README.md

---
language:
- de
- en
- es
- cs
- fr
- hu
- it
- nl
- pt
- ru
- sq
- sv
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- automatic-speech-recognition
- text-to-speech
- translation
- summarization
- audio-to-audio
- question-answering
tags:
- speech prompts
- text prompts
- instruction following
- benchmark
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-*
---

<p align="center">
  <img src="https://github.com/MaikeZuefle/DOWIS/blob/64f807de73bfe1e5ad6e2d07a62c642afb076ad7/dowis_logo.png?raw=true" alt="DOWIS" width="400"/>
</p>

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

<span style="background-color:#fee2e2; color:#b91c1c; padding:2px 6px; border-radius:4px; font-size:0.85em; font-weight:600;">NEW</span> 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](https://arxiv.org/abs/2603.09881). 

Code for benchmarking Speech LLMs with different task benchmarks coupled with DOWIS can be found on [GitHub](https://github.com/MaikeZuefle/DOWIS).

---

## 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 |

---

## Benchmarking Usage

The following instructions for inference and evaluation are provided in the official GitHub repository.

### Inference
Run `main.py` with the following arguments to start inference:

```bash
python main.py \
  --lang de \
  --model phi_multimodal \
  --task ASR \
  --out_folder outputs
```

### Evaluation
The evaluation script `eval_outputs.py` computes metrics on the generated predictions.

```bash
python eval_outputs.py \
  --lang de \
  --model phi_multimodal \
  --task ASR \
  --predictions_folder outputs \
  --out_folder evaluation_results
```

---

## 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:
```bibtex
@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