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"""Librispeech automatic speech recognition dataset."""
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from __future__ import absolute_import, division, print_function
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import glob
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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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Note that in order to limit the required storage for preparing this dataset, the audio
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is stored in the .flac format and is not converted to a float32 array. To convert, the audio
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file to a float32 array, please make use of the `.map()` function as follows:
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```python
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import soundfile as sf
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def map_to_array(batch):
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speech_array, _ = sf.read(batch["file"])
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batch["speech"] = speech_array
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return batch
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dataset = dataset.map(map_to_array, remove_columns=["file"])
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```
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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 = {
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"clean": {
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"test": _DL_URL + "test-clean.tar.gz",
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},
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"other": {
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"test": _DL_URL + "test-other.tar.gz",
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},
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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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BUILDER_CONFIGS = [
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LibrispeechASRConfig(name="clean", description="'Clean' speech."),
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LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."),
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]
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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=("speech", "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_and_extract(_DL_URLS[self.config.name])
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return [
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path["dev"], "split_name": f"dev_{self.config.name}"}),
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]
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def _generate_examples(self, archive_path, split_name):
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"""Generate examples from a Librispeech archive_path."""
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transcripts_glob = os.path.join(archive_path, split_name, "*/*/*.txt")
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for transcript_file in glob.glob(transcripts_glob):
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path = os.path.dirname(transcript_file)
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with open(os.path.join(path, transcript_file)) as f:
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for line in f:
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line = line.strip()
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key, transcript = line.split(" ", 1)
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audio_file = f"{key}.flac"
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speaker_id, chapter_id = [int(el) for el in key.split("-")[:2]]
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example = {
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"id": key,
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"speaker_id": speaker_id,
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"chapter_id": chapter_id,
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"file": os.path.join(path, audio_file),
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"audio": os.path.join(path, audio_file),
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"text": transcript,
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}
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yield key, example |