sliding window extrctor

nnet/xvector.py

@@ -1572,14 +1572,10 @@ def extract_sliding_embedding(idmap_name,
                 name = name + '.weight'
             emb_size = model.module.before_speaker_embedding.state_dict()[name].shape[0]
 
-        # Create the StatServer
-        embeddings = StatServer()
-        embeddings.modelset = idmap.leftids
-        embeddings.segset = idmap.rightids
-        embeddings.start = idmap.start
-        embeddings.stop = idmap.stop
-        embeddings.stat0 = numpy.ones((embeddings.modelset.shape[0], 1))
-        embeddings.stat1 = numpy.ones((embeddings.modelset.shape[0], emb_size))
+        embeddings = []
+        modelset= []
+        segset = []
+        starts = []
 
         # Process the data
         with torch.no_grad():
@@ -1588,9 +1584,18 @@ def extract_sliding_embedding(idmap_name,
                                                                           mininterval=1)):
                 with torch.cuda.amp.autocast(enabled=mixed_precision):
                     vec = model(x=data.to(device), is_eval=True)
-                embeddings.stat1= vec.detach().cpu()
-
-
+                    embeddings.append(vec.detach().cpu())
+                    modelset += [mod,] *  embeddings.shape[0]
+                    segset += [seg,] *  embeddings.shape[0]
+                    starts += numpy.arange(start, start + embeddings.shape[0] * window_shift , window_shift)
 
+        # Create the StatServer
+        embeddings = StatServer()
+        embeddings.modelset = numpy.array(modelset).astype('>U')
+        embeddings.segset = numpy.array(segset).astype('>U')
+        embeddings.start = numpy.array(starts)
+        embeddings.stop = numpy.array(starts) + window_len
+        embeddings.stat0 = numpy.ones((embeddings.modelset.shape[0], 1))
+        embeddings.stat1 = numpy.concatenate(embeddings)
 
     return embeddings