|
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)) |
|
|
|
|
|
all_files_filtered = filter(lambda x: x != "Readme.txt" and x != "/nota/", all_files) |
|
|
|
for download_file in all_files_filtered: |
|
|
|
if "INSL20210003.zip" in download_file: |
|
continue |
|
|
|
|
|
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 = {} |
|
|
