Add dataset loading script

tv3_parla.py

@@ -0,0 +1,109 @@
+# 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/albertvillanova/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]
+                text = transcripts.pop(uid)
+                audio = {"path": path, "bytes": file.read()}
+                yield key, {"path": path, "audio": audio, "text": text}