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--- |
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language: |
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- en |
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- th |
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license: mit |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- automatic-speech-recognition |
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dataset_info: |
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features: |
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- name: name |
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dtype: string |
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- name: audio |
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dtype: audio |
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- name: label |
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dtype: string |
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- name: start |
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dtype: float64 |
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- name: end |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 1068060017.248 |
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num_examples: 10137 |
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- name: test |
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num_bytes: 184950411.426 |
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num_examples: 2791 |
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download_size: 1037733018 |
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dataset_size: 1253010428.674 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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tags: |
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- finance |
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--- |
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# RATCHADA-STT Dataset |
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## Overview |
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The dataset includes recordings from earnings calls of publicly traded companies in Thailand. Each audio file is accompanied by a transcription and metadata such as company name, reporting period, and other relevant details. |
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### Dataset Info |
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- **Total Duration:** |
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- Train: 26507.87 seconds (~ 7.36 hours) |
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- Test: 8376.28 seconds (~ 2.33 hours) |
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- **File Count:** |
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- Train: 10912 files |
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- Test: 2804 files |
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### Dataset Structure |
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The dataset consists of recordings from various financial presentations categorized into different subfolders based on event and company. |
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#### Metadata |
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- **Features:** |
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- path: string |
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- array: sequence of float32 |
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- label: string |
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- start: float64 |
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- end: float64 |
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- **Dataset Size:** 2.11 GB |
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### Example Usage |
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#### Load the Ratchada-STT dataset |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("ThinkingMachinesDataScience/Ratchada-STT") |
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``` |
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#### Access train and test splits |
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```python |
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train = dataset['train'] |
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test = dataset['test'] |
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``` |
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#### Example usage with first sample |
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```python |
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print(train[0]) |
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``` |
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### License |
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This dataset is distributed under the `MIT` License. |
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### Citation |
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If you use this dataset in your research or work, please cite it as: |
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``` |
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@dataset{thinkingmachinesdatascience/ratchada-stt, |
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title = {Ratchada-STT Dataset}, |
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publisher = {Hugging Face}, |
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year = {2024}, |
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author = {Thinking Machines Data Science}, |
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license = {MIT}, |
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} |
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``` |
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### Contribution Guidelines |
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- Ensure your PR adheres to the repository's coding standards. |
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- Include relevant tests and ensure they pass. |
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- Update documentation if your contribution changes any functionality. |
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- Follow the Hugging Face Community Code of Conduct. |
