Datasets:
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from collections import defaultdict
import os
import json
import csv
import datasets
import torchaudio
import warnings
_NAME="samromur_children"
_VERSION="1.0.0"
_AUDIO_EXTENSIONS=".flac"
_DESCRIPTION = """
The Samrómur Children corpus contains more than 137000 validated speech-recordings uttered by Icelandic children.
"""
_CITATION = """
@misc{menasamromurchildren2022,
title={Samrómur Children Icelandic Speech 1.0},
ldc_catalog_no={LDC2022S11},
DOI={https://doi.org/10.35111/frrj-qd60},
author={Hernández Mena, Carlos Daniel and Borsky, Michal and Mollberg, David Erik and Guðmundsson, Smári Freyr and Hedström, Staffan and Pálsson, Ragnar and Jónsson, Ólafur Helgi and Þorsteinsdóttir, Sunneva and Guðmundsdóttir, Jóhanna Vigdís and Magnúsdóttir, Eydís Huld and Þórhallsdóttir, Ragnheiður and Guðnason, Jón},
publisher={Reykjavík University}
journal={Linguistic Data Consortium, Philadelphia},
year={2019},
url={https://catalog.ldc.upenn.edu/LDC2022S11},
}
"""
_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2022S11"
_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 SamromurChildrenConfig(datasets.BuilderConfig):
"""BuilderConfig for Samromur Children"""
def __init__(self, name, **kwargs):
name=_NAME
super().__init__(name=name, **kwargs)
class SamromurChildren(datasets.GeneratorBasedBuilder):
"""Samrómur Children Icelandic Speech 1.0"""
VERSION = datasets.Version(_VERSION)
BUILDER_CONFIGS = [
SamromurChildrenConfig(
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]
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
# Load the audio file using torchaudio
waveform, sample_rate = torchaudio.load(path)
# Check if the waveform is empty
if waveform.numel() == 0:
warnings.warn(f"Empty audio file: {str(audio_id)}")
continue
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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