cleaning extract embedding

nnet/xsets.py

@@ -414,8 +414,8 @@ class IdMapSet(Dataset):
             # add this in case the segment is too short
             if duration <= self.min_duration * self.sample_rate:
                 middle = start + duration // 2
-                start = max(0, int(middle - (self.min_sample_nb / 2)))
-                duration = int(self.min_sample_nb)
+                start = int(max(0, int(middle - (self.min_duration * self.sample_rate / 2))))
+                duration = int(self.min_duration * self.sample_rate)
 
             speech, speech_fs = torchaudio.load(f"{self.data_path}/{self.idmap.rightids[index]}.{self.file_extension}",
                                                 frame_offset=start,
@@ -435,7 +435,7 @@ class IdMapSet(Dataset):
                                        rir_df=self.rir_df)
 
         speech = speech.squeeze()
-
+        
         return speech, self.idmap.leftids[index], self.idmap.rightids[index], start, start + duration
 
     def __len__(self):

nnet/xvector.py

@@ -228,7 +228,6 @@ def test_metrics(model,
                                  model_filename=model,
                                  data_root_name=data_opts["test"]["data_path"],
                                  device=device,
-                                 loss=model_opts["loss"]["type"],
                                  transform_pipeline=transform_pipeline,
                                  num_thread=train_opts["num_cpu"],
                                  mixed_precision=train_opts["mixed_precision"])
@@ -1065,8 +1064,6 @@ def get_loaders(dataset_opts, training_opts, model_opts, local_rank=0):
                                    batch_size=batch_size,
                                    seed=training_opts['torch_seed'],
                                    rank=local_rank,
-                                   num_process=torch.cuda.device_count(),
-                                   num_replicas=dataset_opts["train"]["sampler"]["augmentation_replica"]
                                    )
     else:
         batch_size = dataset_opts["batch_size"]
@@ -1613,7 +1610,7 @@ def extract_embeddings(idmap_name,
                        window_len=win_duration,
                        window_shift=win_shift,
                        sample_rate=sample_rate,
-                       min_duration=sliding_window
+                       min_duration=win_duration
                        )
 
 
@@ -1643,13 +1640,19 @@ def extract_embeddings(idmap_name,
 
 
         # 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 = 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))
+
+
+        embed = []
+        modelset= []
+        segset = []
+        starts = []
 
         # Process the data
         with torch.no_grad():
@@ -1657,11 +1660,31 @@ def extract_embeddings(idmap_name,
                                                                           desc='xvector extraction',
                                                                           mininterval=1,
                                                                           disable=None)):
-                if data.shape[1] > 20000000:
-                    data = data[...,:20000000]
+                #if data.shape[1] > 20000000:
+                #    data = data[...,:20000000]
+                print(f"data.shape = {data.shape}")
+                if data.dim() > 2:
+                    data = data.squeeze()
+                print(f"data.shape = {data.shape}")
+
                 with torch.cuda.amp.autocast(enabled=mixed_precision):
-                    _, vec = model(x=data.to(device), is_eval=True)
-                embeddings.stat1[idx, :] = vec.detach().cpu()
+                    tmp_data = torch.split(data,data.shape[0]//(max(1, data.shape[0]//100)))
+                    for td in tmp_data:
+                        _, vec = model(x=td.to(device), is_eval=True)
+                        embed.append(vec.detach().cpu())
+
+                    modelset += [mod,] *  data.shape[0]
+                    segset += [seg,] *  data.shape[0]
+                    starts += [numpy.arange(start, start + vec.shape[0] * win_shift , win_shift),]
+
+        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) + win_duration
+        embeddings.stat0 = numpy.ones((embeddings.modelset.shape[0], 1))
+        print(f"type = {type(embed)}, {type(embed[0])}") 
+        embeddings.stat1 = numpy.concatenate(embed)
 
     return embeddings