VoiceStudio/tests/probe

probe — spec-driven test harness

probe is a portable test harness for OmniVoice Studio (and, by design, other projects/APIs). It is mostly deterministic with three narrowly-scoped agent roles, not an "AI does everything" system — that distinction is the whole point.

The one principle: separate the Actor from the Judge

ACTOR  (AI agent / HTTP call / browser)  ──drives──▶  app under test
                                                          │ produces artifacts
                                                          ▼
JUDGE  (deterministic code + metrics)    ◀──renders the verdict

• The Actor may be flexible, self-healing, non-deterministic.
• The Judge is deterministic code + objective metrics only. No LLM ever sits on the verdict path — except a clearly-labelled, non-blocking advisory lane. Letting an agent both act and judge produces false passes (green tests on broken software), which is worse than no test.

Feature coverage (specs)

Beyond the layer skeleton, the suite covers these features — one *.probe.yaml spec each, run against the real app where possible (a single subprocess boot is shared across the backend-touching specs):

Spec	Layer	Verifies
first_run	env	fresh-dir boot: health + DB init + endpoints
migration	env	alembic UPGRADE on existing omnivoice_data fixture (backward-compat)
engines	engine	TTS/ASR registry: active engine available, every unavailable engine explains why (11 TTS / 7 ASR backends)
security	security	system routes reject non-loopback origins (403)
dictation	dictation	streaming-ASR WebSocket /ws/transcribe registered + accepts loopback handshake
tts_smoke / voice_clone / voice_design	media	audio-correctness ladder (decode/duration/not-silent/clipping/WER) + speaker-sim (clone)
dub_export	dubbing	segment duration-ratio, SRT/VTT well-formed, export-archive contents, output language-ID (advisory)
i18n_parity	i18n	locale files valid JSON (gate); orphan-keys + coverage (advisory)
desktop_smoke	desktop	Tauri config integrity (version parity, dev/build wiring, bundled bins, CSP)
launchpad	web	UI render via Playwright Driver (FakePage offline)
coverage_critic	meta	every declared layer still has a spec (drift gate) + API-surface inventory (advisory)

The i18n orphan-key check surfaced a real bug: all 20 non-en locales carry gallery.cat_* (and some bootstrap.lines) keys absent from the en reference. It's reported in the advisory lane (non-blocking) rather than gating, since the fix is a product change.

Layers

Layer	Module	Status	What it does
L1 API fuzz	api_fuzz.py / test_api_fuzz.py	✅ wired, enable-on-demand	Schemathesis property-fuzzes the FastAPI app in-process over ASGI for 500s / schema violations.
L2 Web UI	web.py · judges/web.py · test_probe_web.py	✅ built (live = enable-on-demand)	Playwright Driver + deterministic self-heal + judges. Self-heal logic + judges are unit-tested offline against a FakePage; the live browser skips without Playwright/frontend.
L3 Desktop	desktop.py · judges/desktop.py · test_probe_desktop.py	✅ built + tested	Tauri config-integrity (version parity vs pyproject, dev/build wiring, bundled uv/ffmpeg binaries, CSP permits the local backend — a desktop-only failure mode) against the real tauri.conf.json incl. platform-override merge. Plus a guarded live bundle launch (skips without a built bundle/display). Tauri macOS has no official WebDriver — backend-over-HTTP (L5) + browser (L2) substitute for E2E, per the architecture decision.
L4 Media	judges/	✅ built + tested	Audio correctness verification: exists/decodes/duration/not-silent/not-clipping/no-NaN, round-trip ASR WER, speaker similarity.
L5 Env / first-run	env.py · _boot_runner.py · test_probe_env.py	✅ built + tested	Fresh-data-dir backend boot in a subprocess (no session contamination); asserts health, DB init, endpoint reachability. Docker boot gated behind a daemon check.
Triager	triage.py · test_triage.py	✅ built + tested	Clusters/dedupes blocking failures, sanitizes (home paths + tokens), and drafts a prefilled GitHub issue URL (no auto-submit, no credential). The HTML report shows a one-click "Draft GitHub issue" button when a run fails.

The hybrid spec format

Simple tests are declarative YAML (specs/*.probe.yaml); anything the schema can't express drops to a plain pytest function calling the same judge functions (the escape hatch). See specs/tts_smoke.probe.yaml. A spec separates:

• steps — what the Actor does (executed per-layer; captures artifacts into the run context as $.name).
• judge — deterministic blocking verdicts. A failure fails the test.
• advisory — non-blocking metrics (naturalness predictors, trends). Never gate; reported only.

from tests.probe import load_spec, run_judges, blocking_failures   # (relative within the probe pkg)
spec = load_spec("specs/tts_smoke.probe.yaml")
results = run_judges(spec, context={"audio": out_path},
                     backends={"transcriber": FasterWhisperTranscriber()})
assert not blocking_failures(results)

Running

uv run pytest tests/probe -q          # judges + spec engine (offline, no models)

The harness's own tests use synthetic audio + a FakeTranscriber, so they run in milliseconds with no GPU and no model downloads. Real verification injects the live backends (FasterWhisperTranscriber, a Resemblyzer/ECAPA embedder).

HTML report

Every probe session writes a self-contained HTML report (inline CSS+JS, no external assets) to tests/probe/reports/ and opens it in the browser:

tests/probe/reports/report-YYYYMMDD-HHMMSS.html   # this run
tests/probe/reports/report-latest.html            # stable pointer to the newest

The report shows the verdict (blocking failures only), summary cards (passed / failed / skipped / advisory), per-spec tables with status badges and measured values, filter buttons, and the honest-ceiling note. The advisory lane and SKIPs are visually separated and never affect the verdict.

Auto-open is suppressed automatically in CI, on headless Linux (no DISPLAY), or when PROBE_NO_OPEN=1. Override the output location with PROBE_REPORT_DIR.

Tests feed the report via the session-scoped probe_report fixture:

def test_something(probe_report):
    results = run_judges(spec, context={...}, backends={...})
    probe_report.record(spec, results)        # → a row group in the report

Render programmatically without pytest:

from tests.probe.report import Report, SpecOutcome, save_and_open
save_and_open(Report(outcomes=[SpecOutcome.from_spec(spec, results)]))

Enabling the heavier layers

uv add schemathesis                          # L1 API fuzzing (skips until then)
uv add resemblyzer                           # L4 speaker-similarity (skips until then)
uv add playwright && uv run playwright install chromium   # L2 live browser
uv add anthropic        # L2 agentic self-heal (LLMHealer); set ANTHROPIC_API_KEY
# faster-whisper + whisperx are already in the base venv (round-trip ASR works now)
# L5 Docker boot activates automatically when a Docker daemon is reachable.

What runs offline vs. enable-on-demand

Runs now (base venv, no models/GPU)	Skips until enabled
L4 judges + spec engine + report	L1 fuzz (needs schemathesis)
L5 first-run boot (subprocess, model short-circuited)	L4 speaker-sim (needs resemblyzer)
L2 self-heal logic + judges (FakePage)	L2 live browser (needs Playwright + bun run dev)
	L5 Docker boot (needs a daemon)

L2's agentic self-heal escalates: primary selector → deterministic fallback candidates (id→test-id→text, loosened CSS) → pluggable Healer. The default is NoopHealer (deterministic). For genuine agentic self-heal, pass launch(healer=anthropic_healer()) — LLMHealer asks a model to propose a selector from the live page HTML. It's provider-agnostic (LLMHealer(complete_fn)) and unit-tested offline with an injected completion. The verdict always comes from the deterministic judges, never the Driver or the model.

The honest ceiling — read this before trusting a green dashboard

probe verifies that output is correct and not broken. It does not verify that output is good.

• ✅ Trustworthy autonomously (~70–80% of features): crashes, 500s, schema breaks, silent/gibberish/wrong-language audio, truncation, duration drift, broken installs, locator drift.
• ❌ Human-judgment-only (~10–15%): naturalness, prosody, emotional appropriateness, accent authenticity, "sounds like a convincing me", "the UI feels right". The metrics that claim to score these (MOS predictors like UTMOS/NISQA/SQUIM) fail out-of-domain, and most of 646 languages is out-of-domain — so they live in the advisory lane and never gate.

Design rules that enforce this, baked into the code:

• No golden-WAV fixtures. PyTorch is non-reproducible CPU-vs-GPU even with fixed seeds; a byte compare would manufacture platform-only regressions (a P0 violation of the cross-platform-parity rule). Gate on metrics, not waveforms.
• WER gates at ~0.10–0.15, never 0 — it measures your TTS plus the ASR's own errors. A rising WER on a fixed sentence beats any absolute number.
• Speaker similarity is a relative gate — calibrate per-engine, alert on drops; the default English-biased encoder is unreliable for other languages.
• NISQA's weights are CC-BY-NC-SA (non-commercial) — do not bundle it in a shipped build; prefer UTMOS / TorchAudio-SQUIM / TTSDS2.

A harness that hides what it can't verify is worse than nothing. probe reports skips and advisories explicitly so a green run never overstates its confidence.