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README.md

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+# OpenAI Whisper-Base Fine-Tuned Model for Speech-to-Text
+
+This repository hosts a fine-tuned version of the OpenAI Whisper-Base model optimized for speech-to-text tasks using the [Mozilla Common Voice 13.0](https://commonvoice.mozilla.org/) dataset. The model is designed to efficiently transcribe speech into text while maintaining high accuracy.
+
+## Model Details
+- **Model Architecture**: OpenAI Whisper-Base  
+- **Task**: Speech-to-Text  
+- **Dataset**: [Mozilla Common Voice 13.0](https://commonvoice.mozilla.org/)  
+- **Quantization**: FP16  
+- **Fine-tuning Framework**: Hugging Face Transformers  
+
+## 🚀 Usage
+
+### Installation
+```bash
+pip install transformers torch
+```
+
+### Loading the Model
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = "AventIQ-AI/whisper-speech-text"
+model = WhisperForConditionalGeneration.from_pretrained(model_name).to(device)
+processor = WhisperProcessor.from_pretrained(model_name)
+```
+
+### Speech-to-Text Inference
+```python
+import torchaudio
+
+# Load and process audio file
+def transcribe(audio_path):
+    waveform, sample_rate = torchaudio.load(audio_path)
+    inputs = processor(waveform, sampling_rate=sample_rate, return_tensors="pt").input_features.to(device)
+    
+    # Generate transcription
+    with torch.no_grad():
+        predicted_ids = model.generate(inputs)
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+    return transcription
+
+# Example usage
+audio_file = "sample_audio.wav"
+print(transcribe(audio_file))
+```
+
+## 📊 Evaluation Results
+After fine-tuning the Whisper-Base model for speech-to-text, we evaluated the model's performance on the validation set from the Common Voice 13.0 dataset. The following results were obtained:
+
+| Metric      | Score  | Meaning |
+|------------|--------|------------------------------------------------|
+| **WER**    | 8.2%   | Word Error Rate: Measures transcription accuracy |
+| **CER**    | 4.5%   | Character Error Rate: Measures character-level accuracy |
+
+## Fine-Tuning Details
+
+### Dataset
+The Mozilla Common Voice 13.0 dataset, containing diverse multilingual speech samples, was used for fine-tuning the model.
+
+### Training
+- **Number of epochs**: 3  
+- **Batch size**: 8  
+- **Evaluation strategy**: epochs  
+
+### Quantization
+Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
+
+## 📂 Repository Structure
+```bash
+.
+├── model/               # Contains the quantized model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safetensors/   # Quantized Model
+├── README.md            # Model documentation
+```
+
+## ⚠️ Limitations
+- The model may struggle with highly noisy or overlapping speech.
+- Quantization may lead to slight degradation in accuracy compared to full-precision models.
+- Performance may vary across different accents and dialects.
+
+## 🤝 Contributing
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
+