load batch name list

fluid/DeepASR/data_utils/async_data_reader.py

@@ -31,16 +31,18 @@ class SampleInfo(object):
         label_bin_path (str): File containing the label data.
         label_size (int): Byte count of the sample's label data.
         label_frame_num (int): Label number of the sample.
+        sample_name (str): key of the sample
     """
 
     def __init__(self, feature_bin_path, feature_start, feature_size,
                  feature_frame_num, feature_dim, label_bin_path, label_start,
-                 label_size, label_frame_num):
+                 label_size, label_frame_num, sample_name):
         self.feature_bin_path = feature_bin_path
         self.feature_start = feature_start
         self.feature_size = feature_size
         self.feature_frame_num = feature_frame_num
         self.feature_dim = feature_dim
+        self.sample_name = sample_name
 
         self.label_bin_path = label_bin_path
         self.label_start = label_start
@@ -113,6 +115,7 @@ class SampleInfoBucket(object):
 
             for i in xrange(sample_num):
                 feature_desc_split = feature_desc_lines[i + 1].split()
+                sample_name = feature_desc_split[0]
                 feature_start = int(feature_desc_split[2])
                 feature_size = int(feature_desc_split[3])
                 feature_frame_num = int(feature_desc_split[4])
@@ -136,7 +139,7 @@ class SampleInfoBucket(object):
                         SampleInfo(feature_bin_path, feature_start,
                                    feature_size, feature_frame_num, feature_dim,
                                    label_bin_path, label_start, label_size,
-                                   label_frame_num))
+                                   label_frame_num, sample_name))
                 #split sentence
                 else:
                     cur_frame_pos = 0
@@ -157,7 +160,7 @@ class SampleInfoBucket(object):
                                 * feature_dim * 4, cur_frame_len * feature_dim *
                                 4, cur_frame_len, feature_dim, label_bin_path,
                                 label_start + cur_frame_pos * 4, cur_frame_len *
-                                4, cur_frame_len))
+                                4, cur_frame_len, sample_name))
 
                         remain_frame_num -= cur_frame_len
                         cur_frame_pos += cur_frame_len
@@ -361,8 +364,8 @@ class AsyncDataReader(object):
                 feature_data = np.array(
                     feature_array, dtype='float32').reshape((
                         sample_info.feature_frame_num, sample_info.feature_dim))
-
-                sample_data = (feature_data, label_data)
+                sample_data = (feature_data, label_data,
+                               sample_info.sample_name)
                 for transformer in self._transformers:
                     # @TODO(pkuyym) to make transfomer only accept feature_data
                     sample_data = transformer.perform_trans(sample_data)
@@ -415,24 +418,26 @@ class AsyncDataReader(object):
                     batch_samples.append(sample)
                     lod.append(lod[-1] + sample[0].shape[0])
                     if len(batch_samples) == batch_size:
-                        feature, label = batch_to_ndarray(batch_samples, lod)
+                        feature, label, name_lst = batch_to_ndarray(
+                            batch_samples, lod)
 
                         feature = conv_to_shared(feature)
                         label = conv_to_shared(label)
                         lod = conv_to_shared(np.array(lod).astype('int64'))
 
-                        batch_queue.put((feature, label, lod))
+                        batch_queue.put((feature, label, lod, name_lst))
                         batch_samples = []
                         lod = [0]
 
             if len(batch_samples) >= minimum_batch_size:
-                (feature, label) = batch_to_ndarray(batch_samples, lod)
+                (feature, label, name_lst) = batch_to_ndarray(batch_samples,
+                                                              lod)
 
                 feature = conv_to_shared(feature)
                 label = conv_to_shared(label)
                 lod = conv_to_shared(np.array(lod).astype('int64'))
 
-                batch_queue.put((feature, label, lod))
+                batch_queue.put((feature, label, lod, name_lst))
 
             batch_queue.put(EpochEndSignal())
 

fluid/DeepASR/data_utils/augmentor/tests/test_data_trans.py

@@ -22,7 +22,7 @@ class TestTransMeanVarianceNorm(unittest.TestCase):
         feature = np.zeros((2, 120), dtype="float32")
         feature.fill(1)
         trans = trans_mean_variance_norm.TransMeanVarianceNorm(self._file_path)
-        (feature1, label1) = trans.perform_trans((feature, None))
+        (feature1, label1, name) = trans.perform_trans((feature, None, None))
         (mean, var) = trans.get_mean_var()
         feature_flat1 = feature1.flatten()
         feature_flat = feature.flatten()
@@ -70,7 +70,7 @@ class TestTransAddDelta(unittest.TestCase):
         feature[2, 0:40].fill(3)
         feature[3, 0:40].fill(4)
         trans = trans_add_delta.TransAddDelta()
-        (feature, label) = trans.perform_trans((feature, None))
+        (feature, label, name) = trans.perform_trans((feature, None, None))
         self.assertAlmostEqual(feature.shape[0], 4)
         self.assertAlmostEqual(feature.shape[1], 120)
         self.assertAlmostEqual(1.0, feature[0][0])
@@ -93,7 +93,7 @@ class TestTransSplict(unittest.TestCase):
             feature[i, :].fill(i)
 
         trans = trans_splice.TransSplice()
-        (feature, label) = trans.perform_trans((feature, None))
+        (feature, label, name) = trans.perform_trans((feature, None, None))
         self.assertEqual(feature.shape[1], 110)
 
         for i in xrange(8):

fluid/DeepASR/data_utils/augmentor/trans_add_delta.py

@@ -32,9 +32,9 @@ class TransAddDelta(object):
             Args: 
                 sample(object,tuple): contain feature numpy and label numpy
             Returns:
-                (feature, label)
+                (feature, label, sample_name)
         """
-        (feature, label) = sample
+        (feature, label, sample_name) = sample
         frame_dim = feature.shape[1]
         d_frame_dim = frame_dim * 3
         head_filled = 5
@@ -64,7 +64,8 @@ class TransAddDelta(object):
                       start * d_frame_dim + 2 * frame_dim, frame_dim, nframe,
                       d_frame_dim)
         mat.shape = tmp_shape
-        return (mat[head_filled:mat.shape[0] - tail_filled, :], label)
+        return (mat[head_filled:mat.shape[0] - tail_filled, :], label,
+                sample_name)
 
     def _regress(self, data_in, start_in, data_out, start_out, size, n, step):
         """ regress

fluid/DeepASR/data_utils/augmentor/trans_mean_variance_norm.py

@@ -53,9 +53,9 @@ class TransMeanVarianceNorm(object):
             Args:
                 sample(object):input sample, contain feature numpy and label numpy
             Returns:
-                (feature, label)
+                (feature, label, sample_name)
         """
-        (feature, label) = sample
+        (feature, label, sample_name) = sample
         shape = feature.shape
         assert len(shape) == 2
         nfeature_len = shape[0] * shape[1]
@@ -68,4 +68,4 @@ class TransMeanVarianceNorm(object):
             feature[ncur_idx:ncur_idx + self._nLen] = block
             ncur_idx += self._nLen
         feature = feature.reshape(shape)
-        return (feature, label)
+        return (feature, label, sample_name)

fluid/DeepASR/data_utils/augmentor/trans_splice.py

@@ -30,9 +30,9 @@ class TransSplice(object):
         Args:
             sample(object): input sample(feature, label)
         Return:
-            (feature, label)
+            (feature, label, sample_name)
         """
-        (feature, label) = sample
+        (feature, label, sample_name) = sample
         nframe_num = feature.shape[0]
         nframe_dim = feature.shape[1]
         nnew_frame_dim = nframe_dim * (
@@ -61,4 +61,4 @@ class TransSplice(object):
             np.copyto(ret[i * nnew_frame_dim:(i + 1) * nnew_frame_dim],
                       mat[i * nframe_dim:i * nframe_dim + nnew_frame_dim])
         ret = ret.reshape((nframe_num, nnew_frame_dim))
-        return (ret, label)
+        return (ret, label, sample_name)

fluid/DeepASR/data_utils/util.py

@@ -42,12 +42,14 @@ def batch_to_ndarray(batch_samples, lod):
     batch_feature = np.zeros((lod[-1], frame_dim), dtype="float32")
     batch_label = np.zeros((lod[-1], 1), dtype="int64")
     start = 0
+    name_lst = []
     for sample in batch_samples:
         frame_num = sample[0].shape[0]
         batch_feature[start:start + frame_num, :] = sample[0]
         batch_label[start:start + frame_num, :] = sample[1]
         start += frame_num
-    return (batch_feature, batch_label)
+        name_lst.append(sample[2])
+    return (batch_feature, batch_label, name_lst)
 
 
 def split_infer_result(infer_seq, lod):

fluid/DeepASR/train.py

@@ -220,7 +220,7 @@ def train(args):
                 train_data_reader.batch_iterator(args.batch_size,
                                                  args.minimum_batch_size)):
             # load_data
-            (features, labels, lod) = batch_data
+            (features, labels, lod, name_lst) = batch_data
             feature_t.set(features.ndarray, place)
             feature_t.set_lod([lod.ndarray])
             label_t.set(labels.ndarray, place)