import datasets from datasets.tasks import AutomaticSpeechRecognition import os _CITATION = """\ @inproceedings{panayotov2015librispeech, title={Myspeech: 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 = """\ MySpeech 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 = "/content/drive/MyDrive/" _DL_URLS = { "clean": { "dev": _DL_URL + "dev-other.tar.gz", "test": _DL_URL + "dev-other.tar.gz", "train.100": _DL_URL + "dev-other.tar.gz", "train.360": _DL_URL + "dev-other.tar.gz", }, "other": { "test": _DL_URL + "dev-other.tar.gz", "dev": _DL_URL + "dev-other.tar.gz", "train.500": _DL_URL + "dev-other.tar.gz", }, "all": { "dev.clean": _DL_URL + "dev-other.tar.gz", "dev.other": _DL_URL + "dev-other.tar.gz", "test.clean": _DL_URL + "dev-other.tar.gz", "test.other": _DL_URL + "dev-other.tar.gz", "train.clean.100": _DL_URL + "dev-other.tar.gz", "train.clean.360": _DL_URL + "dev-other.tar.gz", "train.other.500": _DL_URL + "dev-other.tar.gz", }, } class MyspeechASRConfig(datasets.BuilderConfig): 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(MyspeechASRConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) class MyspeechASR(datasets.GeneratorBasedBuilder): """Librispeech dataset.""" DEFAULT_WRITER_BATCH_SIZE = 256 DEFAULT_CONFIG_NAME = "all" BUILDER_CONFIGS = [ MyspeechASRConfig(name="clean", description="'Clean' speech."), MyspeechASRConfig(name="other", description="'Other', more challenging, speech."), MyspeechASRConfig(name="all", description="Combined clean and other dataset."), ] 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, task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")], ) def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) # (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 {} if self.config.name == "clean": train_splits = [ 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"]), }, ), datasets.SplitGenerator( name="train.360", gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("train.360"), "files": dl_manager.iter_archive(archive_path["train.360"]), }, ), ] dev_splits = [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("dev"), "files": dl_manager.iter_archive(archive_path["dev"]), }, ) ] test_splits = [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("test"), "files": dl_manager.iter_archive(archive_path["test"]), }, ) ] elif self.config.name == "other": train_splits = [ datasets.SplitGenerator( name="train.500", gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("train.500"), "files": dl_manager.iter_archive(archive_path["train.500"]), }, ) ] dev_splits = [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("dev"), "files": dl_manager.iter_archive(archive_path["dev"]), }, ) ] test_splits = [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("test"), "files": dl_manager.iter_archive(archive_path["test"]), }, ) ] elif self.config.name == "all": train_splits = [ datasets.SplitGenerator( name="train.clean.100", gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("train.clean.100"), "files": dl_manager.iter_archive(archive_path["train.clean.100"]), }, ), datasets.SplitGenerator( name="train.clean.360", gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("train.clean.360"), "files": dl_manager.iter_archive(archive_path["train.clean.360"]), }, ), datasets.SplitGenerator( name="train.other.500", gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("train.other.500"), "files": dl_manager.iter_archive(archive_path["train.other.500"]), }, ), ] dev_splits = [ datasets.SplitGenerator( name="validation.clean", gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("validation.clean"), "files": dl_manager.iter_archive(archive_path["dev.clean"]), }, ), datasets.SplitGenerator( name="validation.other", gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("validation.other"), "files": dl_manager.iter_archive(archive_path["dev.other"]), }, ), ] test_splits = [ datasets.SplitGenerator( name="test.clean", gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("test.clean"), "files": dl_manager.iter_archive(archive_path["test.clean"]), }, ), datasets.SplitGenerator( name="test.other", gen_kwargs={ "local_extracted_archive": local_extracted_archive.get("test.other"), "files": dl_manager.iter_archive(archive_path["test.other"]), }, ), ] return train_splits + dev_splits + test_splits 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 = []