metadata

license: apache-2.0
datasets:
  - ARTPARK-IISc/Vaani
language:
  - hi
base_model:
  - openai/whisper-small
pipeline_tag: automatic-speech-recognition

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
IndicTTS	02.86
MUCS	14.60
Commonvoice	13.84
Kathbath	08.85
Kathbath Noisy	11.80
Vaani	24.66
RESPIN	07.36