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README.md

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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"])
+```
+