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# 🗣️ Speech-to-Text Model: Whisper Small (openai/whisper-small)
This repository demonstrates how to fine-tune, evaluate, quantize, and deploy the [OpenAI Whisper Small](https://huggingface.co/openai/whisper-small) model for automatic speech recognition (ASR).
---
## 📦 Model Used
- **Model Name**: `openai/whisper-small`
- **Architecture**: Transformer-based encoder-decoder
- **Task**: Automatic Speech Recognition (ASR)
- **Pretrained by**: OpenAI
---
## 🧾 Dataset
We use the [common_voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) dataset from Hugging Face.
### 🔹 Load English Subset:
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="train[:1%]")
```
# 🧠 Evaluation / Scoring (WER)
```python
from datasets import load_metric
import numpy as np
wer_metric = load_metric("wer")
def compute_wer(predictions, references):
return wer_metric.compute(predictions=predictions, references=references)
```
# 🎤 Inference Example
```python
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="./Speech_To_Text_OpenAIWhisper_Model", device=0)
result = pipe("harvard.wav")
print("Transcription:", result["text"])
```
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