update anthony

nnet/xsets.py

@@ -429,7 +429,7 @@ class IdMapSet(Dataset):
                                                 frame_offset=start,
                                                 num_frames=duration)
 
-        speech += 10e-6 * torch.randn(speech.shape)
+        #speech += 10e-6 * torch.randn(speech.shape)
 
         if self.sliding_window:
             speech = speech.squeeze().unfold(0, self.window_len, self.window_shift)
@@ -564,7 +564,7 @@ class IdMapSetPerSpeaker(Dataset):
                 tmp_data.append(speech)
 
         speech = torch.cat(tmp_data, dim=1)
-        speech += 10e-6 * torch.randn(speech.shape)
+        #speech += 10e-6 * torch.randn(speech.shape)
 
         if len(self.transformation.keys()) > 0:
             speech = data_augmentation(speech,

nnet/xvector.py

@@ -978,6 +978,8 @@ def update_training_dictionary(dataset_description,
     fill_dict(model_opts, tmp_model_dict)
     fill_dict(training_opts, tmp_train_dict)
 
+    print(model_opts)
+
     # Overwrite with manually given parameters
     if "lr" in kwargs:
         training_opts["lr"] = kwargs['lr']
@@ -1010,26 +1012,28 @@ def get_network(model_opts, local_rank):
         model = Xtractor(model_opts["speaker_number"], model_opts, loss=model_opts["loss"]["type"], embedding_size=model_opts["embedding_size"])
 
     # Load the model if it exists
-    if model_opts["initial_model_name"] is not None and os.path.isfile(model_opts["initial_model_name"]):
-        logging.critical(f"*** Load model from = {model_opts['initial_model_name']}")
-        checkpoint = torch.load(model_opts["initial_model_name"])
+    if model_opts["initial_model_name"] is not None:
+        if os.path.isfile(model_opts["initial_model_name"]):
+            print(f"model_opts['initial_model_name'] = {model_opts['initial_model_name']} et os.path.isfile(model_opts['initial_model_name']): {os.path.isfile(model_opts['initial_model_name'])}")
+            logging.critical(f"*** Load model from = {model_opts['initial_model_name']}")
+            checkpoint = torch.load(model_opts["initial_model_name"], map_location={"cuda:0" : "cuda:%d" % local_rank})
 
-        """
-        Here we remove all layers that we don't want to reload
+            """
+            Here we remove all layers that we don't want to reload
 
-        """
-        pretrained_dict = checkpoint["model_state_dict"]
-        for part in model_opts["reset_parts"]:
-            pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith(part)}
+            """
+            pretrained_dict = checkpoint["model_state_dict"]
+            for part in model_opts["reset_parts"]:
+                pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith(part)}
 
-        new_model_dict = model.state_dict()
-        new_model_dict.update(pretrained_dict)
-        model.load_state_dict(new_model_dict)
+            new_model_dict = model.state_dict()
+            new_model_dict.update(pretrained_dict)
+            model.load_state_dict(new_model_dict)
 
-        # Freeze required layers
-        for name, param in model.named_parameters():
-            if name.split(".")[0] in model_opts["reset_parts"]:
-                param.requires_grad = False
+            # Freeze required layers
+            for name, param in model.named_parameters():
+                if name.split(".")[0] in model_opts["reset_parts"]:
+                    param.requires_grad = False
 
     if model_opts["loss"]["type"] == "aam" and not (model_opts["loss"]["aam_margin"] == 0.2 and model_opts["loss"]["aam_s"] == 30):
         model.after_speaker_embedding.change_params(model_opts["loss"]["aam_s"], model_opts["loss"]["aam_margin"])
@@ -1755,6 +1759,7 @@ def extract_embeddings(idmap_name,
         modelset= []
         segset = []
         starts = []
+        stops = []
 
         for idx, (data, mod, seg, start, stop) in enumerate(tqdm.tqdm(dataloader,
                                                                       desc='xvector extraction',
@@ -1774,15 +1779,20 @@ def extract_embeddings(idmap_name,
                 if sliding_window:
                     tmp_start = numpy.arange(0, data.shape[0] * win_shift, win_shift)
                     starts.extend(tmp_start * sample_rate + start.detach().cpu().numpy())
+                    win_duration = int(len(tmp_data))
                 else:
                     starts.append(start.numpy())
+                    stops.append(tmp_data[0].shape[1])
 
         embeddings = StatServer()
         embeddings.stat1 = numpy.concatenate(embed)
         embeddings.modelset = numpy.array(modelset).astype('>U')
         embeddings.segset = numpy.array(segset).astype('>U')
         embeddings.start = numpy.array(starts).squeeze()
-        embeddings.stop = embeddings.start + win_duration
+        if sliding_window:
+            embeddings.stop = embeddings.start + win_duration
+        else:
+            embeddings.stop = embeddings.start + numpy.array(stops).squeeze()
         embeddings.stat0 = numpy.ones((embeddings.modelset.shape[0], 1))
 
     return embeddings