Add ASCEND loading script

ascend.py

@@ -0,0 +1,131 @@
+# 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"""
+
+
+import os
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+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"
+
+
+class ASCENDConfig(datasets.BuilderConfig):
+    """BuilderConfig for ASCEND."""
+
+    def __init__(self, name, **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="train",
+            version=datasets.Version("1.0.0", ""),
+            description=_DESCRIPTION,
+        ),
+        ASCENDConfig(
+            name="validation",
+            version=datasets.Version("1.0.0", ""),
+            description=_DESCRIPTION,
+        ),
+        ASCENDConfig(
+            name="test",
+            version=datasets.Version("1.0.0", ""),
+            description=_DESCRIPTION,
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "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):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "metadata_path": "train_metadata.csv",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "metadata_path": "test_metadata.csv",
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "metadata_path": "validation_metadata.csv",
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, metadata_path):
+        metadata_df = pd.read_csv(metadata_path)
+
+        for index, row in metadata_df.iterrows():
+            example = {
+                "id": index,
+                "audio": row["audio"],
+                "transcription": row["transcription"]
+            }
+            yield index, example