Upload Openslr.py

Openslr.py

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+# coding=utf-8
+# Copyright 2021 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.
+""" OpenSLR Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+import re
+from pathlib import Path
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_DATA_URL = "https://openslr.org/resources/{}"
+
+_CITATION = """\
+SLR70, SLR71:
+@inproceedings{guevara-rukoz-etal-2020-crowdsourcing,
+    title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}},
+    author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin,
+    Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur},
+    booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
+    year = {2020},
+    month = may,
+    address = {Marseille, France},
+    publisher = {European Language Resources Association (ELRA)},
+    url = {https://www.aclweb.org/anthology/2020.lrec-1.801},
+    pages = {6504--6513},
+    ISBN = {979-10-95546-34-4},
+}
+
+"""
+
+_DESCRIPTION = """\
+OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition,
+and software related to speech recognition. We intend to be a convenient place for anyone to put resources that
+they have created, so that they can be downloaded publicly.
+"""
+
+_HOMEPAGE = "https://openslr.org/"
+
+_LICENSE = ""
+
+_RESOURCES = {
+   
+   
+    "SLR70": {
+        "Language": "Nigerian English",
+        "LongName": "Crowdsourced high-quality Nigerian English speech data set",
+        "Category": "Speech",
+        "Summary": "Data set which contains recordings of Nigerian English",
+        "Files": ["en_ng_female.zip", "en_ng_male.zip"],
+        "IndexFiles": ["line_index.tsv", "line_index.tsv"],
+        "DataDirs": ["", ""],
+    },
+    "SLR71": {
+        "Language": "Chilean Spanish",
+        "LongName": "Crowdsourced high-quality Chilean Spanish speech data set",
+        "Category": "Speech",
+        "Summary": "Data set which contains recordings of Chilean Spanish",
+        "Files": ["es_cl_female.zip", "es_cl_male.zip"],
+        "IndexFiles": ["line_index.tsv", "line_index.tsv"],
+        "DataDirs": ["", ""],
+    
+    },
+    
+}
+
+
+class OpenSlrConfig(datasets.BuilderConfig):
+    """BuilderConfig for OpenSlr."""
+
+    def __init__(self, name, **kwargs):
+        """
+        Args:
+          data_dir: `string`, the path to the folder containing the files in the
+            downloaded .tar
+          citation: `string`, citation for the data set
+          url: `string`, url for information about the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        self.language = kwargs.pop("language", None)
+        self.long_name = kwargs.pop("long_name", None)
+        self.category = kwargs.pop("category", None)
+        self.summary = kwargs.pop("summary", None)
+        self.files = kwargs.pop("files", None)
+        self.index_files = kwargs.pop("index_files", None)
+        self.data_dirs = kwargs.pop("data_dirs", None)
+        description = (
+            f"Open Speech and Language Resources dataset in {self.language}. Name: {self.name}, "
+            f"Summary: {self.summary}."
+        )
+        super(OpenSlrConfig, self).__init__(name=name, description=description, **kwargs)
+
+
+class OpenSlr(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 32
+
+    BUILDER_CONFIGS = [
+        OpenSlrConfig(
+            name=resource_id,
+            language=_RESOURCES[resource_id]["Language"],
+            long_name=_RESOURCES[resource_id]["LongName"],
+            category=_RESOURCES[resource_id]["Category"],
+            summary=_RESOURCES[resource_id]["Summary"],
+            files=_RESOURCES[resource_id]["Files"],
+            index_files=_RESOURCES[resource_id]["IndexFiles"],
+            data_dirs=_RESOURCES[resource_id]["DataDirs"],
+        )
+        for resource_id in _RESOURCES.keys()
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "path": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=48_000),
+                "sentence": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="sentence")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        resource_number = self.config.name.replace("SLR", "")
+        urls = [f"{_DATA_URL.format(resource_number)}/{file}" for file in self.config.files]
+        if urls[0].endswith(".zip"):
+            dl_paths = dl_manager.download_and_extract(urls)
+            path_to_indexs = [os.path.join(path, f"{self.config.index_files[i]}") for i, path in enumerate(dl_paths)]
+            path_to_datas = [os.path.join(path, f"{self.config.data_dirs[i]}") for i, path in enumerate(dl_paths)]
+            archives = None
+        else:
+            archives = dl_manager.download(urls)
+            path_to_indexs = dl_manager.download(self.config.index_files)
+            path_to_datas = self.config.data_dirs
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "path_to_indexs": path_to_indexs,
+                    "path_to_datas": path_to_datas,
+                    "archive_files": [dl_manager.iter_archive(archive) for archive in archives] if archives else None,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, path_to_indexs, path_to_datas, archive_files):
+        """Yields examples."""
+
+        counter = -1
+        for i, path_to_index in enumerate(path_to_indexs):
+            with open(path_to_index, encoding="utf-8") as f:
+                lines = f.readlines()
+                for id_, line in enumerate(lines):
+                    # Following regexs are needed to normalise the lines, since the datasets
+                    # are not always consistent and have bugs:
+                    line = re.sub(r"\t[^\t]*\t", "\t", line.strip())
+                    field_values = re.split(r"\t\t?", line)
+                    if len(field_values) != 2:
+                        continue
+                    filename, sentence = field_values
+                    # set absolute path for audio file
+                    path = os.path.join(path_to_datas[i], f"{filename}.wav")
+                    counter += 1
+                    yield counter, {"path": path, "audio": path, "sentence": sentence}
+