Datasets:
license: cc-by-nc-nd-4.0
task_categories:
- automatic-speech-recognition
language:
- hi
tags:
- audio
- Hindi
- speech
- speech recognition
- machine
- machine learning
size_categories:
- n<1K
🎧 Hindi Speech Dataset
The Hindi Speech Dataset is a high-quality and structured speech audio dataset developed to support modern AI systems that rely on diverse audio data and scalable voice data. It contains 132 hours of recordings distributed across 565 files, available in MP3 and WAV formats, with a total size of 101 MB. This carefully curated audio dataset provides balanced speaker representation with 49% female and 51% male contributors, covering an age range from 18 to 50+ years. The dataset language is Hindi, with additional speaker diversity from countries including India, Nepal, Fiji, Mauritius, Suriname, Guyana, and Trinidad and Tobago, making it a globally relevant language speech dataset.
🔗 Learn more:
https://speech-data.ai/datasets/hindi-speech/
🚀 Use Cases
This Hindi speech dataset is designed for advanced AI applications such as speech recognition, voice assistant development, and natural language processing. The structured speech data supports acoustic modeling, speaker identification, and robust machine learning pipelines. It is particularly valuable for building multilingual and cross-regional systems that depend on high-quality speech audio dataset resources. As a reliable speech recognition dataset, it can be used in both research environments and production-grade deployments of voice-enabled technologies.
📊 Dataset Metadata
| Field | Value |
|---|---|
| 📜 License | CC BY-NC-ND 4.0 |
| 🎯 Task Categories | Automatic Speech Recognition |
| 🌍 Language | Hindi (hi) |
| 🏷️ Tags | Audio, Hindi, Speech, Speech Recognition, Machine, Machine Learning |
| 📦 Size Category | n < 1K |
⭐ Key Value
The key value of this voice dataset lies in its linguistic coverage, balanced demographics, and high-quality audio data optimized for real-world AI training. It enhances model accuracy across accents and regional variations, making it a strong foundation for scalable speech dataset development and multilingual speech intelligence systems.