Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
License:
File size: 2,506 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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
"""Librispeech language modeling dataset."""
from __future__ import absolute_import, division, print_function
import datasets
_CITATION = """\
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
}
"""
_DESCRIPTION = """\
Language modeling resources to be used in conjunction with the LibriSpeech ASR corpus.
"""
_URL = "http://www.openslr.org/11"
_DL_URL = "http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz"
class LibrispeechLm(datasets.GeneratorBasedBuilder):
"""Librispeech language modeling dataset."""
VERSION = datasets.Version("0.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
}
),
supervised_keys=("text", "text"),
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
archive_path = dl_manager.download_and_extract(_DL_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path}),
]
def _generate_examples(self, archive_path):
"""Yields examples."""
with open(archive_path, "r", encoding="utf-8") as f:
for key, line in enumerate(f):
text = line.strip()
if text: # Skip empty lines.
yield key, {"text": text}
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