Datasets:

pain
/

MASC

Tasks:

Automatic Speech Recognition

Languages:

Arabic

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MASC / MASC.py

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	# coding=utf-8
	# Copyright 2022 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.
	""" MASC Dataset"""

	# This script has been adopted from this dataset: "mozilla-foundation/common_voice_11_0"

	import csv
	import os
	import json

	import datasets
	from datasets.utils.py_utils import size_str
	from tqdm import tqdm

	_CITATION = """\
	@INPROCEEDINGS{10022652,
	author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
	booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
	title={MASC: Massive Arabic Speech Corpus},
	year={2023},
	volume={},
	number={},
	pages={1006-1013},
	doi={10.1109/SLT54892.2023.10022652}}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	MASC is a dataset that contains 1,000 hours of speech sampled at 16 kHz and crawled from over 700 YouTube channels. The dataset is multi-regional, multi-genre, and multi-dialect intended to advance the research and development of Arabic speech technology with a special emphasis on Arabic speech recognition.
	"""

	_HOMEPAGE = "https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus"
	_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
	_BASE_URL = "https://huggingface.co/datasets/pain/MASC/resolve/main/"
	_AUDIO_URL1 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.gz"
	_AUDIO_URL2 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.xz"
	_TRANSCRIPT_URL = _BASE_URL + "transcript/{split}/{split}.csv"

	class MASC(datasets.GeneratorBasedBuilder):

	VERSION = datasets.Version("1.0.0")

	def _info(self):

	features = datasets.Features(
	{
	"video_id": datasets.Value("string"),
	"start": datasets.Value("float64"),
	"end": datasets.Value("float64"),
	"duration": datasets.Value("float64"),
	"text": datasets.Value("string"),
	"type": datasets.Value("string"),
	"file_path": datasets.Value("string"),
	"audio": datasets.features.Audio(sampling_rate=16_000),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	version=self.config.version,
	)

	def _split_generators(self, dl_manager):

	n_shards = {"train": 8,"dev": 1, "test": 1}
	audio_urls = {}
	splits = ("train", "dev", "test")

	for split in splits:
	audio_urls[split] = [
	_AUDIO_URL2.format(split=split, shard_idx="{:02d}".format(i+1)) if split=="train" else _AUDIO_URL1.format(split=split, shard_idx="{:02d}".format(i+1)) for i in range(n_shards[split])
	]
	archive_paths = dl_manager.download(audio_urls)
	local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}

	meta_urls = {split: _TRANSCRIPT_URL.format(split=split) for split in splits}

	meta_paths = dl_manager.download(meta_urls)

	split_generators = []
	split_names = {
	"train": datasets.Split.TRAIN,
	"dev": datasets.Split.VALIDATION,
	"test": datasets.Split.TEST,
	}
	for split in splits:
	split_generators.append(
	datasets.SplitGenerator(
	name=split_names.get(split, split),
	gen_kwargs={
	"local_extracted_archive_paths": local_extracted_archive_paths.get(split),
	"archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
	"meta_path": meta_paths[split],
	},
	),
	)

	return split_generators

	def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
	data_fields = list(self._info().features.keys())
	metadata = {}
	with open(meta_path, encoding="utf-8") as f:
	reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_NONE)
	for row in reader:
	if not row["file_path"].endswith(".wav"):
	row["file_path"] += ".wav"
	for field in data_fields:
	if field not in row:
	row[field] = ""
	metadata[row["file_path"]] = row

	for i, audio_archive in enumerate(archives):
	for filename, file in audio_archive:
	_, filename = os.path.split(filename)
	if filename in metadata:
	result = dict(metadata[filename])
	# set the audio feature and the path to the extracted file
	path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename

	try:
	result["audio"] = {"path": path, "bytes": file.read()}
	except ReadError as e:
	# Handle the ReadError
	print("An error occurred while reading the data:", str(e))
	continiue
	# set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
	result["file_path"] = path if local_extracted_archive_paths else filename
	yield path, result