debg

nnet/xsets.py

@@ -412,7 +412,7 @@ class IdMapSet(Dataset):
         else:
             duration = int(self.idmap.stop[index] * 0.01) * self.sample_rate - start
             # add this in case the segment is too short
-            if duration <= self.self.min_duration * self.sample_rate:
+            if duration <= self.min_duration * self.sample_rate:
                 middle = start + duration // 2
                 start = max(0, int(middle - (self.min_sample_nb / 2)))
                 duration = int(self.min_sample_nb)

nnet/xvector.py

@@ -1585,8 +1585,8 @@ def extract_embeddings(idmap_name,
     if isinstance(model_filename, str):
         checkpoint = torch.load(model_filename, map_location=device)
         speaker_number = checkpoint["speaker_number"]
-        model_archi = checkpoint["model_archi"]
-        model = Xtractor(speaker_number, model_archi=model_archi, loss=checkpoint["loss"])
+        model_opts = checkpoint["model_archi"]
+        model = Xtractor(speaker_number, model_archi=model_opts["model_type"], loss=model_opts["loss"]["type"])
         model.load_state_dict(checkpoint["model_state_dict"])
     else:
         model = model_filename