Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
crowdsourced
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:
File size: 4,170 Bytes
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# coding=utf-8
# Copyright 2021 YANDEX LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""CrowdSpeech: Benchmark Dataset for Crowdsourced Audio Transcription."""
import json
import datasets
from datasets.tasks import Summarization
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
CrowdSpeech is a publicly available large-scale dataset of crowdsourced audio transcriptions. \
It contains annotations for more than 50 hours of English speech transcriptions from more \
than 1,000 crowd workers.
"""
_URL = "https://raw.githubusercontent.com/pilot7747/VoxDIY/main/data/huggingface/"
_URLS = {
"train-clean": _URL + "train-clean.json",
"dev-clean": _URL + "dev-clean.json",
"dev-other": _URL + "dev-other.json",
"test-clean": _URL + "test-clean.json",
"test-other": _URL + "test-other.json",
}
class CrowdSpeechConfig(datasets.BuilderConfig):
"""BuilderConfig for CrowdSpeech."""
def __init__(self, **kwargs):
"""BuilderConfig for CrowdSpeech.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(CrowdSpeechConfig, self).__init__(**kwargs)
class CrowdSpeech(datasets.GeneratorBasedBuilder):
"""CrowdSpeech: Benchmark Dataset for Crowdsourced Audio Transcription."""
BUILDER_CONFIGS = [
CrowdSpeechConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"task": datasets.Value("string"),
"transcriptions": datasets.Value("string"),
"performers": datasets.Value("string"),
"gt": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/pilot7747/VoxDIY/",
# citation=_CITATION,
task_templates=[
Summarization(
text_column="transcriptions", summary_column="gt"
)
],
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train-clean"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test-clean"]}),
datasets.SplitGenerator(name='test.other', gen_kwargs={"filepath": downloaded_files["test-other"]}),
datasets.SplitGenerator(name='dev.clean', gen_kwargs={"filepath": downloaded_files["dev-clean"]}),
datasets.SplitGenerator(name='dev.other', gen_kwargs={"filepath": downloaded_files["dev-clean"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
crowdspeech = json.load(f)
for audio in crowdspeech["data"]:
task = audio.get("task", "")
transcriptions = audio.get("transcriptions", "")
performers = audio.get("performers", "")
gt = audio.get("gt", "")
yield task, {
"task": task,
"transcriptions": transcriptions,
"performers": performers,
"gt": gt,
}
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