diff --git a/mteb/tasks/Audio/AudioClassification/__init__.py b/mteb/tasks/Audio/AudioClassification/__init__.py
index e0b131e9c6..de92ce07da 100644
--- a/mteb/tasks/Audio/AudioClassification/__init__.py
+++ b/mteb/tasks/Audio/AudioClassification/__init__.py
@@ -7,6 +7,7 @@
 from .eng.GTZANGenre import *
 from .eng.GunshotTriangulation import *
 from .eng.LibriCount import *
+from .eng.IEMOCAP import *
 from .eng.MridinghamStroke import *
 from .eng.MridinghamTonic import *
 from .eng.NSynth import *
diff --git a/mteb/tasks/Audio/AudioClassification/eng/IEMOCAP.py b/mteb/tasks/Audio/AudioClassification/eng/IEMOCAP.py
new file mode 100644
index 0000000000..eddbbdbe85
--- /dev/null
+++ b/mteb/tasks/Audio/AudioClassification/eng/IEMOCAP.py
@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+from mteb.abstasks.Audio.AbsTaskAudioClassification import (
+    AbsTaskAudioClassification,
+)
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class IEMOCAP(AbsTaskAudioClassification):
+    metadata = TaskMetadata(
+        name="IEMOCAP",
+        description="""IEMOCAP was recorded from ten actors in dyadic sessions with markers on the face, head,
+            and hands, which provide detailed information about their facial expression and
+            hand movements during scripted and spontaneous spoken communication scenarios.
+            actors performed selected emotional scripts and also improvised hypothetical
+            scenarios designed to elicit specific types of emotions (happiness, anger, sadness, frustration and neutral state).
+            After autmoated annotations, the final emotional categories selected for annotation were :
+            anger, sadness, happiness, disgust, fear and surprise, plus frustration, excited and neutral states
+        """,
+        reference="https://huggingface.co/datasets/AbstractTTS/IEMOCAP",
+        dataset={
+            "path": "AbstractTTS/IEMOCAP",
+            "revision": "9f1696a135a65ce997d898d4121c952269a822ca",
+        },
+        type="AudioClassification",
+        category="a2t",
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="accuracy",
+        date=("2024-08-09", "2024-08-11"),
+        domains=["Spoken"],
+        task_subtypes=["Emotion classification"],
+        license="not specified",
+        annotations_creators="automatic-and-reviewed",
+        dialect=[],
+        modalities=["audio"],
+        sample_creation="created",
+        bibtex_citation="""@article{article,
+            author = {Busso, Carlos and Bulut, Murtaza and Lee, Chi-Chun and Kazemzadeh, Abe and Mower Provost, Emily and Kim, Samuel and Chang, Jeannette and Lee, Sungbok and Narayanan, Shrikanth},
+            year = {2008},
+            month = {12},
+            pages = {335-359},
+            title = {IEMOCAP: Interactive emotional dyadic motion capture database},
+            volume = {42},
+            journal = {Language Resources and Evaluation},
+            doi = {10.1007/s10579-008-9076-6}
+        }""",
+        # https://ecs.utdallas.edu/research/researchlabs/msp-lab/publications/Busso_2008_5.pdf
+        descriptive_stats={
+            "n_samples": {"train": 10039},
+        },
+    )
+
+    audio_column_name: str = "audio"
+    label_column_name: str = "major_emotion"
+    samples_per_label: int = 10
+    is_cross_validation: bool = True
+
+    def dataset_transform(self):
+        ## required to run the dataloader for cross-validation
+        import torch
+
+        torch.multiprocessing.set_sharing_strategy("file_system")
+        #########################################################