Datasets:

MLRS
/

masri_dev

	from collections import defaultdict
	import os
	import json
	import csv

	import datasets

	_NAME="masri_dev"
	_VERSION="1.0.0"
	_AUDIO_EXTENSIONS=".flac"

	_DESCRIPTION = """
	The MASRI-DEV CORPUS was created out of YouTube videos belonging to the channel of the University of Malta. It has a length of 1 hour and it is gender balanced, as it has the same number of male and female speakers.
	"""

	_CITATION = """
	@misc{carlosmenamasridev2020,
	title={MASRI-DEV CORPUS: Audio and Transcriptions in Maltese extracted from the YouTube channel of the University of Malta.},
	author={Hernandez Mena, Carlos Daniel and Brincat, Ayrton Didier and Gatt, Albert and DeMarco, Andrea and Borg, Claudia and van der Plas, Lonneke and Meza Ruiz, Iván Vladimir},
	journal={MASRI Project, Malta},
	year={2020},
	url={https://www.um.edu.mt/projects/masri/},
	}
	"""

	_HOMEPAGE = "https://www.um.edu.mt/projects/masri/"

	_LICENSE = "CC-BY-4.0. The copyright remains with the original owners of the video. See https://creativecommons.org/licenses/by/4.0/"

	_BASE_DATA_DIR = "corpus/"
	_METADATA_DEV = os.path.join(_BASE_DATA_DIR,"files", "metadata_dev.tsv")

	_TARS_DEV = os.path.join(_BASE_DATA_DIR,"files", "tars_dev.paths")

	class MasriDevConfig(datasets.BuilderConfig):
	"""BuilderConfig for MASRI-DEV Corpus"""

	def __init__(self, name, **kwargs):
	name=_NAME
	super().__init__(name=name, **kwargs)

	class MasriDev(datasets.GeneratorBasedBuilder):
	"""MASRI-DEV Corpus"""

	VERSION = datasets.Version(_VERSION)
	BUILDER_CONFIGS = [
	MasriDevConfig(
	name=_NAME,
	version=datasets.Version(_VERSION),
	)
	]

	def _info(self):
	features = datasets.Features(
	{
	"audio_id": datasets.Value("string"),
	"audio": datasets.Audio(sampling_rate=16000),
	"speaker_id": datasets.Value("string"),
	"gender": datasets.Value("string"),
	"duration": datasets.Value("float32"),
	"normalized_text": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):

	metadata_dev=dl_manager.download_and_extract(_METADATA_DEV)

	tars_dev=dl_manager.download_and_extract(_TARS_DEV)

	hash_tar_files=defaultdict(dict)

	with open(tars_dev,'r') as f:
	hash_tar_files['dev']=[path.replace('\n','') for path in f]

	hash_meta_paths={"dev":metadata_dev}
	audio_paths = dl_manager.download(hash_tar_files)

	splits=["dev"]
	local_extracted_audio_paths = (
	dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
	{
	split:[None] * len(audio_paths[split]) for split in splits
	}
	)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["dev"]],
	"local_extracted_archives_paths": local_extracted_audio_paths["dev"],
	"metadata_paths": hash_meta_paths["dev"],
	}
	),
	]

	def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):

	features = ["speaker_id","gender","duration","normalized_text"]

	with open(metadata_paths) as f:
	metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}

	for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
	for audio_filename, audio_file in audio_archive:
	#audio_id = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[0]
	audio_id =os.path.splitext(os.path.basename(audio_filename))[0]
	path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename

	yield audio_id, {
	"audio_id": audio_id,
	**{feature: metadata[audio_id][feature] for feature in features},
	"audio": {"path": path, "bytes": audio_file.read()},
	}