import json import gzip import os from pathlib import Path import re from time import sleep import datasets import numpy as np from tqdm import tqdm import requests logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """\ Libriheavy is a labeled version of Librilight. This (unofficial) huggingface dataset contains the medium (4500 hours) split of the Libriheavy dataset with alignments and mel spectrograms. """ _URL = """\ https://github.com/k2-fsa/libriheavy """ _CITATION = """\ @article{kang2023libriheavy, title={Libriheavy: a 50,000 hours asr corpus with punctuation casing and context}, author={Kang, Wei and Yang, Xiaoyu and Yao, Zengwei and Kuang, Fangjun and Yang, Yifan and Guo, Liyong and Lin, Long and Povey, Daniel}, journal={arXiv preprint arXiv:2309.08105}, year={2023} } """ PATH = "./medium_data" class LibriheavyConfig(datasets.BuilderConfig): """BuilderConfig for Libriheavy.""" def __init__(self, **kwargs): """BuilderConfig for Libriheavy. Args: **kwargs: keyword arguments forwarded to super. """ super(LibriheavyConfig, self).__init__(**kwargs) class Libriheavy(datasets.GeneratorBasedBuilder): """Libriheavy dataset.""" BUILDER_CONFIGS = [ LibriheavyConfig(name="libriheavy", version=datasets.Version("1.0.0"), description="Libriheavy dataset."), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "speaker_id": datasets.Value("string"), "speaker_name": datasets.Value("string"), "speaker_vec": datasets.Sequence(datasets.Value("float32")), "audio": datasets.Value("string"), "text": datasets.Value("string"), "word_segments": datasets.Sequence( { "start": datasets.Value("float32"), "end": datasets.Value("float32"), "word": datasets.Value("string"), } ), "phone_segments": datasets.Sequence( { "start": datasets.Value("float32"), "end": datasets.Value("float32"), "phone": datasets.Value("string"), } ), "mel_spectrogram": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))), "attributes": datasets.Features( { "pitch": datasets.Sequence(datasets.Value("float32")), "energy": datasets.Sequence(datasets.Value("float32")), "snr": datasets.Sequence(datasets.Value("float32")), "srmr": datasets.Sequence(datasets.Value("float32")), } ), "overall_attributes": datasets.Features( { "pitch": datasets.Value("float32"), "energy": datasets.Value("float32"), "snr": datasets.Value("float32"), "srmr": datasets.Value("float32"), } ), } ), supervised_keys=None, homepage=_URL, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # first, we load speaker_list.json speaker_list = f"{PATH}/speaker_list.json" speaker_list = dl_manager.download_and_extract(speaker_list) with open(speaker_list, "r") as f: speaker_list = json.load(f) # now we load the individual speaker metadata speaker_metadata = {} for speaker_id, metadata_path in tqdm(speaker_list.items()): hf_home = os.environ.get("HF_HOME", "~/.cache/huggingface") metadata_cache = f"{hf_home}/libriheavy_metadata" # we always cache the speaker metadata, as it is small if os.path.exists(f"{metadata_cache}/{speaker_id}.json"): with open(f"{metadata_cache}/{speaker_id}.json", "r") as f: speaker_metadata[speaker_id] = json.load(f) else: Path(metadata_cache).mkdir(parents=True, exist_ok=True) metadata_path = f"{PATH}/{speaker_id}/{metadata_path}" metadata_path = dl_manager.download_and_extract(metadata_path) with open(metadata_path, "r") as f: speaker_metadata[speaker_id] = json.load(f) try: speaker_name = requests.get(f"https://librivox.org/reader/{speaker_id}").text speaker_name = re.findall("

([^<>]+)

", speaker_name)[0] sleep(0.5) except IndexError: print(f"No name found for speaker with id {speaker_id}") speaker_name = "None" speaker_metadata[speaker_id]["name"] = speaker_name with open(f"{metadata_cache}/{speaker_id}.json", "w") as f: json.dump(speaker_metadata[speaker_id], f) speaker_chunks = [] even_speaker_chunks = [] odd_speaker_chunks = [] for speaker_id, metadata in speaker_metadata.items(): for chunk_id, chunk in metadata["chunks"].items(): chunk_dict = { "speaker_id": speaker_id, "speaker_name": metadata["name"], "id": f"{speaker_id}_{chunk_id}", "audio": dl_manager.download(f"{PATH}/{speaker_id}/{chunk['npz'].replace('.gz', '')}"), "text": dl_manager.download(f"{PATH}/{speaker_id}/{chunk['json']}"), } speaker_chunks.append(chunk_dict) if int(chunk_id) % 2 == 0: even_speaker_chunks.append(chunk_dict) else: odd_speaker_chunks.append(chunk_dict) # shuffle the chunks np.random.seed(42) np.random.shuffle(speaker_chunks) return [ datasets.SplitGenerator( name="train", gen_kwargs={"speaker_chunks": speaker_chunks, "split": "train"} ), datasets.SplitGenerator( name="validation", gen_kwargs={"speaker_chunks": speaker_chunks, "split": "validation"} ), datasets.SplitGenerator( name="even", gen_kwargs={"speaker_chunks": even_speaker_chunks, "split": "even"} ), datasets.SplitGenerator( name="odd", gen_kwargs={"speaker_chunks": odd_speaker_chunks, "split": "odd"} ), datasets.SplitGenerator( name="even100", gen_kwargs={"speaker_chunks": even_speaker_chunks, "split": "even", "hours": 100} ), datasets.SplitGenerator( name="odd100", gen_kwargs={"speaker_chunks": odd_speaker_chunks, "split": "odd", "hours": 100} ), datasets.SplitGenerator( name="even500", gen_kwargs={"speaker_chunks": even_speaker_chunks, "split": "even", "hours": 500} ), datasets.SplitGenerator( name="odd500", gen_kwargs={"speaker_chunks": odd_speaker_chunks, "split": "odd", "hours": 500} ), datasets.SplitGenerator( name="even1000", gen_kwargs={"speaker_chunks": even_speaker_chunks, "split": "even", "hours": 1000} ), datasets.SplitGenerator( name="odd1000", gen_kwargs={"speaker_chunks": odd_speaker_chunks, "split": "odd", "hours": 1000} ), ] def _generate_examples(self, speaker_chunks, split, hours=None): """Yields examples.""" hours_streamed = 0 finish_stream = False if hours is None: hours = float("inf") for chunk in speaker_chunks: if finish_stream: break retry = 0 while retry < 10: try: npz = dict(np.load(chunk["audio"], allow_pickle=True)) break except Exception as e: print(e, "retrying in 60s") sleep(60) retry += 1 utterances = npz.keys() with gzip.open(chunk["text"], "rt") as f: text = json.load(f) if split in ["train", "even", "odd"]: for utterance_id, utterance in text.items(): # skip the last utterance if utterance_id == sorted(list(text.keys()))[-1]: continue npz_item = npz[str(utterance_id)].item() result = { "id": chunk["speaker_id"] + "_" + utterance_id, "speaker_id": chunk["speaker_id"], "speaker_name": chunk["speaker_name"], "speaker_vec": npz_item["d_vector"][0], "audio": chunk["audio"], "text": " ".join([segment[2] for segment in utterance["word_segments"] if "<" not in segment[2]]), "word_segments": [ {"start": segment[0], "end": segment[1], "word": segment[2]} for segment in utterance["word_segments"] ], "phone_segments": [ {"start": segment[0], "end": segment[1], "phone": segment[2]} for segment in utterance["phone_segments"] ], "mel_spectrogram": npz_item["mel"][0][0], "attributes": { "pitch": npz_item["pitch"][0], "energy": npz_item["energy"][0], "snr": npz_item["snr"][0], "srmr": npz_item["srmr"][0], }, "overall_attributes": { "pitch": npz_item["overall_pitch"], "energy": npz_item["overall_energy"], "snr": npz_item["overall_snr"], "srmr": npz_item["overall_srmr"], }, } hours_streamed += (utterance["word_segments"][-1][1] - utterance["word_segments"][0][0]) / 3600 yield chunk["speaker_id"] + "_" + utterance_id, result if hours_streamed >= hours: finish_stream = True break else: # only use the last utterance utterance_id = sorted(list(text.keys()))[-1] utterance = text[utterance_id] npz_item = npz[str(utterance_id)].item() result = { "id": chunk["speaker_id"] + "_" + utterance_id, "speaker_id": chunk["speaker_id"], "speaker_vec": npz_item["d_vector"][0], "speaker_name": chunk["speaker_name"], "audio": chunk["audio"], "text": " ".join([segment[2] for segment in utterance["word_segments"] if "<" not in segment[2]]), "word_segments": [ {"start": segment[0], "end": segment[1], "word": segment[2]} for segment in utterance["word_segments"] ], "phone_segments": [ {"start": segment[0], "end": segment[1], "phone": segment[2]} for segment in utterance["phone_segments"] ], "mel_spectrogram": npz_item["mel"][0][0], "attributes": { "pitch": npz_item["pitch"][0], "energy": npz_item["energy"][0], "snr": npz_item["snr"][0], "srmr": npz_item["srmr"][0], }, "overall_attributes": { "pitch": npz_item["overall_pitch"], "energy": npz_item["overall_energy"], "snr": npz_item["overall_snr"], "srmr": npz_item["overall_srmr"], }, } hours_streamed += (utterance["word_segments"][-1][1] - utterance["word_segments"][0][0]) / 3600 yield chunk["speaker_id"] + "_" + utterance_id, result if hours_streamed >= hours: finish_stream = True break