Datasets:
File size: 6,568 Bytes
4ae7deb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
from collections import defaultdict
import os
import json
import csv
import datasets
_NAME="malromur_asr"
_VERSION="1.0.0"
_AUDIO_EXTENSIONS=".flac"
_DESCRIPTION = """
The Málrómur corpus is an open source corpus of Icelandic voice samples.
"""
_CITATION = """
@inproceedings{steingrimsson2017malromur,
title={Málrómur: A manually verified corpus of recorded Icelandic speech},
author={Steingrímsson, Steinþór and Guðnason, Jón and Helgadóttir, Sigrún and Rögnvaldsson, Eiríkur},
booktitle={Proceedings of the 21st Nordic Conference on Computational Linguistics},
pages={237--240},
year={2017}
}
"""
_HOMEPAGE = "https://clarin.is/en/resources/malromur/"
_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 MalromurAsrConfig(datasets.BuilderConfig):
"""BuilderConfig for The Málrómur Corpus"""
def __init__(self, name, **kwargs):
name=_NAME
super().__init__(name=name, **kwargs)
class MalromurAsr(datasets.GeneratorBasedBuilder):
"""The Málrómur Corpus"""
VERSION = datasets.Version(_VERSION)
BUILDER_CONFIGS = [
MalromurAsrConfig(
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()},
}
|