Datasets:

openslr
/

librispeech_lm

	# 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}