debug

nnet/xsets.py

@@ -32,6 +32,7 @@ import pandas
 import random
 import torch
 import torchaudio
+torchaudio.set_audio_backend("sox_io")
 import tqdm
 import soundfile
 import yaml
@@ -347,7 +348,7 @@ class IdMapSet(Dataset):
 
         if self.idmap.stop[index] is None:
             speech, speech_fs = torchaudio.load(f"{self.data_path}/{self.idmap.rightids[index]}.{self.file_extension}")
-            duration = len(speech) - start
+            duration = speech.shape[1] - start
         else:
             start = int(self.idmap.start[index])
             duration = int(self.idmap.stop[index]) - start

nnet/xvector.py

@@ -1193,7 +1193,7 @@ def cross_validation(model, validation_loader, device, validation_shape, mixed_p
     :return:
     """
     model.eval()
-
+    print("In cross validation")
     if isinstance(model, Xtractor):
         loss_criteria = model.loss
     else:
@@ -1205,7 +1205,10 @@ def cross_validation(model, validation_loader, device, validation_shape, mixed_p
     embeddings = torch.zeros(validation_shape)
     classes = torch.zeros([validation_shape[0]])
     with torch.no_grad():
-        for batch_idx, (data, target) in enumerate(tqdm.tqdm(validation_loader, desc='validation compute', mininterval=1)):
+        #for batch_idx, (data, target) in enumerate(tqdm.tqdm(validation_loader, desc='validation compute', mininterval=1)):
+        print("In cross validation 2")
+        for batch_idx, (data, target) in enumerate(validation_loader):
+            print("In cross validation 3")
             batch_size = target.shape[0]
             target = target.squeeze().to(device)
             data = data.squeeze().to(device)
@@ -1364,7 +1367,7 @@ def extract_embeddings_per_speaker(idmap_name,
 
         model_archi = checkpoint["model_archi"]
 
-        model = Xtractor(checkpoint["speaker_number"], model_archi=model_archi)
+        model = Xtractor(checkpoint["speaker_number"], model_archi=model_archi, loss="aam")
         model.load_state_dict(checkpoint["model_state_dict"])
     else:
         model = model_filename
@@ -1429,7 +1432,7 @@ def extract_sliding_embedding(idmap_name,
                               file_extension="wav",
                               transform_pipeline=None,
                               num_thread=1):
-    """
+    
 
     :param idmap_name:
     :param window_length:
@@ -1443,7 +1446,7 @@ def extract_sliding_embedding(idmap_name,
     :param file_extension:
     :param transform_pipeline:
     :return:
-    """
+    
     # From the original IdMap, create the new one to extract x-vectors
     if not isinstance(idmap_name, IdMap):
         input_idmap = IdMap(idmap_name)