metadata

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 Synthesized with Kokoro TTS. 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".

We refer you to the SlamKit codebase 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.

Usage

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}, 
}