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import json |
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import gzip |
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import os |
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from pathlib import Path |
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import re |
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from time import sleep |
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import datasets |
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import numpy as np |
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from tqdm import tqdm |
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import requests |
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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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PATH = "./medium_data" |
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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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"speaker_name": datasets.Value("string"), |
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"speaker_vec": datasets.Sequence(datasets.Value("float32")), |
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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("float32"), |
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"end": datasets.Value("float32"), |
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"word": datasets.Value("string"), |
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} |
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), |
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"phone_segments": datasets.Sequence( |
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{ |
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"start": datasets.Value("float32"), |
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"end": datasets.Value("float32"), |
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"phone": 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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"attributes": datasets.Features( |
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{ |
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"pitch": datasets.Sequence(datasets.Value("float32")), |
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"energy": datasets.Sequence(datasets.Value("float32")), |
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"snr": datasets.Sequence(datasets.Value("float32")), |
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"srmr": datasets.Sequence(datasets.Value("float32")), |
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} |
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), |
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"overall_attributes": datasets.Features( |
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{ |
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"pitch": datasets.Value("float32"), |
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"energy": datasets.Value("float32"), |
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"snr": datasets.Value("float32"), |
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"srmr": datasets.Value("float32"), |
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} |
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), |
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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 = f"{PATH}/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 tqdm(speaker_list.items()): |
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hf_home = os.environ.get("HF_HOME", "~/.cache/huggingface") |
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metadata_cache = f"{hf_home}/libriheavy_metadata" |
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if os.path.exists(f"{metadata_cache}/{speaker_id}.json"): |
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with open(f"{metadata_cache}/{speaker_id}.json", "r") as f: |
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speaker_metadata[speaker_id] = json.load(f) |
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else: |
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Path(metadata_cache).mkdir(parents=True, exist_ok=True) |
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metadata_path = f"{PATH}/{speaker_id}/{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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try: |
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speaker_name = requests.get(f"https://librivox.org/reader/{speaker_id}").text |
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speaker_name = re.findall("<h1>([^<>]+)</h1>", speaker_name)[0] |
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sleep(0.5) |
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except IndexError: |
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print(f"No name found for speaker with id {speaker_id}") |
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speaker_name = "None" |
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speaker_metadata[speaker_id]["name"] = speaker_name |
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with open(f"{metadata_cache}/{speaker_id}.json", "w") as f: |
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json.dump(speaker_metadata[speaker_id], f) |
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speaker_chunks = [] |
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even_speaker_chunks = [] |
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odd_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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chunk_dict = { |
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"speaker_id": speaker_id, |
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"speaker_name": metadata["name"], |
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"id": f"{speaker_id}_{chunk_id}", |
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"audio": dl_manager.download(f"{PATH}/{speaker_id}/{chunk['npz'].replace('.gz', '')}"), |
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"text": dl_manager.download(f"{PATH}/{speaker_id}/{chunk['json']}"), |
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} |
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speaker_chunks.append(chunk_dict) |
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if int(chunk_id) % 2 == 0: |
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even_speaker_chunks.append(chunk_dict) |
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else: |
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odd_speaker_chunks.append(chunk_dict) |
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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="train", |
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gen_kwargs={"speaker_chunks": speaker_chunks, "split": "train"} |
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), |
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datasets.SplitGenerator( |
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name="validation", |
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gen_kwargs={"speaker_chunks": speaker_chunks, "split": "validation"} |
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), |
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datasets.SplitGenerator( |
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name="even", |
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gen_kwargs={"speaker_chunks": even_speaker_chunks, "split": "even"} |
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), |
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datasets.SplitGenerator( |
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name="odd", |
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gen_kwargs={"speaker_chunks": odd_speaker_chunks, "split": "odd"} |
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), |
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] |
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def _generate_examples(self, speaker_chunks, split): |
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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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if split in ["train", "even", "odd"]: |
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for utterance_id, utterance in text.items(): |
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if utterance_id == sorted(list(text.keys()))[-1]: |
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continue |
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npz_item = npz[str(utterance_id)].item() |
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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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"speaker_name": chunk["speaker_name"], |
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"speaker_vec": npz_item["d_vector"][0], |
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"audio": chunk["audio"], |
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"text": " ".join([segment[2] for segment in utterance["word_segments"] if "<" not in segment[2]]), |
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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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"phone_segments": [ |
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{"start": segment[0], "end": segment[1], "phone": segment[2]} for segment in utterance["phone_segments"] |
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], |
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"mel_spectrogram": npz_item["mel"][0][0], |
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"attributes": { |
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"pitch": npz_item["pitch"][0], |
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"energy": npz_item["energy"][0], |
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"snr": npz_item["snr"][0], |
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"srmr": npz_item["srmr"][0], |
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}, |
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"overall_attributes": { |
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"pitch": npz_item["overall_pitch"], |
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"energy": npz_item["overall_energy"], |
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"snr": npz_item["overall_snr"], |
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"srmr": npz_item["overall_srmr"], |
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}, |
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} |
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yield chunk["speaker_id"] + "_" + utterance_id, result |
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else: |
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utterance_id = sorted(list(text.keys()))[-1] |
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utterance = text[utterance_id] |
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npz_item = npz[str(utterance_id)].item() |
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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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"speaker_vec": npz_item["d_vector"][0], |
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"speaker_name": chunk["speaker_name"], |
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"audio": chunk["audio"], |
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"text": " ".join([segment[2] for segment in utterance["word_segments"] if "<" not in segment[2]]), |
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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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"phone_segments": [ |
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{"start": segment[0], "end": segment[1], "phone": segment[2]} for segment in utterance["phone_segments"] |
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], |
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"mel_spectrogram": npz_item["mel"][0][0], |
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"attributes": { |
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"pitch": npz_item["pitch"][0], |
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"energy": npz_item["energy"][0], |
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"snr": npz_item["snr"][0], |
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"srmr": npz_item["srmr"][0], |
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}, |
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"overall_attributes": { |
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"pitch": npz_item["overall_pitch"], |
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"energy": npz_item["overall_energy"], |
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"snr": npz_item["overall_snr"], |
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"srmr": npz_item["overall_srmr"], |
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}, |
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} |
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yield chunk["speaker_id"] + "_" + utterance_id, result |
