Add dataloader

YueMotion.py

@@ -0,0 +1,139 @@
+# 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 = """\
+@misc{cahyawijaya2023crosslingual,
+    title={Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition}, 
+    author={Samuel Cahyawijaya and Holy Lovenia and Willy Chung and Rita Frieske and Zihan Liu and Pascale Fung},
+    year={2023},
+    eprint={2306.14517},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+YueMotion is a Cantonese speech emotion dataset.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/CAiRE/YueMotion"
+
+_URL = "https://huggingface.co/datasets/CAiRE/YueMotion/raw/main/"
+_URLS = {
+    "train": _URL + "train_metadata.csv",
+    "test": _URL + "test_metadata.csv",
+    "validation": _URL + "validation_metadata.csv",
+    "waves": "https://huggingface.co/datasets/CAiRE/YueMotion/resolve/main/data.tar.bz2",
+}
+
+
+class YueMotionConfig(datasets.BuilderConfig):
+    """BuilderConfig for YueMotion."""
+
+    def __init__(self, name="main", **kwargs):
+        """
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(YueMotionConfig, self).__init__(name, **kwargs)
+
+
+class YueMotion(datasets.GeneratorBasedBuilder):
+    """YueMotion: Cantonese speech emotion recognition for both adults and elderly. Snapshot date: 28 June 2023."""
+
+    BUILDER_CONFIGS = [
+        YueMotionConfig(
+            name="main",
+            version=datasets.Version("1.0.0", ""),
+            description=_DESCRIPTION,
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "split": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+                "path": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "gender": datasets.Value("string"),
+                "age": datasets.Value("int64"),
+                "sentence_id": datasets.Value("string"),
+                "label_id": datasets.Value("int64"),
+                "label": 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 = {
+                "split": row["split"],
+                "speaker_id": row["speaker_id"],
+                "path": os.path.join(wave_path, row["file_name"]),
+                "audio": os.path.join(wave_path, row["file_name"]),
+                "gender": row["gender"],
+                "age": row["age"],
+                "sentence_id": row["sentence_id"],
+                "label_id": row["label_id"],
+                "label": row["label"],
+            }
+            yield index, example