README.md

---
license: mit
task_categories:
- automatic-speech-recognition
- text-to-speech
tags:
- speech
- audio
- haitian_creole
- healthcare
- human
- multilingual
language:
- ha
size_categories:
- n<1K
pretty_name: Multi-Domain Haitian_creole Speech Dataset
---

# Multi-Domain Haitian_creole Speech Dataset

This dataset contains 12 audio recordings with corresponding text transcriptions across multiple languages and domains.

## Dataset Description

A comprehensive collection of audio files paired with text transcriptions, featuring both synthetic and natural speech across various domains. Suitable for automatic speech recognition (ASR), text-to-speech (TTS), and domain-specific speech processing tasks.

## Dataset Structure

Each entry contains:
- `id`: Unique identifier (UUID)
- `text`: Transcription text in the specified language
- `audio`: URL to the audio file (with AWS S3 signed URLs)
- `nature`: Type of audio (e.g., "synthetic", "natural")
- `language`: Language of the audio/text
- `domain`: Domain/topic category (e.g., "agriculture", "healthcare", "education")

## Languages

This dataset includes the following languages:
- **Haitian_creole** (ha): haitian_creole

## Domains

Content spans across multiple domains:
- **Healthcare**: Domain-specific terminology and context

## Audio Nature

The dataset includes different types of audio:
- **Human**: Natural human speech

## Usage

```python
from datasets import load_dataset
import json
import requests
from io import BytesIO
import pandas as pd

# Load using datasets library
dataset = load_dataset("jsbeaudry/med-cre")

# Or load JSON directly
with open("dataset.json", "r", encoding="utf-8") as f:
    data = json.load(f)

print(f"Dataset contains {len(data)} audio-text pairs")

# Create DataFrame for analysis
df = pd.DataFrame(data)
print("\nDataset breakdown:")
print(f"Languages: {df['language'].value_counts().to_dict()}")
print(f"Domains: {df['domain'].value_counts().to_dict()}")
print(f"Nature: {df['nature'].value_counts().to_dict()}")

# Filter by criteria
swahili_agriculture = [item for item in data 
                      if item['language'] == 'swahili' and item['domain'] == 'agriculture']
print(f"\nSwahili agriculture samples: {len(swahili_agriculture)}")

# Example: Download and process audio
def download_audio(url):
    response = requests.get(url)
    return BytesIO(response.content)

# Get first audio file
audio_data = download_audio(data[0]['audio'])
print(f"Audio downloaded for: {data[0]['text'][:50]}...")
```

## Sample Data

```json
{
  "id": "2317776c-d722-4870-bcfa-99b3b58526c4",
  "source": "Jean",
  "text": "Kijan ou santi ou jodi a, paske pran swen tèt ou enpòtan anpil pou sante mantal ou.",
  "audio": "https://voiceovers-haiti.s3.us-east-2.amazonaws.com/0e230ff9-f13e-4d3b-be7d-fb3b19977511_d28cfdee-75e0-489a-a790-49db5343dd8a_human.wav?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIAXF2BWYGA2CKJX4LH%2F20251005%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20251005T063333Z&X-Amz-Expires=3600&X-Amz-Signature=db472454266f6ace8db4563a71a3705ba9e49635f17a97217c4975ef9596e07e&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject",
  "nature": "human",
  "language": "haitian_creole",
  "domain": "healthcare"
}
```

## Sample Transcriptions by Domain

### Healthcare Domain

1. **Haitian_creole** (human):  
   "Kijan ou santi ou jodi a, paske pran swen tèt ou enpòtan anpil pou sante mantal ou."  
   *ID*: `2317776c...`

2. **Haitian_creole** (human):  
   "Si w santi estrès oswa tristès, pa pè chèche sipò yon pwofesyonèl nan sante mantal, yo la pou ede w."  
   *ID*: `9665761e...`

## Dataset Statistics

- **Total audio files**: 12
- **Languages**: 1 (Haitian_creole)
- **Domains**: 1 (healthcare)
- **Audio types**: human
- **Average text length**: 74 characters
- **Audio hosting**: voiceovers-haiti.s3.us-east-2.amazonaws.com

### Distribution by Category

| Category | Values |
|----------|---------|
| Haitian_creole | 12 samples |
| Healthcare | 12 samples |
| Human | 12 samples |

## Audio Format

Audio files are stored locally in the dataset as WAV files. When loaded with the datasets library, audio is automatically converted to the standard format:
- **Format**: WAV
- **Sampling Rate**: Preserved from original (typically 16kHz or 22kHz)
- **Channels**: Mono
- **Bit Depth**: 16-bit or 32-bit float
- **Access**: Direct array access via `dataset['train'][index]['audio']['array']`

## Use Cases

This dataset can be used for:

### Speech Recognition (ASR)
- Multi-language speech recognition systems
- Domain-specific ASR models (agriculture, healthcare, etc.)
- Synthetic vs. natural speech detection

### Text-to-Speech (TTS)
- Multi-language TTS systems
- Domain-adaptive speech synthesis
- Voice quality evaluation (synthetic vs. natural)

### Research Applications
- Cross-domain speech analysis
- Language-specific phonetic studies
- Synthetic speech quality assessment
- Multi-modal AI training

### Commercial Applications
- Voice assistants for specific domains
- Educational pronunciation tools
- Accessibility applications
- Multilingual customer service systems

## Data Quality

- All audio files are accessible via HTTPS URLs with AWS authentication
- Text transcriptions are domain-verified and language-specific
- Unique identifiers ensure data integrity and traceability
- Consistent schema across all entries
- Balanced representation across domains and languages

## License

This dataset is released under the MIT License.

## Citation

```bibtex
@dataset{multi_domain_speech_2025,
  title={Multi-Domain Haitian_creole Speech Dataset},
  author={Dataset Creator},
  year={2025},
  languages={ha},
  domains={healthcare},
  url={https://huggingface.co/datasets/jsbeaudry/med-cre}
}
```

## Acknowledgments

Special thanks to contributors who provided audio recordings and transcriptions across multiple languages and domains to make this comprehensive dataset possible.