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1from transformers import AutoModel
2import torch
3import joblib
4import librosa
5import numpy as np
6
7# Load model
8model = AutoModel.from_pretrained("your-username/your-model-name")
9label_encoder = joblib.load("label_encoder.joblib")
10feature_params = joblib.load("feature_params.joblib")
11
12# Prediction function
13def predict_voice(file_path):
14 # Extract features (same as during training)
15 features = extract_features(file_path, feature_params['max_pad_len'])
16 features = torch.tensor(features).unsqueeze(0).unsqueeze(0)
17
18 # Predict
19 with torch.no_grad():
20 outputs = model(features)
21 _, predicted = torch.max(outputs, 1)
22
23 return label_encoder.inverse_transform([predicted.item()])[0]