1 files changed, 23 insertions, 6 deletions
diff --git a/become_yukarin/dataset/dataset.py b/become_yukarin/dataset/dataset.py
index f9db53e..39331c3 100644
--- a/become_yukarin/dataset/dataset.py
+++ b/become_yukarin/dataset/dataset.py
@@ -128,10 +128,20 @@ class ReshapeFeatureProcess(BaseDataProcess):
 
     def __call__(self, data: AcousticFeature, test):
         feature = numpy.concatenate([getattr(data, t) for t in self._targets])
-        feature = feature[numpy.newaxis]
+        feature = feature.T
         return feature
 
 
+class ShapeAlignProcess(BaseDataProcess):
+    def __call__(self, data, test):
+        data1, data2 = data['input'], data['target']
+        m = max(data1.shape[1], data2.shape[1])
+        data1 = numpy.pad(data1, ((0, 0), (0, m - data1.shape[1])), mode='constant')
+        data2 = numpy.pad(data2, ((0, 0), (0, m - data2.shape[1])), mode='constant')
+        data['input'], data['target'] = data1, data2
+        return data
+
+
 class DataProcessDataset(chainer.dataset.DatasetMixin):
     def __init__(self, data: typing.List, data_process: BaseDataProcess):
         self._data = data
@@ -144,28 +154,35 @@ class DataProcessDataset(chainer.dataset.DatasetMixin):
         return self._data_process(data=self._data[i], test=not chainer.config.train)
 
 
-def choose(config: DatasetConfig):
+def create(config: DatasetConfig):
     import glob
     input_paths = list(sorted([Path(p) for p in glob.glob(config.input_glob)]))
     target_paths = list(sorted([Path(p) for p in glob.glob(config.target_glob)]))
     assert len(input_paths) == len(target_paths)
 
+    acoustic_feature_load_process = AcousticFeatureLoadProcess()
+    input_mean = acoustic_feature_load_process(config.input_mean_path, test=True)
+    input_var = acoustic_feature_load_process(config.input_var_path, test=True)
+    target_mean = acoustic_feature_load_process(config.target_mean_path, test=True)
+    target_var = acoustic_feature_load_process(config.target_var_path, test=True)
+
     # {input_path, target_path}
     data_process = ChainProcess([
         SplitProcess(dict(
             input=ChainProcess([
                 LambdaProcess(lambda d, test: d['input_path']),
-                AcousticFeatureLoadProcess(),
-                AcousticFeatureNormalizeProcess(mean=config.input_mean, var=config.input_var),
+                acoustic_feature_load_process,
+                AcousticFeatureNormalizeProcess(mean=input_mean, var=input_var),
                 ReshapeFeatureProcess(['mfcc']),
             ]),
             target=ChainProcess([
                 LambdaProcess(lambda d, test: d['target_path']),
-                AcousticFeatureLoadProcess(),
-                AcousticFeatureNormalizeProcess(mean=config.target_mean, var=config.target_var),
+                acoustic_feature_load_process,
+                AcousticFeatureNormalizeProcess(mean=target_mean, var=target_var),
                 ReshapeFeatureProcess(['mfcc']),
             ]),
         )),
+        ShapeAlignProcess(),
     ])
 
     num_test = config.num_test