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---
language: en
tags:
- audio
- voice-recognition
- security
- pytorch
license: apache-2.0
datasets:
- your-dataset-name
---
# Voice Recognition Security Model
This model provides secure voice recognition with transfer learning and data augmentation.
## Usage
```python
from transformers import AutoModel
import torch
import joblib
import librosa
import numpy as np
# Load model
model = AutoModel.from_pretrained("your-username/your-model-name")
label_encoder = joblib.load("label_encoder.joblib")
feature_params = joblib.load("feature_params.joblib")
# Prediction function
def predict_voice(file_path):
# Extract features (same as during training)
features = extract_features(file_path, feature_params['max_pad_len'])
features = torch.tensor(features).unsqueeze(0).unsqueeze(0)
# Predict
with torch.no_grad():
outputs = model(features)
_, predicted = torch.max(outputs, 1)
return label_encoder.inverse_transform([predicted.item()])[0] |