Randomly double the training sequence length

Helps with stability with little cost in training speed

2 files changed, 14 insertions, 3 deletions

diff --git a/dnn/torch/fargan/dataset.py b/dnn/torch/fargan/dataset.py
index f33bed36..6195c6af 100644
--- a/dnn/torch/fargan/dataset.py
+++ b/dnn/torch/fargan/dataset.py
@@ -20,16 +20,19 @@ class FARGANDataset(torch.utils.data.Dataset):
 
         self.data = np.memmap(signal_file, dtype='int16', mode='r')
         #self.data = self.data[1::2]
-        self.nb_sequences = len(self.data)//(pcm_chunk_size)-1
+        self.nb_sequences = len(self.data)//(pcm_chunk_size)-4
         self.data = self.data[(4-self.lookahead)*self.frame_size:]
         self.data = self.data[:self.nb_sequences*pcm_chunk_size]
 
 
-        self.data = np.reshape(self.data, (self.nb_sequences, pcm_chunk_size))
+        #self.data = np.reshape(self.data, (self.nb_sequences, pcm_chunk_size))
+        sizeof = self.data.strides[-1]
+        self.data = np.lib.stride_tricks.as_strided(self.data, shape=(self.nb_sequences, pcm_chunk_size*2),
+                                           strides=(pcm_chunk_size*sizeof, sizeof))
 
         self.features = np.reshape(np.memmap(feature_file, dtype='float32', mode='r'), (-1, nb_features))
         sizeof = self.features.strides[-1]
-        self.features = np.lib.stride_tricks.as_strided(self.features, shape=(self.nb_sequences, self.sequence_length+4, nb_features),
+        self.features = np.lib.stride_tricks.as_strided(self.features, shape=(self.nb_sequences, self.sequence_length*2+4, nb_features),
                                            strides=(self.sequence_length*self.nb_features*sizeof, self.nb_features*sizeof, sizeof))
         self.periods = np.round(50*self.features[:,:,self.nb_used_features-2]+100).astype('int')
 
diff --git a/dnn/torch/fargan/train_fargan.py b/dnn/torch/fargan/train_fargan.py
index b4eef6fd..852b87be 100644
--- a/dnn/torch/fargan/train_fargan.py
+++ b/dnn/torch/fargan/train_fargan.py
@@ -118,6 +118,14 @@ if __name__ == '__main__':
                 features = features.to(device)
                 lpc = lpc.to(device)
                 periods = periods.to(device)
+                if (np.random.rand() > 0.1):
+                    target = target[:, :sequence_length*160]
+                    lpc = lpc[:,:sequence_length,:]
+                else:
+                    target=target[::2, :]
+                    lpc=lpc[::2,:]
+                    features=features[::2,:]
+                    periods=periods[::2,:]
                 target = target.to(device)
                 target = fargan.analysis_filter(target, lpc[:,:,:], gamma=args.gamma)