---
license: mit  
language:  
- en  
pretty_name: Speech Brown  
size_categories:  
- 10K<n<100K  
task_categories:  
- text-to-speech  

---

## Dataset Summary

**Speech Brown** is a comprehensive, synthetic, and diverse paired speech-text dataset in 15 categories, covering a wide range of topics from fiction to religion. This dataset consists of over 55,000 sentence-level samples.  

To train the [CLASP](https://huggingface.co/llm-lab/CLASP) model, we created this dataset based on the Brown Corpus. The synthetic speech was generated using the [NVIDIA Tacotron 2](https://pytorch.org/hub/nvidia_deeplearningexamples_tacotron2/) text-to-speech model.  

For more information about our proposed model, please refer to this [paper](https://arxiv.org/abs/2412.13071). The dataset generation pipeline, along with code and usage instructions, is available on this [GitHub page](https://github.com/language-modeling-lab/CLASP).  

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64ba58d377dd483716aba098/5dy1Cb3-ZmGytf3QbQN9a.png)  

## Dataset Statistics
1. Total size: Approximately 30 GB.  
2. Number of samples: 55,173 pairs of speech and text.  
3. Average words per sample: 17.78.  
4. Maximum words in a sample: 48.  
5. Average characters per sample: 96.72.  
6. Categories: 15 categories consist of `adventure`, `belles_lettres`, `editorial`, `fiction`, `government`, `hobbies`, `humor`, `learned`, `lore`, `mystery`, `news`, `religion`, `reviews`, `romance`, `science_fiction`.  

## Dataset Structure
To ensure ease of use, the dataset is partitioned into 10 parts. Each part can be used independently if it meets the requirements of your task and model.  

## Metadata Files:
1. **global_metadata**: A JSON file containing metadata for all 55,173 samples.  
2. **localized_metadata**: A JSON file containing metadata for all samples, categorized into the 10 dataset partitions.  

## Metadata Fields:
1. **id**: The unique identifier for the sample.  
2. **audio_file_path**: The file path for the audio in the dataset.  
3. **category**: The category of the sample's text.  
4. **text**: The corresponding text of the audio file.  

## Citations
If you find our paper, code, data, or models useful, please cite the paper:  
```
@misc{abootorabi2024claspcontrastivelanguagespeechpretraining,
      title={CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval}, 
      author={Mohammad Mahdi Abootorabi and Ehsaneddin Asgari},
      year={2024},
      eprint={2412.13071},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.13071}, 
}
```

## Contact
If you have questions, please email mahdi.abootorabi2@gmail.com or asgari@berkeley.edu.