Datasets:

Lacito
/

pangloss

	# 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