README.md

metadata

title: SignBridge
emoji: 🤟
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false
thumbnail: assets/cover.png
license: mit
short_description: Real-time ASL → English speech on AMD MI300X.

SignBridge — real-time ASL → speech

Two people who couldn't communicate, now can.

A deaf person signs into the webcam. SignBridge — a multi-stage vision + reasoning + voice pipeline running on a single AMD Instinct MI300X — translates the signs into spoken English in under 2 seconds.

Submission for the AMD Developer Hackathon (LabLab.ai, May 2026) — Track 3: Vision & Multimodal AI.

How it works

webcam frames  →  MediaPipe Holistic   →  trained sign classifier
   (1–5 fps)        (543-dim pose)        (WLASL Top-100 + alphabet)
                                                  │
                                                  ▼
                                      Llama-3.1-8B sentence composer
                                                  │
                                                  ▼
                                            Coqui XTTS-v2  →  speech

All four stages run concurrently on a single AMD Instinct MI300X via AMD Developer Cloud. Total weights ~22 GB on a 192 GB GPU — fits with margin for KV cache + serving overhead.

V1 use cases

1. ASL fingerspelling alphabet — sign A–Z and 0–9 → AI speaks the letters / numbers
2. Top-50 WLASL signs (hello, thank you, name, please, sorry, family, eat, drink, work, …) → AI composes grammatical English sentences

V1 is one-way: deaf signs → hearing hears. Reverse direction (speech → on-screen text) is V2.

Why AMD

The MI300X's 192 GB HBM3 fits the entire pipeline (Qwen3-VL-8B + Llama-3.1-8B + XTTS-v2) on one GPU with margin. NVIDIA H100 (80 GB) requires sharding, and the V2 plan to upgrade to a 70B reasoner is impossible on H100 without a 3-GPU cluster. Single-GPU concurrency + 5.3 TB/s memory bandwidth is the actual AMD pitch — practical accessibility tools running globally need the cost-and-availability profile that AMD enables.

Why this matters (business case)

Sign-language interpreters cost $50–200 per hour and are scarce. Courts, hospitals, schools, and public services must by law provide interpretation (ADA Title II/III in the US, EAA 2025 in the EU). Sorenson VRS — the dominant relay-services provider — books $4B+ in annual revenue in this space. SignBridge is the open-source backbone that any country, NGO, or enterprise can deploy on their own AMD compute.

Privacy

Session-only. Frames and audio are processed in-memory and not persisted server-side beyond the WebSocket / HTTP session.

For Deaf-led teams

SignBridge is open-source under MIT license and intentionally scoped to ASL-only V1. The pipeline is a substrate, not a finished product — Deaf-led organisations (schools-for-the-Deaf, NGOs, ministries) are the intended deployers. Other sign languages (BSL, MSL, CSL, ISL, +200 more) deserve their own teams, training data, and Deaf community leadership. See docs/walkthrough.md → "Deployment ethics" for the design principles drawn from the Deaf-led academic literature.

Local dev

# Setup
pip install -r requirements.txt
cp .env.example .env   # fill in HF_TOKEN, AMD_DEV_CLOUD_*, OPENAI_API_KEY (fallback)

# Run the Gradio app
python app.py

# Run the inference backend (point at AMD Dev Cloud or local ROCm)
python -m signbridge.backend

# Train the classifier on WLASL Top-100 (Day 2 task — run on AMD Dev Cloud)
python -m signbridge.scripts.train_classifier --dataset data/wlasl --epochs 30

Datasets used

• WLASL — Word-Level American Sign Language; we use the Top-100 subset
• ASL fingerspelling alphabet (open dataset)

Models pulled from Hugging Face Hub

• meta-llama/Llama-3.1-8B-Instruct — sentence composer
• coqui/XTTS-v2 — text-to-speech
• (V2 stretch) openai/whisper-large-v3 — for the reverse direction

License

MIT. See LICENSE.

Status

Active development — see CLAUDE.md for the working state and docs/walkthrough.md for the technical writeup.