Update app.py

app.py

@@ -9,6 +9,9 @@ import onnxruntime, onnx
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+from torchmetrics.audio import ShortTimeObjectiveIntelligibility as STOI
+from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality as PESQ
+import pandas as pd
 
 @st.cache
 def load_model():
@@ -119,4 +122,90 @@ if st.button('Сгенерировать потери'):
     st.text('Аудио с потерями')
     st.audio('lossy.wav')
     st.text('Улучшенное аудио')
-    st.audio('enhanced.wav')
+    st.audio('enhanced.wav')
+    data_clean, samplerate = torchaudio.load('target.wav')
+
+    data_lossy, samplerate = torchaudio.load('lossy.wav')
+    data_enhanced, samplerate = torchaudio.load('enhanced.wav')
+    min_len = min(data_clean.shape[1], data_lossy.shape[1])
+    data_clean = data_clean[:, :min_len]
+    data_lossy = data_lossy[:, :min_len]
+    data_enhanced = data_enhanced[:, :min_len]
+
+
+    stoi = STOI(48000)
+    stoi_orig = np.array(stoi(data_clean, data_clean))
+    stoi_lossy = np.array(stoi(data_clean, data_lossy))
+    stoi_enhanced = np.array(stoi(data_clean, data_enhanced))
+    stoi_mass=[stoi_orig, stoi_lossy, stoi_enhanced]
+
+
+    pesq = PESQ(16000, 'nb')
+    data_clean = data_clean.cpu().numpy()
+    data_lossy = data_lossy.detach().cpu().numpy()
+    data_enhanced = data_enhanced.cpu().numpy()
+
+    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 = np.array(pesq(torch.tensor(data_clean), torch.tensor(data_clean)))
+        pesq_lossy = np.array(pesq(torch.tensor(data_lossy), torch.tensor(data_clean)))
+        pesq_enhanced = np.array(pesq(torch.tensor(data_enhanced), torch.tensor(data_clean)))
+
+    psq_mas=[pesq_orig, pesq_lossy, pesq_enhanced]
+
+
+
+    df = pd.DataFrame(columns=['Audio', 'PESQ', 'STOI', 'PLCMOS', 'LSD'])
+
+    df['Audio'] = ['Clean', 'Lossy', 'Enhanced']
+
+    df['PESQ'] = psq_mas
+
+    df['STOI'] = stoi_mass
+
+    st.table(df)
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