Loading datasets...
Task: ASR
Release Date: 5/16/2026
Format: OGG , SRT
Size: 63.03 MB
Share
This dataset consists of Hindi audio recordings paired with their corresponding text transcriptions. It includes a variety of speech samples that may cover different speakers, accents, speaking styles, and recording conditions, reflecting real-world audio diversity. The dataset is suitable for tasks such as automatic speech recognition (ASR), speech-to-text modeling, and language processing. It can be used for training and evaluating models in speech recognition, transcription accuracy, and related natural language processing applications.
Licensing
Creative Commons Attribution Non Commercial Share Alike 4.0 International (CC-BY-NC-SA-4.0)
Restrictions/Special Constraints
This dataset is intended for research and non-commercial use only.
Forbidden Usage
This dataset may not be used for commercial purposes without prior authorization for commercial licensing inquiries, please contact us.
Intended Use
Intended for training, evaluation, and research of automatic speech recognition (ASR), Hindi speech-to-text, and other speech processing models.
The dataset consists of Hindi audio recordings collected from diverse sources, capturing real-world variability across different speakers, recording environments, and devices. This multi-source approach ensures the dataset reflects the natural range of Hindi speech as it occurs in everyday contexts, from controlled studio settings to informal conversational environments.
Audio files have undergone standard preprocessing steps including noise reduction, silence trimming, segmentation, and amplitude normalization to ensure consistency across recordings. Transcriptions have been cleaned and standardized to maintain alignment accuracy between audio segments and their corresponding text.
Each audio sample is paired with a corresponding text transcription, manually or automatically generated depending on the source. Annotation quality has been reviewed for consistency; however, minor variations may exist across different data sources and transcription methods. Users are advised to validate annotations for high-precision applications.
The dataset includes multiple speakers with natural variations in accent, tone, speaking speed, and regional pronunciation, reflecting the linguistic diversity of Hindi across India. This speaker diversity makes the dataset particularly well-suited for training robust and generalized ASR models that perform reliably across a wide range of speaker profiles.
This dataset is well-suited for a range of speech and language processing tasks, including:
Automatic speech recognition (ASR) system development
Speech-to-text transcription modeling
Speaker identification and verification
Hindi language modeling and acoustic research
Low-resource and multilingual NLP pipeline development
Speaker representation may be imbalanced, with certain accents, dialects, or demographics having fewer samples than others.
Some recordings may contain background noise, overlapping speech, or audio artifacts that could affect model performance.
Minor transcription inconsistencies may be present across different source batches.
Additional cleaning, validation, or augmentation may be required depending on the specific application and accuracy requirements.
