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README.md

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----
-license: mit
----
+---
+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
+{
+  "speaker_id": "2317776c-d722-4870-bcfa-99b3b58526c4",
+  "id": "2317776c-d722-4870-bcfa-99b3b58526c4",
+  "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%2F20251003%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20251003T132746Z&X-Amz-Expires=3600&X-Amz-Signature=905a5d745efff9ed19663649d2a87c56377008223314632b46029d4539eea643&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.
+    

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dataset_info.json

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+{
+  "citation": "",
+  "description": "Multi-domain speech dataset with 12 audio-text pairs across 1 languages and 1 domains",
+  "features": {
+    "id": {
+      "dtype": "string",
+      "description": "Unique identifier (UUID) for each audio-text pair"
+    },
+    "text": {
+      "dtype": "string",
+      "description": "Text transcription in the specified language"
+    },
+    "audio": {
+      "sampling_rate": 24000,
+      "_type": "Audio"
+    },
+    "nature": {
+      "dtype": "string",
+      "description": "Type of audio generation (synthetic, natural, etc.)"
+    },
+    "language": {
+      "dtype": "string",
+      "description": "Language of the audio and text content"
+    },
+    "domain": {
+      "dtype": "string",
+      "description": "Domain or topic category (agriculture, healthcare, etc.)"
+    }
+  },
+  "homepage": "",
+  "license": "mit",
+  "task_categories": [
+    "automatic-speech-recognition",
+    "text-to-speech"
+  ],
+  "tags": [
+    "speech",
+    "audio",
+    "multilingual",
+    "haitian_creole",
+    "healthcare",
+    "human"
+  ],
+  "languages": [
+    "ha"
+  ],
+  "size_categories": "n<1K",
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 8919,
+      "num_examples": 12
+    }
+  }
+}

metadata.csv

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+id,text,file_name,nature,language,domain
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+9b373149-9be4-420d-8d69-86a870bc93fc,"Mwen santi m gen lafyèv depi yè swa, èske mwen bezwen fè yon tès?",data/9b373149-9be4-420d-8d69-86a870bc93fc.wav,human,haitian_creole,healthcare
+37d780b1-bd75-42a7-88c5-245beefac17c,Telemedsin fasilite w jwenn konsèy medikal san ou pa kite kay ou.,data/37d780b1-bd75-42a7-88c5-245beefac17c.wav,human,haitian_creole,healthcare
+4cab0134-3314-4d70-a2be-9f911a7ec8a3,Ou ka voye foto blese a nan aplikasyon an pou doktè a ka wè li pi byen.,data/4cab0134-3314-4d70-a2be-9f911a7ec8a3.wav,human,haitian_creole,healthcare
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+5a1153aa-5c0c-4db2-8a55-c26c297b60d3,Telemedsin se yon bon solisyon pou moun ki twò lwen pou yon vizit nan klinik la.,data/5a1153aa-5c0c-4db2-8a55-c26c297b60d3.wav,human,haitian_creole,healthcare
+c24b6d06-677e-481f-9cb2-5c6507ccb14e,Èske ou gen tout medikaman ou bezwen anvan ou kòmanse yon konsiltasyon videyo?,data/c24b6d06-677e-481f-9cb2-5c6507ccb14e.wav,human,haitian_creole,healthcare
+66ca86cf-bad3-43ec-98c3-b675873fc1dd,Mwen ka ede w pran yon randevou ak yon espesyalis san ou pa deplase.,data/66ca86cf-bad3-43ec-98c3-b675873fc1dd.wav,human,haitian_creole,healthcare
+1d9885ce-b532-49cb-b09b-26bed65ca979,Li enpòtan pou w di tout sentòm yo byen klè pandan konsiltasyon an.,data/1d9885ce-b532-49cb-b09b-26bed65ca979.wav,human,haitian_creole,healthcare