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README.md

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+---
+license: apache-2.0
+tags:
+- audio-classification
+- deep-speech-detection
+- tensorflow
+- keras
+---
+# Model Card for Deep Speech Detection
+
+## Model Description
+This is a TensorFlow/Keras CNN model trained to detect deepfake or synthetic speech with >95% accuracy. It uses audio features (MFCCs, chroma, spectral centroid, etc.) extracted with `librosa`.
+
+## Intended Use
+- Deepfake speech detection
+- Audio authenticity verification
+
+## Dependencies
+```bash
+pip install tensorflow==2.10.0 librosa==0.10.1 joblib==1.3.2 numpy==1.22.4 pandas==1.5.3 scikit-learn==1.2.2
+```
+
+## Usage
+```python
+import tensorflow as tf
+import librosa
+import joblib
+import numpy as np
+import pandas as pd
+from huggingface_hub import hf_hub_download, HfApi
+import os
+
+# Download model and files
+repo_name = "Prince53/deep-speech-detection"
+model_dir = "downloaded_model"
+scaler_path = hf_hub_download(repo_name, "scaler.pkl", local_dir=model_dir)
+label_encoder_path = hf_hub_download(repo_name, "label_encoder.pkl", local_dir=model_dir)
+api = HfApi()
+api.snapshot_download(repo_name, local_dir=model_dir, allow_patterns="saved_model/*")
+
+# Load model and preprocessing objects
+model = tf.keras.models.load_model(os.path.join(model_dir, "saved_model"))
+scaler = joblib.load(scaler_path)
+label_encoder = joblib.load(label_encoder_path)
+
+# Feature extraction function
+def segment_and_extract_features(audio, sr=16000):
+    segment_samples = int(2.0 * sr)
+    step_samples = int(0.25 * sr)
+    segments = [audio[i:i+segment_samples] for i in range(0, len(audio) - segment_samples + 1, step_samples)]
+    features = []
+    for segment in segments:
+        if len(segment) < segment_samples:
+            continue
+        mfccs = librosa.feature.mfcc(y=segment, sr=sr, n_mfcc=13)
+        chroma = librosa.feature.chroma_stft(y=segment, sr=sr)
+        spectral_centroid = librosa.feature.spectral_centroid(y=segment, sr=sr)
+        spectral_bandwidth = librosa.feature.spectral_bandwidth(y=segment, sr=sr)
+        rolloff = librosa.feature.spectral_rolloff(y=segment, sr=sr)
+        zero_crossing_rate = librosa.feature.zero_crossing_rate(y=segment)
+        feature_dict = {
+            'mfcc_mean': np.mean(mfccs, axis=1),
+            'mfcc_std': np.std(mfccs, axis=1),
+            'chroma': np.mean(chroma, axis=1),
+            'spectral_centroid': np.mean(spectral_centroid),
+            'spectral_bandwidth': np.mean(spectral_bandwidth),
+            'rolloff': np.mean(rolloff),
+            'zero_crossing_rate': np.mean(zero_crossing_rate)
+        }
+        features.append(feature_dict)
+    return features
+
+# Classify audio
+audio, sr = librosa.load("path/to/audio.wav", sr=16000)
+segments = segment_and_extract_features(audio, sr)
+segment_features = pd.concat([
+    pd.DataFrame([seg['mfcc_mean'] for seg in segments]),
+    pd.DataFrame([seg['mfcc_std'] for seg in segments]),
+    pd.DataFrame([seg['chroma'] for seg in segments]),
+    pd.DataFrame([[seg['spectral_centroid'], seg['spectral_bandwidth'], seg['rolloff'], seg['zero_crossing_rate']] for seg in segments])
+], axis=1)
+segment_features = scaler.transform(segment_features)
+segment_features = segment_features.reshape(segment_features.shape[0], segment_features.shape[1], 1)
+predictions = model.predict(segment_features)
+segment_labels = np.argmax(predictions, axis=1)
+confidence_scores = np.mean(predictions, axis=0)
+final_label = label_encoder.inverse_transform([np.argmax(np.bincount(segment_labels))])[0]
+print(f"Confidence Scores: Real={confidence_scores[0]:.4f}, Fake={confidence_scores[1]:.4f}")
+print(f"Classification: {final_label} ({0 if final_label == 'Real' else 1})")
+```
+
+## Limitations
+- Requires mono audio at 16kHz sampling rate.
+- May struggle with low-quality audio or unseen domains.
+- Trained on the Comb4 dataset.
+
+## Training Data
+- Dataset: Comb4 (custom dataset with real and fake audio)
+- Size: [Update with number of samples]
+
+## Evaluation
+- Test Accuracy: [Update with >95%]