| --- |
| title: WitnessBox |
| emoji: ⚖️ |
| colorFrom: yellow |
| colorTo: red |
| sdk: gradio |
| sdk_version: 5.50.0 |
| app_file: app.py |
| pinned: false |
| license: mit |
| tags: |
| - track:wood |
| - sponsor:modal |
| - sponsor:openbmb |
| - achievement:offbrand |
| - build-small-hackathon |
| - gradio |
| - minicpm |
| - voxcpm |
| - modal |
| - voice |
| - game |
| --- |
| |
| # ⚖️ WitnessBox — cross-examine a hostile AI witness with your *voice* |
|
|
| > Interrogate **Marcus Reid, CFO of Halcyon Dynamics**. He reads *how you deliver* |
| > — sound confident and he clams up; sound hesitant and he gets cocky and |
| > overshares. Surface **three contradictions** and his voice **cracks** as he breaks. |
| > |
| > **Track:** 🍄 An Adventure in Thousand Token Wood · **Targeting:** Best Use of Modal + Best MiniCPM Build |
|
|
| --- |
|
|
| ## Why it's different |
| Every other "interrogate a witness" build in this jam is text-and-logic. WitnessBox |
| is the only one where **your vocal delivery is the input**: a `librosa` pass reads |
| your *perceived* confidence (pauses + pace) and steers the witness in real time, |
| and the witness answers back in a **voice that escalates** from composed to |
| cracking. The moat is the audio loop, not the puzzle. |
|
|
| > **The delivery meter is *perceived delivery*, never a lie detector.** It reads |
| > how you sound (pauses, pace, pitch steadiness) — not whether anything is true. |
|
|
| ## How a turn works |
| ``` |
| you speak ─┬─► Whisper ASR ───────────────► your question |
| └─► librosa stance ─► CONFIDENT / NEUTRAL / HESITANT (steers the witness) |
| your question ─► deterministic Contradiction Engine ─► catch? (reproducible verdict) |
| persona + stance + tier + leak ─► MiniCPM4.1-8B ─► witness's line |
| state ─► VoxCPM2 (voice style = game state) ─► audio (cached voice-crack on the win) |
| ``` |
| Hesitant delivery makes Reid leak a thread toward an uncaught lie. Confident |
| delivery shuts him down. Catch all three (timeline · authorization · relationship) |
| and he breaks; whiff too many and the bench excuses him — you lose. |
|
|
| ## Models — all <32B, ~11B combined |
| | Role | Model | Size | |
| |---|---|---| |
| | Witness brain | `openbmb/MiniCPM4.1-8B` | 8.2B | |
| | Witness voice | `openbmb/VoxCPM2` (style tag = game state) | 2.3B | |
| | Player ASR | `openai/whisper-small` (deployed) — `nvidia/nemotron-…-0.6b` is a one-image-swap upgrade (NeMo-only) | 0.24B | |
| | Delivery stance | `librosa` (no model) | — | |
|
|
| ## ⚙️ Best Use of Modal |
| Modal is the **runtime** for all three GPU models and the beat pre-generator — |
| used as a *platform*, not just a host (the prize counts "inference… all"): |
|
|
| 1. **GPU inference behind `@app.cls`, scale-to-zero.** Three models on three |
| right-sized GPUs (A100 + 2×A10G); idle → `$0` via `scaledown_window`. |
| 2. **Opt-in keep-warm.** `min_containers` defaults to `0` — genuinely `$0` |
| between examinations — and flips to `1` (`WITNESSBOX_KEEP_WARM=1`) for a live |
| demo so turns don't eat a cold start. Scale-to-zero is the default; warmth is |
| a deliberate, costed choice, not an always-on bill. |
| 3. **Parallel `.map()`** pre-generates every scripted beat at deploy time, fanning |
| the **32 voice-crack takes across containers at once** and keeping the best. |
| 4. **Volume** persists the designed CFO reference voice + model cache + chosen beats. |
| 5. **Right-sized GPUs** — an A100 only for the 8B witness brain; the 2B voice and |
| the ASR ride cheaper A10Gs. |
|
|
| **Measured (warm, this deploy).** A live dynamic turn is `MiniCPM4.1-8B` **→ 5.3s** |
| for the witness's reply, then `VoxCPM2` **→ 8.6s** for ~4.5s of 48 kHz speech |
| (RTF ≈ 1.9) — the line lands as **text first**, the voice follows. The five |
| **scripted beats** (intro · opening · the voice-crack · win · lose) are pre-rendered |
| by the parallel `.map()` pass and served straight from the Volume, so every |
| *dramatic* moment plays **instantly** off the per-turn path. Idle containers → |
| `$0` via `scaledown_window`. (Container-seconds / $-per-match read live from the |
| Modal dashboard, not fabricated.) |
|
|
| ## 🧠 Best MiniCPM Build |
| The witness *is* a MiniCPM model. `openbmb/MiniCPM4.1-8B` runs the entire persona — |
| it reads the delivery stance, decides what Reid admits or hides, and leaks a thread |
| toward an uncaught lie when you sound unsure — and `openbmb/VoxCPM2` gives him the |
| voice that cracks on the break. The 8B brain is the **core of the experience, not a |
| bolt-on**: every line Reid speaks is MiniCPM under a stance- and tier-conditioned |
| system prompt, so the drama lives or dies on how well a small model holds a character |
| under pressure. |
|
|
| ## Run it |
| **Offline (no GPU, no Modal — boots anywhere):** |
| ```bash |
| pip install -r requirements.txt |
| python app.py # WITNESSBOX_BACKEND defaults to "mock"; type your questions |
| ``` |
| The full game loop — stance, the catch engine, state, win/lose, audio autoplay — |
| runs locally against a rule-based mock witness, so the end-to-end flow is provable |
| without a single GPU. |
|
|
| **Live (real models):** |
| ```bash |
| modal deploy modal_app.py # serves MiniCPM4.1-8B, VoxCPM2, Whisper ASR |
| modal run modal_app.py # pre-generate the scripted beats (.map) |
| WITNESSBOX_BACKEND=modal python app.py |
| ``` |
| On a Space, set `MODAL_TOKEN_ID` / `MODAL_TOKEN_SECRET` as secrets. Lookups are |
| lazy and fall back to mock if Modal is unreachable, so the Space always boots. |
|
|
| ## Integrity |
| Detection fires against three **planted** lies with concrete cues — reliable, not |
| "magical." The model never grades itself. Cost/latency numbers are measured. No |
| "only entry that…" claims about a moving field. |
|
|
