Faster forwarding

data_io.py

@@ -305,9 +305,9 @@ def _update_data(data_set, labs, fea_dict, fea, fea_index, data_set_fea, labs_fe
                 fea_index=fea_index+data_set_fea.shape[1]
                 fea_dict[fea].append(fea_index)
                 fea_dict[fea].append(fea_dict[fea][6]-fea_dict[fea][5])
-            elif cnt_fea==0 and (not cnt_fea==0):
+            elif cnt_fea==0 and (not cnt_lab==0):
                 labs=np.column_stack((labs,labs_fea))
-            elif (not cnt_fea==0) and cnt_fea==0:
+            elif (not cnt_fea==0) and cnt_lab==0:
                 data_set=np.column_stack((data_set,data_set_fea))
                 fea_dict[fea].append(fea_index)
                 fea_index=fea_index+data_set_fea.shape[1]
@@ -368,12 +368,13 @@ def _load_chunk_refac01(fea_scp,fea_opts,lab_folder,lab_opts,left,right,max_sequ
             for lab in lab_dict.keys():
                 lab_folder, lab_opts = _get_lab_config_from_dict(lab_dict[lab], fea_only)
                 data_name_fea, data_set_fea, data_set_lab, data_end_index_fea, data_end_index_lab = _load_chunk_refac01(fea_scp, fea_opts, lab_folder, lab_opts, cw_left, cw_right, max_seq_length, output_folder, fea_only)
-                labs_fea, data_set_fea, data_end_index_fea, data_end_index_lab = _compensate_for_different_context_windows(data_set_fea, data_set_lab, cw_left_max, cw_left, cw_right_max, cw_right, data_end_index_fea, data_end_index_lab)
+                if sum([abs(e) for e in [cw_left_max, cw_right_max, cw_left, cw_right]]) != 0: 
+                    data_set_lab, data_set_fea, data_end_index_fea, data_end_index_lab = _compensate_for_different_context_windows(data_set_fea, data_set_lab, cw_left_max, cw_left, cw_right_max, cw_right, data_end_index_fea, data_end_index_lab)
                 if cnt_fea == 0 and cnt_lab == 0:
                     data_end_index_fea_ini = data_end_index_fea
                     data_end_index_lab_ini = data_end_index_lab
                     data_name = data_name_fea
-                data_set, labs, fea_dict, fea_index = _update_data(data_set, labs, fea_dict, fea, fea_index, data_set_fea, labs_fea, cnt_fea, cnt_lab)
+                data_set, labs, fea_dict, fea_index = _update_data(data_set, labs, fea_dict, fea, fea_index, data_set_fea, data_set_lab, cnt_fea, cnt_lab)
                 _check_consistency(data_name, data_name_fea, data_end_index_fea_ini, data_end_index_fea, data_end_index_lab_ini, data_end_index_lab)
                 cnt_lab=cnt_lab+1
             cnt_fea=cnt_fea+1

neural_networks.py

@@ -694,6 +694,23 @@ def _safe_log(inp, epsilon=1e-20):
         out = log_mel_spec 
         return out
 
+class channel_averaging(nn.Module):
+    def __init__(self, options,inp_dim):
+        super(channel_averaging, self).__init__()
+        self._use_cuda = strtobool(options['use_cuda'])
+        channel_weights = [float(e) for e in options['chAvg_channelWeights'].split(',')]
+        self._nr_of_channels = len(channel_weights)
+        numpy_weights = np.asarray(channel_weights, dtype=np.float32) * 1.0 / np.sum(channel_weights)
+        self._weights = torch.from_numpy(numpy_weights) 
+        if self._use_cuda:
+            self._weights = self._weights.cuda()
+        self.out_dim = 1
+
+    def forward(self, x):
+        assert self._nr_of_channels == x.shape[-1]
+        out = torch.einsum('tbc,c->tb', x, self._weights).unsqueeze(-1)
+        return out
+
 class liGRU(nn.Module):
 
     def __init__(self, options,inp_dim):

proto/channelAvg.proto

@@ -0,0 +1,4 @@
+[proto]
+chAvg_channelWeights=str
+
+

utils.py

@@ -934,6 +934,12 @@ def _get_validation_data_for_chunks(fea_names, list_fea, N_chunks):
         random.shuffle(full_list_fea_conc)
         valid_chunks_fea=list(split_chunks(full_list_fea_conc,N_chunks))
         return valid_chunks_fea
+    def _shuffle_forward_data(config):
+        if 'shuffle_forwarding_data' in config['forward']:
+            suffle_on_forwarding = strtobool(config['forward']['shuffle_forwarding_data'])
+            if not suffle_on_forwarding:
+                return False
+        return True
 
     # splitting data into chunks (see out_folder/additional_files)
     out_folder=config['exp']['out_folder']
@@ -1030,7 +1036,8 @@ def _get_validation_data_for_chunks(fea_names, list_fea, N_chunks):
 
 
         # randomize the list
-        random.shuffle(full_list_fea_conc)
+        if _shuffle_forward_data(config):
+            random.shuffle(full_list_fea_conc)
         forward_chunks_fea=list(split_chunks(full_list_fea_conc,N_chunks))