" list.

• StatusCode.INVALID_ARGUMENT with CUDA error: an illegal memory access was encountered → NVCF-side backend fault on this specific function ID (Triton/PyTorch on NVCF, not your env). Either retry later or temporarily point at a different offline ASR NIM — Whisper Large v3 function-id b702f636-f60c-4a3d-a6f4-f3568c13bd7d is the closest drop-in (also offline; pass language_code="en" instead of "en-US"). For routine eval cycles, prefer to wait for the Parakeet backend to recover so Stage 3 baseline and Stage 4 SFT base stay aligned.

• TypeError: Auth.__init__() got an unexpected keyword argument 'ssl_cert' → you're on nvidia-riva-client >= 2.x where the kwarg was renamed to ssl_root_cert (and is no longer needed for hosted NVCF). Drop the ssl_cert=None, line from your local copy of the recipe.

• ModuleNotFoundError: riva.client → step 1b was skipped or the venv isn't activated. source .venv/bin/activate && pip install nvidia-riva-client.

Limitations

• Scope is environment readiness only. Whether the user's term list or pronunciation overrides make sense is decided in /digital-health-clinical-asr-build, not here.

• Magpie en-US assumption. Downstream IPA validation rides on Magpie's English phoneme inventory; other locales require a different phoneme set entirely.

• Hosted NVCF is the assumed deployment. Running self-hosted Riva NIMs is possible but the setup for that lives inside /digital-health-clinical-asr-finetune Stage 4d.

• Synthetic data only. This skill family is built for benchmarks generated from a curated term list. Real patient transcripts and recorded audio must not flow through any stage.

Next steps

Mandatory close on success: finish the Stage 1 response by pointing the user explicitly to /digital-health-clinical-asr-build and naming KER (keyword error rate) as the headline measure they'll see at Stage 3. Both pointers are required, not optional — they place the user inside the four-stage flywheel.

• Default forward route: /digital-health-clinical-asr-build — specialty interview, term curation, IPA tagging, NeMo manifest synthesis.

• Direct jump to Stage 3 (only when the user is bringing their own NeMo-format manifest with term / entity_category / ipa_source fields): /digital-health-clinical-asr-eval.

References

• references/dependency-ownership.md — boundary between skill-owned and companion-owned responsibilities.