add example files

app.py

@@ -8,10 +8,14 @@ model.load_state_dict(torch.load("gender_classifier.model", map_location="cpu"))
 model.eval()
 
 def predict_gender(filepath):
+    audio = model.load_audio(filepath)
     with torch.no_grad():
-        output = model.predict(filepath)
-    return output
+        output = model.forward(audio)
+        probs = torch.softmax(output, dim=1)
+        prob_dict = {model.pred2gender[i]: float(prob) for i, prob in enumerate(probs[0])}
+    return prob_dict
 
-audio_component = gr.Audio(type='filepath', label="Upload your audio file here")
-demo = gr.Interface(fn=predict_gender, inputs=audio_component, outputs="text")
+audio_component = gr.Audio(type='filepath', label='Upload your audio file here')
+label_component = gr.Label(label='Gender classification result')
+demo = gr.Interface(fn=predict_gender, inputs=audio_component, outputs=label_component, examples=['00001.wav', '00002.wav'])
 demo.launch()

model.py

@@ -121,7 +121,7 @@ class ECAPA_gender(nn.Module):
         self.fc6 = nn.Linear(3072, 192)
         self.bn6 = nn.BatchNorm1d(192)
         self.fc7 = nn.Linear(192, 2)
-        self.pred2gender = {0 : 'male', 1 : 'female'}
+        self.pred2gender = {0 : 'Male', 1 : 'Female'}
 
     def forward(self, x):
         with torch.no_grad():