Datasets:

language-and-voice-lab
/

raddromur_asr

	from collections import defaultdict
	import os
	import json
	import csv

	import datasets

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

	_DESCRIPTION = """
	The Raddrómur Corpus is intended for the speech recognition field and it is made out of radio podcasts mostly taken from RÚV (ruv.is). Such podcasts were selected because they contained a text script that matches with certain fidelity what is said during the show. After automatic segmentation of the episodes, the transcriptions were inferred using the scripts along with a forced alignment technique.
	"""

	_CITATION = """
	@misc{carlosmenaraddromur2022,
	title={Raddrómur Icelandic Speech 22.09},
	author={Hernández Mena, Carlos Daniel and Hedström, Staffan and Þórhallsdóttir, Ragnheiður and Fong, Judy Y. and Gunnarsson, Þorsteinn Daði and Sigurðardóttir, Helga Svala and Þorsteinsdóttir, Helga Lára and Guðnason, Jón},
	year={2022},
	url={http://hdl.handle.net/20.500.12537/286},
	}
	"""

	_HOMEPAGE = "http://hdl.handle.net/20.500.12537/286"

	_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/"

	_BASE_DATA_DIR = "corpus/"
	_METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files","metadata_train.tsv")

	_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths")

	class RaddromurAsrConfig(datasets.BuilderConfig):
	"""BuilderConfig for Raddrómur Corpus"""

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

	class RaddromurAsr(datasets.GeneratorBasedBuilder):
	"""Raddrómur Icelandic Speech 22.09"""

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

	def _info(self):
	features = datasets.Features(
	{
	"audio_id": datasets.Value("string"),
	"audio": datasets.Audio(sampling_rate=16000),
	"podcast_id": datasets.Value("string"),
	"segment_num": datasets.Value("int32"),
	"start_time": datasets.Value("string"),
	"duration": datasets.Value("float32"),
	"mafia_score": 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_train=dl_manager.download_and_extract(_METADATA_TRAIN)

	tars_train=dl_manager.download_and_extract(_TARS_TRAIN)

	hash_tar_files=defaultdict(dict)
	with open(tars_train,'r') as f:
	hash_tar_files['train']=[path.replace('\n','') for path in f]

	hash_meta_paths={"train":metadata_train}
	audio_paths = dl_manager.download(hash_tar_files)

	splits=["train"]
	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.TRAIN,
	gen_kwargs={
	"audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["train"]],
	"local_extracted_archives_paths": local_extracted_audio_paths["train"],
	"metadata_paths": hash_meta_paths["train"],
	}
	),
	]

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

	features = ["podcast_id","segment_num","start_time","duration","mafia_score","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()},
	}