Datasets:

mteb
/

SoundDescsA2TRetrieval

annotations_creators:
  - derived
language:
  - zxx
license: apache-2.0
multilinguality: monolingual
source_datasets:
  - mteb/sounddescs_a2t
task_categories:
  - other
  - text-to-audio
task_ids: []
dataset_info:
  - config_name: corpus
    features:
      - name: id
        dtype: string
      - name: modality
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 646207
        num_examples: 4947
    download_size: 352239
    dataset_size: 646207
  - config_name: qrels
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: int64
    splits:
      - name: test
        num_bytes: 192854
        num_examples: 4947
    download_size: 76317
    dataset_size: 192854
  - config_name: queries
    features:
      - name: id
        dtype: string
      - name: modality
        dtype: string
      - name: audio
        dtype: audio
    splits:
      - name: test
        num_bytes: 96943930060
        num_examples: 4946
    download_size: 96962675747
    dataset_size: 96943930060
configs:
  - config_name: corpus
    data_files:
      - split: test
        path: corpus/test-*
  - config_name: qrels
    data_files:
      - split: test
        path: qrels/test-*
  - config_name: queries
    data_files:
      - split: test
        path: queries/test-*
tags:
  - mteb
  - text
  - audio

SoundDescsA2TRetrieval

An MTEB dataset

Massive Text Embedding Benchmark

Natural language description for different audio sources from the BBC Sound Effects webpage.


Task category	Any2AnyRetrieval (audio-to-text)
Domains	Encyclopaedic, Written
Reference	IEEE Transactions on Multimedia

Source datasets:

mteb/sounddescs_a2t

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_task("SoundDescsA2TRetrieval")
model = mteb.get_model(YOUR_MODEL)
mteb.evaluate(model, task)

To learn more about how to run models on mteb task check out the GitHub repository.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.


@inproceedings{Koepke2022,
  author = {Koepke, A.S. and Oncescu, A.-M. and Henriques, J. and Akata, Z. and Albanie, S.},
  booktitle = {IEEE Transactions on Multimedia},
  title = {Audio Retrieval with Natural Language Queries: A Benchmark Study},
  year = {2022},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("SoundDescsA2TRetrieval")

desc_stats = task.metadata.descriptive_stats

{}

This dataset card was automatically generated using MTEB