Update app.py

app.py

@@ -11,13 +11,11 @@ import onnxruntime, onnx
 import matplotlib.pyplot as plt
 from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
 from pystoi import stoi
-#from pesq import pesq
+from pesq import pesq
 import pandas as pd
 import torchaudio
 
 
-from torch_pesq import PesqLoss
-
 
 @st.cache
 def load_model():
@@ -149,21 +147,16 @@ if st.button('Сгенерировать потери'):
 
 
     
-    #if samplerate != 16000:
-    #    data_lossy = librosa.resample(data_lossy, orig_sr=48000, target_sr=16000)
-    #    data_clean = librosa.resample(data_clean, orig_sr=48000, target_sr=16000)
-    #    data_enhanced = librosa.resample(data_enhanced, orig_sr=48000, target_sr=16000)
-    #
-
-    pesq = PesqLoss(0.5, sample_rate=48000)
+    if samplerate != 16000:
+        data_lossy = librosa.resample(data_lossy, orig_sr=48000, target_sr=16000)
+        data_clean = librosa.resample(data_clean, orig_sr=48000, target_sr=16000)
+        data_enhanced = librosa.resample(data_enhanced, orig_sr=48000, target_sr=16000)
+    
 
-    pesq_orig = pesq.mos(data_clean, data_clean)
-    pesq_lossy = pesq.mos(data_clean, data_lossy)
-    pesq_enhanced= pesq.mos(data_clean, data_enhanced)
 
-    #pesq_orig = pesq(fs = 16000, ref = data_clean, deg = data_clean, mode='nb')
-    #pesq_lossy = pesq(fs = 16000, ref = data_clean, deg = data_lossy, mode='nb')
-    #pesq_enhanced = pesq(fs = 16000, ref = data_clean, deg = data_enhanced, mode='nb')
+    pesq_orig = pesq(fs = 16000, ref = data_clean, deg = data_clean, mode='nb')
+    pesq_lossy = pesq(fs = 16000, ref = data_clean, deg = data_lossy, mode='nb')
+    pesq_enhanced = pesq(fs = 16000, ref = data_clean, deg = data_enhanced, mode='nb')
 
     psq_mas=[pesq_orig, pesq_lossy, pesq_enhanced]