bugfix validation

nnet/xsets.py

@@ -208,7 +208,7 @@ class SideSet(Dataset):
                 chunk_nb = len(possible_starts)
             else:
                 chunk_nb = min(len(possible_starts), chunk_per_segment)
-                starts = numpy.random.permutation(possible_starts)[:chunk_nb] / self.sample_rate
+                starts = numpy.random.permutation(possible_starts)[:chunk_nb]
 
             # Once we know how many segments are selected, create the other fields to fill the DataFrame
             for ii in range(chunk_nb):
@@ -256,13 +256,16 @@ class SideSet(Dataset):
         # TODO is this required ?
         nfo = soundfile.info(f"{self.data_path}/{current_session['file_id']}{self.data_file_extension}")
         original_start = int(current_session['start'])
-        lowest_shift = self.overlap/2
-        highest_shift = self.overlap/2
-        if original_start < (current_session['file_start']*self.sample_rate + self.sample_number/2):
-            lowest_shift = int(original_start - current_session['file_start']*self.sample_rate)
-        if original_start + self.sample_number > (current_session['file_start'] + current_session['file_duration'])*self.sample_rate - self.sample_number/2:
-            highest_shift = int((current_session['file_start'] + current_session['file_duration'])*self.sample_rate - (original_start + self.sample_number))
-        start_frame = original_start + int(random.uniform(-lowest_shift, highest_shift))
+        if self.overlap > 0:
+            lowest_shift = self.overlap/2
+            highest_shift = self.overlap/2
+            if original_start < (current_session['file_start']*self.sample_rate + self.sample_number/2):
+                lowest_shift = int(original_start - current_session['file_start']*self.sample_rate)
+            if original_start + self.sample_number > (current_session['file_start'] + current_session['file_duration'])*self.sample_rate - self.sample_number/2:
+                highest_shift = int((current_session['file_start'] + current_session['file_duration'])*self.sample_rate - (original_start + self.sample_number))
+            start_frame = original_start + int(random.uniform(-lowest_shift, highest_shift))
+        else:
+            start_frame = original_start
 
         if start_frame + self.sample_number >= nfo.frames:
             start_frame = numpy.min(nfo.frames - self.sample_number - 1)

nnet/xvector.py

@@ -1105,7 +1105,7 @@ def xtrain(speaker_number,
 
     test_eer = 100.
 
-    classes = torch.ByteTensor(validation_set.sessions['speaker_idx'].to_numpy())
+    classes = torch.ShortTensor(validation_set.sessions['speaker_idx'].to_numpy())
     mask = classes.unsqueeze(1) == classes.unsqueeze(1).T
     tar_indices = torch.tril(mask, -1).numpy()
     non_indices = torch.tril(~mask, -1).numpy()
@@ -1302,6 +1302,7 @@ def cross_validation(model, validation_loader, device, validation_shape, tar_ind
     criterion = torch.nn.CrossEntropyLoss()
     embeddings = torch.zeros(validation_shape)
     #classes = torch.zeros([validation_shape[0]])
+    cursor = 0
     with torch.no_grad():
         for batch_idx, (data, target) in enumerate(tqdm.tqdm(validation_loader, desc='validation compute', mininterval=1, disable=None)):
             target = target.squeeze()
@@ -1318,8 +1319,9 @@ def cross_validation(model, validation_loader, device, validation_shape, tar_ind
                     batch_embeddings = l2_norm(batch_embeddings)
                 accuracy += (torch.argmax(batch_predictions.data, 1) == target).sum()
                 loss += criterion(batch_predictions, target)
-            embeddings[batch_idx * batch_size:batch_idx * batch_size + batch_predictions.shape[0],:] = batch_embeddings.detach().cpu()
-            #classes[batch_idx * batch_size:batch_idx * batch_size + batch_predictions.shape[0]] = target.detach().cpu()
+            embeddings[cursor:cursor + batch_size,:] = batch_embeddings.detach().cpu()
+            #classes[cursor:cursor + batch_size] = target.detach().cpu()
+            cursor += batch_size
 
     #print(classes.shape[0])
     local_device = "cpu" if embeddings.shape[0] > 3e4 else device