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 datasets


	_CITATION = """\
	@inproceedings{luong-vu-2016-non,
	title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
	author = "Luong, Hieu-Thi and
	Vu, Hai-Quan",
	booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
	month = dec,
	year = "2016",
	address = "Osaka, Japan",
	publisher = "The COLING 2016 Organizing Committee",
	url = "https://aclanthology.org/W16-5207",
	pages = "51--55",
	}
	"""

	_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://doi.org/10.5281/zenodo.7068130"

	_LICENSE = "CC BY-NC-SA 4.0"

	_DATA_URL = "https://zenodo.org/record/7068130/files/vivos.tar.gz"

	_PROMPTS_URLS = {
	"train": "https://s3.amazonaws.com/datasets.huggingface.co/vivos/train/prompts.txt",
	"test": "https://s3.amazonaws.com/datasets.huggingface.co/vivos/test/prompts.txt",
	}


	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"),
	"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
	prompts_paths = dl_manager.download(_PROMPTS_URLS)
	archive = dl_manager.download(_DATA_URL)
	train_dir = "vivos/train"
	test_dir = "vivos/test"

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"prompts_path": prompts_paths["train"],
	"path_to_clips": train_dir + "/waves",
	"audio_files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"prompts_path": prompts_paths["test"],
	"path_to_clips": test_dir + "/waves",
	"audio_files": dl_manager.iter_archive(archive),
	},
	),
	]

	def _generate_examples(self, prompts_path, path_to_clips, audio_files):
	"""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.
	examples = {}
	with open(prompts_path, encoding="utf-8") as f:
	for row in f:
	data = row.strip().split(" ", 1)
	speaker_id = data[0].split("_")[0]
	audio_path = "/".join([path_to_clips, speaker_id, data[0] + ".wav"])
	examples[audio_path] = {
	"speaker_id": speaker_id,
	"path": audio_path,
	"sentence": data[1],
	}
	inside_clips_dir = False
	id_ = 0
	for path, f in audio_files:
	if path.startswith(path_to_clips):
	inside_clips_dir = True
	if path in examples:
	audio = {"path": path, "bytes": f.read()}
	yield id_, {**examples[path], "audio": audio}
	id_ += 1
	elif inside_clips_dir:
	break