debug vad

s4d/nnet/seqtoseq.py

@@ -82,7 +82,7 @@ class BLSTM(torch.nn.Module):
         super(BLSTM, self).__init__()
         self.input_size = input_size
         self.blstm_sizes = blstm_sizes
-        self.output_size = blstm_sizes[0] * 2
+        self.output_size = blstm_sizes * 2
 
         self.blstm_layers = torch.nn.LSTM(input_size,
                                           blstm_sizes,
@@ -373,7 +373,7 @@ def train_epoch(model, epoch, training_loader, optimizer, log_interval, device):
     """
     model.to(device)
     model.train()
-    criterion = torch.nn.CrossEntropyLoss(reduction='mean', weight=torch.FloatTensor([0.1,0.9]))
+    criterion = torch.nn.CrossEntropyLoss(reduction='mean', weight=torch.FloatTensor([0.1,0.9]).to(device))
 
     recall = 0.0
     precision = 0.0
@@ -501,4 +501,4 @@ def calc_recall(output,target,device):
     pr/=len(y_trueb[0])
     acc/=len(y_trueb[0])
 
-    return rc,pr,acc
\ No newline at end of file
+    return rc,pr,acc

s4d/nnet/wavsets.py

@@ -94,7 +94,6 @@ def load_wav_segment(wav_file_name, idx, duration, seg_shift, framerate=16000):
 
 
 def mdtm_to_label(mdtm_filename,
-                  mode,
                   start_time,
                   stop_time,
                   sample_number,
@@ -122,7 +121,7 @@ def mdtm_to_label(mdtm_filename,
                 diarization.segments[ii]['start'] = diarization.segments[ii - 1]['stop']
 
     # Create the empty labels
-    label = list(numpy.zeros(sample_number, dtype=int))
+    label = []
 
     # Compute the time stamp of each sample
     time_stamps = numpy.zeros(sample_number, dtype=numpy.float32)
@@ -136,6 +135,11 @@ def mdtm_to_label(mdtm_filename,
             if seg['start'] / 100. <= time <= seg['stop'] / 100.:
                 lbls.append(speaker_dict[seg['cluster']])
 
+        if len(lbls) > 0:
+            label.append(lbls)
+        else:
+            label.append([])
+
     return label
 
 
@@ -393,8 +397,7 @@ class SeqSet(Dataset):
                                   start_time=start,
                                   stop_time=start + self.duration,
                                   sample_number=sig.shape[1],
-                                  speaker_dict=self.speaker_dict,
-                                  overlap=self.mode=="overlap")
+                                  speaker_dict=self.speaker_dict)
 
         label = process_segment_label(label=tmp_label,
                                       mode=self.mode,
@@ -432,7 +435,7 @@ def create_train_val_seqtoseq(dataset_yaml):
 
     # Read all MDTM files and ouptut a list of segments with minimum duration as well as a speaker dictionary
     segment_list, speaker_dict = seqSplit(mdtm_dir=dataset_params["mdtm_dir"],
-                                          duration=dataset_params["duration"])
+                                          duration=dataset_params["train"]["duration"])
 
     split_idx = numpy.random.choice([True, False],
                                     size=(len(segment_list),),
@@ -448,26 +451,26 @@ def create_train_val_seqtoseq(dataset_yaml):
     # Split the list of segment between training and validation sets
     train_set = SeqSet(wav_dir=dataset_params["wav_dir"],
                        mdtm_dir=dataset_params["mdtm_dir"],
-                       mode=dataset_param["mode"],
+                       mode=dataset_params["mode"],
                        segment_list=segment_list_train,
                        speaker_dict=speaker_dict,
-                       duration=dataset_param["train"]["duration"],
-                       filter_type=dataset_param["filter_type"],
-                       collar_duration=dataset_param["collar_duration"],
-                       audio_framerate=dataset_param["sample_rate"],
-                       output_framerate=dataset_param["output_rate"],
-                       transform_pipeline=dataset_param["train"]["transformation"]["pipeline"])
+                       duration=dataset_params["train"]["duration"],
+                       filter_type=dataset_params["filter_type"],
+                       collar_duration=dataset_params["collar_duration"],
+                       audio_framerate=dataset_params["sample_rate"],
+                       output_framerate=dataset_params["output_rate"],
+                       transform_pipeline=dataset_params["train"]["transformation"]["pipeline"])
 
     validation_set = SeqSet(wav_dir=dataset_params["wav_dir"],
                             mdtm_dir=dataset_params["mdtm_dir"],
-                            mode=dataset_param["mode"],
+                            mode=dataset_params["mode"],
                             segment_list=segment_list_val,
                             speaker_dict=speaker_dict,
-                            duration=dataset_param["eval"]["duration"],
-                            filter_type=dataset_param["filter_type"],
-                            collar_duration=dataset_param["collar_duration"],
-                            audio_framerate=dataset_param["sample_rate"],
-                            output_framerate=dataset_param["output_rate"],
-                            transform_pipeline=dataset_param["eval"]["transformation"]["pipeline"])
-
-    return train_set, validation_set
\ No newline at end of file
+                            duration=dataset_params["eval"]["duration"],
+                            filter_type=dataset_params["filter_type"],
+                            collar_duration=dataset_params["collar_duration"],
+                            audio_framerate=dataset_params["sample_rate"],
+                            output_framerate=dataset_params["output_rate"],
+                            transform_pipeline=dataset_params["eval"]["transformation"]["pipeline"])
+
+    return train_set, validation_set