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NINJAL-Ainu-Folklore.py

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-# coding=utf-8
-# Copyright 2023 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""NINJAL Ainu folklore corpus"""
-
-import os
-import json
-
-import datasets
-
-
-_DESCRIPTION = ""
-_CITATION = ""
-_HOMEPAGE_URL = ""
-
-_BASE_PATH = "data/"
-_DATA_URL = _BASE_PATH + "audio/{split}.tar.gz"
-_META_URL = _BASE_PATH + "{split}.json"
-
-
-class AinuFolkloreConfig(datasets.BuilderConfig):
-    def __init__(self, name, **kwargs):
-        super().__init__(name=name, version=datasets.Version("0.0.0", ""), **kwargs)
-
-
-class AinuFolklore(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [AinuFolkloreConfig("all")]
-
-    def _info(self):
-        task_templates = None
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "audio": datasets.features.Audio(sampling_rate=16_000),
-                "transcription": datasets.Value("string"),
-                "speaker": datasets.Value("string"),
-                "surface": datasets.Value("string"),
-                "underlying": datasets.Value("string"),
-                "gloss": datasets.Value("string"),
-                "translation": datasets.Value("string"),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=("audio", "transcription"),
-            homepage=_HOMEPAGE_URL,
-            citation=_CITATION,
-            task_templates=task_templates,
-        )
-
-    def _split_generators(self, dl_manager):
-        splits = ["train", "dev", "test"]
-
-        data_urls = {split: [_DATA_URL.format(split=split)] for split in splits}
-        meta_urls = {split: [_META_URL.format(split=split)] for split in splits}
-
-        archive_paths = dl_manager.download(data_urls)
-        local_extracted_archives = (
-            dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
-        )
-        archive_iters = {
-            split: [dl_manager.iter_archive(path) for path in paths]
-            for split, paths in archive_paths.items()
-        }
-
-        meta_paths = dl_manager.download(meta_urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "local_extracted_archives": local_extracted_archives.get(
-                        "train", [None] * len(meta_paths.get("train"))
-                    ),
-                    "archive_iters": archive_iters.get("train"),
-                    "text_paths": meta_paths.get("train"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "local_extracted_archives": local_extracted_archives.get(
-                        "dev", [None] * len(meta_paths.get("dev"))
-                    ),
-                    "archive_iters": archive_iters.get("dev"),
-                    "text_paths": meta_paths.get("dev"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "local_extracted_archives": local_extracted_archives.get(
-                        "test", [None] * len(meta_paths.get("test"))
-                    ),
-                    "archive_iters": archive_iters.get("test"),
-                    "text_paths": meta_paths.get("test"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, local_extracted_archives, archive_iters, text_paths):
-        assert len(local_extracted_archives) == len(archive_iters) == len(text_paths)
-        key = 0
-
-        for archive, text_path, local_extracted_path in zip(
-            archive_iters, text_paths, local_extracted_archives
-        ):
-            with open(text_path, encoding="utf-8") as fin:
-                data = json.load(fin)
-
-            for audio_path, audio_file in archive:
-                audio_filename = audio_path.split("/")[-1]
-                if audio_filename not in data:
-                    continue
-
-                result = data[audio_filename]
-                extracted_audio_path = (
-                    os.path.join(local_extracted_path, audio_filename)
-                    if local_extracted_path is not None
-                    else None
-                )
-                result["audio"] = {"path": audio_path, "bytes": audio_file.read()}
-                yield key, result
-                key += 1

README.md

@@ -1,34 +0,0 @@
----
-license: cc-by-sa-4.0
----
-
-# Dataset Card for NINJAL Ainu Folklore
-
-## Dataset Description
-
-- **Original source** [A Glossed Audio Corpus of Ainu folklore](https://ainu.ninjal.ac.jp/folklore/en/)
-
-### Dataset Summary
-
-
-### Annotations
-
-
-## Additional Information
-
-### Limitations
-
-### License
-
-
-### Original Source
-
-```
-@misc{ninjal-ainu-folklore,
-  title={A Glossed Audio Corpus of Ainu Folklore},
-  url={https://ainu.ninjal.ac.jp/folklore/},
-  author={Nakagawa, Hiroshi and Bugaeva, Anna and Kobayashi, Miki and Yoshikawa, Yoshimi},
-  publisher={The National Institute for Japanese Language and Linguistics ({NINJAL})},
-  date={2016--2021}
-} 
-```

data/test.json → all/ninjal-ainu-folklore-test.parquet

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