doc

README.md

@@ -46,13 +46,13 @@ e1 = compute_embedding(wav1, model)
 e2 = compute_embedding(wav2, model)
 sim = float(torch.matmul(e1,e2.t()))
 
-print(sim) #
+print(sim) #0.7334115505218506
 ```
 
 # Evaluations
 The model has been evaluated on the standard ASV [VoxCeleb1-clean test set](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt).
-It results in an Equal Error Rate (EER, lower value denotes a better identification, random prediction leads to a value of 50%) of **0.98%** 
-(with a decision threshold of **0.37**).
+It results in an Equal Error Rate (EER, lower value denotes a better identification, random prediction leads to a value of 50%) of **0.946%** 
+(with a decision threshold of **0.388**).
 
 Please note that the EER value can vary a little depending on the max_size defined to reduce long audios (max 30 seconds in our case).