clerc
ÉPÉE v0.4 · OPEN ASL KEYPOINT DATASET · CC BY-NC-SA 4.0

You have the model.This is the missing half.

2,400 clips of ASL from 8 Deaf signers, free to download on Hugging Face. Keypoints, gloss annotations with temporal boundaries, and a frozen benchmark: +12 points on a signer the model has never seen, same architecture, same 24 signs. Half of it from fixing the hands, half from the new data.

2,400
ASL clips
×2.0 vs v0.3
8
Deaf signers
6 → 8
179.9 min
Signed duration
×2.5 vs v0.3
1,501
Unique glosses
×2.1 vs v0.3
128
Keypoints / frame
hands, body, face
Open
On Hugging Face
CC BY-NC-SA 4.0
CLERC · THE DATA LAYER FOR SIGN LANGUAGE AI huggingface.co/datasets/CLERC-DATA/epee
WHAT IT IS FOR · THREE USE CASES, ONE DATASET

Three products.One dataset away.

The models already exist, and you can have one running today. What no model can invent is the data underneath it. Each of these three is one model you already have, plus Épée.

+12 PTS ON A STRANGER
Small BiLSTM↗ + clercÉpée v0.4 =Sign to word
The model is the easy part: ask Claude or ChatGPT and you have one in a minute. What it cannot invent is thousands of real signs, from different Deaf bodies, labelled frame by frame. That is Épée. Try it: a small open model trained only on Épée reads you live, on Hugging Face.
LIVE ON HUGGING FACE CLERC-DATA/read-the-hands
WEBCAM · RUNS IN YOUR BROWSER
Read the Hands.
How many signs can it read from you in 60 seconds?
Try it on Hugging Face ↗
Open data + open model. Anyone can build this. The video never leaves your device.
WHAT IT READS ON A STRANGER
9 signs it reads at 70%+
overall, unseen signer69% → 81%
signs with 5+ examples205 → 438
signs with 10+ examples71 → 209
READ IT RIGHT. Listed: only signs that clear 70% recall on a signer the model never saw, with the precision to match. 24 signs is the frozen benchmark, not the corpus ceiling: depth makes a wider vocabulary trainable, it does not mean it is trained. One sign, one word is the first step of translation, not translation: sentences need grammar on top, which is what the parallel grid trains.
ALL FIGURES COMPUTED FROM THE v0.3 AND v0.4 FILES huggingface.co/datasets/CLERC-DATA/epee
THE EVOLUTION BEHIND IT · PROTOCOL FROZEN SINCE v0.2

One fix, twice the data.12 points on a stranger.

Same small BiLSTM, same 24 signs, same held-out Deaf signers, same seeds. Two things changed in the dataset, and we measured them one at a time.

ACCURACY ON A SIGNER OUTSIDE THE DATASET · BY # TRAINING SIGNERS

mean over signer combinations · 8 seeds · chance 4.2%
[ VIEW AS TABLE ]

ACCURACY · BY AMOUNT OF DATA

same unseen signer, growing pool
[ VIEW AS TABLE ]
69%→81%

On a signer never seen, v0.3 as published to v0.4. Second unseen signer: 58% → 69%.

+6.3 pts

The fix. The same v0.3 clips with the hands put back in their own slots: 69.4% → 75.7%.

+5.7 pts

The new data. Twice the clips, two more Deaf signers: 75.7% → 81.4%, close to the 82.5% it scores on signers it has seen.

READ IT HONESTLY. v0.3 shipped with the two hands swapped in 61.7% of its clips, so its published curve is shown as it was measured; v0.4 is checked clip by clip · 24-gloss vocabulary frozen from v0.2 · unseen means zero shared clips (prompts are shared by protocol) · at equal volume, eight signers reach 79.0% against 76.7% for six: inside the run-to-run noise, so this release does not yet separate more bodies from more clips · hands are stored by side of the body, not dominant hand, so a single signer whose dominant hand is on the other side transfers poorly alone (ALPHA: 14%). It is a data property.
SAME MODEL · SAME TEST · ONLY THE DATA CHANGED huggingface.co/datasets/CLERC-DATA/epee
FOUR RELEASES · TWENTY WEEKS · SINCE JUNE 2026

Doubled three times.Same protocol throughout.

Same repo, same format, same pseudonymized signers since v0.1. v0.4 is the newest; every version stays pinned under its git tag and Zenodo DOI. Pick a release.

TAGS: v0.1 · v0.2 · v0.3 · v0.4 (MAIN) DOI 10.5281/zenodo.20268565
QUESTIONS · BEFORE YOU DOWNLOAD

Asked often.Answered once.

What is in the CLERC Épée sign language dataset?
2,400 ASL clips from 8 Deaf signers: 179.9 minutes of signing, 128 motion keypoints per frame (hands, body, face, head), and ASL gloss annotations with temporal segmentation. A 302-phrase parallel grid lets you compare the same phrase across signers directly. The release ships extracted keypoints and annotations only. Video never ships, in the open release or under a commercial license.
How do I download the ASL dataset?
The Épée release is open on Hugging Face (CLERC-DATA/epee) under CC BY-NC-SA 4.0: plain .npy and .json files, loadable in a few lines of Python with huggingface_hub. Commercial licensing of the larger corpus: florian@clerc.io.
Why does multi-signer ASL training data matter?
The benchmark holds test signers entirely outside the training set: accuracy on a brand-new signer climbs from 42% with 1 training signer to 81% with 8, with the same model and hyperparameters. A model trained on one signer has learned one person, not a language, which is why data, not architecture, is the bottleneck for sign language AI.
What changed between Épée v0.3 and v0.4?
The corpus doubled again: 1,200 to 2,400 clips, 6 to 8 Deaf signers, 724 to 1,501 unique glosses. The hand slots are now consistent: v0.3 had the two hands swapped in 61.7% of its clips, v0.4 in none. On the frozen benchmark, accuracy on an unseen signer went from 69% (v0.3 as published) to 81%: 6.3 points from fixing the hands, 5.7 from the new data. Previous versions stay pinned under git tags and Zenodo DOIs.
Can I train a commercial model on this data?
The public release is non-commercial (CC BY-NC-SA 4.0): research, education and prototyping are covered. Training commercial models or foundation models requires a commercial license, which also unlocks a larger multi-signer corpus beyond this release, as keypoints and annotations: florian@clerc.io.
OPEN RELEASE · CC BY-NC-SA 4.0

Free to use.One line to load.

Keypoints and gloss annotations, numpy-ready. No raw video ships: signers stay pseudonymized, and every number on this page is reproducible from the download.

QUICK START · PYTHONmetadata.csv · keypoints/*.npy
from huggingface_hub import snapshot_download import numpy as np, pandas as pd root = snapshot_download(repo_id="CLERC-DATA/epee", repo_type="dataset") meta = pd.read_csv(f"{root}/metadata.csv") # five renderings of the same phrase, ready to compare phrase = meta[(meta.parallel == "yes") & (meta.phrase_id == 7)] clips = {r.signer_id: np.load(f"{root}/keypoints/{r.clip_id}.npy") for r in phrase.itertuples()} # each (n_frames, 128, 3)
Research and evaluation are free under CC BY-NC-SA 4.0, with attribution and the DOI. The larger CLERC corpus, growing weekly, is licensed commercially as keypoints and annotations, never as video: florian@clerc.io
CLERC · SIGN LANGUAGE DATA INFRASTRUCTURE CLERC.IO