Whisper-large-v3-vaani-hindi

This is a fine-tuned version of OpenAI's Whisper-Large-V3, trained on approximately 718 hours of transcribed Hindi speech from multiple datasets.

Usage

This can be used with the pipeline function from the Transformers module.

import torch
from transformers import pipeline

audio = "path to the audio file to be transcribed"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
modelTags="ARTPARK-IISc/whisper-large-v3-vaani-hindi"
transcribe = pipeline(task="automatic-speech-recognition", model=modelTags, chunk_length_s=30, device=device)
transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="hi", task="transcribe")

print('Transcription: ', transcribe(audio)["text"])

Training and Evaluation

The models has finetuned using folllowing dataset Vaani ,Gramvaani IndicVoices, Fleurs,IndicTTS and Commonvoice

The performance of the model was evaluated using multiple datasets, and the evaluation results are provided below.

Dataset	WER
Gramvaani	25.11
Fleurs	11.20
MUCS	14.60
Commonvoice	13.84
Kathbath	08.85
Kathbath Noisy	11.80
Vaani	24.66
RESPIN	07.36

Citation

If you use this model, please cite the following:

@misc{pulikodan2026vaanicapturinglanguagelandscape,
      title={VAANI: Capturing the language landscape for an inclusive digital India}, 
      author={Sujith Pulikodan and Abhayjeet Singh and Agneedh Basu and Nihar Desai and Pavan Kumar J and Pranav D Bhat and Raghu Dharmaraju and Ritika Gupta and Sathvik Udupa and Saurabh Kumar and Sumit Sharma and Vaibhav Vishwakarma and Visruth Sanka and Dinesh Tewari and Harsh Dhand and Amrita Kamat and Sukhwinder Singh and Shikhar Vashishth and Partha Talukdar and Raj Acharya and Prasanta Kumar Ghosh},
      year={2026},
      eprint={2603.28714},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2603.28714}, 
}