Datasets:

collectivat
/

tv3_parla

	# 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.
	"""TV3Parla."""

	import re

	import datasets
	from datasets.tasks import AutomaticSpeechRecognition


	_CITATION = """\
	@inproceedings{kulebi18_iberspeech,
	author={Baybars Külebi and Alp Öktem},
	title={{Building an Open Source Automatic Speech Recognition System for Catalan}},
	year=2018,
	booktitle={Proc. IberSPEECH 2018},
	pages={25--29},
	doi={10.21437/IberSPEECH.2018-6}
	}
	"""

	_DESCRIPTION = """\
	This corpus includes 240 hours of Catalan speech from broadcast material.
	The details of segmentation, data processing and also model training are explained in Külebi, Öktem; 2018.
	The content is owned by Corporació Catalana de Mitjans Audiovisuals, SA (CCMA);
	we processed their material and hereby making it available under their terms of use.

	This project was supported by the Softcatalà Association.
	"""

	_HOMEPAGE = "https://collectivat.cat/asr#tv3parla"

	_LICENSE = "Creative Commons Attribution-NonCommercial 4.0 International"

	_REPO = "https://huggingface.co/datasets/collectivat/tv3_parla/resolve/main/"
	_URLS = {
	"transcripts": _REPO + "tv3_0.3_{split}.transcription",
	"audio": _REPO + "tv3_0.3.tar.gz",
	}
	_SPLITS = [datasets.Split.TRAIN, datasets.Split.TEST]

	_PATTERN = re.compile(r"^<s> (?P<text>.+) </s> $(?P<id>\S+)$$")


	class Tv3Parla(datasets.GeneratorBasedBuilder):
	"""TV3Parla."""

	VERSION = datasets.Version("0.3.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"path": datasets.Value("string"),
	"audio": datasets.features.Audio(),
	"text": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[
	AutomaticSpeechRecognition(transcription_column="text")
	],
	)

	def _split_generators(self, dl_manager):
	urls = {
	split: {key: url.format(split=split) for key, url in _URLS.items()} for split in _SPLITS
	}
	dl_dir = dl_manager.download(urls)
	return [
	datasets.SplitGenerator(
	name=split,
	gen_kwargs={
	"transcripts_path": dl_dir[split]["transcripts"],
	"audio_files": dl_manager.iter_archive(dl_dir[split]["audio"]),
	"split": split,
	},
	) for split in _SPLITS
	]

	def _generate_examples(self, transcripts_path, audio_files, split):
	transcripts = {}
	with open(transcripts_path, encoding="utf-8") as transcripts_file:
	for line in transcripts_file:
	match = _PATTERN.match(line)
	transcripts[match["id"]] = match["text"]
	# train: 159242; test: 2220
	for key, (path, file) in enumerate(audio_files):
	if path.endswith(".wav") and f"/{split}/" in path:
	uid = path.split("/")[-1][:-4]
	if uid not in transcripts:
	continue
	text = transcripts.pop(uid)
	audio = {"path": path, "bytes": file.read()}
	yield key, {"path": path, "audio": audio, "text": text}