README.md

---
language:
- bn
tags:
- whisper
- asr
- speech-recognition
- fine-tuned
- automatic-speech-recognition
- bengali
- audio
- generated_from_trainer
license: apache-2.0
metrics:
- wer
pipeline_tag: automatic-speech-recognition
---

# Whisper Small Bengali

This is a fine-tuned Whisper Small model for Bengali (Bangla) speech recognition.

## Model Details

- **Base Model**: openai/whisper-small
- **Language**: Bengali (bn)
- **Training Steps**: 2000
- **Final Training Loss**: N/A

## Usage

```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration, WhisperTokenizer
import torch
import librosa

# Load model and tokenizer
model = WhisperForConditionalGeneration.from_pretrained("Noobbbbb/whisper-small-bn")
tokenizer = WhisperTokenizer.from_pretrained("Noobbbbb/whisper-small-bn")
processor = WhisperProcessor.from_pretrained("Noobbbbb/whisper-small-bn")

# Load audio (must be 16kHz)
audio, sr = librosa.load("audio.wav", sr=16000)

# Extract features
input_features = processor.feature_extractor(
    audio, 
    sampling_rate=16000, 
    return_tensors="pt"
).input_features

# Generate transcription
with torch.no_grad():
    generated_ids = model.generate(input_features, max_length=448)
    
# Decode
transcription = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(transcription)
```

## Training Details

- **Training Data**: openslr37
- **Language**: Bengali (bn)
- **Training Steps**: 2000
- **Batch Size**: 4
- **Learning Rate**: 1e-05
- **Optimizer**: AdamW
- **eval_wer**: 0.3080158337456705

## Limitations

- Optimized for Bengali speech only
- Works best with clear audio at 16kHz sampling rate
- May not perform well on heavily accented or noisy audio


## Acknowledgments

Based on OpenAI's Whisper model: https://github.com/openai/whisper