Update app.py

app.py

@@ -11,7 +11,7 @@ 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
 
@@ -143,16 +143,16 @@ if st.button('Сгенерировать потери'):
     stoi_mass=[stoi_orig, stoi_lossy, stoi_enhanced]
 
 
-    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)
+    #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(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]
+    #psq_mas=[pesq_orig, pesq_lossy, pesq_enhanced]
 
 
 
@@ -160,7 +160,7 @@ if st.button('Сгенерировать потери'):
 
     df['Audio'] = ['Clean', 'Lossy', 'Enhanced']
 
-    df['PESQ'] = psq_mas
+    #df['PESQ'] = psq_mas
 
     df['STOI'] = stoi_mass