Upload Main.py

Main.py

@@ -0,0 +1,61 @@
+import torch
+
+# To speed-up training process
+torch.autograd.set_detect_anomaly(False)
+torch.autograd.profiler.profile(False)
+torch.autograd.profiler.emit_nvtx(False)
+
+import warnings
+import pickle
+
+from Transformer import Transformer
+import librosa
+
+import os.path as path
+import json
+from tqdm import tqdm
+import time
+
+import numpy as np
+
+warnings.filterwarnings("ignore")
+
+with open("config.json") as json_data_file:
+    data = json.load(json_data_file)
+
+lr = data['learn_rate']
+epochs = data['epochs']
+batch_size = data['batch_size']
+Training = data['Training']
+Testing = data['Testing']
+main_path = data['MainPath']
+
+device = data['Device']
+diag_attn = data['DiagAttn']
+
+BestModelPath = 'Best_GlobalModel_500_0_0.pt'
+
+def pre_process_mfcc(mfcc):
+    
+    mfcc = mfcc.T
+
+    mean_G = np.mean(mfcc, axis=0)
+    std_G = np.std(mfcc, axis=0)
+    mfcc = 0.5*(mfcc-mean_G)/std_G
+    
+    return mfcc
+
+def wav2art(wav):
+    rate = 16000
+    mfcc = librosa.feature.mfcc(wav, 16000, n_mfcc=13, hop_length=int(0.010*rate), n_fft=int(0.020*rate))
+    mfcc = pre_process_mfcc(mfcc)
+
+    mfcc = torch.tensor([mfcc]).float()
+
+    test_model = torch.load(BestModelPath, map_location=torch.device('cpu')).float()
+    test_model.eval()
+
+    p = test_model(mfcc, 0, 0, 0)
+    p = p[0].detach().numpy()
+
+    return p