Delete loading script

tv3_parla.py

@@ -1,111 +0,0 @@
-# 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(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}