jsbeaudry/test-esp

Multi-Domain Spanish Speech Dataset

This dataset contains 1 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:

• Spanish (es): spanish

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

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/test-esp")

# 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

{
  "id": "9195564e-737a-4a71-8562-92b89a0fb652",
  "source": "Jean",
  "text": "¿Cómo te has sentido últimamente con todo lo que está pasando?",
  "audio": "https://voiceovers-haiti.s3.us-east-2.amazonaws.com/0f775ec4-f100-4bab-9477-2ee3cde613f2_d28cfdee-75e0-489a-a790-49db5343dd8a_human.wav?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIAXF2BWYGA2CKJX4LH%2F20251013%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20251013T202720Z&X-Amz-Expires=3600&X-Amz-Signature=5e4308d2a90ed913e3e234745730f1ba9df3fba4ea092cbd8800e7b0939f5ad6&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject",
  "nature": "human",
  "language": "spanish",
  "domain": "healthcare"
}

Sample Transcriptions by Domain

Healthcare Domain

1. Spanish (human):
   "¿Cómo te has sentido últimamente con todo lo que está pasando?"
   ID: 9195564e...

Dataset Statistics

• Total audio files: 1
• Languages: 1 (Spanish)
• Domains: 1 (healthcare)
• Audio types: human
• Average text length: 62 characters
• Audio hosting: voiceovers-haiti.s3.us-east-2.amazonaws.com

Distribution by Category

Category	Values
Spanish	1 samples
Healthcare	1 samples
Human	1 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

@dataset{multi_domain_speech_2025,
  title={Multi-Domain Spanish Speech Dataset},
  author={Dataset Creator},
  year={2025},
  languages={es},
  domains={healthcare},
  url={https://huggingface.co/datasets/jsbeaudry/test-esp}
}

Acknowledgments

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