diff --git a/bob/learn/em/factor_analysis.py b/bob/learn/em/factor_analysis.py
index 779068ba07708df1afdcf51e6c867d3b181fecbf..cd9a769553b5a41368aa11ece53c35045a31e1a4 100644
--- a/bob/learn/em/factor_analysis.py
+++ b/bob/learn/em/factor_analysis.py
@@ -230,12 +230,13 @@ class FactorAnalysisBase(BaseEstimator):
         """
 
         if self.ubm is None:
-            logger.info("FA: Creating a new GMMMachine and training it.")
+            logger.info("Creating a new GMMMachine and training it.")
             gmm_class = self.ubm_kwargs.pop("gmm_class", GMMMachine)
             self.ubm: GMMMachine = gmm_class(**self.ubm_kwargs)
             self.ubm.fit(X)
 
         if self.ubm._means is None:
+            logger.info("UBM means are None, training the UBM.")
             self.ubm.fit(X)
 
         # Initializing the state matrix
@@ -1276,21 +1277,17 @@ class FactorAnalysisBase(BaseEstimator):
                 class_indices = y == class_id
                 X_new.append(X[class_indices])
                 y_new.append(y[class_indices])
-            __import__("ipdb").set_trace()
             X, y = X_new, y_new
+            del X_new, y_new
 
             stats = [
                 dask.delayed(self.ubm.stats_per_sample)(xx).persist()
                 for xx in X
             ]
-            # try:
-            #     client = dask.distributed.Client.current()
-            #     stats = client.scatter(stats)
-            # except ValueError:
-            #     pass
         else:
             stats = self.ubm.stats_per_sample(X)
 
+        del X
         self.fit_using_stats(stats, y)
         return self