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""" Common Voice Dataset""" |
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from datasets import AutomaticSpeechRecognition |
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import datasets |
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import os |
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import pandas as pd |
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_CITATION = """\ |
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@misc{cahyawijaya2023crosslingual, |
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title={Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition}, |
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author={Samuel Cahyawijaya and Holy Lovenia and Willy Chung and Rita Frieske and Zihan Liu and Pascale Fung}, |
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year={2023}, |
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eprint={2306.14517}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """\ |
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YueMotion is a Cantonese speech emotion dataset. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/CAiRE/YueMotion" |
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_URL = "https://huggingface.co/datasets/CAiRE/YueMotion/raw/main/" |
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_URLS = { |
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"train": _URL + "train_metadata.csv", |
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"test": _URL + "test_metadata.csv", |
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"validation": _URL + "validation_metadata.csv", |
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"waves": "https://huggingface.co/datasets/CAiRE/YueMotion/resolve/main/data.tar.bz2", |
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} |
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class YueMotionConfig(datasets.BuilderConfig): |
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"""BuilderConfig for YueMotion.""" |
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def __init__(self, name="main", **kwargs): |
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""" |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(YueMotionConfig, self).__init__(name, **kwargs) |
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class YueMotion(datasets.GeneratorBasedBuilder): |
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"""YueMotion: Cantonese speech emotion recognition for both adults and elderly. Snapshot date: 28 June 2023.""" |
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BUILDER_CONFIGS = [ |
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YueMotionConfig( |
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name="main", |
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version=datasets.Version("1.0.0", ""), |
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description=_DESCRIPTION, |
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) |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"split": datasets.Value("string"), |
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"speaker_id": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"gender": datasets.Value("string"), |
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"age": datasets.Value("int64"), |
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"sentence_id": datasets.Value("string"), |
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"label_id": datasets.Value("int64"), |
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"label": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="transcription")], |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"metadata_path": downloaded_files["train"], |
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"wave_path": downloaded_files["waves"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"metadata_path": downloaded_files["test"], |
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"wave_path": downloaded_files["waves"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"metadata_path": downloaded_files["validation"], |
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"wave_path": downloaded_files["waves"], |
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}, |
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), |
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] |
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def _generate_examples(self, metadata_path, wave_path): |
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print(metadata_path) |
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metadata_df = pd.read_csv(metadata_path) |
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for index, row in metadata_df.iterrows(): |
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example = { |
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"split": row["split"], |
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"speaker_id": row["speaker_id"], |
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"path": os.path.join(wave_path, row["file_name"]), |
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"audio": os.path.join(wave_path, row["file_name"]), |
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"gender": row["gender"], |
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"age": row["age"], |
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"sentence_id": row["sentence_id"], |
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"label_id": row["label_id"], |
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"label": row["label"], |
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} |
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yield index, example |
