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
Tags:
License:
# 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.""" | |
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} | |