Gnani AI has announced the launch of its latest speech-to-text model, trained on an extensive dataset of 14 million hours of proprietary Indic speech. The model covers 12 languages and incorporates real dialect variation, ambient noise, and natural code-switching into its training distribution.
Enhanced Training Data for Better Accuracy
The new model leverages a diverse corpus that reflects the linguistic diversity of the Indian subcontinent. By including real-world variations such as regional dialects, background noise, and the common practice of switching between languages mid-sentence, Gnani AI aims to deliver superior transcription accuracy in everyday scenarios.
Key Features of the Model
- Trained on 14 million hours of proprietary Indic speech data.
- Supports 12 major Indian languages.
- Incorporates dialect variations, ambient noise, and code-switching.
- Designed for robust performance in real-world environments.
The model is expected to benefit applications in voice assistants, transcription services, and accessibility tools for Indic language speakers. Gnani AI continues to focus on advancing speech recognition technology for underserved languages.
Implications for the Industry
This launch positions Gnani AI as a key player in the Indic speech recognition space. The inclusion of code-switching and dialectal data addresses a critical gap in existing models, which often struggle with the linguistic complexity of India. The company plans to integrate this model into its product suite for enterprise and consumer use.
The announcement was made on 19 June 2026, reflecting ongoing innovation in artificial intelligence and natural language processing for regional languages.
