Nikita Pavlichenko
commited on
Commit
•
907dd52
1
Parent(s):
c2c3579
First version of VoxDIY
Browse files- VoxDIY-RusNews.py +105 -0
VoxDIY-RusNews.py
ADDED
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# coding=utf-8
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# Copyright 2021 YANDEX LLC.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""VoxDIY: Benchmark Dataset for Russian Crowdsourced Audio Transcription."""
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import json
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import datasets
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from datasets.tasks import Summarization
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logger = datasets.logging.get_logger(__name__)
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_DESCRIPTION = """\
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VoxDIY: Benchmark Dataset for Russian Crowdsourced Audio Transcription.
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"""
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_URL = "https://raw.githubusercontent.com/pilot7747/VoxDIY/main/data/huggingface/"
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_URLS = {
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"train": "vox-diy-rusnews.json"
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}
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class VoxDIYRusNewsConfig(datasets.BuilderConfig):
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"""BuilderConfig for VoxDIY-RusNews."""
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def __init__(self, **kwargs):
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"""BuilderConfig for VoxDIY-RusNews.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(CrowdSpeechConfig, self).__init__(**kwargs)
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class VoxDIYRusNews(datasets.GeneratorBasedBuilder):
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"""VoxDIY: Benchmark Dataset for Russian Crowdsourced Audio Transcription."""
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BUILDER_CONFIGS = [
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VoxDIYRusNewsConfig(
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name="plain_text",
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version=datasets.Version("1.0.0", ""),
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description="Plain text",
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),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"task": datasets.Value("string"),
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"transcriptions": datasets.Value("string"),
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"performers": datasets.Value("string"),
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"gt": datasets.Value("string"),
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}
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),
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supervised_keys=None,
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homepage="https://github.com/pilot7747/VoxDIY/",
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# citation=_CITATION,
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task_templates=[
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Summarization(
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text_column="transcriptions", summary_column="gt"
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)
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],
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)
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def _split_generators(self, dl_manager):
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downloaded_files = dl_manager.download_and_extract(_URLS)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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]
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def _generate_examples(self, filepath):
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"""This function returns the examples in the raw (text) form."""
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logger.info("generating examples from = %s", filepath)
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with open(filepath, encoding="utf-8") as f:
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crowdspeech = json.load(f)
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for audio in crowdspeech["data"]:
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task = audio.get("task", "")
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transcriptions = audio.get("transcriptions", "")
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performers = audio.get("performers", "")
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gt = audio.get("gt", "")
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yield task, {
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"task": task,
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"transcriptions": transcriptions,
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"performers": performers,
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"gt": gt,
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}
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