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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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dataset_infos.json ADDED
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+ {"default": {"description": "Language modeling resources to be used in conjunction with the LibriSpeech ASR corpus.\n", "citation": "@inproceedings{panayotov2015librispeech,\n title={Librispeech: an ASR corpus based on public domain audio books},\n author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},\n booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},\n pages={5206--5210},\n year={2015},\n organization={IEEE}\n}\n", "homepage": "http://www.openslr.org/11", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "text", "output": "text"}, "builder_name": "librispeech_lm", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4418577129, "num_examples": 40418260, "dataset_name": "librispeech_lm"}}, "download_checksums": {"http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz": {"num_bytes": 1507274412, "checksum": "927d47656638c2ca93566640e56bcbeb2aa4ca237b8490fe1eff7d6e610a53c7"}}, "download_size": 1507274412, "dataset_size": 4418577129, "size_in_bytes": 5925851541}}
dummy/0.1.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0c3ae75bf70f3c1f996fc67f3f8517923db76772b1389715b1c313bd651f7f18
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+ size 213
librispeech_lm.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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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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+
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+ # Lint as: python3
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+ """Librispeech language modeling dataset."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import datasets
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+
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+
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+ _CITATION = """\
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+ @inproceedings{panayotov2015librispeech,
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+ title={Librispeech: an ASR corpus based on public domain audio books},
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+ author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
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+ booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
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+ pages={5206--5210},
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+ year={2015},
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+ organization={IEEE}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ Language modeling resources to be used in conjunction with the LibriSpeech ASR corpus.
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+ """
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+
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+ _URL = "http://www.openslr.org/11"
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+
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+ _DL_URL = "http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz"
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+
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+
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+ class LibrispeechLm(datasets.GeneratorBasedBuilder):
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+ """Librispeech language modeling dataset."""
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+
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+ VERSION = datasets.Version("0.1.0")
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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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+ "text": datasets.Value("string"),
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+ }
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+ ),
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+ supervised_keys=("text", "text"),
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+ homepage=_URL,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ archive_path = dl_manager.download_and_extract(_DL_URL)
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path}),
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+ ]
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+
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+ def _generate_examples(self, archive_path):
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+ """Yields examples."""
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+ with open(archive_path, "r", encoding="utf-8") as f:
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+ for key, line in enumerate(f):
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+ text = line.strip()
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+ if text: # Skip empty lines.
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+ yield key, {"text": text}