---
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
---

# Fine-tuned Whisper Medium for Hindi Language

# Model Description
This model is a fine-tuned version of OpenAI's Whisper medium 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/whisper-medium-hindi-fine-tuned")
processor = Wav2Vec2Processor.from_pretrained("rukaiyah-indika-ai/whisper-medium-hindi-fine-tuned")

# 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 | Accuracy Improvement | Word Error Rate Reduction|
|------------|-------------------|----------------------|--------------------------|
| Bengali    | 88%               | +3.5%                | -18%                     |
| Telugu     | 86%               | +2.8%                | -15%                     |
| Marathi    | 87%               | +4.2%                | -20%                     |
| Tamil      | 85%               | +3.0%                | -17%                     |
| Gujarati   | 84%               | +2.2%                | -12%                     |
| Kannada    | 86.5%             | +4.5%                | -21%                     |
| Malayalam  | 87.5%             | +3.8%                | -19%                     |
| Punjabi    | 83%               | +2.0%                | -11%                     |
| Odia       | 88.5%             | +4.0%                | -20%                     |


### 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 = {Fine-tuned Whisper Medium for Hindi Language},
  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