Add AESDD dataset

mteb/tasks/Audio/AudioClassification/eng/AESDD.py

@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+from mteb.abstasks.Audio.AbsTaskAudioClassification import (
+    AbsTaskAudioClassification,
+)
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class AESDDClassification(AbsTaskAudioClassification):
+    metadata = TaskMetadata(
+        name="AESDD",
+        description="Speech Emotion Recognition Dataset.",
+        reference="https://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/",
+        dataset={
+            "path": "mteb/AESDD",
+            "revision": "afbff14d927de14412d8124502313ea6d9d140e0",
+        },
+        type="AudioClassification",
+        category="a2t",
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="accuracy",
+        date=("2018-01-01", "2019-01-01"),
+        domains=["Spoken"],
+        task_subtypes=["Emotion recognition"],
+        license="cc-by-sa-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        modalities=["audio"],
+        sample_creation="found",
+        bibtex_citation="""@article{vryzas2018speech,
+            title={Speech emotion recognition for performance interaction},
+            author={Vryzas, Nikolaos and Kotsakis, Rigas and Liatsou, Aikaterini and Dimoulas, Charalampos A and Kalliris, George},
+            journal={Journal of the Audio Engineering Society},
+            volume={66},
+            number={6},
+            pages={457--467},
+            year={2018},
+            publisher={Audio Engineering Society}
+            }""",
+        descriptive_stats={
+            "n_samples": {"train": 3200},
+        },
+    )
+
+    audio_column_name: str = "audio"
+    label_column_name: str = "label"
+    samples_per_label: int = 32