File size: 5,336 Bytes
22ef493 |
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 |
"""Librispeech automatic speech recognition dataset."""
import os
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 = """\
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 = {"test": _DL_URL + "test-clean.tar.gz",
"train.100": _DL_URL + "train-clean-100.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_CONFIG = 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,
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS)
# (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 {}
train_split = [
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"]),
},
),
]
test_split = [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("test"),
"files": dl_manager.iter_archive(archive_path["test"]),
},
)
]
return train_split + test_split
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 = [] |