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import datasets |
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from datasets.tasks import AutomaticSpeechRecognition |
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import os |
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_CITATION = """\ |
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@inproceedings{panayotov2015librispeech, |
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title={Myspeech: 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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MySpeech 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 = "/content/drive/MyDrive/" |
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_DL_URLS = { |
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"clean": { |
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"dev": _DL_URL + "dev-other.tar.gz", |
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"test": _DL_URL + "dev-other.tar.gz", |
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"train.100": _DL_URL + "dev-other.tar.gz", |
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"train.360": _DL_URL + "dev-other.tar.gz", |
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}, |
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"other": { |
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"test": _DL_URL + "dev-other.tar.gz", |
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"dev": _DL_URL + "dev-other.tar.gz", |
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"train.500": _DL_URL + "dev-other.tar.gz", |
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}, |
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"all": { |
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"dev.clean": _DL_URL + "dev-other.tar.gz", |
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"dev.other": _DL_URL + "dev-other.tar.gz", |
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"test.clean": _DL_URL + "dev-other.tar.gz", |
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"test.other": _DL_URL + "dev-other.tar.gz", |
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"train.clean.100": _DL_URL + "dev-other.tar.gz", |
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"train.clean.360": _DL_URL + "dev-other.tar.gz", |
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"train.other.500": _DL_URL + "dev-other.tar.gz", |
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}, |
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} |
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class MyspeechASRConfig(datasets.BuilderConfig): |
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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(MyspeechASRConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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class MyspeechASR(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_CONFIGS = [ |
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MyspeechASRConfig(name="clean", description="'Clean' speech."), |
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MyspeechASRConfig(name="other", description="'Other', more challenging, speech."), |
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MyspeechASRConfig(name="all", description="Combined clean and other dataset."), |
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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=("file", "text"), |
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homepage=_URL, |
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citation=_CITATION, |
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task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")], |
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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[self.config.name]) |
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local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {} |
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if self.config.name == "clean": |
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train_splits = [ |
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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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datasets.SplitGenerator( |
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name="train.360", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("train.360"), |
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"files": dl_manager.iter_archive(archive_path["train.360"]), |
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}, |
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), |
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] |
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dev_splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("dev"), |
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"files": dl_manager.iter_archive(archive_path["dev"]), |
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}, |
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) |
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] |
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test_splits = [ |
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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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elif self.config.name == "other": |
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train_splits = [ |
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datasets.SplitGenerator( |
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name="train.500", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("train.500"), |
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"files": dl_manager.iter_archive(archive_path["train.500"]), |
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}, |
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) |
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] |
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dev_splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("dev"), |
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"files": dl_manager.iter_archive(archive_path["dev"]), |
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}, |
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) |
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] |
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test_splits = [ |
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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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elif self.config.name == "all": |
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train_splits = [ |
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datasets.SplitGenerator( |
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name="train.clean.100", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("train.clean.100"), |
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"files": dl_manager.iter_archive(archive_path["train.clean.100"]), |
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}, |
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), |
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datasets.SplitGenerator( |
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name="train.clean.360", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("train.clean.360"), |
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"files": dl_manager.iter_archive(archive_path["train.clean.360"]), |
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}, |
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), |
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datasets.SplitGenerator( |
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name="train.other.500", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("train.other.500"), |
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"files": dl_manager.iter_archive(archive_path["train.other.500"]), |
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}, |
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), |
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] |
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dev_splits = [ |
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datasets.SplitGenerator( |
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name="validation.clean", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("validation.clean"), |
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"files": dl_manager.iter_archive(archive_path["dev.clean"]), |
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}, |
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), |
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datasets.SplitGenerator( |
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name="validation.other", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("validation.other"), |
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"files": dl_manager.iter_archive(archive_path["dev.other"]), |
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}, |
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), |
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] |
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test_splits = [ |
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datasets.SplitGenerator( |
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name="test.clean", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("test.clean"), |
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"files": dl_manager.iter_archive(archive_path["test.clean"]), |
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}, |
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), |
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datasets.SplitGenerator( |
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name="test.other", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("test.other"), |
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"files": dl_manager.iter_archive(archive_path["test.other"]), |
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}, |
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), |
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] |
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return train_splits + dev_splits + test_splits |
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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 = [] |
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