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voice-code-bench / scripts /MODAL.md
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Add Meta OmniASR Modal baseline (part 2)
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Running Modal ASR Baselines

BES-4805 evaluates NVIDIA Parakeet TDT 0.6B v3 as the batch baseline modal_nvidia_parakeet_tdt_0_6b_v3. The checked-in deployment source is modal/parakeet_tdt_0_6b_v3.py; do not use owner-private deployment code or the old non-ticket model ID.

Deployment Constants

  • Benchmark model ID: modal_nvidia_parakeet_tdt_0_6b_v3
  • Upstream model: nvidia/parakeet-tdt-0.6b-v3
  • Upstream revision: b51b7dc0fbf7f266a97880fb4b626c56d28f4b96
  • Modal app: voice-code-bench-parakeet
  • Modal GPU: L40S
  • Modal Volume: voice-code-bench-parakeet-hf-cache
  • Runtime: Transformers 5.12.0
  • Endpoint secret: MODAL_PARAKEET_ENDPOINT

The app stores official Hugging Face weights in the Modal Volume at /models and loads them once per warm container with @modal.enter. The benchmark sends one complete 16 kHz mono WAV per request. There is no chunking, no prompts, no custom vocabulary, and no post-ASR correction.

The Meta OmniASR section below covers BES-4806. Inworld STT is not part of these Modal deployment paths.

Modal Credentials

Install and configure the Modal CLI/API credentials for the workspace that will own the deployment:

python -m pip install modal==1.4.1
modal setup

Create a Modal proxy token for the environment that will serve the Web Function:

modal workspace proxy-tokens create

If your workspace uses scoped proxy tokens, allow the token in the deployment environment. Store the endpoint and proxy token in the ignored scripts/.secret file:

MODAL_PARAKEET_ENDPOINT=https://<your-parakeet-web-function>.modal.run
MODAL_PROXY_TOKEN_ID=wk-...
MODAL_PROXY_TOKEN_SECRET=ws-...

The entity-verification stage of vcb-run also needs OPENAI_API_KEY=.... Never commit scripts/.secret.

Warm The Model Cache

Run the cache warm-up before deployment:

modal run modal/parakeet_tdt_0_6b_v3.py::download_model

Verify in Modal logs that the download used nvidia/parakeet-tdt-0.6b-v3 at b51b7dc0fbf7f266a97880fb4b626c56d28f4b96 and wrote under /models/nvidia/parakeet-tdt-0.6b-v3. The revision literal in modal/parakeet_tdt_0_6b_v3.py, the volume directory, and the Hugging Face revision should match before releasing results.

Deploy

Deploy the Web Function:

modal deploy modal/parakeet_tdt_0_6b_v3.py

Copy the authenticated Web Function URL printed by Modal, or find it in the Modal dashboard for voice-code-bench-parakeet, and save it as MODAL_PARAKEET_ENDPOINT. The benchmark adapter accepts the exact Web Function URL and normalizes trailing slashes.

The endpoint accepts JSON only:

{
  "audio_base64": "<base64-encoded 16 kHz mono WAV bytes>",
  "audio_format": "wav"
}

It also accepts optional "language": "en" for manual compatibility checks, but the registered benchmark configuration does not send a language hint. The response body is exactly:

{
  "transcript": "<verbatim model output>"
}

Requests must include the proxy-token headers:

Modal-Key: <MODAL_PROXY_TOKEN_ID>
Modal-Secret: <MODAL_PROXY_TOKEN_SECRET>

The benchmark stores only the returned transcript verbatim.

Longest-Recording Smoke Test

Create a one-row metadata file for the longest recording, audio_id=education_workplace_006 (data/audio/261.wav, 122.875 seconds):

mkdir -p runs/parakeet-longest-smoke
python - <<'PY'
import json
from pathlib import Path
source = Path('data/metadata.jsonl')
out = Path('runs/parakeet-longest-smoke/metadata.longest.jsonl')
for line in source.read_text().splitlines():
    row = json.loads(line)
    if row['audio_id'] == 'education_workplace_006':
        out.write_text(json.dumps(row, separators=(',', ':')) + '\n')
        break
else:
    raise SystemExit('education_workplace_006 not found')
PY

Run the smoke transcription without using --limit 1:

vcb-transcribe \
  --metadata runs/parakeet-longest-smoke/metadata.longest.jsonl \
  --stt-model-ids modal_nvidia_parakeet_tdt_0_6b_v3 \
  --output-dir runs/parakeet-longest-smoke

Inspect runs/parakeet-longest-smoke/modal_nvidia_parakeet_tdt_0_6b_v3/transcripts.json and confirm it has one non-empty model_transcript for education_workplace_006. Modal logs should show one complete request for the 122.875-second WAV and no client-side chunking.

Full Run

With MODAL_PARAKEET_ENDPOINT, MODAL_PROXY_TOKEN_ID, MODAL_PROXY_TOKEN_SECRET, and OPENAI_API_KEY configured, run:

vcb-run \
  --stt-model-ids modal_nvidia_parakeet_tdt_0_6b_v3 \
  --resume \
  --output-dir runs/parakeet-full

If interrupted, rerun the same command with --resume. After the run completes, validate that transcripts and entity matches cover all 300 audio IDs in data/metadata.jsonl order, then copy the generated entity-match artifact to baselines/predictions/modal_nvidia_parakeet_tdt_0_6b_v3.json without editing transcripts, entity decisions, or run metadata by hand.

Regenerate current aggregate outputs after the prediction artifact exists:

vcb-score-entities \
  --dataset-root . \
  --metadata data/metadata.jsonl \
  --entity-matches-dir baselines/predictions \
  --output-dir baselines \
  --scores-dir baselines/scores/entities \
  --output-csv baselines/results.csv

vcb-score-wer \
  --dataset-root . \
  --metadata data/metadata.jsonl \
  --transcripts-dir baselines/predictions \
  --output-dir baselines \
  --scores-dir baselines/scores/wer \
  --output-csv baselines/results.csv

vcb-make-figures --model-set all

./scripts/reproduce_release.sh remains the frozen paper reproduction path and must not require Modal credentials.

Meta OmniASR LLM Unlimited 7B v2

BES-4806 evaluates Meta OmniASR Unlimited 7B v2 as the batch model ID modal_meta_omniasr_llm_unlimited_7b_v2. The checked-in deployment source is modal/omniasr_llm_unlimited_7b_v2.py; do not use the standard 40-second OmniASR models, the public Replicate endpoint, private deployment code, or a client-side chunking wrapper.

OmniASR Deployment Constants

  • Benchmark model ID: modal_meta_omniasr_llm_unlimited_7b_v2
  • Upstream model card: omniASR_LLM_Unlimited_7B_v2
  • Runtime and catalog revision: omnilingual-asr==0.2.0
  • Modal app: voice-code-bench-omniasr
  • Modal GPU: L40S
  • Modal Volume: voice-code-bench-omniasr-fairseq2-cache
  • Fairseq2 asset cache: /root/.cache/fairseq2/assets
  • Language conditioning: eng_Latn
  • Endpoint secret: MODAL_OMNIASR_ENDPOINT

The app mounts the persistent fairseq2 asset cache Volume at /root/.cache/fairseq2/assets, warms the official assets before deployment, and constructs ASRInferencePipeline(model_card="omniASR_LLM_Unlimited_7B_v2") once per warm container with @modal.enter. Each benchmark request sends one complete 16 kHz mono WAV. There is no client-side chunking, server-side chunking, prompt, target entity list, custom vocabulary, canonicalization, or post-ASR correction.

Live OmniASR deployment and release are blocked until the runner has a usable Modal deployment credential, a deployed Web Function URL, MODAL_PROXY_TOKEN_ID, MODAL_PROXY_TOKEN_SECRET, and either a live entity-verifier credential or a complete verifier cache accepted by vcb-run. Mock output is never releasable.

OmniASR Credentials

Install and configure Modal for the workspace that will own the app:

python -m pip install modal==1.4.1
modal setup

Create a Modal proxy token for the deployment environment:

modal workspace proxy-tokens create

Store only private values in ignored scripts/.secret or equivalent environment variables:

MODAL_OMNIASR_ENDPOINT=https://<your-omniasr-web-function>.modal.run
MODAL_PROXY_TOKEN_ID=wk-...
MODAL_PROXY_TOKEN_SECRET=ws-...
OPENAI_API_KEY=...

Never commit scripts/.secret or proxy token values.

Warm The OmniASR Cache

Run cache warm-up before deployment:

modal run modal/omniasr_llm_unlimited_7b_v2.py::warm_model_cache

Verify Modal logs show omniASR_LLM_Unlimited_7B_v2, omnilingual-asr==0.2.0, and /root/.cache/fairseq2/assets. Rerun the warm-up if it is interrupted; a successful rerun should reuse the persistent Volume instead of downloading the full asset set again. If serving reports a missing asset cache, rerun only this warm-up path before redeploying.

Deploy OmniASR

Deploy the authenticated Web Function:

modal deploy modal/omniasr_llm_unlimited_7b_v2.py

Copy the Web Function URL printed by Modal, or find it in the dashboard for voice-code-bench-omniasr, and save it as MODAL_OMNIASR_ENDPOINT. The benchmark adapter accepts the URL and normalizes trailing slashes.

The endpoint accepts JSON only:

{
  "audio_base64": "<base64-encoded 16 kHz mono WAV bytes>",
  "audio_format": "wav",
  "language": "eng_Latn"
}

The response body is exactly:

{
  "transcript": "<verbatim model output>"
}

Requests must include the proxy-token headers:

Modal-Key: <MODAL_PROXY_TOKEN_ID>
Modal-Secret: <MODAL_PROXY_TOKEN_SECRET>

A request without those headers should be rejected before inference. Endpoint errors must not include proxy token values.

OmniASR Longest-Recording Smoke Test

Create a one-row metadata file for the longest recording, audio_id=education_workplace_006 (data/audio/261.wav, 122.875 seconds). Do not use --limit 1, because that selects the first metadata row rather than the longest recording.

mkdir -p runs/omniasr-longest-smoke
python - <<'PY'
import json
from pathlib import Path
source = Path('data/metadata.jsonl')
out = Path('runs/omniasr-longest-smoke/metadata.longest.jsonl')
for line in source.read_text().splitlines():
    row = json.loads(line)
    if row['audio_id'] == 'education_workplace_006':
        out.write_text(json.dumps(row, separators=(',', ':')) + '\n')
        break
else:
    raise SystemExit('education_workplace_006 not found')
PY

Run the smoke transcription:

vcb-transcribe \
  --metadata runs/omniasr-longest-smoke/metadata.longest.jsonl \
  --stt-model-ids modal_meta_omniasr_llm_unlimited_7b_v2 \
  --output-dir runs/omniasr-longest-smoke

Inspect runs/omniasr-longest-smoke/modal_meta_omniasr_llm_unlimited_7b_v2/transcripts.json and Modal logs. Confirm exactly one non-empty model_transcript for education_workplace_006, one complete 122.875-second request, language eng_Latn, batch size 1, and no client-side chunking. Send a second authenticated smoke request while the container is warm and confirm logs do not show another pipeline construction for the same warm container.

OmniASR Full Run And Release Artifact

After the longest-recording smoke test succeeds, run all 300 recordings with resume enabled from the first invocation:

vcb-run \
  --stt-model-ids modal_meta_omniasr_llm_unlimited_7b_v2 \
  --resume \
  --output-dir runs/omniasr-full

If interrupted, rerun the identical command with --resume. Do not regenerate completed rows. Do not score or release mock transcripts.

Before copying anything to baselines/predictions/, validate the final artifact against data/metadata.jsonl: exactly 300 rows, exactly 300 unique audio_id values, identical order, one non-empty model_transcript for every row, entity matches for every target entity, and run metadata containing provider modal, model omniASR_LLM_Unlimited_7B_v2, revision omnilingual-asr==0.2.0, language="eng_Latn", the real normalized endpoint, evaluation date, mode batch, L40S, batch size 1, timeout 600 seconds, and 10 max attempts.

Only after that validation passes, copy the generated entity-match artifact to:

baselines/predictions/modal_meta_omniasr_llm_unlimited_7b_v2.json

Do not hand-edit transcripts, entity decisions, or run metadata.

Regenerate current aggregate outputs after the real prediction artifact exists:

vcb-score-entities \
  --dataset-root . \
  --metadata data/metadata.jsonl \
  --entity-matches-dir baselines/predictions \
  --output-dir baselines \
  --scores-dir baselines/scores/entities \
  --output-csv baselines/results.csv

vcb-score-wer \
  --dataset-root . \
  --metadata data/metadata.jsonl \
  --transcripts-dir baselines/predictions \
  --output-dir baselines \
  --scores-dir baselines/scores/wer \
  --output-csv baselines/results.csv

vcb-make-figures --model-set all

Update README counts, ranges, and exact OmniASR WER/CTEM/TSR only from the computed baselines/results.csv row. ./scripts/reproduce_release.sh remains the frozen paper reproduction path and must not require Modal credentials or add OmniASR to PAPER_MODEL_IDS.

OmniASR Cleanup

Stop the app when no more requests are expected:

modal app stop voice-code-bench-omniasr

Retain voice-code-bench-omniasr-fairseq2-cache while the released baseline may need reruns. Delete the Volume only when you intentionally want to remove the cached official assets and are ready to rerun warm_model_cache.

Teardown

After release, stop the app if no more requests are expected:

modal app stop voice-code-bench-parakeet

Retain the voice-code-bench-parakeet-hf-cache volume while the released baseline may need reruns. Delete the volume only when you intentionally want to remove the cached official weights and are ready to rerun the warm-up step.