Datasets:
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from collections import defaultdict
import os
import json
import csv
import datasets
_NAME="samromur_asr"
_VERSION="1.0.0"
_AUDIO_EXTENSIONS=".flac"
_DESCRIPTION = """
Samrómur Icelandic Speech 1.0.
"""
_CITATION = """
@misc{mollbergsamromur2022,
title={Samrómur Icelandic Speech 1.0},
ldc_catalog_no={LDC2022S05},
DOI={https://doi.org/10.35111/thx3-f170},
author={Mollberg, David Erik and Jónsson, Ólafur Helgi and Þorsteinsdóttir, Sunneva and Guðmundsdóttir, Jóhanna Vigdís and Steingrimsson, Steinthor and Magnusdottir, Eydis Huld and Fong, Judy Y. and Borsky, Michal and Guðnason, Jón},
publisher={Reykjavík University}
journal={Linguistic Data Consortium, Philadelphia},
year={2022},
url={https://catalog.ldc.upenn.edu/LDC2022S05},
}
"""
_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2022S05/"
_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")
_METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv")
_METADATA_DEV = os.path.join(_BASE_DATA_DIR,"files", "metadata_dev.tsv")
_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths")
_TARS_TEST = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths")
_TARS_DEV = os.path.join(_BASE_DATA_DIR,"files", "tars_dev.paths")
class SamromurAsrConfig(datasets.BuilderConfig):
"""BuilderConfig for The Samrómur Corpus"""
def __init__(self, name, **kwargs):
name=_NAME
super().__init__(name=name, **kwargs)
class SamromurAsr(datasets.GeneratorBasedBuilder):
"""Samrómur Icelandic Speech 1.0"""
VERSION = datasets.Version(_VERSION)
BUILDER_CONFIGS = [
SamromurAsrConfig(
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"),
"age": 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_train=dl_manager.download_and_extract(_METADATA_TRAIN)
metadata_test=dl_manager.download_and_extract(_METADATA_TEST)
metadata_dev=dl_manager.download_and_extract(_METADATA_DEV)
tars_train=dl_manager.download_and_extract(_TARS_TRAIN)
tars_test=dl_manager.download_and_extract(_TARS_TEST)
tars_dev=dl_manager.download_and_extract(_TARS_DEV)
hash_tar_files=defaultdict(dict)
with open(tars_train,'r') as f:
hash_tar_files['train']=[path.replace('\n','') for path in f]
with open(tars_test,'r') as f:
hash_tar_files['test']=[path.replace('\n','') for path in f]
with open(tars_dev,'r') as f:
hash_tar_files['dev']=[path.replace('\n','') for path in f]
hash_meta_paths={"train":metadata_train,"test":metadata_test,"dev":metadata_dev}
audio_paths = dl_manager.download(hash_tar_files)
splits=["train","dev","test"]
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"],
}
),
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"],
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["test"]],
"local_extracted_archives_paths": local_extracted_audio_paths["test"],
"metadata_paths": hash_meta_paths["test"],
}
),
]
def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
features = ["speaker_id","gender","age","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]
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()},
}
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