cleaning

nnet/xsets.py

@@ -120,7 +120,6 @@ class SideSampler(torch.utils.data.Sampler):
             if self.segment_cursors[value] > len(self.labels_to_indices[value]) - 1:
                 random.shuffle(self.labels_to_indices[value])
                 self.segment_cursors[value] = 0
-
             self.index_iterator[idx] = self.labels_to_indices[value][self.segment_cursors[value]]
             self.segment_cursors[value] += 1
         return iter(self.index_iterator)

nnet/xvector.py

@@ -223,10 +223,10 @@ def test_metrics(model,
         [type]: [description]
     """
 
-    idmap_test_filename = 'h5f/vox1_test_cleaned_idmap.h5'
-    ndx_test_filename = 'h5f/vox1_test_cleaned_ndx.h5'
-    key_test_filename = 'h5f/vox1_test_cleaned_key.h5'
-    data_root_name='/hdd/data/vox1/test/wav'
+    idmap_test_filename = '/lium/raid01_c/larcher/data/allies_dev_verif_idmap.h5'
+    ndx_test_filename = '/lium/raid01_c/larcher/data/allies_dev_verif_ndx.h5'
+    key_test_filename = '/lium/raid01_c/larcher/data/allies_dev_verif_key.h5'
+    data_root_name='/lium/corpus/base/ALLIES/wav'
 
     transform_pipeline = dict()
 
@@ -234,10 +234,10 @@ def test_metrics(model,
                                  model_filename=model,
                                  data_root_name=data_root_name,
                                  device=device,
+                                 loss="aam",
                                  transform_pipeline=transform_pipeline,
                                  num_thread=num_thread,
-                                 mixed_precision=mixed_precision,
-                                 backward=False)
+                                 mixed_precision=mixed_precision)
 
     tar, non = cosine_scoring(xv_stat,
                               xv_stat,
@@ -891,7 +891,7 @@ class Xtractor(torch.nn.Module):
 
 def update_training_dictionary(dataset_description,
                                model_description,
-                               kwargs)
+                               kwargs):
     """
         speaker_number,
                                dataset_yaml,
@@ -1562,7 +1562,7 @@ def xtrain(speaker_number,
     First we load the dataframe from CSV file in order to split it for training and validation purpose
     Then we provide those two 
     """
-    training_df, validation_df = train_test_split(df, test_size=dataset_params["validation_ratio"]) #, stratify=df["speaker_idx"])
+    training_df, validation_df = train_test_split(df, test_size=dataset_params["validation_ratio"] , stratify=df["speaker_idx"])
 
     torch.manual_seed(dataset_params['seed'])
 
@@ -1602,6 +1602,7 @@ def xtrain(speaker_number,
                                    num_workers=num_thread,
                                    persistent_workers=False)
 
+
     """
     Set the training options
     """