from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch, soundfile as sf

model = WhisperForConditionalGeneration.from_pretrained("Tmanna/whisper-bengali-final")
processor = WhisperProcessor.from_pretrained("Tmanna/whisper-bengali-final")

audio, sr = sf.read("audio.wav")
inputs = processor(audio, sampling_rate=16000, return_tensors="pt").input_features

with torch.no_grad():
    ids = model.generate(inputs)

print(processor.batch_decode(ids, skip_special_tokens=True)[0])

📈 Limitations

Trained on synthetic audio (not real speech)
Accuracy is limited
Not production-ready
For research/demo use

🔮 Future Work

Train on real Bengali speech dataset
Use Whisper Medium / Large
Add speaker diarization
Improve WER accuracy

👤 Author

Tapan Manna
Machine Learning / Speech AI

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