nena_speech_1_0.py

""" NENA Speech Dataset"""


import csv
import os
import json

import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm


# _CITATION = """\
# """

# _HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"

# _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"

# TODO: change this
_BASE_URL = "./"

_AUDIO_URL = _BASE_URL + "audio/{dialect}/{split}.tar"

_TRANSCRIPT_URL = _BASE_URL + "transcript/{dialect}/{split}.tsv"

import datasets

class NENASpeechConfig(datasets.BuilderConfig):
    """BuilderConfig for NENASpeech."""
    def __init__(self, name, version, **kwargs):
        self.language = kwargs.pop("language", None)
        description = (
            f"This is a test. "
        )
        super(NENASpeechConfig, self).__init__(
            name=name,
            version=datasets.Version(version),
            description=description,
            **kwargs,
        )

class NENASpeech(datasets.GeneratorBasedBuilder):
    DEFAULT_WRITER_BATCH_SIZE = 1000

    BUILDER_CONFIGS = [
        NENASpeechConfig(
            name='curmi',
            version='1.0.4',
            language='assyrian',
        ),
        NENASpeechConfig(
            name='jurmi',
            version='1.0.4',
            language='assyrian',
        )
        # for lang, lang_stats in STATS["locales"].items()
    ]

    def _info(self):
        # total_languages = len(STATS["locales"])
        # total_valid_hours = STATS["totalValidHrs"]
        description = (
            "description from _info"
            # "Common Voice is Mozilla's initiative to help teach machines how real people speak. "
            # f"The dataset currently consists of {total_valid_hours} validated hours of speech "
            # f" in {total_languages} languages, but more voices and languages are always added."
        )
        features = datasets.Features(
            {
                "transcription": datasets.Value("string"),
                "translation": datasets.Value("string"),
                "audio": datasets.features.Audio(sampling_rate=48_000),
                "path": datasets.Value("string"),
                "age": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=description,
            # citation=_CITATION,
            # homepage=_HOMEPAGE,
            # license=_LICENSE,
            features=features,
            supervised_keys=None,
            version=self.config.version,
        )

    def _split_generators(self, dl_manager):
        dialect = self.config.name

        audio_urls = {}
        splits = ("train", "dev", "test", "other", "invalidated")
        for split in splits:
            audio_urls[split] = _AUDIO_URL.format(dialect=dialect, split=split)
        archive_paths = dl_manager.download(audio_urls)
        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}

        meta_urls = {split: _TRANSCRIPT_URL.format(dialect=dialect, split=split) for split in splits}
        meta_paths = dl_manager.download_and_extract(meta_urls)

        split_generators = []
        split_names = {
            "train": datasets.Split.TRAIN,
            "dev": datasets.Split.VALIDATION,
            "test": datasets.Split.TEST,
        }
        for split in splits:
            split_generators.append(
                datasets.SplitGenerator(
                    name=split_names.get(split, split),
                    gen_kwargs={
                        "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
                        "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
                        "meta_path": meta_paths[split],
                    },
                ),
            )

        return split_generators

    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
        data_fields = list(self._info().features.keys())
        metadata = {}
        with open(meta_path, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            for row in tqdm(reader, desc="Reading metadata..."):
                if not row["path"].endswith(".mp3"):
                    row["path"] += ".mp3"
                # accent -> accents in CV 8.0
                if "accents" in row:
                    row["accent"] = row["accents"]
                    del row["accents"]
                # if data is incomplete, fill with empty values
                for field in data_fields:
                    if field not in row:
                        row[field] = ""
                metadata[row["path"]] = row

        for i, audio_archive in enumerate(archives):
            for path, file in audio_archive:
                _, filename = os.path.split(path)
                if filename in metadata:
                    result = dict(metadata[filename])
                    # set the audio feature and the path to the extracted file
                    path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
                    result["audio"] = {"path": path, "bytes": file.read()}
                    result["path"] = path
                    yield path, result