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"""Librispeech automatic speech recognition dataset."""
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import os
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import datasets
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_CITATION = """\
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@inproceedings{panayotov2015librispeech,
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title={Librispeech: an ASR corpus based on public domain audio books},
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author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
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booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
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pages={5206--5210},
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year={2015},
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organization={IEEE}
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}
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"""
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_DESCRIPTION = """\
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LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
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prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
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audiobooks from the LibriVox project, and has been carefully segmented and aligned.87
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"""
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_URL = "http://www.openslr.org/12"
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_DL_URL = "http://www.openslr.org/resources/12/"
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_DL_URLS = {"test": _DL_URL + "test-clean.tar.gz",
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"train.100": _DL_URL + "train-clean-100.tar.gz",
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}
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class LibrispeechASRConfig(datasets.BuilderConfig):
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"""BuilderConfig for LibriSpeechASR."""
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def __init__(self, **kwargs):
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"""
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Args:
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data_dir: `string`, the path to the folder containing the files in the
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downloaded .tar
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citation: `string`, citation for the data set
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url: `string`, url for information about the data set
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**kwargs: keyword arguments forwarded to super.
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"""
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super(LibrispeechASRConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
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class LibrispeechASR(datasets.GeneratorBasedBuilder):
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"""Librispeech dataset."""
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DEFAULT_WRITER_BATCH_SIZE = 256
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DEFAULT_CONFIG_NAME = "all"
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BUILDER_CONFIG = LibrispeechASRConfig(name="clean", description="'Clean' speech.")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"file": datasets.Value("string"),
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"audio": datasets.Audio(sampling_rate=16_000),
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"text": datasets.Value("string"),
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"speaker_id": datasets.Value("int64"),
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"chapter_id": datasets.Value("int64"),
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"id": datasets.Value("string"),
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}
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),
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supervised_keys=("file", "text"),
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homepage=_URL,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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archive_path = dl_manager.download(_DL_URLS)
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local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
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train_split = [
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datasets.SplitGenerator(
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name="train.100",
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gen_kwargs={
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"local_extracted_archive": local_extracted_archive.get("train.100"),
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"files": dl_manager.iter_archive(archive_path["train.100"]),
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},
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),
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]
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test_split = [
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"local_extracted_archive": local_extracted_archive.get("test"),
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"files": dl_manager.iter_archive(archive_path["test"]),
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},
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)
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]
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return train_split + test_split
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def _generate_examples(self, files, local_extracted_archive):
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"""Generate examples from a LibriSpeech archive_path."""
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key = 0
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audio_data = {}
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transcripts = []
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for path, f in files:
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if path.endswith(".flac"):
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id_ = path.split("/")[-1][: -len(".flac")]
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audio_data[id_] = f.read()
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elif path.endswith(".trans.txt"):
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for line in f:
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if line:
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line = line.decode("utf-8").strip()
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id_, transcript = line.split(" ", 1)
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audio_file = f"{id_}.flac"
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speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
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audio_file = (
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os.path.join(local_extracted_archive, audio_file)
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if local_extracted_archive
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else audio_file
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)
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transcripts.append(
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{
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"id": id_,
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"speaker_id": speaker_id,
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"chapter_id": chapter_id,
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"file": audio_file,
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"text": transcript,
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}
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)
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if audio_data and len(audio_data) == len(transcripts):
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for transcript in transcripts:
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audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
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yield key, {"audio": audio, **transcript}
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key += 1
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audio_data = {}
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transcripts = [] |
