parallel

nnet/xvector.py

@@ -1056,7 +1056,7 @@ def get_network(model_opts):
     return model
 
 
-def get_loaders(dataset_opts, training_opts, model_opts, speaker_number):
+def get_loaders(dataset_opts, training_opts, model_opts):
     """
 
     :param dataset_yaml:
@@ -1100,14 +1100,14 @@ def get_loaders(dataset_opts, training_opts, model_opts, speaker_number):
         batch_size = dataset_opts["batch_size"]//torch.cuda.device_count()
 
         side_sampler = SideSampler(training_set.sessions['speaker_idx'],
-                                   speaker_number,
+                                   model_opts["speaker_number"],
                                    dataset_opts["train"]["sampler"]["examples_per_speaker"],
                                    dataset_opts["train"]["sampler"]["samples_per_speaker"],
                                    dataset_opts["batch_size"])
     else:
         batch_size = dataset_opts["batch_size"]
         side_sampler = SideSampler(training_set.sessions['speaker_idx'],
-                                   speaker_number,
+                                   model_opts["speaker_number"],
                                    samples_per_speaker,
                                    batch_size,
                                    batch_size,