language:
- hi
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium finetuned Hindi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 99.8077099166743
iVaani - Fine-tuned ASR model for Hindi Language
Model Description
This is iVaani model, specifically optimized for the Hindi language. The fine-tuning process has led to an improvement in accuracy by 2.5% compared to the original Whisper model.
Performance
After fine-tuning, the model shows a 2.5% increase in transcription accuracy for Hindi language audio compared to the base Whisper medium model.
How to Use
You can use this model directly with a simple API call in Hugging Face. Here is a Python code snippet for using the model:
from transformers import AutoModelForCTC, Wav2Vec2Processor
model = AutoModelForCTC.from_pretrained("rukaiyah-indika-ai/iVaani")
processor = Wav2Vec2Processor.from_pretrained("rukaiyah-indika-ai/iVaani")
# Replace 'path_to_audio_file' with the path to your Hindi audio file
input_audio = processor(path_to_audio_file, return_tensors="pt", padding=True)
# Perform the transcription
transcription = model.generate(**input_audio)
print("Transcription:", transcription)
Additional Language Models
Indika AI has also fine-tuned ASR (Automatic Speech Recognition) models for several other Indic languages, enhancing the accuracy by 2-5% for each language. The word error rate has also been significantly reduced.
The additional languages include:
| Language | Original Accuracy |
|---|---|
| Bengali | 88% |
| Telugu | 86% |
| Marathi | 87% |
| Tamil | 88% |
| Gujarati | 90% |
| Kannada | 86.5% |
| Malayalam | 87.5% |
| Punjabi | 89% |
| Odia | 88.5% |
BibTeX entry and citation info
If you use this model in your research, please cite it as follows:
@misc{whisper-medium-hindi-fine-tuned,
author = {Indika AI},
title = {iVaani},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face Model Hub}
}
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0