Hugging Face's logo

Datasets:
CAiRE
/
YueMotion

Modalities:
Audio
Text
Languages:
Yue Chinese
Size:
1K<n<10K
ArXiv:
Tags:
speech
speech-emotion-recognition
Libraries:
Datasets
Croissant
License:
Dataset card Viewer Files Community
YueMotion
File size: 4,790 Bytes 
48c5ff8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
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