# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""Librispeech language modeling dataset."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow_datasets.public_api as tfds

_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(tfds.core.GeneratorBasedBuilder):
  """Librispeech language modeling dataset."""

  VERSION = tfds.core.Version('0.1.0')

  def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            'text': tfds.features.Text(),
        }),
        supervised_keys=('text', 'text'),
        homepage=_URL,
        citation=_CITATION,
    )

  def _split_generators(self, dl_manager):
    """Returns SplitGenerators."""
    archive_path = dl_manager.download(_DL_URL)
    return [
        tfds.core.SplitGenerator(
            name=tfds.Split.TRAIN,
            gen_kwargs={'files_iter': dl_manager.iter_archive(archive_path)},
        ),
    ]

  def _generate_examples(self, files_iter):
    """Yields examples."""
    # The archive contains a single file.
    _, f = next(files_iter)
    for key, line in enumerate(f):
      text = line.strip()
      if text:  # Skip empty lines.
        yield key, {'text': text}