Update README.md

README.md

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----
-license: mit
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+# Truth Detection from Audio Stories
+
+This model predicts whether a short audio story is truthful or deceptive using MFCC feature extraction and a Random Forest classifier.
+
+## Model Details
+
+* **Type:** Random Forest Classifier
+* **Features:** 13-dimensional MFCC (Mel-Frequency Cepstral Coefficients)
+* **Training Framework:** scikit-learn (`joblib` serialization)
+* **Input:** WAV audio file
+* **Output:** Predicted label: `True Story` or `Deceptive Story`
+
+## Intended Uses & Limitations
+
+**Intended Uses:**
+
+* Detecting potential deception in short, spoken stories or statements.
+* Research experiments on vocal biomarkers of deception.
+* Educational demonstrations on audio feature extraction and classification.
+
+**Limitations & Risks:**
+
+* The model was trained on a limited dataset; performance may degrade on different languages, audio quality, or speaking styles.
+* Predictions are probabilistic and should not be used as sole evidence in high-stakes scenarios (e.g., legal or security decisions).
+* Cultural, linguistic, or demographic biases in the training data can lead to unfair predictions.
+
+## Evaluation Metrics
+
+* **Accuracy:** 91%
+* **Languages in Training Data:** 15+ spoken languages
+
+## Training Data
+
+* **Source:** Curated dataset of narrated stories labeled as truthful or deceptive.
+* **Preprocessing:** Resampled to original sampling rates, trimmed to 30 seconds, MFCC extraction.
+
+## How to Use
+
+### Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+### Loading the Model in Python
+
+```python
+import joblib
+from huggingface_hub import hf_hub_download
+
+repo_id = "sangambhamare/TruthDetection"
+model_file = hf_hub_download(repo_id=repo_id, filename="model.joblib")
+model = joblib.load(model_file)
+```
+
+### Making Predictions
+
+```python
+import librosa
+import numpy as np
+
+def extract_mfcc(file_path):
+    y, sr = librosa.load(file_path, sr=None)
+    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    return np.mean(mfcc, axis=1)
+
+features = extract_mfcc("path/to/audio.wav").reshape(1, -1)
+prediction = model.predict(features)[0]
+label = "True Story" if prediction == 1 else "Deceptive Story"
+print(label)
+```
+
+## Gradio Demo
+
+A live demo of this model is available via a Gradio interface. To launch locally:
+
+```bash
+python app.py
+```
+
+This will start a web app where you can upload a WAV file and see the prediction.
+
+tag::end
+
+---
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```
+@misc{bhamare2025truthdetection,
+  title={Truth Detection from Audio Stories},
+  author={Sangam Sanjay Bhamare},
+  year={2025},
+  howpublished={\url{https://huggingface.co/sangambhamare/TruthDetection}}
+}
+```
+
+## License
+
+This model is released under the MIT License.