Delete loading script

ASCEND.py

@@ -1,139 +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.
-""" Common Voice Dataset"""
-
-from datasets import AutomaticSpeechRecognition
-
-
-import datasets
-import os
-import pandas as pd
-
-
-_CITATION = """\
-@inproceedings{lovenia2021ascend,
-  title     = {ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation},
-  author    = {Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others},
-  booktitle = {Proceedings of the International Conference on Language Resources and Evaluation, {LREC} 2022, 20-25 June 2022, Lu Palais du Pharo, France},
-  publisher = {European Language Resources Association},
-  year      = {2022},
-  pages = {}
-}
-"""
-
-_DESCRIPTION = """\
-ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/CAiRE/ASCEND"
-
-_URL = "https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/"
-_URLS = {
-    "train": _URL + "train_metadata.csv",
-    "test": _URL + "test_metadata.csv",
-    "validation": _URL + "validation_metadata.csv",
-    "waves": "https://huggingface.co/datasets/CAiRE/ASCEND/resolve/main/waves.tar.bz2",
-}
-
-
-class ASCENDConfig(datasets.BuilderConfig):
-    """BuilderConfig for ASCEND."""
-
-    def __init__(self, name="main", **kwargs):
-        """
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(ASCENDConfig, self).__init__(name, **kwargs)
-
-
-class ASCEND(datasets.GeneratorBasedBuilder):
-    """ASCEND: A Spontaneous Chinese-English Dataset for code-switching. Snapshot date: 5 January 2022."""
-
-    BUILDER_CONFIGS = [
-        ASCENDConfig(
-            name="main",
-            version=datasets.Version("1.0.0", ""),
-            description=_DESCRIPTION,
-        )
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "path": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=16_000),
-                "transcription": datasets.Value("string"),
-                "duration": datasets.Value("float32"),
-                "language": datasets.Value("string"),
-                "original_speaker_id": datasets.Value("int64"),
-                "session_id": datasets.Value("int64"),
-                "topic": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="transcription")],
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "metadata_path": downloaded_files["train"],
-                    "wave_path": downloaded_files["waves"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "metadata_path": downloaded_files["test"],
-                    "wave_path": downloaded_files["waves"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "metadata_path": downloaded_files["validation"],
-                    "wave_path": downloaded_files["waves"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, metadata_path, wave_path):
-        print(metadata_path)
-        metadata_df = pd.read_csv(metadata_path)
-
-        for index, row in metadata_df.iterrows():
-            example = {
-                "id": str(index).zfill(5),
-                "path": os.path.join(wave_path, row["file_name"]),
-                "audio": os.path.join(wave_path, row["file_name"]),
-                "transcription": row["transcription"],
-                "duration": row["duration"],
-                "language": row["language"],
-                "original_speaker_id": row["original_speaker_id"],
-                "session_id": row["session_id"],
-                "topic": row["topic"],
-            }
-            yield index, example