Datasets:
license: cc-by-nc-nd-4.0
task_categories:
- automatic-speech-recognition
language:
- hu
tags:
- audio
- speech
- speech recognition
- machine
- machine learning
- Hungarian
size_categories:
- n<1K
🎧 Hungarian Speech Dataset
The Hungarian Speech Dataset is a high-quality speech audio dataset designed to support advanced AI systems that depend on diverse audio data and reliable voice data for multilingual model training. It comprises 169 hours of recordings across 743 files, provided in MP3 and WAV formats, with a total size of 134 MB. This structured audio dataset ensures balanced speaker representation, featuring 46% female and 54% male speakers, and an age distribution spanning 18 to 50+ years. The dataset language is Hungarian, with contributions from speakers across Hungary, Romania, Slovakia, Serbia, Ukraine, and Austria, making it a geographically diverse language speech dataset suitable for real-world AI applications.
🔗 Learn more:
https://speech-data.ai/datasets/hungarian/
🚀 Use Cases
This Hungarian speech dataset is designed for building robust AI solutions, including speech recognition, voice assistant development, and natural language processing systems. The structured speech data supports acoustic modeling, speaker identification, and scalable machine learning workflows. It is especially useful for developing multilingual applications that require high-quality speech audio dataset resources. As a reliable speech recognition dataset, it can be applied in both research environments and production-level deployment of voice-driven technologies.
📊 Dataset Metadata
| Field | Value |
|---|---|
| 📜 License | CC BY-NC-ND 4.0 |
| 🎯 Task Categories | Automatic Speech Recognition |
| 🌍 Language | Hungarian (hu) |
| 🏷️ Tags | Audio, Speech, Hungarian, Speech Recognition, Machine, Machine Learning |
| 📦 Size Category | n < 1K |
⭐ Key Value
The key value of this voice dataset lies in its structured composition, demographic balance, and high-quality audio data optimized for real-world AI training scenarios. It improves model robustness across accents and regional variations, making it a strong foundation for scalable speech dataset development and multilingual speech technology systems.