Datasets:
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---
dataset_info:
features:
- name: id
dtype: string
- name: correct_text
dtype: string
- name: correct_audio
dtype: audio
- name: negative_text
dtype: string
- name: negative_audio
dtype: audio
splits:
- name: train
num_bytes: 2660941782.232
num_examples: 1871
download_size: 2439350149
dataset_size: 2660941782.232
configs:
- config_name: default
data_files:
- split: tsc_bm
path: tSC/bm/train-*
- split: tsc_am
path: tSC/am/train-*
- split: tsc_bf
path: tSC/bf/train-*
- split: tsc_af
path: tSC/af/train-*
- split: ssc_bm
path: sSC/bm/train-*
- split: ssc_am
path: sSC/am/train-*
- split: ssc_bf
path: sSC/bf/train-*
- split: ssc_af
path: sSC/af/train-*
license: mit
language:
- en
---
# Multi Speaker StoryCloze
A multispeaker spoken version of [StoryCloze](https://paperswithcode.com/dataset/storycloze) Synthesized with [Kokoro TTS](https://huggingface.co/hexgrad/Kokoro-82M).
The dataset was synthesized to evaluate the performance of speech language models as detailed in the paper ["Scaling Analysis of Interleaved Speech-Text Language Models"](https://arxiv.org/abs/2504.02398).
We refer you to the _SlamKit_ [codebase](https://github.com/slp-rl/slamkit) to see how you can evaluate your SpeechLM with this dataset.
## sSC and tSC
We split the generation for spoken-stroycloze and topic-storycloze as detailed in [Twist](https://arxiv.org/abs/2305.13009).
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("slprl/multispeaker-storycloze")
```
## Data Fields
The data has several fields:
- `id`: the file id as in the original [StoryCloze]() dataset.
- `correct_text`: the text of the correct sample.
- `correct_audio`: the synthesized audio of the correct sample.
- `incorrect_text`: the text of the incorrect sample.
- `incorrect_audio`: the synthesized audio of the incorrect sample.
## Citation
If you use this version of the dataset please cite our work:
```
@misc{maimon2025scaling,
title={Scaling Analysis of Interleaved Speech-Text Language Models},
author={Gallil Maimon and Michael Hassid and Amit Roth and Yossi Adi},
year={2025},
eprint={2504.02398},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.02398},
}
``` |