README.md

metadata

license: cc-by-4.0
language:
  - de
  - en
  - es
  - cs
  - fr
  - hu
  - it
  - nl
  - pt
  - ru
  - sq
  - sv
tags:
  - speech prompts
  - text prompts
  - instruction following
  - benchmark
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: text_prompt
      dtype: string
    - name: audio_prompt_female_1
      dtype: audio
    - name: audio_prompt_female_2
      dtype: audio
    - name: audio_prompt_male_1
      dtype: audio
    - name: audio_prompt_male_2
      dtype: audio
    - name: language
      dtype: string
    - name: task
      dtype: string
    - name: prompt_type
      dtype: string
  splits:
    - name: test
      num_bytes: 2704378267.6
      num_examples: 1320
  download_size: 1772318018
  dataset_size: 2704378267.6
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*

Do What I Say (DOWIS): A Spoken Prompt Dataset for Instruction-Following

NEW DOWIS now also contains spoken and written prompts in Albanian (sq), and for the tasks LIPREAD and SLU!

TL;DR — DOWIS is a multilingual dataset of human-recorded spoken and written instruction prompts, designed to enable realistic evaluation of Speech Large Language Models across 11 tasks and 12 languages.

---

Dataset Summary

Most Speech LLM benchmarks use text-based prompts, which does not reflect how users actually interact with these models in the real world. DOWIS fills this gap by providing human-recorded spoken prompts, paired with their written equivalents, across a wide range of tasks, languages, and prompt styles. Each prompt can be directly paired with any existing speech benchmark to evaluate how well Speech LLMs follow spoken instructions.

The dataset contains 1,320 rows, with up to 4 audio recordings per row (2 female, 2 male speakers where available), covering:

• 12 languages: cs, de, en, es, fr, hu, it, nl, pt, ru, sq, sv
• 11 tasks: ACHAP, ASR, MT, S2ST, SQA, SSUM, ST, TSUM, TTS, LIPREAD, SLU
• 5 prompt styles: basic, formal, informal, detailed, short
• 10 prompt variants per task-language pair

Details can be found in the corresponding paper on arXiv.

Code for benchmarking Speech LLMs with different task benchmarks coupled with DOWIS can be found on GitHub.

---

Tasks

Task Code	Description
ACHAP	Audio Chaptering
ASR	Automatic Speech Recognition
MT	Machine Translation
S2ST	Speech-to-Speech Translation
SQA	Spoken Question Answering
SSUM	Speech Summarization
ST	Speech Translation
TSUM	Text Summarization
TTS	Text-to-Speech
LIPREAD	Lip-Reading
SLU	Spoken Language Understanding

Prompt Styles

Style	Description
basic	Natural, everyday phrasing a researcher would use
formal	Professional, polished language
informal	Conversational and casual
detailed	Explicit and precise instructions on how to perform the task
short	Concise as possible while remaining unambiguous

---

Dataset Fields

Field	Type	Description
text_prompt	string	Written version of the instruction prompt
audio_prompt_female_1	Audio	Human-recorded female speaker (speaker 1), null if unavailable
audio_prompt_female_2	Audio	Human-recorded female speaker (speaker 2), null if unavailable
audio_prompt_male_1	Audio	Human-recorded male speaker (speaker 1), null if unavailable
audio_prompt_male_2	Audio	Human-recorded male speaker (speaker 2), null if unavailable
language	string	ISO 639-1 language code (e.g. en, de)
task	string	Task code the prompt is designed for (e.g. asr, mt)
prompt_type	string	Prompt style: basic, formal, informal, detailed, or short

---

Citation

If you use this work, please cite:

@misc{züfle2026isayspokenprompt,
      title={Do What I Say: A Spoken Prompt Dataset for Instruction-Following}, 
      author={Maike Züfle and
              Sara Papi and
              Fabian Retkowski and
              Szymon Mazurek and
              Marek Kasztelnik and
              Alexander Waibel and
              Luisa Bentivogli and
              Jan Niehues},
      year={2026},
      eprint={2603.09881},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.09881}}

---

Dataset Contact: maike.zuefle@kit.edu