File size: 6,316 Bytes
ed2f49e 06305e4 ed2f49e 06305e4 ed2f49e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
# coding=utf-8
# Copyright 2021 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 automatic speech recognition dataset."""
import os
import datasets
from datasets.tasks import AutomaticSpeechRecognition
_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 = """\
LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87
"""
_URL = "http://www.openslr.org/12"
_DL_URL = "http://www.openslr.org/resources/12/"
_DL_URLS = {
"clean": {
"train.100": _DL_URL + "train-clean-100.tar.gz",
"train.360": _DL_URL + "train-clean-360.tar.gz",
},
"all": {
"train.100": _DL_URL + "train-clean-100.tar.gz",
"train.360": _DL_URL + "train-clean-360.tar.gz",
},
}
class LibrispeechASRConfig(datasets.BuilderConfig):
"""BuilderConfig for LibriSpeechASR."""
def __init__(self, **kwargs):
"""
Args:
data_dir: `string`, the path to the folder containing the files in the
downloaded .tar
citation: `string`, citation for the data set
url: `string`, url for information about the data set
**kwargs: keyword arguments forwarded to super.
"""
super(LibrispeechASRConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
class LibrispeechASR(datasets.GeneratorBasedBuilder):
"""Librispeech dataset."""
DEFAULT_WRITER_BATCH_SIZE = 256
DEFAULT_CONFIG_NAME = "all"
BUILDER_CONFIGS = [LibrispeechASRConfig(name="clean", description="'Clean' speech.")]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"text": datasets.Value("string"),
"speaker_id": datasets.Value("int64"),
"chapter_id": datasets.Value("int64"),
"id": datasets.Value("string"),
}
),
supervised_keys=("file", "text"),
homepage=_URL,
citation=_CITATION,
task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
# (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
if self.config.name == "clean" or self.config.name == "all":
train_splits = [
datasets.SplitGenerator(
name="train.100",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("train.100"),
"files": dl_manager.iter_archive(archive_path["train.100"]),
},
),
datasets.SplitGenerator(
name="train.360",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("train.360"),
"files": dl_manager.iter_archive(archive_path["train.360"]),
},
),
]
return train_splits
def _generate_examples(self, files, local_extracted_archive):
"""Generate examples from a LibriSpeech archive_path."""
key = 0
audio_data = {}
transcripts = []
for path, f in files:
if path.endswith(".flac"):
id_ = path.split("/")[-1][: -len(".flac")]
audio_data[id_] = f.read()
elif path.endswith(".trans.txt"):
for line in f:
if line:
line = line.decode("utf-8").strip()
id_, transcript = line.split(" ", 1)
audio_file = f"{id_}.flac"
speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
audio_file = (
os.path.join(local_extracted_archive, audio_file)
if local_extracted_archive
else audio_file
)
transcripts.append(
{
"id": id_,
"speaker_id": speaker_id,
"chapter_id": chapter_id,
"file": audio_file,
"text": transcript,
}
)
if audio_data and len(audio_data) == len(transcripts):
for transcript in transcripts:
audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
yield key, {"audio": audio, **transcript}
key += 1
audio_data = {}
transcripts = [] |