---
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

```python
from datasets import load_dataset

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

#### Access train and test splits

```python
train = dataset['train']
test = dataset['test']
```

#### Example usage with first sample

```python
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.