Datasets:

dash8x
/

dv-presidential-speech

	# coding=utf-8
	# 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.
	import csv
	import os
	import datasets


	_CITATION = """\
	@misc{Sofwath_2023,
	title = "Dhivehi Presidential Speech Dataset",
	url = "https://huggingface.co/datasets/dash8x/presidential_speech",
	journal = "Hugging Face",
	author = "Sofwath",
	year = "2018",
	month = jul
	}
	"""

	_DESCRIPTION = """\
	Dhivehi Presidential Speech is a Dhivehi speech dataset created from data extracted and
	processed by [Sofwath](https://github.com/Sofwath) as part of a collection of Dhivehi
	datasets found [here](https://github.com/Sofwath/DhivehiDatasets).

	The dataset contains around 2.5 hrs (1 GB) of speech collected from Maldives President's Office
	consisting of 7 speeches given by President Yaameen Abdhul Gayyoom.
	"""

	_HOMEPAGE = 'https://github.com/Sofwath/DhivehiDatasets'

	_LICENSE = 'CC BY-NC-SA 4.0'

	# Source data: 'https://drive.google.com/file/d/1vhMXoB2L23i4HfAGX7EYa4L-sfE4ThU5/view?usp=sharing'
	_DATA_URL = 'data'

	class DhivehiPresidentialSpeech(datasets.GeneratorBasedBuilder):
	"""Dhivehi Presidential Speech is a free Dhivehi speech corpus consisting of around 2.5 hours of
	recorded speech prepared for Dhivehi Automatic Speech Recognition task."""

	VERSION = datasets.Version('1.0.0')

	# 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

	def _info(self):
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	'path': datasets.Value('string'),
	'audio': datasets.Audio(sampling_rate=16_000),
	'sentence': datasets.Value('string'),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# 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
	dl_manager.download_config.ignore_url_params = True
	audio_path = {}
	local_extracted_archive = {}
	metadata_path = {}

	split_type = {
	'train': datasets.Split.TRAIN,
	'test': datasets.Split.TEST,
	'validation': datasets.Split.VALIDATION,
	}

	for split in split_type:
	audio_path[split] = dl_manager.download(f'{_DATA_URL}/audio_{split}.tar.gz')
	local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
	metadata_path[split] = dl_manager.download_and_extract(f'{_DATA_URL}/metadata_{split}.csv')

	path_to_clips = 'dv-presidential-speech'

	return [
	datasets.SplitGenerator(
	name=split_type[split],
	gen_kwargs={
	'local_extracted_archive': local_extracted_archive[split],
	'audio_files': dl_manager.iter_archive(audio_path[split]),
	'metadata_path': metadata_path[split],
	'path_to_clips': f'{path_to_clips}-{split}/waves',
	},
	) for split in split_type
	]

	def _generate_examples(
	self,
	local_extracted_archive,
	audio_files,
	metadata_path,
	path_to_clips,
	):
	"""Yields examples."""
	data_fields = list(self._info().features.keys())
	metadata = {}
	with open(metadata_path, 'r', encoding='utf-8') as f:
	reader = csv.reader(f, delimiter=',', quotechar='"')

	for row in reader:
	row_dict = {}
	row_dict['path'] = row[0]
	row_dict['sentence'] = row[1]

	# if data is incomplete, fill with empty values
	for field in data_fields:
	if field not in row_dict:
	row_dict[field] = ''

	metadata[row_dict['path']] = row_dict

	id_ = 0
	for path, f in audio_files:
	file_name = os.path.splitext(os.path.basename(path))[0]

	if file_name in metadata:
	result = dict(metadata[file_name])
	# set the audio feature and the path to the extracted file
	path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
	result['audio'] = {'path': path, 'bytes': f.read()}
	result['path'] = path
	yield id_, result
	id_ += 1