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id_msvd.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
+                                      Licenses, Tasks)
+
+_CITATION = """\
+@article{hendria2023msvd,
+    author    = {Willy Fitra Hendria},
+    title     = {MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian},
+    journal   = {arXiv preprint arXiv:2306.11341},
+    year      = {2023},
+    url       = {https://arxiv.org/abs/2306.11341},
+}
+"""
+
+_DATASETNAME = "id_msvd"
+_DESCRIPTION = """\
+MSVD-Indonesian is derived from the MSVD (Microsoft Video Description) dataset, which is
+obtained with the help of a machine translation service (Google Translate API). This
+dataset can be used for multimodal video-text tasks, including text-to-video retrieval,
+video-to-text retrieval, and video captioning. Same as the original English dataset, the
+MSVD-Indonesian dataset contains about 80k video-text pairs.
+"""
+
+_HOMEPAGE = "https://github.com/willyfh/msvd-indonesian"
+_LANGUAGES = ["ind"]
+_LICENSE = Licenses.MIT.value
+_URLS = {"text": "https://raw.githubusercontent.com/willyfh/msvd-indonesian/main/data/MSVD-indonesian.txt", "video": "https://www.cs.utexas.edu/users/ml/clamp/videoDescription/YouTubeClips.tar"}
+
+_SUPPORTED_TASKS = [Tasks.VIDEO_TO_TEXT_RETRIEVAL]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_LOCAL = False
+
+
+class IdMsvdDataset(datasets.GeneratorBasedBuilder):
+    """MSVD dataset with Indonesian translation."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()  # "vidtext"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "video_path": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = SCHEMA_TO_FEATURES[self.SEACROWD_SCHEMA_NAME.upper()]  # video_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        # expect several minutes to download video data ~1.7GB
+        data_path = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "text_path": Path(data_path["text"]),
+                    "video_path": Path(data_path["video"]) / "YouTubeClips",
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, text_path: Path, video_path: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        text_data = []
+        with open(text_path, "r", encoding="utf-8") as f:
+            for line in f:
+                id = line.find(" ")
+                video = line[:id]
+                text = line[id + 1 :].strip()
+                text_data.append([video, text])
+
+        df = pd.DataFrame(text_data, columns=["video_path", "text"])
+        df["video_path"] = df["video_path"].apply(lambda x: video_path / f"{x}.avi")
+
+        if self.config.schema == "source":
+            for i, row in df.iterrows():
+                yield i, {
+                    "video_path": str(row["video_path"]),
+                    "text": row["text"],
+                }
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for i, row in df.iterrows():
+                yield i, {
+                    "id": str(i),
+                    "video_path": str(row["video_path"]),
+                    "text": row["text"],
+                    "metadata": {
+                        "resolution": {
+                            "width": None,
+                            "height": None,
+                        },
+                        "duration": None,
+                        "fps": None,
+                    },
+                }