libriheavy.py

import json
import gzip
import os
from pathlib import Path
import re

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_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)
                speaker_name = requests.get(f"https://librivox.org/reader/{speaker_id}").text
                speaker_name = re.findall("<h1>([^<>]+)</h1>", speaker_name)[0]
                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"}
            ),
        ]

    def _generate_examples(self, speaker_chunks, split):
        """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)
            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": chunk["text"],
                        "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"],
                        },
                    }
                    yield chunk["speaker_id"] + "_" + utterance_id, result
            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],
                    "audio": chunk["audio"],
                    "text": chunk["text"],
                    "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"],
                    },
                }
                yield chunk["speaker_id"] + "_" + utterance_id, result