initialize dataset

.gitattributes

@@ -52,3 +52,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text

cocoon-gloss.py

@@ -0,0 +1,190 @@
+# 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.
+"""Cocoon datasets that contrain interlinear gloss"""
+
+import os
+import json
+
+import datasets
+
+
+_ALL_LANGS = [
+    "tvk",
+    "kke",
+    "ers",
+    "sxg",
+    "svm",
+    "kkt",
+    "nee",
+    "bej",
+    "say",
+    "ady",
+    "kab",
+    "ixc",
+    "rmn",
+    "swb",
+    "nxq",
+    "nru",
+]
+_ALL_CONFIGS = [*_ALL_LANGS, "all"]
+
+_DESCRIPTION = ""
+_CITATION = ""
+_HOMEPAGE_URL = ""
+
+_BASE_PATH = "data/{langs}/"
+_DATA_URL = _BASE_PATH + "audio/{split}.tar.gz"
+_META_URL = _BASE_PATH + "{split}.json"
+
+
+class CocoonConfig(datasets.BuilderConfig):
+    def __init__(self, name, **kwargs):
+        super().__init__(name=name, version=datasets.Version("0.0.0", ""), **kwargs)
+
+
+class Cocoon(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+    BUILDER_CONFIGS = [CocoonConfig(name) for name in _ALL_CONFIGS]
+
+    def _info(self):
+        task_templates = None
+        langs = _ALL_CONFIGS
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "audio": datasets.features.Audio(sampling_rate=16_000),
+                "transcription": datasets.Value("string"),
+                "language": datasets.Value("string"),
+                "speaker": datasets.Value("string"),
+                "surface": datasets.Value("string"),
+                "underlying": datasets.Value("string"),
+                "gloss": datasets.Value("string"),
+                "translation": datasets.Value("string"),
+                "url": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("audio", "transcription"),
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+            task_templates=None,
+        )
+
+    # Fleurs
+    def _split_generators(self, dl_manager):
+        splits = ["train", "dev", "test"]
+
+        # metadata_path = dl_manager.download_and_extract(_METADATA_URL)
+
+        if self.config.name == "all":
+            data_urls = {
+                split: [
+                    _DATA_URL.format(langs=langs, split=split) for langs in _ALL_LANGS
+                ]
+                for split in splits
+            }
+            meta_urls = {
+                split: [
+                    _META_URL.format(langs=langs, split=split) for langs in _ALL_LANGS
+                ]
+                for split in splits
+            }
+        else:
+            data_urls = {
+                split: [_DATA_URL.format(langs=self.config.name, split=split)]
+                for split in splits
+            }
+            meta_urls = {
+                split: [_META_URL.format(langs=self.config.name, 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
+
+        if self.config.name == "all":
+            langs = _ALL_LANGS
+        else:
+            langs = [self.config.name]
+
+        for archive, text_path, local_extracted_path, lang in zip(
+            archive_iters, text_paths, local_extracted_archives, langs
+        ):
+            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["language"] = lang
+                result["audio"] = {"path": audio_path, "bytes": audio_file.read()}
+                yield key, result
+                key += 1

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