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| title: Voice Access Control System | |
| emoji: 🎤 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.12.0 | |
| app_file: app.py | |
| pinned: false | |
| # Voice Access Control System | |
| This is a deep learning-based voice access control system that can verify whether a person should be granted access based on their voice recording. | |
| ## Description | |
| The system uses a convolutional neural network to analyze mel spectrograms of voice recordings and determine if the speaker is authorized. It processes audio input through several steps: | |
| 1. Audio preprocessing (resampling, normalization) | |
| 2. Mel spectrogram generation | |
| 3. Deep learning model analysis | |
| 4. Access decision with confidence score | |
| ## Usage | |
| 1. Click the audio input button or drag and drop an audio file | |
| 2. Wait for the system to process the recording | |
| 3. View the access result and confidence score | |
| ## Technical Details | |
| - Model: Custom CNN architecture (VoiceAccessNet) | |
| - Input: Audio files (WAV, MP3) | |
| - Audio processing: 16kHz sample rate, mel spectrogram features | |
| - Output: Binary classification (Access Granted/Denied) with confidence score | |
| ## References | |
| - Model training code and dataset details: [Link to your repository] | |
| - Based on PyTorch and torchaudio | |
| - Deployed using Gradio and Hugging Face Spaces | |
| ## License | |
| [Your chosen license] |