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dataset.py

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+import os
+import numpy as np
+import datasets
+
+# Mô tả dataset
+_DESCRIPTION = """
+VOYA Vietnamese Sign Language (VSL) dataset.
+Dataset gồm các chuỗi keypoints đã trích xuất bằng MediaPipe cho nhận dạng ngôn ngữ ký hiệu.
+Mỗi sample có shape (60, 1605), lưu trong 'sequences', với nhãn tương ứng trong 'labels'.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/Kateht/VOYA_VSL"
+_LICENSE = "MIT"
+_CITATION = """
+@misc{voya_vsl_2025,
+  author       = {Kateht et al.},
+  title        = {VOYA Vietnamese Sign Language Dataset},
+  year         = {2025},
+  publisher    = {Hugging Face},
+  howpublished = {\\url{https://huggingface.co/datasets/Kateht/VOYA_VSL}}
+}
+"""
+
+
+class VOYAVSLConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+class VOYAVSL(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        VOYAVSLConfig(
+            name="default",
+            version=datasets.Version("1.0.0"),
+            description="VOYA Vietnamese Sign Language dataset",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sequences": datasets.Array2D(
+                        shape=(60, 1605), dtype="float32"
+                    ),
+                    "labels": datasets.Value("int32"),
+                }
+            ),
+            supervised_keys=("sequences", "labels"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # Tải cả folder Merged thay vì 1 file
+        data_dir = dl_manager.download_and_extract(
+            "https://huggingface.co/datasets/Kateht/VOYA_VSL/resolve/main/Merged"
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,  # bạn sẽ tự chia train/val/test sau
+                gen_kwargs={"data_dir": data_dir},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir):
+        idx = 0
+        for fname in sorted(os.listdir(data_dir)):
+            if not fname.endswith(".npz"):
+                continue
+            fpath = os.path.join(data_dir, fname)
+            data = np.load(fpath)
+
+            sequences, labels = data["sequences"], data["labels"]
+            for seq, label in zip(sequences, labels):
+                yield idx, {
+                    "sequences": seq.astype("float32"),
+                    "labels": int(label),
+                }
+                idx += 1