Delete Openslr.py

Openslr.py

@@ -1,189 +0,0 @@
-# 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}
-