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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TV3Parla."""
import re
import datasets
from datasets.tasks import AutomaticSpeechRecognition
_CITATION = """\
@inproceedings{kulebi18_iberspeech,
author={Baybars Külebi and Alp Öktem},
title={{Building an Open Source Automatic Speech Recognition System for Catalan}},
year=2018,
booktitle={Proc. IberSPEECH 2018},
pages={25--29},
doi={10.21437/IberSPEECH.2018-6}
}
"""
_DESCRIPTION = """\
This corpus includes 240 hours of Catalan speech from broadcast material.
The details of segmentation, data processing and also model training are explained in Külebi, Öktem; 2018.
The content is owned by Corporació Catalana de Mitjans Audiovisuals, SA (CCMA);
we processed their material and hereby making it available under their terms of use.
This project was supported by the Softcatalà Association.
"""
_HOMEPAGE = "https://collectivat.cat/asr#tv3parla"
_LICENSE = "Creative Commons Attribution-NonCommercial 4.0 International"
_REPO = "https://huggingface.co/datasets/collectivat/tv3_parla/resolve/main/"
_URLS = {
"transcripts": _REPO + "tv3_0.3_{split}.transcription",
"audio": _REPO + "tv3_0.3.tar.gz",
}
_SPLITS = [datasets.Split.TRAIN, datasets.Split.TEST]
_PATTERN = re.compile(r"^<s> (?P<text>.+) </s> \((?P<id>\S+)\)$")
class Tv3Parla(datasets.GeneratorBasedBuilder):
"""TV3Parla."""
VERSION = datasets.Version("0.3.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"path": datasets.Value("string"),
"audio": datasets.features.Audio(),
"text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
task_templates=[
AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="text")
],
)
def _split_generators(self, dl_manager):
urls = {
split: {key: url.format(split=split) for key, url in _URLS.items()} for split in _SPLITS
}
dl_dir = dl_manager.download(urls)
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"transcripts_path": dl_dir[split]["transcripts"],
"audio_files": dl_manager.iter_archive(dl_dir[split]["audio"]),
"split": split,
},
) for split in _SPLITS
]
def _generate_examples(self, transcripts_path, audio_files, split):
transcripts = {}
with open(transcripts_path, encoding="utf-8") as transcripts_file:
for line in transcripts_file:
match = _PATTERN.match(line)
transcripts[match["id"]] = match["text"]
# train: 159242; test: 2220
for key, (path, file) in enumerate(audio_files):
if path.endswith(".wav") and f"/{split}/" in path:
uid = path.split("/")[-1][:-4]
if uid not in transcripts:
continue
text = transcripts.pop(uid)
audio = {"path": path, "bytes": file.read()}
yield key, {"path": path, "audio": audio, "text": text}
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