Datasets:

toloka
/

CrowdSpeech

	# coding=utf-8
	# Copyright 2021 YANDEX LLC.
	#
	# 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.

	# Lint as: python3
	"""CrowdSpeech: Benchmark Dataset for Crowdsourced Audio Transcription."""


	import json

	import datasets
	from datasets.tasks import Summarization


	logger = datasets.logging.get_logger(__name__)

	_DESCRIPTION = """\
	CrowdSpeech is a publicly available large-scale dataset of crowdsourced audio transcriptions. \
	It contains annotations for more than 50 hours of English speech transcriptions from more \
	than 1,000 crowd workers.
	"""

	_URL = "https://raw.githubusercontent.com/pilot7747/VoxDIY/main/data/huggingface/"
	_URLS = {
	"train-clean": _URL + "train-clean.json",
	"dev-clean": _URL + "dev-clean.json",
	"dev-other": _URL + "dev-other.json",
	"test-clean": _URL + "test-clean.json",
	"test-other": _URL + "test-other.json",
	}


	class CrowdSpeechConfig(datasets.BuilderConfig):
	"""BuilderConfig for CrowdSpeech."""

	def __init__(self, **kwargs):
	"""BuilderConfig for CrowdSpeech.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(CrowdSpeechConfig, self).__init__(**kwargs)


	class CrowdSpeech(datasets.GeneratorBasedBuilder):
	"""CrowdSpeech: Benchmark Dataset for Crowdsourced Audio Transcription."""

	BUILDER_CONFIGS = [
	CrowdSpeechConfig(
	name="plain_text",
	version=datasets.Version("1.0.0", ""),
	description="Plain text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"task": datasets.Value("string"),
	"transcriptions": datasets.Value("string"),
	"performers": datasets.Value("string"),
	"gt": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/pilot7747/VoxDIY/",
	# citation=_CITATION,
	task_templates=[
	Summarization(
	text_column="transcriptions", summary_column="gt"
	)
	],
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download_and_extract(_URLS)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train-clean"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test-clean"]}),
	datasets.SplitGenerator(name='test.other', gen_kwargs={"filepath": downloaded_files["test-other"]}),
	datasets.SplitGenerator(name='dev.clean', gen_kwargs={"filepath": downloaded_files["dev-clean"]}),
	datasets.SplitGenerator(name='dev.other', gen_kwargs={"filepath": downloaded_files["dev-clean"]}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	crowdspeech = json.load(f)
	for audio in crowdspeech["data"]:
	task = audio.get("task", "")
	transcriptions = audio.get("transcriptions", "")
	performers = audio.get("performers", "")
	gt = audio.get("gt", "")

	yield task, {
	"task": task,
	"transcriptions": transcriptions,
	"performers": performers,
	"gt": gt,
	}