init

.gitignore

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+.DS_Store
+Pipfile
+Pipfile.lock
+example.py

crema-d.py

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+# coding=utf-8
+# Copyright 2022 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.
+
+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 = {
+    "ANG": "anger",
+    "DIS": "disgust",
+    "FEA": "fear",
+    "HAP": "happy",
+    "NEU": "neutral",
+    "SAD": "sad",
+}
+
+_INTENSITY_MAP = {
+    "LO": "Low",
+    "MD": "Medium",
+    "HI": "High",
+    "XX": "Unspecified",
+    ## on 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"),
+                "emotion_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

data/crema_d.tar.gz

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