Datasets:
vivos

	# 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 os

	import datasets


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{vivos:2016,
	Address = {Ho Chi Minh, Vietnam}
	title = {VIVOS: 15 hours of recording speech prepared for Vietnamese Automatic Speech Recognition},
	author={Prof. Vu Hai Quan},
	year={2016}
	}
	"""

	_DESCRIPTION = """\
	VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
	Vietnamese Automatic Speech Recognition task.
	The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.
	We publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems.
	"""

	_HOMEPAGE = "https://ailab.hcmus.edu.vn/vivos"

	_LICENSE = "cc-by-sa-4.0"

	_DATA_URL = "https://ailab.hcmus.edu.vn/assets/vivos.tar.gz"


	class VivosDataset(datasets.GeneratorBasedBuilder):
	"""VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
	Vietnamese Automatic Speech Recognition task."""

	VERSION = datasets.Version("1.1.0")

	# 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(
	{
	"speaker_id": datasets.Value("string"),
	"path": datasets.Value("string"),
	"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_path = dl_manager.download_and_extract(_DATA_URL)
	data_dir = os.path.join(dl_path, "vivos")
	train_dir = os.path.join(data_dir, "train")
	test_dir = os.path.join(data_dir, "test")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(train_dir, "prompts.txt"),
	"path_to_clips": os.path.join(train_dir, "waves"),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(test_dir, "prompts.txt"),
	"path_to_clips": os.path.join(test_dir, "waves"),
	},
	),
	]

	def _generate_examples(
	self,
	filepath,
	path_to_clips, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	"""Yields examples as (key, example) tuples."""
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.

	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = row.strip().split(" ", 1)
	speaker_id = data[0].split("_")[0]
	yield id_, {
	"speaker_id": speaker_id,
	"path": os.path.join(path_to_clips, speaker_id, data[0] + ".wav"),
	"sentence": data[1],
	}