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 model, we created this dataset based on the Brown Corpus. The synthetic speech was generated using the NVIDIA Tacotron 2 text-to-speech model.
For more information about our proposed model, please refer to this paper. The dataset generation pipeline, along with code and usage instructions, is available on this GitHub page.
Dataset Statistics
- Total size: Approximately 30 GB.
- Number of samples: 55,173 pairs of speech and text.
- Average words per sample: 17.78.
- Maximum words in a sample: 48.
- Average characters per sample: 96.72.
- 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:
- global_metadata: A JSON file containing metadata for all 55,173 samples.
- localized_metadata: A JSON file containing metadata for all samples, categorized into the 10 dataset partitions.
Metadata Fields:
- id: The unique identifier for the sample.
- audio_file_path: The file path for the audio in the dataset.
- category: The category of the sample's text.
- 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].