Convert dataset to Parquet (part 00001-of-00002)

LICENSE.txt

@@ -1,11 +0,0 @@
-Copyright (c) 2018 Col·lectivaT SCCL
-
-The TV3Parla speech corpus is distributed under a Creative Commons
-Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.
-For details see <https://creativecommons.org/licenses/by-nc/4.0/>
-
-The audio files and transcriptions that are the basis of the corpus are the
-property of the Corporació Catalana de Mitjans Audiovisuals, SA (CCMA). Any
-derivative work needs to refer to Parlament de Catalunya as the original owner
-of the content and confirm with their terms of use as explained in their
-website. <http://www.ccma.cat/avis-legal/condicions-utilitzacio-del-portal/>

README.md

@@ -19,6 +19,30 @@ task_categories:
 task_ids:
 - language-modeling
 pretty_name: TV3Parla
+dataset_info:
+  features:
+  - name: path
+    dtype: string
+  - name: audio
+    dtype: audio
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 33609299680.048
+    num_examples: 159242
+  - name: test
+    num_bytes: 627492346.34
+    num_examples: 2220
+  download_size: 34204460155
+  dataset_size: 34236792026.388
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for TV3Parla

tv3_0.3_train.fileids → data/test-00000-of-00002.parquet

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tv3_0.3_train.transcription → data/test-00001-of-00002.parquet

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tv3_0.3_test.fileids → data/train-00050-of-00068.parquet

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tv3_0.3.tar.gz

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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}