
# 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:

```python
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:

```bibtex
@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

{"language": ["hi"], "license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-medium", "model-index": [{"name": "Whisper Medium finetuned Hindi", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "common_voice_11_0", "type": "mozilla-foundation/common_voice_11_0", "config": "hi", "split": "test", "args": "hi"}, "metrics": [{"type": "wer", "value": 99.8077099166743, "name": "Wer"}]}]}]}
transformers tensorboard safetensors whisper automatic-speech-recognition whisper-event generated_from_trainer hi dataset:mozilla-foundation/common_voice_11_0 base_model:openai/whisper-medium license:apache-2.0 model-index endpoints_compatible region:us