metadata

language:
  - en
  - th
license: mit
size_categories:
  - 10K<n<100K
task_categories:
  - automatic-speech-recognition
dataset_info:
  features:
    - name: name
      dtype: string
    - name: audio
      dtype: audio
    - name: label
      dtype: string
    - name: start
      dtype: float64
    - name: end
      dtype: float64
  splits:
    - name: train
      num_bytes: 1068060017.248
      num_examples: 10137
    - name: test
      num_bytes: 184950411.426
      num_examples: 2791
  download_size: 1037733018
  dataset_size: 1253010428.674
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
tags:
  - finance

RATCHADA-STT Dataset

Overview

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.

Dataset Info

Total Duration:
- Train: 26507.87 seconds (~ 7.36 hours)
- Test: 8376.28 seconds (~ 2.33 hours)
File Count:
- Train: 10912 files
- Test: 2804 files

Dataset Structure

The dataset consists of recordings from various financial presentations categorized into different subfolders based on event and company.

Metadata

Features:
- path: string
- array: sequence of float32
- label: string
- start: float64
- end: float64
Dataset Size: 2.11 GB

Example Usage

Load the Ratchada-STT dataset

from datasets import load_dataset

dataset = load_dataset("ThinkingMachinesDataScience/Ratchada-STT")

Access train and test splits

train = dataset['train']
test = dataset['test']

Example usage with first sample

print(train[0])

License

This dataset is distributed under the MIT License.

Citation

If you use this dataset in your research or work, please cite it as:

@dataset{thinkingmachinesdatascience/ratchada-stt,
  title = {Ratchada-STT Dataset},
  publisher = {Hugging Face},
  year = {2024},
  author = {Thinking Machines Data Science},
  license = {MIT},
}

Contribution Guidelines

Ensure your PR adheres to the repository's coding standards.
Include relevant tests and ensure they pass.
Update documentation if your contribution changes any functionality.
Follow the Hugging Face Community Code of Conduct.