docs/pitch-deck.md

SignBridge — Pitch Deck (8 slides)

Open a Google Slides deck (or Pitch). Paste each slide's content into the matching blank slide. Visuals are described in italics — replace with actual screenshots / diagrams / table renders. Aspect ratio: 16:9. Theme: indigo→pink gradient (matches HF Space card).

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Slide 1 — Title

Title (huge): SignBridge

Subtitle: Real-time ASL → English speech, on a single AMD Instinct MI300X.

Footer (small): Track 3 · Vision & Multimodal AI · AMD Developer Hackathon 2026 · Lucas Loo Tan Yu Heng

Visual: the cover.png we already shipped (1280×640 indigo→pink gradient with 🤟 + project name).

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Slide 2 — The problem

Headline: 70 million deaf people. Sign-language interpreters cost $50–200 per hour. They're scarce.

Body bullets:

• Courts, hospitals, schools, public services must by law provide interpretation (ADA Title II/III in the US; European Accessibility Act 2025 in the EU).
• Sorenson VRS, the dominant sign-language relay-services provider, books $4B+ in annual revenue filling this gap — proof the demand is enormous and budgeted-for.
• Existing AI alternatives (Be My Eyes, Microsoft Seeing AI) are turn-based, photo-only, English-default, and closed-source. Real ASL is motion — they fundamentally can't translate "HELLO" or "THANK YOU".

Visual: a row of three context icons — courthouse / hospital / classroom — labeled with the mandates.

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Slide 3 — The solution

Headline: Hold to record. Sign. Speak.

Body (3-step arc):

1. Hold-to-record button captures 1.5 seconds of your sign.
2. A multi-stage pipeline (vision → reasoning → speech) translates it.
3. The other person hears natural English.

Tag line under the arc: Two people who couldn't communicate, now can.

Visual: 3 screenshots of the live Gradio Space — (a) user signing into webcam; (b) "detected: HELLO (85%)"; (c) audio waveform playing "Hello.". If single screenshot: just the Gradio "Record sign" tab mid-demo.

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Slide 4 — Architecture (the AMD pitch)

Headline: We fine-tuned Qwen3-VL-8B on a single MI300X — 54 minutes, 92% accuracy.

Diagram (build in Slides; described as bullets):

[ Webcam frame ]
       │
       ├─►  MediaPipe Hand → trained MLP classifier
       │      (90% on ASL fingerspelling, 50ms CPU)
       │      └─ falls through to ↓ when no hand detected
       │
       └─►  Fine-tuned Qwen3-VL-8B (LoRA on MI300X)
              ── webcam clip → ffmpeg → vLLM video_url block
              ── Qwen3-VL native temporal encoder (no manual frame sampling)
                                       │
                                       ▼
              [ Qwen3-8B composer ── sign tokens → English ]
                                       │
                                       ▼
              [ gTTS ── free, fast speech synthesis ]
                                       │
                                       ▼
                              [ Audio out ]

Comparison table (small print under diagram):

Component	Weights (FP16)	MI300X 1× (192 GB)	H100 80 GB
Fine-tuned Qwen3-VL-8B	~16 GB	✅ fits	✅
Qwen3-8B composer	~16 GB	✅ fits	✅
Whisper (V2 stretch)	~3 GB	✅ fits	⚠ tight
(V2) Llama-3.1-70B FP8 reasoner	~70 GB	✅ still fits	❌ doesn't fit at all

(gTTS runs as a small Python call from the Space; no GPU memory.)

The MI300X did three jobs in this project: (1) ran the LoRA fine-tune in 54 min, (2) hosts the merged 8B model for inference, (3) hosts the 8B composer in parallel — all on one GPU. That's the AMD pitch.

Visual: the diagram + table as a single composite slide. Use a brand colour for the AMD column to highlight.

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Slide 5 — Live demo

Headline: (blank — this slide is the live demo)

Speaker note: Switch to the live HF Space at huggingface.co/spaces/lablab-ai-amd-developer-hackathon/signbridge. 30 seconds:

1. Snapshot tab — fingerspell L-U-C-A-S → click Speak → AI says "Lucas."
2. Record sign tab — record HELLO → click Submit → "hello" detected → click Speak → AI says "Hello."

If demo fails / network down → fall back to the pre-recorded 2-min video on slide 6.

Visual: leave the slide blank or use a single QR code linking to the Space URL for the audience to scan and try themselves.

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Slide 6 — Demo video (fallback)

Headline: (blank — this slide embeds the demo video)

Embed: The 2–3 minute demo video, looping, autoplay-on-slide-show.

Visual: video player.

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Slide 6.5 — Qwen3-VL is the brain

Headline: LoRA-fine-tuned Qwen3-VL-8B — the visual intelligence behind every sign.

Body bullets:

• The recognizer is our LoRA-fine-tuned Qwen3-VL-8B (huggingface.co/LucasLooTan/signbridge-qwen3vl-8b-asl), trained in 54 minutes on a single AMD Instinct MI300X. Lifts ASL accuracy from 19% zero-shot → 92%.
• For motion signs (HELLO, THANK_YOU, PLEASE, EAT) we send the whole recorded clip natively to Qwen3-VL via vLLM's video_url content block — Qwen3-VL's own temporal encoder handles the motion. No manual frame sampling.
• Closed-vocabulary forcing + domain priming keep Qwen on-rails for the 87-token sign vocab.
• Qwen3-8B then composes Qwen-VL's tokens into grammatical English (also on the MI300X via vLLM, separate port); gTTS synthesises the spoken sentence.

Closer: Qwen3-VL is the only thing in the pipeline making the visual judgement. The rest is plumbing.

Visual: a single screenshot of signbridge/recognizer/vlm.py showing the video_url Qwen call, alongside an arrow into a "detected: HELLO (85%)" overlay.

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Slide 7 — Why this is the right submission for Track 3

Headline: Four judging criteria, four deliberate choices.

Two-column layout:

Judging criterion	Our choice
Application of Technology	Multi-modal pipeline (vision + reasoning + voice) running concurrently on a single MI300X — exactly what Track 3's "massive memory bandwidth of AMD GPUs" was for.
Presentation	Demo is experienced: judge holds phone, signs HELLO, hears "Hello." 30 seconds, no explanation needed.
Business Value	$4B+ existing market (Sorenson VRS comparable), legally-mandated interpretation budgets, open-source so any Deaf-led NGO / ministry / school can self-host on their own AMD compute.
Originality	Streaming continuous multi-frame VLM agent for sign language — no peer-reviewed benchmark exists for this approach yet (we checked the literature). Real ASL motion-words, not just fingerspelling.

Visual: 2×2 grid of icons, one per criterion.

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Slide 8 — Substrate, not product · Open · Deaf-led future

Headline: SignBridge is a substrate. Deaf-led teams are the deployers.

Body:

• MIT-licensed, code at github.com/seekerPrice/signbridge — anyone can self-host.
• ASL only V1 is a scope decision. BSL, MSL, CSL, ISL, +200 sign languages each deserve their own teams, training data, and Deaf community leadership. (Citing Bragg et al., "Systemic Biases in Sign Language AI Research", arXiv 2403.02563.)
• Privacy by default — frames and audio are processed in-memory and not persisted server-side beyond the request lifetime.

Closing line (large): The hardest part of accessibility isn't building. It's deploying. AMD makes the deploying possible.

Visual: world map outline with sign-language regional dots; or just the SignBridge logo with the closing tagline.

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Speaker-note tips (read these before recording)

1. Lead with the human problem (Slide 2), not the architecture. Architecture is for criterion 1; emotion is what closes criteria 2–4.
2. Time the live demo — 30 seconds max. If it fails, switch to fallback video without comment.
3. Always say "AMD MI300X" by name at least 3 times in the talk track. Sponsors notice.
4. End on the substrate framing — pre-empts the "savior tech" critique that Deaf-AI judges look out for.

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