Upload dataset_2.py

dataset_2.py

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+# coding=utf-8
+# Copyright 2022 The PolyAI and 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.
+
+import csv
+import os
+
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+
+""" Self Dataset"""
+
+_CITATION = """\
+@article{gerz2021multilingual,
+  title={Multilingual and cross-lingual intent detection from spoken data},
+  author={Gerz, Daniela and Su, Pei-Hao and Kusztos, Razvan and Mondal, Avishek and Lis, Michal and Singhal, Eshan and Mrk{\v{s}}i{\'c}, Nikola and Wen, Tsung-Hsien and Vuli{\'c}, Ivan},
+  journal={arXiv preprint arXiv:2104.08524},
+  year={2021}
+}
+"""
+
+_DESCRIPTION = """\
+Self Dataset is training and evaluation resource for intent
+detection task with spoken data. It covers 14
+intents extracted from a commercial system
+in the e-banking domain, associated with spoken examples in 14 diverse language varieties.
+"""
+
+_ALL_CONFIGS = sorted([
+    "cs-CZ", "de-DE", "en-AU", "en-GB", "en-US", "es-ES", "fr-FR", "it-IT", "ko-KR", "nl-NL", "pl-PL", "pt-PT", "ru-RU", "zh-CN"
+])
+
+
+_DESCRIPTION = "Self Dataset is a dataset for the intent detection task with spoken data. It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties."
+
+_HOMEPAGE_URL = "https://arxiv.org/abs/2104.08524"
+
+_DATA_URL = "https://www.dropbox.com/scl/fi/wr608qdxo5jnm0azwkbak/dataset.zip?rlkey=wktncggwoka8qdm46cjb41yac&dl=0"
+
+
+class ATCConfig(datasets.BuilderConfig):
+    """BuilderConfig for xtreme-s"""
+
+    def __init__(
+        self, name, description, homepage, data_url
+    ):
+        super(ATCConfig, self).__init__(
+            name=self.name,
+            version=datasets.Version("1.0.0", ""),
+            description=self.description,
+        )
+        self.name = name
+        self.description = description
+        self.homepage = homepage
+        self.data_url = data_url
+
+
+def _build_config(name):
+    return ATCConfig(
+        name=name,
+        description=_DESCRIPTION,
+        homepage=_HOMEPAGE_URL,
+        data_url=_DATA_URL,
+    )
+
+
+class ATC(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+    BUILDER_CONFIGS = [_build_config(name) for name in _ALL_CONFIGS + ["all"]]
+
+    def _info(self):
+        task_templates = None
+        langs = _ALL_CONFIGS
+        features = datasets.Features(
+            {
+                "file_path": datasets.Value("string"),
+                "reference": datasets.Value("string"),
+             
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("file_path", "reference"),
+            homepage=self.config.homepage,
+            citation=_CITATION,
+            task_templates=task_templates,
+        )
+
+    def _split_generators(self, dl_manager):
+        langs = (
+            _ALL_CONFIGS
+            if self.config.name == "all"
+            else [self.config.name]
+        )
+
+        archive_path = dl_manager.download_and_extract(self.config.data_url)
+        audio_path = dl_manager.extract(
+            os.path.join(archive_path, "dataset", "audio.zip")
+        )
+        text_path = dl_manager.extract(
+            os.path.join(archive_path, "dataset", "text.zip")
+        )
+
+        text_path = {l: os.path.join(text_path, f"{l}.csv") for l in langs}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "audio_path": audio_path,
+                    "text_paths": text_path,
+                },
+            )
+        ]
+
+
+    def _generate_examples(self, audio_path, text_paths):
+        key = 0
+        for lang in text_paths.keys():
+            text_path = text_paths[lang]
+            with open(text_path, encoding="utf-8") as csv_file:
+                csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
+                next(csv_reader)
+                for row in csv_reader:
+                    file_path, reference = row
+
+                    file_path = os.path.join(audio_path, *file_path.split("/"))
+                    yield key, {
+                        "path": file_path,                        
+                        "reference": transcription,
+                    }
+                    key += 1