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import json
import gzip
import datasets
import numpy as np
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}
}
"""
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"),
"audio": datasets.Value("string"),
"text": datasets.Value("string"),
"word_segments": datasets.Sequence(
{
"start": datasets.Value("int32"),
"end": datasets.Value("int32"),
"word": datasets.Value("string"),
}
),
"mel_spectrogram": datasets.Sequence(datasets.Sequence(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 = "medium_data/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 speaker_list.items():
metadata_path = f"medium_data/{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)
speaker_chunks = []
for speaker_id, metadata in speaker_metadata.items():
for chunk_id, chunk in metadata["chunks"].items():
speaker_chunks.append(
{
"speaker_id": speaker_id,
"id": f"{speaker_id}_{chunk_id}",
"audio": dl_manager.download(f"medium_data/{chunk['npz'].replace('.gz', '')}"),
"text": dl_manager.download(f"medium_data/{chunk['json']}"),
}
)
# shuffle the chunks
np.random.seed(42)
np.random.shuffle(speaker_chunks)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"speaker_chunks": speaker_chunks},
)
]
def _generate_examples(self, speaker_chunks):
"""Yields examples."""
for chunk in speaker_chunks:
npz = dict(np.load(chunk["audio"], allow_pickle=True))
utterances = npz.keys()
with gzip.open(chunk["text"], "rt") as f:
text = json.load(f)
for utterance_id, utterance in text.items():
result = {
"id": chunk["speaker_id"] + "_" + utterance_id,
"speaker_id": chunk["speaker_id"],
"audio": chunk["audio"],
"text": chunk["text"],
"word_segments": [
{"start": segment[0], "end": segment[1], "word": segment[2]} for segment in utterance["word_segments"]
],
"mel_spectrogram": npz[str(utterance_id)].item()["mel"][0][0],
}
yield chunk["speaker_id"] + "_" + utterance_id, result |