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import datasets |
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import pandas as pd |
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from datasets import ClassLabel |
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import os |
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"""The Audio, Speech, and Vision Processing Lab - Emotional Sound Database (ASVP - ESD)""" |
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_HOMEPAGE = "https://affective-meld.github.io/" |
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_CITATION = """\ |
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@article{poria2018meld, |
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title={Meld: A multimodal multi-party dataset for emotion recognition in conversations}, |
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author={Poria, Soujanya and Hazarika, Devamanyu and Majumder, Navonil and Naik, Gautam and Cambria, Erik and Mihalcea, Rada}, |
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journal={arXiv preprint arXiv:1810.02508}, |
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year={2018} |
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} |
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@article{chen2018emotionlines, |
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title={Emotionlines: An emotion corpus of multi-party conversations}, |
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author={Chen, Sheng-Yeh and Hsu, Chao-Chun and Kuo, Chuan-Chun and Ku, Lun-Wei and others}, |
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journal={arXiv preprint arXiv:1802.08379}, |
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year={2018} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Multimodal EmotionLines Dataset (MELD) has been created by enhancing and extending EmotionLines dataset. |
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MELD contains the same dialogue instances available in EmotionLines, but it also encompasses audio and |
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visual modality along with text. MELD has more than 1400 dialogues and 13000 utterances from Friends TV series. |
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Multiple speakers participated in the dialogues. Each utterance in a dialogue has been labeled by any of these |
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seven emotions -- Anger, Disgust, Sadness, Joy, Neutral, Surprise and Fear. MELD also has sentiment (positive, |
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negative and neutral) annotation for each utterance. |
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This dataset is modified from https://huggingface.co/datasets/zrr1999/MELD_Text_Audio. |
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The audio is extracted from MELD mp4 files while the audio only has one channel with sample rate 16khz. |
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""" |
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_LICENSE = "gpl-3.0" |
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class MELD_Audio(datasets.GeneratorBasedBuilder): |
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DEFAULT_WRITER_BATCH_SIZE = 256 |
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DEFAULT_CONFIG_NAME = "MELD_Audio" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="MELD_Audio", version=datasets.Version("0.0.1"), description="MELD audio"), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16000), |
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"emotion": datasets.Value("string"), |
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"sentiment": datasets.Value("string"), |
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} |
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), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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metadata_dir: dict[str, str] = dl_manager.download_and_extract( |
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{"train": "train.csv", "dev": "dev.csv", "test": "test.csv"} |
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) |
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data_path: dict[str, str] = dl_manager.download( |
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{ |
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"train": "archive/train.tar.gz", |
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"dev": "archive/dev.tar.gz", |
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"test": "archive/test.tar.gz", |
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} |
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) |
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local_extracted_archive: dict[str, str] = ( |
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dl_manager.extract(data_path) |
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if not dl_manager.is_streaming |
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else { |
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"train": None, |
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"dev": None, |
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"test": None, |
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} |
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) |
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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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"filepath": metadata_dir["train"], |
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"split": "train", |
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"local_extracted_archive": local_extracted_archive["train"], |
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"audio_files": dl_manager.iter_archive(data_path["train"]), |
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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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"filepath": metadata_dir["dev"], |
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"split": "dev", |
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"local_extracted_archive": local_extracted_archive["dev"], |
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"audio_files": dl_manager.iter_archive(data_path["dev"]), |
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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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"filepath": metadata_dir["test"], |
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"split": "test", |
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"local_extracted_archive": local_extracted_archive["test"], |
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"audio_files": dl_manager.iter_archive(data_path["test"]), |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split, local_extracted_archive, audio_files): |
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"""Yields examples.""" |
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metadata_df = pd.read_csv(filepath, sep=",", index_col=0, header=0) |
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metadata = {} |
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for _, row in metadata_df.iterrows(): |
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id_ = f"dia{row['Dialogue_ID']}_utt{row['Utterance_ID']}" |
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audio_path = f"{split}/{id_}.flac" |
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metadata[audio_path] = row |
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id_ = 0 |
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for path, f in audio_files: |
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if path in metadata: |
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row = metadata[path] |
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path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path |
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audio = {"path": path, bytes: f.read()} |
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yield ( |
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id_, |
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{ |
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"text": row["Utterance"], |
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"path": path, |
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"audio": audio, |
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"emotion": row["Emotion"], |
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"sentiment": row["Sentiment"], |
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}, |
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) |
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id_ += 1 |
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