crema-d.py

# coding=utf-8
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#     http://www.apache.org/licenses/LICENSE-2.0
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import os
import datasets  # type: ignore

logger = datasets.logging.get_logger(__name__)


"""CREMA-D (Crowd-sourced Emotional Multimodal Actors Dataset)"""

_CITATION = """\
@article{cao2014crema,
  title={CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset},
  author={Cao, H. and Cooper, D. G. and Keutmann, M. K. and Gur, R. C. and Nenkova, A. and Verma, R.},
  journal={IEEE transactions on affective computing},
  volume={5},
  number={4},
  pages={377--390},
  year={2014},
  doi={10.1109/TAFFC.2014.2336244},
  url={https://doi.org/10.1109/TAFFC.2014.2336244}
}
"""

_DESCRIPTION = """\
CREMA-D is a data set of 7,442 original clips from 91 actors.
These clips were from 48 male and 43 female actors between the ages of 20 and 74
coming from a variety of races and ethnicities (African America, Asian, Caucasian, Hispanic, and Unspecified).
Actors spoke from a selection of 12 sentences.
The sentences were presented using one of six different emotions (Anger, Disgust, Fear, Happy, Neutral and Sad)
and four different emotion levels (Low, Medium, High and Unspecified).
Participants rated the emotion and emotion levels based on the combined audiovisual presentation,
the video alone, and the audio alone. Due to the large number of ratings needed, this effort was crowd-sourced
and a total of 2443 participants each rated 90 unique clips, 30 audio, 30 visual, and 30 audio-visual.
95% of the clips have more than 7 rating.
"""

_HOMEPAGE = "https://github.com/CheyneyComputerScience/CREMA-D"
_LICENSE = "ODbL"

_ROOT_DIR = "crema_d"
_DATA_URL = f"data/{_ROOT_DIR}.tar.gz"


_SENTENCE_MAP = {
    "IEO": "It's eleven o'clock",
    "TIE": "That is exactly what happened",
    "IOM": "I'm on my way to the meeting",
    "IWW": "I wonder what this is about",
    "TAI": "The airplane is almost full",
    "MTI": "Maybe tomorrow it will be cold",
    "IWL": "I would like a new alarm clock",
    "ITH": "I think I have a doctor's appointment",
    "DFA": "Don't forget a jacket",
    "ITS": "I think I've seen this before",
    "TSI": "The surface is slick",
    "WSI": "We'll stop in a couple of minutes",
}


_EMOTION_MAP = {
    "NEU": "neutral",
    "HAP": "happy",
    "SAD": "sad",
    "ANG": "anger",
    "FEA": "fear",
    "DIS": "disgust",
}

_INTENSITY_MAP = {
    "LO": "Low",
    "MD": "Medium",
    "HI": "High",
    "XX": "Unspecified",
    ## one stray file
    "X": "Unspecified",
}

_CLASS_NAMES = list(_EMOTION_MAP.values())


class CremaDDataset(datasets.GeneratorBasedBuilder):
    """The Crema-D dataset"""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        sampling_rate = 16_000
        features = datasets.Features(
            {
                # "path": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=sampling_rate),
                "actor_id": datasets.Value("string"),
                "sentence": datasets.Value("string"),
                # "emotion": datasets.Value("string"),
                "intensity": datasets.Value("string"),
                "label": datasets.ClassLabel(names=_CLASS_NAMES),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
            # task_templates=[datasets.TaskTemplate("audio-classification")],
        )

    def _split_generators(self, dl_manager):

        archive = dl_manager.download(_DATA_URL)
        local_extracted_archive = (
            dl_manager.extract(archive) if not dl_manager.is_streaming else None
        )

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    # "archive_path": _ROOT_DIR,
                    "local_extracted_archive": local_extracted_archive,
                    "audio_files": dl_manager.iter_archive(archive),
                },
            )
        ]

    def _generate_examples(self, local_extracted_archive, audio_files):
        "4digitActorId_sentenceId_emotionId_emotionLevel"

        id_ = 0
        for path, f in audio_files:
            path = os.path.join(
                local_extracted_archive, path
            )  # if local_extracted_archive else path
            filename = os.path.basename(path)
            with open(path, "rb") as f:
                audio_bytes = f.read()
            actor_id, sentence_id, emotion_id, emotion_level = filename.split(".")[
                0
            ].split("_")
            base = {
                "path": path,
                "actor_id": actor_id,
                "sentence": _SENTENCE_MAP[sentence_id],
                "label": _EMOTION_MAP[emotion_id],
                "emotion_intensity": _INTENSITY_MAP[emotion_level],
            }
            audio = {"path": path, "bytes": audio_bytes}
            yield id_, {**base, "audio": audio}
            id_ += 1