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"""Librispeech automatic speech recognition dataset.""" |
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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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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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] |
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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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"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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manual_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) |
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"archive_path": manual_dir})] |
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def _generate_examples(self, archive_path): |
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"""Generate examples from a Librispeech archive_path.""" |
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transcripts_glob = os.path.join(archive_path, "LibriSpeech", "*/*/*/*.txt") |
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for transcript_file in sorted(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), "r", encoding="utf-8") 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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"text": transcript, |
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} |
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yield key, example |
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