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---
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).
## 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 [email protected] or [email protected]. |