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import json |
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import gzip |
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import datasets |
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import numpy as np |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """\ |
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Libriheavy is a labeled version of Librilight. |
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This (unofficial) huggingface dataset contains the medium (4500 hours) split of the Libriheavy dataset with alignments and mel spectrograms. |
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""" |
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_URL = """\ |
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https://github.com/k2-fsa/libriheavy |
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""" |
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_CITATION = """\ |
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@article{kang2023libriheavy, |
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title={Libriheavy: a 50,000 hours asr corpus with punctuation casing and context}, |
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author={Kang, Wei and Yang, Xiaoyu and Yao, Zengwei and Kuang, Fangjun and Yang, Yifan and Guo, Liyong and Lin, Long and Povey, Daniel}, |
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journal={arXiv preprint arXiv:2309.08105}, |
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year={2023} |
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} |
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""" |
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class LibriheavyConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Libriheavy.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Libriheavy. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(LibriheavyConfig, self).__init__(**kwargs) |
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class Libriheavy(datasets.GeneratorBasedBuilder): |
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"""Libriheavy dataset.""" |
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BUILDER_CONFIGS = [ |
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LibriheavyConfig(name="libriheavy", version=datasets.Version("1.0.0"), description="Libriheavy 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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"id": datasets.Value("string"), |
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"speaker_id": datasets.Value("string"), |
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"audio": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"word_segments": datasets.Sequence( |
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{ |
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"start": datasets.Value("int32"), |
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"end": datasets.Value("int32"), |
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"word": datasets.Value("string"), |
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} |
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), |
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"mel_spectrogram": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))), |
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} |
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), |
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supervised_keys=None, |
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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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"""Returns SplitGenerators.""" |
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speaker_list = "medium_data/speaker_list.json" |
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speaker_list = dl_manager.download_and_extract(speaker_list) |
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with open(speaker_list, "r") as f: |
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speaker_list = json.load(f) |
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speaker_metadata = {} |
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for speaker_id, metadata_path in speaker_list.items(): |
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metadata_path = f"medium_data/{metadata_path}" |
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metadata_path = dl_manager.download_and_extract(metadata_path) |
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with open(metadata_path, "r") as f: |
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speaker_metadata[speaker_id] = json.load(f) |
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speaker_chunks = [] |
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for speaker_id, metadata in speaker_metadata.items(): |
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for chunk_id, chunk in metadata["chunks"].items(): |
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speaker_chunks.append( |
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{ |
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"speaker_id": speaker_id, |
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"id": f"{speaker_id}_{chunk_id}", |
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"audio": dl_manager.download(f"medium_data/{chunk['npz'].replace('.gz', '')}"), |
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"text": dl_manager.download(f"medium_data/{chunk['json']}"), |
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} |
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) |
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np.random.seed(42) |
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np.random.shuffle(speaker_chunks) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"speaker_chunks": speaker_chunks}, |
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) |
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] |
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def _generate_examples(self, speaker_chunks): |
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"""Yields examples.""" |
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for chunk in speaker_chunks: |
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npz = dict(np.load(chunk["audio"], allow_pickle=True)) |
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utterances = npz.keys() |
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with gzip.open(chunk["text"], "rt") as f: |
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text = json.load(f) |
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for utterance_id, utterance in text.items(): |
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result = { |
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"id": chunk["speaker_id"] + "_" + utterance_id, |
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"speaker_id": chunk["speaker_id"], |
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"audio": chunk["audio"], |
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"text": chunk["text"], |
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"word_segments": [ |
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{"start": segment[0], "end": segment[1], "word": segment[2]} for segment in utterance["word_segments"] |
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], |
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"mel_spectrogram": npz[str(utterance_id)].item()["mel"][0][0], |
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} |
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yield chunk["speaker_id"] + "_" + utterance_id, result |
