Delete loading script

pangloss.py

@@ -1,202 +0,0 @@
-# Copyright 2020 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.
-"""Pangloss datasets for Yongning Na (yong1288) and Japhug (japh1234)"""
-
-import csv
-import json
-import os
-import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-
-_CITATION = {
-    "yong1288": """
-@misc{michaud_alexis_2021_5336698,
-author       = {Michaud, Alexis and
-                Galliot, Benjamin and
-                Guillaume, Séverine},
-title        = {{Yongning Na for Natural Language Processing: a
-                single-speaker audio corpus with transcriptions}},
-month        = aug,
-year         = 2021,
-publisher    = {Zenodo},
-version      = {1.0},
-doi          = {10.5281/zenodo.5336698},
-url          = {https://doi.org/10.5281/zenodo.5336698}
-    }
-    """,
-    "japh1234": """\
-@misc{jacques_guillaume_2021_5521112,
-author       = {Jacques, Guillaume and
-                Galliot, Benjamin and
-                Guillaume, Séverine},
-title        = {{Japhug for Natural Language Processing: a single-
-                speaker audio corpus with transcriptions}},
-month        = sep,
-year         = 2021,
-publisher    = {Zenodo},
-version      = {1.0},
-doi          = {10.5281/zenodo.5521112},
-url          = {https://doi.org/10.5281/zenodo.5521112}
-    }
-"""
-}
-
-_DESCRIPTION = """\
-These datasets are extracts from the Pangloss collection and have
-been preprocessed for ASR experiments in Na and Japhug.
-"""
-
-_HOMEPAGE = "https://pangloss.cnrs.fr/"
-
-_LICENSE = "https://creativecommons.org/licenses/by-nc-sa/4.0/fr/legalcode"
-
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-
-_VERSION = datasets.Version("1.0.0")
-
-_LANGUAGES = {
-    "yong1288": {
-        "url": "https://mycore.core-cloud.net/index.php/s/vaGMeRf4Iij8MWR/download",
-        "homepage": "https://zenodo.org/record/5336698",
-        "description": "Yongning Na dataset",
-        "translations": ["fr", "en", "zh"]
-    },
-    "japh1234": {
-        "url": "https://mycore.core-cloud.net/index.php/s/kuQCxmyVcUFWroV/download",
-        "homepage": "https://zenodo.org/record/5521112",
-        "description": "Japhug dataset",
-        "translations": ["fr", "zh"]
-    }
-}
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class PanglossDataset(datasets.GeneratorBasedBuilder):
-    """The Pangloss datasets are extracts from Pangloss Collections that can be used for ASR experiments in these languages."""
-    field_translations = {
-        "chemin_audio": "path",
-        "nature": "doctype",
-        "forme": "sentence",
-        "locuteur": "speaker",
-        "traduction:fr": "translation:fr",
-        "traduction:en": "translation:en",
-        "traduction:zh": "translation:zh"
-    }
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=language_name, version=_VERSION, description=language_data["description"])
-        for language_name, language_data in _LANGUAGES.items()
-    ]
-
-    #DEFAULT_CONFIG_NAME = "na"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        features = datasets.Features(
-            {
-                "path": datasets.Value("string"),
-                "audio": datasets.features.Audio(sampling_rate=16_000),
-                "sentence": datasets.Value("string"),
-                "doctype": datasets.Value("string"),
-                "speaker": datasets.Value("string"),
-                **{f"translation:{language_code}": datasets.Value("string") for language_code in _LANGUAGES[self.config.name]["translations"]}
-            }
-        )
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="forme")],
-
-        )
-
-    def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _LANGUAGES[self.config.name]["url"]
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, self.config.name, "train.csv"),
-                    "split": "train"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, self.config.name, "test.csv"),
-                    "split": "test"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, self.config.name, "validation.csv"),
-                    "split": "validation"
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as file_descriptor:
-            reader = csv.DictReader(file_descriptor)
-            for key, row in enumerate(reader):
-                translated_fieldnames = [self.field_translations[fieldname] for fieldname in reader.fieldnames if fieldname in self.field_translations.keys()]
-                data = dict(zip(translated_fieldnames, row.values()))
-                data["audio"] = os.path.join(os.path.dirname(filepath), data["path"])
-                # Yields examples as (key, example) tuples
-                yield key, data
-
-
-if __name__ == "__main__":
-    # for language in _LANGUAGES.keys():
-    datasets.load_dataset("datasets/pangloss/pangloss.py", "japh1234")
-
-# datasets-cli test datasets/pangloss --save_infos --all_configs
-# datasets-cli dummy_data datasets/pangloss --auto_generate
-