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--- |
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license: mit |
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language: |
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- en |
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pretty_name: Speech Brown |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- text-to-speech |
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--- |
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[](https://arxiv.org/abs/2412.13071) [](https://github.com/language-modeling-lab/CLASP) |
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## Dataset Summary |
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**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. |
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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. |
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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). |
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## Dataset Statistics |
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1. Total size: Approximately 30 GB. |
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2. Number of samples: 55,173 pairs of speech and text. |
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3. Average tokens per sample: 19.00. |
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4. Maximum tokens in a sample: 48. |
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5. Average characters per sample: 96.72. |
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6. Number of unique tokens: 50,667 |
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7. Categories: 15 categories consist of `adventure`, `belles_lettres`, `editorial`, `fiction`, `government`, `hobbies`, `humor`, `learned`, `lore`, `mystery`, `news`, `religion`, `reviews`, `romance`, `science_fiction`. |
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## Dataset Structure |
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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. |
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### Metadata Files |
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1. **global_metadata**: A JSON file containing metadata for all 55,173 samples. |
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2. **localized_metadata**: A JSON file containing metadata for all samples, categorized into the 10 dataset partitions. |
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### Metadata Fields |
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1. **id**: The unique identifier for the sample. |
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2. **audio_file_path**: The file path for the audio in the dataset. |
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3. **category**: The category of the sample's text. |
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4. **text**: The corresponding text of the audio file. |
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## Usage Instructions |
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To use this dataset, download the parts and metadata files as follows: |
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#### Option 1: Manual Download |
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Visit the [dataset repository](https://huggingface.co/datasets/llm-lab/SpeechBrown/tree/main) and download all `dataset_partX.zip` files and the `global_metadata.json` file. |
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#### Option 2: Programmatic Download |
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Use the `huggingface_hub` library to download the files programmatically: |
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```python |
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from huggingface_hub import hf_hub_download |
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from zipfile import ZipFile |
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import os |
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import json |
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# Download dataset parts |
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zip_file_path1 = hf_hub_download(repo_id="llm-lab/SpeechBrown", filename="dataset_part1.zip", repo_type="dataset") |
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zip_file_path2 = hf_hub_download(repo_id="llm-lab/SpeechBrown", filename="dataset_part2.zip", repo_type="dataset") |
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# Download other parts... |
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# Download metadata |
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metadata_file_path = hf_hub_download(repo_id="llm-lab/SpeechBrown", filename="global_metadata.json", repo_type="dataset") |
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for i in range(1, 11): |
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with ZipFile(f'dataset_part{i}.zip', 'r') as zip_ref: |
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zip_ref.extractall(f'dataset_part{i}') |
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os.remove(f'dataset_part{i}.zip') |
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with open('global_metadata.json', 'r') as f: |
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metadata = json.load(f) |
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metadata.keys() |
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``` |
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## Citations |
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If you find our paper, code, data, or models useful, please cite the paper: |
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``` |
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@misc{abootorabi2024claspcontrastivelanguagespeechpretraining, |
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title={CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval}, |
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author={Mohammad Mahdi Abootorabi and Ehsaneddin Asgari}, |
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year={2024}, |
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eprint={2412.13071}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2412.13071}, |
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} |
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``` |
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## Contact |
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If you have questions, please email [email protected] or [email protected]. |
