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Datasets:
arpelarpe
/
nota

Tasks:
Automatic Speech Recognition
Languages:
Danish
Multilinguality:
monolingual
License:
Dataset card Files Community
nota
File size: 4,992 Bytes 
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import os
import re

import datasets
import requests
from datasets import AutomaticSpeechRecognition

_DATA_URLS = ["https://sprogtek-ressources.digst.govcloud.dk/nota/Inspiration%202016%20-%202021/",
              "https://sprogtek-ressources.digst.govcloud.dk/nota/Inspiration%202008%20-%202016/",
              "https://sprogtek-ressources.digst.govcloud.dk/nota/Radio-TV%20program%202007%20-%202012/",
              "https://sprogtek-ressources.digst.govcloud.dk/nota/Radio-TV%20Program%202013%20-%202015/",
              "https://sprogtek-ressources.digst.govcloud.dk/nota/Radio-TV%20Program%202016%20-%202018/",
              "https://sprogtek-ressources.digst.govcloud.dk/nota/Radio-TV%20Program%202019%20-%202022/"
              ]

_DESCRIPTION = """\
Nota lyd- og tekstdata
Datasættet indeholder både tekst- og taledata fra udvalgte dele af Nota's lydbogsbiblotek. Datasættet består af 
over 500 timers oplæsninger og medfølgende transkriptioner på dansk. Al lyddata er i .wav-format, mens tekstdata 
er i .txt-format.

I data indgår indlæsninger af Notas eget blad "Inspiration" og "Radio/TV", som er udgivet i perioden 2007 til 2022.
Nota krediteres for arbejdet med at strukturere data, således at tekst og lyd stemmer overens.

Nota er en institution under Kulturministeriet, der gør trykte tekster tilgængelige i digitale formater til personer 
med synshandicap og læsevanskeligheder, fx via produktion af lydbøger og oplæsning af aviser, magasiner, mv.
"""

_HOMEPAGE = "https://sprogteknologi.dk/dataset/notalyd-ogtekstdata"

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


def extract_file_links():
    """
    Extracts the web locations of the zip files containing the data
    :return: List of web urls
    """
    download_paths = []

    download_files_regex = re.compile("<a href=\"(.+?)\">")

    for download_root in _DATA_URLS:
        r = requests.get(download_root)
        all_files = download_files_regex.findall(str(r.content))

        # We ignore Parent and Readme files
        all_files_filtered = filter(lambda x: x != "Readme.txt" and x != "/nota/", all_files)

        for download_file in all_files_filtered:
            # Empty file
            if "INSL20210003.zip" in download_file:
                continue

            # Because of wget behaviour, we have to replace correct %20 with space
            full_download_path = download_root + download_file
            full_download_path = full_download_path.replace("%20", " ")
            download_paths.append(full_download_path)

    return download_paths


class NotaDanishSoundAndTextDataset(datasets.GeneratorBasedBuilder):
    DEFAULT_CONFIG_NAME = "all"

    def _info(self):
        features = datasets.Features(
            {
                "audio": datasets.Audio(sampling_rate=44_100),
                "sentence": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="sentence")],
        )

    def _split_generators(self, dl_manager):
        download_urls = extract_file_links()
        dl_path = dl_manager.download_and_extract(download_urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "dl_path": dl_path,
                },
            )
        ]

    @staticmethod
    def _extract_transcript(file_path):
        with open(file_path, "r", encoding="utf-8") as f:
            data = f.read()
        return data

    def _generate_examples(self, dl_path):
        key = 0
        transcripts = {}

        for parent_directory in dl_path:
            parent_directory_path = os.listdir(os.path.join(dl_path, parent_directory))
            for sub_directory in parent_directory_path:
                data_directory_path = os.path.join(dl_path, parent_directory, sub_directory)
                data_files = os.listdir(data_directory_path)
                for data_file in data_files:
                    file_type = data_file[-3:]
                    file_id = data_file[:-4]
                    if file_id not in transcripts:
                        transcripts[file_id] = {}

                    if file_type == "wav":
                        transcripts[file_id]["audio_path"] = os.path.join(data_directory_path, data_file)
                    elif file_type == "txt":
                        transcripts[file_id]["sentence"] = self._extract_transcript(
                            os.path.join(data_directory_path, data_file))

        for sample_id, info in transcripts.items():
            audio = {"path": info["audio_path"]}
            yield key, {"audio": audio, "sentence": info["sentence"]}
            key += 1

        transcripts = {}